diff --git a/DeepGraft/AttMIL_simple_no_viral.yaml b/DeepGraft/AttMIL_simple_no_viral.yaml
index ac18f9cea8c08d0cf2f82f06f7cec31c7c6cec34..4aeac661a56d260f945d7baaa40cb75c7e8600e1 100644
--- a/DeepGraft/AttMIL_simple_no_viral.yaml
+++ b/DeepGraft/AttMIL_simple_no_viral.yaml
@@ -11,12 +11,12 @@ General:
frozen_bn: False
patience: 50
server: test #train #test
- log_path: logs/
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/data/DeepGraft/224_128um/'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_no_viral.json'
fold: 1
nfold: 4
diff --git a/DeepGraft/AttMIL_simple_norm_rej_rest.yaml b/DeepGraft/AttMIL_simple_norm_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8d27c5e7712081664823804fb1ea4b4dc91def26
--- /dev/null
+++ b/DeepGraft/AttMIL_simple_norm_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/limit_20_split_PAS_HE_Jones_norm_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: AttMIL
+ n_classes: 3
+ backbone: simple
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/AttMIL_simple_norm_rest.yaml b/DeepGraft/AttMIL_simple_norm_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8dafb657ed479545690f970953f089a1d16a5502
--- /dev/null
+++ b/DeepGraft/AttMIL_simple_norm_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_split_PAS_HE_Jones_norm_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: AttMIL
+ n_classes: 2
+ backbone: simple
+ in_features: 1024
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0004
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/AttMIL_simple_rej_rest.yaml b/DeepGraft/AttMIL_simple_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b65afc6c5fb6e698eaf3bd6a5ab652aabaaee3ac
--- /dev/null
+++ b/DeepGraft/AttMIL_simple_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: AttMIL
+ n_classes: 2
+ backbone: simple
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/AttMIL_simple_tcmr_viral.yaml b/DeepGraft/AttMIL_simple_tcmr_viral.yaml
index 2aa4ed853c9a324ce4ce6c3ca938c91c637b1e97..b6221c1c9c88599f52e14f676d74e36d62986053 100644
--- a/DeepGraft/AttMIL_simple_tcmr_viral.yaml
+++ b/DeepGraft/AttMIL_simple_tcmr_viral.yaml
@@ -11,7 +11,7 @@ General:
frozen_bn: False
patience: 20
server: train #train #test
- log_path: logs/
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
Data:
dataset_name: custom
diff --git a/DeepGraft/Chowder_resnet50_bin_1.yaml b/DeepGraft/Chowder_resnet50_bin_1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d94e5781ce5a0f67409962c4d15e825d0787d333
--- /dev/null
+++ b/DeepGraft/Chowder_resnet50_bin_1.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/reduced_split_PAS_bin_1.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 16
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 16
+
+Model:
+ name: Chowder
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/Chowder_resnet50_tcmr_viral.yaml b/DeepGraft/Chowder_resnet50_tcmr_viral.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c1765328896a6793a5f68b11578d9b152b6a4509
--- /dev/null
+++ b/DeepGraft/Chowder_resnet50_tcmr_viral.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 32
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 32
+
+Model:
+ name: Chowder
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransMIL_feat_norm_rest.yaml b/DeepGraft/TransMIL_feat_norm_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dab6568ee8f7895affa58eec70bacc0f8adccfef
--- /dev/null
+++ b/DeepGraft/TransMIL_feat_norm_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_split_PAS_HE_Jones_norm_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransMIL
+ n_classes: 2
+ backbone: features
+ in_features: 512
+ out_features: 1024
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0001
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransMIL_resnet50_no_viral.yaml b/DeepGraft/TransMIL_resnet50_no_viral.yaml
index 2e3394a8342caab9824528b5ec254bc6c91b7ba3..b9989c2a5231ee5f5a65629b24d4a5f8ea784f0d 100644
--- a/DeepGraft/TransMIL_resnet50_no_viral.yaml
+++ b/DeepGraft/TransMIL_resnet50_no_viral.yaml
@@ -11,12 +11,12 @@ General:
frozen_bn: False
patience: 50
server: test #train #test
- log_path: logs/
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_no_viral.json'
fold: 1
nfold: 4
@@ -41,7 +41,7 @@ Optimizer:
opt_eps: null
opt_betas: null
momentum: null
- weight_decay: 0.00001
+ weight_decay: 0.01
Loss:
base_loss: CrossEntropyLoss
diff --git a/DeepGraft/TransMIL_resnet50_norm_rej_rest.yaml b/DeepGraft/TransMIL_resnet50_norm_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..98857434cac5d09f25c7e68e05e41abf0c16c325
--- /dev/null
+++ b/DeepGraft/TransMIL_resnet50_norm_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/limit_20_split_PAS_HE_Jones_norm_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransMIL
+ n_classes: 3
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransMIL_resnet50_norm_rest.yaml b/DeepGraft/TransMIL_resnet50_norm_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b58c6d880b5707fcdd2aa39e65ae2fb77871bfcd
--- /dev/null
+++ b/DeepGraft/TransMIL_resnet50_norm_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 1024
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransMIL_resnet50_rej_rest.yaml b/DeepGraft/TransMIL_resnet50_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..35cfe86ae9c2fbe79b8c0919574139ed83c723dc
--- /dev/null
+++ b/DeepGraft/TransMIL_resnet50_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 1024
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransMIL_resnet50_rejections.yaml b/DeepGraft/TransMIL_resnet50_rejections.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e8e75561aace9ea467664e9ccfc6e2ceaee13039
--- /dev/null
+++ b/DeepGraft/TransMIL_resnet50_rejections.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/reduced_split_PAS_HE_Jones_rejections.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransMIL
+ n_classes: 3
+ backbone: resnet50
+ in_features: 1024
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransMIL_resnet50_tcmr_viral.yaml b/DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
index df205a2342386af0e48aedb777b8e5a5e6aa7aba..19b26f5cfe864fd03516e137c4b3c1d188b1d77e 100644
--- a/DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
+++ b/DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
@@ -16,7 +16,7 @@ General:
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/data/DeepGraft/224_128um/'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
fold: 1
nfold: 3
diff --git a/DeepGraft/TransMIL_retccl_norm_rej_rest.yaml b/DeepGraft/TransMIL_retccl_norm_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..52a78ad5fb8d0c2c8a467ed6c990378e052822d5
--- /dev/null
+++ b/DeepGraft/TransMIL_retccl_norm_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_split_PAS_HE_Jones_norm_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransMIL
+ n_classes: 3
+ backbone: retccl
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransMIL_retccl_norm_rest.yaml b/DeepGraft/TransMIL_retccl_norm_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fa9988de796bf5e7f2d8ce418ccac95b6d28863e
--- /dev/null
+++ b/DeepGraft/TransMIL_retccl_norm_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_split_PAS_HE_Jones_norm_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransMIL
+ n_classes: 2
+ backbone: retccl
+ in_features: 512
+ out_features: 1024
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransMIL_retccl_rej_rest.yaml b/DeepGraft/TransMIL_retccl_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9bf335ed309c92dbd02f73f41931f0499f22ae1d
--- /dev/null
+++ b/DeepGraft/TransMIL_retccl_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_split_PAS_HE_Jones_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransMIL
+ n_classes: 2
+ backbone: retccl
+ in_features: 1024
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_abmr_tcmr.yaml b/DeepGraft/TransformerMIL_resnet50_abmr_tcmr.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6cdfaff0b8aed8ad4c57a1081d1e3796885128c2
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_abmr_tcmr.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/limit_20_split_PAS_HE_Jones_abmr_tcmr.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_all.yaml b/DeepGraft/TransformerMIL_resnet50_all.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2f08bd3a46e094e900d41e23d7aec88db4a75348
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_all.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/limit_20_split_PAS_HE_Jones_all.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 6
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_no_viral.yaml b/DeepGraft/TransformerMIL_resnet50_no_viral.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..faccfa187ea084c2b0ceb58c6043888e9f670de6
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_no_viral.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/reduced_split_PAS_HE_Jones_no_viral.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 4
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0004
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_norm_rej_rest.yaml b/DeepGraft/TransformerMIL_resnet50_norm_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a6f0c827a6cd2f4601f9927d4b346a6b5092834f
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_norm_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/limit_20_split_PAS_HE_Jones_norm_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransformerMIL
+ n_classes: 3
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_norm_rest.yaml b/DeepGraft/TransformerMIL_resnet50_norm_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..755d10408fdb8f9d79394626847f5a2eb294bc30
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_norm_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_rej_rest.yaml b/DeepGraft/TransformerMIL_resnet50_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2f375b315b4c6b4dba8fc17e5d4ca4ae6763f3ce
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_rejections.yaml b/DeepGraft/TransformerMIL_resnet50_rejections.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..87cf4e1dfc6328f631a5cacca6446f36a2bca4de
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_rejections.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/reduced_split_PAS_HE_Jones_rejections.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 3
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_tcmr_viral.yaml b/DeepGraft/TransformerMIL_resnet50_tcmr_viral.yaml
index 896bef13fb4cde9841f9afcb7e87a7fe6d72dd91..b5a6b68041116ea7726d5e023e769307e18a86bc 100644
--- a/DeepGraft/TransformerMIL_resnet50_tcmr_viral.yaml
+++ b/DeepGraft/TransformerMIL_resnet50_tcmr_viral.yaml
@@ -16,7 +16,7 @@ General:
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/data/DeepGraft/224_128um/'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
fold: 1
nfold: 3
@@ -24,11 +24,11 @@ Data:
train_dataloader:
batch_size: 1
- num_workers: 8
+ num_workers: 32
test_dataloader:
batch_size: 1
- num_workers: 8
+ num_workers: 32
Model:
name: TransformerMIL
diff --git a/DeepGraft/TransformerMIL_resnet50_tcmr_viral_U.yaml b/DeepGraft/TransformerMIL_resnet50_tcmr_viral_U.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fab6e3207510ef8d7b72f6697a8bd2e282e6bf5f
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_tcmr_viral_U.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_Utrecht.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_val_RU.yaml b/DeepGraft/TransformerMIL_resnet50_val_RU.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..959cef9dde5b5e3f89af8471c93842c9101136ad
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_val_RU.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_val_RU.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_resnet50_viral_other.yaml b/DeepGraft/TransformerMIL_resnet50_viral_other.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..07ceedae98c1c9b2d28c6fa58fe0e2caf99fbba2
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_viral_other.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_HE_Jones_viral_other.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_retccl_bin_1.yaml b/DeepGraft/TransformerMIL_retccl_bin_1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..64bcc7b19fd2f5163614bb9371d3920b4ab983b0
--- /dev/null
+++ b/DeepGraft/TransformerMIL_retccl_bin_1.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /homeStor1/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/homeStor1/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/homeStor1/ylan/DeepGraft/training_tables/reduced_split_PAS_bin_1.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: retccl
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_retccl_no_viral.yaml b/DeepGraft/TransformerMIL_retccl_no_viral.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5590f9d9449fda73fadb880022efe2c3b6751896
--- /dev/null
+++ b/DeepGraft/TransformerMIL_retccl_no_viral.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/reduced_split_PAS_no_viral.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 4
+ backbone: retccl
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_retccl_norm_rej_rest.yaml b/DeepGraft/TransformerMIL_retccl_norm_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..99c94ffe030eeb1be446ef7110b69838896e5a40
--- /dev/null
+++ b/DeepGraft/TransformerMIL_retccl_norm_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/limit_20_split_PAS_HE_Jones_norm_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransformerMIL
+ n_classes: 3
+ backbone: retccl
+ in_features: 512
+ out_features: 1024
+
+
+Optimizer:
+ opt: adamw
+ lr: 0.0004
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_retccl_norm_rest.yaml b/DeepGraft/TransformerMIL_retccl_norm_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9c7341d5c0c901be73312bde35f15b1122f2bc78
--- /dev/null
+++ b/DeepGraft/TransformerMIL_retccl_norm_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 4
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: retccl
+ in_features: 512
+ out_features: 1024
+
+
+Optimizer:
+ opt: adamw
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_retccl_rej_rest.yaml b/DeepGraft/TransformerMIL_retccl_rej_rest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..20db4da8cfc22daf557025feaa12632e58d2f57c
--- /dev/null
+++ b/DeepGraft/TransformerMIL_retccl_rej_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_rej_rest.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: retccl
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/TransformerMIL_retccl_tcmr_viral.yaml b/DeepGraft/TransformerMIL_retccl_tcmr_viral.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2cc80d1aca23f748acac825226323093ede5bbe3
--- /dev/null
+++ b/DeepGraft/TransformerMIL_retccl_tcmr_viral.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um_v2/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: retccl
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/code/MyLoss/__init__.py b/code/MyLoss/__init__.py
index bd772e346e2d91f749e52282bb72bcb1616b1eff..637208132c62d0ccd6a767840ce6c29cbf63f9f8 100755
--- a/code/MyLoss/__init__.py
+++ b/code/MyLoss/__init__.py
@@ -6,6 +6,7 @@ from .dice_loss import GDiceLoss, GDiceLossV2, SSLoss, SoftDiceLoss,\
IoULoss, TverskyLoss, FocalTversky_loss, AsymLoss, DC_and_CE_loss,\
PenaltyGDiceLoss, DC_and_topk_loss, ExpLog_loss
from .focal_loss import FocalLoss
+from .focal_loss_ori import FocalLoss_Ori
from .hausdorff import HausdorffDTLoss, HausdorffERLoss
from .lovasz_loss import LovaszSoftmax
from .ND_Crossentropy import CrossentropyND, TopKLoss, WeightedCrossEntropyLoss,\
diff --git a/code/MyLoss/__pycache__/__init__.cpython-39.pyc b/code/MyLoss/__pycache__/__init__.cpython-39.pyc
index 119cc4d4a02cb0c100c171e5e23385ccb8309724..7da3e9984ce1b902f3b782c5d227bc06682e938a 100644
Binary files a/code/MyLoss/__pycache__/__init__.cpython-39.pyc and b/code/MyLoss/__pycache__/__init__.cpython-39.pyc differ
diff --git a/code/MyLoss/__pycache__/focal_loss.cpython-39.pyc b/code/MyLoss/__pycache__/focal_loss.cpython-39.pyc
index 4b2734f4edcae0885494fb2002e9678f127ccc8d..c6095b09fb0de1b97c0210ca024f36a0570d93c4 100644
Binary files a/code/MyLoss/__pycache__/focal_loss.cpython-39.pyc and b/code/MyLoss/__pycache__/focal_loss.cpython-39.pyc differ
diff --git a/code/MyLoss/__pycache__/focal_loss_ori.cpython-39.pyc b/code/MyLoss/__pycache__/focal_loss_ori.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5a4cf4db95dd0e42102480ca738d4dd95633ddef
Binary files /dev/null and b/code/MyLoss/__pycache__/focal_loss_ori.cpython-39.pyc differ
diff --git a/code/MyLoss/__pycache__/loss_factory.cpython-39.pyc b/code/MyLoss/__pycache__/loss_factory.cpython-39.pyc
index 4a56099c87d6626e35a3f5d5f82502c95139ee5b..db4ebcceee99d56aa365237e82f0e543b7ba5cc1 100644
Binary files a/code/MyLoss/__pycache__/loss_factory.cpython-39.pyc and b/code/MyLoss/__pycache__/loss_factory.cpython-39.pyc differ
diff --git a/code/MyLoss/focal_loss.py b/code/MyLoss/focal_loss.py
index 05171a9753f40c7116803809487ccabc1d9fdec5..2c3c2e22d3e83dd18657cabbc1ab6b0067d44540 100755
--- a/code/MyLoss/focal_loss.py
+++ b/code/MyLoss/focal_loss.py
@@ -90,4 +90,4 @@ class FocalLoss(nn.Module):
loss = loss.sum()
return loss
-
+
diff --git a/code/MyLoss/focal_loss_ori.py b/code/MyLoss/focal_loss_ori.py
new file mode 100644
index 0000000000000000000000000000000000000000..31ea24cf6260db8a0c10da69367b60cbf7f24d98
--- /dev/null
+++ b/code/MyLoss/focal_loss_ori.py
@@ -0,0 +1,86 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class FocalLoss_Ori(nn.Module):
+ """
+ This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in
+ 'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)'
+ Focal_Loss= -1*alpha*((1-pt)**gamma)*log(pt)
+ Args:
+ num_class: number of classes
+ alpha: class balance factor
+ gamma:
+ ignore_index:
+ reduction:
+ """
+
+ def __init__(self, num_class, alpha=None, gamma=2, ignore_index=None, reduction='mean'):
+ super(FocalLoss_Ori, self).__init__()
+ self.num_class = num_class
+ self.gamma = gamma
+ self.reduction = reduction
+ self.smooth = 1e-4
+ self.ignore_index = ignore_index
+ self.alpha = alpha
+ if alpha is None:
+ self.alpha = torch.ones(num_class, )
+ elif isinstance(alpha, (int, float)):
+ self.alpha = torch.as_tensor([alpha] * num_class)
+ elif isinstance(alpha, (list, np.ndarray)):
+ self.alpha = torch.as_tensor(alpha)
+ if self.alpha.shape[0] != num_class:
+ raise RuntimeError('the length not equal to number of class')
+
+ # if isinstance(self.alpha, (list, tuple, np.ndarray)):
+ # assert len(self.alpha) == self.num_class
+ # self.alpha = torch.Tensor(list(self.alpha))
+ # elif isinstance(self.alpha, (float, int)):
+ # assert 0 < self.alpha < 1.0, 'alpha should be in `(0,1)`)'
+ # assert balance_index > -1
+ # alpha = torch.ones((self.num_class))
+ # alpha *= 1 - self.alpha
+ # alpha[balance_index] = self.alpha
+ # self.alpha = alpha
+ # elif isinstance(self.alpha, torch.Tensor):
+ # self.alpha = self.alpha
+ # else:
+ # raise TypeError('Not support alpha type, expect `int|float|list|tuple|torch.Tensor`')
+
+ def forward(self, logit, target):
+ # assert isinstance(self.alpha,torch.Tensor)\
+ N, C = logit.shape[:2]
+ alpha = self.alpha.to(logit.device)
+ prob = F.softmax(logit, dim=1)
+ if prob.dim() > 2:
+ # N,C,d1,d2 -> N,C,m (m=d1*d2*...)
+ prob = prob.view(N, C, -1)
+ prob = prob.transpose(1, 2).contiguous() # [N,C,d1*d2..] -> [N,d1*d2..,C]
+ prob = prob.view(-1, prob.size(-1)) # [N,d1*d2..,C]-> [N*d1*d2..,C]
+ ori_shp = target.shape
+ target = target.view(-1, 1) # [N,d1,d2,...]->[N*d1*d2*...,1]
+ valid_mask = None
+ if self.ignore_index is not None:
+ valid_mask = target != self.ignore_index
+ target = target * valid_mask
+
+ # ----------memory saving way--------
+
+ prob = prob.gather(1, target).view(-1) + self.smooth # avoid nan
+ logpt = torch.log(prob)
+ # alpha_class = alpha.gather(0, target.view(-1))
+ alpha_class = alpha[target.squeeze().long()]
+ class_weight = -alpha_class * torch.pow(torch.sub(1.0, prob), self.gamma)
+ loss = class_weight * logpt
+ if valid_mask is not None:
+ loss = loss * valid_mask.squeeze()
+
+ if self.reduction == 'mean':
+ loss = loss.mean()
+ if valid_mask is not None:
+ loss = loss.sum() / valid_mask.sum()
+ elif self.reduction == 'none':
+ loss = loss.view(ori_shp)
+ return loss
+
diff --git a/code/MyLoss/loss_factory.py b/code/MyLoss/loss_factory.py
index 8ff6d1814af1bb43a9e8e16a243f6fe87fe476cf..61502bf66fda9c2d2f4ebb8df1566a1d56583b5e 100755
--- a/code/MyLoss/loss_factory.py
+++ b/code/MyLoss/loss_factory.py
@@ -9,6 +9,7 @@ from .dice_loss import GDiceLoss, GDiceLossV2, SSLoss, SoftDiceLoss,\
IoULoss, TverskyLoss, FocalTversky_loss, AsymLoss, DC_and_CE_loss,\
PenaltyGDiceLoss, DC_and_topk_loss, ExpLog_loss
from .focal_loss import FocalLoss
+from .focal_loss_ori import FocalLoss_Ori
from .hausdorff import HausdorffDTLoss, HausdorffERLoss
from .lovasz_loss import LovaszSoftmax
from .ND_Crossentropy import CrossentropyND, TopKLoss, WeightedCrossEntropyLoss,\
@@ -17,20 +18,22 @@ from .poly_loss import PolyLoss
from pytorch_toolbelt import losses as L
-def create_loss(args, w1=1.0, w2=0.5):
+def create_loss(args, n_classes, w1=1.0, w2=0.5):
conf_loss = args.base_loss
- if args.loss_weight:
- weight = torch.tensor(args.loss_weight)
- else: weight = None
+ # n_classes = args.model.n_classes
+ # if args.loss_weight:
+ # weight = torch.tensor(args.loss_weight)
+ # else: weight = None
### MulticlassJaccardLoss(classes=np.arange(11)
# mode = args.base_loss #BINARY_MODE \MULTICLASS_MODE \MULTILABEL_MODE
loss = None
+ print(conf_loss)
if hasattr(nn, conf_loss):
- loss = getattr(nn, conf_loss)(weight=weight, label_smoothing=0.5)
+ loss = getattr(nn, conf_loss)(label_smoothing=0.5)
# loss = getattr(nn, conf_loss)(label_smoothing=0.5)
#binary loss
elif conf_loss == "focal":
- loss = L.BinaryFocalLoss()
+ loss = FocalLoss_Ori(n_classes)
elif conf_loss == "jaccard":
loss = L.BinaryJaccardLoss()
elif conf_loss == "jaccard_log":
diff --git a/code/__pycache__/test_visualize.cpython-39.pyc b/code/__pycache__/test_visualize.cpython-39.pyc
index f22258b82070b835ec1875cf4e46622cbad9198a..e961ce76be403bfa75de922eeedcbaa056c2b97c 100644
Binary files a/code/__pycache__/test_visualize.cpython-39.pyc and b/code/__pycache__/test_visualize.cpython-39.pyc differ
diff --git a/code/models/resnet50.py b/code/datasets/ResNet.py
similarity index 63%
rename from code/models/resnet50.py
rename to code/datasets/ResNet.py
index 89e23d7460df4b37d9455d9ab7a07d4350fd802f..f8fe70648f55b58eacc5da0578821d7842c563f3 100644
--- a/code/models/resnet50.py
+++ b/code/datasets/ResNet.py
@@ -1,6 +1,8 @@
import torch
import torch.nn as nn
-from .utils import load_state_dict_from_url
+from torch.hub import load_state_dict_from_url
+import torch.nn.functional as F
+from torch.nn import Parameter
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
@@ -73,27 +75,21 @@ class BasicBlock(nn.Module):
class Bottleneck(nn.Module):
- # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
- # while original implementation places the stride at the first 1x1 convolution(self.conv1)
- # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
- # This variant is also known as ResNet V1.5 and improves accuracy according to
- # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
-
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
- base_width=64, dilation=1, norm_layer=None):
+ base_width=64, dilation=1, norm_layer=None, momentum_bn=0.1):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
- self.bn1 = norm_layer(width)
+ self.bn1 = norm_layer(width, momentum=momentum_bn)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
- self.bn2 = norm_layer(width)
+ self.bn2 = norm_layer(width, momentum=momentum_bn)
self.conv3 = conv1x1(width, planes * self.expansion)
- self.bn3 = norm_layer(planes * self.expansion)
+ self.bn3 = norm_layer(planes * self.expansion, momentum=momentum_bn)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
@@ -120,12 +116,23 @@ class Bottleneck(nn.Module):
return out
+class NormedLinear(nn.Module):
+
+ def __init__(self, in_features, out_features):
+ super(NormedLinear, self).__init__()
+ self.weight = Parameter(torch.Tensor(in_features, out_features))
+ self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
+
+ def forward(self, x):
+ out = F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0))
+ return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
- norm_layer=None):
+ norm_layer=None, two_branch=False, mlp=False, normlinear=False,
+ momentum_bn=0.1, attention=False, attention_layers=3, return_attn=False):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
@@ -133,6 +140,7 @@ class ResNet(nn.Module):
self.inplanes = 64
self.dilation = 1
+ self.return_attn = return_attn
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
@@ -142,9 +150,14 @@ class ResNet(nn.Module):
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
+ self.two_branch = two_branch
+ self.momentum_bn = momentum_bn
+ self.mlp = mlp
+ linear = NormedLinear if normlinear else nn.Linear
+
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
- self.bn1 = norm_layer(self.inplanes)
+ self.bn1 = norm_layer(self.inplanes, momentum=momentum_bn)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
@@ -154,8 +167,33 @@ class ResNet(nn.Module):
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
+
+ if attention:
+ self.att_branch = self._make_layer(block, 512, attention_layers, 1, attention=True)
+ else:
+ self.att_branch = None
+
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
- self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+ if self.mlp:
+ if self.two_branch:
+ self.fc = nn.Sequential(
+ nn.Linear(512 * block.expansion, 512 * block.expansion),
+ nn.ReLU()
+ )
+ self.instDis = linear(512 * block.expansion, num_classes)
+ self.groupDis = linear(512 * block.expansion, num_classes)
+ else:
+ self.fc = nn.Sequential(
+ nn.Linear(512 * block.expansion, 512 * block.expansion),
+ nn.ReLU(),
+ linear(512 * block.expansion, num_classes)
+ )
+ else:
+ self.fc = nn.Linear(512 * block.expansion, num_classes)
+ if self.two_branch:
+ self.groupDis = nn.Linear(512 * block.expansion, num_classes)
+
for m in self.modules():
if isinstance(m, nn.Conv2d):
@@ -174,7 +212,7 @@ class ResNet(nn.Module):
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
- def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+ def _make_layer(self, block, planes, blocks, stride=1, dilate=False, attention=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
@@ -184,22 +222,31 @@ class ResNet(nn.Module):
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
- norm_layer(planes * block.expansion),
+ norm_layer(planes * block.expansion, momentum=self.momentum_bn),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
- self.base_width, previous_dilation, norm_layer))
+ self.base_width, previous_dilation, norm_layer, momentum_bn=self.momentum_bn))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
- norm_layer=norm_layer))
+ norm_layer=norm_layer, momentum_bn=self.momentum_bn))
+
+ if attention:
+ layers.append(nn.Sequential(
+ conv1x1(self.inplanes, 128),
+ nn.BatchNorm2d(128),
+ nn.ReLU(inplace=True),
+ conv1x1(128, 1),
+ nn.BatchNorm2d(1),
+ nn.Sigmoid()
+ ))
return nn.Sequential(*layers)
- def _forward_impl(self, x):
- # See note [TorchScript super()]
+ def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
@@ -209,15 +256,23 @@ class ResNet(nn.Module):
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
+ if self.att_branch is not None:
+ att_map = self.att_branch(x)
+ x = x + att_map * x
x = self.avgpool(x)
x = torch.flatten(x, 1)
- x = self.fc(x)
-
- return x
-
- def forward(self, x):
- return self._forward_impl(x)
+ if self.mlp and self.two_branch:
+ x = self.fc(x)
+ x1 = self.instDis(x)
+ x2 = self.groupDis(x)
+ return [x1, x2]
+ else:
+ x1 = self.fc(x)
+ if self.two_branch:
+ x2 = self.groupDis(x)
+ return [x1, x2]
+ return x1
def _resnet(arch, block, layers, pretrained, progress, **kwargs):
@@ -232,7 +287,6 @@ def _resnet(arch, block, layers, pretrained, progress, **kwargs):
def resnet18(pretrained=False, progress=True, **kwargs):
r"""ResNet-18 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
@@ -241,11 +295,9 @@ def resnet18(pretrained=False, progress=True, **kwargs):
**kwargs)
-
def resnet34(pretrained=False, progress=True, **kwargs):
r"""ResNet-34 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
@@ -254,11 +306,9 @@ def resnet34(pretrained=False, progress=True, **kwargs):
**kwargs)
-
def resnet50(pretrained=False, progress=True, **kwargs):
r"""ResNet-50 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
@@ -267,11 +317,9 @@ def resnet50(pretrained=False, progress=True, **kwargs):
**kwargs)
-
def resnet101(pretrained=False, progress=True, **kwargs):
r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
@@ -280,14 +328,70 @@ def resnet101(pretrained=False, progress=True, **kwargs):
**kwargs)
-
def resnet152(pretrained=False, progress=True, **kwargs):
r"""ResNet-152 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
-
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
- **kwargs)
\ No newline at end of file
+ **kwargs)
+
+
+def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
+ r"""ResNeXt-50 32x4d model from
+ `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ kwargs['groups'] = 32
+ kwargs['width_per_group'] = 4
+ return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
+ pretrained, progress, **kwargs)
+
+
+def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
+ r"""ResNeXt-101 32x8d model from
+ `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ kwargs['groups'] = 32
+ kwargs['width_per_group'] = 8
+ return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
+ pretrained, progress, **kwargs)
+
+
+def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
+ r"""Wide ResNet-50-2 model from
+ `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+ The model is the same as ResNet except for the bottleneck number of channels
+ which is twice larger in every block. The number of channels in outer 1x1
+ convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+ channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ kwargs['width_per_group'] = 64 * 2
+ return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
+ pretrained, progress, **kwargs)
+
+
+def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
+ r"""Wide ResNet-101-2 model from
+ `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+ The model is the same as ResNet except for the bottleneck number of channels
+ which is twice larger in every block. The number of channels in outer 1x1
+ convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+ channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ kwargs['width_per_group'] = 64 * 2
+ return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
+ pretrained, progress, **kwargs)
\ No newline at end of file
diff --git a/code/datasets/__init__.py b/code/datasets/__init__.py
index 0eb1fe74d53130bd211a4475d70a12507e9cf879..2989858e6e652de44eaa34b0eb5ba798f1ffefdf 100644
--- a/code/datasets/__init__.py
+++ b/code/datasets/__init__.py
@@ -1,3 +1,4 @@
from .custom_jpg_dataloader import JPGMILDataloader
from .data_interface import MILDataModule
+from .fast_tensor_dl import FastTensorDataLoader
diff --git a/code/datasets/__pycache__/ResNet.cpython-39.pyc b/code/datasets/__pycache__/ResNet.cpython-39.pyc
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diff --git a/code/datasets/__pycache__/zarr_feature_dataloader.cpython-39.pyc b/code/datasets/__pycache__/zarr_feature_dataloader.cpython-39.pyc
new file mode 100644
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diff --git a/code/datasets/custom_dataloader.py b/code/datasets/custom_dataloader.py
index cf65534d749f1cdc764bbdd8193245043c7853f6..f66246188040f435a184fae2cff904c12c2b7c90 100644
--- a/code/datasets/custom_dataloader.py
+++ b/code/datasets/custom_dataloader.py
@@ -18,7 +18,7 @@ import pandas as pd
import json
import albumentations as A
from albumentations.pytorch import ToTensorV2
-from transformers import AutoFeatureExtractor
+# from transformers import AutoFeatureExtractor
from imgaug import augmenters as iaa
import imgaug as ia
from torchsampler import ImbalancedDatasetSampler
diff --git a/code/datasets/custom_jpg_dataloader.py b/code/datasets/custom_jpg_dataloader.py
index b47fc7fbe2b461a5e446106f3d8c5a9edb55ba4a..c28acc1a378e95ad770a045e583f6ee0011e2181 100644
--- a/code/datasets/custom_jpg_dataloader.py
+++ b/code/datasets/custom_jpg_dataloader.py
@@ -3,30 +3,45 @@ ToDo: remove bag_size
'''
+# from custom_resnet50 import resnet50_baseline
import numpy as np
from pathlib import Path
import torch
from torch.utils import data
-from torch.utils.data.dataloader import DataLoader
+from torch.utils.data import random_split, DataLoader
from tqdm import tqdm
import torchvision.transforms as transforms
from PIL import Image
import cv2
import json
import albumentations as A
+from albumentations.pytorch import ToTensorV2
from imgaug import augmenters as iaa
import imgaug as ia
from torchsampler import ImbalancedDatasetSampler
+
class RangeNormalization(object):
def __call__(self, sample):
- img = sample
- return (img / 255.0 - 0.5) / 0.5
+
+ MEAN = 255 * torch.tensor([0.485, 0.456, 0.406])
+ STD = 255 * torch.tensor([0.229, 0.224, 0.225])
+
+ # x = torch.from_numpy(sample)
+ x = sample.type(torch.float32)
+ # x = x.permute(-1, 0, 1)
+ x = 2 * x / 255 - 1
+
+ # x = (x - MEAN[:, None, None])/ STD[:, None, None]
+
+ #
+
+ return x
class JPGMILDataloader(data.Dataset):
- def __init__(self, file_path, label_path, mode, n_classes, load_data=False, data_cache_size=10, max_bag_size=1296):
+ def __init__(self, file_path, label_path, mode, n_classes, cache=False, data_cache_size=100, max_bag_size=1000):
super().__init__()
self.data_info = []
@@ -42,15 +57,16 @@ class JPGMILDataloader(data.Dataset):
self.max_bag_size = max_bag_size
self.min_bag_size = 120
self.empty_slides = []
+ self.corrupt_slides = []
# self.label_file = label_path
recursive = True
# read labels and slide_path from csv
with open(self.label_path, 'r') as f:
temp_slide_label_dict = json.load(f)[mode]
+ print(len(temp_slide_label_dict))
for (x, y) in temp_slide_label_dict:
x = Path(x).stem
-
# x_complete_path = Path(self.file_path)/Path(x)
for cohort in Path(self.file_path).iterdir():
x_complete_path = Path(self.file_path) / cohort / 'BLOCKS' / Path(x)
@@ -60,12 +76,20 @@ class JPGMILDataloader(data.Dataset):
self.slideLabelDict[x] = y
self.files.append(x_complete_path)
else: self.empty_slides.append(x_complete_path)
- # print(len(self.empty_slides))
- # print(self.empty_slides)
+
+ print(f'Slides with bag size under {self.min_bag_size}: ', len(self.empty_slides))
+ # print(self.empty_slides)
+ # print(len(self.files))
+ # print(len(self.corrupt_slides))
+ # print(self.corrupt_slides)
+ home = Path.cwd().parts[1]
+ self.slide_patient_dict_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict.json'
+ with open(self.slide_patient_dict_path, 'r') as f:
+ slide_patient_dict = json.load(f)
for slide_dir in tqdm(self.files):
- self._add_data_infos(str(slide_dir.resolve()), load_data)
+ self._add_data_infos(str(slide_dir.resolve()), cache, slide_patient_dict)
self.resize_transforms = A.Compose([
@@ -90,7 +114,24 @@ class JPGMILDataloader(data.Dataset):
], name="MyOneOf")
], name="MyAug")
-
+ self.albu_transforms = A.Compose([
+ A.HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, always_apply=False, p=0.5),
+ A.ColorJitter(always_apply=False, p=0.5),
+ A.RandomGamma(gamma_limit=(80,120)),
+ A.Flip(p=0.5),
+ A.RandomRotate90(p=0.5),
+ # A.OneOf([
+ # A.ElasticTransform(alpha=150, sigma=20, alpha_affine=50),
+ # A.Affine(
+ # scale={'x': (0.95, 1.05), 'y': (0.95, 1.05)},
+ # rotate=(-45, 45),
+ # shear=(-4, 4),
+ # cval=8,
+ # )
+ # ]),
+ A.Normalize(),
+ ToTensorV2(),
+ ])
# self.train_transforms = A.Compose([
# A.HueSaturationValue(hue_shift_limit=13, sat_shift_limit=2, val_shift_limit=0, always_apply=True, p=1.0),
# # A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=0, val_shift_limit=0, always_apply=False, p=0.5),
@@ -111,11 +152,13 @@ class JPGMILDataloader(data.Dataset):
# ToTensorV2(),
# ])
self.val_transforms = transforms.Compose([
- # A.Normalize(),
- # ToTensorV2(),
- RangeNormalization(),
+ #
transforms.ToTensor(),
-
+ transforms.Normalize(
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225],
+ ),
+ # RangeNormalization(),
])
self.img_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(p=1),
@@ -130,7 +173,7 @@ class JPGMILDataloader(data.Dataset):
def __getitem__(self, index):
# get data
- (batch, batch_names), label, name = self.get_data(index)
+ (batch, batch_names), label, name, patient = self.get_data(index)
out_batch = []
seq_img_d = self.train_transforms.to_deterministic()
@@ -139,10 +182,15 @@ class JPGMILDataloader(data.Dataset):
# print(.shape)
for img in batch: # expects numpy
img = img.numpy().astype(np.uint8)
+ # img = self.albu_transforms(image=img)
+ # print(img)
# print(img.shape)
img = seq_img_d.augment_image(img)
- img = self.val_transforms(img)
+ img = self.val_transforms(img.copy())
+ # print(img)
out_batch.append(img)
+ # img = self.albu_transforms(image=img)
+ # out_batch.append(img['image'])
else:
for img in batch:
@@ -154,6 +202,7 @@ class JPGMILDataloader(data.Dataset):
# # print(name)
# out_batch = torch.randn(self.bag_size,3,256,256)
# else:
+ # print(len(out_batch))
out_batch = torch.stack(out_batch)
# print(out_batch.shape)
# out_batch = out_batch[torch.randperm(out_batch.shape[0])] #shuffle tiles within batch
@@ -165,19 +214,21 @@ class JPGMILDataloader(data.Dataset):
label = torch.as_tensor(label)
label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
# print(out_batch)
- return out_batch, label, (name, batch_names) #, name_batch
+ return out_batch, label, (name, batch_names, patient) #, name_batch
def __len__(self):
return len(self.data_info)
- def _add_data_infos(self, file_path, load_data):
+ def _add_data_infos(self, file_path, cache, slide_patient_dict):
+
+
wsi_name = Path(file_path).stem
if wsi_name in self.slideLabelDict:
# if wsi_name[:2] != 'RU': #skip RU because of container problems in dataset
label = self.slideLabelDict[wsi_name]
- # print(wsi_name)
+ patient = slide_patient_dict[wsi_name]
idx = -1
- self.data_info.append({'data_path': file_path, 'label': label, 'name': wsi_name, 'cache_idx': idx})
+ self.data_info.append({'data_path': file_path, 'label': label, 'name': wsi_name, 'patient': patient,'cache_idx': idx})
def _load_data(self, file_path):
"""Load data to the cache given the file
@@ -186,20 +237,13 @@ class JPGMILDataloader(data.Dataset):
"""
wsi_batch = []
name_batch = []
- # print(wsi_batch)
- # for tile_path in Path(file_path).iterdir():
- # print(tile_path)
for tile_path in Path(file_path).iterdir():
- # print(tile_path)
img = np.asarray(Image.open(tile_path)).astype(np.uint8)
img = torch.from_numpy(img)
-
- # print(wsi_batch)
wsi_batch.append(img)
name_batch.append(tile_path.stem)
- # if wsi_batch:
wsi_batch = torch.stack(wsi_batch)
if len(wsi_batch.shape) < 4:
wsi_batch.unsqueeze(0)
@@ -213,6 +257,7 @@ class JPGMILDataloader(data.Dataset):
# print(wsi_batch.shape)
if wsi_batch.size(0) > self.max_bag_size:
wsi_batch, name_batch, _ = to_fixed_size_bag(wsi_batch, name_batch, self.max_bag_size)
+ wsi_batch, name_batch = self.data_dropout(wsi_batch, name_batch, drop_rate=0.1)
idx = self._add_to_cache((wsi_batch,name_batch), file_path)
file_idx = next(i for i,v in enumerate(self.data_info) if v['data_path'] == file_path)
self.data_info[file_idx + idx]['cache_idx'] = idx
@@ -225,7 +270,7 @@ class JPGMILDataloader(data.Dataset):
self.data_cache.pop(removal_keys[0])
# remove invalid cache_idx
# self.data_info = [{'data_path': di['data_path'], 'label': di['label'], 'shape': di['shape'], 'name': di['name'], 'cache_idx': -1} if di['data_path'] == removal_keys[0] else di for di in self.data_info]
- self.data_info = [{'data_path': di['data_path'], 'label': di['label'], 'name': di['name'], 'cache_idx': -1} if di['data_path'] == removal_keys[0] else di for di in self.data_info]
+ self.data_info = [{'data_path': di['data_path'], 'label': di['label'], 'name': di['name'], 'patient':di['patient'], 'cache_idx': -1} if di['data_path'] == removal_keys[0] else di for di in self.data_info]
def _add_to_cache(self, data, data_path):
"""Adds data to the cache and returns its index. There is one cache
@@ -269,9 +314,18 @@ class JPGMILDataloader(data.Dataset):
cache_idx = self.data_info[i]['cache_idx']
label = self.data_info[i]['label']
name = self.data_info[i]['name']
+ patient = self.data_info[i]['patient']
+
# print(self.data_cache[fp][cache_idx])
- return self.data_cache[fp][cache_idx], label, name
+ return self.data_cache[fp][cache_idx], label, name, patient
+ def data_dropout(self, bag, batch_names, drop_rate):
+ bag_size = bag.shape[0]
+ bag_idxs = torch.randperm(bag_size)[:int(bag_size*(1-drop_rate))]
+ bag_samples = bag[bag_idxs]
+ name_samples = [batch_names[i] for i in bag_idxs]
+
+ return bag_samples, name_samples
class RandomHueSaturationValue(object):
@@ -318,7 +372,7 @@ def to_fixed_size_bag(bag, names, bag_size: int = 512):
# zero-pad if we don't have enough samples
# zero_padded = torch.cat((bag_samples,
- # torch.zeros(bag_size-bag_samples.shape[0], bag_samples.shape[1], bag_samples.shape[2], bag_samples.shape[3])))
+ # torch.zeros(bag_size-bag_samples.shape[0], bag_samples.shape[1], bag_samples.shape[2], bag_samples.shape[3])))
return bag_samples, name_samples, min(bag_size, len(bag))
@@ -355,28 +409,45 @@ class RandomHueSaturationValue(object):
if __name__ == '__main__':
from pathlib import Path
import os
+ import time
+ from fast_tensor_dl import FastTensorDataLoader
+ from custom_resnet50 import resnet50_baseline
+
+
home = Path.cwd().parts[1]
train_csv = f'/{home}/ylan/DeepGraft_project/code/debug_train.csv'
- data_root = f'/{home}/ylan/data/DeepGraft/224_128um'
+ data_root = f'/{home}/ylan/data/DeepGraft/224_128um_v2'
# data_root = f'/{home}/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
# label_path = f'/{home}/ylan/DeepGraft_project/code/split_PAS_bin.json'
- label_path = f'/{home}/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
+ label_path = f'/{home}/ylan/DeepGraft/training_tables/split_debug.json'
+ # label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
output_dir = f'/{data_root}/debug/augments'
os.makedirs(output_dir, exist_ok=True)
n_classes = 2
- dataset = JPGMILDataloader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes)
+ dataset = JPGMILDataloader(data_root, label_path=label_path, mode='train', cache=False, n_classes=n_classes)
+ a = int(len(dataset)* 0.8)
+ b = int(len(dataset) - a)
+ train_data, valid_data = random_split(dataset, [a, b])
# print(dataset.dataset)
# a = int(len(dataset)* 0.8)
# b = int(len(dataset) - a)
# train_ds, val_ds = torch.utils.data.random_split(dataset, [a, b])
- dl = DataLoader(dataset, None, num_workers=1, shuffle=False)
+ # dl = FastTensorDataLoader(dataset, batch_size=1, shuffle=False)
+ dl = DataLoader(train_data, batch_size=1, num_workers=16, sampler=ImbalancedDatasetSampler(train_data), pin_memory=True)
# print(len(dl))
# dl = DataLoader(dataset, batch_size=1, sampler=ImbalancedDatasetSampler(dataset), num_workers=5)
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ scaler = torch.cuda.amp.GradScaler()
+ model_ft = resnet50_baseline(pretrained=True)
+ for param in model_ft.parameters():
+ param.requires_grad = False
+ model_ft.to(device)
+
# data = DataLoader(dataset, batch_size=1)
@@ -385,12 +456,27 @@ if __name__ == '__main__':
#/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/RU0248_PASD_jke_PASD_20200201_195900_BIG.hdf5
c = 0
label_count = [0] *n_classes
- print(len(dl))
- for item in dl:
- if c >= 5:
- break
- bag, label, (name, _) = item
- label_count[torch.argmax(label)] += 1
+ # print(len(dl))
+ start = time.time()
+ for item in tqdm(dl):
+
+ # if c >= 10:
+ # break
+ bag, label, (name, batch_names, patient) = item
+ print(bag.shape)
+ print(len(batch_names))
+ bag = bag.squeeze(0).float().to(device)
+ label = label.to(device)
+ with torch.cuda.amp.autocast():
+ output = model_ft(bag)
+ c += 1
+ end = time.time()
+
+ print('Bag Time: ', end-start)
+ # print(label)
+ # print(name)
+ # print(patient)
+ # label_count[torch.argmax(label)] += 1
# print(name)
# if name == 'RU0248_PASD_jke_PASD_20200201_195900_BIG':
@@ -414,23 +500,23 @@ if __name__ == '__main__':
# # bag = item[0]
# bag = bag.squeeze()
# original = original.squeeze()
- output_path = Path(output_dir) / name
- output_path.mkdir(exist_ok=True)
- for i in range(bag.shape[0]):
- img = bag[i, :, :, :]
- img = img.squeeze()
+ # output_path = Path(output_dir) / name
+ # output_path.mkdir(exist_ok=True)
+ # for i in range(bag.shape[0]):
+ # img = bag[i, :, :, :]
+ # img = img.squeeze()
- img = ((img-img.min())/(img.max() - img.min())) * 255
- # print(img)
- # print(img)
- img = img.numpy().astype(np.uint8).transpose(1,2,0)
+ # img = ((img-img.min())/(img.max() - img.min())) * 255
+ # # print(img)
+ # # print(img)
+ # img = img.numpy().astype(np.uint8).transpose(1,2,0)
- img = Image.fromarray(img)
- img = img.convert('RGB')
- img.save(f'{output_path}/{i}.png')
+ # img = Image.fromarray(img)
+ # img = img.convert('RGB')
+ # img.save(f'{output_path}/{i}.png')
- c += 1
+ # c += 1
# o_img = original[i,:,:,:]
# o_img = o_img.squeeze()
@@ -449,4 +535,4 @@ if __name__ == '__main__':
# b = torch.stack(a)
# print(b)
# c = to_fixed_size_bag(b, 512)
- # print(c)
\ No newline at end of file
+ # print(c)
diff --git a/code/datasets/custom_npy_dataloader.py b/code/datasets/custom_npy_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2c99e7a08f796dd210e2d967317681a07ae16c5
--- /dev/null
+++ b/code/datasets/custom_npy_dataloader.py
@@ -0,0 +1,526 @@
+'''
+ToDo: remove bag_size
+'''
+
+
+from custom_resnet50 import resnet50_baseline
+import numpy as np
+from pathlib import Path
+import torch
+from torch.utils import data
+from torch.utils.data import random_split, DataLoader
+from tqdm import tqdm
+import torchvision.transforms as transforms
+from PIL import Image
+import cv2
+import json
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+from imgaug import augmenters as iaa
+import imgaug as ia
+from torchsampler import ImbalancedDatasetSampler
+
+
+
+class RangeNormalization(object):
+ def __call__(self, sample):
+ img = sample
+ return (img / 255.0 - 0.5) / 0.5
+
+class NPYMILDataloader(data.Dataset):
+
+ def __init__(self, file_path, label_path, mode, n_classes, load_data=False, data_cache_size=500, max_bag_size=1000):
+ super().__init__()
+
+ self.data_info = []
+ self.data_cache = {}
+ self.slideLabelDict = {}
+ self.files = []
+ self.data_cache_size = data_cache_size
+ self.mode = mode
+ self.file_path = file_path
+ # self.csv_path = csv_path
+ self.label_path = label_path
+ self.n_classes = n_classes
+ self.max_bag_size = max_bag_size
+ self.min_bag_size = 120
+ self.empty_slides = []
+ self.corrupt_slides = []
+ # self.label_file = label_path
+ recursive = True
+ exclude_cohorts = ["debug", "test"]
+ # read labels and slide_path from csv
+ with open(self.label_path, 'r') as f:
+ temp_slide_label_dict = json.load(f)[mode]
+ print(len(temp_slide_label_dict))
+ for (x, y) in temp_slide_label_dict:
+ x = Path(x).stem
+ # x_complete_path = Path(self.file_path)/Path(x)
+ for cohort in Path(self.file_path).iterdir():
+ if cohort.stem not in exclude_cohorts:
+ x_complete_path = cohort / 'BLOCKS'
+ for slide_dir in Path(x_complete_path).iterdir():
+ if slide_dir.suffix == '.npy':
+
+ if slide_dir.stem == x:
+ # if len(list(x_complete_path.iterdir())) > self.min_bag_size:
+ # print(x_complete_path)
+ self.slideLabelDict[x] = y
+ self.files.append(slide_dir)
+ # else: self.empty_slides.append(x_complete_path)
+
+ print(len(self.files))
+
+ print(f'Slides with bag size under {self.min_bag_size}: ', len(self.empty_slides))
+ # print(self.empty_slides)
+ # print(len(self.files))
+ # print(len(self.corrupt_slides))
+ # print(self.corrupt_slides)
+ home = Path.cwd().parts[1]
+ slide_patient_dict_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict.json'
+ with open(slide_patient_dict_path, 'r') as f:
+ slide_patient_dict = json.load(f)
+
+ for slide_dir in tqdm(self.files):
+ self._add_data_infos(str(slide_dir.resolve()), load_data, slide_patient_dict)
+
+
+ self.resize_transforms = A.Compose([
+ A.SmallestMaxSize(max_size=256)
+ ])
+ sometimes = lambda aug: iaa.Sometimes(0.5, aug, name="Random1")
+ sometimes2 = lambda aug: iaa.Sometimes(0.2, aug, name="Random2")
+ sometimes3 = lambda aug: iaa.Sometimes(0.9, aug, name="Random3")
+ sometimes4 = lambda aug: iaa.Sometimes(0.9, aug, name="Random4")
+ sometimes5 = lambda aug: iaa.Sometimes(0.9, aug, name="Random5")
+
+ self.train_transforms = iaa.Sequential([
+ iaa.AddToHueAndSaturation(value=(-30, 30), name="MyHSV"), #13
+ sometimes2(iaa.GammaContrast(gamma=(0.85, 1.15), name="MyGamma")),
+ iaa.Fliplr(0.5, name="MyFlipLR"),
+ iaa.Flipud(0.5, name="MyFlipUD"),
+ sometimes(iaa.Rot90(k=1, keep_size=True, name="MyRot90")),
+ # iaa.OneOf([
+ # sometimes3(iaa.PiecewiseAffine(scale=(0.015, 0.02), cval=0, name="MyPiece")),
+ # sometimes4(iaa.ElasticTransformation(alpha=(100, 200), sigma=20, cval=0, name="MyElastic")),
+ # sometimes5(iaa.Affine(scale={"x": (0.95, 1.05), "y": (0.95, 1.05)}, rotate=(-45, 45), shear=(-4, 4), cval=0, name="MyAffine"))
+ # ], name="MyOneOf")
+
+ ], name="MyAug")
+ self.albu_transforms = A.Compose([
+ A.HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, always_apply=False, p=0.5),
+ A.ColorJitter(always_apply=False, p=0.5),
+ A.RandomGamma(gamma_limit=(80,120)),
+ A.Flip(p=0.5),
+ A.RandomRotate90(p=0.5),
+ # A.OneOf([
+ # A.ElasticTransform(alpha=150, sigma=20, alpha_affine=50),
+ # A.Affine(
+ # scale={'x': (0.95, 1.05), 'y': (0.95, 1.05)},
+ # rotate=(-45, 45),
+ # shear=(-4, 4),
+ # cval=8,
+ # )
+ # ]),
+ A.Normalize(),
+ ToTensorV2(),
+ ])
+ # self.train_transforms = A.Compose([
+ # A.HueSaturationValue(hue_shift_limit=13, sat_shift_limit=2, val_shift_limit=0, always_apply=True, p=1.0),
+ # # A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=0, val_shift_limit=0, always_apply=False, p=0.5),
+ # # A.RandomGamma(),
+ # # A.HorizontalFlip(),
+ # # A.VerticalFlip(),
+ # # A.RandomRotate90(),
+ # # A.OneOf([
+ # # A.ElasticTransform(alpha=150, sigma=20, alpha_affine=50),
+ # # A.Affine(
+ # # scale={'x': (0.95, 1.05), 'y': (0.95, 1.05)},
+ # # rotate=(-45, 45),
+ # # shear=(-4, 4),
+ # # cval=8,
+ # # )
+ # # ]),
+ # A.Normalize(),
+ # ToTensorV2(),
+ # ])
+ self.val_transforms = transforms.Compose([
+ # A.Normalize(),
+ # ToTensorV2(),
+ RangeNormalization(),
+ transforms.ToTensor(),
+
+ ])
+ self.img_transforms = transforms.Compose([
+ transforms.RandomHorizontalFlip(p=1),
+ transforms.RandomVerticalFlip(p=1),
+ # histoTransforms.AutoRandomRotation(),
+ transforms.Lambda(lambda a: np.array(a)),
+ ])
+ self.hsv_transforms = transforms.Compose([
+ RandomHueSaturationValue(hue_shift_limit=(-13,13), sat_shift_limit=(-13,13), val_shift_limit=(-13,13)),
+ transforms.ToTensor()
+ ])
+
+ def __getitem__(self, index):
+ # get data
+ (batch, batch_names), label, name, patient = self.get_data(index)
+ out_batch = []
+ seq_img_d = self.train_transforms.to_deterministic()
+
+ if self.mode == 'train':
+ # print(img)
+ # print(.shape)
+ for img in batch: # expects numpy
+ # img = img.numpy().astype(np.uint8)
+ # img = self.albu_transforms(image=img)
+ # print(img)
+ # print(img.shape)
+ # print(img)
+ img = img.astype(np.uint8)
+
+ img = seq_img_d.augment_image(img)
+ img = self.val_transforms(img)
+ out_batch.append(img)
+ # img = self.albu_transforms(image=img)
+ # out_batch.append(img['image'])
+
+ else:
+ for img in batch:
+ img = img.numpy().astype(np.uint8)
+ img = self.val_transforms(img)
+ out_batch.append(img)
+
+ # if len(out_batch) == 0:
+ # # print(name)
+ # out_batch = torch.randn(self.bag_size,3,256,256)
+ # else:
+ # print(len(out_batch))
+ out_batch = torch.stack(out_batch)
+ # print(out_batch.shape)
+ # out_batch = out_batch[torch.randperm(out_batch.shape[0])] #shuffle tiles within batch
+ # print(out_batch.shape)
+ # if out_batch.shape != torch.Size([self.bag_size, 256, 256, 3]) and out_batch.shape != torch.Size([self.bag_size, 3,256,256]):
+ # print(name)
+ # print(out_batch.shape)
+ # out_batch = torch.permute(out_batch, (0, 2,1,3))
+ label = torch.as_tensor(label)
+ label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+ # print(out_batch)
+ return out_batch, label, (name, batch_names, patient) #, name_batch
+
+ def __len__(self):
+ return len(self.data_info)
+
+ def _add_data_infos(self, file_path, load_data, slide_patient_dict):
+
+
+ wsi_name = Path(file_path).stem
+ if wsi_name in self.slideLabelDict:
+ # if wsi_name[:2] != 'RU': #skip RU because of container problems in dataset
+ label = self.slideLabelDict[wsi_name]
+ patient = slide_patient_dict[wsi_name]
+ idx = -1
+ self.data_info.append({'data_path': file_path, 'label': label, 'name': wsi_name, 'patient': patient,'cache_idx': idx})
+
+ def _load_data(self, file_path):
+ """Load data to the cache given the file
+ path and update the cache index in the
+ data_info structure.
+ """
+ wsi_batch = []
+ name_batch = []
+ # print(wsi_batch)
+ # for tile_path in Path(file_path).iterdir():
+ # print(tile_path)
+ wsi_batch = np.load(file_path)
+
+
+ if wsi_batch.shape[0] > self.max_bag_size:
+ wsi_batch, name_batch, _ = to_fixed_size_bag(wsi_batch, name_batch, self.max_bag_size)
+ idx = self._add_to_cache((wsi_batch,name_batch), file_path)
+ file_idx = next(i for i,v in enumerate(self.data_info) if v['data_path'] == file_path)
+ self.data_info[file_idx + idx]['cache_idx'] = idx
+
+ # remove an element from data cache if size was exceeded
+ if len(self.data_cache) > self.data_cache_size:
+ # remove one item from the cache at random
+ removal_keys = list(self.data_cache)
+ removal_keys.remove(file_path)
+ self.data_cache.pop(removal_keys[0])
+ # remove invalid cache_idx
+ # self.data_info = [{'data_path': di['data_path'], 'label': di['label'], 'shape': di['shape'], 'name': di['name'], 'cache_idx': -1} if di['data_path'] == removal_keys[0] else di for di in self.data_info]
+ self.data_info = [{'data_path': di['data_path'], 'label': di['label'], 'name': di['name'], 'patient':di['patient'], 'cache_idx': -1} if di['data_path'] == removal_keys[0] else di for di in self.data_info]
+
+ def _add_to_cache(self, data, data_path):
+ """Adds data to the cache and returns its index. There is one cache
+ list for every file_path, containing all datasets in that file.
+ """
+ if data_path not in self.data_cache:
+ self.data_cache[data_path] = [data]
+ else:
+ self.data_cache[data_path].append(data)
+ return len(self.data_cache[data_path]) - 1
+
+ # def get_data_infos(self, type):
+ # """Get data infos belonging to a certain type of data.
+ # """
+ # data_info_type = [di for di in self.data_info if di['type'] == type]
+ # return data_info_type
+
+ def get_name(self, i):
+ # name = self.get_data_infos(type)[i]['name']
+ name = self.data_info[i]['name']
+ return name
+
+ def get_labels(self, indices):
+
+ return [self.data_info[i]['label'] for i in indices]
+ # return self.slideLabelDict.values()
+
+ def get_data(self, i):
+ """Call this function anytime you want to access a chunk of data from the
+ dataset. This will make sure that the data is loaded in case it is
+ not part of the data cache.
+ i = index
+ """
+ # fp = self.get_data_infos(type)[i]['data_path']
+ fp = self.data_info[i]['data_path']
+ if fp not in self.data_cache:
+ self._load_data(fp)
+
+ # get new cache_idx assigned by _load_data_info
+ # cache_idx = self.get_data_infos(type)[i]['cache_idx']
+ cache_idx = self.data_info[i]['cache_idx']
+ label = self.data_info[i]['label']
+ name = self.data_info[i]['name']
+ patient = self.data_info[i]['patient']
+ # print(self.data_cache[fp][cache_idx])
+ return self.data_cache[fp][cache_idx], label, name, patient
+
+
+
+class RandomHueSaturationValue(object):
+
+ def __init__(self, hue_shift_limit=(-180, 180), sat_shift_limit=(-255, 255), val_shift_limit=(-255, 255), p=0.5):
+
+ self.hue_shift_limit = hue_shift_limit
+ self.sat_shift_limit = sat_shift_limit
+ self.val_shift_limit = val_shift_limit
+ self.p = p
+
+ def __call__(self, sample):
+
+ img = sample #,lbl
+
+ if np.random.random() < self.p:
+ img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) #takes np.float32
+ h, s, v = cv2.split(img)
+ hue_shift = np.random.randint(self.hue_shift_limit[0], self.hue_shift_limit[1] + 1)
+ hue_shift = np.uint8(hue_shift)
+ h += hue_shift
+ sat_shift = np.random.uniform(self.sat_shift_limit[0], self.sat_shift_limit[1])
+ s = cv2.add(s, sat_shift)
+ val_shift = np.random.uniform(self.val_shift_limit[0], self.val_shift_limit[1])
+ v = cv2.add(v, val_shift)
+ img = cv2.merge((h, s, v))
+ img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
+ return img #, lbl
+
+def to_fixed_size_bag(bag, names, bag_size: int = 512):
+
+ #duplicate bag instances unitl
+
+ # get up to bag_size elements
+ rng = np.random.default_rng()
+ # if bag.shape[0] > bag_size:
+ rng.shuffle(bag)
+ bag_samples = bag[:bag_size, :, :, :]
+ # bag_samples = bag[bag_idxs]
+ # else:
+ # original_size = bag.shape[0]
+ # q, r = divmod(bag_size - bag.shape[0], bag.shape[0])
+ # # print(bag_size)
+ # # print(bag.shape[0])
+ # # print(q, r)
+
+ # if q > 0:
+ # bag = np.concatenate([bag for _ in range(q)])
+ # if r > 0:
+ # temp = bag
+ # rng.shuffle(temp)
+ # temp = temp[:r, :, :, :]
+ # bag = np.concatenate((bag, temp))
+ # bag_samples = bag
+ name_batch = [''] * bag_size
+ # name_samples = [names[i] for i in bag_idxs]
+ # bag_sample_names = [bag_names[i] for i in bag_idxs]
+ # q, r = divmod(bag_size, bag_samples.shape[0])
+ # if q > 0:
+ # bag_samples = torch.cat([bag_samples]*q, 0)
+
+ # self_padded = torch.cat([bag_samples, bag_samples[:r,:, :, :]])
+
+ # zero-pad if we don't have enough samples
+ # zero_padded = torch.cat((bag_samples,
+ # torch.zeros(bag_size-bag_samples.shape[0], bag_samples.shape[1], bag_samples.shape[2], bag_samples.shape[3])))
+
+ return bag_samples, name_batch, min(bag_size, len(bag))
+
+
+class RandomHueSaturationValue(object):
+
+ def __init__(self, hue_shift_limit=(-180, 180), sat_shift_limit=(-255, 255), val_shift_limit=(-255, 255), p=0.5):
+
+ self.hue_shift_limit = hue_shift_limit
+ self.sat_shift_limit = sat_shift_limit
+ self.val_shift_limit = val_shift_limit
+ self.p = p
+
+ def __call__(self, sample):
+
+ img = sample #,lbl
+
+ if np.random.random() < self.p:
+ img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) #takes np.float32
+ h, s, v = cv2.split(img)
+ hue_shift = np.random.randint(self.hue_shift_limit[0], self.hue_shift_limit[1] + 1)
+ hue_shift = np.uint8(hue_shift)
+ h += hue_shift
+ sat_shift = np.random.uniform(self.sat_shift_limit[0], self.sat_shift_limit[1])
+ s = cv2.add(s, sat_shift)
+ val_shift = np.random.uniform(self.val_shift_limit[0], self.val_shift_limit[1])
+ v = cv2.add(v, val_shift)
+ img = cv2.merge((h, s, v))
+ img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
+ return img #, lbl
+
+
+
+if __name__ == '__main__':
+ from pathlib import Path
+ import os
+ import time
+ from fast_tensor_dl import FastTensorDataLoader
+
+
+
+ home = Path.cwd().parts[1]
+ train_csv = f'/{home}/ylan/DeepGraft_project/code/debug_train.csv'
+ data_root = f'/{home}/ylan/data/DeepGraft/224_128um_v2'
+ # data_root = f'/{home}/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ # label_path = f'/{home}/ylan/DeepGraft_project/code/split_PAS_bin.json'
+ label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ output_dir = f'/{data_root}/debug/augments'
+ os.makedirs(output_dir, exist_ok=True)
+
+ n_classes = 2
+
+ dataset = NPYMILDataloader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes)
+ a = int(len(dataset)* 0.8)
+ b = int(len(dataset) - a)
+ train_data, valid_data = random_split(dataset, [a, b])
+ # print(dataset.dataset)
+ # a = int(len(dataset)* 0.8)
+ # b = int(len(dataset) - a)
+ # train_ds, val_ds = torch.utils.data.random_split(dataset, [a, b])
+ # dl = FastTensorDataLoader(dataset, batch_size=1, shuffle=False)
+ dl = DataLoader(train_data, batch_size=1, num_workers=16, sampler=ImbalancedDatasetSampler(train_data), pin_memory=True)
+ # print(len(dl))
+ # dl = DataLoader(dataset, batch_size=1, sampler=ImbalancedDatasetSampler(dataset), num_workers=5)
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ scaler = torch.cuda.amp.GradScaler()
+
+ model_ft = resnet50_baseline(pretrained=True)
+ for param in model_ft.parameters():
+ param.requires_grad = False
+ model_ft.to(device)
+
+
+
+ # data = DataLoader(dataset, batch_size=1)
+
+ # print(len(dataset))
+ # # x = 0
+ #/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/RU0248_PASD_jke_PASD_20200201_195900_BIG.hdf5
+ c = 0
+ label_count = [0] *n_classes
+ # print(len(dl))
+ start = time.time()
+ for item in tqdm(dl):
+
+
+ # if c >= 10:
+ # break
+ bag, label, (name, _, patient) = item
+ bag = bag.squeeze(0).float().to(device)
+ label = label.to(device)
+ with torch.cuda.amp.autocast():
+ output = model_ft(bag)
+ c += 1
+ end = time.time()
+
+ print('Bag Time: ', end-start)
+ # print(label)
+ # print(name)
+ # print(patient)
+ # label_count[torch.argmax(label)] += 1
+ # print(name)
+ # if name == 'RU0248_PASD_jke_PASD_20200201_195900_BIG':
+
+ # print(bag)
+ # print(label)
+
+ # # # print(bag.shape)
+ # # if bag.shape[1] == 1:
+ # # print(name)
+ # # print(bag.shape)
+ # print(bag.shape)
+
+ # # out_dir = Path(output_path) / name
+ # # os.makedirs(out_dir, exist_ok=True)
+
+ # # # print(item[2])
+ # # # print(len(item))
+ # # # print(item[1])
+ # # # print(data.shape)
+ # # # data = data.squeeze()
+ # # bag = item[0]
+ # bag = bag.squeeze()
+ # original = original.squeeze()
+ # output_path = Path(output_dir) / name
+ # output_path.mkdir(exist_ok=True)
+ # for i in range(bag.shape[0]):
+ # img = bag[i, :, :, :]
+ # img = img.squeeze()
+
+ # img = ((img-img.min())/(img.max() - img.min())) * 255
+ # # print(img)
+ # # print(img)
+ # img = img.numpy().astype(np.uint8).transpose(1,2,0)
+
+
+ # img = Image.fromarray(img)
+ # img = img.convert('RGB')
+ # img.save(f'{output_path}/{i}.png')
+
+ # c += 1
+
+ # o_img = original[i,:,:,:]
+ # o_img = o_img.squeeze()
+ # print(o_img.shape)
+ # o_img = ((o_img-o_img.min())/(o_img.max()-o_img.min()))*255
+ # o_img = o_img.numpy().astype(np.uint8).transpose(1,2,0)
+ # o_img = Image.fromarray(o_img)
+ # o_img = o_img.convert('RGB')
+ # o_img.save(f'{output_path}/{i}_original.png')
+
+ # break
+ # else: break
+ # print(data.shape)
+ # print(label)
+ # a = [torch.Tensor((3,256,256))]*3
+ # b = torch.stack(a)
+ # print(b)
+ # c = to_fixed_size_bag(b, 512)
+ # print(c)
\ No newline at end of file
diff --git a/code/datasets/custom_resnet50.py b/code/datasets/custom_resnet50.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c3f33518bc53a1611b94767bc50fbbb169154d0
--- /dev/null
+++ b/code/datasets/custom_resnet50.py
@@ -0,0 +1,122 @@
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+import torch
+import torch.nn.functional as F
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+ 'resnet152']
+
+model_urls = {
+ 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+ 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+ 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+ 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+ 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+class Bottleneck_Baseline(nn.Module):
+ expansion = 4
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
+ super(Bottleneck_Baseline, self).__init__()
+ self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+ self.bn1 = nn.BatchNorm2d(planes)
+ self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+ padding=1, bias=False)
+ self.bn2 = nn.BatchNorm2d(planes)
+ self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
+ self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+ out = self.relu(out)
+
+ out = self.conv3(out)
+ out = self.bn3(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+class ResNet_Baseline(nn.Module):
+
+ def __init__(self, block, layers):
+ self.inplanes = 64
+ super(ResNet_Baseline, self).__init__()
+ self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+ bias=False)
+ self.bn1 = nn.BatchNorm2d(64)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = self._make_layer(block, 64, layers[0])
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+ self.avgpool = nn.AdaptiveAvgPool2d(1)
+
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ elif isinstance(m, nn.BatchNorm2d):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+
+ def _make_layer(self, block, planes, blocks, stride=1):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ nn.Conv2d(self.inplanes, planes * block.expansion,
+ kernel_size=1, stride=stride, bias=False),
+ nn.BatchNorm2d(planes * block.expansion),
+ )
+
+ layers = []
+ layers.append(block(self.inplanes, planes, stride, downsample))
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(block(self.inplanes, planes))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+
+ x = self.layer1(x)
+ x = self.layer2(x)
+ x = self.layer3(x)
+
+ x = self.avgpool(x)
+ x = x.view(x.size(0), -1)
+
+ return x
+
+def resnet50_baseline(pretrained=False):
+ """Constructs a Modified ResNet-50 model.
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ """
+ model = ResNet_Baseline(Bottleneck_Baseline, [3, 4, 6, 3])
+ if pretrained:
+ model = load_pretrained_weights(model, 'resnet50')
+ return model
+
+def load_pretrained_weights(model, name):
+ pretrained_dict = model_zoo.load_url(model_urls[name])
+ model.load_state_dict(pretrained_dict, strict=False)
+ return model
diff --git a/code/datasets/custom_zarr_dataloader.py b/code/datasets/custom_zarr_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bdf0906adaea225fb630038412ac709b1c125ec
--- /dev/null
+++ b/code/datasets/custom_zarr_dataloader.py
@@ -0,0 +1,528 @@
+'''
+ToDo: remove bag_size
+'''
+
+
+from custom_resnet50 import resnet50_baseline
+import numpy as np
+from pathlib import Path
+import torch
+from torch.utils import data
+from torch.utils.data import random_split, DataLoader
+from tqdm import tqdm
+import torchvision.transforms as transforms
+from PIL import Image
+import cv2
+import json
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+from imgaug import augmenters as iaa
+import imgaug as ia
+from torchsampler import ImbalancedDatasetSampler
+import zarr
+
+
+
+class RangeNormalization(object):
+ def __call__(self, sample):
+ img = sample
+ return (img / 255.0 - 0.5) / 0.5
+
+class ZarrMILDataloader(data.Dataset):
+
+ def __init__(self, file_path, label_path, mode, n_classes, load_data=False, data_cache_size=500, max_bag_size=1000):
+ super().__init__()
+
+ self.data_info = []
+ self.data_cache = {}
+ self.slideLabelDict = {}
+ self.files = []
+ self.data_cache_size = data_cache_size
+ self.mode = mode
+ self.file_path = file_path
+ # self.csv_path = csv_path
+ self.label_path = label_path
+ self.n_classes = n_classes
+ self.max_bag_size = max_bag_size
+ self.min_bag_size = 120
+ self.empty_slides = []
+ self.corrupt_slides = []
+ # self.label_file = label_path
+ recursive = True
+ exclude_cohorts = ["debug", "test"]
+ # read labels and slide_path from csv
+ with open(self.label_path, 'r') as f:
+ temp_slide_label_dict = json.load(f)[mode]
+ print(len(temp_slide_label_dict))
+ for (x, y) in temp_slide_label_dict:
+ x = Path(x).stem
+ # x_complete_path = Path(self.file_path)/Path(x)
+ for cohort in Path(self.file_path).iterdir():
+ if cohort.stem not in exclude_cohorts:
+ x_complete_path = cohort / 'BLOCKS'
+ for slide_dir in Path(x_complete_path).iterdir():
+ if slide_dir.suffix == '.zarr':
+
+ if slide_dir.stem == x:
+ # if len(list(x_complete_path.iterdir())) > self.min_bag_size:
+ # print(x_complete_path)
+ self.slideLabelDict[x] = y
+ self.files.append(slide_dir)
+ # else: self.empty_slides.append(x_complete_path)
+
+ print(len(self.files))
+
+ print(f'Slides with bag size under {self.min_bag_size}: ', len(self.empty_slides))
+ # print(self.empty_slides)
+ # print(len(self.files))
+ # print(len(self.corrupt_slides))
+ # print(self.corrupt_slides)
+ home = Path.cwd().parts[1]
+ slide_patient_dict_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict.json'
+ with open(slide_patient_dict_path, 'r') as f:
+ slide_patient_dict = json.load(f)
+
+ for slide_dir in tqdm(self.files):
+ self._add_data_infos(str(slide_dir.resolve()), load_data, slide_patient_dict)
+
+
+ self.resize_transforms = A.Compose([
+ A.SmallestMaxSize(max_size=256)
+ ])
+ sometimes = lambda aug: iaa.Sometimes(0.5, aug, name="Random1")
+ sometimes2 = lambda aug: iaa.Sometimes(0.2, aug, name="Random2")
+ sometimes3 = lambda aug: iaa.Sometimes(0.9, aug, name="Random3")
+ sometimes4 = lambda aug: iaa.Sometimes(0.9, aug, name="Random4")
+ sometimes5 = lambda aug: iaa.Sometimes(0.9, aug, name="Random5")
+
+ self.train_transforms = iaa.Sequential([
+ iaa.AddToHueAndSaturation(value=(-30, 30), name="MyHSV"), #13
+ sometimes2(iaa.GammaContrast(gamma=(0.85, 1.15), name="MyGamma")),
+ iaa.Fliplr(0.5, name="MyFlipLR"),
+ iaa.Flipud(0.5, name="MyFlipUD"),
+ sometimes(iaa.Rot90(k=1, keep_size=True, name="MyRot90")),
+ # iaa.OneOf([
+ # sometimes3(iaa.PiecewiseAffine(scale=(0.015, 0.02), cval=0, name="MyPiece")),
+ # sometimes4(iaa.ElasticTransformation(alpha=(100, 200), sigma=20, cval=0, name="MyElastic")),
+ # sometimes5(iaa.Affine(scale={"x": (0.95, 1.05), "y": (0.95, 1.05)}, rotate=(-45, 45), shear=(-4, 4), cval=0, name="MyAffine"))
+ # ], name="MyOneOf")
+
+ ], name="MyAug")
+ self.albu_transforms = A.Compose([
+ A.HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, always_apply=False, p=0.5),
+ A.ColorJitter(always_apply=False, p=0.5),
+ A.RandomGamma(gamma_limit=(80,120)),
+ A.Flip(p=0.5),
+ A.RandomRotate90(p=0.5),
+ # A.OneOf([
+ # A.ElasticTransform(alpha=150, sigma=20, alpha_affine=50),
+ # A.Affine(
+ # scale={'x': (0.95, 1.05), 'y': (0.95, 1.05)},
+ # rotate=(-45, 45),
+ # shear=(-4, 4),
+ # cval=8,
+ # )
+ # ]),
+ A.Normalize(),
+ ToTensorV2(),
+ ])
+ # self.train_transforms = A.Compose([
+ # A.HueSaturationValue(hue_shift_limit=13, sat_shift_limit=2, val_shift_limit=0, always_apply=True, p=1.0),
+ # # A.HueSaturationValue(hue_shift_limit=5, sat_shift_limit=0, val_shift_limit=0, always_apply=False, p=0.5),
+ # # A.RandomGamma(),
+ # # A.HorizontalFlip(),
+ # # A.VerticalFlip(),
+ # # A.RandomRotate90(),
+ # # A.OneOf([
+ # # A.ElasticTransform(alpha=150, sigma=20, alpha_affine=50),
+ # # A.Affine(
+ # # scale={'x': (0.95, 1.05), 'y': (0.95, 1.05)},
+ # # rotate=(-45, 45),
+ # # shear=(-4, 4),
+ # # cval=8,
+ # # )
+ # # ]),
+ # A.Normalize(),
+ # ToTensorV2(),
+ # ])
+ self.val_transforms = transforms.Compose([
+ # A.Normalize(),
+ # ToTensorV2(),
+ RangeNormalization(),
+ transforms.ToTensor(),
+
+ ])
+ self.img_transforms = transforms.Compose([
+ transforms.RandomHorizontalFlip(p=1),
+ transforms.RandomVerticalFlip(p=1),
+ # histoTransforms.AutoRandomRotation(),
+ transforms.Lambda(lambda a: np.array(a)),
+ ])
+ self.hsv_transforms = transforms.Compose([
+ RandomHueSaturationValue(hue_shift_limit=(-13,13), sat_shift_limit=(-13,13), val_shift_limit=(-13,13)),
+ transforms.ToTensor()
+ ])
+
+ def __getitem__(self, index):
+ # get data
+ (batch, batch_names), label, name, patient = self.get_data(index)
+ out_batch = []
+ seq_img_d = self.train_transforms.to_deterministic()
+
+ if self.mode == 'train':
+ # print(img)
+ # print(.shape)
+ for img in batch: # expects numpy
+ # img = img.numpy().astype(np.uint8)
+ # img = self.albu_transforms(image=img)
+ # print(img)
+ # print(img.shape)
+ # print(img)
+ img = img.astype(np.uint8)
+
+ img = seq_img_d.augment_image(img)
+ img = self.val_transforms(img)
+ out_batch.append(img)
+ # img = self.albu_transforms(image=img)
+ # out_batch.append(img['image'])
+
+ else:
+ for img in batch:
+ img = img.numpy().astype(np.uint8)
+ img = self.val_transforms(img)
+ out_batch.append(img)
+
+ # if len(out_batch) == 0:
+ # # print(name)
+ # out_batch = torch.randn(self.bag_size,3,256,256)
+ # else:
+ # print(len(out_batch))
+ out_batch = torch.stack(out_batch)
+ # print(out_batch.shape)
+ # out_batch = out_batch[torch.randperm(out_batch.shape[0])] #shuffle tiles within batch
+ # print(out_batch.shape)
+ # if out_batch.shape != torch.Size([self.bag_size, 256, 256, 3]) and out_batch.shape != torch.Size([self.bag_size, 3,256,256]):
+ # print(name)
+ # print(out_batch.shape)
+ # out_batch = torch.permute(out_batch, (0, 2,1,3))
+ label = torch.as_tensor(label)
+ label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+ # print(out_batch)
+ return out_batch, label, (name, batch_names, patient) #, name_batch
+
+ def __len__(self):
+ return len(self.data_info)
+
+ def _add_data_infos(self, file_path, load_data, slide_patient_dict):
+
+
+ wsi_name = Path(file_path).stem
+ if wsi_name in self.slideLabelDict:
+ # if wsi_name[:2] != 'RU': #skip RU because of container problems in dataset
+ label = self.slideLabelDict[wsi_name]
+ patient = slide_patient_dict[wsi_name]
+ idx = -1
+ self.data_info.append({'data_path': file_path, 'label': label, 'name': wsi_name, 'patient': patient,'cache_idx': idx})
+
+ def _load_data(self, file_path):
+ """Load data to the cache given the file
+ path and update the cache index in the
+ data_info structure.
+ """
+ wsi_batch = []
+ name_batch = []
+ # print(wsi_batch)
+ # for tile_path in Path(file_path).iterdir():
+ # print(tile_path)
+ # wsi_batch = np.load(file_path)
+ wsi_batch = zarr.open(file_path, 'r')[:]
+
+
+ if wsi_batch.shape[0] > self.max_bag_size:
+ wsi_batch, name_batch, _ = to_fixed_size_bag(wsi_batch, name_batch, self.max_bag_size)
+ idx = self._add_to_cache((wsi_batch,name_batch), file_path)
+ file_idx = next(i for i,v in enumerate(self.data_info) if v['data_path'] == file_path)
+ self.data_info[file_idx + idx]['cache_idx'] = idx
+
+ # remove an element from data cache if size was exceeded
+ if len(self.data_cache) > self.data_cache_size:
+ # remove one item from the cache at random
+ removal_keys = list(self.data_cache)
+ removal_keys.remove(file_path)
+ self.data_cache.pop(removal_keys[0])
+ # remove invalid cache_idx
+ # self.data_info = [{'data_path': di['data_path'], 'label': di['label'], 'shape': di['shape'], 'name': di['name'], 'cache_idx': -1} if di['data_path'] == removal_keys[0] else di for di in self.data_info]
+ self.data_info = [{'data_path': di['data_path'], 'label': di['label'], 'name': di['name'], 'patient':di['patient'], 'cache_idx': -1} if di['data_path'] == removal_keys[0] else di for di in self.data_info]
+
+ def _add_to_cache(self, data, data_path):
+ """Adds data to the cache and returns its index. There is one cache
+ list for every file_path, containing all datasets in that file.
+ """
+ if data_path not in self.data_cache:
+ self.data_cache[data_path] = [data]
+ else:
+ self.data_cache[data_path].append(data)
+ return len(self.data_cache[data_path]) - 1
+
+ # def get_data_infos(self, type):
+ # """Get data infos belonging to a certain type of data.
+ # """
+ # data_info_type = [di for di in self.data_info if di['type'] == type]
+ # return data_info_type
+
+ def get_name(self, i):
+ # name = self.get_data_infos(type)[i]['name']
+ name = self.data_info[i]['name']
+ return name
+
+ def get_labels(self, indices):
+
+ return [self.data_info[i]['label'] for i in indices]
+ # return self.slideLabelDict.values()
+
+ def get_data(self, i):
+ """Call this function anytime you want to access a chunk of data from the
+ dataset. This will make sure that the data is loaded in case it is
+ not part of the data cache.
+ i = index
+ """
+ # fp = self.get_data_infos(type)[i]['data_path']
+ fp = self.data_info[i]['data_path']
+ if fp not in self.data_cache:
+ self._load_data(fp)
+
+ # get new cache_idx assigned by _load_data_info
+ # cache_idx = self.get_data_infos(type)[i]['cache_idx']
+ cache_idx = self.data_info[i]['cache_idx']
+ label = self.data_info[i]['label']
+ name = self.data_info[i]['name']
+ patient = self.data_info[i]['patient']
+ # print(self.data_cache[fp][cache_idx])
+ return self.data_cache[fp][cache_idx], label, name, patient
+
+
+
+class RandomHueSaturationValue(object):
+
+ def __init__(self, hue_shift_limit=(-180, 180), sat_shift_limit=(-255, 255), val_shift_limit=(-255, 255), p=0.5):
+
+ self.hue_shift_limit = hue_shift_limit
+ self.sat_shift_limit = sat_shift_limit
+ self.val_shift_limit = val_shift_limit
+ self.p = p
+
+ def __call__(self, sample):
+
+ img = sample #,lbl
+
+ if np.random.random() < self.p:
+ img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) #takes np.float32
+ h, s, v = cv2.split(img)
+ hue_shift = np.random.randint(self.hue_shift_limit[0], self.hue_shift_limit[1] + 1)
+ hue_shift = np.uint8(hue_shift)
+ h += hue_shift
+ sat_shift = np.random.uniform(self.sat_shift_limit[0], self.sat_shift_limit[1])
+ s = cv2.add(s, sat_shift)
+ val_shift = np.random.uniform(self.val_shift_limit[0], self.val_shift_limit[1])
+ v = cv2.add(v, val_shift)
+ img = cv2.merge((h, s, v))
+ img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
+ return img #, lbl
+
+def to_fixed_size_bag(bag, names, bag_size: int = 512):
+
+ #duplicate bag instances unitl
+
+ # get up to bag_size elements
+ rng = np.random.default_rng()
+ # if bag.shape[0] > bag_size:
+ rng.shuffle(bag)
+ bag_samples = bag[:bag_size, :, :, :]
+ # bag_samples = bag[bag_idxs]
+ # else:
+ # original_size = bag.shape[0]
+ # q, r = divmod(bag_size - bag.shape[0], bag.shape[0])
+ # # print(bag_size)
+ # # print(bag.shape[0])
+ # # print(q, r)
+
+ # if q > 0:
+ # bag = np.concatenate([bag for _ in range(q)])
+ # if r > 0:
+ # temp = bag
+ # rng.shuffle(temp)
+ # temp = temp[:r, :, :, :]
+ # bag = np.concatenate((bag, temp))
+ # bag_samples = bag
+ name_batch = [''] * bag_size
+ # name_samples = [names[i] for i in bag_idxs]
+ # bag_sample_names = [bag_names[i] for i in bag_idxs]
+ # q, r = divmod(bag_size, bag_samples.shape[0])
+ # if q > 0:
+ # bag_samples = torch.cat([bag_samples]*q, 0)
+
+ # self_padded = torch.cat([bag_samples, bag_samples[:r,:, :, :]])
+
+ # zero-pad if we don't have enough samples
+ # zero_padded = torch.cat((bag_samples,
+ # torch.zeros(bag_size-bag_samples.shape[0], bag_samples.shape[1], bag_samples.shape[2], bag_samples.shape[3])))
+
+ return bag_samples, name_batch, min(bag_size, len(bag))
+
+
+class RandomHueSaturationValue(object):
+
+ def __init__(self, hue_shift_limit=(-180, 180), sat_shift_limit=(-255, 255), val_shift_limit=(-255, 255), p=0.5):
+
+ self.hue_shift_limit = hue_shift_limit
+ self.sat_shift_limit = sat_shift_limit
+ self.val_shift_limit = val_shift_limit
+ self.p = p
+
+ def __call__(self, sample):
+
+ img = sample #,lbl
+
+ if np.random.random() < self.p:
+ img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) #takes np.float32
+ h, s, v = cv2.split(img)
+ hue_shift = np.random.randint(self.hue_shift_limit[0], self.hue_shift_limit[1] + 1)
+ hue_shift = np.uint8(hue_shift)
+ h += hue_shift
+ sat_shift = np.random.uniform(self.sat_shift_limit[0], self.sat_shift_limit[1])
+ s = cv2.add(s, sat_shift)
+ val_shift = np.random.uniform(self.val_shift_limit[0], self.val_shift_limit[1])
+ v = cv2.add(v, val_shift)
+ img = cv2.merge((h, s, v))
+ img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
+ return img #, lbl
+
+
+
+if __name__ == '__main__':
+ from pathlib import Path
+ import os
+ import time
+ from fast_tensor_dl import FastTensorDataLoader
+
+
+
+ home = Path.cwd().parts[1]
+ train_csv = f'/{home}/ylan/DeepGraft_project/code/debug_train.csv'
+ data_root = f'/{home}/ylan/data/DeepGraft/224_128um_v2'
+ # data_root = f'/{home}/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ # label_path = f'/{home}/ylan/DeepGraft_project/code/split_PAS_bin.json'
+ label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ output_dir = f'/{data_root}/debug/augments'
+ os.makedirs(output_dir, exist_ok=True)
+
+ n_classes = 2
+
+ dataset = ZarrMILDataloader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes)
+ a = int(len(dataset)* 0.8)
+ b = int(len(dataset) - a)
+ train_data, valid_data = random_split(dataset, [a, b])
+ # print(dataset.dataset)
+ # a = int(len(dataset)* 0.8)
+ # b = int(len(dataset) - a)
+ # train_ds, val_ds = torch.utils.data.random_split(dataset, [a, b])
+ # dl = FastTensorDataLoader(dataset, batch_size=1, shuffle=False)
+ dl = DataLoader(train_data, batch_size=1, num_workers=16, sampler=ImbalancedDatasetSampler(train_data), pin_memory=True)
+ # print(len(dl))
+ # dl = DataLoader(dataset, batch_size=1, sampler=ImbalancedDatasetSampler(dataset), num_workers=5)
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ scaler = torch.cuda.amp.GradScaler()
+
+ model_ft = resnet50_baseline(pretrained=True)
+ for param in model_ft.parameters():
+ param.requires_grad = False
+ model_ft.to(device)
+
+
+
+ # data = DataLoader(dataset, batch_size=1)
+
+ # print(len(dataset))
+ # # x = 0
+ #/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/RU0248_PASD_jke_PASD_20200201_195900_BIG.hdf5
+ c = 0
+ label_count = [0] *n_classes
+ # print(len(dl))
+ start = time.time()
+ for item in tqdm(dl):
+
+
+ # if c >= 10:
+ # break
+ bag, label, (name, _, patient) = item
+ bag = bag.squeeze(0).float().to(device)
+ label = label.to(device)
+ with torch.cuda.amp.autocast():
+ output = model_ft(bag)
+ c += 1
+ end = time.time()
+
+ print('Bag Time: ', end-start)
+ # print(label)
+ # print(name)
+ # print(patient)
+ # label_count[torch.argmax(label)] += 1
+ # print(name)
+ # if name == 'RU0248_PASD_jke_PASD_20200201_195900_BIG':
+
+ # print(bag)
+ # print(label)
+
+ # # # print(bag.shape)
+ # # if bag.shape[1] == 1:
+ # # print(name)
+ # # print(bag.shape)
+ # print(bag.shape)
+
+ # # out_dir = Path(output_path) / name
+ # # os.makedirs(out_dir, exist_ok=True)
+
+ # # # print(item[2])
+ # # # print(len(item))
+ # # # print(item[1])
+ # # # print(data.shape)
+ # # # data = data.squeeze()
+ # # bag = item[0]
+ # bag = bag.squeeze()
+ # original = original.squeeze()
+ # output_path = Path(output_dir) / name
+ # output_path.mkdir(exist_ok=True)
+ # for i in range(bag.shape[0]):
+ # img = bag[i, :, :, :]
+ # img = img.squeeze()
+
+ # img = ((img-img.min())/(img.max() - img.min())) * 255
+ # # print(img)
+ # # print(img)
+ # img = img.numpy().astype(np.uint8).transpose(1,2,0)
+
+
+ # img = Image.fromarray(img)
+ # img = img.convert('RGB')
+ # img.save(f'{output_path}/{i}.png')
+
+ # c += 1
+
+ # o_img = original[i,:,:,:]
+ # o_img = o_img.squeeze()
+ # print(o_img.shape)
+ # o_img = ((o_img-o_img.min())/(o_img.max()-o_img.min()))*255
+ # o_img = o_img.numpy().astype(np.uint8).transpose(1,2,0)
+ # o_img = Image.fromarray(o_img)
+ # o_img = o_img.convert('RGB')
+ # o_img.save(f'{output_path}/{i}_original.png')
+
+ # break
+ # else: break
+ # print(data.shape)
+ # print(label)
+ # a = [torch.Tensor((3,256,256))]*3
+ # b = torch.stack(a)
+ # print(b)
+ # c = to_fixed_size_bag(b, 512)
+ # print(c)
\ No newline at end of file
diff --git a/code/datasets/dali_dataloader.py b/code/datasets/dali_dataloader.py
index 0851613c97ebc3705d324e03cb22ce6060e12315..a5efe96fc66f9940ed6ee387dc50d24d01a63cdd 100644
--- a/code/datasets/dali_dataloader.py
+++ b/code/datasets/dali_dataloader.py
@@ -1,139 +1,236 @@
+import nvidia.dali as dali
from nvidia.dali import pipeline_def
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.fn as fn
+# import nvidia.dali.fn.readers.file as file
+from nvidia.dali.fn.decoders import image, image_random_crop
+
import nvidia.dali.types as types
+from nvidia.dali.plugin.pytorch import DALIClassificationIterator, LastBatchPolicy
from pathlib import Path
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-import math
import json
+from tqdm import tqdm
+from random import shuffle
import numpy as np
-import cupy as cp
+from PIL import Image
import torch
-import imageio
+import os
+
+sequence_length = 800
-batch_size = 10
-home = Path.cwd().parts[1]
-# image_filename = f"/{home}/ylan/data/DeepGraft/224_128um/Aachen_Biopsy_Slides/BLOCKS/"
+# Path to MNIST dataset
+# data_path = os.path.join(os.environ['DALI_EXTRA_PATH'], 'db/MNIST/training/')
+# file_path = f'/home/ylan/data/DeepGraft/224_128um_v2'
class ExternalInputIterator(object):
- def __init__(self, batch_size):
- self.file_path = f"/{home}/ylan/data/DeepGraft/224_128um/"
- # self.label_file = f'/{home}/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
- self.label_path = f'/{home}/ylan/DeepGraft/training_tables/split_Aachen_PAS_tcmr_viral.json'
+ def __init__(self, file_path, label_path, mode, n_classes, device_id, num_gpus, batch_size = 1, max_bag_size=sequence_length):
+
+ self.file_path = file_path
+ self.label_path = label_path
+ self.n_classes = n_classes
+ self.mode = mode
+ self.max_bag_size = max_bag_size
+ self.min_bag_size = 120
self.batch_size = batch_size
-
- mode = 'test'
- # with open(self.images_dir + "file_list.txt", 'r') as f:
- # self.files = [line.rstrip() for line in f if line != '']
- self.slideLabelDict = {}
+
+ self.data_info = []
+ self.data_cache = {}
self.files = []
+ self.slideLabelDict = {}
self.empty_slides = []
+
+ home = Path.cwd().parts[1]
+ slide_patient_dict_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict.json'
+ with open(slide_patient_dict_path, 'r') as f:
+ self.slidePatientDict = json.load(f)
+
with open(self.label_path, 'r') as f:
temp_slide_label_dict = json.load(f)[mode]
+ print(len(temp_slide_label_dict))
for (x, y) in temp_slide_label_dict:
x = Path(x).stem
-
# x_complete_path = Path(self.file_path)/Path(x)
for cohort in Path(self.file_path).iterdir():
x_complete_path = Path(self.file_path) / cohort / 'BLOCKS' / Path(x)
if x_complete_path.is_dir():
- if len(list(x_complete_path.iterdir())) > 50:
+ if len(list(x_complete_path.iterdir())) > self.min_bag_size:
# print(x_complete_path)
- # self.slideLabelDict[x] = y
- self.files.append((x_complete_path, y))
+ self.slideLabelDict[x] = y
+ patient = self.slidePatientDict[x_complete_path.stem]
+ self.files.append((x_complete_path, y, patient))
else: self.empty_slides.append(x_complete_path)
- # shuffle(self.files)
+
+ # for slide_dir in tqdm(self.files):
+ # # self._add_data_infos(str(slide_dir.resolve()), load_data, slide_patient_dict)
+ # wsi_name = Path(slide_dir).stem
+ # if wsi_name in self.slideLabelDict:
+ # label = self.slideLabelDict[wsi_name]
+ # patient = self.slidePatientDict[wsi_name]
+ # idx = -1
+ # self.data_info.append({'data_path': file_path, 'label': label, 'name': wsi_name, 'patient': patient,'cache_idx': idx})
+
+ self.dataset_len = len(self.files)
+
+ self.files = self.files[self.dataset_len * device_id//num_gpus:self.dataset_len*(device_id+1)//num_gpus]
+
+ self.n = len(self.files)
+
+ test_data_root = os.environ['DALI_EXTRA_PATH']
+ jpeg_file = os.path.join(test_data_root, 'db', 'single', 'jpeg', '510', 'ship-1083562_640.jpg')
def __iter__(self):
self.i = 0
- self.n = len(self.files)
+ shuffle(self.files)
return self
-
+
def __next__(self):
- batch = []
+ batch = []
labels = []
- file_names = []
-
- for _ in range(self.batch_size):
- wsi_batch = []
- wsi_filename, label = self.files[self.i]
- # jpeg_filename, label = self.files[self.i].split(' ')
- name = Path(wsi_filename).stem
- file_names.append(name)
- for img_path in Path(wsi_filename).iterdir():
- # f = open(img, 'rb')
-
- f = imageio.imread(img_path)
- img = cp.asarray(f)
- wsi_batch.append(img.astype(cp.uint8))
- wsi_batch = cp.stack(wsi_batch)
- batch.append(wsi_batch)
- labels.append(cp.array([label], dtype = cp.uint8))
+
+ if self.i >=self.n:
+ self.__iter__()
+ raise StopIteration
+
+ # for _ in range(self.batch_size):
+ wsi_path, label, patient = self.files[self.i]
+ wsi_batch = []
+ for tile_path in Path(wsi_path).iterdir():
+ np_img = np.fromfile(tile_path, dtype=np.uint8)
+
+ batch.append(np_img)
+
+ # test_data_root = os.environ['DALI_EXTRA_PATH']
+ # jpeg_file = os.path.join(test_data_root, 'db', 'single', 'jpeg', '510', 'ship-1083562_640.jpg')
+ # wsi_batch = [np.fromfile(jpeg_file, dtype=np.uint8) for _ in range(sequence_length)]
+
+ # np_img = np.asarray(Image.open(tile_path)).astype(np.uint8)
+ # print(np_img.shape)
+ # wsi_batch.append(np_img)
- self.i = (self.i + 1) % self.n
+ # print(np_img)
+
+
+
+ # wsi_batch = np.stack(wsi_batch, axis=0)
+ # # print(wsi_batch.shape)
+ # print(wsi_batch)
+ # print(len(wsi_batch))
+ # if len(wsi_batch) > self.max_bag_size:
+ wsi_batch, _ = self.to_fixed_size_bag(batch, self.max_bag_size)
+ # batch.append(wsi_batch)
+ batch = wsi_batch
+ batch.append(torch.tensor([int(label)], dtype=torch.uint8))
+ self.i += 1
+ # for i in range(len(batch)):
+ # print(batch[i].shape)
+ # print(labels[i])
# print(batch)
- # print(labels)
- return (batch, labels)
-
-
-eii = ExternalInputIterator(batch_size=10)
-
-@pipeline_def()
-def hsv_pipeline(device, hue, saturation, value):
- # files, labels = fn.readers.file(file_root=image_filename)
- files, labels = fn.external_source(source=eii, num_outputs=2, dtype=types.UINT8)
- images = fn.decoders.image(files, device = 'cpu' if device == 'cpu' else 'mixed')
- converted = fn.hsv(images, hue=hue, saturation=saturation, value=value)
- return images, converted
-
-def display(outputs, idx, columns=2, captions=None, cpu=True):
- rows = int(math.ceil(len(outputs) / columns))
- fig = plt.figure()
- fig.set_size_inches(16, 6 * rows)
- gs = gridspec.GridSpec(rows, columns)
- row = 0
- col = 0
- for i, out in enumerate(outputs):
- plt.subplot(gs[i])
- plt.axis("off")
- if captions is not None:
- plt.title(captions[i])
- plt.imshow(out.at(idx) if cpu else out.as_cpu().at(idx))
-
-# pipe_cpu = hsv_pipeline(device='cpu', hue=120, saturation=1, value=0.4, batch_size=batch_size, num_threads=1, device_id=0)
-# pipe_cpu.build()
-# cpu_output = pipe_cpu.run()
-
-# display(cpu_output, 3, captions=["Original", "Hue=120, Saturation=1, Value=0.4"])
-
-# pipe_gpu = hsv_pipeline(device='gpu', hue=120, saturation=2, value=1, batch_size=batch_size, num_threads=1, device_id=0)
-# pipe_gpu.build()
-# gpu_output = pipe_gpu.run()
-
-# display(gpu_output, 0, cpu=False, captions=["Original", "Hue=120, Saturation=2, Value=1"])
-
-
-pipe_gpu = Pipeline(batch_size=batch_size, num_threads=2, device_id=0)
-with pipe_gpu:
- images, labels = fn.external_source(source=eii, num_outputs=2, device="gpu", dtype=types.UINT8)
- enhance = fn.brightness_contrast(images, contrast=2)
- pipe_gpu.set_outputs(enhance, labels)
-
-pipe_gpu.build()
-pipe_out_gpu = pipe_gpu.run()
-batch_gpu = pipe_out_gpu[0].as_cpu()
-labels_gpu = pipe_out_gpu[1].as_cpu()
-# file_names = pipe_out_gpu[2].as_cpu()
-
-output_path = f"/{home}/ylan/data/DeepGraft/224_128um/debug/augments/"
-output_path.mkdir(exist_ok=True)
-
-
-img = batch_gpu.at(2)
-print(img.shape)
-print(labels_gpu.at(2))
-plt.axis('off')
-plt.imsave(f'{output_path}/0.jpg', img[0, :, :, :])
\ No newline at end of file
+ return batch
+ # return (batch, labels)
+
+ def __len__(self):
+ return self.dataset_len
+
+ def to_fixed_size_bag(self, bag, bag_size):
+
+ current_size = len(bag)
+ # print(bag)
+
+ if current_size < bag_size:
+ zero_padded = [np.empty(1000, dtype=np.uint8)] * (bag_size - current_size)
+ bag_samples = bag + zero_padded
+ # while current_size < bag_size:
+ # bag.append(np.empty(1, dtype=np.uint8))
+
+ # zero_padded = np.empty(5000)
+
+ else:
+
+ bag_samples = list(np.random.permutation(bag)[:bag_size])
+
+ # bag_samples = list(np.array(bag, dtype=object)[bag_idxs])
+
+ print(len(bag_samples))
+
+ return bag_samples, min(bag_size, len(bag))
+ next = __next__
+
+def ExternalSourcePipeline(batch_size, num_threads, device_id, external_data):
+ pipe = Pipeline(batch_size, num_threads, device_id)
+ with pipe:
+ *jpegs, label = fn.external_source(source=external_data, num_outputs=sequence_length+1, dtype=types.UINT8, batch=False)
+
+ images = fn.decoders.image(jpegs, device="mixed")
+ images = fn.resize(images, resize_x=224, resize_y=224)
+
+ bag = fn.stack(*images)
+ # bag = fn.reshape(bag, layout='')
+ # output = fn.cast(bag, dtype=types.UINT8)
+
+ # output.append(images)
+ # print(output)
+ pipe.set_outputs(bag, label)
+ return pipe
+# @pipeline_def
+# def get_pipeline(file_root:str, size:int=224, validation_size: Optional[int]=256, random_shuffle: bool=False, training:bool=True, decoder_device:str= 'mixed', device:str='gpu'):
+
+# images, labels = file(file_root=file_root, random_shuffle=random_shuffle, name='Reader')
+
+# if training:
+# images = image_random_crop(images,
+# random_area=[0.08, 1.0],
+# random_aspect_ratio = [0.75, 1.3],
+# device=decoder_device,
+# )
+# images = fn.resize(images,
+# size=size,
+# device=device)
+# mirror = fn.random.coin_flip(
+# # probability refers to the probability of getting a 1
+# probability=0.5,
+# )
+# else:
+# images = image(images, device=decoder_device)
+# images = resize(images, size=validation_size, mode='not_smaller', device=device)
+# mirror = False
+
+# images = fn.crop_mirror_normalize(images,
+# crop=(size,size),
+# mirror=mirror,
+# mean=[0.485 * 255,0.456 * 255,0.406 * 255],
+# std=[0.229 * 255,0.224 * 255,0.225 * 255],
+# device=device,
+# )
+# if device == 'gpu':
+# labels = labels.gpu()
+
+# return images, labels
+
+# training_pipeline = get_pipeline(batch_size=1, num_threads=8, device_id=0, file_root=f'/home/ylan/data/DeepGraft/224_128um_v2', random_shuffle=True, training=True, size=224)
+# validation_pipeline = get_pipeline(batch_size=1, num_threads=8, device_id=0, file_root=f'/home/ylan/data/DeepGraft/224_128um_v2', random_shuffle=True, training=False, size=224)
+
+# training_pipeline.build()
+# validation_pipeline.build()
+
+# training_dataloader = DALIClassificationIterator(pipelines=training_pipeline, reader_name='Reader', last_batch_policy=LastBatchPolicy.PARTIAL, auto_reset=True)
+# validation_pipeline = DALIClassificationIterator(pipelines=validation_pipeline, reader_name='Reader', last_batch_policy=LastBatchPolicy.PARTIAL, auto_reset=True)
+
+if __name__ == '__main__':
+
+ home = Path.cwd().parts[1]
+ file_path = f'/{home}/ylan/data/DeepGraft/224_128um_v2'
+ label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ eii = ExternalInputIterator(file_path, label_path, mode="train", n_classes=2, device_id=0, num_gpus=1)
+
+ pipe = ExternalSourcePipeline(batch_size=1, num_threads=2, device_id = 0,
+ external_data = eii)
+ pii = DALIClassificationIterator(pipe, last_batch_padded=True, last_batch_policy=LastBatchPolicy.PARTIAL)
+
+ for e in range(3):
+ for i, data in enumerate(pii):
+ # print(data)
+ print("epoch: {}, iter {}, real batch size: {}".format(e, i, len(data[0]["data"])))
+ pii.reset()
+
diff --git a/code/datasets/data_interface.py b/code/datasets/data_interface.py
index efa104c4f3a1c0ce12ece5cfc285202c8f8f1825..fc4acabfae48ace4abb9833a2b67cbbecde6f2e8 100644
--- a/code/datasets/data_interface.py
+++ b/code/datasets/data_interface.py
@@ -2,8 +2,8 @@ import inspect # 查看python 类的参数和模块、函数代码
import importlib # In order to dynamically import the library
from typing import Optional
import pytorch_lightning as pl
-from pytorch_lightning.loops.base import Loop
-from pytorch_lightning.loops.fit_loop import FitLoop
+# from pytorch_lightning.loops.base import Loop
+# from pytorch_lightning.loops.fit_loop import FitLoop
from torch.utils.data import random_split, DataLoader
from torch.utils.data.dataset import Dataset, Subset
@@ -12,8 +12,9 @@ from torchvision import transforms
from .camel_dataloader import FeatureBagLoader
from .custom_dataloader import HDF5MILDataloader
from .custom_jpg_dataloader import JPGMILDataloader
+from .zarr_feature_dataloader import ZarrFeatureBagLoader
from pathlib import Path
-from transformers import AutoFeatureExtractor
+# from transformers import AutoFeatureExtractor
from torchsampler import ImbalancedDatasetSampler
from abc import ABC, abstractclassmethod, abstractmethod
@@ -119,13 +120,13 @@ class DataInterface(pl.LightningDataModule):
class MILDataModule(pl.LightningDataModule):
- def __init__(self, data_root: str, label_path: str, batch_size: int=1, num_workers: int=50, n_classes=2, cache: bool=True, *args, **kwargs):
+ def __init__(self, data_root: str, label_path: str, batch_size: int=1, num_workers: int=8, n_classes=2, cache: bool=True, use_features=False, *args, **kwargs):
super().__init__()
self.data_root = data_root
self.label_path = label_path
self.batch_size = batch_size
self.num_workers = num_workers
- self.image_size = 384
+ self.image_size = 224
self.n_classes = n_classes
self.target_number = 9
self.mean_bag_length = 10
@@ -134,8 +135,15 @@ class MILDataModule(pl.LightningDataModule):
self.num_bags_test = 50
self.seed = 1
- self.cache = True
+
+ self.cache = cache
self.fe_transform = None
+ if not use_features:
+ self.base_dataloader = JPGMILDataloader
+ else:
+
+ self.base_dataloader = ZarrFeatureBagLoader
+ self.cache = True
@@ -143,19 +151,22 @@ class MILDataModule(pl.LightningDataModule):
home = Path.cwd().parts[1]
if stage in (None, 'fit'):
- dataset = JPGMILDataloader(self.data_root, label_path=self.label_path, mode='train', n_classes=self.n_classes)
+ dataset = self.base_dataloader(self.data_root, label_path=self.label_path, mode='train', n_classes=self.n_classes, cache=self.cache)
+ # dataset = JPGMILDataloader(self.data_root, label_path=self.label_path, mode='train', n_classes=self.n_classes)
+ print(len(dataset))
a = int(len(dataset)* 0.8)
b = int(len(dataset) - a)
self.train_data, self.valid_data = random_split(dataset, [a, b])
if stage in (None, 'test'):
- self.test_data = JPGMILDataloader(self.data_root, label_path=self.label_path, mode='test', n_classes=self.n_classes, data_cache_size=1)
+ self.test_data = self.base_dataloader(self.data_root, label_path=self.label_path, mode='test', n_classes=self.n_classes, data_cache_size=1)
return super().setup(stage=stage)
def train_dataloader(self) -> DataLoader:
+ # return DataLoader(self.train_data, batch_size = self.batch_size, num_workers=self.num_workers) #batch_transforms=self.transform, pseudo_batch_dim=True,
return DataLoader(self.train_data, batch_size = self.batch_size, sampler=ImbalancedDatasetSampler(self.train_data), num_workers=self.num_workers) #batch_transforms=self.transform, pseudo_batch_dim=True,
#sampler=ImbalancedDatasetSampler(self.train_data)
def val_dataloader(self) -> DataLoader:
diff --git a/code/datasets/fast_tensor_dl.py b/code/datasets/fast_tensor_dl.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4eff2611089bd62d6ccb8a579d857610d04890c
--- /dev/null
+++ b/code/datasets/fast_tensor_dl.py
@@ -0,0 +1,46 @@
+import torch
+
+class FastTensorDataLoader:
+ """
+ A DataLoader-like object for a set of tensors that can be much faster than
+ TensorDataset + DataLoader because dataloader grabs individual indices of
+ the dataset and calls cat (slow).
+ Source: https://discuss.pytorch.org/t/dataloader-much-slower-than-manual-batching/27014/6
+ """
+ def __init__(self, *tensors, batch_size=32, shuffle=False):
+ """
+ Initialize a FastTensorDataLoader.
+ :param *tensors: tensors to store. Must have the same length @ dim 0.
+ :param batch_size: batch size to load.
+ :param shuffle: if True, shuffle the data *in-place* whenever an
+ iterator is created out of this object.
+ :returns: A FastTensorDataLoader.
+ """
+ assert all(t.shape[0] == tensors[0].shape[0] for t in tensors)
+ self.tensors = tensors
+
+ self.dataset_len = self.tensors[0].shape[0]
+ self.batch_size = batch_size
+ self.shuffle = shuffle
+
+ # Calculate # batches
+ n_batches, remainder = divmod(self.dataset_len, self.batch_size)
+ if remainder > 0:
+ n_batches += 1
+ self.n_batches = n_batches
+ def __iter__(self):
+ if self.shuffle:
+ r = torch.randperm(self.dataset_len)
+ self.tensors = [t[r] for t in self.tensors]
+ self.i = 0
+ return self
+
+ def __next__(self):
+ if self.i >= self.dataset_len:
+ raise StopIteration
+ batch = tuple(t[self.i:self.i+self.batch_size] for t in self.tensors)
+ self.i += self.batch_size
+ return batch
+
+ def __len__(self):
+ return self.n_batches
\ No newline at end of file
diff --git a/code/datasets/feature_extractor.py b/code/datasets/feature_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..0057c8effb518dd68d00f33035081578d9615166
--- /dev/null
+++ b/code/datasets/feature_extractor.py
@@ -0,0 +1,186 @@
+import numpy as np
+from pathlib import Path
+from PIL import Image
+from tqdm import tqdm
+import zarr
+from numcodecs import Blosc
+import torch
+import torch.nn as nn
+import ResNet as ResNet
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+import re
+from imgaug import augmenters as iaa
+
+def chunker(seq, size):
+ return (seq[pos:pos + size] for pos in range(0, len(seq), size))
+
+def get_coords(batch_names): #ToDO: Change function for precise coords
+ coords = []
+
+ for tile_name in batch_names:
+ # print(tile_name)
+ pos = re.findall(r'\((.*?)\)', tile_name)
+ x, y = pos[-1].split('_')
+ coords.append((int(x),int(y)))
+ return coords
+
+def augment(img):
+
+ sometimes = lambda aug: iaa.Sometimes(0.5, aug, name="Random1")
+ sometimes2 = lambda aug: iaa.Sometimes(0.2, aug, name="Random2")
+ sometimes3 = lambda aug: iaa.Sometimes(0.9, aug, name="Random3")
+ sometimes4 = lambda aug: iaa.Sometimes(0.9, aug, name="Random4")
+ sometimes5 = lambda aug: iaa.Sometimes(0.9, aug, name="Random5")
+
+ transforms = iaa.Sequential([
+ iaa.AddToHueAndSaturation(value=(-30, 30), name="MyHSV"), #13
+ sometimes2(iaa.GammaContrast(gamma=(0.85, 1.15), name="MyGamma")),
+ iaa.Fliplr(0.5, name="MyFlipLR"),
+ iaa.Flipud(0.5, name="MyFlipUD"),
+ sometimes(iaa.Rot90(k=1, keep_size=True, name="MyRot90")),
+ iaa.OneOf([
+ sometimes3(iaa.PiecewiseAffine(scale=(0.015, 0.02), cval=0, name="MyPiece")),
+ sometimes4(iaa.ElasticTransformation(alpha=(100, 200), sigma=20, cval=0, name="MyElastic")),
+ sometimes5(iaa.Affine(scale={"x": (0.95, 1.05), "y": (0.95, 1.05)}, rotate=(-45, 45), shear=(-4, 4), cval=0, name="MyAffine"))
+ ], name="MyOneOf")
+ ])
+ seq_img_d = transforms.to_deterministic()
+ img = seq_img_d.augment_image(img)
+
+ return img
+
+
+if __name__ == '__main__':
+
+
+ home = Path.cwd().parts[1]
+
+ data_root = Path(f'/{home}/ylan/data/DeepGraft/224_128um_v2')
+ # output_path = Path(f'/{home}/ylan/wsi_tools/debug/zarr')
+ cohorts = ['DEEPGRAFT_RA', 'Leuven'] #,
+ # cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU'] #,
+ # cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU', 'DEEPGRAFT_RA', 'Leuven'] #,
+ compressor = Blosc(cname='blosclz', clevel=3)
+
+ val_transforms = transforms.Compose([
+ #
+ transforms.ToTensor(),
+ transforms.Normalize(
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225],
+ ),
+ # RangeNormalization(),
+ ])
+
+
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ scaler = torch.cuda.amp.GradScaler()
+ n_classes = 2
+ out_features = 1024
+ model_ft = ResNet.resnet50(num_classes=n_classes, mlp=False, two_branch=False, normlinear=True)
+ home = Path.cwd().parts[1]
+ model_ft.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=False)
+ for param in model_ft.parameters():
+ param.requires_grad = False
+ for m in model_ft.modules():
+ if isinstance(m, torch.nn.modules.batchnorm.BatchNorm2d):
+ m.eval()
+ m.weight.requires_grad = False
+ m.bias.requires_grad = False
+ model_ft.fc = nn.Linear(2048, out_features)
+ model_ft.eval()
+ model_ft.to(device)
+
+ batch_size = 100
+
+ for f in data_root.iterdir():
+
+ if f.stem in cohorts:
+ print(f)
+ fe_path = f / 'FEATURES_RETCCL'
+ fe_path.mkdir(parents=True, exist_ok=True)
+
+ # num_files = len(list((f / 'BLOCKS').iterdir()))
+ slide_list = []
+ for slide in (f / 'BLOCKS').iterdir():
+ if Path(slide).is_dir():
+ if slide.suffix != '.zarr':
+ slide_list.append(slide)
+
+ with tqdm(total=len(slide_list)) as pbar:
+ for slide in slide_list:
+ # print('slide: ', slide)
+
+ # run every slide 5 times for augments
+ for n in range(5):
+
+
+ output_path = fe_path / Path(str(slide.stem) + f'_aug{n}.zarr')
+ if output_path.is_dir():
+ pbar.update(1)
+ print(output_path, ' skipped.')
+ continue
+ # else:
+ output_array = []
+ output_batch_names = []
+ for tile_path_batch in chunker(list(slide.iterdir()), batch_size):
+ batch_array = []
+ batch_names = []
+ for t in tile_path_batch:
+ # for n in range(5):
+ img = np.asarray(Image.open(str(t))).astype(np.uint8) #.astype(np.uint8)
+
+ img = augment(img)
+
+ img = val_transforms(img.copy()).to(device)
+ batch_array.append(img)
+
+ tile_name = t.stem
+ batch_names.append(tile_name)
+ if len(batch_array) == 0:
+ continue
+ else:
+ batch_array = torch.stack(batch_array)
+ # with torch.cuda.amp.autocast():
+ model_output = model_ft(batch_array).detach()
+ output_array.append(model_output)
+ output_batch_names += batch_names
+ if len(output_array) == 0:
+ pbar.update(1)
+ continue
+ else:
+ output_array = torch.cat(output_array, dim=0).cpu().numpy()
+ output_batch_coords = get_coords(output_batch_names)
+ # print(output_batch_coords)
+ # z = zarr.group()
+ # data = z.create_group('data')
+ # tile_names = z.create_group('tile_names')
+ # d1 = data.create_dataset('bag', shape=output_array.shape, chunks=True, compressor = compressor, synchronizer=zarr.ThreadSynchronizer(), dtype='i4')
+ # d2 = tile_names.create_dataset(output_batch_coords, shape=[len(output_batch_coords), 2], chunks=True, compressor = compressor, synchronizer=zarr.ThreadSynchronizer(), dtype='i4')
+
+ # z['data'] = zarr.array(output_array, chunks=True, compressor = compressor, synchronizer=zarr.ThreadSynchronizer(), dtype='float') # 1792 = 224*8
+ # z['tile_names'] = zarr.array(output_batch_coords, chunks=True, compressor = compressor, synchronizer=zarr.ThreadSynchronizer(), dtype='int32') # 1792 = 224*8
+ # z.save
+ # print(z['data'])
+ # print(z['data'][:])
+ # print(z['tile_names'][:])
+ # zarr.save(output_path, z)
+ zarr.save_group(output_path, data=output_array, coords=output_batch_coords)
+
+ # z_test = zarr.open(output_path, 'r')
+ # print(z_test['data'][:])
+ # # print(z_test.tree())
+
+ # if np.all(output_array== z_test['data'][:]):
+ # print('data true')
+ # if np.all(z['tile_names'][:] == z_test['tile_names'][:]):
+ # print('tile_names true')
+ # print(output_path ' ')
+ # print(np.all(z[:] == z_test[:]))
+
+ # np.save(f'{str(slide)}.npy', slide_np)
+ pbar.update(1)
+
+
+
\ No newline at end of file
diff --git a/code/datasets/feature_extractor_2.py b/code/datasets/feature_extractor_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..0057c8effb518dd68d00f33035081578d9615166
--- /dev/null
+++ b/code/datasets/feature_extractor_2.py
@@ -0,0 +1,186 @@
+import numpy as np
+from pathlib import Path
+from PIL import Image
+from tqdm import tqdm
+import zarr
+from numcodecs import Blosc
+import torch
+import torch.nn as nn
+import ResNet as ResNet
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+import re
+from imgaug import augmenters as iaa
+
+def chunker(seq, size):
+ return (seq[pos:pos + size] for pos in range(0, len(seq), size))
+
+def get_coords(batch_names): #ToDO: Change function for precise coords
+ coords = []
+
+ for tile_name in batch_names:
+ # print(tile_name)
+ pos = re.findall(r'\((.*?)\)', tile_name)
+ x, y = pos[-1].split('_')
+ coords.append((int(x),int(y)))
+ return coords
+
+def augment(img):
+
+ sometimes = lambda aug: iaa.Sometimes(0.5, aug, name="Random1")
+ sometimes2 = lambda aug: iaa.Sometimes(0.2, aug, name="Random2")
+ sometimes3 = lambda aug: iaa.Sometimes(0.9, aug, name="Random3")
+ sometimes4 = lambda aug: iaa.Sometimes(0.9, aug, name="Random4")
+ sometimes5 = lambda aug: iaa.Sometimes(0.9, aug, name="Random5")
+
+ transforms = iaa.Sequential([
+ iaa.AddToHueAndSaturation(value=(-30, 30), name="MyHSV"), #13
+ sometimes2(iaa.GammaContrast(gamma=(0.85, 1.15), name="MyGamma")),
+ iaa.Fliplr(0.5, name="MyFlipLR"),
+ iaa.Flipud(0.5, name="MyFlipUD"),
+ sometimes(iaa.Rot90(k=1, keep_size=True, name="MyRot90")),
+ iaa.OneOf([
+ sometimes3(iaa.PiecewiseAffine(scale=(0.015, 0.02), cval=0, name="MyPiece")),
+ sometimes4(iaa.ElasticTransformation(alpha=(100, 200), sigma=20, cval=0, name="MyElastic")),
+ sometimes5(iaa.Affine(scale={"x": (0.95, 1.05), "y": (0.95, 1.05)}, rotate=(-45, 45), shear=(-4, 4), cval=0, name="MyAffine"))
+ ], name="MyOneOf")
+ ])
+ seq_img_d = transforms.to_deterministic()
+ img = seq_img_d.augment_image(img)
+
+ return img
+
+
+if __name__ == '__main__':
+
+
+ home = Path.cwd().parts[1]
+
+ data_root = Path(f'/{home}/ylan/data/DeepGraft/224_128um_v2')
+ # output_path = Path(f'/{home}/ylan/wsi_tools/debug/zarr')
+ cohorts = ['DEEPGRAFT_RA', 'Leuven'] #,
+ # cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU'] #,
+ # cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU', 'DEEPGRAFT_RA', 'Leuven'] #,
+ compressor = Blosc(cname='blosclz', clevel=3)
+
+ val_transforms = transforms.Compose([
+ #
+ transforms.ToTensor(),
+ transforms.Normalize(
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225],
+ ),
+ # RangeNormalization(),
+ ])
+
+
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ scaler = torch.cuda.amp.GradScaler()
+ n_classes = 2
+ out_features = 1024
+ model_ft = ResNet.resnet50(num_classes=n_classes, mlp=False, two_branch=False, normlinear=True)
+ home = Path.cwd().parts[1]
+ model_ft.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=False)
+ for param in model_ft.parameters():
+ param.requires_grad = False
+ for m in model_ft.modules():
+ if isinstance(m, torch.nn.modules.batchnorm.BatchNorm2d):
+ m.eval()
+ m.weight.requires_grad = False
+ m.bias.requires_grad = False
+ model_ft.fc = nn.Linear(2048, out_features)
+ model_ft.eval()
+ model_ft.to(device)
+
+ batch_size = 100
+
+ for f in data_root.iterdir():
+
+ if f.stem in cohorts:
+ print(f)
+ fe_path = f / 'FEATURES_RETCCL'
+ fe_path.mkdir(parents=True, exist_ok=True)
+
+ # num_files = len(list((f / 'BLOCKS').iterdir()))
+ slide_list = []
+ for slide in (f / 'BLOCKS').iterdir():
+ if Path(slide).is_dir():
+ if slide.suffix != '.zarr':
+ slide_list.append(slide)
+
+ with tqdm(total=len(slide_list)) as pbar:
+ for slide in slide_list:
+ # print('slide: ', slide)
+
+ # run every slide 5 times for augments
+ for n in range(5):
+
+
+ output_path = fe_path / Path(str(slide.stem) + f'_aug{n}.zarr')
+ if output_path.is_dir():
+ pbar.update(1)
+ print(output_path, ' skipped.')
+ continue
+ # else:
+ output_array = []
+ output_batch_names = []
+ for tile_path_batch in chunker(list(slide.iterdir()), batch_size):
+ batch_array = []
+ batch_names = []
+ for t in tile_path_batch:
+ # for n in range(5):
+ img = np.asarray(Image.open(str(t))).astype(np.uint8) #.astype(np.uint8)
+
+ img = augment(img)
+
+ img = val_transforms(img.copy()).to(device)
+ batch_array.append(img)
+
+ tile_name = t.stem
+ batch_names.append(tile_name)
+ if len(batch_array) == 0:
+ continue
+ else:
+ batch_array = torch.stack(batch_array)
+ # with torch.cuda.amp.autocast():
+ model_output = model_ft(batch_array).detach()
+ output_array.append(model_output)
+ output_batch_names += batch_names
+ if len(output_array) == 0:
+ pbar.update(1)
+ continue
+ else:
+ output_array = torch.cat(output_array, dim=0).cpu().numpy()
+ output_batch_coords = get_coords(output_batch_names)
+ # print(output_batch_coords)
+ # z = zarr.group()
+ # data = z.create_group('data')
+ # tile_names = z.create_group('tile_names')
+ # d1 = data.create_dataset('bag', shape=output_array.shape, chunks=True, compressor = compressor, synchronizer=zarr.ThreadSynchronizer(), dtype='i4')
+ # d2 = tile_names.create_dataset(output_batch_coords, shape=[len(output_batch_coords), 2], chunks=True, compressor = compressor, synchronizer=zarr.ThreadSynchronizer(), dtype='i4')
+
+ # z['data'] = zarr.array(output_array, chunks=True, compressor = compressor, synchronizer=zarr.ThreadSynchronizer(), dtype='float') # 1792 = 224*8
+ # z['tile_names'] = zarr.array(output_batch_coords, chunks=True, compressor = compressor, synchronizer=zarr.ThreadSynchronizer(), dtype='int32') # 1792 = 224*8
+ # z.save
+ # print(z['data'])
+ # print(z['data'][:])
+ # print(z['tile_names'][:])
+ # zarr.save(output_path, z)
+ zarr.save_group(output_path, data=output_array, coords=output_batch_coords)
+
+ # z_test = zarr.open(output_path, 'r')
+ # print(z_test['data'][:])
+ # # print(z_test.tree())
+
+ # if np.all(output_array== z_test['data'][:]):
+ # print('data true')
+ # if np.all(z['tile_names'][:] == z_test['tile_names'][:]):
+ # print('tile_names true')
+ # print(output_path ' ')
+ # print(np.all(z[:] == z_test[:]))
+
+ # np.save(f'{str(slide)}.npy', slide_np)
+ pbar.update(1)
+
+
+
\ No newline at end of file
diff --git a/code/datasets/simple_jpg_dataloader.py b/code/datasets/simple_jpg_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..332920a6b269ff1f4593e2f60e50ffa3109a4d12
--- /dev/null
+++ b/code/datasets/simple_jpg_dataloader.py
@@ -0,0 +1,322 @@
+# import pandas as pd
+
+import numpy as np
+import torch
+from torch import Tensor
+from torch.utils import data
+from torch.utils.data import random_split, DataLoader
+from torch.autograd import Variable
+from torch.nn.functional import one_hot
+import torch.utils.data as data_utils
+import torchvision.transforms as transforms
+import pandas as pd
+from sklearn.utils import shuffle
+from pathlib import Path
+from tqdm import tqdm
+from PIL import Image
+import cv2
+import json
+from imgaug import augmenters as iaa
+from torchsampler import ImbalancedDatasetSampler
+
+
+class FeatureBagLoader(data_utils.Dataset):
+ def __init__(self, file_path, label_path, mode, n_classes, load_data=False, data_cache_size=100, max_bag_size=1000):
+ super().__init__()
+
+ self.data_info = []
+ self.data_cache = {}
+ self.slideLabelDict = {}
+ self.files = []
+ self.data_cache_size = data_cache_size
+ self.mode = mode
+ self.file_path = file_path
+ # self.csv_path = csv_path
+ self.label_path = label_path
+ self.n_classes = n_classes
+ self.max_bag_size = max_bag_size
+ self.min_bag_size = 120
+ self.empty_slides = []
+ self.corrupt_slides = []
+ self.cache = True
+
+ # read labels and slide_path from csv
+ with open(self.label_path, 'r') as f:
+ temp_slide_label_dict = json.load(f)[mode]
+ print(len(temp_slide_label_dict))
+ for (x, y) in temp_slide_label_dict:
+ x = Path(x).stem
+ # x_complete_path = Path(self.file_path)/Path(x)
+ for cohort in Path(self.file_path).iterdir():
+ x_complete_path = Path(self.file_path) / cohort / 'BLOCKS' / Path(x)
+ if x_complete_path.is_dir():
+ if len(list(x_complete_path.iterdir())) > self.min_bag_size:
+ # print(x_complete_path)
+ self.slideLabelDict[x] = y
+ self.files.append(x_complete_path)
+ else: self.empty_slides.append(x_complete_path)
+
+ home = Path.cwd().parts[1]
+ self.slide_patient_dict_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict.json'
+ with open(self.slide_patient_dict_path, 'r') as f:
+ self.slide_patient_dict = json.load(f)
+
+ sometimes = lambda aug: iaa.Sometimes(0.5, aug, name="Random1")
+ sometimes2 = lambda aug: iaa.Sometimes(0.2, aug, name="Random2")
+ sometimes3 = lambda aug: iaa.Sometimes(0.9, aug, name="Random3")
+ sometimes4 = lambda aug: iaa.Sometimes(0.9, aug, name="Random4")
+ sometimes5 = lambda aug: iaa.Sometimes(0.9, aug, name="Random5")
+
+ self.train_transforms = iaa.Sequential([
+ iaa.AddToHueAndSaturation(value=(-30, 30), name="MyHSV"), #13
+ sometimes2(iaa.GammaContrast(gamma=(0.85, 1.15), name="MyGamma")),
+ iaa.Fliplr(0.5, name="MyFlipLR"),
+ iaa.Flipud(0.5, name="MyFlipUD"),
+ sometimes(iaa.Rot90(k=1, keep_size=True, name="MyRot90")),
+ # iaa.OneOf([
+ # sometimes3(iaa.PiecewiseAffine(scale=(0.015, 0.02), cval=0, name="MyPiece")),
+ # sometimes4(iaa.ElasticTransformation(alpha=(100, 200), sigma=20, cval=0, name="MyElastic")),
+ # sometimes5(iaa.Affine(scale={"x": (0.95, 1.05), "y": (0.95, 1.05)}, rotate=(-45, 45), shear=(-4, 4), cval=0, name="MyAffine"))
+ # ], name="MyOneOf")
+
+ ], name="MyAug")
+ self.val_transforms = transforms.Compose([
+ #
+ transforms.ToTensor(),
+ transforms.Normalize(
+ mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225],
+ ),
+ # RangeNormalization(),
+ ])
+
+
+
+
+ self.features = []
+ self.labels = []
+ self.wsi_names = []
+ self.name_batches = []
+ self.patients = []
+ if self.cache:
+ if mode=='train':
+ seq_img_d = self.train_transforms.to_deterministic()
+
+ # with tqdm(total=len(self.files)) as pbar:
+
+ for t in tqdm(self.files):
+ batch, label, (wsi_name, name_batch, patient) = self.get_data(t)
+ # print('label: ', label)
+ out_batch = []
+ for img in batch:
+ img = img.numpy().astype(np.uint8)
+ img = seq_img_d.augment_image(img)
+ img = self.val_transforms(img.copy())
+ out_batch.append(img)
+ # ft = ft.view(-1, 512)
+
+ out_batch = torch.stack(out_batch)
+ self.labels.append(label)
+ self.features.append(out_batch)
+ self.wsi_names.append(wsi_name)
+ self.name_batches.append(name_batch)
+ self.patients.append(patient)
+ # pbar.update()
+ else:
+ # with tqdm(total=len(self.file_path)) as pbar:
+ for t in tqdm(self.file_path):
+ batch, label, (wsi_name, name_batch, patient) = self.get_data(t)
+ out_batch = []
+ for img in batch:
+ img = img.numpy().astype(np.uint8)
+ img = self.val_transforms(img.copy())
+ out_batch.append(img)
+ # ft = ft.view(-1, 512)
+ out_batch = torch.stack(out_batch)
+ self.labels.append(label)
+ self.features.append(out_batch)
+ self.wsi_names.append(wsi_name)
+ self.name_batches.append(name_batch)
+ self.patients.append(patient)
+ # pbar.update()
+ # print(self.get_bag_feats(self.train_path))
+ # self.r = np.random.RandomState(seed)
+
+ # self.num_in_train = 60000
+ # self.num_in_test = 10000
+
+ # if self.train:
+ # self.train_bags_list, self.train_labels_list = self._create_bags()
+ # else:
+ # self.test_bags_list, self.test_labels_list = self._create_bags()
+
+ def get_data(self, file_path):
+
+ wsi_batch=[]
+ name_batch=[]
+
+ for tile_path in Path(file_path).iterdir():
+ img = np.asarray(Image.open(tile_path)).astype(np.uint8)
+ img = torch.from_numpy(img)
+ wsi_batch.append(img)
+ name_batch.append(tile_path.stem)
+
+ wsi_batch = torch.stack(wsi_batch)
+
+ if wsi_batch.size(0) > self.max_bag_size:
+ wsi_batch, name_batch, _ = self.to_fixed_size_bag(wsi_batch, name_batch, self.max_bag_size)
+
+
+ wsi_batch, name_batch = self.data_dropout(wsi_batch, name_batch, drop_rate=0.1)
+
+ wsi_name = Path(file_path).stem
+ try:
+ label = self.slideLabelDict[wsi_name]
+ except KeyError:
+ print(f'{wsi_name} is not included in label file {self.label_path}')
+
+ try:
+ patient = self.slide_patient_dict[wsi_name]
+ except KeyError:
+ print(f'{wsi_name} is not included in label file {self.slide_patient_dict_path}')
+
+ return wsi_batch, label, (wsi_name, name_batch, patient)
+
+ def get_labels(self, indices):
+ return [self.labels[i] for i in indices]
+
+
+ def to_fixed_size_bag(self, bag, names, bag_size: int = 512):
+
+ #duplicate bag instances unitl
+
+ bag_idxs = torch.randperm(bag.shape[0])[:bag_size]
+ bag_samples = bag[bag_idxs]
+ name_samples = [names[i] for i in bag_idxs]
+ # bag_sample_names = [bag_names[i] for i in bag_idxs]
+ # q, r = divmod(bag_size, bag_samples.shape[0])
+ # if q > 0:
+ # bag_samples = torch.cat([bag_samples]*q, 0)
+
+ # self_padded = torch.cat([bag_samples, bag_samples[:r,:, :, :]])
+
+ # zero-pad if we don't have enough samples
+ # zero_padded = torch.cat((bag_samples,
+ # torch.zeros(bag_size-bag_samples.shape[0], bag_samples.shape[1], bag_samples.shape[2], bag_samples.shape[3])))
+
+ return bag_samples, name_samples, min(bag_size, len(bag))
+
+ def data_dropout(self, bag, batch_names, drop_rate):
+ bag_size = bag.shape[0]
+ bag_idxs = torch.randperm(bag_size)[:int(bag_size*(1-drop_rate))]
+ bag_samples = bag[bag_idxs]
+ name_samples = [batch_names[i] for i in bag_idxs]
+
+ return bag_samples, name_samples
+
+ def __len__(self):
+ return len(self.files)
+
+ def __getitem__(self, index):
+
+ if self.cache:
+ label = self.labels[index]
+ wsi = self.features[index]
+ label = Variable(Tensor(label))
+ wsi_name = self.wsi_names[index]
+ name_batch = self.name_batches[index]
+ patient = self.patients[index]
+ # feats = Variable(Tensor(feats))
+ return wsi, label, (wsi_name, name_batch, patient)
+ else:
+ if self.mode=='train':
+ batch, label, (wsi_name, name_batch, patient) = self.get_data(self.files[index])
+ label = Variable(Tensor(label))
+ # wsi = Variable(Tensor(wsi_batch))
+ out_batch = []
+ seq_img_d = self.train_transforms.to_deterministic()
+ for img in batch:
+ img = img.numpy().astype(np.uint8)
+ img = seq_img_d.augment_image(img)
+ img = self.val_transforms(img.copy())
+ out_batch.append(img)
+ out_batch = torch.stack(out_batch)
+ # ft = ft.view(-1, 512)
+
+ else:
+ batch, label, (wsi_name, name_batch, patient) = self.get_data(self.files[index])
+ label = Variable(Tensor(label))
+ out_batch = []
+ seq_img_d = self.train_transforms.to_deterministic()
+ for img in batch:
+ img = img.numpy().astype(np.uint8)
+ img = self.val_transforms(img.copy())
+ out_batch.append(img)
+ out_batch = torch.stack(out_batch)
+
+ return out_batch, label, (wsi_name, name_batch, patient)
+
+if __name__ == '__main__':
+
+ from pathlib import Path
+ import os
+ import time
+ from fast_tensor_dl import FastTensorDataLoader
+ from custom_resnet50 import resnet50_baseline
+
+
+
+ home = Path.cwd().parts[1]
+ train_csv = f'/{home}/ylan/DeepGraft_project/code/debug_train.csv'
+ data_root = f'/{home}/ylan/data/DeepGraft/224_128um_v2'
+ # data_root = f'/{home}/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ # label_path = f'/{home}/ylan/DeepGraft_project/code/split_PAS_bin.json'
+ label_path = f'/{home}/ylan/DeepGraft/training_tables/split_debug.json'
+ # label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ output_dir = f'/{data_root}/debug/augments'
+ os.makedirs(output_dir, exist_ok=True)
+
+ n_classes = 2
+
+ dataset = FeatureBagLoader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes)
+
+ # print(dataset.get_labels(0))
+ a = int(len(dataset)* 0.8)
+ b = int(len(dataset) - a)
+ train_data, valid_data = random_split(dataset, [a, b])
+ # print(dataset.dataset)
+ # a = int(len(dataset)* 0.8)
+ # b = int(len(dataset) - a)
+ # train_ds, val_ds = torch.utils.data.random_split(dataset, [a, b])
+ # dl = FastTensorDataLoader(dataset, batch_size=1, shuffle=False)
+ dl = DataLoader(train_data, batch_size=1, num_workers=8, sampler=ImbalancedDatasetSampler(train_data), pin_memory=True)
+ # print(len(dl))
+ # dl = DataLoader(dataset, batch_size=1, sampler=ImbalancedDatasetSampler(dataset), num_workers=5)
+ device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
+ scaler = torch.cuda.amp.GradScaler()
+
+ model_ft = resnet50_baseline(pretrained=True)
+ for param in model_ft.parameters():
+ param.requires_grad = False
+ model_ft.to(device)
+
+ c = 0
+ label_count = [0] *n_classes
+ # print(len(dl))
+ start = time.time()
+ for item in tqdm(dl):
+
+ # if c >= 10:
+ # break
+ bag, label, (name, batch_names, patient) = item
+ # print(bag.shape)
+ # print(len(batch_names))
+
+ bag = bag.squeeze(0).float().to(device)
+ label = label.to(device)
+ with torch.cuda.amp.autocast():
+ output = model_ft(bag)
+ c += 1
+ end = time.time()
+
+ print('Bag Time: ', end-start)
\ No newline at end of file
diff --git a/code/datasets/zarr_feature_dataloader.py b/code/datasets/zarr_feature_dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..b244b87c83a128d9b40b604fef8df647690d482b
--- /dev/null
+++ b/code/datasets/zarr_feature_dataloader.py
@@ -0,0 +1,238 @@
+import pandas as pd
+
+import numpy as np
+import torch
+from torch import Tensor
+from torch.autograd import Variable
+from torch.nn.functional import one_hot
+from torch.utils import data
+from torch.utils.data import random_split, DataLoader
+from torchsampler import ImbalancedDatasetSampler
+from torchvision import datasets, transforms
+import pandas as pd
+from sklearn.utils import shuffle
+from pathlib import Path
+from tqdm import tqdm
+import zarr
+import json
+import cv2
+from PIL import Image
+# from models import TransMIL
+
+
+
+class ZarrFeatureBagLoader(data.Dataset):
+ def __init__(self, file_path, label_path, mode, n_classes, cache=False, data_cache_size=100, max_bag_size=1000):
+ super().__init__()
+
+ self.data_info = []
+ self.data_cache = {}
+ self.slideLabelDict = {}
+ self.files = []
+ self.data_cache_size = data_cache_size
+ self.mode = mode
+ self.file_path = file_path
+ # self.csv_path = csv_path
+ self.label_path = label_path
+ self.n_classes = n_classes
+ self.max_bag_size = max_bag_size
+ self.min_bag_size = 120
+ self.empty_slides = []
+ self.corrupt_slides = []
+ self.cache = True
+
+ # read labels and slide_path from csv
+ with open(self.label_path, 'r') as f:
+ temp_slide_label_dict = json.load(f)[mode]
+ # print(len(temp_slide_label_dict))
+ for (x, y) in temp_slide_label_dict:
+ x = Path(x).stem
+ # x_complete_path = Path(self.file_path)/Path(x)
+ for cohort in Path(self.file_path).iterdir():
+ x_complete_path = Path(self.file_path) / cohort / 'FEATURES_RETCCL' / (str(x) + '.zarr')
+ if x_complete_path.is_dir():
+ # if len(list(x_complete_path.iterdir())) > self.min_bag_size:
+ # # print(x_complete_path)
+ self.slideLabelDict[x] = y
+ self.files.append(x_complete_path)
+
+ home = Path.cwd().parts[1]
+ self.slide_patient_dict_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict.json'
+ with open(self.slide_patient_dict_path, 'r') as f:
+ self.slide_patient_dict = json.load(f)
+
+ self.feature_bags = []
+ self.labels = []
+ self.wsi_names = []
+ self.name_batches = []
+ self.patients = []
+ if self.cache:
+ for t in tqdm(self.files):
+ # zarr_t = str(t) + '.zarr'
+ batch, label, (wsi_name, name_batch, patient) = self.get_data(t)
+
+ self.labels.append(label)
+ self.feature_bags.append(batch)
+ self.wsi_names.append(wsi_name)
+ self.name_batches.append(name_batch)
+ self.patients.append(patient)
+
+ def get_data(self, file_path, drop_rate=0.1):
+
+ batch_names=[] #add function for name_batch read out
+
+ wsi_name = Path(file_path).stem
+ if wsi_name in self.slideLabelDict:
+ label = self.slideLabelDict[wsi_name]
+
+ patient = self.slide_patient_dict[wsi_name]
+ z = zarr.open(file_path, 'r')
+ np_bag = np.array(z['data'][:])
+ # np_bag = np.array(zarr.open(file_path, 'r')).astype(np.uint8)
+ wsi_bag = torch.from_numpy(np_bag)
+ batch_coords = torch.from_numpy(np.array(z['coords'][:]))
+
+ # print(wsi_bag.shape)
+ bag_size = wsi_bag.shape[0]
+
+ # random drop
+ bag_idxs = torch.randperm(bag_size)[:int(bag_size*(1-drop_rate))]
+ wsi_bag = wsi_bag[bag_idxs, :]
+ batch_coords = batch_coords[bag_idxs]
+ # print(wsi_bag.shape)
+ # name_samples = [batch_names[i] for i in bag_idxs]
+ return wsi_bag, label, (wsi_name, batch_coords, patient)
+
+ def get_labels(self, indices):
+ return [self.labels[i] for i in indices]
+
+
+ def to_fixed_size_bag(self, bag, names, bag_size: int = 512):
+
+ #duplicate bag instances unitl
+
+ bag_idxs = torch.randperm(bag.shape[0])[:bag_size]
+ bag_samples = bag[bag_idxs]
+ name_samples = [names[i] for i in bag_idxs]
+ # bag_sample_names = [bag_names[i] for i in bag_idxs]
+ # q, r = divmod(bag_size, bag_samples.shape[0])
+ # if q > 0:
+ # bag_samples = torch.cat([bag_samples]*q, 0)
+
+ # self_padded = torch.cat([bag_samples, bag_samples[:r,:, :, :]])
+
+ # zero-pad if we don't have enough samples
+ # zero_padded = torch.cat((bag_samples,
+ # torch.zeros(bag_size-bag_samples.shape[0], bag_samples.shape[1], bag_samples.shape[2], bag_samples.shape[3])))
+
+ return bag_samples, name_samples, min(bag_size, len(bag))
+
+ def data_dropout(self, bag, batch_names, drop_rate):
+ bag_size = bag.shape[0]
+ bag_idxs = torch.randperm(bag_size)[:int(bag_size*(1-drop_rate))]
+ bag_samples = bag[bag_idxs]
+ name_samples = [batch_names[i] for i in bag_idxs]
+
+ return bag_samples, name_samples
+
+ def __len__(self):
+ return len(self.files)
+
+ def __getitem__(self, index):
+
+ if self.cache:
+ label = self.labels[index]
+ wsi = self.feature_bags[index]
+ # label = Variable(Tensor(label))
+ label = torch.as_tensor(label)
+ label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+ wsi_name = self.wsi_names[index]
+ name_batch = self.name_batches[index]
+ patient = self.patients[index]
+
+ #random dropout
+ #shuffle
+
+ # feats = Variable(Tensor(feats))
+ return wsi, label, (wsi_name, name_batch, patient)
+ else:
+ t = self.files[index]
+ batch, label, (wsi_name, name_batch, patient) = self.get_data(t)
+
+ # self.labels.append(label)
+ # self.feature_bags.append(batch)
+ # self.wsi_names.append(wsi_name)
+ # self.name_batches.append(name_batch)
+ # self.patients.append(patient)
+
+ return batch, label, (wsi_name, name_batch, patient)
+
+if __name__ == '__main__':
+
+ from pathlib import Path
+ import os
+ import time
+ # from fast_tensor_dl import FastTensorDataLoader
+ # from custom_resnet50 import resnet50_baseline
+
+
+
+ home = Path.cwd().parts[1]
+ train_csv = f'/{home}/ylan/DeepGraft_project/code/debug_train.csv'
+ data_root = f'/{home}/ylan/data/DeepGraft/224_128um_v2'
+ # data_root = f'/{home}/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ # label_path = f'/{home}/ylan/DeepGraft_project/code/split_PAS_bin.json'
+ label_path = f'/{home}/ylan/DeepGraft/training_tables/split_debug.json'
+ # label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ output_dir = f'/{data_root}/debug/augments'
+ os.makedirs(output_dir, exist_ok=True)
+
+ n_classes = 2
+
+ dataset = ZarrFeatureBagLoader(data_root, label_path=label_path, mode='train', cache=True, n_classes=n_classes)
+
+ # print(dataset.get_labels(0))
+ a = int(len(dataset)* 0.8)
+ b = int(len(dataset) - a)
+ train_data, valid_data = random_split(dataset, [a, b])
+ # print(dataset.dataset)
+ # a = int(len(dataset)* 0.8)
+ # b = int(len(dataset) - a)
+ # train_ds, val_ds = torch.utils.data.random_split(dataset, [a, b])
+ # dl = FastTensorDataLoader(dataset, batch_size=1, shuffle=False)
+ dl = DataLoader(train_data, batch_size=1, num_workers=8, sampler=ImbalancedDatasetSampler(train_data), pin_memory=True)
+ # print(len(dl))
+ # dl = DataLoader(dataset, batch_size=1, sampler=ImbalancedDatasetSampler(dataset), num_workers=5)
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ scaler = torch.cuda.amp.GradScaler()
+
+ # model_ft = resnet50_baseline(pretrained=True)
+ # for param in model_ft.parameters():
+ # param.requires_grad = False
+ # model_ft.to(device)
+ model = TransMIL(n_classes=n_classes).to(device)
+
+ c = 0
+ label_count = [0] *n_classes
+ epochs = 1
+ # print(len(dl))
+ # start = time.time()
+ for i in range(epochs):
+ start = time.time()
+ for item in tqdm(dl):
+
+ # if c >= 10:
+ # break
+ bag, label, (name, batch_names, patient) = item
+ # print(bag.shape)
+ # print(len(batch_names))
+ print(label)
+ print(batch_names)
+ bag = bag.float().to(device)
+ # print(bag.shape)
+ # label = label.to(device)
+ with torch.cuda.amp.autocast():
+ output = model(bag)
+ # c += 1
+ end = time.time()
+ print('Bag Time: ', end-start)
\ No newline at end of file
diff --git a/code/label_map.json b/code/label_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..d77f55d8bc082c1fe7b3fddf2ad348167861f521
--- /dev/null
+++ b/code/label_map.json
@@ -0,0 +1 @@
+{"tcmr_viral":{"0": "TCMR", "1": "VIRAL"}, "no_viral": {"0": "STABLE", "1": "TCMR", "2": "ABMR", "3": "MIXED"}, "no_other":{"0": "STABLE", "1": "TCMR", "2": "ABMR", "3": "MIXED", "4": "VIRAL"}, "rejections": {"0": "TCMR", "1": "ABMR", "2": "MIXED"}, "all": {"0": "STABLE", "1": "TCMR", "2": "ABMR", "3": "MIXED", "4": "VIRAL", "5": "OTHER"}, "rej_rest": {"0": "REJECTION", "1": "REST"}, "norm_rest": {"0": "STABLE", "1": "DISEASE"}, "norm_rej_rest":{"0": "STABLE", "1": "REJECTION", "2": "REST"}}
\ No newline at end of file
diff --git a/code/lightning_logs/version_0/cm_test.png b/code/lightning_logs/version_0/cm_test.png
deleted file mode 100644
index 6fb672f0640e83968af9558a7d438cbf42b51815..0000000000000000000000000000000000000000
Binary files a/code/lightning_logs/version_0/cm_test.png and /dev/null differ
diff --git a/code/lightning_logs/version_0/events.out.tfevents.1657535217.dgx2.2080039.0 b/code/lightning_logs/version_0/events.out.tfevents.1657535217.dgx2.2080039.0
deleted file mode 100644
index 734f0a9372e6608b0ab5aed218222431f0044e31..0000000000000000000000000000000000000000
Binary files a/code/lightning_logs/version_0/events.out.tfevents.1657535217.dgx2.2080039.0 and /dev/null differ
diff --git a/code/lightning_logs/version_0/hparams.yaml b/code/lightning_logs/version_0/hparams.yaml
deleted file mode 100644
index de11b2861a631025df79cacf8d8a13415bbe1769..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_0/hparams.yaml
+++ /dev/null
@@ -1,368 +0,0 @@
-backbone: resnet50
-cfg: &id010 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - General
- - &id002 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - comment
- - null
- - !!python/tuple
- - seed
- - 2021
- - !!python/tuple
- - fp16
- - true
- - !!python/tuple
- - amp_level
- - O2
- - !!python/tuple
- - precision
- - 16
- - !!python/tuple
- - multi_gpu_mode
- - dp
- - !!python/tuple
- - gpus
- - &id001
- - 1
- - !!python/tuple
- - epochs
- - 200
- - !!python/tuple
- - grad_acc
- - 2
- - !!python/tuple
- - frozen_bn
- - false
- - !!python/tuple
- - patience
- - 50
- - !!python/tuple
- - server
- - test
- - !!python/tuple
- - log_path
- - /home/ylan/workspace/TransMIL-DeepGraft/logs/
- dictitems:
- amp_level: O2
- comment: null
- epochs: 200
- fp16: true
- frozen_bn: false
- gpus: *id001
- grad_acc: 2
- log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
- multi_gpu_mode: dp
- patience: 50
- precision: 16
- seed: 2021
- server: test
- state: *id002
- - !!python/tuple
- - Data
- - &id005 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - dataset_name
- - custom
- - !!python/tuple
- - data_shuffle
- - false
- - !!python/tuple
- - data_dir
- - /home/ylan/data/DeepGraft/256_256um/
- - !!python/tuple
- - label_file
- - /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
- - !!python/tuple
- - fold
- - 0
- - !!python/tuple
- - nfold
- - 3
- - !!python/tuple
- - cross_val
- - false
- - !!python/tuple
- - train_dataloader
- - &id003 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - batch_size
- - 1
- - !!python/tuple
- - num_workers
- - 8
- dictitems:
- batch_size: 1
- num_workers: 8
- state: *id003
- - !!python/tuple
- - test_dataloader
- - &id004 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - batch_size
- - 1
- - !!python/tuple
- - num_workers
- - 8
- dictitems:
- batch_size: 1
- num_workers: 8
- state: *id004
- - !!python/tuple
- - bag_size
- - 1024
- dictitems:
- bag_size: 1024
- cross_val: false
- data_dir: /home/ylan/data/DeepGraft/256_256um/
- data_shuffle: false
- dataset_name: custom
- fold: 0
- label_file: /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
- nfold: 3
- test_dataloader: *id004
- train_dataloader: *id003
- state: *id005
- - !!python/tuple
- - Model
- - &id006 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - name
- - TransMIL
- - !!python/tuple
- - n_classes
- - 2
- - !!python/tuple
- - backbone
- - resnet50
- - !!python/tuple
- - in_features
- - 512
- - !!python/tuple
- - out_features
- - 512
- dictitems:
- backbone: resnet50
- in_features: 512
- n_classes: 2
- name: TransMIL
- out_features: 512
- state: *id006
- - !!python/tuple
- - Optimizer
- - &id007 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - opt
- - lookahead_radam
- - !!python/tuple
- - lr
- - 0.0002
- - !!python/tuple
- - opt_eps
- - null
- - !!python/tuple
- - opt_betas
- - null
- - !!python/tuple
- - momentum
- - null
- - !!python/tuple
- - weight_decay
- - 0.01
- dictitems:
- lr: 0.0002
- momentum: null
- opt: lookahead_radam
- opt_betas: null
- opt_eps: null
- weight_decay: 0.01
- state: *id007
- - !!python/tuple
- - Loss
- - &id008 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - base_loss
- - CrossEntropyLoss
- dictitems:
- base_loss: CrossEntropyLoss
- state: *id008
- - !!python/tuple
- - config
- - ../DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
- - !!python/tuple
- - version
- - 4
- - !!python/tuple
- - epoch
- - '159'
- - !!python/tuple
- - log_path
- - &id009 !!python/object/apply:pathlib.PosixPath
- - /
- - home
- - ylan
- - workspace
- - TransMIL-DeepGraft
- - logs
- - DeepGraft
- - TransMIL
- - tcmr_viral
- - _resnet50_CrossEntropyLoss
- - lightning_logs
- - version_4
- dictitems:
- Data: *id005
- General: *id002
- Loss: *id008
- Model: *id006
- Optimizer: *id007
- config: ../DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
- epoch: '159'
- log_path: *id009
- version: 4
- state: *id010
-data: &id013 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - dataset_name
- - custom
- - !!python/tuple
- - data_shuffle
- - false
- - !!python/tuple
- - data_dir
- - /home/ylan/data/DeepGraft/224_128um/
- - !!python/tuple
- - label_file
- - /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
- - !!python/tuple
- - fold
- - 0
- - !!python/tuple
- - nfold
- - 3
- - !!python/tuple
- - cross_val
- - false
- - !!python/tuple
- - train_dataloader
- - &id011 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - batch_size
- - 1
- - !!python/tuple
- - num_workers
- - 8
- dictitems:
- batch_size: 1
- num_workers: 8
- state: *id011
- - !!python/tuple
- - test_dataloader
- - &id012 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - batch_size
- - 1
- - !!python/tuple
- - num_workers
- - 8
- dictitems:
- batch_size: 1
- num_workers: 8
- state: *id012
- - !!python/tuple
- - bag_size
- - 1024
- dictitems:
- bag_size: 1024
- cross_val: false
- data_dir: /home/ylan/data/DeepGraft/224_128um/
- data_shuffle: false
- dataset_name: custom
- fold: 0
- label_file: /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
- nfold: 3
- test_dataloader: *id012
- train_dataloader: *id011
- state: *id013
-log: !!python/object/apply:pathlib.PosixPath
-- /
-- home
-- ylan
-- workspace
-- TransMIL-DeepGraft
-- logs
-- DeepGraft
-- TransMIL
-- tcmr_viral
-- _resnet50_CrossEntropyLoss
-loss: &id014 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - base_loss
- - CrossEntropyLoss
- dictitems:
- base_loss: CrossEntropyLoss
- state: *id014
-model: &id015 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - name
- - TransMIL
- - !!python/tuple
- - n_classes
- - 2
- - !!python/tuple
- - backbone
- - resnet50
- - !!python/tuple
- - in_features
- - 512
- - !!python/tuple
- - out_features
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deleted file mode 100644
index 3027219ffc70ee8a4f037c88505b459131ec11e4..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_1/hparams.yaml
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-- /
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deleted file mode 100644
index 385e52b4b6623ae9f0b42aa6e66bf1642b66bf4f..0000000000000000000000000000000000000000
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-- /
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Binary files a/code/lightning_logs/version_11/events.out.tfevents.1657546322.dgx1.48740.0 and /dev/null differ
diff --git a/code/lightning_logs/version_11/hparams.yaml b/code/lightning_logs/version_11/hparams.yaml
deleted file mode 100644
index 385e52b4b6623ae9f0b42aa6e66bf1642b66bf4f..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_11/hparams.yaml
+++ /dev/null
@@ -1,368 +0,0 @@
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diff --git a/code/lightning_logs/version_12/hparams.yaml b/code/lightning_logs/version_12/hparams.yaml
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deleted file mode 100644
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diff --git a/code/lightning_logs/version_15/hparams.yaml b/code/lightning_logs/version_15/hparams.yaml
deleted file mode 100644
index 385e52b4b6623ae9f0b42aa6e66bf1642b66bf4f..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_15/hparams.yaml
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diff --git a/code/lightning_logs/version_16/hparams.yaml b/code/lightning_logs/version_16/hparams.yaml
deleted file mode 100644
index 385e52b4b6623ae9f0b42aa6e66bf1642b66bf4f..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_16/hparams.yaml
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diff --git a/code/lightning_logs/version_17/events.out.tfevents.1657623153.dgx1.41577.0 b/code/lightning_logs/version_17/events.out.tfevents.1657623153.dgx1.41577.0
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diff --git a/code/lightning_logs/version_17/hparams.yaml b/code/lightning_logs/version_17/hparams.yaml
deleted file mode 100644
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deleted file mode 100644
index 5d3068796e8af6c6aacc06462015239f67239118..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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deleted file mode 100644
index d8ca6a0997effb2e3843c368c93978c2621870cb..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_20/hparams.yaml
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diff --git a/code/lightning_logs/version_21/hparams.yaml b/code/lightning_logs/version_21/hparams.yaml
deleted file mode 100644
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deleted file mode 100644
index d8ca6a0997effb2e3843c368c93978c2621870cb..0000000000000000000000000000000000000000
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diff --git a/code/lightning_logs/version_23/hparams.yaml b/code/lightning_logs/version_23/hparams.yaml
deleted file mode 100644
index d8ca6a0997effb2e3843c368c93978c2621870cb..0000000000000000000000000000000000000000
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diff --git a/code/lightning_logs/version_24/hparams.yaml b/code/lightning_logs/version_24/hparams.yaml
deleted file mode 100644
index d8ca6a0997effb2e3843c368c93978c2621870cb..0000000000000000000000000000000000000000
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diff --git a/code/lightning_logs/version_25/events.out.tfevents.1657625613.dgx1.27343.0 b/code/lightning_logs/version_25/events.out.tfevents.1657625613.dgx1.27343.0
deleted file mode 100644
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Binary files a/code/lightning_logs/version_25/events.out.tfevents.1657625613.dgx1.27343.0 and /dev/null differ
diff --git a/code/lightning_logs/version_25/hparams.yaml b/code/lightning_logs/version_25/hparams.yaml
deleted file mode 100644
index d8ca6a0997effb2e3843c368c93978c2621870cb..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_25/hparams.yaml
+++ /dev/null
@@ -1,368 +0,0 @@
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diff --git a/code/lightning_logs/version_26/hparams.yaml b/code/lightning_logs/version_26/hparams.yaml
deleted file mode 100644
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-- /
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Binary files a/code/lightning_logs/version_27/events.out.tfevents.1657625819.dgx1.36236.0 and /dev/null differ
diff --git a/code/lightning_logs/version_27/hparams.yaml b/code/lightning_logs/version_27/hparams.yaml
deleted file mode 100644
index d8ca6a0997effb2e3843c368c93978c2621870cb..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_27/hparams.yaml
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deleted file mode 100644
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diff --git a/code/lightning_logs/version_3/hparams.yaml b/code/lightning_logs/version_3/hparams.yaml
deleted file mode 100644
index a662047b861838dd7c0c87b5d45111c35a9a254d..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_3/hparams.yaml
+++ /dev/null
@@ -1,368 +0,0 @@
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-log: !!python/object/apply:pathlib.PosixPath
-- /
-- home
-- ylan
-- workspace
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Binary files a/code/lightning_logs/version_30/events.out.tfevents.1657625960.dgx1.43463.0 and /dev/null differ
diff --git a/code/lightning_logs/version_30/hparams.yaml b/code/lightning_logs/version_30/hparams.yaml
deleted file mode 100644
index d8ca6a0997effb2e3843c368c93978c2621870cb..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_30/hparams.yaml
+++ /dev/null
@@ -1,368 +0,0 @@
-backbone: resnet50
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-data: &id013 !!python/object/new:addict.addict.Dict
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-log: !!python/object/apply:pathlib.PosixPath
-- /
-- home
-- ylan
-- workspace
-- TransMIL-DeepGraft
-- logs
-- DeepGraft
-- TransMIL
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diff --git a/code/lightning_logs/version_31/events.out.tfevents.1657626120.dgx1.48909.0 b/code/lightning_logs/version_31/events.out.tfevents.1657626120.dgx1.48909.0
deleted file mode 100644
index 125ad0c8b29dcda71447e15108aa5dc4d5d085aa..0000000000000000000000000000000000000000
Binary files a/code/lightning_logs/version_31/events.out.tfevents.1657626120.dgx1.48909.0 and /dev/null differ
diff --git a/code/lightning_logs/version_31/hparams.yaml b/code/lightning_logs/version_31/hparams.yaml
deleted file mode 100644
index d8ca6a0997effb2e3843c368c93978c2621870cb..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_31/hparams.yaml
+++ /dev/null
@@ -1,368 +0,0 @@
-backbone: resnet50
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diff --git a/code/lightning_logs/version_32/hparams.yaml b/code/lightning_logs/version_32/hparams.yaml
deleted file mode 100644
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deleted file mode 100644
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diff --git a/code/lightning_logs/version_4/hparams.yaml b/code/lightning_logs/version_4/hparams.yaml
deleted file mode 100644
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diff --git a/code/lightning_logs/version_5/hparams.yaml b/code/lightning_logs/version_5/hparams.yaml
deleted file mode 100644
index 385e52b4b6623ae9f0b42aa6e66bf1642b66bf4f..0000000000000000000000000000000000000000
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Binary files a/code/lightning_logs/version_6/events.out.tfevents.1657545574.dgx1.43290.0 and /dev/null differ
diff --git a/code/lightning_logs/version_6/hparams.yaml b/code/lightning_logs/version_6/hparams.yaml
deleted file mode 100644
index 385e52b4b6623ae9f0b42aa6e66bf1642b66bf4f..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_6/hparams.yaml
+++ /dev/null
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diff --git a/code/lightning_logs/version_7/hparams.yaml b/code/lightning_logs/version_7/hparams.yaml
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- dataset_name: custom
- fold: 0
- label_file: /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
- nfold: 3
- test_dataloader: *id012
- train_dataloader: *id011
- state: *id013
-log: !!python/object/apply:pathlib.PosixPath
-- /
-- home
-- ylan
-- workspace
-- TransMIL-DeepGraft
-- logs
-- DeepGraft
-- TransMIL
-- tcmr_viral
-- _resnet50_CrossEntropyLoss
-loss: &id014 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - base_loss
- - CrossEntropyLoss
- dictitems:
- base_loss: CrossEntropyLoss
- state: *id014
-model: &id015 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - name
- - TransMIL
- - !!python/tuple
- - n_classes
- - 2
- - !!python/tuple
- - backbone
- - resnet50
- - !!python/tuple
- - in_features
- - 512
- - !!python/tuple
- - out_features
- - 512
- dictitems:
- backbone: resnet50
- in_features: 512
- n_classes: 2
- name: TransMIL
- out_features: 512
- state: *id015
-optimizer: &id016 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - opt
- - lookahead_radam
- - !!python/tuple
- - lr
- - 0.0002
- - !!python/tuple
- - opt_eps
- - null
- - !!python/tuple
- - opt_betas
- - null
- - !!python/tuple
- - momentum
- - null
- - !!python/tuple
- - weight_decay
- - 0.01
- dictitems:
- lr: 0.0002
- momentum: null
- opt: lookahead_radam
- opt_betas: null
- opt_eps: null
- weight_decay: 0.01
- state: *id016
diff --git a/code/lightning_logs/version_9/events.out.tfevents.1657545985.dgx1.46385.0 b/code/lightning_logs/version_9/events.out.tfevents.1657545985.dgx1.46385.0
deleted file mode 100644
index 1f6b05a4e88ebf59fe40d3af43cf85a4991a21c0..0000000000000000000000000000000000000000
Binary files a/code/lightning_logs/version_9/events.out.tfevents.1657545985.dgx1.46385.0 and /dev/null differ
diff --git a/code/lightning_logs/version_9/hparams.yaml b/code/lightning_logs/version_9/hparams.yaml
deleted file mode 100644
index 385e52b4b6623ae9f0b42aa6e66bf1642b66bf4f..0000000000000000000000000000000000000000
--- a/code/lightning_logs/version_9/hparams.yaml
+++ /dev/null
@@ -1,368 +0,0 @@
-backbone: resnet50
-cfg: &id010 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - General
- - &id002 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - comment
- - null
- - !!python/tuple
- - seed
- - 2021
- - !!python/tuple
- - fp16
- - true
- - !!python/tuple
- - amp_level
- - O2
- - !!python/tuple
- - precision
- - 16
- - !!python/tuple
- - multi_gpu_mode
- - dp
- - !!python/tuple
- - gpus
- - &id001
- - 1
- - !!python/tuple
- - epochs
- - 200
- - !!python/tuple
- - grad_acc
- - 2
- - !!python/tuple
- - frozen_bn
- - false
- - !!python/tuple
- - patience
- - 50
- - !!python/tuple
- - server
- - train
- - !!python/tuple
- - log_path
- - /home/ylan/workspace/TransMIL-DeepGraft/logs/
- dictitems:
- amp_level: O2
- comment: null
- epochs: 200
- fp16: true
- frozen_bn: false
- gpus: *id001
- grad_acc: 2
- log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
- multi_gpu_mode: dp
- patience: 50
- precision: 16
- seed: 2021
- server: train
- state: *id002
- - !!python/tuple
- - Data
- - &id005 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - dataset_name
- - custom
- - !!python/tuple
- - data_shuffle
- - false
- - !!python/tuple
- - data_dir
- - /home/ylan/data/DeepGraft/224_128um/
- - !!python/tuple
- - label_file
- - /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_test.json
- - !!python/tuple
- - fold
- - 0
- - !!python/tuple
- - nfold
- - 3
- - !!python/tuple
- - cross_val
- - false
- - !!python/tuple
- - train_dataloader
- - &id003 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - batch_size
- - 1
- - !!python/tuple
- - num_workers
- - 8
- dictitems:
- batch_size: 1
- num_workers: 8
- state: *id003
- - !!python/tuple
- - test_dataloader
- - &id004 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - batch_size
- - 1
- - !!python/tuple
- - num_workers
- - 8
- dictitems:
- batch_size: 1
- num_workers: 8
- state: *id004
- - !!python/tuple
- - bag_size
- - 1024
- dictitems:
- bag_size: 1024
- cross_val: false
- data_dir: /home/ylan/data/DeepGraft/224_128um/
- data_shuffle: false
- dataset_name: custom
- fold: 0
- label_file: /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_test.json
- nfold: 3
- test_dataloader: *id004
- train_dataloader: *id003
- state: *id005
- - !!python/tuple
- - Model
- - &id006 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - name
- - TransMIL
- - !!python/tuple
- - n_classes
- - 2
- - !!python/tuple
- - backbone
- - resnet50
- - !!python/tuple
- - in_features
- - 512
- - !!python/tuple
- - out_features
- - 512
- dictitems:
- backbone: resnet50
- in_features: 512
- n_classes: 2
- name: TransMIL
- out_features: 512
- state: *id006
- - !!python/tuple
- - Optimizer
- - &id007 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - opt
- - lookahead_radam
- - !!python/tuple
- - lr
- - 0.0002
- - !!python/tuple
- - opt_eps
- - null
- - !!python/tuple
- - opt_betas
- - null
- - !!python/tuple
- - momentum
- - null
- - !!python/tuple
- - weight_decay
- - 0.01
- dictitems:
- lr: 0.0002
- momentum: null
- opt: lookahead_radam
- opt_betas: null
- opt_eps: null
- weight_decay: 0.01
- state: *id007
- - !!python/tuple
- - Loss
- - &id008 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - base_loss
- - CrossEntropyLoss
- dictitems:
- base_loss: CrossEntropyLoss
- state: *id008
- - !!python/tuple
- - config
- - ../DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
- - !!python/tuple
- - version
- - 4
- - !!python/tuple
- - epoch
- - '159'
- - !!python/tuple
- - log_path
- - &id009 !!python/object/apply:pathlib.PosixPath
- - /
- - home
- - ylan
- - workspace
- - TransMIL-DeepGraft
- - logs
- - DeepGraft
- - TransMIL
- - tcmr_viral
- - _resnet50_CrossEntropyLoss
- - lightning_logs
- - version_4
- dictitems:
- Data: *id005
- General: *id002
- Loss: *id008
- Model: *id006
- Optimizer: *id007
- config: ../DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
- epoch: '159'
- log_path: *id009
- version: 4
- state: *id010
-data: &id013 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - dataset_name
- - custom
- - !!python/tuple
- - data_shuffle
- - false
- - !!python/tuple
- - data_dir
- - /home/ylan/data/DeepGraft/224_128um/
- - !!python/tuple
- - label_file
- - /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
- - !!python/tuple
- - fold
- - 0
- - !!python/tuple
- - nfold
- - 3
- - !!python/tuple
- - cross_val
- - false
- - !!python/tuple
- - train_dataloader
- - &id011 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - batch_size
- - 1
- - !!python/tuple
- - num_workers
- - 8
- dictitems:
- batch_size: 1
- num_workers: 8
- state: *id011
- - !!python/tuple
- - test_dataloader
- - &id012 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - batch_size
- - 1
- - !!python/tuple
- - num_workers
- - 8
- dictitems:
- batch_size: 1
- num_workers: 8
- state: *id012
- - !!python/tuple
- - bag_size
- - 1024
- dictitems:
- bag_size: 1024
- cross_val: false
- data_dir: /home/ylan/data/DeepGraft/224_128um/
- data_shuffle: false
- dataset_name: custom
- fold: 0
- label_file: /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
- nfold: 3
- test_dataloader: *id012
- train_dataloader: *id011
- state: *id013
-log: !!python/object/apply:pathlib.PosixPath
-- /
-- home
-- ylan
-- workspace
-- TransMIL-DeepGraft
-- logs
-- DeepGraft
-- TransMIL
-- tcmr_viral
-- _resnet50_CrossEntropyLoss
-loss: &id014 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - base_loss
- - CrossEntropyLoss
- dictitems:
- base_loss: CrossEntropyLoss
- state: *id014
-model: &id015 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - name
- - TransMIL
- - !!python/tuple
- - n_classes
- - 2
- - !!python/tuple
- - backbone
- - resnet50
- - !!python/tuple
- - in_features
- - 512
- - !!python/tuple
- - out_features
- - 512
- dictitems:
- backbone: resnet50
- in_features: 512
- n_classes: 2
- name: TransMIL
- out_features: 512
- state: *id015
-optimizer: &id016 !!python/object/new:addict.addict.Dict
- args:
- - !!python/tuple
- - opt
- - lookahead_radam
- - !!python/tuple
- - lr
- - 0.0002
- - !!python/tuple
- - opt_eps
- - null
- - !!python/tuple
- - opt_betas
- - null
- - !!python/tuple
- - momentum
- - null
- - !!python/tuple
- - weight_decay
- - 0.01
- dictitems:
- lr: 0.0002
- momentum: null
- opt: lookahead_radam
- opt_betas: null
- opt_eps: null
- weight_decay: 0.01
- state: *id016
diff --git a/code/models/AttMIL.py b/code/models/AttMIL.py
index d4a5938c1a27531cf992f9cad099ba445e7d093c..048fb0fc782aa856c1e7c6edcc850e0e3cb39552 100644
--- a/code/models/AttMIL.py
+++ b/code/models/AttMIL.py
@@ -76,4 +76,5 @@ class AttMIL(nn.Module): #gated attention
M = torch.mm(A, H) # KxL
logits = self.classifier(M)
- return logits, A
\ No newline at end of file
+ return logits, A
+
diff --git a/code/models/Chowder.py b/code/models/Chowder.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8c4e186b3506a17720dbb08160548ae6e35e3e4
--- /dev/null
+++ b/code/models/Chowder.py
@@ -0,0 +1,59 @@
+import os
+import logging
+from argparse import ArgumentParser
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+
+import pytorch_lightning as pl
+from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
+
+'''
+Chowder implementation from Courtiol 2018(https://openreview.net/pdf?id=ryserbZR-)
+'''
+
+
+class Chowder(nn.Module):
+ def __init__(self, n_classes, features=512, r=5):
+ super(Chowder, self).__init__()
+ self.L = features
+ self.n_classes = n_classes
+ self.R = r
+
+
+ self.f1 = nn.Sequential(
+ nn.Conv1d(self.L, 1, 1),
+ )
+ self.f2 = nn.Sequential(
+ nn.Linear(r*2, 200),
+ nn.Linear(200, 100),
+ nn.Linear(100, self.n_classes),
+ # nn.Sigmoid()
+ )
+
+ def forward(self, x):
+
+ x = x.float()
+ x = torch.transpose(x, 1, 2)
+
+ x = self.f1(x)
+ max_indices = torch.topk(x, self.R).values
+ min_indices = torch.topk(x, self.R, largest=False).values
+
+ cat_minmax = torch.cat((min_indices, max_indices), dim=2)
+
+ out = self.f2(cat_minmax).squeeze(0)
+
+ return out, None
+
+if __name__ == '__main__':
+
+ data = torch.randn((1, 6000, 512)).cuda()
+ model = Chowder(n_classes=2).cuda()
+ print(model.eval())
+ logits, _ = model(data)
+
+ print(logits.shape)
\ No newline at end of file
diff --git a/code/models/ConvMixer.py b/code/models/ConvMixer.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c9da60d10d18e00a83150cbb29e1c9cb28f1854
--- /dev/null
+++ b/code/models/ConvMixer.py
@@ -0,0 +1,182 @@
+import torch
+from torch import nn
+
+from labml_helpers.module import Module
+from labml_nn.utils import clone_module_list
+
+
+class ConvMixerLayer(Module):
+ """
+ <a id="ConvMixerLayer"></a>
+ ## ConvMixer layer
+ This is a single ConvMixer layer. The model will have a series of these.
+ """
+
+ def __init__(self, d_model: int, kernel_size: int):
+ """
+ * `d_model` is the number of channels in patch embeddings, $h$
+ * `kernel_size` is the size of the kernel of spatial convolution, $k$
+ """
+ super().__init__()
+ # Depth-wise convolution is separate convolution for each channel.
+ # We do this with a convolution layer with the number of groups equal to the number of channels.
+ # So that each channel is it's own group.
+ self.depth_wise_conv = nn.Conv2d(d_model, d_model,
+ kernel_size=kernel_size,
+ groups=d_model,
+ padding=(kernel_size - 1) // 2)
+ # Activation after depth-wise convolution
+ self.act1 = nn.GELU()
+ # Normalization after depth-wise convolution
+ self.norm1 = nn.BatchNorm2d(d_model)
+
+ # Point-wise convolution is a $1 \times 1$ convolution.
+ # i.e. a linear transformation of patch embeddings
+ self.point_wise_conv = nn.Conv2d(d_model, d_model, kernel_size=1)
+ # Activation after point-wise convolution
+ self.act2 = nn.GELU()
+ # Normalization after point-wise convolution
+ self.norm2 = nn.BatchNorm2d(d_model)
+
+ def forward(self, x: torch.Tensor):
+ # For the residual connection around the depth-wise convolution
+ residual = x
+
+ # Depth-wise convolution, activation and normalization
+ x = self.depth_wise_conv(x)
+ x = self.act1(x)
+ x = self.norm1(x)
+
+ # Add residual connection
+ x += residual
+
+ # Point-wise convolution, activation and normalization
+ x = self.point_wise_conv(x)
+ x = self.act2(x)
+ x = self.norm2(x)
+
+ #
+ return x
+
+
+class PatchEmbeddings(Module):
+ """
+ <a id="PatchEmbeddings"></a>
+ ## Get patch embeddings
+ This splits the image into patches of size $p \times p$ and gives an embedding for each patch.
+ """
+
+ def __init__(self, d_model: int, patch_size: int, in_channels: int):
+ """
+ * `d_model` is the number of channels in patch embeddings $h$
+ * `patch_size` is the size of the patch, $p$
+ * `in_channels` is the number of channels in the input image (3 for rgb)
+ """
+ super().__init__()
+
+ # We create a convolution layer with a kernel size and and stride length equal to patch size.
+ # This is equivalent to splitting the image into patches and doing a linear
+ # transformation on each patch.
+ self.conv = nn.Conv2d(in_channels, d_model, kernel_size=patch_size, stride=patch_size)
+ # Activation function
+ self.act = nn.GELU()
+ # Batch normalization
+ self.norm = nn.BatchNorm2d(d_model)
+
+ def forward(self, x: torch.Tensor):
+ """
+ * `x` is the input image of shape `[batch_size, channels, height, width]`
+ """
+ # Apply convolution layer
+ x = self.conv(x)
+ # Activation and normalization
+ x = self.act(x)
+ x = self.norm(x)
+
+ #
+ return x
+
+
+class ClassificationHead(Module):
+ """
+ <a id="ClassificationHead"></a>
+ ## Classification Head
+ They do average pooling (taking the mean of all patch embeddings) and a final linear transformation
+ to predict the log-probabilities of the image classes.
+ """
+
+ def __init__(self, d_model: int, n_classes: int):
+ """
+ * `d_model` is the number of channels in patch embeddings, $h$
+ * `n_classes` is the number of classes in the classification task
+ """
+ super().__init__()
+ # Average Pool
+ self.pool = nn.AdaptiveAvgPool2d((1, 1))
+ # Linear layer
+ self.linear = nn.Linear(d_model, n_classes)
+
+ def forward(self, x: torch.Tensor):
+ # Average pooling
+ x = self.pool(x)
+ # Get the embedding, `x` will have shape `[batch_size, d_model, 1, 1]`
+ x = x[:, :, 0, 0]
+ # Linear layer
+ x = self.linear(x)
+
+ #
+ return x
+
+
+class ConvMixer(Module):
+ """
+ ## ConvMixer
+ This combines the patch embeddings block, a number of ConvMixer layers and a classification head.
+ """
+
+ def __init__(self, conv_mixer_layer: ConvMixerLayer, n_layers: int,
+ patch_emb: PatchEmbeddings,
+ classification: ClassificationHead):
+ """
+ * `conv_mixer_layer` is a copy of a single [ConvMixer layer](#ConvMixerLayer).
+ We make copies of it to make ConvMixer with `n_layers`.
+ * `n_layers` is the number of ConvMixer layers (or depth), $d$.
+ * `patch_emb` is the [patch embeddings layer](#PatchEmbeddings).
+ * `classification` is the [classification head](#ClassificationHead).
+ """
+ super().__init__()
+ # Patch embeddings
+ self.patch_emb = patch_emb
+ # Classification head
+ self.classification = classification
+ # Make copies of the [ConvMixer layer](#ConvMixerLayer)
+ self.conv_mixer_layers = clone_module_list(conv_mixer_layer, n_layers)
+
+ def forward(self, x: torch.Tensor):
+ """
+ * `x` is the input image of shape `[batch_size, channels, height, width]`
+ """
+ # Get patch embeddings. This gives a tensor of shape `[batch_size, d_model, height / patch_size, width / patch_size]`.
+ x = self.patch_emb(x)
+ print(x.shape)
+ # Pass through [ConvMixer layers](#ConvMixerLayer)
+ for layer in self.conv_mixer_layers:
+ x = layer(x)
+
+ # Classification head, to get logits
+ x = self.classification(x)
+
+ #
+ return x
+
+if __name__ == '__main__':
+
+
+ convmix = ConvMixerLayer(d_model=20, kernel_size=9)
+ patch_emb = PatchEmbeddings(d_model=20, patch_size=7, in_channels=3)
+ classification = ClassificationHead(d_model=20, n_classes=2)
+ model = ConvMixer(conv_mixer_layer=convmix, n_layers=20, patch_emb = patch_emb, classification=classification )
+ x = torch.randn([1,3,224,224])
+
+ y = model(x)
+ print(y.shape)
\ No newline at end of file
diff --git a/code/models/MDMIL.py b/code/models/MDMIL.py
new file mode 100644
index 0000000000000000000000000000000000000000..db61da50e7fddd160d49a711ca006a63cf2c6ebc
--- /dev/null
+++ b/code/models/MDMIL.py
@@ -0,0 +1,141 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from nystrom_attention import NystromAttention
+
+
+class TransLayer(nn.Module):
+
+ def __init__(self, norm_layer=nn.LayerNorm, dim=512):
+ super().__init__()
+ self.norm = norm_layer(dim)
+ self.attn = NystromAttention(
+ dim = dim,
+ dim_head = dim//8,
+ heads = 8,
+ num_landmarks = dim//2, # number of landmarks
+ pinv_iterations = 6, # number of moore-penrose iterations for approximating pinverse. 6 was recommended by the paper
+ residual = True, # whether to do an extra residual with the value or not. supposedly faster convergence if turned on
+ dropout=0.7 #0.1
+ )
+
+ def forward(self, x):
+ out, attn = self.attn(self.norm(x), return_attn=True)
+ x = x + out
+ # x = x + self.attn(self.norm(x))
+
+ return x, attn
+
+
+class PPEG(nn.Module):
+ def __init__(self, dim=512):
+ super(PPEG, self).__init__()
+ self.proj = nn.Conv2d(dim, dim, 7, 1, 7//2, groups=dim)
+ self.proj1 = nn.Conv2d(dim, dim, 5, 1, 5//2, groups=dim)
+ self.proj2 = nn.Conv2d(dim, dim, 3, 1, 3//2, groups=dim)
+
+ def forward(self, x, H, W):
+ B, _, C = x.shape
+ cls_token, feat_token = x[:, 0], x[:, 1:]
+ cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
+ x = self.proj(cnn_feat)+cnn_feat+self.proj1(cnn_feat)+self.proj2(cnn_feat)
+ x = x.flatten(2).transpose(1, 2)
+ x = torch.cat((cls_token.unsqueeze(1), x), dim=1)
+ return x
+
+class IQGM(nn.Module):
+ def __init__(self, in_features, n_classes):
+ super(IQGM, self).__init__()
+
+ self.in_features = in_features
+ self.n_classes = self.n_classes
+ self.fc = nn.Linear(self.in_features, self.n_classes)
+
+ def forward(feats):
+ c = F.softmax(self.fc(feats))
+ _, m_indices = torch.sort(c, 0, descending=True)
+ m_feats = torch.index_select(feats, dim=0, index=m_indices[0,:]) #critical index?
+
+class MDMIL(nn.Module):
+ def __init__(self, n_classes):
+ super(MDMIL, self).__init__()
+ in_features = 1024
+ out_features = 512
+ self.pos_layer = PPEG(dim=out_features)
+ self._fc1 = nn.Sequential(nn.Linear(in_features, out_features), nn.GELU())
+ # self._fc1 = nn.Sequential(nn.Linear(1024, 512), nn.ReLU())
+ self.cls_token = nn.Parameter(torch.randn(1, 1, out_features))
+ self.n_classes = n_classes
+ self.layer1 = TransLayer(dim=out_features)
+ self.layer2 = TransLayer(dim=out_features)
+ self.norm = nn.LayerNorm(out_features)
+ self._fc2 = nn.Linear(out_features, self.n_classes)
+
+
+ def forward(self, x): #, **kwargs
+
+ h = x.float() #[B, n, 1024]
+ h = self._fc1(h) #[B, n, 512]
+
+ # print('Feature Representation: ', h.shape)
+ #---->duplicate pad
+ H = h.shape[1]
+ _H, _W = int(np.ceil(np.sqrt(H))), int(np.ceil(np.sqrt(H)))
+ add_length = _H * _W - H
+ h = torch.cat([h, h[:,:add_length,:]],dim = 1) #[B, N, 512]
+
+
+ #---->cls_token
+ B = h.shape[0]
+ cls_tokens = self.cls_token.expand(B, -1, -1).cuda()
+ h = torch.cat((cls_tokens, h), dim=1)
+
+
+ #---->Translayer x1
+ h, attn1 = self.layer1(h) #[B, N, 512]
+
+ # print('After first TransLayer: ', h.shape)
+
+ #---->PPEG
+ h = self.pos_layer(h, _H, _W) #[B, N, 512]
+ # print('After PPEG: ', h.shape)
+
+ #---->Translayer x2
+ h, attn2 = self.layer2(h) #[B, N, 512]
+
+ # print('After second TransLayer: ', h.shape) #[1, 1025, 512] 1025 = cls_token + 1024
+ #---->cls_token
+
+ h = self.norm(h)[:,0]
+
+ #---->predict
+ logits = self._fc2(h) #[B, n_classes]
+ return logits, attn2
+
+if __name__ == "__main__":
+ data = torch.randn((1, 6000, 512)).cuda()
+ model = TransMIL(n_classes=2).cuda()
+ print(model.eval())
+ logits, attn = model(data)
+ cls_attention = attn[:,:, 0, :6000]
+ values, indices = torch.max(cls_attention, 1)
+ mean = values.mean()
+ zeros = torch.zeros(values.shape).cuda()
+ filtered = torch.where(values > mean, values, zeros)
+
+ # filter = values > values.mean()
+ # filtered_values = values[filter]
+ # values = np.where(values>values.mean(), values, 0)
+
+ print(filtered.shape)
+
+
+ # values = [v if v > values.mean().item() else 0 for v in values]
+ # print(values)
+ # print(len(values))
+
+ # logits = results_dict['logits']
+ # Y_prob = results_dict['Y_prob']
+ # Y_hat = results_dict['Y_hat']
+ # print(F.sigmoid(logits))
diff --git a/code/models/ResNet.py b/code/models/ResNet.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8fe70648f55b58eacc5da0578821d7842c563f3
--- /dev/null
+++ b/code/models/ResNet.py
@@ -0,0 +1,397 @@
+import torch
+import torch.nn as nn
+from torch.hub import load_state_dict_from_url
+import torch.nn.functional as F
+from torch.nn import Parameter
+
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+ 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
+ 'wide_resnet50_2', 'wide_resnet101_2']
+
+
+model_urls = {
+ 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+ 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+ 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+ 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+ 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+ 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
+ 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
+ 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
+ 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+ """3x3 convolution with padding"""
+ return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+ padding=dilation, groups=groups, bias=False, dilation=dilation)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+ """1x1 convolution"""
+ return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class BasicBlock(nn.Module):
+ expansion = 1
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+ base_width=64, dilation=1, norm_layer=None):
+ super(BasicBlock, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ if groups != 1 or base_width != 64:
+ raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+ if dilation > 1:
+ raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+ # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+ self.conv1 = conv3x3(inplanes, planes, stride)
+ self.bn1 = norm_layer(planes)
+ self.relu = nn.ReLU(inplace=True)
+ self.conv2 = conv3x3(planes, planes)
+ self.bn2 = norm_layer(planes)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x):
+ identity = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+
+ if self.downsample is not None:
+ identity = self.downsample(x)
+
+ out += identity
+ out = self.relu(out)
+
+ return out
+
+
+class Bottleneck(nn.Module):
+ expansion = 4
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+ base_width=64, dilation=1, norm_layer=None, momentum_bn=0.1):
+ super(Bottleneck, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ width = int(planes * (base_width / 64.)) * groups
+ # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+ self.conv1 = conv1x1(inplanes, width)
+ self.bn1 = norm_layer(width, momentum=momentum_bn)
+ self.conv2 = conv3x3(width, width, stride, groups, dilation)
+ self.bn2 = norm_layer(width, momentum=momentum_bn)
+ self.conv3 = conv1x1(width, planes * self.expansion)
+ self.bn3 = norm_layer(planes * self.expansion, momentum=momentum_bn)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x):
+ identity = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+ out = self.relu(out)
+
+ out = self.conv3(out)
+ out = self.bn3(out)
+
+ if self.downsample is not None:
+ identity = self.downsample(x)
+
+ out += identity
+ out = self.relu(out)
+
+ return out
+
+class NormedLinear(nn.Module):
+
+ def __init__(self, in_features, out_features):
+ super(NormedLinear, self).__init__()
+ self.weight = Parameter(torch.Tensor(in_features, out_features))
+ self.weight.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
+
+ def forward(self, x):
+ out = F.normalize(x, dim=1).mm(F.normalize(self.weight, dim=0))
+ return out
+
+class ResNet(nn.Module):
+
+ def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
+ groups=1, width_per_group=64, replace_stride_with_dilation=None,
+ norm_layer=None, two_branch=False, mlp=False, normlinear=False,
+ momentum_bn=0.1, attention=False, attention_layers=3, return_attn=False):
+ super(ResNet, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ self._norm_layer = norm_layer
+
+ self.inplanes = 64
+ self.dilation = 1
+ self.return_attn = return_attn
+ if replace_stride_with_dilation is None:
+ # each element in the tuple indicates if we should replace
+ # the 2x2 stride with a dilated convolution instead
+ replace_stride_with_dilation = [False, False, False]
+ if len(replace_stride_with_dilation) != 3:
+ raise ValueError("replace_stride_with_dilation should be None "
+ "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+ self.groups = groups
+ self.base_width = width_per_group
+ self.two_branch = two_branch
+ self.momentum_bn = momentum_bn
+ self.mlp = mlp
+ linear = NormedLinear if normlinear else nn.Linear
+
+ self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
+ bias=False)
+ self.bn1 = norm_layer(self.inplanes, momentum=momentum_bn)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = self._make_layer(block, 64, layers[0])
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
+ dilate=replace_stride_with_dilation[0])
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
+ dilate=replace_stride_with_dilation[1])
+ self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
+ dilate=replace_stride_with_dilation[2])
+
+ if attention:
+ self.att_branch = self._make_layer(block, 512, attention_layers, 1, attention=True)
+ else:
+ self.att_branch = None
+
+ self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+ if self.mlp:
+ if self.two_branch:
+ self.fc = nn.Sequential(
+ nn.Linear(512 * block.expansion, 512 * block.expansion),
+ nn.ReLU()
+ )
+ self.instDis = linear(512 * block.expansion, num_classes)
+ self.groupDis = linear(512 * block.expansion, num_classes)
+ else:
+ self.fc = nn.Sequential(
+ nn.Linear(512 * block.expansion, 512 * block.expansion),
+ nn.ReLU(),
+ linear(512 * block.expansion, num_classes)
+ )
+ else:
+ self.fc = nn.Linear(512 * block.expansion, num_classes)
+ if self.two_branch:
+ self.groupDis = nn.Linear(512 * block.expansion, num_classes)
+
+
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+
+ # Zero-initialize the last BN in each residual branch,
+ # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+ # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+ if zero_init_residual:
+ for m in self.modules():
+ if isinstance(m, Bottleneck):
+ nn.init.constant_(m.bn3.weight, 0)
+ elif isinstance(m, BasicBlock):
+ nn.init.constant_(m.bn2.weight, 0)
+
+ def _make_layer(self, block, planes, blocks, stride=1, dilate=False, attention=False):
+ norm_layer = self._norm_layer
+ downsample = None
+ previous_dilation = self.dilation
+ if dilate:
+ self.dilation *= stride
+ stride = 1
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ conv1x1(self.inplanes, planes * block.expansion, stride),
+ norm_layer(planes * block.expansion, momentum=self.momentum_bn),
+ )
+
+ layers = []
+ layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
+ self.base_width, previous_dilation, norm_layer, momentum_bn=self.momentum_bn))
+ self.inplanes = planes * block.expansion
+ for _ in range(1, blocks):
+ layers.append(block(self.inplanes, planes, groups=self.groups,
+ base_width=self.base_width, dilation=self.dilation,
+ norm_layer=norm_layer, momentum_bn=self.momentum_bn))
+
+ if attention:
+ layers.append(nn.Sequential(
+ conv1x1(self.inplanes, 128),
+ nn.BatchNorm2d(128),
+ nn.ReLU(inplace=True),
+ conv1x1(128, 1),
+ nn.BatchNorm2d(1),
+ nn.Sigmoid()
+ ))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+
+ x = self.layer1(x)
+ x = self.layer2(x)
+ x = self.layer3(x)
+ x = self.layer4(x)
+ if self.att_branch is not None:
+ att_map = self.att_branch(x)
+ x = x + att_map * x
+
+ x = self.avgpool(x)
+ x = torch.flatten(x, 1)
+ if self.mlp and self.two_branch:
+ x = self.fc(x)
+ x1 = self.instDis(x)
+ x2 = self.groupDis(x)
+ return [x1, x2]
+ else:
+ x1 = self.fc(x)
+ if self.two_branch:
+ x2 = self.groupDis(x)
+ return [x1, x2]
+ return x1
+
+
+def _resnet(arch, block, layers, pretrained, progress, **kwargs):
+ model = ResNet(block, layers, **kwargs)
+ if pretrained:
+ state_dict = load_state_dict_from_url(model_urls[arch],
+ progress=progress)
+ model.load_state_dict(state_dict)
+ return model
+
+
+def resnet18(pretrained=False, progress=True, **kwargs):
+ r"""ResNet-18 model from
+ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
+ **kwargs)
+
+
+def resnet34(pretrained=False, progress=True, **kwargs):
+ r"""ResNet-34 model from
+ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
+ **kwargs)
+
+
+def resnet50(pretrained=False, progress=True, **kwargs):
+ r"""ResNet-50 model from
+ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
+ **kwargs)
+
+
+def resnet101(pretrained=False, progress=True, **kwargs):
+ r"""ResNet-101 model from
+ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
+ **kwargs)
+
+
+def resnet152(pretrained=False, progress=True, **kwargs):
+ r"""ResNet-152 model from
+ `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
+ **kwargs)
+
+
+def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
+ r"""ResNeXt-50 32x4d model from
+ `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ kwargs['groups'] = 32
+ kwargs['width_per_group'] = 4
+ return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
+ pretrained, progress, **kwargs)
+
+
+def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
+ r"""ResNeXt-101 32x8d model from
+ `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ kwargs['groups'] = 32
+ kwargs['width_per_group'] = 8
+ return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
+ pretrained, progress, **kwargs)
+
+
+def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
+ r"""Wide ResNet-50-2 model from
+ `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+ The model is the same as ResNet except for the bottleneck number of channels
+ which is twice larger in every block. The number of channels in outer 1x1
+ convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+ channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ kwargs['width_per_group'] = 64 * 2
+ return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
+ pretrained, progress, **kwargs)
+
+
+def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
+ r"""Wide ResNet-101-2 model from
+ `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+ The model is the same as ResNet except for the bottleneck number of channels
+ which is twice larger in every block. The number of channels in outer 1x1
+ convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+ channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ progress (bool): If True, displays a progress bar of the download to stderr
+ """
+ kwargs['width_per_group'] = 64 * 2
+ return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
+ pretrained, progress, **kwargs)
\ No newline at end of file
diff --git a/code/models/TransMIL.py b/code/models/TransMIL.py
index c78599d34d7faf2cc54fa182f5fb11ef63b13ea0..ddc126286835639ded83fd68054401c500237061 100755
--- a/code/models/TransMIL.py
+++ b/code/models/TransMIL.py
@@ -3,6 +3,17 @@ import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from nystrom_attention import NystromAttention
+import models.ResNet as ResNet
+from pathlib import Path
+
+try:
+ import apex
+ apex_available=True
+except ModuleNotFoundError:
+ # Error handling
+ apex_available = False
+ pass
+
class TransLayer(nn.Module):
@@ -38,9 +49,9 @@ class PPEG(nn.Module):
def forward(self, x, H, W):
B, _, C = x.shape
cls_token, feat_token = x[:, 0], x[:, 1:]
- cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
+ cnn_feat = feat_token.transpose(1, 2).contiguous().view(B, C, H, W)
x = self.proj(cnn_feat)+cnn_feat+self.proj1(cnn_feat)+self.proj2(cnn_feat)
- x = x.flatten(2).transpose(1, 2)
+ x = x.flatten(2).transpose(1, 2).contiguous()
x = torch.cat((cls_token.unsqueeze(1), x), dim=1)
return x
@@ -48,21 +59,37 @@ class PPEG(nn.Module):
class TransMIL(nn.Module):
def __init__(self, n_classes):
super(TransMIL, self).__init__()
- in_features = 512
- out_features=512
+ in_features = 1024
+ out_features = 512
self.pos_layer = PPEG(dim=out_features)
self._fc1 = nn.Sequential(nn.Linear(in_features, out_features), nn.GELU())
# self._fc1 = nn.Sequential(nn.Linear(1024, 512), nn.ReLU())
+ if apex_available:
+ norm_layer = apex.normalization.FusedLayerNorm
+
+ else:
+ norm_layer = nn.LayerNorm
self.cls_token = nn.Parameter(torch.randn(1, 1, out_features))
self.n_classes = n_classes
- self.layer1 = TransLayer(dim=out_features)
- self.layer2 = TransLayer(dim=out_features)
- self.norm = nn.LayerNorm(out_features)
+ self.layer1 = TransLayer(norm_layer=norm_layer, dim=out_features)
+ self.layer2 = TransLayer(norm_layer=norm_layer, dim=out_features)
+ # self.norm = nn.LayerNorm(out_features)
+ self.norm = norm_layer(out_features)
self._fc2 = nn.Linear(out_features, self.n_classes)
+ # self.model_ft = ResNet.resnet50(num_classes=self.n_classes, mlp=False, two_branch=False, normlinear=True).to(self.device)
+ # home = Path.cwd().parts[1]
+ # # self.model_ft.fc = nn.Identity()
+ # # self.model_ft.load_from_checkpoint(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth', strict=False)
+ # self.model_ft.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=False)
+ # for param in self.model_ft.parameters():
+ # param.requires_grad = False
+ # self.model_ft.fc = nn.Linear(2048, self.in_features)
+
def forward(self, x): #, **kwargs
+ # x = self.model_ft(x).unsqueeze(0)
h = x.float() #[B, n, 1024]
h = self._fc1(h) #[B, n, 512]
@@ -71,6 +98,8 @@ class TransMIL(nn.Module):
H = h.shape[1]
_H, _W = int(np.ceil(np.sqrt(H))), int(np.ceil(np.sqrt(H)))
add_length = _H * _W - H
+
+ # print(h.shape)
h = torch.cat([h, h[:,:add_length,:]],dim = 1) #[B, N, 512]
@@ -102,7 +131,7 @@ class TransMIL(nn.Module):
return logits, attn2
if __name__ == "__main__":
- data = torch.randn((1, 6000, 512)).cuda()
+ data = torch.randn((1, 6000, 1024)).cuda()
model = TransMIL(n_classes=2).cuda()
print(model.eval())
logits, attn = model(data)
diff --git a/code/models/TransformerMIL.py b/code/models/TransformerMIL.py
index ea249ba371fcfa7113625f6459ceb73421c13182..d5e6b89fa4088afc1b143362b627f33de7d57ce6 100644
--- a/code/models/TransformerMIL.py
+++ b/code/models/TransformerMIL.py
@@ -17,7 +17,7 @@ class TransLayer(nn.Module):
num_landmarks = dim//2, # number of landmarks
pinv_iterations = 6, # number of moore-penrose iterations for approximating pinverse. 6 was recommended by the paper
residual = True, # whether to do an extra residual with the value or not. supposedly faster convergence if turned on
- dropout=0.25 #0.1
+ dropout=0.7 #0.1
)
def forward(self, x):
@@ -48,15 +48,15 @@ class PPEG(nn.Module):
class TransformerMIL(nn.Module):
def __init__(self, n_classes):
super(TransformerMIL, self).__init__()
- in_features = 512
+ in_features = 1024
out_features = 512
- self.pos_layer = PPEG(dim=out_features)
+ # self.pos_layer = PPEG(dim=out_features)
self._fc1 = nn.Sequential(nn.Linear(in_features, out_features), nn.GELU())
# self._fc1 = nn.Sequential(nn.Linear(1024, 512), nn.ReLU())
self.cls_token = nn.Parameter(torch.randn(1, 1, out_features))
self.n_classes = n_classes
self.layer1 = TransLayer(dim=out_features)
- self.layer2 = TransLayer(dim=out_features)
+ # self.layer2 = TransLayer(dim=out_features)
self.norm = nn.LayerNorm(out_features)
self._fc2 = nn.Linear(out_features, self.n_classes)
@@ -83,6 +83,8 @@ class TransformerMIL(nn.Module):
#---->Translayer x1
h, attn1 = self.layer1(h) #[B, N, 512]
+
+
# print('After first TransLayer: ', h.shape)
#---->PPEG
@@ -99,6 +101,7 @@ class TransformerMIL(nn.Module):
#---->predict
logits = self._fc2(h) #[B, n_classes]
+ # return logits, attn2
return logits, attn1
if __name__ == "__main__":
diff --git a/code/models/__init__.py b/code/models/__init__.py
index 73aad9d74d278565976a1dd2c63c69dc7a0997ad..795017ec760368727809f128f00815f1b691ea00 100755
--- a/code/models/__init__.py
+++ b/code/models/__init__.py
@@ -1 +1,3 @@
from .model_interface import ModelInterface
+# import ResNet as ResNet
+from .TransMIL import TransMIL
diff --git a/code/models/__pycache__/AttMIL.cpython-39.pyc b/code/models/__pycache__/AttMIL.cpython-39.pyc
index f0f6f23506b721b78a8172b4ed1b215c65ae45b0..550f6876773393094b93f1d14f352eb024d7917a 100644
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diff --git a/code/models/__pycache__/Chowder.cpython-39.pyc b/code/models/__pycache__/Chowder.cpython-39.pyc
new file mode 100644
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diff --git a/code/models/__pycache__/ResNet.cpython-39.pyc b/code/models/__pycache__/ResNet.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..453943eef8a113608c389d6590ffeb844bbe73aa
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diff --git a/code/models/__pycache__/TransMIL.cpython-39.pyc b/code/models/__pycache__/TransMIL.cpython-39.pyc
index 38ed5a1f3fedfb29af2fc50c7909b312399d39d3..547c0a2e1594a0ef49a2c0482c066af1ca368fc9 100644
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diff --git a/code/models/__pycache__/TransformerMIL.cpython-39.pyc b/code/models/__pycache__/TransformerMIL.cpython-39.pyc
index 999c95ee4caea337f6a77e6cc0eaad0fb3a6c90c..7ed17970f205f815c2b3f9dc1b6529a296ec9548 100644
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diff --git a/code/models/__pycache__/__init__.cpython-39.pyc b/code/models/__pycache__/__init__.cpython-39.pyc
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diff --git a/code/models/__pycache__/model_interface.cpython-39.pyc b/code/models/__pycache__/model_interface.cpython-39.pyc
index 565cfe10390ef1f7b43c48995fdfeb7ea63773a2..176ab832bfacbc7d53356fade7a947b9b78e4a7e 100644
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diff --git a/code/models/ckpt/__init__.py b/code/models/ckpt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/code/models/ckpt/retccl_best_ckpt.pth b/code/models/ckpt/retccl_best_ckpt.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f3b5568d87aa87cf21771297e4d0bff4ebafd892
Binary files /dev/null and b/code/models/ckpt/retccl_best_ckpt.pth differ
diff --git a/code/models/model_interface.py b/code/models/model_interface.py
index 586d80c41c22c5e5f3b1f48a95e130fdb0911571..9f4f282f67a84c5482a3f30fcb40b6d95bdcdd7d 100755
--- a/code/models/model_interface.py
+++ b/code/models/model_interface.py
@@ -15,9 +15,12 @@ from PIL import Image
from MyOptimizer import create_optimizer
from MyLoss import create_loss
from utils.utils import cross_entropy_torch
+from utils.custom_resnet50 import resnet50_baseline
+
from timm.loss import AsymmetricLossSingleLabel
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
-
+from libauc.losses import AUCMLoss, AUCM_MultiLabel, CompositionalAUCLoss
+from libauc.optimizers import PESG, PDSCA
#---->
import torch
import torch.nn as nn
@@ -25,6 +28,7 @@ import torch.nn.functional as F
import torchmetrics
from torchmetrics.functional import stat_scores
from torch import optim as optim
+
# from sklearn.metrics import roc_curve, auc, roc_curve_score
@@ -41,6 +45,22 @@ from pytorch_grad_cam.utils.image import show_cam_on_image
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from captum.attr import LayerGradCam
+import models.ResNet as ResNet
+
+class FeatureExtractor(pl.LightningDataModule):
+ def __init__(self, model_name, n_classes):
+ self.n_classes = n_classes
+
+ self.model_ft = ResNet.resnet50(num_classes=self.n_classes, mlp=False, two_branch=False, normlinear=True)
+ home = Path.cwd().parts[1]
+ self.model_ft.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=False)
+ # self.model_ft.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=False)
+ for param in self.model_ft.parameters():
+ param.requires_grad = False
+ self.model_ft.fc = nn.Linear(2048, self.out_features)
+
+ def forward(self,x):
+ return self.model_ft(x)
class ModelInterface(pl.LightningModule):
@@ -48,17 +68,20 @@ class ModelInterface(pl.LightningModule):
def __init__(self, model, loss, optimizer, **kargs):
super(ModelInterface, self).__init__()
self.save_hyperparameters()
+ self.n_classes = model.n_classes
self.load_model()
- self.loss = create_loss(loss)
+ self.loss = create_loss(loss, model.n_classes)
+ # self.loss = AUCM_MultiLabel(num_classes = model.n_classes, device=self.device)
# self.asl = AsymmetricLossSingleLabel()
# self.loss = LabelSmoothingCrossEntropy(smoothing=0.1)
# self.loss =
# print(self.model)
self.model_name = model.name
+
# self.ecam = EigenGradCAM(model = self.model, target_layers = target_layers, use_cuda=True, reshape_transform=self.reshape_transform)
self.optimizer = optimizer
- self.n_classes = model.n_classes
+
self.save_path = kargs['log']
if Path(self.save_path).parts[3] == 'tcmr':
temp = list(Path(self.save_path).parts)
@@ -66,15 +89,20 @@ class ModelInterface(pl.LightningModule):
temp[3] = 'tcmr_viral'
self.save_path = '/'.join(temp)
+ # if kargs['task']:
+ # self.task = kargs['task']
+ self.task = Path(self.save_path).parts[3]
+
+
#---->acc
self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
# print(self.experiment)
#---->Metrics
if self.n_classes > 2:
- self.AUROC = torchmetrics.AUROC(num_classes = self.n_classes, average = 'weighted')
+ self.AUROC = torchmetrics.AUROC(num_classes = self.n_classes)
metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = self.n_classes,
- average='micro'),
+ average='weighted'),
torchmetrics.CohenKappa(num_classes = self.n_classes),
torchmetrics.F1Score(num_classes = self.n_classes,
average = 'macro'),
@@ -86,10 +114,11 @@ class ModelInterface(pl.LightningModule):
num_classes = self.n_classes)])
else :
- self.AUROC = torchmetrics.AUROC(num_classes=self.n_classes, average = 'weighted')
+ self.AUROC = torchmetrics.AUROC(num_classes=self.n_classes, average='weighted')
+ # self.AUROC = torchmetrics.AUROC(num_classes=self.n_classes, average = 'weighted')
metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = 2,
- average = 'micro'),
+ average = 'weighted'),
torchmetrics.CohenKappa(num_classes = 2),
torchmetrics.F1Score(num_classes = 2,
average = 'macro'),
@@ -109,11 +138,14 @@ class ModelInterface(pl.LightningModule):
self.count = 0
self.backbone = kargs['backbone']
- self.out_features = 512
- if kargs['backbone'] == 'dino':
+ self.out_features = 1024
+
+ if self.backbone == 'features':
+ self.model_ft = None
+ elif self.backbone == 'dino':
self.feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/dino-vitb16')
self.model_ft = ViTModel.from_pretrained('facebook/dino-vitb16')
- elif kargs['backbone'] == 'resnet18':
+ elif self.backbone == 'resnet18':
self.model_ft = models.resnet18(pretrained=True)
# modules = list(resnet18.children())[:-1]
for param in self.model_ft.parameters():
@@ -132,12 +164,33 @@ class ModelInterface(pl.LightningModule):
# nn.Linear(512, self.out_features),
# nn.GELU(),
# )
- elif kargs['backbone'] == 'resnet50':
-
- self.model_ft = models.resnet50(pretrained=True)
+ elif self.backbone == 'retccl':
+ # import models.ResNet as ResNet
+ self.model_ft = ResNet.resnet50(num_classes=self.n_classes, mlp=False, two_branch=False, normlinear=True)
+ home = Path.cwd().parts[1]
+ # pre_model =
+ # self.model_ft.fc = nn.Identity()
+ # self.model_ft.load_from_checkpoint(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth', strict=False)
+ self.model_ft.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=False)
for param in self.model_ft.parameters():
param.requires_grad = False
self.model_ft.fc = nn.Linear(2048, self.out_features)
+
+ # self.model_ft = FeatureExtractor('retccl', self.n_classes)
+
+
+ elif self.backbone == 'resnet50':
+
+ self.model_ft = resnet50_baseline(pretrained=True)
+ for param in self.model_ft.parameters():
+ param.requires_grad = False
+
+ # self.model_ft = models.resnet50(pretrained=True)
+ # for param in self.model_ft.parameters():
+ # param.requires_grad = False
+ # self.model_ft.fc = nn.Linear(2048, self.out_features)
+
+
# modules = list(resnet50.children())[:-3]
# res50 = nn.Sequential(
# *modules,
@@ -150,7 +203,9 @@ class ModelInterface(pl.LightningModule):
# nn.Linear(1024, self.out_features),
# # nn.GELU()
# )
- elif kargs['backbone'] == 'efficientnet':
+ # elif kargs
+
+ elif self.backbone == 'efficientnet':
efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_widese_b0', pretrained=True)
for param in efficientnet.parameters():
param.requires_grad = False
@@ -160,7 +215,7 @@ class ModelInterface(pl.LightningModule):
efficientnet,
nn.GELU(),
)
- elif kargs['backbone'] == 'simple': #mil-ab attention
+ elif self.backbone == 'simple': #mil-ab attention
feature_extracting = False
self.model_ft = nn.Sequential(
nn.Conv2d(3, 20, kernel_size=5),
@@ -176,15 +231,23 @@ class ModelInterface(pl.LightningModule):
# print(self.model_ft[0].features[-1])
# print(self.model_ft)
+ # def __build_
+
def forward(self, x):
# print(x.shape)
- feats = self.model_ft(x).unsqueeze(0)
+ if self.model_ft:
+ feats = self.model_ft(x).unsqueeze(0)
+ else:
+ feats = x.unsqueeze(0)
return self.model(feats)
+ # return self.model(x)
def step(self, input):
input = input.squeeze(0).float()
- logits, _ = self(input)
+ logits, _ = self(input.contiguous())
+
+
Y_hat = torch.argmax(logits, dim=1)
Y_prob = F.softmax(logits, dim=1)
@@ -196,7 +259,8 @@ class ModelInterface(pl.LightningModule):
input, label, _= batch
#random image dropout
- # bag_size = 500
+
+ # bag_size = input.squeeze().shape[0] * 0.7
# bag_idxs = torch.randperm(input.squeeze(0).shape[0])[:bag_size]
# input = input.squeeze(0)[bag_idxs].unsqueeze(0)
@@ -218,20 +282,20 @@ class ModelInterface(pl.LightningModule):
# Y = int(label[0])
self.data[Y]["count"] += 1
self.data[Y]["correct"] += (int(Y_hat) == Y)
- self.log('loss', loss, prog_bar=True, on_epoch=True, logger=True, batch_size=1)
+ self.log('loss', loss, prog_bar=True, on_epoch=True, logger=True, batch_size=1, sync_dist=True)
- if self.current_epoch % 10 == 0:
+ # if self.current_epoch % 10 == 0:
- # images = input.squeeze()[:10, :, :, :]
- # for i in range(10):
- img = input.squeeze(0)[:10, :, :, :]
- img = (img - torch.min(img)/(torch.max(img)-torch.min(img)))*255.0
+ # # images = input.squeeze()[:10, :, :, :]
+ # # for i in range(10):
+ # img = input.squeeze(0)[:10, :, :, :]
+ # img = (img - torch.min(img)/(torch.max(img)-torch.min(img)))*255.0
- # mg = img.cpu().numpy()
- grid = torchvision.utils.make_grid(img, normalize=True, value_range=(0, 255), scale_each=True)
- # grid = img.detach().cpu().numpy()
- # log input images
- self.loggers[0].experiment.add_image(f'{self.current_epoch}/input', grid)
+ # # mg = img.cpu().numpy()
+ # grid = torchvision.utils.make_grid(img, normalize=True, value_range=(0, 255), scale_each=True)
+ # # grid = img.detach().cpu().numpy()
+ # # log input images
+ # self.loggers[0].experiment.add_image(f'{self.current_epoch}/input', grid)
return {'loss': loss, 'Y_prob': Y_prob, 'Y_hat': Y_hat, 'label': Y}
@@ -243,14 +307,16 @@ class ModelInterface(pl.LightningModule):
# target = torch.stack([x['label'] for x in training_step_outputs], dim = 0)
target = torch.stack([x['label'] for x in training_step_outputs])
# target = torch.argmax(target, dim=1)
- for c in range(self.n_classes):
- count = self.data[c]["count"]
- correct = self.data[c]["correct"]
- if count == 0:
- acc = None
- else:
- acc = float(correct) / count
- print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+
+ if self.current_epoch % 5 == 0:
+ for c in range(self.n_classes):
+ count = self.data[c]["count"]
+ correct = self.data[c]["correct"]
+ if count == 0:
+ acc = None
+ else:
+ acc = float(correct) / count
+ print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
# print('max_probs: ', max_probs)
@@ -258,7 +324,7 @@ class ModelInterface(pl.LightningModule):
if self.current_epoch % 10 == 0:
self.log_confusion_matrix(max_probs, target, stage='train')
- self.log('Train/auc', self.AUROC(probs, target), prog_bar=True, on_epoch=True, logger=True)
+ self.log('Train/auc', self.AUROC(probs, target), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
def validation_step(self, batch, batch_idx):
@@ -271,8 +337,10 @@ class ModelInterface(pl.LightningModule):
# Y = int(label[0][1])
Y = torch.argmax(label)
+ # print(Y_hat)
self.data[Y]["count"] += 1
- self.data[Y]["correct"] += (Y_hat.item() == Y)
+ self.data[Y]["correct"] += (int(Y_hat) == Y)
+ # self.data[Y]["correct"] += (Y_hat.item() == Y)
return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : Y}
@@ -284,20 +352,18 @@ class ModelInterface(pl.LightningModule):
target = torch.stack([x['label'] for x in val_step_outputs])
self.log_dict(self.valid_metrics(logits, target),
- on_epoch = True, logger = True)
+ on_epoch = True, logger = True, sync_dist=True)
#---->
# logits = logits.long()
# target = target.squeeze().long()
# logits = logits.squeeze(0)
if len(target.unique()) != 1:
- self.log('val_auc', self.AUROC(probs, target), prog_bar=True, on_epoch=True, logger=True)
+ self.log('val_auc', self.AUROC(probs, target), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
else:
- self.log('val_auc', 0.0, prog_bar=True, on_epoch=True, logger=True)
+ self.log('val_auc', 0.0, prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
-
-
- self.log('val_loss', cross_entropy_torch(logits, target), prog_bar=True, on_epoch=True, logger=True)
+ self.log('val_loss', cross_entropy_torch(logits, target), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
precision, recall, thresholds = self.PRC(probs, target)
@@ -498,6 +564,8 @@ class ModelInterface(pl.LightningModule):
print(f'{keys} = {values}')
metrics[keys] = values.cpu().numpy()
#---->acc log
+
+
for c in range(self.n_classes):
count = self.data[c]["count"]
correct = self.data[c]["correct"]
@@ -530,8 +598,13 @@ class ModelInterface(pl.LightningModule):
def configure_optimizers(self):
# optimizer_ft = optim.Adam(self.model_ft.parameters(), lr=self.optimizer.lr*0.1)
optimizer = create_optimizer(self.optimizer, self.model)
+ # optimizer = PESG(self.model, a=self.loss.a, b=self.loss.b, loss_fn=self.loss, lr=self.optimizer.lr, margin=1.0, epoch_decay=2e-3, weight_decay=1e-5, device=self.device)
+ # optimizer = PDSCA(self.model, loss_fn=self.loss, lr=self.optimizer.lr, margin=1.0, epoch_decay=2e-3, weight_decay=1e-5, device=self.device)
return optimizer
+ def optimizer_zero_grad(self, epoch, batch_idx, optimizer, optimizer_idx):
+ optimizer.zero_grad(set_to_none=True)
+
def reshape_transform(self, tensor):
# print(tensor.shape)
H = tensor.shape[1]
@@ -545,6 +618,7 @@ class ModelInterface(pl.LightningModule):
def load_model(self):
name = self.hparams.model.name
+ backbone = self.hparams.model.backbone
# Change the `trans_unet.py` file name to `TransUnet` class name.
# Please always name your model file name as `trans_unet.py` and
# class name or funciton name corresponding `TransUnet`.
@@ -559,8 +633,26 @@ class ModelInterface(pl.LightningModule):
except:
raise ValueError('Invalid Module File Name or Invalid Class Name!')
self.model = self.instancialize(Model)
+
+ # if backbone == 'retccl':
+
+ # self.model_ft = ResNet.resnet50(num_classes=self.n_classes, mlp=False, two_branch=False, normlinear=True)
+ # home = Path.cwd().parts[1]
+ # # self.model_ft.fc = nn.Identity()
+ # # self.model_ft.load_from_checkpoint(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth', strict=False)
+ # self.model_ft.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=False)
+ # for param in self.model_ft.parameters():
+ # param.requires_grad = False
+ # self.model_ft.fc = nn.Linear(2048, self.out_features)
+
+ # elif backbone == 'resnet50':
+ # self.model_ft = resnet50_baseline(pretrained=True)
+ # for param in self.model_ft.parameters():
+ # param.requires_grad = False
+
pass
+
def instancialize(self, Model, **other_args):
""" Instancialize a model using the corresponding parameters
from self.hparams dictionary. You can also input any args
@@ -573,6 +665,8 @@ class ModelInterface(pl.LightningModule):
if arg in inkeys:
args1[arg] = getattr(self.hparams.model, arg)
args1.update(other_args)
+
+
return Model(**args1)
def log_image(self, tensor, stage, name):
@@ -594,6 +688,8 @@ class ModelInterface(pl.LightningModule):
else:
fig_.savefig(f'{self.loggers[0].log_dir}/cm_test.png', dpi=400)
+ fig_.clf()
+
def log_roc_curve(self, probs, target, stage):
fpr_list, tpr_list, thresholds = self.ROC(probs, target)
diff --git a/code/test.ipynb b/code/test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4149f989b13e724c9ac6f209b4b3ecc887f80b97
--- /dev/null
+++ b/code/test.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "a = [None] * 500\n",
+ "print(a.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "b = np.fromfile(a)"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/code/test_visualize.py b/code/test_visualize.py
index 82f6cb4c6dc9d14404aece06d93c729206b70ad6..5e4a5e76491d1725a78c90216f077c4eac32bd17 100644
--- a/code/test_visualize.py
+++ b/code/test_visualize.py
@@ -17,8 +17,9 @@ from utils.utils import *
import pytorch_lightning as pl
from pytorch_lightning import Trainer
import torch
+import torch.nn as nn
-from pytorch_grad_cam import GradCAM, EigenGradCAM
+from pytorch_grad_cam import GradCAM, EigenGradCAM, EigenCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
@@ -26,28 +27,35 @@ import cv2
from PIL import Image
from matplotlib import pyplot as plt
import pandas as pd
+import json
+import pprint
+
#--->Setting parameters
def make_parse():
parser = argparse.ArgumentParser()
parser.add_argument('--stage', default='test', type=str)
- parser.add_argument('--config', default='DeepGraft/TransMIL.yaml',type=str)
+ parser.add_argument('--config', default='../DeepGraft/TransMIL.yaml',type=str)
parser.add_argument('--version', default=0,type=int)
parser.add_argument('--epoch', default='0',type=str)
parser.add_argument('--gpus', default = 2, type=int)
parser.add_argument('--loss', default = 'CrossEntropyLoss', type=str)
parser.add_argument('--fold', default = 0)
- parser.add_argument('--bag_size', default = 1024, type=int)
+ parser.add_argument('--bag_size', default = 10000, type=int)
args = parser.parse_args()
return args
class custom_test_module(ModelInterface):
+ # self.task = kargs['task']
+ # self.task = 'tcmr_viral'
+
def test_step(self, batch, batch_idx):
torch.set_grad_enabled(True)
- input_data, label, (wsi_name, batch_names) = batch
+ input_data, label, (wsi_name, batch_names, patient) = batch
+ patient = patient[0]
wsi_name = wsi_name[0]
label = label.float()
# logits, Y_prob, Y_hat = self.step(data)
@@ -60,6 +68,10 @@ class custom_test_module(ModelInterface):
Y = torch.argmax(label)
Y_hat = torch.argmax(logits, dim=1)
Y_prob = F.softmax(logits, dim=1)
+
+ # print('Y_hat:', Y_hat)
+ # print('Y_prob:', Y_prob)
+
#----> Get GradCam maps, map each instance to attention value, assemble, overlay on original WSI
if self.model_name == 'TransMIL':
@@ -67,6 +79,10 @@ class custom_test_module(ModelInterface):
# target_layers = [self.model_ft[0].features[-1]] # 32x32
self.cam = GradCAM(model=self.model, target_layers = target_layers, use_cuda=True, reshape_transform=self.reshape_transform) #, reshape_transform=self.reshape_transform
# self.cam_ft = GradCAM(model=self.model, target_layers = target_layers_ft, use_cuda=True) #, reshape_transform=self.reshape_transform
+ elif self.model_name == 'TransformerMIL':
+ target_layers = [self.model.layer1.norm]
+ self.cam = EigenCAM(model=self.model, target_layers = target_layers, use_cuda=True, reshape_transform=self.reshape_transform)
+ # self.cam = GradCAM(model=self.model, target_layers = target_layers, use_cuda=True, reshape_transform=self.reshape_transform)
else:
target_layers = [self.model.attention_weights]
self.cam = GradCAM(model = self.model, target_layers = target_layers, use_cuda=True)
@@ -74,14 +90,15 @@ class custom_test_module(ModelInterface):
data_ft = self.model_ft(input_data).unsqueeze(0).float()
instance_count = input_data.size(0)
target = [ClassifierOutputTarget(Y)]
- grayscale_cam = self.cam(input_tensor=data_ft, targets=target)
+ grayscale_cam = self.cam(input_tensor=data_ft, targets=target, eigen_smooth=True)
grayscale_cam = torch.Tensor(grayscale_cam)[:instance_count, :] #.to(self.device)
#----------------------------------------------------
# Get Topk Tiles and Topk Patients
#----------------------------------------------------
+ k = 10
summed = torch.mean(grayscale_cam, dim=2)
- topk_tiles, topk_indices = torch.topk(summed.squeeze(0), 5, dim=0)
+ topk_tiles, topk_indices = torch.topk(summed.squeeze(0), k, dim=0)
topk_data = input_data[topk_indices].detach()
#----------------------------------------------------
@@ -95,56 +112,158 @@ class custom_test_module(ModelInterface):
# Tile Level Attention Maps
#----------------------------------------------------
- self.save_attention_map(wsi_name, input_data, batch_names, grayscale_cam, target=Y)
+ # print(input_data.shape)
+ # print(len(batch_names))
+ # if visualize:
+ # self.save_attention_map(wsi_name, input_data, batch_names, grayscale_cam, target=Y)
+ # print('test_step_patient: ', patient)
- return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : Y, 'name': wsi_name, 'topk_data': topk_data} #
+ return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : Y, 'name': wsi_name, 'patient': patient, 'topk_data': topk_data} #
# return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : label, 'name': name} #, 'topk_data': topk_data
def test_epoch_end(self, output_results):
+ k_patient = 1
+ k_slide = 1
+
+ pp = pprint.PrettyPrinter(indent=4)
+
+
logits = torch.cat([x['logits'] for x in output_results], dim = 0)
probs = torch.cat([x['Y_prob'] for x in output_results])
max_probs = torch.stack([x['Y_hat'] for x in output_results])
# target = torch.stack([x['label'] for x in output_results], dim = 0)
target = torch.stack([x['label'] for x in output_results])
# target = torch.argmax(target, dim=1)
- patients = [x['name'] for x in output_results]
+ slide = [x['name'] for x in output_results]
+ patients = [x['patient'] for x in output_results]
topk_tiles = [x['topk_data'] for x in output_results]
#---->
- auc = self.AUROC(probs, target)
+ if len(target.unique()) !=1:
+ auc = self.AUROC(probs, target)
+ else: auc = torch.tensor(0)
metrics = self.test_metrics(logits , target)
# metrics = self.test_metrics(max_probs.squeeze() , torch.argmax(target.squeeze(), dim=1))
metrics['test_auc'] = auc
- # self.log('auc', auc, prog_bar=True, on_epoch=True, logger=True)
+ # print(metrics)
+ np_metrics = {k: metrics[k].item() for k in metrics.keys()}
+ # print(np_metrics)
- #---->get highest scoring patients for each class
- # test_path = Path(self.save_path) / 'most_predictive'
- # Path.mkdir(output_path, exist_ok=True)
- topk, topk_indices = torch.topk(probs.squeeze(0), 5, dim=0)
+ complete_patient_dict = {}
+ '''
+ Patient
+ -> slides:
+
+ -> SlideName:
+ ->probs = [0.5, 0.5]
+ ->topk = [10,3,224,224]
+ -> score = []
+ '''
+
+
+ for p, s, l, topkt in zip(patients, slide, probs, topk_tiles):
+ if p not in complete_patient_dict.keys():
+ complete_patient_dict[p] = {'slides':{}}
+ complete_patient_dict[p]['slides'][s] = {'probs': l, 'topk':topkt}
+
+ patient_list = []
+ patient_score = []
+ for p in complete_patient_dict.keys():
+ score = []
+
+ for s in complete_patient_dict[p]['slides'].keys():
+ score.append(complete_patient_dict[p]['slides'][s]['probs'])
+ score = torch.mean(torch.stack(score), dim=0) #.cpu().detach().numpy()
+ complete_patient_dict[p]['score'] = score
+ print(p, score)
+ patient_list.append(p)
+ patient_score.append(score)
+
+ print(patient_list)
+ #topk patients:
+
+
+ # task = 'tcmr_viral'
+ task = Path(self.save_path).parts[-5]
+ label_map_path = 'label_map.json'
+ with open(label_map_path, 'r') as f:
+ label_map = json.load(f)
+
+ # topk_patients, topk_p_indices = torch.topk(score, 5, dim=0)
+
+ # print(probs.squeeze(0))
+ # topk, topk_indices = torch.topk(probs.squeeze(0), 5, dim=0) # topk =
+ # print(topk)
+
+ # topk_indices = topk_indices.transpose(0, 1)
+
+ output_dict = {}
+
+
for n in range(self.n_classes):
- print('class: ', n)
+
+ class_name = f'{n}_{label_map[task][str(n)]}'
+
+ output_dict[class_name] = {}
+ # class_name = str(n)
+ print('class: ', class_name)
+ print(score)
+ _, topk_indices = torch.topk(score, k_patient, dim=0) # change to 3
+ print(topk_indices)
+
+ topk_patients = [patient_list[i] for i in topk_indices]
+
+ patient_top_slides = {}
+ for p in topk_patients:
+ print(p)
+ output_dict[class_name][p] = {}
+ output_dict[class_name][p]['Patient_Score'] = complete_patient_dict[p]['score'].cpu().detach().numpy().tolist()
+
+ slides = list(complete_patient_dict[p]['slides'].keys())
+ slide_scores = [complete_patient_dict[p]['slides'][s]['probs'] for s in slides]
+ slide_scores = torch.stack(slide_scores)
+ print(slide_scores)
+ _, topk_slide_indices = torch.topk(slide_scores, k_slide, dim=0)
+ # topk_slide_indices = topk_slide_indices.squeeze(0)
+ print(topk_slide_indices[0])
+ topk_patient_slides = [slides[i] for i in topk_slide_indices[0]]
+ patient_top_slides[p] = topk_patient_slides
+
+ output_dict[class_name][p]['Top_Slides'] = [{slides[i]: {'Slide_Score': slide_scores[i].cpu().detach().numpy().tolist()}} for i in topk_slide_indices[0]]
- topk_patients = [patients[i[n]] for i in topk_indices]
- topk_patient_tiles = [topk_tiles[i[n]] for i in topk_indices]
- for x, p, t in zip(topk, topk_patients, topk_patient_tiles):
- print(p, x[n])
- patient = p
- # outpath = test_path / str(n) / patient
- outpath = Path(self.save_path) / str(n) / patient
- outpath.mkdir(parents=True, exist_ok=True)
- for i in range(len(t)):
- tile = t[i]
- tile = tile.cpu().numpy().transpose(1,2,0)
- tile = (tile - tile.min())/ (tile.max() - tile.min()) * 255
- tile = tile.astype(np.uint8)
- img = Image.fromarray(tile)
+
+
+
+ for p in topk_patients:
+
+ score = complete_patient_dict[p]['score']
+ print(p, score)
+ print('Topk Slides:')
+ for slide in patient_top_slides[p]:
+ print(slide)
+ outpath = Path(self.save_path) / class_name / p / slide
+ outpath.mkdir(parents=True, exist_ok=True)
+
+ topk_tiles = complete_patient_dict[p]['slides'][slide]['topk']
+ for i in range(topk_tiles.shape[0]):
+ tile = topk_tiles[i]
+ tile = tile.cpu().numpy().transpose(1,2,0)
+ tile = (tile - tile.min())/ (tile.max() - tile.min()) * 255
+ tile = tile.astype(np.uint8)
+ img = Image.fromarray(tile)
img.save(f'{outpath}/{i}.jpg')
+ output_dict['Test_Metrics'] = np_metrics
+ pp.pprint(output_dict)
+ json.dump(output_dict, open(f'{self.save_path}/test_metrics.json', 'w'))
+
+
+
for keys, values in metrics.items():
print(f'{keys} = {values}')
@@ -174,17 +293,21 @@ class custom_test_module(ModelInterface):
for tile_name in batch_names:
pos = re.findall(r'\((.*?)\)', tile_name[0])
- x, y = pos[0].split('_')
+ x, y = pos[-1].split('_')
coords.append((int(x),int(y)))
return coords
coords = get_coords(batch_names)
+ # coords_set = set(coords)
+
+ # print(coords)
# temp_data = data.cpu()
# print(data.shape)
wsi = self.assemble(data, coords).cpu().numpy()
# wsi = (wsi-wsi.min())/(wsi.max()-wsi.min())
# wsi = wsi
-
+ # print(coords)
+ print('wsi.shape: ', wsi.shape)
#--> Get interpolated mask from GradCam
W, H = wsi.shape[0], wsi.shape[1]
@@ -192,10 +315,11 @@ class custom_test_module(ModelInterface):
attention_map = grayscale_cam[:, :, 1].squeeze()
attention_map = F.relu(attention_map)
# print(attention_map)
- input_h = 256
+ input_h = 224
mask = torch.ones(( int(W/input_h), int(H/input_h))).to(self.device)
-
+ print('mask.shape: ', mask.shape)
+ print('attention_map.shape: ', attention_map.shape)
for i, (x,y) in enumerate(coords):
mask[y][x] = attention_map[i]
mask = mask.unsqueeze(0).unsqueeze(0)
@@ -217,8 +341,11 @@ class custom_test_module(ModelInterface):
wsi_cam = show_cam_on_image(wsi, mask)
wsi_cam = ((wsi_cam-wsi_cam.min())/(wsi_cam.max()-wsi_cam.min()) * 255.0).astype(np.uint8)
+ size = (20000, 20000)
+
img = Image.fromarray(wsi_cam)
img = img.convert('RGB')
+ img.thumbnail(size, Image.ANTIALIAS)
output_path = self.save_path / str(target.item())
output_path.mkdir(parents=True, exist_ok=True)
img.save(f'{output_path}/{wsi_name}_gradcam.jpg')
@@ -226,9 +353,14 @@ class custom_test_module(ModelInterface):
wsi = ((wsi-wsi.min())/(wsi.max()-wsi.min()) * 255.0).astype(np.uint8)
img = Image.fromarray(wsi)
img = img.convert('RGB')
+ img.thumbnail(size, Image.ANTIALIAS)
output_path = self.save_path / str(target.item())
output_path.mkdir(parents=True, exist_ok=True)
img.save(f'{output_path}/{wsi_name}.jpg')
+ del wsi
+ del img
+ del wsi_cam
+ del mask
def assemble(self, tiles, coords): # with coordinates (x-y)
@@ -245,72 +377,57 @@ class custom_test_module(ModelInterface):
count = 0
# max_x = max(coords, key = lambda t: t[0])[0]
d = tiles[0,:,:,:].permute(1,2,0).shape
- print(d)
+ # print(d)
white_value = 0
x_max = max([x[0] for x in coords])
y_max = max([x[1] for x in coords])
for i, (x,y) in enumerate(coords):
-
- # name = n[0]
- # image = tiles[i,:,:,:].permute(1,2,0)
-
- # d = image.shape
- # print(image.min())
- # print(image.max())
- # if image.max() > white_value:
- # white_value = image.max()
- # # print(image.shape)
-
- # tile_position = '-'.join(name.split('_')[-2:])
- # x,y = getPosition(tile_position)
-
- # y_max = y if y > y_max else y_max
if x not in position_dict.keys():
position_dict[x] = [(y, i)]
else: position_dict[x].append((y, i))
- # count += 1
- print(position_dict.keys())
- x_positions = sorted(position_dict.keys())
- # print(x_max)
- # complete_image = torch.zeros([x_max, y_max, 3])
-
+ # x_positions = sorted(position_dict.keys())
+ test_img_compl = torch.ones([(y_max+1)*224, (x_max+1)*224, 3]).to(self.device)
for i in range(x_max+1):
-
- # if i in position_dict.keys():
- # print(i)
- column = [None]*(int(y_max+1))
- # if len(d) == 3:
- # empty_tile = torch.zeros(d).to(self.device)
- # else:
- # empty_tile = torch.ones(d)
- empty_tile = torch.ones(d).to(self.device)
- # print(i)
if i in position_dict.keys():
- # print(i)
for j in position_dict[i]:
- print(j)
sample_idx = j[1]
- print(sample_idx)
- # img = tiles[sample_idx, :, :, :].permute(1,2,0)
- column[int(j[0])] = tiles[sample_idx, :, :, :]
- column = [empty_tile if i is None else i for i in column]
- print(column)
- # for c in column:
- # print(c.shape)
- # column = torch.vstack(column)
- # print(column)
- column = torch.stack(column)
- assembled.append((i, column))
+ if tiles[sample_idx, :, :, :].shape != [3,224,224]:
+ img = tiles[sample_idx, :, :, :].permute(1,2,0)
+ else:
+ img = tiles[sample_idx, :, :, :]
+ y_coord = int(j[0])
+ x_coord = int(i)
+ test_img_compl[y_coord*224:(y_coord+1)*224, x_coord*224:(x_coord+1)*224, :] = img
+
+
+
+ # for i in range(x_max+1):
+ # column = [None]*(int(y_max+1))
+ # empty_tile = torch.ones(d).to(self.device)
+ # if i in position_dict.keys():
+ # for j in position_dict[i]:
+ # sample_idx = j[1]
+ # if tiles[sample_idx, :, :, :].shape != [3,224,224]:
+ # img = tiles[sample_idx, :, :, :].permute(1,2,0)
+ # else:
+ # img = tiles[sample_idx, :, :, :]
+ # column[int(j[0])] = img
+ # column = [empty_tile if i is None else i for i in column]
+ # column = torch.vstack(column)
+ # assembled.append((i, column))
- assembled = sorted(assembled, key=lambda x: x[0])
+ # assembled = sorted(assembled, key=lambda x: x[0])
- stack = [i[1] for i in assembled]
- # print(stack)
- img_compl = torch.hstack(stack)
- print(img_compl)
- return img_compl
+ # stack = [i[1] for i in assembled]
+ # # print(stack)
+ # img_compl = torch.hstack(stack)
+ # print(img_compl.shape)
+ # print(test_img_compl)
+ # print(torch.nonzero(img_compl - test_img_compl))
+ # print(img_compl)
+ return test_img_compl.cpu().detach()
#---->main
@@ -331,8 +448,11 @@ def main(cfg):
# cfg.callbacks = load_callbacks(cfg, save_path)
home = Path.cwd().parts[1]
- cfg.Data.label_file = '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
- cfg.Data.data_dir = '/home/ylan/data/DeepGraft/224_128um/'
+ # cfg.Data.label_file = '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_Utrecht.json'
+ # cfg.Data.label_file = '/homeStor1/ylan/DeepGraft/training_tables/split_debug.json'
+ # cfg.Data.label_file = '/home/ylan/DeepGraft/training_tables/dg_limit_20_split_PAS_HE_Jones_norm_rest.json'
+ # cfg.Data.patient_slide = '/homeStor1/ylan/DeepGraft/training_tables/cohort_stain_dict.json'
+ # cfg.Data.data_dir = '/homeStor1/ylan/data/DeepGraft/224_128um_v2/'
DataInterface_dict = {
'data_root': cfg.Data.data_dir,
'label_path': cfg.Data.label_file,
@@ -353,15 +473,19 @@ def main(cfg):
'data': cfg.Data,
'log': cfg.log_path,
'backbone': cfg.Model.backbone,
+ 'task': cfg.task,
}
# model = ModelInterface(**ModelInterface_dict)
model = custom_test_module(**ModelInterface_dict)
+ # model._fc1 = nn.Sequential(nn.Linear(512, 512), nn.GELU())
# model.save_path = cfg.log_path
#---->Instantiate Trainer
+ tb_logger = pl_loggers.TensorBoardLogger(cfg.log_path)
+
trainer = Trainer(
num_sanity_val_steps=0,
- # logger=cfg.load_loggers,
+ logger=tb_logger,
# callbacks=cfg.callbacks,
max_epochs= cfg.General.epochs,
min_epochs = 200,
@@ -384,6 +508,7 @@ def main(cfg):
# log_path = Path('lightning_logs/2/checkpoints')
model_paths = list(log_path.glob('*.ckpt'))
+
if cfg.epoch == 'last':
model_paths = [str(model_path) for model_path in model_paths if f'last' in str(model_path)]
else:
@@ -404,7 +529,27 @@ def main(cfg):
# Top 5 scoring patches for patient
# GradCam
+def check_home(cfg):
+ # replace home directory
+
+ home = Path.cwd().parts[1]
+ x = cfg.General.log_path
+ if Path(x).parts[1] != home:
+ new_path = Path(home).joinpath(*Path(x).parts[2:])
+ cfg.General.log_path = '/' + str(new_path)
+
+ x = cfg.Data.data_dir
+ if Path(x).parts[1] != home:
+ new_path = Path(home).joinpath(*Path(x).parts[2:])
+ cfg.Data.data_dir = '/' + str(new_path)
+
+ x = cfg.Data.label_file
+ if Path(x).parts[1] != home:
+ new_path = Path(home).joinpath(*Path(x).parts[2:])
+ cfg.Data.label_file = '/' + str(new_path)
+
+ return cfg
if __name__ == '__main__':
@@ -421,6 +566,8 @@ if __name__ == '__main__':
cfg.version = args.version
cfg.epoch = args.epoch
+ cfg = check_home(cfg)
+
config_path = '/'.join(Path(cfg.config).parts[1:])
log_path = Path(cfg.General.log_path) / str(Path(config_path).parent)
@@ -428,8 +575,11 @@ if __name__ == '__main__':
log_name = f'_{cfg.Model.backbone}' + f'_{cfg.Loss.base_loss}'
task = '_'.join(Path(cfg.config).name[:-5].split('_')[2:])
# task = Path(cfg.config).name[:-5].split('_')[2:][0]
+ cfg.task = task
cfg.log_path = log_path / f'{cfg.Model.name}' / task / log_name / 'lightning_logs' / f'version_{cfg.version}'
+ # cfg.model_path = cfg.log_patth / 'code' / 'models' /
+
#---->main
diff --git a/code/train.py b/code/train.py
index ddd37e443ffee175cdb37b0e2d2e6728728dc5ba..e01bc52c17b0b8b0698124bb4ae0e61d087a585d 100644
--- a/code/train.py
+++ b/code/train.py
@@ -14,8 +14,48 @@ from utils.utils import *
# pytorch_lightning
import pytorch_lightning as pl
from pytorch_lightning import Trainer
+from pytorch_lightning.strategies import DDPStrategy
import torch
from train_loop import KFoldLoop
+from pytorch_lightning.plugins.training_type import DDPPlugin
+
+
+try:
+ import apex
+ from apex.parallel import DistributedDataParallel
+ print('Apex available.')
+except ModuleNotFoundError:
+ # Error handling
+ pass
+
+def unwrap_lightning_module(wrapped_model):
+ from apex.parallel import DistributedDataParallel
+ from pytorch_lightning.overrides.base import (
+ _LightningModuleWrapperBase,
+ _LightningPrecisionModuleWrapperBase,
+ )
+
+ model = wrapped_model
+ if isinstance(model, DistributedDataParallel):
+ model = unwrap_lightning_module(model.module)
+ if isinstance(
+ model, (_LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase)
+ ):
+ model = unwrap_lightning_module(model.module)
+ return model
+
+
+class ApexDDPPlugin(DDPPlugin):
+ def _setup_model(self, model):
+ from apex.parallel import DistributedDataParallel
+
+ return DistributedDataParallel(model, delay_allreduce=False)
+
+ @property
+ def lightning_module(self):
+ return unwrap_lightning_module(self._model)
+
+
#--->Setting parameters
def make_parse():
@@ -23,11 +63,12 @@ def make_parse():
parser.add_argument('--stage', default='train', type=str)
parser.add_argument('--config', default='DeepGraft/TransMIL.yaml',type=str)
parser.add_argument('--version', default=2,type=int)
- parser.add_argument('--gpus', default = 2, type=int)
+ parser.add_argument('--gpus', nargs='+', default = [2], type=int)
parser.add_argument('--loss', default = 'CrossEntropyLoss', type=str)
parser.add_argument('--fold', default = 0)
parser.add_argument('--bag_size', default = 1024, type=int)
parser.add_argument('--resume_training', action='store_true')
+ parser.add_argument('--label_file', type=str)
# parser.add_argument('--ckpt_path', default = , type=str)
@@ -59,6 +100,9 @@ def main(cfg):
# 'dataset_cfg': cfg.Data,}
# dm = DataInterface(**DataInterface_dict)
home = Path.cwd().parts[1]
+ if cfg.Model.backbone == 'features':
+ use_features = True
+ else: use_features = False
DataInterface_dict = {
'data_root': cfg.Data.data_dir,
'label_path': cfg.Data.label_file,
@@ -66,6 +110,7 @@ def main(cfg):
'num_workers': cfg.Data.train_dataloader.num_workers,
'n_classes': cfg.Model.n_classes,
'bag_size': cfg.Data.bag_size,
+ 'use_features': use_features,
}
if cfg.Data.cross_val:
@@ -80,6 +125,7 @@ def main(cfg):
'data': cfg.Data,
'log': cfg.log_path,
'backbone': cfg.Model.backbone,
+ 'task': cfg.task,
}
if cfg.Model.name == 'DTFDMIL':
model = ModelInterface_DTFD(**ModelInterface_dict)
@@ -87,33 +133,64 @@ def main(cfg):
model = ModelInterface(**ModelInterface_dict)
#---->Instantiate Trainer
- trainer = Trainer(
- # num_sanity_val_steps=0,
- logger=cfg.load_loggers,
- callbacks=cfg.callbacks,
- max_epochs= cfg.General.epochs,
- min_epochs = 200,
- gpus=cfg.General.gpus,
- # gpus = [0,2],
- # strategy='ddp',
- amp_backend='native',
- # amp_level=cfg.General.amp_level,
- precision=cfg.General.precision,
- accumulate_grad_batches=cfg.General.grad_acc,
- gradient_clip_val=0.0,
- # fast_dev_run = True,
- # limit_train_batches=1,
-
- # deterministic=True,
- check_val_every_n_epoch=5,
- )
+ # plugins = []
+ # if apex:
+ # plugins.append(ApexDDPPlugin())
+
+ if len(cfg.General.gpus) > 1:
+ trainer = Trainer(
+ logger=cfg.load_loggers,
+ callbacks=cfg.callbacks,
+ max_epochs= cfg.General.epochs,
+ min_epochs = 100,
+ accelerator='gpu',
+ # plugins=plugins,
+ devices=cfg.General.gpus,
+ strategy=DDPStrategy(find_unused_parameters=False),
+ replace_sampler_ddp=False,
+ amp_backend='native',
+ precision=cfg.General.precision,
+ # accumulate_grad_batches=cfg.General.grad_acc,
+ gradient_clip_val=0.0,
+ # fast_dev_run = True,
+ # limit_train_batches=1,
+
+ # deterministic=True,
+ check_val_every_n_epoch=5,
+ )
+ else:
+ trainer = Trainer(
+ # num_sanity_val_steps=0,
+ logger=cfg.load_loggers,
+ callbacks=cfg.callbacks,
+ max_epochs= cfg.General.epochs,
+ min_epochs = 100,
+
+ # gpus=cfg.General.gpus,
+ accelerator='gpu'
+ devices=cfg.General.gpus,
+ amp_backend='native',
+ # amp_level=cfg.General.amp_level,
+ precision=cfg.General.precision,
+ accumulate_grad_batches=cfg.General.grad_acc,
+ gradient_clip_val=0.0,
+ # fast_dev_run = True,
+ # limit_train_batches=1,
+
+ # deterministic=True,
+ check_val_every_n_epoch=5,
+ )
# print(cfg.log_path)
# print(trainer.loggers[0].log_dir)
# print(trainer.loggers[1].log_dir)
#----> Copy Code
+
+ # home = Path.cwd()[0]
+
copy_path = Path(trainer.loggers[0].log_dir) / 'code'
copy_path.mkdir(parents=True, exist_ok=True)
copy_origin = '/' / Path('/'.join(cfg.log_path.parts[1:5])) / 'code'
+ # print(copy_path)
# print(copy_origin)
shutil.copytree(copy_origin, copy_path, dirs_exist_ok=True)
@@ -123,7 +200,9 @@ def main(cfg):
#---->train or test
if cfg.resume_training:
last_ckpt = log_path = Path(cfg.log_path) / 'lightning_logs' / f'version_{cfg.version}' / 'last.ckpt'
- trainer.fit(model = model, datamodule = dm, ckpt_path=last_ckpt)
+ # model = model.load_from_checkpoint(last_ckpt)
+ # trainer.fit(model, dm) #, datamodule = dm
+ trainer.fit(model = model, ckpt_path=last_ckpt) #, datamodule = dm
if cfg.General.server == 'train':
@@ -152,6 +231,29 @@ def main(cfg):
trainer.test(model=new_model, datamodule=dm)
+def check_home(cfg):
+ # replace home directory
+
+ home = Path.cwd().parts[1]
+
+ x = cfg.General.log_path
+ if Path(x).parts[1] != home:
+ new_path = Path(home).joinpath(*Path(x).parts[2:])
+ cfg.General.log_path = '/' + str(new_path)
+
+ x = cfg.Data.data_dir
+ if Path(x).parts[1] != home:
+ new_path = Path(home).joinpath(*Path(x).parts[2:])
+ cfg.Data.data_dir = '/' + str(new_path)
+
+ x = cfg.Data.label_file
+ if Path(x).parts[1] != home:
+ new_path = Path(home).joinpath(*Path(x).parts[2:])
+ cfg.Data.label_file = '/' + str(new_path)
+
+ return cfg
+
+
if __name__ == '__main__':
args = make_parse()
@@ -159,12 +261,16 @@ if __name__ == '__main__':
#---->update
cfg.config = args.config
- cfg.General.gpus = [args.gpus]
+ cfg.General.gpus = args.gpus
cfg.General.server = args.stage
cfg.Data.fold = args.fold
cfg.Loss.base_loss = args.loss
cfg.Data.bag_size = args.bag_size
cfg.version = args.version
+ if args.label_file:
+ cfg.Data.label_file = '/home/ylan/DeepGraft/training_tables/' + args.label_file
+
+ cfg = check_home(cfg)
config_path = '/'.join(Path(cfg.config).parts[1:])
log_path = Path(cfg.General.log_path) / str(Path(config_path).parent)
@@ -174,12 +280,12 @@ if __name__ == '__main__':
Path(cfg.General.log_path).mkdir(exist_ok=True, parents=True)
log_name = f'_{cfg.Model.backbone}' + f'_{cfg.Loss.base_loss}'
task = '_'.join(Path(cfg.config).name[:-5].split('_')[2:])
+ cfg.task = task
# task = Path(cfg.config).name[:-5].split('_')[2:][0]
cfg.log_path = log_path / f'{cfg.Model.name}' / task / log_name
-
- #---->main
+ # ---->main
main(cfg)
\ No newline at end of file
diff --git a/code/utils/__init__.py b/code/utils/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..5caecf700da5e467edb71103e550ca714f21f962 100644
--- a/code/utils/__init__.py
+++ b/code/utils/__init__.py
@@ -0,0 +1 @@
+from .custom_resnet50 import resnet50_baseline
\ No newline at end of file
diff --git a/code/utils/__pycache__/__init__.cpython-39.pyc b/code/utils/__pycache__/__init__.cpython-39.pyc
index 3dcd009c36ba1c22656489be43171004bc84df85..dcda699d2566f65d9c29aac8a8f8bc255eb8cf7e 100644
Binary files a/code/utils/__pycache__/__init__.cpython-39.pyc and b/code/utils/__pycache__/__init__.cpython-39.pyc differ
diff --git a/code/utils/__pycache__/custom_resnet50.cpython-39.pyc b/code/utils/__pycache__/custom_resnet50.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..84b461b38eae431d5b26a7cbd8e846cb71977b4b
Binary files /dev/null and b/code/utils/__pycache__/custom_resnet50.cpython-39.pyc differ
diff --git a/code/utils/__pycache__/utils.cpython-39.pyc b/code/utils/__pycache__/utils.cpython-39.pyc
index 4e86955d7bdf2df00f04cb9e61d452bc3f04da04..26de104113838d7e9f56ecbe311a3c8813d08d77 100644
Binary files a/code/utils/__pycache__/utils.cpython-39.pyc and b/code/utils/__pycache__/utils.cpython-39.pyc differ
diff --git a/code/utils/custom_resnet50.py b/code/utils/custom_resnet50.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c3f33518bc53a1611b94767bc50fbbb169154d0
--- /dev/null
+++ b/code/utils/custom_resnet50.py
@@ -0,0 +1,122 @@
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+import torch
+import torch.nn.functional as F
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+ 'resnet152']
+
+model_urls = {
+ 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+ 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+ 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+ 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+ 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+class Bottleneck_Baseline(nn.Module):
+ expansion = 4
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
+ super(Bottleneck_Baseline, self).__init__()
+ self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+ self.bn1 = nn.BatchNorm2d(planes)
+ self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+ padding=1, bias=False)
+ self.bn2 = nn.BatchNorm2d(planes)
+ self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
+ self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = downsample
+ self.stride = stride
+
+ def forward(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = self.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+ out = self.relu(out)
+
+ out = self.conv3(out)
+ out = self.bn3(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out += residual
+ out = self.relu(out)
+
+ return out
+
+class ResNet_Baseline(nn.Module):
+
+ def __init__(self, block, layers):
+ self.inplanes = 64
+ super(ResNet_Baseline, self).__init__()
+ self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+ bias=False)
+ self.bn1 = nn.BatchNorm2d(64)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = self._make_layer(block, 64, layers[0])
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+ self.avgpool = nn.AdaptiveAvgPool2d(1)
+
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ elif isinstance(m, nn.BatchNorm2d):
+ nn.init.constant_(m.weight, 1)
+ nn.init.constant_(m.bias, 0)
+
+ def _make_layer(self, block, planes, blocks, stride=1):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ nn.Conv2d(self.inplanes, planes * block.expansion,
+ kernel_size=1, stride=stride, bias=False),
+ nn.BatchNorm2d(planes * block.expansion),
+ )
+
+ layers = []
+ layers.append(block(self.inplanes, planes, stride, downsample))
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(block(self.inplanes, planes))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+
+ x = self.layer1(x)
+ x = self.layer2(x)
+ x = self.layer3(x)
+
+ x = self.avgpool(x)
+ x = x.view(x.size(0), -1)
+
+ return x
+
+def resnet50_baseline(pretrained=False):
+ """Constructs a Modified ResNet-50 model.
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ """
+ model = ResNet_Baseline(Bottleneck_Baseline, [3, 4, 6, 3])
+ if pretrained:
+ model = load_pretrained_weights(model, 'resnet50')
+ return model
+
+def load_pretrained_weights(model, name):
+ pretrained_dict = model_zoo.load_url(model_urls[name])
+ model.load_state_dict(pretrained_dict, strict=False)
+ return model
diff --git a/code/utils/extract_features.py b/code/utils/extract_features.py
index 9ec600e3cf5ee46cd48d57d55173c01ce8bbc7fd..daae76954509eb4d6bdfa45891e26bf23f9ad614 100644
--- a/code/utils/extract_features.py
+++ b/code/utils/extract_features.py
@@ -19,6 +19,38 @@ def extract_features(input_dir, output_dir, model, batch_size):
model = model.to(device)
model.eval()
+
+
+ for bag_candidate_idx in range(total):
+ bag_candidate = bags_dataset[bag_candidate_idx]
+ bag_name = os.path.basename(os.path.normpath(bag_candidate))
+ print(bag_name)
+ print('\nprogress: {}/{}'.format(bag_candidate_idx, total))
+ bag_base = bag_name.split('\\')[-1]
+
+ if not os.path.exists(os.path.join(feat_dir, bag_base + '.pt')):
+
+ print(bag_name)
+
+ output_path = os.path.join(feat_dir, bag_name)
+ file_path = bag_candidate
+ print(file_path)
+ output_file_path = Compute_w_loader(file_path, output_path,
+ model = model, batch_size = batch_size,
+ verbose = 1, print_every = 20,
+ target_patch_size = target_patch_size)
+
+ if os.path.exists (output_file_path):
+ file = h5py.File(output_file_path, "r")
+ features = file['features'][:]
+
+ print('features size: ', features.shape)
+ print('coordinates size: ', file['coords'].shape)
+
+ features = torch.from_numpy(features)
+ torch.save(features, os.path.join(feat_dir, bag_base+'.pt'))
+ file.close()
+
diff --git a/code/utils/utils.py b/code/utils/utils.py
index 7e992cffd810cd7cf54babc70477e8dc174d82e3..5596208e41716da17ac42e18741e09b99ff93667 100755
--- a/code/utils/utils.py
+++ b/code/utils/utils.py
@@ -65,7 +65,7 @@ def load_loggers(cfg):
#---->load Callback
-from pytorch_lightning.callbacks import ModelCheckpoint, RichProgressBar
+from pytorch_lightning.callbacks import ModelCheckpoint, RichProgressBar, StochasticWeightAveraging
from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBarTheme
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
@@ -77,11 +77,11 @@ def load_callbacks(cfg, save_path):
output_path.mkdir(exist_ok=True, parents=True)
early_stop_callback = EarlyStopping(
- monitor='val_loss',
+ monitor='val_auc',
min_delta=0.00,
patience=cfg.General.patience,
verbose=True,
- mode='min'
+ mode='max'
)
Mycallbacks.append(early_stop_callback)
@@ -106,7 +106,7 @@ def load_callbacks(cfg, save_path):
filename = '{epoch:02d}-{val_loss:.4f}-{val_auc: .4f}',
verbose = True,
save_last = True,
- save_top_k = 1,
+ save_top_k = 2,
mode = 'min',
save_weights_only = True))
Mycallbacks.append(ModelCheckpoint(monitor = 'val_auc',
@@ -114,9 +114,13 @@ def load_callbacks(cfg, save_path):
filename = '{epoch:02d}-{val_loss:.4f}-{val_auc:.4f}',
verbose = True,
save_last = True,
- save_top_k = 1,
+ save_top_k = 2,
mode = 'max',
save_weights_only = True))
+
+ swa = StochasticWeightAveraging(swa_lrs=1e-2)
+ Mycallbacks.append(swa)
+
return Mycallbacks
#---->val loss