From 70574bb76a6b0c325c5e48de906f8718e827c99e Mon Sep 17 00:00:00 2001
From: Ycblue <yuchialan@gmail.com>
Date: Fri, 2 Dec 2022 15:44:10 +0100
Subject: [PATCH] project plan, mixup, test eval
---
DeepGraft/AttMIL_feat_norm_rej_rest.yaml | 52 ++
DeepGraft/AttMIL_feat_norm_rest.yaml | 52 ++
DeepGraft/AttMIL_feat_rej_rest.yaml | 52 ++
DeepGraft/AttTrans_resnet50_norm_rest.yaml | 52 ++
DeepGraft/TransMIL_feat_norm_rej_rest.yaml | 52 ++
DeepGraft/TransMIL_feat_norm_rest.yaml | 14 +-
DeepGraft/TransMIL_feat_rej_rest.yaml | 52 ++
DeepGraft/TransMIL_feat_rejections.yaml | 52 ++
.../TransMIL_resnet50_norm_rej_rest.yaml | 6 +-
DeepGraft/TransMIL_resnet50_norm_rest.yaml | 2 +-
DeepGraft/TransformerMIL_feat_norm_rest.yaml | 52 ++
DeepGraft_Project_Plan.pdf | Bin 0 -> 41190 bytes
README.md | 7 +-
.../__pycache__/loss_factory.cpython-39.pyc | Bin 2635 -> 2605 bytes
code/MyLoss/loss_factory.py | 2 +-
.../__pycache__/test_visualize.cpython-39.pyc | Bin 13391 -> 14897 bytes
code/datasets/ResNet.py | 10 +-
.../__pycache__/ResNet.cpython-39.pyc | Bin 12596 -> 12695 bytes
.../custom_jpg_dataloader.cpython-39.pyc | Bin 12376 -> 12309 bytes
.../__pycache__/data_interface.cpython-39.pyc | Bin 10399 -> 8118 bytes
.../feature_dataloader.cpython-39.pyc | Bin 0 -> 7019 bytes
.../simple_jpg_dataloader.cpython-39.pyc | Bin 0 -> 8079 bytes
.../zarr_feature_dataloader.cpython-39.pyc | Bin 5865 -> 6943 bytes
...r_feature_dataloader_simple.cpython-39.pyc | Bin 0 -> 5976 bytes
code/datasets/custom_jpg_dataloader.py | 2 +-
code/datasets/data_interface.py | 210 +++++----
code/datasets/feature_dataloader.py | 394 ++++++++++++++++
code/datasets/feature_dataloader_deca.py | 320 +++++++++++++
code/datasets/feature_extractor.py | 155 ++++--
code/datasets/feature_extractor_2.py | 186 --------
.../feature_extractor_annotated.ipynb | 223 +++++++++
code/datasets/feature_extractor_annotated.py | 267 +++++++++++
code/datasets/feature_file_checker.py | 82 ++++
code/datasets/monai_loader.py | 179 +++++++
code/datasets/simple_jpg_dataloader.py | 17 +-
code/datasets/test_normalization.ipynb | 195 ++++++++
code/datasets/zarr_feature_dataloader.py | 196 +++++---
.../zarr_feature_dataloader_simple.py | 255 ++++++++++
code/models/AttMIL.py | 2 +-
code/models/TransMIL.py | 34 +-
code/models/__pycache__/AttMIL.cpython-39.pyc | Bin 1560 -> 1560 bytes
.../__pycache__/TransMIL.cpython-39.pyc | Bin 3741 -> 3811 bytes
.../__pycache__/TransformerMIL.cpython-39.pyc | Bin 3445 -> 3393 bytes
.../model_interface.cpython-39.pyc | Bin 17157 -> 17801 bytes
code/models/model_interface.py | 444 ++++++++++--------
code/monai_test.json | 1 +
code/test_visualize.py | 271 ++++++++---
code/train.py | 48 +-
code/utils/__pycache__/utils.cpython-39.pyc | Bin 4175 -> 4382 bytes
code/utils/utils.py | 20 +-
monai_test.json | 1 +
paper_structure.md | 35 ++
project_plan.md | 102 ++++
53 files changed, 3380 insertions(+), 716 deletions(-)
create mode 100644 DeepGraft/AttMIL_feat_norm_rej_rest.yaml
create mode 100644 DeepGraft/AttMIL_feat_norm_rest.yaml
create mode 100644 DeepGraft/AttMIL_feat_rej_rest.yaml
create mode 100644 DeepGraft/AttTrans_resnet50_norm_rest.yaml
create mode 100644 DeepGraft/TransMIL_feat_norm_rej_rest.yaml
create mode 100644 DeepGraft/TransMIL_feat_rej_rest.yaml
create mode 100644 DeepGraft/TransMIL_feat_rejections.yaml
create mode 100644 DeepGraft/TransformerMIL_feat_norm_rest.yaml
create mode 100644 DeepGraft_Project_Plan.pdf
create mode 100644 code/datasets/__pycache__/feature_dataloader.cpython-39.pyc
create mode 100644 code/datasets/__pycache__/simple_jpg_dataloader.cpython-39.pyc
create mode 100644 code/datasets/__pycache__/zarr_feature_dataloader_simple.cpython-39.pyc
create mode 100644 code/datasets/feature_dataloader.py
create mode 100644 code/datasets/feature_dataloader_deca.py
delete mode 100644 code/datasets/feature_extractor_2.py
create mode 100644 code/datasets/feature_extractor_annotated.ipynb
create mode 100644 code/datasets/feature_extractor_annotated.py
create mode 100644 code/datasets/feature_file_checker.py
create mode 100644 code/datasets/monai_loader.py
create mode 100644 code/datasets/test_normalization.ipynb
create mode 100644 code/datasets/zarr_feature_dataloader_simple.py
create mode 100644 code/monai_test.json
create mode 100644 monai_test.json
create mode 100644 paper_structure.md
create mode 100644 project_plan.md
diff --git a/DeepGraft/AttMIL_feat_norm_rej_rest.yaml b/DeepGraft/AttMIL_feat_norm_rej_rest.yaml
new file mode 100644
index 0000000..4eda25f
--- /dev/null
+++ b/DeepGraft/AttMIL_feat_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_limit_20_split_PAS_HE_Jones_norm_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: 3
+ backbone: features
+ in_features: 1024
+ out_features: 512
+
+
+Optimizer:
+ opt: Adam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/AttMIL_feat_norm_rest.yaml b/DeepGraft/AttMIL_feat_norm_rest.yaml
new file mode 100644
index 0000000..fefbe5e
--- /dev/null
+++ b/DeepGraft/AttMIL_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 1000
+ grad_acc: 2
+ frozen_bn: False
+ patience: 100
+ 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: AttMIL
+ n_classes: 2
+ backbone: features
+ in_features: 512
+ out_features: 1024
+
+
+Optimizer:
+ opt: Adam
+ lr: 0.0001
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/AttMIL_feat_rej_rest.yaml b/DeepGraft/AttMIL_feat_rej_rest.yaml
new file mode 100644
index 0000000..3d854be
--- /dev/null
+++ b/DeepGraft/AttMIL_feat_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: features
+ in_features: 1024
+ out_features: 512
+
+
+Optimizer:
+ opt: Adam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/DeepGraft/AttTrans_resnet50_norm_rest.yaml b/DeepGraft/AttTrans_resnet50_norm_rest.yaml
new file mode 100644
index 0000000..cfffa7d
--- /dev/null
+++ b/DeepGraft/AttTrans_resnet50_norm_rest.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 32
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 1000
+ grad_acc: 2
+ frozen_bn: False
+ patience: 100
+ server: train #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_annotated/'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_100_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: AttTrans
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 1024
+
+
+Optimizer:
+ opt: Adam
+ 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_rej_rest.yaml b/DeepGraft/TransMIL_feat_norm_rej_rest.yaml
new file mode 100644
index 0000000..3dcc817
--- /dev/null
+++ b/DeepGraft/TransMIL_feat_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 1000
+ grad_acc: 2
+ frozen_bn: False
+ patience: 100
+ 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: features
+ 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
index dab6568..ea452a3 100644
--- a/DeepGraft/TransMIL_feat_norm_rest.yaml
+++ b/DeepGraft/TransMIL_feat_norm_rest.yaml
@@ -3,20 +3,22 @@ General:
seed: 2021
fp16: True
amp_level: O2
- precision: 16
+ precision: 16
multi_gpu_mode: dp
- gpus: [0]
+ gpus: [0, 1]
epochs: &epoch 500
grad_acc: 2
frozen_bn: False
patience: 50
- server: test #train #test
+ server: train #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/'
+ mixup: False
+ aug: True
+ data_dir: '/home/ylan/data/DeepGraft/224_128uM_annotated/'
label_file: '/home/ylan/DeepGraft/training_tables/dg_split_PAS_HE_Jones_norm_rest.json'
fold: 1
nfold: 3
@@ -34,12 +36,12 @@ Model:
name: TransMIL
n_classes: 2
backbone: features
- in_features: 512
+ in_features: 2048
out_features: 1024
Optimizer:
- opt: lookahead_radam
+ opt: Adam
lr: 0.0001
opt_eps: null
opt_betas: null
diff --git a/DeepGraft/TransMIL_feat_rej_rest.yaml b/DeepGraft/TransMIL_feat_rej_rest.yaml
new file mode 100644
index 0000000..ca9c0e4
--- /dev/null
+++ b/DeepGraft/TransMIL_feat_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: features
+ 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_feat_rejections.yaml b/DeepGraft/TransMIL_feat_rejections.yaml
new file mode 100644
index 0000000..a1d2ae6
--- /dev/null
+++ b/DeepGraft/TransMIL_feat_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/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: features
+ 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_norm_rej_rest.yaml b/DeepGraft/TransMIL_resnet50_norm_rej_rest.yaml
index 9885743..1fa23b5 100644
--- a/DeepGraft/TransMIL_resnet50_norm_rej_rest.yaml
+++ b/DeepGraft/TransMIL_resnet50_norm_rej_rest.yaml
@@ -6,11 +6,11 @@ General:
precision: 16
multi_gpu_mode: dp
gpus: [0]
- epochs: &epoch 500
+ epochs: &epoch 1000
grad_acc: 2
frozen_bn: False
- patience: 50
- server: test #train #test
+ patience: 100
+ server: train #train #test
log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
Data:
diff --git a/DeepGraft/TransMIL_resnet50_norm_rest.yaml b/DeepGraft/TransMIL_resnet50_norm_rest.yaml
index b58c6d8..0511d26 100644
--- a/DeepGraft/TransMIL_resnet50_norm_rest.yaml
+++ b/DeepGraft/TransMIL_resnet50_norm_rest.yaml
@@ -17,7 +17,7 @@ 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'
+ label_file: '/home/ylan/DeepGraft/training_tables/dg_limit_100_split_PAS_HE_Jones_norm_rest_RA_RU.json'
fold: 1
nfold: 3
cross_val: False
diff --git a/DeepGraft/TransformerMIL_feat_norm_rest.yaml b/DeepGraft/TransformerMIL_feat_norm_rest.yaml
new file mode 100644
index 0000000..7c90fbf
--- /dev/null
+++ b/DeepGraft/TransformerMIL_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 1000
+ grad_acc: 2
+ frozen_bn: False
+ patience: 100
+ 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: TransformerMIL
+ 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_Project_Plan.pdf b/DeepGraft_Project_Plan.pdf
new file mode 100644
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diff --git a/README.md b/README.md
index c8594bc..810ff74 100644
--- a/README.md
+++ b/README.md
@@ -31,8 +31,7 @@ wd = 0.01
### Ablation
-image drop out:
-tcmr_viral TCMR efficientnet: version 0
+# Important things:
+
+ *
-wd incerease:
-tcmr_viral TCMR efficientnet: version 110
\ No newline at end of file
diff --git a/code/MyLoss/__pycache__/loss_factory.cpython-39.pyc b/code/MyLoss/__pycache__/loss_factory.cpython-39.pyc
index db4ebcceee99d56aa365237e82f0e543b7ba5cc1..5d4cc8bb3818896e5944e5eb2f6b551388b67e48 100644
GIT binary patch
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diff --git a/code/MyLoss/loss_factory.py b/code/MyLoss/loss_factory.py
index 61502bf..f17f698 100755
--- a/code/MyLoss/loss_factory.py
+++ b/code/MyLoss/loss_factory.py
@@ -29,7 +29,7 @@ def create_loss(args, n_classes, w1=1.0, w2=0.5):
loss = None
print(conf_loss)
if hasattr(nn, conf_loss):
- loss = getattr(nn, conf_loss)(label_smoothing=0.5)
+ loss = getattr(nn, conf_loss)()
# loss = getattr(nn, conf_loss)(label_smoothing=0.5)
#binary loss
elif conf_loss == "focal":
diff --git a/code/__pycache__/test_visualize.cpython-39.pyc b/code/__pycache__/test_visualize.cpython-39.pyc
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diff --git a/code/datasets/ResNet.py b/code/datasets/ResNet.py
index f8fe706..5c3c277 100644
--- a/code/datasets/ResNet.py
+++ b/code/datasets/ResNet.py
@@ -394,4 +394,12 @@ def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
"""
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
+ pretrained, progress, **kwargs)
+
+if __name__ == '__main__':
+
+
+ model_ft = resnet50(num_classes=1024, mlp=False, two_branch=False, normlinear=True)
+
+ # model_ft.fc = nn.Identity()
+ print(model_ft)
\ No newline at end of file
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/custom_jpg_dataloader.py b/code/datasets/custom_jpg_dataloader.py
index c28acc1..95d5adb 100644
--- a/code/datasets/custom_jpg_dataloader.py
+++ b/code/datasets/custom_jpg_dataloader.py
@@ -212,7 +212,7 @@ class JPGMILDataloader(data.Dataset):
# 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)
+ # 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
diff --git a/code/datasets/data_interface.py b/code/datasets/data_interface.py
index fc4acab..7049b22 100644
--- a/code/datasets/data_interface.py
+++ b/code/datasets/data_interface.py
@@ -6,121 +6,125 @@ import pytorch_lightning as pl
# from pytorch_lightning.loops.fit_loop import FitLoop
from torch.utils.data import random_split, DataLoader
+from torch.utils.data.sampler import WeightedRandomSampler
from torch.utils.data.dataset import Dataset, Subset
from torchvision.datasets import MNIST
from torchvision import transforms
-from .camel_dataloader import FeatureBagLoader
+# from .camel_dataloader import FeatureBagLoader
from .custom_dataloader import HDF5MILDataloader
-from .custom_jpg_dataloader import JPGMILDataloader
-from .zarr_feature_dataloader import ZarrFeatureBagLoader
+# from .custom_jpg_dataloader import JPGMILDataloader
+from .simple_jpg_dataloader import JPGBagLoader
+from .zarr_feature_dataloader_simple import ZarrFeatureBagLoader
+from .feature_dataloader import FeatureBagLoader
from pathlib import Path
# from transformers import AutoFeatureExtractor
from torchsampler import ImbalancedDatasetSampler
from abc import ABC, abstractclassmethod, abstractmethod
from sklearn.model_selection import KFold
+import numpy as np
+import torch
+# class DataInterface(pl.LightningDataModule):
-class DataInterface(pl.LightningDataModule):
+# def __init__(self, train_batch_size=64, train_num_workers=8, test_batch_size=1, test_num_workers=1,dataset_name=None, **kwargs):
+# """[summary]
- def __init__(self, train_batch_size=64, train_num_workers=8, test_batch_size=1, test_num_workers=1,dataset_name=None, **kwargs):
- """[summary]
+# Args:
+# batch_size (int, optional): [description]. Defaults to 64.
+# num_workers (int, optional): [description]. Defaults to 8.
+# dataset_name (str, optional): [description]. Defaults to ''.
+# """
+# super().__init__()
- Args:
- batch_size (int, optional): [description]. Defaults to 64.
- num_workers (int, optional): [description]. Defaults to 8.
- dataset_name (str, optional): [description]. Defaults to ''.
- """
- super().__init__()
-
- self.train_batch_size = train_batch_size
- self.train_num_workers = train_num_workers
- self.test_batch_size = test_batch_size
- self.test_num_workers = test_num_workers
- self.dataset_name = dataset_name
- self.kwargs = kwargs
- self.load_data_module()
- home = Path.cwd().parts[1]
- self.data_root = f'/{home}/ylan/RCC_project/rcc_classification/datasets/Camelyon16/Camelyon16.csv'
+# self.train_batch_size = train_batch_size
+# self.train_num_workers = train_num_workers
+# self.test_batch_size = test_batch_size
+# self.test_num_workers = test_num_workers
+# self.dataset_name = dataset_name
+# self.kwargs = kwargs
+# self.load_data_module()
+# home = Path.cwd().parts[1]
+# self.data_root = f'/{home}/ylan/RCC_project/rcc_classification/datasets/Camelyon16/Camelyon16.csv'
- def prepare_data(self):
- # 1. how to download
- # MNIST(self.data_dir, train=True, download=True)
- # MNIST(self.data_dir, train=False, download=True)
- ...
-
- def setup(self, stage=None):
- # 2. how to split, argument
- """
- - count number of classes
-
- - build vocabulary
-
- - perform train/val/test splits
-
- - apply transforms (defined explicitly in your datamodule or assigned in init)
- """
- # Assign train/val datasets for use in dataloaders
- if stage == 'fit' or stage is None:
- dataset = FeatureBagLoader(data_root = self.data_root,
- train=True)
- a = int(len(dataset)* 0.8)
- b = int(len(dataset) - a)
- # print(a)
- # print(b)
- self.train_dataset, self.val_dataset = random_split(dataset, [a, b]) # returns data.Subset
-
- # self.train_dataset = self.instancialize(state='train')
- # self.val_dataset = self.instancialize(state='val')
+# def prepare_data(self):
+# # 1. how to download
+# # MNIST(self.data_dir, train=True, download=True)
+# # MNIST(self.data_dir, train=False, download=True)
+# ...
+
+# def setup(self, stage=None):
+# # 2. how to split, argument
+# """
+# - count number of classes
+
+# - build vocabulary
+
+# - perform train/val/test splits
+
+# - apply transforms (defined explicitly in your datamodule or assigned in init)
+# """
+# # Assign train/val datasets for use in dataloaders
+# if stage == 'fit' or stage is None:
+# dataset = FeatureBagLoader(data_root = self.data_root,
+# train=True)
+# a = int(len(dataset)* 0.8)
+# b = int(len(dataset) - a)
+# # print(a)
+# # print(b)
+# self.train_dataset, self.val_dataset = random_split(dataset, [a, b]) # returns data.Subset
+
+# # self.train_dataset = self.instancialize(state='train')
+# # self.val_dataset = self.instancialize(state='val')
- # Assign test dataset for use in dataloader(s)
- if stage == 'test' or stage is None:
- # self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)
- self.test_dataset = FeatureBagLoader(data_root = self.data_root,
- train=False)
- # self.test_dataset = self.instancialize(state='test')
+# # Assign test dataset for use in dataloader(s)
+# if stage == 'test' or stage is None:
+# # self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)
+# self.test_dataset = FeatureBagLoader(data_root = self.data_root,
+# train=False)
+# # self.test_dataset = self.instancialize(state='test')
- def train_dataloader(self):
- return DataLoader(self.train_dataset, batch_size=self.train_batch_size, num_workers=self.train_num_workers, shuffle=False)
+# def train_dataloader(self):
+# return DataLoader(self.train_dataset, batch_size=self.train_batch_size, num_workers=self.train_num_workers, shuffle=False)
- def val_dataloader(self):
- return DataLoader(self.val_dataset, batch_size=self.train_batch_size, num_workers=self.train_num_workers, shuffle=False)
+# def val_dataloader(self):
+# return DataLoader(self.val_dataset, batch_size=self.train_batch_size, num_workers=self.train_num_workers, shuffle=False)
- def test_dataloader(self):
- return DataLoader(self.test_dataset, batch_size=self.test_batch_size, num_workers=self.test_num_workers, shuffle=False)
+# def test_dataloader(self):
+# return DataLoader(self.test_dataset, batch_size=self.test_batch_size, num_workers=self.test_num_workers, shuffle=False)
- def load_data_module(self):
- camel_name = ''.join([i.capitalize() for i in (self.dataset_name).split('_')])
- try:
- self.data_module = getattr(importlib.import_module(
- f'datasets.{self.dataset_name}'), camel_name)
- except:
- raise ValueError(
- 'Invalid Dataset File Name or Invalid Class Name!')
+# def load_data_module(self):
+# camel_name = ''.join([i.capitalize() for i in (self.dataset_name).split('_')])
+# try:
+# self.data_module = getattr(importlib.import_module(
+# f'datasets.{self.dataset_name}'), camel_name)
+# except:
+# raise ValueError(
+# 'Invalid Dataset File Name or Invalid Class Name!')
- def instancialize(self, **other_args):
- """ Instancialize a model using the corresponding parameters
- from self.hparams dictionary. You can also input any args
- to overwrite the corresponding value in self.kwargs.
- """
- class_args = inspect.getargspec(self.data_module.__init__).args[1:]
- inkeys = self.kwargs.keys()
- args1 = {}
- for arg in class_args:
- if arg in inkeys:
- args1[arg] = self.kwargs[arg]
- args1.update(other_args)
- return self.data_module(**args1)
+# def instancialize(self, **other_args):
+# """ Instancialize a model using the corresponding parameters
+# from self.hparams dictionary. You can also input any args
+# to overwrite the corresponding value in self.kwargs.
+# """
+# class_args = inspect.getargspec(self.data_module.__init__).args[1:]
+# inkeys = self.kwargs.keys()
+# args1 = {}
+# for arg in class_args:
+# if arg in inkeys:
+# args1[arg] = self.kwargs[arg]
+# args1.update(other_args)
+# return self.data_module(**args1)
class MILDataModule(pl.LightningDataModule):
- 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):
+ 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, mixup=False, aug=False, *args, **kwargs):
super().__init__()
self.data_root = data_root
self.label_path = label_path
@@ -134,32 +138,37 @@ class MILDataModule(pl.LightningDataModule):
self.num_bags_train = 200
self.num_bags_test = 50
self.seed = 1
+ self.mixup = mixup
+ self.aug = aug
+ self.class_weight = []
self.cache = cache
self.fe_transform = None
if not use_features:
- self.base_dataloader = JPGMILDataloader
+ self.base_dataloader = JPGBagLoader
else:
-
- self.base_dataloader = ZarrFeatureBagLoader
+ self.base_dataloader = FeatureBagLoader
self.cache = True
-
-
def setup(self, stage: Optional[str] = None) -> None:
home = Path.cwd().parts[1]
if stage in (None, 'fit'):
- dataset = self.base_dataloader(self.data_root, label_path=self.label_path, mode='train', n_classes=self.n_classes, cache=self.cache)
+ dataset = self.base_dataloader(self.data_root, label_path=self.label_path, mode='train', n_classes=self.n_classes, cache=self.cache, mixup=self.mixup, aug=self.aug)
# 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])
+ # self.weights = self.get_weights(dataset)
+
+
+
if stage in (None, 'test'):
- 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)
+ self.test_data = self.base_dataloader(self.data_root, label_path=self.label_path, mode='test', n_classes=self.n_classes, cache=False)
+ print(len(self.test_data))
return super().setup(stage=stage)
@@ -167,6 +176,7 @@ class MILDataModule(pl.LightningDataModule):
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 = WeightedRandomSampler(self.weights, len(self.weights)), 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:
@@ -174,6 +184,22 @@ class MILDataModule(pl.LightningDataModule):
def test_dataloader(self) -> DataLoader:
return DataLoader(self.test_data, batch_size = self.batch_size, num_workers=self.num_workers)
+
+ def get_weights(self, dataset):
+
+ label_count = [0]*self.n_classes
+ labels = dataset.get_labels(np.arange(len(dataset)))
+ for i in labels:
+ label_count[i] += 1
+ weights_per_class = [0.] * self.n_classes
+ for i in range(self.n_classes):
+ weights_per_class[i] = float(len(labels) / float(label_count[i]))
+ weights_per_class = [i / sum(weights_per_class) for i in weights_per_class]
+ weights = [0.] * len(labels)
+ for i in range(len(labels)):
+ weights[i] = weights_per_class[labels[i]]
+
+ return torch.DoubleTensor(weights)
class DataModule(pl.LightningDataModule):
diff --git a/code/datasets/feature_dataloader.py b/code/datasets/feature_dataloader.py
new file mode 100644
index 0000000..3e9dbb5
--- /dev/null
+++ b/code/datasets/feature_dataloader.py
@@ -0,0 +1,394 @@
+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
+import h5py
+# from models import TransMIL
+
+
+
+class FeatureBagLoader(data.Dataset):
+ def __init__(self, file_path, label_path, mode, n_classes, cache=False, mixup=False, aug=False, data_cache_size=5000, 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.drop_rate = 0.2
+ # self.min_bag_size = 120
+ self.empty_slides = []
+ self.corrupt_slides = []
+ self.cache = cache
+ self.mixup = mixup
+ self.aug = aug
+
+ self.missing = []
+
+ home = Path.cwd().parts[1]
+ self.slide_patient_dict_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict_an.json'
+ with open(self.slide_patient_dict_path, 'r') as f:
+ self.slide_patient_dict = json.load(f)
+
+ # read labels and slide_path from csv
+ with open(self.label_path, 'r') as f:
+ json_dict = json.load(f)
+ temp_slide_label_dict = json_dict[self.mode]
+ # print(len(temp_slide_label_dict))
+ for (x,y) in temp_slide_label_dict:
+
+ x_name = Path(x).stem
+ x_path_list = [Path(self.file_path)/x]
+ # x_name = x.stem
+ # x_path_list = [Path(self.file_path)/ x for (x,y) in temp_slide_label_dict]
+ if self.aug:
+ for i in range(5):
+ aug_path = Path(self.file_path)/f'{x}_aug{i}'
+ x_path_list.append(aug_path)
+
+ for x_path in x_path_list:
+
+ if x_path.exists():
+ self.slideLabelDict[x_name] = y
+ self.files.append(x_path)
+ elif Path(str(x_path) + '.zarr').exists():
+ self.slideLabelDict[x] = y
+ self.files.append(str(x_path)+'.zarr')
+ else:
+ self.missing.append(x)
+ # print(x, y)
+ # 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 self.mode == 'test': #set to test if using GAN output
+ # # x_path_list = [Path(self.file_path) / cohort / 'FE' / (str(x) + '.zarr')]
+ # # else:
+ # # x_path_list = [Path(self.file_path) / cohort / 'FEATURES' / (str(x))]
+ # x_path_list = [Path(self.file_path) / cohort / 'FEATURES_RETCCL_2048' / (str(x))]
+ # # if not self.mixup:
+ # for i in range(5):
+ # aug_path = Path(self.file_path) / cohort / 'FEATURES_RETCCL_2048' / (str(x) + f'_aug{i}')
+ # if aug_path.exists():
+ # x_path_list.append(aug_path)
+ # # print(x_complete_path)
+ # for x_path in x_path_list:
+ # # print(x_path)
+
+ # if x_path.exists():
+ # # print(x_path)
+ # # if len(list(x_complete_path.iterdir())) > self.min_bag_size:
+ # # # print(x_complete_path)
+ # self.slideLabelDict[x] = y
+ # self.files.append(x_path)
+ # elif Path(str(x_path) + '.zarr').exists():
+ # self.slideLabelDict[x] = y
+ # self.files.append(str(x_path)+'.zarr')
+ # else:
+ # self.missing.append(x)
+
+ # mix in 10 Slides of Test data
+ # if 'test_mixin' in json_dict.keys():
+ # test_slide_label_dict = json_dict['test']
+ # for (x, y) in test_slide_label_dict:
+ # x = Path(x).stem
+ # for cohort in Path(self.file_path).iterdir():
+ # x_path_list = [Path(self.file_path) / cohort / 'FEATURES_RETCCL_2048' / (str(x))]
+ # for x_path in x_path_list:
+ # if x_path.exists():
+ # self.slideLabelDict[x] = y
+ # self.files.append(x_path)
+ # patient = self.slide_patient_dict[x]
+ # elif Path(str(x_path) + '.zarr').exists():
+ # self.slideLabelDict[x] = y
+ # self.files.append(str(x_path)+'.zarr')
+
+
+
+
+
+ self.feature_bags = []
+ self.labels = []
+ self.wsi_names = []
+ self.coords = []
+ self.patients = []
+ if self.cache:
+ for t in tqdm(self.files):
+ # zarr_t = str(t) + '.zarr'
+ batch, label, (wsi_name, batch_coords, patient) = self.get_data(t)
+
+ # print(label)
+ self.labels.append(label)
+ self.feature_bags.append(batch)
+ self.wsi_names.append(wsi_name)
+ self.coords.append(batch_coords)
+ self.patients.append(patient)
+
+
+ def get_data(self, file_path):
+
+ batch_names=[] #add function for name_batch read out
+
+ wsi_name = Path(file_path).stem
+ if wsi_name.split('_')[-1][:3] == 'aug':
+ wsi_name = '_'.join(wsi_name.split('_')[:-1])
+ # if wsi_name in self.slideLabelDict:
+ label = self.slideLabelDict[wsi_name]
+ patient = self.slide_patient_dict[wsi_name]
+
+ if Path(file_path).suffix == '.zarr':
+ z = zarr.open(file_path, 'r')
+ np_bag = np.array(z['data'][:])
+ coords = np.array(z['coords'][:])
+ else:
+ with h5py.File(file_path, 'r') as hdf5_file:
+ np_bag = hdf5_file['features'][:]
+ coords = hdf5_file['coords'][:]
+
+ # np_bag = torch.load(file_path)
+ # z = zarr.open(file_path, 'r')
+ # np_bag = np.array(z['data'][:])
+ # np_bag = np.array(zarr.open(file_path, 'r')).astype(np.uint8)
+ # label = torch.as_tensor(label)
+ label = int(label)
+ wsi_bag = torch.from_numpy(np_bag)
+ batch_coords = torch.from_numpy(coords)
+
+ return wsi_bag, label, (wsi_name, batch_coords, patient)
+
+ def get_labels(self, indices):
+ # for i in indices:
+ # print(self.labels[i])
+ 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 = self.max_bag_size
+ bag_size = bag.shape[0]
+ bag_idxs = torch.randperm(self.max_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 get_mixup_bag(self, bag):
+
+ bag_size = bag.shape[0]
+
+ a = torch.rand([bag_size])
+ b = 0.6
+ rand_x = torch.randint(0, bag_size, [bag_size,])
+ rand_y = torch.randint(0, bag_size, [bag_size,])
+
+ bag_x = bag[rand_x, :]
+ bag_y = bag[rand_y, :]
+
+ # print('bag_x: ', bag_x.shape)
+ # print('bag_y: ', bag_y.shape)
+ # print('a*bag_x: ', (a*bag_x).shape)
+ # print('(1.0-a)*bag_y: ', ((1.0-a)*bag_y).shape)
+
+ temp_bag = (bag_x.t()*a).t() + (bag_y.t()*(1.0-a)).t()
+ # print('temp_bag: ', temp_bag.shape)
+
+ if bag_size < self.max_bag_size:
+ diff = self.max_bag_size - bag_size
+ bag_idxs = torch.randperm(bag_size)[:diff]
+
+ # print('bag: ', bag.shape)
+ # print('bag_idxs: ', bag_idxs.shape)
+ mixup_bag = torch.cat((bag, temp_bag[bag_idxs, :]))
+ # print('mixup_bag: ', mixup_bag.shape)
+ else:
+ random_sample_list = torch.rand(bag_size)
+ mixup_bag = [bag[i] if random_sample_list[i] else temp_bag[i] > b for i in range(bag_size)] #make pytorch native?!
+ mixup_bag = torch.stack(mixup_bag)
+ # print('else')
+ # print(mixup_bag.shape)
+
+ return mixup_bag
+
+ def __len__(self):
+ return len(self.files)
+
+ def __getitem__(self, index):
+
+ if self.cache:
+ label = self.labels[index]
+ bag = 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]
+ batch_coords = self.coords[index]
+ patient = self.patients[index]
+
+
+ #random dropout
+ #shuffle
+
+ # feats = Variable(Tensor(feats))
+ # return wsi, label, (wsi_name, batch_coords, patient)
+ else:
+ t = self.files[index]
+ bag, label, (wsi_name, batch_coords, patient) = self.get_data(t)
+ # label = torch.as_tensor(label)
+ # label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+ # 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)
+ if self.mode == 'train':
+ bag_size = bag.shape[0]
+
+ bag_idxs = torch.randperm(bag_size)[:self.max_bag_size]
+ # bag_idxs = torch.randperm(bag_size)[:int(self.max_bag_size*(1-self.drop_rate))]
+ out_bag = bag[bag_idxs, :]
+ if self.mixup:
+ out_bag = self.get_mixup_bag(out_bag)
+ # batch_coords =
+ if out_bag.shape[0] < self.max_bag_size:
+ out_bag = torch.cat((out_bag, torch.zeros(self.max_bag_size-out_bag.shape[0], out_bag.shape[1])))
+
+ # shuffle again
+ out_bag_idxs = torch.randperm(out_bag.shape[0])
+ out_bag = out_bag[out_bag_idxs]
+
+
+ # batch_coords only useful for test
+ batch_coords = batch_coords[bag_idxs]
+
+
+ # mixup? Linear combination of 2 vectors
+ # add noise
+
+
+ else: out_bag = bag
+
+ return out_bag, label, (wsi_name, batch_coords, 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_annotated'
+ # 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_split_PAS_HE_Jones_norm_rest_test.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', cache=False, mixup=True, aug=True, n_classes=n_classes)
+
+ test_dataset = FeatureBagLoader(data_root, label_path=label_path, mode='test', cache=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])
+
+ train_dl = DataLoader(train_data, batch_size=1, num_workers=5)
+ valid_dl = DataLoader(valid_data, batch_size=1, num_workers=5)
+ test_dl = DataLoader(test_dataset)
+
+ print('train_dl: ', len(train_dl))
+ print('valid_dl: ', len(valid_dl))
+ print('test_dl: ', len(test_dl))
+
+
+ # 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)
+
+
+ # print(dataset.get_labels(np.arange(len(dataset))))
+
+ 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(train_dl):
+
+ # if c >= 10:
+ # break
+ bag, label, (name, batch_coords, patient) = item
+ print(bag.shape)
+ # print(bag.shape, label)
+ # print(len(batch_names))
+ # print(label)
+ # print(batch_coords)
+ # print(name)
+ # 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/datasets/feature_dataloader_deca.py b/code/datasets/feature_dataloader_deca.py
new file mode 100644
index 0000000..a0543c5
--- /dev/null
+++ b/code/datasets/feature_dataloader_deca.py
@@ -0,0 +1,320 @@
+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
+import h5py
+# from models import TransMIL
+
+
+
+class FeatureBagLoader(data.Dataset):
+ def __init__(self, file_path, label_path, mode, n_classes, cache=False, data_cache_size=5000, 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.drop_rate = 0.2
+ # self.min_bag_size = 120
+ self.empty_slides = []
+ self.corrupt_slides = []
+ self.cache = cache
+
+ self.missing = []
+
+ home = Path.cwd().parts[1]
+ self.slide_patient_dict_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict_an.json'
+ with open(self.slide_patient_dict_path, 'r') as f:
+ self.slide_patient_dict = json.load(f)
+
+ # read labels and slide_path from csv
+ with open(self.label_path, 'r') as f:
+ json_dict = json.load(f)
+ temp_slide_label_dict = json_dict[self.mode]
+ # print(len(temp_slide_label_dict))
+ for (x, y) in temp_slide_label_dict:
+ x = Path(x).stem
+
+ # print(x, y)
+ # 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 self.mode == 'test': #set to test if using GAN output
+ # x_path_list = [Path(self.file_path) / cohort / 'FE' / (str(x) + '.zarr')]
+ # else:
+ # x_path_list = [Path(self.file_path) / cohort / 'FEATURES' / (str(x))]
+ x_path_list = [Path(self.file_path) / cohort / 'FEATURES_RETCCL_2048' / (str(x))]
+ for i in range(5):
+ aug_path = Path(self.file_path) / cohort / 'FEATURES_RETCCL_2048' / (str(x) + f'_aug{i}')
+ if aug_path.exists():
+ x_path_list.append(aug_path)
+ # print(x_complete_path)
+ for x_path in x_path_list:
+ # print(x_path)
+
+ if x_path.exists():
+ # print(x_path)
+ # if len(list(x_complete_path.iterdir())) > self.min_bag_size:
+ # # print(x_complete_path)
+ self.slideLabelDict[x] = y
+ self.files.append(x_path)
+ elif Path(str(x_path) + '.zarr').exists():
+ self.slideLabelDict[x] = y
+ self.files.append(str(x_path)+'.zarr')
+ else:
+ self.missing.append(x)
+
+ # mix in 10 Slides of Test data
+ if 'test_mixin' in json_dict.keys():
+ test_slide_label_dict = json_dict['test']
+ for (x, y) in test_slide_label_dict:
+ x = Path(x).stem
+ for cohort in Path(self.file_path).iterdir():
+ x_path_list = [Path(self.file_path) / cohort / 'FEATURES_RETCCL_2048' / (str(x))]
+ for x_path in x_path_list:
+ if x_path.exists():
+ self.slideLabelDict[x] = y
+ self.files.append(x_path)
+ patient = self.slide_patient_dict[x]
+ elif Path(str(x_path) + '.zarr').exists():
+ self.slideLabelDict[x] = y
+ self.files.append(str(x_path)+'.zarr')
+
+
+
+
+
+ self.feature_bags = []
+ self.labels = []
+ self.wsi_names = []
+ self.coords = []
+ self.patients = []
+ if self.cache:
+ for t in tqdm(self.files):
+ # zarr_t = str(t) + '.zarr'
+ batch, label, (wsi_name, batch_coords, patient) = self.get_data(t)
+
+ # print(label)
+ self.labels.append(label)
+ self.feature_bags.append(batch)
+ self.wsi_names.append(wsi_name)
+ self.coords.append(batch_coords)
+ self.patients.append(patient)
+
+
+ def get_data(self, file_path):
+
+ batch_names=[] #add function for name_batch read out
+
+ wsi_name = Path(file_path).stem
+ if wsi_name.split('_')[-1][:3] == 'aug':
+ wsi_name = '_'.join(wsi_name.split('_')[:-1])
+ # if wsi_name in self.slideLabelDict:
+ label = self.slideLabelDict[wsi_name]
+ patient = self.slide_patient_dict[wsi_name]
+
+ if Path(file_path).suffix == '.zarr':
+ z = zarr.open(file_path, 'r')
+ np_bag = np.array(z['data'][:])
+ coords = np.array(z['coords'][:])
+ else:
+ with h5py.File(file_path, 'r') as hdf5_file:
+ np_bag = hdf5_file['features'][:]
+ coords = hdf5_file['coords'][:]
+
+ # np_bag = torch.load(file_path)
+ # z = zarr.open(file_path, 'r')
+ # np_bag = np.array(z['data'][:])
+ # np_bag = np.array(zarr.open(file_path, 'r')).astype(np.uint8)
+ # label = torch.as_tensor(label)
+ label = int(label)
+ wsi_bag = torch.from_numpy(np_bag)
+ batch_coords = torch.from_numpy(coords)
+
+ return wsi_bag, label, (wsi_name, batch_coords, patient)
+
+ def get_labels(self, indices):
+ # for i in indices:
+ # print(self.labels[i])
+ 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 = self.max_bag_size
+ # bag_size = bag.shape[0]
+ bag_idxs = torch.randperm(self.max_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]
+ bag = 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]
+ batch_coords = self.coords[index]
+ patient = self.patients[index]
+
+
+ #random dropout
+ #shuffle
+
+ # feats = Variable(Tensor(feats))
+ # return wsi, label, (wsi_name, batch_coords, patient)
+ else:
+ t = self.files[index]
+ bag, label, (wsi_name, batch_coords, patient) = self.get_data(t)
+ # label = torch.as_tensor(label)
+ # label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+ # 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)
+ if self.mode != 'test':
+ bag_size = bag.shape[0]
+
+ bag_idxs = torch.randperm(bag_size)[:int(self.max_bag_size*(1-self.drop_rate))]
+ out_bag = bag[bag_idxs, :]
+ batch_coords = batch_coords[bag_idxs]
+ if out_bag.shape[0] < self.max_bag_size:
+ out_bag = torch.cat((out_bag, torch.zeros(self.max_bag_size-out_bag.shape[0], out_bag.shape[1])))
+
+ else: out_bag = bag
+
+ return out_bag, label, (wsi_name, batch_coords, 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_annotated'
+ # 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_decathlon_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', cache=True, n_classes=n_classes)
+
+ test_dataset = FeatureBagLoader(data_root, label_path=label_path, mode='test', cache=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])
+
+ train_dl = DataLoader(train_data, batch_size=1, sampler=ImbalancedDatasetSampler(train_data), num_workers=5)
+ valid_dl = DataLoader(valid_data, batch_size=1, num_workers=5)
+ test_dl = DataLoader(test_dataset)
+
+ print('train_dl: ', len(train_dl))
+ print('valid_dl: ', len(valid_dl))
+ print('test_dl: ', len(test_dl))
+
+
+ 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)
+
+
+ # print(dataset.get_labels(np.arange(len(dataset))))
+
+ 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(test_dl):
+
+ # if c >= 10:
+ # break
+ bag, label, (name, batch_coords, patient) = item
+ # print(bag.shape)
+ print(bag.shape, label)
+ # print(len(batch_names))
+ # print(label)
+ # print(batch_coords)
+ # print(name)
+ 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/datasets/feature_extractor.py b/code/datasets/feature_extractor.py
index 0057c8e..0f2f64d 100644
--- a/code/datasets/feature_extractor.py
+++ b/code/datasets/feature_extractor.py
@@ -11,6 +11,15 @@ import torchvision.transforms as transforms
import torch.nn.functional as F
import re
from imgaug import augmenters as iaa
+import argparse
+
+def make_parse():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--augment', default=False, action='store_true')
+ parser.add_argument('--cohort', default='RU', type=str)
+
+ args = parser.parse_args()
+ return args
def chunker(seq, size):
return (seq[pos:pos + size] for pos in range(0, len(seq), size))
@@ -21,11 +30,12 @@ def get_coords(batch_names): #ToDO: Change function for precise coords
for tile_name in batch_names:
# print(tile_name)
pos = re.findall(r'\((.*?)\)', tile_name)
- x, y = pos[-1].split('_')
+ # pos = pos
+ x, y = pos[-1].replace('-', '_').split('_')
coords.append((int(x),int(y)))
return coords
-def augment(img):
+def iaa_augment(img):
sometimes = lambda aug: iaa.Sometimes(0.5, aug, name="Random1")
sometimes2 = lambda aug: iaa.Sometimes(0.2, aug, name="Random2")
@@ -53,13 +63,22 @@ def augment(img):
if __name__ == '__main__':
+ torch.set_num_threads(8)
+ torch.manual_seed(2022)
+
+ args = make_parse()
+
+ augment=args.augment
+ cohorts = [args.cohort]
+ print('Augment Data: ', augment)
+ print('Cohort: ', cohorts)
home = Path.cwd().parts[1]
- data_root = Path(f'/{home}/ylan/data/DeepGraft/224_128um_v2')
+ data_root = Path(f'/{home}/ylan/data/DeepGraft/224_128uM_annotated')
# output_path = Path(f'/{home}/ylan/wsi_tools/debug/zarr')
- cohorts = ['DEEPGRAFT_RA', 'Leuven'] #,
- # cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU'] #,
+ # cohorts = ['RU', 'RA'] #,
+ # cohorts = ['Aachen_Biopsy_Slides'] #,
# cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU', 'DEEPGRAFT_RA', 'Leuven'] #,
compressor = Blosc(cname='blosclz', clevel=3)
@@ -77,28 +96,34 @@ if __name__ == '__main__':
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)
+ # out_features = 1024
+ model_ft = ResNet.resnet50(num_classes=1024, mlp=False, two_branch=False, normlinear=True)
+
+ model_ft.fc = nn.Identity()
+ # print(model_ft)
+ # model_ft.fc = nn.Linear(2048, out_features)
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.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=True)
+ # 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 = Path(test_output_path) / 'FEATURES_RETCCL'
+ fe_path = f / 'FEATURES_RETCCL_2048'
+
fe_path.mkdir(parents=True, exist_ok=True)
# num_files = len(list((f / 'BLOCKS').iterdir()))
@@ -107,21 +132,27 @@ if __name__ == '__main__':
if Path(slide).is_dir():
if slide.suffix != '.zarr':
slide_list.append(slide)
+ if augment:
+ tqdm_len = len(slide_list)*5
+ else: tqdm_len = len(slide_list)
- with tqdm(total=len(slide_list)) as pbar:
+
+ with tqdm(total=tqdm_len) 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:
+ # print('slide: ', slide)
+
+ # run every slide 5 times for augments
+ if not augment:
+ output_path = fe_path / Path(str(slide.stem) + '.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):
@@ -130,9 +161,6 @@ if __name__ == '__main__':
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)
@@ -142,8 +170,8 @@ if __name__ == '__main__':
continue
else:
batch_array = torch.stack(batch_array)
- # with torch.cuda.amp.autocast():
- model_output = model_ft(batch_array).detach()
+ 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:
@@ -152,35 +180,60 @@ if __name__ == '__main__':
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)
+ # test eval mode!
# 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')
+ # print('data same')
+ # else: print(slide)
# 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)
+ else:
+ for n in range(5):
+ # if n != 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 = iaa_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)
+ zarr.save_group(output_path, data=output_array, coords=output_batch_coords)
+
+ 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
deleted file mode 100644
index 0057c8e..0000000
--- a/code/datasets/feature_extractor_2.py
+++ /dev/null
@@ -1,186 +0,0 @@
-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_annotated.ipynb b/code/datasets/feature_extractor_annotated.ipynb
new file mode 100644
index 0000000..2b34715
--- /dev/null
+++ b/code/datasets/feature_extractor_annotated.ipynb
@@ -0,0 +1,223 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from pathlib import Path\n",
+ "from PIL import Image\n",
+ "from tqdm import tqdm\n",
+ "import zarr\n",
+ "from numcodecs import Blosc\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import ResNet as ResNet \n",
+ "import torchvision.transforms as transforms\n",
+ "import torch.nn.functional as F\n",
+ "import re\n",
+ "from imgaug import augmenters as iaa\n",
+ "import argparse\n",
+ "import json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def make_parse():\n",
+ " parser = argparse.ArgumentParser()\n",
+ " parser.add_argument('--augment', default=False, action='store_true')\n",
+ " \n",
+ " args = parser.parse_args()\n",
+ " return args\n",
+ "\n",
+ "def chunker(seq, size):\n",
+ " return (seq[pos:pos + size] for pos in range(0, len(seq), size))\n",
+ "\n",
+ "def get_coords(batch_names): #ToDO: Change function for precise coords\n",
+ " coords = []\n",
+ " \n",
+ " for tile_name in batch_names: \n",
+ " # print(tile_name)\n",
+ " pos = re.findall(r'\\((.*?)\\)', tile_name)\n",
+ " x, y = pos[-1].split('_')\n",
+ " coords.append((int(x),int(y)))\n",
+ " return coords\n",
+ "\n",
+ "def iaa_augment(img):\n",
+ "\n",
+ " sometimes = lambda aug: iaa.Sometimes(0.5, aug, name=\"Random1\")\n",
+ " sometimes2 = lambda aug: iaa.Sometimes(0.2, aug, name=\"Random2\")\n",
+ " sometimes3 = lambda aug: iaa.Sometimes(0.9, aug, name=\"Random3\")\n",
+ " sometimes4 = lambda aug: iaa.Sometimes(0.9, aug, name=\"Random4\")\n",
+ " sometimes5 = lambda aug: iaa.Sometimes(0.9, aug, name=\"Random5\")\n",
+ "\n",
+ " transforms = iaa.Sequential([\n",
+ " iaa.AddToHueAndSaturation(value=(-30, 30), name=\"MyHSV\"), #13\n",
+ " sometimes2(iaa.GammaContrast(gamma=(0.85, 1.15), name=\"MyGamma\")),\n",
+ " iaa.Fliplr(0.5, name=\"MyFlipLR\"),\n",
+ " iaa.Flipud(0.5, name=\"MyFlipUD\"),\n",
+ " sometimes(iaa.Rot90(k=1, keep_size=True, name=\"MyRot90\")),\n",
+ " iaa.OneOf([\n",
+ " sometimes3(iaa.PiecewiseAffine(scale=(0.015, 0.02), cval=0, name=\"MyPiece\")),\n",
+ " sometimes4(iaa.ElasticTransformation(alpha=(100, 200), sigma=20, cval=0, name=\"MyElastic\")),\n",
+ " sometimes5(iaa.Affine(scale={\"x\": (0.95, 1.05), \"y\": (0.95, 1.05)}, rotate=(-45, 45), shear=(-4, 4), cval=0, name=\"MyAffine\"))\n",
+ " ], name=\"MyOneOf\")\n",
+ " ])\n",
+ " seq_img_d = transforms.to_deterministic()\n",
+ " img = seq_img_d.augment_image(img)\n",
+ "\n",
+ " return img\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "usage: ipykernel_launcher.py [-h] [--augment]\n",
+ "ipykernel_launcher.py: error: unrecognized arguments: --ip=127.0.0.1 --stdin=9040 --control=9038 --hb=9037 --Session.signature_scheme=\"hmac-sha256\" --Session.key=b\"fea321c1-c51d-4123-ac4e-21d7b6f0be68\" --shell=9039 --transport=\"tcp\" --iopub=9041 --f=/home/ylan/.local/share/jupyter/runtime/kernel-v2-8466dCu9m1xIy2SG.json\n"
+ ]
+ },
+ {
+ "ename": "SystemExit",
+ "evalue": "2",
+ "output_type": "error",
+ "traceback": [
+ "An exception has occurred, use %tb to see the full traceback.\n",
+ "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 2\n"
+ ]
+ }
+ ],
+ "source": [
+ "torch.set_num_threads(8)\n",
+ "torch.manual_seed(2022)\n",
+ "\n",
+ "args = make_parse()\n",
+ "\n",
+ "augment=args.augment\n",
+ "print('Augment Data: ', augment)\n",
+ "\n",
+ "home = Path.cwd().parts[1]\n",
+ "data_root = Path(f'/{home}/ylan/data/DeepGraft/tissue_detection/224_128uM/images')\n",
+ "slide_patient_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict.json'\n",
+ "cohort_stain_path = f'/{home}/ylan/DeepGraft/training_tables/cohort_stain_dict.json'\n",
+ "with open(slide_patient_path, 'r') as f:\n",
+ " slide_patient_dict = json.load(f)\n",
+ "with open(cohort_stain_path, 'r') as f:\n",
+ " cohort_stain_dict = json.load(f)\n",
+ "# output_path = Path(f'/{home}/ylan/wsi_tools/debug/zarr')\n",
+ "# cohorts = ['DEEPGRAFT_RU'] #, \n",
+ "# cohorts = ['Aachen_Biopsy_Slides'] #, \n",
+ "# cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU', 'DEEPGRAFT_RA', 'Leuven'] #, \n",
+ "compressor = Blosc(cname='blosclz', clevel=3)\n",
+ "\n",
+ "val_transforms = transforms.Compose([\n",
+ " # \n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize(\n",
+ " mean=[0.485, 0.456, 0.406],\n",
+ " std=[0.229, 0.224, 0.225],\n",
+ " ),\n",
+ " # RangeNormalization(),\n",
+ " ])\n",
+ "\n",
+ "\n",
+ "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+ "scaler = torch.cuda.amp.GradScaler()\n",
+ "n_classes = 2\n",
+ "# out_features = 1024\n",
+ "model_ft = ResNet.resnet50(num_classes=128, mlp=False, two_branch=False, normlinear=True)\n",
+ "\n",
+ "model_ft.fc = nn.Identity()\n",
+ "# print(model_ft)\n",
+ "# model_ft.fc = nn.Linear(2048, out_features)\n",
+ "home = Path.cwd().parts[1]\n",
+ "model_ft.load_state_dict(torch.load(f'/{home}/ylan/workspace/TransMIL-DeepGraft/code/models/ckpt/retccl_best_ckpt.pth'), strict=True)\n",
+ "# for param in model_ft.parameters():\n",
+ "# param.requires_grad = False\n",
+ "# for m in model_ft.modules():\n",
+ "# if isinstance(m, torch.nn.modules.batchnorm.BatchNorm2d):\n",
+ "# m.eval()\n",
+ "# m.weight.requires_grad = False\n",
+ "# m.bias.requires_grad = False\n",
+ "# model_ft.fc = nn.Linear(2048, out_features)\n",
+ "model_ft.eval()\n",
+ "model_ft.to(device)\n",
+ "\n",
+ "batch_size = 100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31mRunning cells with 'Python 3.8.10 64-bit' requires ipykernel package.\n",
+ "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
+ "\u001b[1;31mCommand: '/usr/bin/python3 -m pip install ipykernel -U --user --force-reinstall'"
+ ]
+ }
+ ],
+ "source": [
+ "patient_cohort_dict = {}\n",
+ "for cohort in cohort_stain_dict.keys():\n",
+ " cohort_patient_list = list(cohort_stain_dict[cohort].keys())\n",
+ " for patient in cohort_patient_list:\n",
+ " patient_cohort_dict[patient] = cohort"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for f in data_root.iterdir():\n",
+ " slide_name = f.stem.split('_', 1)[0]\n",
+ " patient = slide_patient_dict[slide_name]"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.9.13 ('torch')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.8"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "7b7fb95db5714bbf59d6a04f6057e8fa5746fef9d16f5c42f2fdbc713170171a"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/code/datasets/feature_extractor_annotated.py b/code/datasets/feature_extractor_annotated.py
new file mode 100644
index 0000000..b6e6a78
--- /dev/null
+++ b/code/datasets/feature_extractor_annotated.py
@@ -0,0 +1,267 @@
+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
+import argparse
+import json
+
+def make_parse():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--augment', default=False, action='store_true')
+
+ args = parser.parse_args()
+ return args
+
+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 iaa_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
+
+
+torch.set_num_threads(8)
+torch.manual_seed(2022)
+
+args = make_parse()
+
+augment=args.augment
+print('Augment Data: ', augment)
+
+home = Path.cwd().parts[1]
+data_root = Path(f'/{home}/ylan/data/DeepGraft/tissue_detection/224_128uM/training/images')
+output_dataset_path = Path(f'/{home}/ylan/data/DeepGraft/224_128uM_annotated/')
+slide_patient_path = f'/{home}/ylan/DeepGraft/training_tables/slide_patient_dict.json'
+cohort_stain_path = f'/{home}/ylan/DeepGraft/training_tables/cohort_stain_dict.json'
+
+with open(slide_patient_path, 'r') as f:
+ slide_patient_dict = json.load(f)
+with open(cohort_stain_path, 'r') as f:
+ cohort_stain_dict = json.load(f)
+# output_path = Path(f'/{home}/ylan/wsi_tools/debug/zarr')
+# cohorts = ['DEEPGRAFT_RU'] #,
+# cohorts = ['Aachen_Biopsy_Slides'] #,
+# 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=1024, mlp=False, two_branch=False, normlinear=True)
+
+model_ft.fc = nn.Identity()
+# print(model_ft)
+# model_ft.fc = nn.Linear(2048, out_features)
+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=True)
+# 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
+
+
+skipped_slides = []
+
+patient_cohort_dict = {}
+for cohort in cohort_stain_dict.keys():
+ cohort_patient_list = list(cohort_stain_dict[cohort].keys())
+ for patient in cohort_patient_list:
+ patient_cohort_dict[patient] = cohort
+
+print('patient_cohort_dict completed.')
+
+cohort_slide_tiles_dict = {}
+for tile in data_root.iterdir():
+
+ slide_name = tile.stem.rsplit('_', 1)[0]
+ if slide_name in slide_patient_dict.keys():
+ patient = slide_patient_dict[slide_name]
+ cohort = patient_cohort_dict[patient]
+
+ if cohort not in cohort_slide_tiles_dict.keys():
+ cohort_slide_tiles_dict[cohort] = {}
+ if slide_name not in cohort_slide_tiles_dict[cohort].keys():
+ cohort_slide_tiles_dict[cohort][slide_name] = []
+ cohort_slide_tiles_dict[cohort][slide_name].append(tile)
+
+ else:
+ skipped_slides.append(slide_name)
+
+print('cohort_slide_tiles_dict complete.')
+for cohort in cohort_slide_tiles_dict.keys():
+ print(cohort)
+ print(len(list(cohort_slide_tiles_dict[cohort].keys())))
+
+for c in cohort_slide_tiles_dict.keys():
+
+ fe_path = output_dataset_path / c / 'FEATURES_RETCCL_2048'
+ fe_path.mkdir(parents=True, exist_ok=True)
+
+ slide_list = list(cohort_slide_tiles_dict[c].keys())
+
+ if augment:
+ tqdm_len = len(slide_list)*5
+ else: tqdm_len = len(slide_list)
+
+ with tqdm(total=tqdm_len) as pbar:
+ for slide in slide_list:
+
+ # run every slide 5 times for augments
+ if not augment:
+ output_path = fe_path / Path(slide + '.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):
+ for tile_path_batch in chunker(cohort_slide_tiles_dict[c][slide], 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 = val_transforms(img.copy()).to(device)
+ batch_array.append(img)
+
+ # print(t)
+ # print(t.stem)
+ 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)
+ zarr.save_group(output_path, data=output_array, coords=output_batch_coords)
+
+ # test eval mode!
+ z_test = zarr.open(output_path, 'r')
+ # print(z_test.tree())
+
+ if np.all(output_array != z_test['data'][:]):
+ # print('data same')
+ print(slide)
+ # if np.all(z['tile_names'][:] == z_test['tile_names'][:]):
+ # print('tile_names true')
+ # print(output_path ' ')
+ # print(np.all(z[:] == z_test[:]))
+ pbar.update(1)
+ else:
+ for n in range(4):
+
+ # if n != 5:
+ output_path = fe_path / Path(slide + '.zarr')
+ # output_path = fe_path / Path(slide + 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):
+ for tile_path_batch in chunker(cohort_slide_tiles_dict[c][slide], 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 = iaa_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)
+ zarr.save_group(output_path, data=output_array, coords=output_batch_coords)
+
+ pbar.update(1)
+
+
+print('skipped slides:')
+print(skipped_slides)
diff --git a/code/datasets/feature_file_checker.py b/code/datasets/feature_file_checker.py
new file mode 100644
index 0000000..e594e4b
--- /dev/null
+++ b/code/datasets/feature_file_checker.py
@@ -0,0 +1,82 @@
+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
+
+
+
+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 = ['Leuven'] #,
+ cohorts = ['DEEPGRAFT_RU'] #,
+ # cohorts = ['Aachen_Biopsy_Slides'] #,
+ # cohorts = ['DEEPGRAFT_RU', 'DEEPGRAFT_RA', 'Leuven'] #,
+ # cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU', 'DEEPGRAFT_RA'] #,
+ # cohorts = ['Aachen_Biopsy_Slides', 'DEEPGRAFT_RU', 'DEEPGRAFT_RA', 'Leuven'] #,
+
+ 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)
+ slide_list = []
+ counter = 0
+ for slide in (f / 'BLOCKS').iterdir():
+ if Path(slide).is_dir():
+ if slide.suffix != '.zarr':
+ slide_list.append(slide)
+
+ print(len(slide_list))
+
+ with tqdm(total=len(slide_list)) as pbar:
+ for slide in slide_list:
+ output_path = fe_path / Path(str(slide.stem) + '.zarr')
+ # print('slide: ', slide)
+
+ # run every slide 5 times for augments
+ for n in range(6):
+
+ if n != 5:
+ output_path = fe_path / Path(str(slide.stem) + f'_aug{n}.zarr')
+ else:
+ output_path = fe_path / Path(str(slide.stem) + '.zarr')
+ if output_path.is_dir():
+ # print(output_path, ' skipped.')
+ pbar.update(1)
+ continue
+
+ else:
+ counter += 1
+ print(output_path)
+ pbar.update(1)
+ print(counter)
+
+
\ No newline at end of file
diff --git a/code/datasets/monai_loader.py b/code/datasets/monai_loader.py
new file mode 100644
index 0000000..0bbe803
--- /dev/null
+++ b/code/datasets/monai_loader.py
@@ -0,0 +1,179 @@
+import numpy as np
+import collections.abc
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+
+from monai.config import KeysCollection
+from monai.data import Dataset, load_decathlon_datalist, PersistentDataset
+from monai.data.wsi_reader import WSIReader, CuCIMWSIReader
+# from monai.data.image_reader import CuCIMWSIReader
+from monai.networks.nets import milmodel
+from monai.transforms import (
+ Compose,
+ GridPatchd,
+ LoadImaged,
+ LoadImage,
+ MapTransform,
+ RandFlipd,
+ RandGridPatchd,
+ RandRotate90d,
+ ScaleIntensityRanged,
+ SplitDimd,
+ ToTensord,
+)
+from sklearn.metrics import cohen_kappa_score
+from torch.cuda.amp import GradScaler, autocast
+from torch.utils.data.dataloader import default_collate
+from torch.utils.data.distributed import DistributedSampler
+from torch.utils.tensorboard import SummaryWriter
+import json
+from pathlib import Path
+import time
+
+class LabelEncodeIntegerGraded(MapTransform):
+ """
+ Convert an integer label to encoded array representation of length num_classes,
+ with 1 filled in up to label index, and 0 otherwise. For example for num_classes=5,
+ embedding of 2 -> (1,1,0,0,0)
+ Args:
+ num_classes: the number of classes to convert to encoded format.
+ keys: keys of the corresponding items to be transformed. Defaults to ``'label'``.
+ allow_missing_keys: don't raise exception if key is missing.
+ """
+
+ def __init__(
+ self,
+ num_classes: int,
+ keys: KeysCollection = "label",
+ allow_missing_keys: bool = False,
+ ):
+ super().__init__(keys, allow_missing_keys)
+ self.num_classes = num_classes
+
+ def __call__(self, data):
+
+ d = dict(data)
+ for key in self.keys:
+ label = int(d[key])
+
+ lz = np.zeros(self.num_classes, dtype=np.float32)
+ lz[:label] = 1.0
+ # alternative oneliner lz=(np.arange(self.num_classes)<int(label)).astype(np.float32) #same oneliner
+ d[key] = lz
+
+ return d
+
+def list_data_collate(batch: collections.abc.Sequence):
+ # print(f"{i} = {item['image'].shape=} >> {item['image'].keys=}")
+ for i, item in enumerate(batch):
+ data = item[0]
+ data["image"] = torch.stack([ix["image"] for ix in item], dim=0)
+ # data["patch_location"] = torch.stack([ix["patch_location"] for ix in item], dim=0)
+ batch[i] = data
+ return default_collate(batch)
+
+
+
+
+
+if __name__ == '__main__':
+
+ num_classes = 2
+ batch_size=1
+ tile_size = 224
+ tile_count = 1000
+ home = Path.cwd().parts[1]
+ data_root = f'/{home}/ylan/DeepGraft/'
+ # labels = [0]
+ # data_root = f'/{home}/public/DeepGraft/Aachen_Biopsy_Slides_Extended'
+ data = {"training": [{
+ "image": 'Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs',
+ "label": 0
+ }, {
+ "image": 'Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs',
+ "label": 0
+ }, {
+ "image": 'Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs',
+ "label": 0
+ }],
+ "validation": [{
+ "image": 'Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs',
+ "label": 0
+ }]
+ }
+ with open('monai_test.json', 'w') as jf:
+ json.dump(data, jf)
+ json_data_path = f'/{home}/ylan/DeepGraft/training_tables/dg_decathlon_PAS_HE_Jones_norm_rest.json'
+
+ training_list = load_decathlon_datalist(
+ data_list_file_path=json_data_path,
+ data_list_key="training",
+ base_dir=data_root,
+ )
+
+ train_transform = Compose(
+ [
+ LoadImaged(keys=["image"], reader=WSIReader, backend="cucim", dtype=np.uint8, level=0, image_only=True, num_workers=8),
+ LabelEncodeIntegerGraded(keys=["label"], num_classes=num_classes),
+ RandGridPatchd(
+ keys=["image"],
+ patch_size=(tile_size, tile_size),
+ threshold=0.999 * 3 * 255 * tile_size * tile_size,
+ num_patches=None,
+ sort_fn="min",
+ pad_mode=None,
+ constant_values=255,
+ ),
+ SplitDimd(keys=["image"], dim=0, keepdim=False, list_output=True),
+ RandFlipd(keys=["image"], spatial_axis=0, prob=0.5),
+ RandFlipd(keys=["image"], spatial_axis=1, prob=0.5),
+ RandRotate90d(keys=["image"], prob=0.5),
+ ScaleIntensityRanged(keys=["image"], a_min=np.float32(255), a_max=np.float32(0)),
+ ToTensord(keys=["image", "label"]),
+ ]
+ )
+ train_data_list = data['training']
+ # dataset_train = Dataset(data=training_list)
+ dataset_train = Dataset(data=training_list, transform=train_transform)
+ # persistent_dataset = PersistentDataset(data=training_list, transform=train_transform, cache_dir='/home/ylan/workspace/test')
+
+
+ train_loader = torch.utils.data.DataLoader(
+ dataset_train,
+ batch_size=batch_size,
+ shuffle=True,
+ num_workers=1,
+ pin_memory=True,
+ sampler=None,
+ collate_fn=list_data_collate,
+ )
+
+ print(len(train_loader))
+ start = time.time()
+ count = 0
+
+ # train_transform = LoadImage(reader=WSIReader, backend='openslide', level=3)
+ # filename = '/home/ylan/DeepGraft/DEEPGRAFT_RU/T19-01474_I1_HE 10_959004.ndpi'
+ # X = train_transform(filename)
+ # print(X)
+ # img, meta = reader.read(data='/home/ylan/DeepGraft/DEEPGRAFT_RU/T19-01474_I1_HE 10_959004.ndpi')
+
+ # print(meta)
+
+ for idx, batch_data in enumerate(train_loader):
+ # print(batch_data)
+ if count > 10:
+ break
+ data, target = batch_data["image"], batch_data["label"]
+ print(target)
+ count += 1
+ end = time.time()
+ print('Time: ', end-start)
+
+ # image_reader = WSIReader(backend='cucim')
+ # for i in training_list:
+ # # print(i)
+ # wsi = image_reader.read(i['image'])
+ # img_data, meta_data = image_reader.get_data(wsi)
+ # print(meta_data)
\ No newline at end of file
diff --git a/code/datasets/simple_jpg_dataloader.py b/code/datasets/simple_jpg_dataloader.py
index 332920a..c5e349f 100644
--- a/code/datasets/simple_jpg_dataloader.py
+++ b/code/datasets/simple_jpg_dataloader.py
@@ -20,8 +20,8 @@ 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):
+class JPGBagLoader(data_utils.Dataset):
+ def __init__(self, file_path, label_path, mode, n_classes, load_data=False, data_cache_size=100, max_bag_size=1000, cache=False):
super().__init__()
self.data_info = []
@@ -35,7 +35,7 @@ class FeatureBagLoader(data_utils.Dataset):
self.label_path = label_path
self.n_classes = n_classes
self.max_bag_size = max_bag_size
- self.min_bag_size = 120
+ self.min_bag_size = 50
self.empty_slides = []
self.corrupt_slides = []
self.cache = True
@@ -222,7 +222,7 @@ class FeatureBagLoader(data_utils.Dataset):
if self.cache:
label = self.labels[index]
wsi = self.features[index]
- label = Variable(Tensor(label))
+ label = int(label)
wsi_name = self.wsi_names[index]
name_batch = self.name_batches[index]
patient = self.patients[index]
@@ -231,13 +231,14 @@ class FeatureBagLoader(data_utils.Dataset):
else:
if self.mode=='train':
batch, label, (wsi_name, name_batch, patient) = self.get_data(self.files[index])
- label = Variable(Tensor(label))
+ # 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 = seq_img_d.augment_image(img)
img = self.val_transforms(img.copy())
out_batch.append(img)
out_batch = torch.stack(out_batch)
@@ -278,7 +279,7 @@ if __name__ == '__main__':
n_classes = 2
- dataset = FeatureBagLoader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes)
+ dataset = JPGBagLoader(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)
@@ -311,7 +312,7 @@ if __name__ == '__main__':
bag, label, (name, batch_names, patient) = item
# print(bag.shape)
# print(len(batch_names))
-
+ print(label)
bag = bag.squeeze(0).float().to(device)
label = label.to(device)
with torch.cuda.amp.autocast():
diff --git a/code/datasets/test_normalization.ipynb b/code/datasets/test_normalization.ipynb
new file mode 100644
index 0000000..c9e0a6b
--- /dev/null
+++ b/code/datasets/test_normalization.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# from simple_jpg_dataloader import JPGBagLoader\n",
+ "import torch\n",
+ "from torch.utils.data import random_split, DataLoader\n",
+ "from pathlib import Path\n",
+ "import numpy as np\n",
+ "import random\n",
+ "from torchvision.transforms import transforms\n",
+ "import matplotlib.pyplot as plt\n",
+ "from PIL import Image\n",
+ "import cv2\n",
+ "import json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "home = Path.cwd().parts[1]\n",
+ "label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_split_PAS_HE_Jones_norm_rest.json'\n",
+ "data_root = f'/{home}/ylan/data/DeepGraft/224_128uM_annotated'\n",
+ "n_classes = 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "val_transforms = transforms.Compose([\n",
+ " # \n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize(\n",
+ " mean=[0.485, 0.456, 0.406],\n",
+ " std=[0.229, 0.224, 0.225],\n",
+ " ),\n",
+ " # RangeNormalization(),\n",
+ "])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualize(cohort):\n",
+ "\n",
+ " cohort_path = Path(data_root) / cohort / 'BLOCKS'\n",
+ " print(cohort_path)\n",
+ " cohort_slides = list(Path(cohort_path).iterdir())\n",
+ " random_idx = random.sample(range(0, len(cohort_slides)), 10)\n",
+ " random_slides = [cohort_slides[i] for i in random_idx]\n",
+ " print(random_slides)\n",
+ "\n",
+ " fig = plt.figure(figsize=(100,100))\n",
+ " columns = 10\n",
+ " rows = 10\n",
+ "\n",
+ " for i, slide in enumerate(random_slides):\n",
+ " tile_list = list(slide.iterdir())\n",
+ " if len(tile_list) < 10:\n",
+ " # continue\n",
+ " tile_list = list(cohort_slides[random.randint(0,len(cohort_slides))].iterdir())\n",
+ " random_idx = random.sample(range(0, len(tile_list)), 10)\n",
+ " for j, tile_path in enumerate([tile_list[i] for i in random_idx]):\n",
+ " img = np.asarray(Image.open(tile_path)).astype(np.uint8)\n",
+ " img = img.astype(np.uint8)\n",
+ " img = val_transforms(img.copy())\n",
+ " img = ((img-img.min())/(img.max()-img.min()))*255\n",
+ " img = img.numpy().astype(np.uint8).transpose(1,2,0)\n",
+ " img = Image.fromarray(img)\n",
+ " img = img.convert('RGB')\n",
+ " # print((i+1)*rows+j)\n",
+ " fig.add_subplot(rows, columns, (i)*rows+(j+1))\n",
+ " # fig.add_subplot(rows, columns, (i+1)*rows+(j+1))\n",
+ " plt.imshow(img)\n",
+ " plt.show\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def hexencode(rgb):\n",
+ " r=rgb[0]\n",
+ " g=rgb[1]\n",
+ " b=rgb[2]\n",
+ " return '#%02x%02x%02x' % (r,g,b)\n",
+ "\n",
+ "def normalize(slides):\n",
+ "\n",
+ " # cohort_path = Path(data_root) / 'debug' / slide\n",
+ " # # print(cohort_path)\n",
+ " # cohort_slides = list(Path(cohort_path).iterdir())\n",
+ " # random_idx = random.sample(range(0, len(cohort_slides)), 5)\n",
+ " # random_slides = [cohort_slides[i] for i in random/_idx]\n",
+ " # print(random_slides)\n",
+ "\n",
+ " fig = plt.figure(figsize=(100,100))\n",
+ " columns = 10\n",
+ " rows = 10\n",
+ "\n",
+ " for i, slide in enumerate(slides):\n",
+ " slide_path = Path(data_root) / 'debug' / slide\n",
+ " tile_list = list(slide_path.iterdir())\n",
+ " if len(tile_list) < 10:\n",
+ " # continue\n",
+ " tile_list = list(cohort_slides[random.randint(0,len(cohort_slides))].iterdir())\n",
+ " random_idx = random.sample(range(0, len(tile_list)), 5)\n",
+ " for j, tile_path in enumerate([tile_list[i] for i in random_idx]):\n",
+ " # print(tile_path)\n",
+ " img = np.asarray(Image.open(tile_path)).astype(np.uint8)\n",
+ " img = img.astype(np.uint8)\n",
+ " img = val_transforms(img.copy())\n",
+ " img = ((img-img.min())/(img.max()-img.min()))*255\n",
+ " img_np = img.numpy().astype(np.uint8).transpose(1,2,0)\n",
+ " img = Image.fromarray(img_np)\n",
+ " img = img.convert('RGB')\n",
+ " # print((i+1)*rows+j)\n",
+ " # fig.add_subplot(rows, columns, (i*2)*rows+(j+1))\n",
+ " # # fig.add_subplot(rows, columns, (i+1)*rows+(j+1))\n",
+ " # plt.imshow(img)\n",
+ "\n",
+ " color = ('b','g','r')\n",
+ " fig.add_subplot(rows, columns, (i*2)*rows+(j+1))\n",
+ " for i,col in enumerate(color):\n",
+ " histr = cv2.calcHist([img_np],[i],None,[256],[0,256])\n",
+ " plt.plot(histr,color = col)\n",
+ " plt.xlim([0,256])\n",
+ " plt.show\n",
+ " # plt.imshow(img)\n",
+ "\n",
+ " \n",
+ " plt.show\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "json_path = f'/{home}/ylan/training_tables/dg_split_PAS_HE_Jones_norm_rest.json'\n",
+ "with open(json_path, 'r') as jf:\n",
+ " split_dict = json.read(jf)\n",
+ "\n",
+ "print(split_dict)\n",
+ "\n",
+ "slides = ['DEEPGRAFT_RA/RA0002_PASD_jkers_PASD_20180829_142406', 'DEEPGRAFT_RU/RU0001_PASD_jke_PASD_20200129_122805_BIG', 'Aachen_Biopsy_Slides/Aachen_KiBiDatabase_KiBiAcALSZ690_01_004_PAS']\n",
+ "\n",
+ "# normalize(slides)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.8.8 ('torch')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "7b7fb95db5714bbf59d6a04f6057e8fa5746fef9d16f5c42f2fdbc713170171a"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/code/datasets/zarr_feature_dataloader.py b/code/datasets/zarr_feature_dataloader.py
index b244b87..9387e13 100644
--- a/code/datasets/zarr_feature_dataloader.py
+++ b/code/datasets/zarr_feature_dataloader.py
@@ -22,11 +22,11 @@ from PIL import Image
class ZarrFeatureBagLoader(data.Dataset):
- def __init__(self, file_path, label_path, mode, n_classes, cache=False, data_cache_size=100, max_bag_size=1000):
+ def __init__(self, file_path, label_path, mode, n_classes, cache=False, data_cache_size=50, max_bag_size=1000):
super().__init__()
self.data_info = []
- self.data_cache = {}
+ self.data_cache = []
self.slideLabelDict = {}
self.files = []
self.data_cache_size = data_cache_size
@@ -39,17 +39,23 @@ class ZarrFeatureBagLoader(data.Dataset):
self.min_bag_size = 120
self.empty_slides = []
self.corrupt_slides = []
- self.cache = True
-
+ self.cache = cache
+ self.drop_rate=0.1
+ self.cache=True
+ print('mode: ', self.mode)
# read labels and slide_path from csv
with open(self.label_path, 'r') as f:
- temp_slide_label_dict = json.load(f)[mode]
+ temp_slide_label_dict = json.load(f)[self.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 self.mode == 'test':
+ x_complete_path = Path(self.file_path) / cohort / 'FEATURES_RETCCL_GAN' / (str(x) + '.zarr')
+ else:
+ x_complete_path = Path(self.file_path) / cohort / 'FEATURES_RETCCL' / (str(x) + '.zarr')
+ print(x_complete_path)
if x_complete_path.is_dir():
# if len(list(x_complete_path.iterdir())) > self.min_bag_size:
# # print(x_complete_path)
@@ -66,26 +72,65 @@ class ZarrFeatureBagLoader(data.Dataset):
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
+ for t in tqdm(self.files):
+ self._add_data_infos(t, cache=cache)
+
+
+ print('data_cache_size: ', self.data_cache_size)
+ print('data_info: ', len(self.data_info))
+ # if self.cache:
+ # print('Loading data into 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)
+ # else:
+
+ def _add_data_infos(self, file_path, cache):
+
+ # if cache:
wsi_name = Path(file_path).stem
- if wsi_name in self.slideLabelDict:
- label = self.slideLabelDict[wsi_name]
-
- patient = self.slide_patient_dict[wsi_name]
+ # if wsi_name in self.slideLabelDict:
+ label = self.slideLabelDict[wsi_name]
+ patient = self.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 get_data(self, i):
+
+ fp = self.data_info[i]['data_path']
+ idx = self.data_info[i]['cache_idx']
+ if idx == -1:
+
+ # if fp not in self.data_cache:
+ self._load_data(fp)
+
+
+ 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']
+
+ return self.data_cache[cache_idx], label, name, patient
+ # return self.data_cache[fp][cache_idx], label, name, patient
+
+
+
+ def _load_data(self, file_path):
+
+
+ # 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)
@@ -96,15 +141,45 @@ class ZarrFeatureBagLoader(data.Dataset):
bag_size = wsi_bag.shape[0]
# random drop
- bag_idxs = torch.randperm(bag_size)[:int(bag_size*(1-drop_rate))]
+ bag_idxs = torch.randperm(bag_size)[:int(bag_size*(1-self.drop_rate))]
wsi_bag = wsi_bag[bag_idxs, :]
batch_coords = batch_coords[bag_idxs]
+
+ idx = self._add_to_cache((wsi_bag, batch_coords), file_path)
+ file_idx = next(i for i, v in enumerate(self.data_info) if v['data_path'] == file_path)
+ # print('file_idx: ', file_idx)
+ # print('idx: ', idx)
+ self.data_info[file_idx]['cache_idx'] = idx
# print(wsi_bag.shape)
# name_samples = [batch_names[i] for i in bag_idxs]
- return wsi_bag, label, (wsi_name, batch_coords, patient)
+ # return wsi_bag, label, (wsi_name, batch_coords, patient)
+
+ if len(self.data_cache) > self.data_cache_size:
+ # removal_keys = list(self.data_cache)
+ # removal_keys.remove(file_path)
+
+ self.data_cache.pop(idx)
+
+ self.data_info = [{'data_path': di['data_path'], 'label': di['label'], 'name': di['name'], 'patient':di['patient'], 'cache_idx':-1} if di['cache_idx'] == idx else di for di in self.data_info]
+
+
+
+ def _add_to_cache(self, data, data_path):
+
+
+ # if data_path not in self.data_cache:
+ # self.data_cache[data_path] = [data]
+ # else:
+ # self.data_cache[data_path].append(data)
+ self.data_cache.append(data)
+ # print(len(self.data_cache))
+ # return len(self.data_cache)
+ return len(self.data_cache) - 1
+
def get_labels(self, indices):
- return [self.labels[i] for i in indices]
+ # return [self.labels[i] for i in indices]
+ return [self.data_info[i]['label'] for i in indices]
def to_fixed_size_bag(self, bag, names, bag_size: int = 512):
@@ -136,36 +211,17 @@ class ZarrFeatureBagLoader(data.Dataset):
return bag_samples, name_samples
def __len__(self):
- return len(self.files)
+ # return len(self.files)
+ return len(self.data_info)
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)
+ (wsi, batch_coords), label, wsi_name, patient = self.get_data(index)
+
+ label = torch.as_tensor(label)
+ label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+
+ return wsi, label, (wsi_name, batch_coords, patient)
if __name__ == '__main__':
@@ -182,14 +238,14 @@ if __name__ == '__main__':
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'
+ # label_path = f'/{home}/ylan/DeepGraft/training_tables/split_debug.json'
+ label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_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)
+ dataset = ZarrFeatureBagLoader(data_root, label_path=label_path, mode='train', cache=False, data_cache_size=3000, n_classes=n_classes)
# print(dataset.get_labels(0))
a = int(len(dataset)* 0.8)
@@ -200,7 +256,7 @@ if __name__ == '__main__':
# 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)
+ dl = DataLoader(train_data, batch_size=1, num_workers=8)#, pin_memory=True , sampler=ImbalancedDatasetSampler(train_data)
# 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")
@@ -210,29 +266,35 @@ if __name__ == '__main__':
# for param in model_ft.parameters():
# param.requires_grad = False
# model_ft.to(device)
- model = TransMIL(n_classes=n_classes).to(device)
+ # model = TransMIL(n_classes=n_classes).to(device)
c = 0
label_count = [0] *n_classes
epochs = 1
- # print(len(dl))
+ print(len(dl))
# start = time.time()
+
+ count = 0
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)
+ # print(label)
+ # print(batch_names)
bag = bag.float().to(device)
+ # print(bag)
+ # print(name)
+ # bag = bag.float().to(device)
# print(bag.shape)
# label = label.to(device)
- with torch.cuda.amp.autocast():
- output = model(bag)
- # c += 1
+ # with torch.cuda.amp.autocast():
+ # output = model(bag)
+ count += 1
+
end = time.time()
- print('Bag Time: ', end-start)
\ No newline at end of file
+ print('Bag Time: ', end-start)
+ print(count)
\ No newline at end of file
diff --git a/code/datasets/zarr_feature_dataloader_simple.py b/code/datasets/zarr_feature_dataloader_simple.py
new file mode 100644
index 0000000..c9313c9
--- /dev/null
+++ b/code/datasets/zarr_feature_dataloader_simple.py
@@ -0,0 +1,255 @@
+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=5000, 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.drop_rate = 0.1
+ # self.min_bag_size = 120
+ self.empty_slides = []
+ self.corrupt_slides = []
+ self.cache = cache
+
+ # 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 self.mode == 'test': #set to test if using GAN output
+ x_path_list = [Path(self.file_path) / cohort / 'FEATURES_RETCCL_2048' / (str(x) + '.zarr')]
+ else:
+ x_path_list = [Path(self.file_path) / cohort / 'FEATURES_RETCCL_2048' / (str(x) + '.zarr')]
+ for i in range(5):
+ x_path_list.append(Path(self.file_path) / cohort / 'FEATURES_RETCCL' / (str(x) + f'_aug{i}.zarr'))
+ # print(x_complete_path)
+ for x_path in x_path_list:
+ if x_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_path)
+
+ # print(self.files)
+ 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):
+
+ batch_names=[] #add function for name_batch read out
+
+ wsi_name = Path(file_path).stem
+ if wsi_name.split('_')[-1][:3] == 'aug':
+ wsi_name = '_'.join(wsi_name.split('_')[:-1])
+ # if wsi_name in self.slideLabelDict:
+ label = self.slideLabelDict[wsi_name]
+ label = torch.as_tensor(label)
+ label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+ 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(self.max_bag_size*(1-self.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 = self.max_bag_size
+ # bag_size = bag.shape[0]
+ bag_idxs = torch.randperm(self.max_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)
+ # label = torch.as_tensor(label)
+ # label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+ # 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, 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_coords, patient) = item
+ # print(bag.shape)
+ # print(len(batch_names))
+ # print(label)
+ # print(batch_coords)
+ print(name)
+ 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/models/AttMIL.py b/code/models/AttMIL.py
index 048fb0f..89ff5d5 100644
--- a/code/models/AttMIL.py
+++ b/code/models/AttMIL.py
@@ -13,7 +13,7 @@ from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
class AttMIL(nn.Module): #gated attention
- def __init__(self, n_classes, features=512):
+ def __init__(self, n_classes, features=1024):
super(AttMIL, self).__init__()
self.L = features
self.D = 128
diff --git a/code/models/TransMIL.py b/code/models/TransMIL.py
index ddc1262..01a0fa7 100755
--- a/code/models/TransMIL.py
+++ b/code/models/TransMIL.py
@@ -32,11 +32,12 @@ class TransLayer(nn.Module):
)
def forward(self, x):
- out, attn = self.attn(self.norm(x), return_attn=True)
+ out= self.attn(self.norm(x))
+ # out, attn = self.attn(self.norm(x))
x = x + out
# x = x + self.attn(self.norm(x))
- return x, attn
+ return x
class PPEG(nn.Module):
@@ -59,16 +60,19 @@ class PPEG(nn.Module):
class TransMIL(nn.Module):
def __init__(self, n_classes):
super(TransMIL, self).__init__()
- in_features = 1024
+ in_features = 2048
+ inter_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.pos_layer = PPEG(dim=out_features)
+ self._fc1 = nn.Sequential(nn.Linear(in_features, inter_features), nn.GELU(), nn.Dropout(p=0.5), norm_layer(inter_features))
+ self._fc1_2 = nn.Sequential(nn.Linear(inter_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(norm_layer=norm_layer, dim=out_features)
@@ -90,8 +94,10 @@ class TransMIL(nn.Module):
def forward(self, x): #, **kwargs
# x = self.model_ft(x).unsqueeze(0)
- h = x.float() #[B, n, 1024]
+ h = x.squeeze(0).float() #[B, n, 1024]
h = self._fc1(h) #[B, n, 512]
+ # h = self.drop(h)
+ h = self._fc1_2(h) #[B, n, 512]
# print('Feature Representation: ', h.shape)
#---->duplicate pad
@@ -110,7 +116,8 @@ class TransMIL(nn.Module):
#---->Translayer x1
- h, attn1 = self.layer1(h) #[B, N, 512]
+ h = self.layer1(h) #[B, N, 512]
+ # h, attn1 = self.layer1(h) #[B, N, 512]
# print('After first TransLayer: ', h.shape)
@@ -119,7 +126,8 @@ class TransMIL(nn.Module):
# print('After PPEG: ', h.shape)
#---->Translayer x2
- h, attn2 = self.layer2(h) #[B, N, 512]
+ h = self.layer2(h) #[B, N, 512]
+ # h, attn2 = self.layer2(h) #[B, N, 512]
# print('After second TransLayer: ', h.shape) #[1, 1025, 512] 1025 = cls_token + 1024
#---->cls_token
@@ -128,7 +136,11 @@ class TransMIL(nn.Module):
#---->predict
logits = self._fc2(h) #[B, n_classes]
- return logits, attn2
+ # Y_hat = torch.argmax(logits, dim=1)
+ # Y_prob = F.softmax(logits, dim = 1)
+ # results_dict = {'logits': logits, 'Y_prob': Y_prob, 'Y_hat': Y_hat}
+ return logits
+ # return logits, attn2
if __name__ == "__main__":
data = torch.randn((1, 6000, 1024)).cuda()
diff --git a/code/models/__pycache__/AttMIL.cpython-39.pyc b/code/models/__pycache__/AttMIL.cpython-39.pyc
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diff --git a/code/models/__pycache__/TransMIL.cpython-39.pyc b/code/models/__pycache__/TransMIL.cpython-39.pyc
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diff --git a/code/models/__pycache__/TransformerMIL.cpython-39.pyc b/code/models/__pycache__/TransformerMIL.cpython-39.pyc
index 7ed17970f205f815c2b3f9dc1b6529a296ec9548..f2c5bda0715cd2d8601f4a33e2187a553a3f322a 100644
GIT binary patch
delta 1053
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diff --git a/code/models/__pycache__/model_interface.cpython-39.pyc b/code/models/__pycache__/model_interface.cpython-39.pyc
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GIT binary patch
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diff --git a/code/models/model_interface.py b/code/models/model_interface.py
index 9f4f282..0186c06 100755
--- a/code/models/model_interface.py
+++ b/code/models/model_interface.py
@@ -10,6 +10,7 @@ from pathlib import Path
from matplotlib import pyplot as plt
import cv2
from PIL import Image
+from pytorch_pretrained_vit import ViT
#---->
from MyOptimizer import create_optimizer
@@ -28,6 +29,13 @@ import torch.nn.functional as F
import torchmetrics
from torchmetrics.functional import stat_scores
from torch import optim as optim
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+
+from monai.config import KeysCollection
+from monai.data import Dataset, load_decathlon_datalist
+from monai.data.wsi_reader import WSIReader
+from monai.metrics import Cumulative, CumulativeAverage
+from monai.networks.nets import milmodel
# from sklearn.metrics import roc_curve, auc, roc_curve_score
@@ -69,11 +77,19 @@ class ModelInterface(pl.LightningModule):
super(ModelInterface, self).__init__()
self.save_hyperparameters()
self.n_classes = model.n_classes
- self.load_model()
- self.loss = create_loss(loss, model.n_classes)
- # self.loss = AUCM_MultiLabel(num_classes = model.n_classes, device=self.device)
+
+ if model.name == 'AttTrans':
+ self.model = milmodel.MILModel(num_classes=self.n_classes, pretrained=True, mil_mode='att_trans', backbone_num_features=1024)
+ else: self.load_model()
+ # self.loss = create_loss(loss, model.n_classes)
+ # self.loss =
+ if self.n_classes>2:
+ self.aucm_loss = AUCM_MultiLabel(num_classes = model.n_classes, device=self.device)
+ else:
+ self.aucm_loss = AUCMLoss()
# self.asl = AsymmetricLossSingleLabel()
- # self.loss = LabelSmoothingCrossEntropy(smoothing=0.1)
+ self.loss = LabelSmoothingCrossEntropy(smoothing=0.1)
+
# self.loss =
# print(self.model)
self.model_name = model.name
@@ -99,7 +115,7 @@ class ModelInterface(pl.LightningModule):
# print(self.experiment)
#---->Metrics
if self.n_classes > 2:
- self.AUROC = torchmetrics.AUROC(num_classes = self.n_classes)
+ self.AUROC = torchmetrics.AUROC(num_classes = self.n_classes, average='macro')
metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = self.n_classes,
average='weighted'),
@@ -131,7 +147,9 @@ class ModelInterface(pl.LightningModule):
# self.pr_curve = torchmetrics.BinnedPrecisionRecallCurve(num_classes = self.n_classes, thresholds=10)
self.confusion_matrix = torchmetrics.ConfusionMatrix(num_classes = self.n_classes)
self.valid_metrics = metrics.clone(prefix = 'val_')
+ self.valid_patient_metrics = metrics.clone(prefix = 'val_patient_')
self.test_metrics = metrics.clone(prefix = 'test_')
+ self.test_patient_metrics = metrics.clone(prefix = 'test_patient')
#--->random
self.shuffle = kargs['data'].data_shuffle
@@ -146,12 +164,16 @@ class ModelInterface(pl.LightningModule):
self.feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/dino-vitb16')
self.model_ft = ViTModel.from_pretrained('facebook/dino-vitb16')
elif self.backbone == 'resnet18':
- self.model_ft = models.resnet18(pretrained=True)
+ self.model_ft = models.resnet18(weights='IMAGENET1K_V1')
# modules = list(resnet18.children())[:-1]
+ # frozen_layers = 8
+ # for child in self.model_ft.children():
+
for param in self.model_ft.parameters():
param.requires_grad = False
self.model_ft.fc = nn.Linear(512, self.out_features)
+
# res18 = nn.Sequential(
# *modules,
# )
@@ -235,22 +257,28 @@ class ModelInterface(pl.LightningModule):
def forward(self, x):
# print(x.shape)
+ if self.model_name == 'AttTrans':
+ return self.model(x)
if self.model_ft:
+ x = x.squeeze(0)
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.contiguous())
+ input = input.float()
+ # logits, _ = self(input.contiguous())
+ logits = self(input.contiguous())
+ Y_hat = torch.argmax(logits, dim=1)
+ Y_prob = F.softmax(logits, dim = 1)
-
- Y_hat = torch.argmax(logits, dim=1)
- Y_prob = F.softmax(logits, dim=1)
+ # Y_hat = torch.argmax(logits, dim=1)
+ # Y_prob = F.softmax(logits, dim=1)
return logits, Y_prob, Y_hat
@@ -264,12 +292,19 @@ class ModelInterface(pl.LightningModule):
# bag_idxs = torch.randperm(input.squeeze(0).shape[0])[:bag_size]
# input = input.squeeze(0)[bag_idxs].unsqueeze(0)
- label = label.float()
+ # label = label.float()
logits, Y_prob, Y_hat = self.step(input)
#---->loss
loss = self.loss(logits, label)
+
+ one_hot_label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
+ # aucm_loss = self.aucm_loss(torch.sigmoid(logits), one_hot_label)
+ # total_loss = torch.mean(loss + aucm_loss)
+ Y = int(label)
+ # print(logits, label)
+ # loss = cross_entropy_torch(logits.squeeze(0), label)
# loss = self.asl(logits, label.squeeze())
#---->acc log
@@ -278,11 +313,14 @@ class ModelInterface(pl.LightningModule):
# if self.n_classes == 2:
# Y = int(label[0][1])
# else:
- Y = torch.argmax(label)
+ # Y = torch.argmax(label)
+
# 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, sync_dist=True)
+ # self.log('total_loss', total_loss, prog_bar=True, on_epoch=True, logger=True, batch_size=1, sync_dist=True)
+ # self.log('aucm_loss', aucm_loss, prog_bar=True, on_epoch=True, logger=True, batch_size=1, sync_dist=True)
+ self.log('lsce_loss', loss, prog_bar=True, on_epoch=True, logger=True, batch_size=1, sync_dist=True)
# if self.current_epoch % 10 == 0:
@@ -298,7 +336,7 @@ class ModelInterface(pl.LightningModule):
# 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}
+ return {'loss': loss, 'Y_prob': Y_prob, 'Y_hat': Y_hat, 'label': label}
def training_epoch_end(self, training_step_outputs):
# logits = torch.cat([x['logits'] for x in training_step_outputs], dim = 0)
@@ -324,50 +362,65 @@ 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, sync_dist=True)
+ self.log('Train/auc', self.AUROC(probs, target.squeeze()), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
def validation_step(self, batch, batch_idx):
- input, label, _ = batch
- label = label.float()
+ input, label, (wsi_name, batch_names, patient) = batch
+ # label = label.float()
logits, Y_prob, Y_hat = self.step(input)
#---->acc log
# Y = int(label[0][1])
- Y = torch.argmax(label)
+ # Y = torch.argmax(label)
+ loss = self.loss(logits, label)
+ # loss = self.loss(logits, label)
+ # print(loss)
+ Y = int(label)
# print(Y_hat)
self.data[Y]["count"] += 1
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}
+ return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : label, 'name': wsi_name, 'patient': patient, 'loss':loss}
def validation_epoch_end(self, val_step_outputs):
+
+ # print(val_step_outputs)
+ # print(torch.cat([x['Y_prob'] for x in val_step_outputs], dim=0))
+ # print(torch.stack([x['Y_prob'] for x in val_step_outputs]))
+
logits = torch.cat([x['logits'] for x in val_step_outputs], dim = 0)
probs = torch.cat([x['Y_prob'] for x in val_step_outputs])
max_probs = torch.stack([x['Y_hat'] for x in val_step_outputs])
- target = torch.stack([x['label'] for x in val_step_outputs])
+ target = torch.stack([x['label'] for x in val_step_outputs], dim=0).int()
+ slide_names = [x['name'] for x in val_step_outputs]
+ patients = [x['patient'] for x in val_step_outputs]
+
+ loss = torch.stack([x['loss'] for x in val_step_outputs])
+ # loss = torch.cat([x['loss'] for x in val_step_outputs])
+ # print(loss.shape)
- self.log_dict(self.valid_metrics(logits, target),
+
+ # self.log('val_loss', cross_entropy_torch(logits.squeeze(), target.squeeze()), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
+ self.log('val_loss', loss, prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
+
+ # print(logits)
+ # print(target)
+ self.log_dict(self.valid_metrics(max_probs.squeeze(), target.squeeze()),
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, sync_dist=True)
+ self.log('val_auc', self.AUROC(probs, target.squeeze()), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
+ # self.log('val_patient_auc', self.AUROC(patient_score, patient_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, sync_dist=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)
-
# print(max_probs.squeeze(0).shape)
@@ -376,6 +429,62 @@ class ModelInterface(pl.LightningModule):
#----> log confusion matrix
self.log_confusion_matrix(max_probs, target, stage='val')
+
+ #----> log per patient metrics
+ complete_patient_dict = {}
+ patient_list = []
+ patient_score = []
+ patient_target = []
+
+ for p, s, pr, t in zip(patients, slide_names, probs, target):
+ if p not in complete_patient_dict.keys():
+ complete_patient_dict[p] = [(s, pr)]
+ patient_target.append(t)
+ else:
+ complete_patient_dict[p].append((s, pr))
+
+
+
+ for p in complete_patient_dict.keys():
+ score = []
+ for (slide, probs) in complete_patient_dict[p]:
+ # max_probs = torch.argmax(probs)
+ # if self.n_classes == 2:
+ # score.append(max_probs)
+ # else: score.append(probs)
+ score.append(probs)
+
+ # if self.n_classes == 2:
+ # score =
+ 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)
+
+ patient_score = torch.stack(patient_score)
+ # print(patient_target)
+ # print(torch.cat(patient_target))
+ # print(self.AUROC(patient_score.squeeze(), torch.cat(patient_target)))
+
+
+ patient_target = torch.cat(patient_target)
+
+ # print(patient_score.shape)
+ # print(patient_target.shape)
+
+ if len(patient_target.unique()) != 1:
+ self.log('val_patient_auc', self.AUROC(patient_score.squeeze(), patient_target.squeeze()), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
+ else:
+ self.log('val_patient_auc', 0.0, prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
+
+ self.log_dict(self.valid_patient_metrics(patient_score, patient_target),
+ on_epoch = True, logger = True, sync_dist=True)
+
+
+
+ # precision, recall, thresholds = self.PRC(probs, target)
+
#---->acc log
@@ -394,178 +503,117 @@ class ModelInterface(pl.LightningModule):
self.count = self.count+1
random.seed(self.count*50)
- def test_step(self, batch, batch_idx):
- torch.set_grad_enabled(True)
- data, label, (wsi_name, batch_names) = batch
- wsi_name = wsi_name[0]
- label = label.float()
- # logits, Y_prob, Y_hat = self.step(data)
- # print(data.shape)
- data = data.squeeze(0).float()
- logits, attn = self(data)
- attn = attn.detach()
- logits = logits.detach()
-
- Y = torch.argmax(label)
- Y_hat = torch.argmax(logits, dim=1)
- Y_prob = F.softmax(logits, dim = 1)
-
- #----> Get GradCam maps, map each instance to attention value, assemble, overlay on original WSI
- if self.model_name == 'TransMIL':
-
- target_layers = [self.model.layer2.norm] # 32x32
- # 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
- else:
- target_layers = [self.model.attention_weights]
- self.cam = GradCAM(model = self.model, target_layers = target_layers, use_cuda=True)
-
-
- data_ft = self.model_ft(data).unsqueeze(0).float()
- instance_count = data.size(0)
- target = [ClassifierOutputTarget(Y)]
- grayscale_cam = self.cam(input_tensor=data_ft, targets=target)
- grayscale_cam = torch.Tensor(grayscale_cam)[:instance_count, :]
-
- # attention_map = grayscale_cam[:, :, 1].squeeze()
- # attention_map = F.relu(attention_map)
- # mask = torch.zeros((instance_count, 3, 256, 256)).to(self.device)
- # for i, v in enumerate(attention_map):
- # mask[i, :, :, :] = v
-
- # mask = self.assemble(mask, batch_names)
- # mask = (mask - mask.min())/(mask.max()-mask.min())
- # mask = mask.cpu().numpy()
- # wsi = self.assemble(data, batch_names)
- # wsi = wsi.cpu().numpy()
-
- # def show_cam_on_image(img, mask):
- # heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)
- # heatmap = np.float32(heatmap) / 255
- # cam = heatmap*0.4 + np.float32(img)
- # cam = cam / np.max(cam)
- # return cam
-
- # wsi = show_cam_on_image(wsi, mask)
- # wsi = ((wsi-wsi.min())/(wsi.max()-wsi.min()) * 255.0).astype(np.uint8)
-
- # img = Image.fromarray(wsi)
- # img = img.convert('RGB')
-
-
- # output_path = self.save_path / str(Y.item())
- # output_path.mkdir(parents=True, exist_ok=True)
- # img.save(f'{output_path}/{wsi_name}.jpg')
+ def test_step(self, batch, batch_idx):
- #----> Get Topk Tiles and Topk Patients
- summed = torch.mean(grayscale_cam, dim=2)
- topk_tiles, topk_indices = torch.topk(summed.squeeze(0), 5, dim=0)
- topk_data = data[topk_indices].detach()
+ input, label, (wsi_name, batch_names, patient) = batch
+ label = label.float()
- # target_ft =
- # grayscale_cam_ft = self.cam_ft(input_tensor=data, )
- # for i in range(data.shape[0]):
-
- # vis_img = data[i, :, :, :].cpu().numpy()
- # vis_img = np.transpose(vis_img, (1,2,0))
- # print(vis_img.shape)
- # cam_img = grayscale_cam.squeeze(0)
- # cam_img = self.reshape_transform(grayscale_cam)
-
- # print(cam_img.shape)
-
- # visualization = show_cam_on_image(vis_img, cam_img, use_rgb=True)
- # visualization = ((visualization/visualization.max())*255.0).astype(np.uint8)
- # print(visualization)
- # cv2.imwrite(f'{test_path}/{Y}/{name}/gradcam.jpg', cam_img)
+ logits, Y_prob, Y_hat = self.step(input)
#---->acc log
- Y = torch.argmax(label)
+ Y = int(label)
+ # 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, 'name': wsi_name, 'topk_data': topk_data} #
- # return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : label, 'name': name} #, 'topk_data': topk_data
+ return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : label, 'name': wsi_name, 'patient': patient}
def test_epoch_end(self, output_results):
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]
- topk_tiles = [x['topk_data'] for x in output_results]
- #---->
- auc = self.AUROC(probs, target)
- fpr, tpr, thresholds = self.ROC(probs, target)
- fpr = fpr.cpu().numpy()
- tpr = tpr.cpu().numpy()
+ target = torch.stack([x['label'] for x in output_results]).int()
+ slide_names = [x['name'] for x in output_results]
+ patients = [x['patient'] for x in output_results]
+
+ self.log_dict(self.test_metrics(max_probs.squeeze(), target.squeeze()),
+ on_epoch = True, logger = True, sync_dist=True)
+ self.log('test_loss', cross_entropy_torch(logits.squeeze(), target.squeeze()), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
- plt.figure(1)
- plt.plot(fpr, tpr)
- plt.xlabel('False positive rate')
- plt.ylabel('True positive rate')
- plt.title('ROC curve')
- plt.savefig(f'{self.save_path}/roc.jpg')
- # self.loggers[0].experiment.add_figure(f'{stage}/Confusion matrix', fig_, self.current_epoch)
+ if len(target.unique()) != 1:
+ self.log('test_auc', self.AUROC(probs, target.squeeze()), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
+ # self.log('val_patient_auc', self.AUROC(patient_score, patient_target), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
+ else:
+ self.log('test_auc', 0.0, prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
- metrics = self.test_metrics(logits , target)
- # metrics = self.test_metrics(max_probs.squeeze() , torch.argmax(target.squeeze(), dim=1))
- metrics['test_auc'] = auc
+ #----> log confusion matrix
+ self.log_confusion_matrix(max_probs, target, stage='test')
- # self.log('auc', auc, prog_bar=True, on_epoch=True, logger=True)
+ #----> log per patient metrics
+ complete_patient_dict = {}
+ patient_list = []
+ patient_score = []
+ patient_target = []
+ patient_class_score = 0
+
+ for p, s, pr, t in zip(patients, slide_names, probs, target):
+ if p not in complete_patient_dict.keys():
+ complete_patient_dict[p] = [(s, pr)]
+ patient_target.append(t)
+ else:
+ complete_patient_dict[p].append((s, pr))
- #---->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)
- for n in range(self.n_classes):
- print('class: ', n)
-
- 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 complete_patient_dict.keys():
+ score = []
+ for (slide, probs) in complete_patient_dict[p]:
+ # if self.n_classes == 2:
+ # if probs.argmax().item() == 1: # only if binary and if class 1 is more important!!! Normal vs Diseased or Rejection vs Other
+ # score.append(probs)
- img.save(f'{outpath}/{i}.jpg')
+ # else:
+ score.append(probs)
+ # print(score)
+ score = torch.stack(score)
+ # print(score)
+ if self.n_classes == 2:
+ positive_positions = (score.argmax(dim=1) == 1).nonzero().squeeze()
+ if positive_positions.numel() != 0:
+ score = score[positive_positions]
+ else:
+ # score = torch.stack(torch.score)
+ ## get scores that predict class 1:
+ # positive_scores = score.argmax(dim=1)
+ # score = torch.sum(score.argmax(dim=1))
+
+ # if score.item() == 1:
+ # patient_class_score = 1
+ score = torch.mean(score) #.cpu().detach().numpy()
+ # complete_patient_dict[p]['score'] = score
+ # print(p, score)
+ # patient_list.append(p)
+ patient_score.append(score)
+
+ print(patient_score)
+
+ patient_score = torch.stack(patient_score)
+ # patient_target = torch.stack(patient_target)
+ patient_target = torch.cat(patient_target)
-
-
- #----->visualize top predictive tiles
+ if len(patient_target.unique()) != 1:
+ self.log('test_patient_auc', self.AUROC(patient_score.squeeze(), patient_target.squeeze()), prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
+ else:
+ self.log('test_patient_auc', 0.0, prog_bar=True, on_epoch=True, logger=True, sync_dist=True)
-
+ self.log_dict(self.test_patient_metrics(patient_score, patient_target),
+ on_epoch = True, logger = True, sync_dist=True)
- # img = img.squeeze(0).cpu().numpy()
- # img = np.transpose(img, (1,2,0))
- # # print(img)
- # # print(grayscale_cam.shape)
- # visualization = show_cam_on_image(img, grayscale_cam, use_rgb=True)
-
+
- for keys, values in metrics.items():
- print(f'{keys} = {values}')
- metrics[keys] = values.cpu().numpy()
- #---->acc log
+ # precision, recall, thresholds = self.PRC(probs, target)
+
+ #---->acc log
for c in range(self.n_classes):
count = self.data[c]["count"]
correct = self.data[c]["correct"]
@@ -573,37 +621,25 @@ class ModelInterface(pl.LightningModule):
acc = None
else:
acc = float(correct) / count
- print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+ print('test class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
-
- #---->plot auroc curve
- # stats = stat_scores(probs, target, reduce='macro', num_classes=self.n_classes)
- # fpr = {}
- # tpr = {}
- # for n in self.n_classes:
-
- # fpr, tpr, thresh = roc_curve(target.cpu().numpy(), probs.cpu().numpy())
- #[tp, fp, tn, fn, tp+fn]
-
-
- self.log_confusion_matrix(max_probs, target, stage='test')
- #---->
- result = pd.DataFrame([metrics])
- result.to_csv(Path(self.save_path) / f'test_result.csv', mode='a', header=not Path(self.save_path).exists())
-
- # with open(f'{self.save_path}/test_metrics.txt', 'a') as f:
-
- # f.write([metrics])
+
+ #---->random, if shuffle data, change seed
+ if self.shuffle == True:
+ self.count = self.count+1
+ random.seed(self.count*50)
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 = PESG(self.model, loss_fn=self.aucm_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
+ scheduler = {'scheduler': ReduceLROnPlateau(optimizer, mode='min', factor=0.5), 'monitor': 'val_loss', 'frequency': 5}
+
+ return [optimizer], [scheduler]
- def optimizer_zero_grad(self, epoch, batch_idx, optimizer, optimizer_idx):
- optimizer.zero_grad(set_to_none=True)
+ # 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)
@@ -618,10 +654,12 @@ 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`.
+ if name == 'ViT':
+ self.model = ViT
+
if '_' in name:
camel_name = ''.join([i.capitalize() for i in name.split('_')])
else:
@@ -686,7 +724,7 @@ class ModelInterface(pl.LightningModule):
if stage == 'train':
self.loggers[0].experiment.add_figure(f'{stage}/Confusion matrix', fig_, self.current_epoch)
else:
- fig_.savefig(f'{self.loggers[0].log_dir}/cm_test.png', dpi=400)
+ fig_.savefig(f'{self.loggers[0].log_dir}/cm_{stage}.png', dpi=400)
fig_.clf()
diff --git a/code/monai_test.json b/code/monai_test.json
new file mode 100644
index 0000000..093623d
--- /dev/null
+++ b/code/monai_test.json
@@ -0,0 +1 @@
+{"training": [{"image": "Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs", "label": 0}, {"image": "Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs", "label": 0}, {"image": "Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs", "label": 0}], "validation": [{"image": "Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs", "label": 0}]}
\ No newline at end of file
diff --git a/code/test_visualize.py b/code/test_visualize.py
index 5e4a5e7..79f814c 100644
--- a/code/test_visualize.py
+++ b/code/test_visualize.py
@@ -38,7 +38,7 @@ def make_parse():
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('--gpus', default = 0, type=int)
parser.add_argument('--loss', default = 'CrossEntropyLoss', type=str)
parser.add_argument('--fold', default = 0)
parser.add_argument('--bag_size', default = 10000, type=int)
@@ -54,6 +54,7 @@ class custom_test_module(ModelInterface):
def test_step(self, batch, batch_idx):
torch.set_grad_enabled(True)
+
input_data, label, (wsi_name, batch_names, patient) = batch
patient = patient[0]
wsi_name = wsi_name[0]
@@ -61,14 +62,18 @@ class custom_test_module(ModelInterface):
# logits, Y_prob, Y_hat = self.step(data)
# print(data.shape)
input_data = input_data.squeeze(0).float()
- logits, attn = self(input_data)
- attn = attn.detach()
- logits = logits.detach()
+ # print(self.model_ft)
+ # print(self.model)
+ logits, _ = self(input_data)
+ # attn = attn.detach()
+ # logits = logits.detach()
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)
@@ -87,9 +92,16 @@ class custom_test_module(ModelInterface):
target_layers = [self.model.attention_weights]
self.cam = GradCAM(model = self.model, target_layers = target_layers, use_cuda=True)
- data_ft = self.model_ft(input_data).unsqueeze(0).float()
+ if self.model_ft:
+ data_ft = self.model_ft(input_data).unsqueeze(0).float()
+ else:
+ data_ft = input_data.unsqueeze(0).float()
instance_count = input_data.size(0)
+ # data_ft.requires_grad=True
+
target = [ClassifierOutputTarget(Y)]
+ # print(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)
@@ -100,6 +112,7 @@ class custom_test_module(ModelInterface):
summed = torch.mean(grayscale_cam, dim=2)
topk_tiles, topk_indices = torch.topk(summed.squeeze(0), k, dim=0)
topk_data = input_data[topk_indices].detach()
+ # print(topk_tiles)
#----------------------------------------------------
# Log Correct/Count
@@ -115,7 +128,7 @@ class custom_test_module(ModelInterface):
# print(input_data.shape)
# print(len(batch_names))
# if visualize:
- # self.save_attention_map(wsi_name, input_data, batch_names, grayscale_cam, target=Y)
+ # self.save_attention_map(wsi_name, 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, 'patient': patient, 'topk_data': topk_data} #
@@ -128,7 +141,6 @@ class custom_test_module(ModelInterface):
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])
@@ -158,7 +170,6 @@ class custom_test_module(ModelInterface):
'''
Patient
-> slides:
-
-> SlideName:
->probs = [0.5, 0.5]
->topk = [10,3,224,224]
@@ -180,11 +191,11 @@ class custom_test_module(ModelInterface):
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)
+ # print(p, score)
patient_list.append(p)
patient_score.append(score)
- print(patient_list)
+ # print(patient_list)
#topk patients:
@@ -212,37 +223,34 @@ class custom_test_module(ModelInterface):
output_dict[class_name] = {}
# class_name = str(n)
print('class: ', class_name)
- print(score)
+ # print(score)
_, topk_indices = torch.topk(score, k_patient, dim=0) # change to 3
- print(topk_indices)
+ # print(topk_indices)
topk_patients = [patient_list[i] for i in topk_indices]
patient_top_slides = {}
for p in topk_patients:
- print(p)
+ # 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)
+ # 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])
+ # 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]]
-
-
-
for p in topk_patients:
score = complete_patient_dict[p]['score']
- print(p, score)
+ # print(p, score)
print('Topk Slides:')
for slide in patient_top_slides[p]:
print(slide)
@@ -250,21 +258,18 @@ class custom_test_module(ModelInterface):
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)
+ # 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')
+ # 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}')
metrics[keys] = values.cpu().numpy()
@@ -286,20 +291,35 @@ class custom_test_module(ModelInterface):
result = pd.DataFrame([metrics])
result.to_csv(Path(self.save_path) / f'test_result.csv', mode='a', header=not Path(self.save_path).exists())
- def save_attention_map(self, wsi_name, data, batch_names, grayscale_cam, target):
+ def save_attention_map(self, wsi_name, batch_names, grayscale_cam, target):
- def get_coords(batch_names): #ToDO: Change function for precise coords
- coords = []
+ # def get_coords(batch_names): #ToDO: Change function for precise coords
+ # coords = []
- for tile_name in batch_names:
- pos = re.findall(r'\((.*?)\)', tile_name[0])
- x, y = pos[-1].split('_')
- coords.append((int(x),int(y)))
- return coords
-
- coords = get_coords(batch_names)
+ # for tile_name in batch_names:
+ # pos = re.findall(r'\((.*?)\)', tile_name[0])
+ # x, y = pos[-1].split('_')
+ # coords.append((int(x),int(y)))
+ # return coords
+
+ home = Path.cwd().parts[1]
+ jpg_dir = f'/{home}/ylan/data/DeepGraft/224_128um_annotated/Aachen_Biopsy_Slides/BLOCKS'
+
+ coords = batch_names.squeeze()
+ data = []
+ for co in coords:
+
+ tile_path = Path(jpg_dir) / wsi_name / f'{wsi_name}_({co[0]}_{co[1]}).jpg'
+ img = np.asarray(Image.open(tile_path)).astype(np.uint8)
+ img = torch.from_numpy(img)
+ # print(img.shape)
+ data.append(img)
# coords_set = set(coords)
-
+ # data = data.unsqueeze(0)
+ # print(data.shape)
+ data = torch.stack(data)
+ # print(data.max())
+ # print(data.min())
# print(coords)
# temp_data = data.cpu()
# print(data.shape)
@@ -307,7 +327,7 @@ class custom_test_module(ModelInterface):
# wsi = (wsi-wsi.min())/(wsi.max()-wsi.min())
# wsi = wsi
# print(coords)
- print('wsi.shape: ', wsi.shape)
+ # print('wsi.shape: ', wsi.shape)
#--> Get interpolated mask from GradCam
W, H = wsi.shape[0], wsi.shape[1]
@@ -318,8 +338,8 @@ class custom_test_module(ModelInterface):
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)
+ # 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)
@@ -343,12 +363,12 @@ class custom_test_module(ModelInterface):
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')
+ # 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')
wsi = ((wsi-wsi.min())/(wsi.max()-wsi.min()) * 255.0).astype(np.uint8)
img = Image.fromarray(wsi)
@@ -365,11 +385,11 @@ class custom_test_module(ModelInterface):
def assemble(self, tiles, coords): # with coordinates (x-y)
- def getPosition(img_name):
- pos = re.findall(r'\((.*?)\)', img_name) #get strings in brackets (0-0)
- a = int(pos[0].split('-')[0])
- b = int(pos[0].split('-')[1])
- return a, b
+ # def getPosition(img_name):
+ # pos = re.findall(r'\((.*?)\)', img_name) #get strings in brackets (0-0)
+ # a = int(pos[0].split('-')[0])
+ # b = int(pos[0].split('-')[1])
+ # return a, b
position_dict = {}
assembled = []
@@ -384,19 +404,23 @@ class custom_test_module(ModelInterface):
for i, (x,y) in enumerate(coords):
if x not in position_dict.keys():
- position_dict[x] = [(y, i)]
- else: position_dict[x].append((y, i))
+ position_dict[x.item()] = [(y.item(), i)]
+ else: position_dict[x.item()].append((y.item(), i))
# 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():
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, :, :, :]
+ # if tiles[sample_idx, :, :, :].shape != [3,224,224]:
+ # img = tiles[sample_idx, :, :, :].permute(2,0,1)
+ # else:
+ img = tiles[sample_idx, :, :, :]
+ # print(img.shape)
+ # print(img.max())
+ # print(img.min())
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
@@ -453,6 +477,9 @@ def main(cfg):
# 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/'
+ 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,
@@ -461,6 +488,7 @@ def main(cfg):
'n_classes': cfg.Model.n_classes,
'backbone': cfg.Model.backbone,
'bag_size': cfg.Data.bag_size,
+ 'use_features': use_features,
}
dm = MILDataModule(**DataInterface_dict)
@@ -489,7 +517,8 @@ def main(cfg):
# callbacks=cfg.callbacks,
max_epochs= cfg.General.epochs,
min_epochs = 200,
- gpus=cfg.General.gpus,
+ accelerator='gpu',
+ devices=cfg.General.gpus,
# gpus = [0,2],
# strategy='ddp',
amp_backend='native',
@@ -508,7 +537,7 @@ def main(cfg):
# log_path = Path('lightning_logs/2/checkpoints')
model_paths = list(log_path.glob('*.ckpt'))
-
+ # print(model_paths)
if cfg.epoch == 'last':
model_paths = [str(model_path) for model_path in model_paths if f'last' in str(model_path)]
else:
@@ -583,5 +612,121 @@ if __name__ == '__main__':
#---->main
- main(cfg)
+ # main(cfg)
+ from models import TransMIL
+ from datasets.zarr_feature_dataloader_simple import ZarrFeatureBagLoader
+ from datasets.feature_dataloader import FeatureBagLoader
+ from torch.utils.data import random_split, DataLoader
+ import time
+ from tqdm import tqdm
+ import torchmetrics
+
+ device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+ print(device)
+ scaler = torch.cuda.amp.GradScaler()
+
+ log_path = Path(cfg.log_path) / 'checkpoints'
+ model_paths = list(log_path.glob('*.ckpt'))
+
+ # print(model_paths)
+ if cfg.epoch == 'last':
+ model_paths = [str(model_path) for model_path in model_paths if f'last' in str(model_path)]
+ else:
+ model_paths = [str(model_path) for model_path in model_paths if f'epoch={cfg.epoch}' in str(model_path)]
+
+ # checkpoint = torch.load(f'{cfg.log_path}/checkpoints/epoch=04-val_loss=0.4243-val_auc=0.8243-val_patient_auc=0.8282244801521301.ckpt')
+ # checkpoint = torch.load(f'{cfg.log_path}/checkpoints/epoch=73-val_loss=0.8574-val_auc=0.9682-val_patient_auc=0.9724310636520386.ckpt')
+ checkpoint = torch.load(model_paths[0])
+
+ hyper_parameters = checkpoint['hyper_parameters']
+ n_classes = hyper_parameters['model']['n_classes']
+
+ # model = TransMIL()
+ model = TransMIL(n_classes).to(device)
+ model_weights = checkpoint['state_dict']
+
+ for key in list(model_weights):
+ model_weights[key.replace('model.', '')] = model_weights.pop(key)
+
+ model.load_state_dict(model_weights)
+
+ count = 0
+ # for m in model.modules():
+ # if isinstance(m, torch.nn.BatchNorm2d):
+ # # # m.track_running_stats = False
+ # # count += 1 #skip the first BatchNorm layer in my ResNet50 based encoder
+ # # if count >= 2:
+ # # m.eval()
+ # print(m)
+ # m.track_running_stats = False
+ # m.running_mean = None
+ # m.running_var = None
+
+ for param in model.parameters():
+ param.requires_grad = False
+ model.eval()
+
+ home = Path.cwd().parts[1]
+ data_root = f'/{home}/ylan/data/DeepGraft/224_128uM_annotated'
+ label_path = f'/{home}/ylan/DeepGraft/training_tables/dg_split_PAS_HE_Jones_norm_rest.json'
+ dataset = FeatureBagLoader(data_root, label_path=label_path, mode='test', cache=False, n_classes=n_classes)
+
+ dl = DataLoader(dataset, batch_size=1, num_workers=8)
+
+
+
+ AUROC = torchmetrics.AUROC(num_classes = n_classes)
+
+ start = time.time()
+ test_logits = []
+ test_probs = []
+ test_labels = []
+ data = [{"count": 0, "correct": 0} for i in range(n_classes)]
+
+ for item in tqdm(dl):
+
+ bag, label, (name, batch_coords, patient) = item
+ # label = label.float()
+ Y = int(label)
+
+ bag = bag.float().to(device)
+ # print(bag.shape)
+ bag = bag.unsqueeze(0)
+ with torch.cuda.amp.autocast():
+ logits = model(bag)
+ Y_hat = torch.argmax(logits, dim=1)
+ Y_prob = F.softmax(logits, dim = 1)
+
+ # print(Y_prob)
+
+ test_logits.append(logits)
+ test_probs.append(Y_prob)
+
+ test_labels.append(label)
+ data[Y]['count'] += 1
+ data[Y]['correct'] += (int(Y_hat) == Y)
+ probs = torch.cat(test_probs).detach().cpu()
+ targets = torch.stack(test_labels).squeeze().detach().cpu()
+ print(probs.shape)
+ print(targets.shape)
+
+
+ for c in range(n_classes):
+ count = data[c]['count']
+ correct = data[c]['correct']
+ if count == 0:
+ acc = None
+ else:
+ acc = float(correct) / count
+ print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+
+
+
+ auroc = AUROC(probs, targets)
+ print(auroc)
+ end = time.time()
+ print('Bag Time: ', end-start)
+
+
+
\ No newline at end of file
diff --git a/code/train.py b/code/train.py
index e01bc52..53ab165 100644
--- a/code/train.py
+++ b/code/train.py
@@ -5,7 +5,8 @@ import glob
from sklearn.model_selection import KFold
-from datasets.data_interface import DataInterface, MILDataModule, CrossVal_MILDataModule
+from datasets.data_interface import MILDataModule, CrossVal_MILDataModule
+# from datasets.data_interface import DataInterface, MILDataModule, CrossVal_MILDataModule
from models.model_interface import ModelInterface
from models.model_interface_dtfd import ModelInterface_DTFD
import models.vision_transformer as vits
@@ -63,7 +64,9 @@ 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', nargs='+', default = [2], type=int)
+ parser.add_argument('--epoch', default='0',type=str)
+
+ parser.add_argument('--gpus', nargs='+', default = [0], 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)
@@ -78,7 +81,7 @@ def make_parse():
#---->main
def main(cfg):
- torch.set_num_threads(16)
+ torch.set_num_threads(8)
#---->Initialize seed
pl.seed_everything(cfg.General.seed)
@@ -111,6 +114,8 @@ def main(cfg):
'n_classes': cfg.Model.n_classes,
'bag_size': cfg.Data.bag_size,
'use_features': use_features,
+ 'mixup': cfg.Data.mixup,
+ 'aug': cfg.Data.aug,
}
if cfg.Data.cross_val:
@@ -142,7 +147,7 @@ def main(cfg):
logger=cfg.load_loggers,
callbacks=cfg.callbacks,
max_epochs= cfg.General.epochs,
- min_epochs = 100,
+ min_epochs = 500,
accelerator='gpu',
# plugins=plugins,
devices=cfg.General.gpus,
@@ -156,7 +161,7 @@ def main(cfg):
# limit_train_batches=1,
# deterministic=True,
- check_val_every_n_epoch=5,
+ check_val_every_n_epoch=1,
)
else:
trainer = Trainer(
@@ -167,7 +172,7 @@ def main(cfg):
min_epochs = 100,
# gpus=cfg.General.gpus,
- accelerator='gpu'
+ accelerator='gpu',
devices=cfg.General.gpus,
amp_backend='native',
# amp_level=cfg.General.amp_level,
@@ -178,7 +183,7 @@ def main(cfg):
# limit_train_batches=1,
# deterministic=True,
- check_val_every_n_epoch=5,
+ check_val_every_n_epoch=1,
)
# print(cfg.log_path)
# print(trainer.loggers[0].log_dir)
@@ -215,18 +220,29 @@ def main(cfg):
else:
trainer.fit(model = model, datamodule = dm)
else:
- log_path = Path(cfg.log_path) / 'lightning_logs' / f'version_{cfg.version}'
+ log_path = Path(cfg.log_path) / 'lightning_logs' / f'version_{cfg.version}'/'checkpoints'
+ print(log_path)
test_path = Path(log_path) / 'test'
- for n in range(cfg.Model.n_classes):
- n_output_path = test_path / str(n)
- n_output_path.mkdir(parents=True, exist_ok=True)
+ # for n in range(cfg.Model.n_classes):
+ # n_output_path = test_path / str(n)
+ # n_output_path.mkdir(parents=True, exist_ok=True)
# print(cfg.log_path)
model_paths = list(log_path.glob('*.ckpt'))
- model_paths = [str(model_path) for model_path in model_paths if 'epoch' in str(model_path)]
+ # print(model_paths)
+ # print(cfg.epoch)
+ # model_paths = [str(model_path) for model_path in model_paths if 'epoch' in str(model_path)]
+ if cfg.epoch == 'last':
+ model_paths = [str(model_path) for model_path in model_paths if f'last' in str(model_path)]
+ elif int(cfg.epoch) < 10:
+ cfg.epoch = f'0{cfg.epoch}'
+
+ else:
+ model_paths = [str(model_path) for model_path in model_paths if f'epoch={cfg.epoch}' in str(model_path)]
# model_paths = [f'{log_path}/epoch=279-val_loss=0.4009.ckpt']
+
for path in model_paths:
- print(path)
+ # print(path)
new_model = model.load_from_checkpoint(checkpoint_path=path, cfg=cfg)
trainer.test(model=new_model, datamodule=dm)
@@ -257,6 +273,7 @@ def check_home(cfg):
if __name__ == '__main__':
args = make_parse()
+
cfg = read_yaml(args.config)
#---->update
@@ -283,7 +300,10 @@ if __name__ == '__main__':
cfg.task = task
# task = Path(cfg.config).name[:-5].split('_')[2:][0]
cfg.log_path = log_path / f'{cfg.Model.name}' / task / log_name
-
+
+
+
+ cfg.epoch = args.epoch
# ---->main
diff --git a/code/utils/__pycache__/utils.cpython-39.pyc b/code/utils/__pycache__/utils.cpython-39.pyc
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diff --git a/code/utils/utils.py b/code/utils/utils.py
index 5596208..9a756d5 100755
--- a/code/utils/utils.py
+++ b/code/utils/utils.py
@@ -14,9 +14,11 @@ from pytorch_lightning import LightningModule
from pytorch_lightning.loops.base import Loop
from pytorch_lightning.loops.fit_loop import FitLoop
from pytorch_lightning.trainer.states import TrainerFn
+from pytorch_lightning.callbacks import LearningRateMonitor
from typing import Any, Dict, List, Optional, Type
import shutil
+
#---->read yaml
import yaml
from addict import Dict
@@ -103,7 +105,7 @@ def load_callbacks(cfg, save_path):
# save_path = Path(cfg.log_path) / 'lightning_logs' / f'version_{cfg.resume_version}' / last.ckpt
Mycallbacks.append(ModelCheckpoint(monitor = 'val_loss',
dirpath = str(output_path),
- filename = '{epoch:02d}-{val_loss:.4f}-{val_auc: .4f}',
+ filename = '{epoch:02d}-{val_loss:.4f}-{val_auc: .4f}-{val_patient_auc}',
verbose = True,
save_last = True,
save_top_k = 2,
@@ -111,7 +113,15 @@ def load_callbacks(cfg, save_path):
save_weights_only = True))
Mycallbacks.append(ModelCheckpoint(monitor = 'val_auc',
dirpath = str(output_path),
- filename = '{epoch:02d}-{val_loss:.4f}-{val_auc:.4f}',
+ filename = '{epoch:02d}-{val_loss:.4f}-{val_auc:.4f}-{val_patient_auc}',
+ verbose = True,
+ save_last = True,
+ save_top_k = 2,
+ mode = 'max',
+ save_weights_only = True))
+ Mycallbacks.append(ModelCheckpoint(monitor = 'val_patient_auc',
+ dirpath = str(output_path),
+ filename = '{epoch:02d}-{val_loss:.4f}-{val_auc:.4f}-{val_patient_auc}',
verbose = True,
save_last = True,
save_top_k = 2,
@@ -121,6 +131,9 @@ def load_callbacks(cfg, save_path):
swa = StochasticWeightAveraging(swa_lrs=1e-2)
Mycallbacks.append(swa)
+ lr_monitor = LearningRateMonitor(logging_interval='step')
+ Mycallbacks.append(lr_monitor)
+
return Mycallbacks
#---->val loss
@@ -128,7 +141,8 @@ import torch
import torch.nn.functional as F
def cross_entropy_torch(x, y):
x_softmax = [F.softmax(x[i], dim=0) for i in range(len(x))]
- x_log = torch.tensor([torch.log(x_softmax[i][y[i]]) for i in range(y.shape[0])])
+ x_log = torch.tensor([torch.log(x_softmax[i][y[i]]) for i in range(len(y))])
+ # x_log = torch.tensor([torch.log(x_softmax[i][y[i]]) for i in range(y.shape[0])])
loss = - torch.sum(x_log) / y.shape[0]
return loss
diff --git a/monai_test.json b/monai_test.json
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--- /dev/null
+++ b/monai_test.json
@@ -0,0 +1 @@
+{"training": [{"image": "Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs", "label": 0}, {"image": "Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs", "label": 0}, {"image": "Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs", "label": 0}], "validation": [{"image": "Aachen_KiBiDatabase_KiBiAcRCIQ360_01_018_PAS.svs", "label": 0}]}
\ No newline at end of file
diff --git a/paper_structure.md b/paper_structure.md
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+++ b/paper_structure.md
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+# Paper Outline
+
+## Abstract
+
+## Introduction
+
+## Methods
+
+ - Fig 1: Model/Workflow
+
+## Dataset
+
+ - Fig: cohorts, data selection
+ - Fig: Preprocessing
+
+## Results
+
+ - Fig: Metrics on Testset for each task:
+
+| Model | Accuracy | Precision | Recall | AUROC |
+| ------------ | -------- | --------- | ------ | ----- |
+| Resnet18 | | | | |
+| ViT | | | | |
+| CLAM | | | | |
+| AttentionMIL | | | | |
+| TransMIL | | | | |
+| | | | | |
+
+ - Fig: AUROC Curves (Best Model, Rest in Appendix)
+
+ - Fig: Attention Maps (Best Model, Rest in Appendix)
+
+## Discussion
+
+## Appendix
diff --git a/project_plan.md b/project_plan.md
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+++ b/project_plan.md
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+# Benchmarking weakly supervised deep learning models for transplant pathology classification
+
+With this project, we aim to esatablish a benchmark for weakly supervised deep learning models for transplant pathology classification, especially for multiple instance learning approaches.
+
+
+## Cohorts:
+
+#### Original Lancet Set:
+
+ * Training:
+ * AMS: 1130 Biopsies (3390 WSI)
+ * Utrecht: 717 Biopsies (2151WSI)
+ * Testing:
+ * Aachen: 101 Biopsies (303 WSI)
+
+
+#### Extended:
+
+* Training:
+ * AMS + Utrecht + Leuven
+* Testing:
+ * Aachen_extended:
+
+## Models:
+
+ For our Benchmark, we chose the following models:
+
+ - AttentionMIL
+ - Resnet18/50
+ - ViT
+ - CLAM
+ - TransMIL
+ - Monai MIL (optional)
+
+ Resnet18 and Resnet50 are basic CNNs that can be applied for a variety of tasks. Although domain or task specific architectures mostly outperform them, they remain a good baseline for comparison.
+
+ The vision transformer is the first transformer based model that was adapted to computer vision tasks. Benchmarking on ViT can provide more insight on the performance of generic transformer based models on multiple instance learning.
+
+ The AttentionMIL was the first simple, yet relatively successful deep MIL model and should be used as a baseline for benchmarking MIL methods.
+
+ CLAM is a recent model proposed by Mahmood lab which was explicitely trained for histopathological whole slide images and should be used as a baseline for benchmarking MIL methods in histopathology.
+
+ TransMIL is another model proposed by Shao et al, which achieved SOTA on histopathological WSI classification tasks using MIL. It was benchmarked on TCGA and compared to CLAM and AttMIL. It utilizes the self-attention module from transformer models.
+
+ Monai MIL (not official name) is a MIL architecture proposed by Myronenk et al (Nvidia). It applies the self-attention mechanism as well. It is included because it shows promising results and it's included in MONAI.
+
+## Tasks:
+
+ The Original tasks mimic the ones published in the original DeepGraft Lancet paper.
+ Before we go for more challenging tasks (future tasks), we want to establish that our models outperform the simpler approach from the previous paper and that going for MIL in this setting is indeed profitable.
+
+ All available classes:
+ * Normal
+ * TCMR
+ * ABMR
+ * Mixed
+ * Viral
+ * Other
+
+#### Original:
+
+ The explicit classes are simplified/grouped together such as this:
+ Diseased = all classes other than Normal
+ Rejection = TCMR, ABMR, Mixed
+
+ - (1) Normal vs Diseased (all other classes)
+ - (2) Rejection vs (Viral + Others)
+ - (3) Normal vs Rejection vs (Viral + Others)
+
+#### Future:
+
+ After validating Original tasks, the next step is to challenge the models by attempting more complicated tasks.
+ These experiments may vary depending on the results from previous experiments
+
+ - (4) Normal vs TCMR vs Mixed vs ABMR vs Viral vs Others
+ - (5) TCMR vs Mixed vs ABMR
+
+## Plan:
+
+ 1. Train models for current tasks on AMS+Utrecht -> Validate on Aachen
+
+ 2. Visualization, AUC Curves
+
+ 3. Train best model on extended training set (AMS+Utrecht+Leuven) (Tasks 1,2,3) -> Validate on Aachen_extended
+ - Investigate if a larger training cohort increases performance
+ 4. Train best model on extended dataset on future tasks (Task 4, 5)
+
+
+ Notes:
+ * Resnet18, ViT and CLAM are all trained on HIA (Training Framework from Kather / Narmin)
+
+
+## Status:
+
+ - Resnet18: Trained on all tasks via HIA
+ - Vit: Trained on all tasks via HIA
+ - CLAM: Trained on (1) via HIA
+ - TransMIL: Trained, but overfitting
+ - Check if the problems are not on model side by evaluating on RCC data.
+ - (mixing in 10 slides from Aachen increases auc performance from 0.7 to 0.89)
+ - AttentionMIL: WIP
+ - Monai MIL: WIP
--
GitLab