From 9ca1a12e00edafc5deb2318defbfb174f9119706 Mon Sep 17 00:00:00 2001
From: Ycblue <yuchialan@gmail.com>
Date: Thu, 14 Jul 2022 14:30:21 +0200
Subject: [PATCH] changed folder hierarchy
---
DeepGraft/AttMIL_simple_no_other.yaml | 2 +-
DeepGraft/AttMIL_simple_no_viral.yaml | 6 +-
DeepGraft/AttMIL_simple_tcmr_viral.yaml | 7 +-
DeepGraft/DTFDMIL_resnet50_tcmr_viral.yaml | 51 ++
DeepGraft/TransMIL_efficientnet_no_viral.yaml | 5 +-
.../TransMIL_efficientnet_tcmr_viral.yaml | 13 +-
DeepGraft/TransMIL_resnet18_tcmr_viral.yaml | 17 +-
DeepGraft/TransMIL_resnet50_tcmr_viral.yaml | 15 +-
.../TransformerMIL_resnet50_tcmr_viral.yaml | 52 ++
.../__pycache__/loss_factory.cpython-39.pyc | Bin 2545 -> 0 bytes
README.md | 23 +
{MyBackbone => code/MyBackbone}/__init__.py | 0
.../MyBackbone}/backbone_factory.py | 1 -
{MyLoss => code/MyLoss}/ND_Crossentropy.py | 0
{MyLoss => code/MyLoss}/__init__.py | 0
.../ND_Crossentropy.cpython-39.pyc | Bin
.../__pycache__/__init__.cpython-39.pyc | Bin
.../__pycache__/boundary_loss.cpython-39.pyc | Bin
.../__pycache__/dice_loss.cpython-39.pyc | Bin
.../__pycache__/focal_loss.cpython-39.pyc | Bin
.../__pycache__/hausdorff.cpython-39.pyc | Bin
.../__pycache__/loss_factory.cpython-39.pyc | Bin 0 -> 2623 bytes
.../__pycache__/lovasz_loss.cpython-39.pyc | Bin
.../__pycache__/poly_loss.cpython-39.pyc | Bin
{MyLoss => code/MyLoss}/boundary_loss.py | 0
{MyLoss => code/MyLoss}/dice_loss.py | 0
{MyLoss => code/MyLoss}/focal_loss.py | 0
{MyLoss => code/MyLoss}/hausdorff.py | 0
{MyLoss => code/MyLoss}/loss_factory.py | 6 +-
{MyLoss => code/MyLoss}/lovasz_loss.py | 0
{MyLoss => code/MyLoss}/poly_loss.py | 0
{MyOptimizer => code/MyOptimizer}/__init__.py | 0
.../__pycache__/__init__.cpython-39.pyc | Bin
.../__pycache__/adafactor.cpython-39.pyc | Bin
.../__pycache__/adahessian.cpython-39.pyc | Bin
.../__pycache__/adamp.cpython-39.pyc | Bin
.../__pycache__/adamw.cpython-39.pyc | Bin
.../__pycache__/lookahead.cpython-39.pyc | Bin
.../__pycache__/nadam.cpython-39.pyc | Bin
.../__pycache__/novograd.cpython-39.pyc | Bin
.../__pycache__/nvnovograd.cpython-39.pyc | Bin
.../__pycache__/optim_factory.cpython-39.pyc | Bin
.../__pycache__/radam.cpython-39.pyc | Bin
.../__pycache__/rmsprop_tf.cpython-39.pyc | Bin
.../__pycache__/sgdp.cpython-39.pyc | Bin
.../MyOptimizer}/adafactor.py | 0
.../MyOptimizer}/adahessian.py | 0
{MyOptimizer => code/MyOptimizer}/adamp.py | 0
{MyOptimizer => code/MyOptimizer}/adamw.py | 0
.../MyOptimizer}/lookahead.py | 0
{MyOptimizer => code/MyOptimizer}/nadam.py | 0
{MyOptimizer => code/MyOptimizer}/novograd.py | 0
.../MyOptimizer}/nvnovograd.py | 0
.../MyOptimizer}/optim_factory.py | 0
{MyOptimizer => code/MyOptimizer}/radam.py | 0
.../MyOptimizer}/rmsprop_tf.py | 0
{MyOptimizer => code/MyOptimizer}/sgdp.py | 0
.../__pycache__/test_visualize.cpython-39.pyc | Bin 0 -> 11095 bytes
code/__pycache__/train_loop.cpython-39.pyc | Bin 0 -> 14557 bytes
code/datasets/__init__.py | 3 +
.../__pycache__/__init__.cpython-39.pyc | Bin 0 -> 270 bytes
.../__pycache__/camel_data.cpython-39.pyc | Bin
.../camel_dataloader.cpython-39.pyc | Bin
.../custom_dataloader.cpython-39.pyc | Bin
.../custom_jpg_dataloader.cpython-39.pyc | Bin 0 -> 10649 bytes
.../__pycache__/data_interface.cpython-39.pyc | Bin
{datasets => code/datasets}/camel_data.py | 0
.../datasets}/camel_dataloader.py | 0
.../datasets}/custom_dataloader.py | 0
.../datasets}/custom_jpg_dataloader.py | 93 ++-
code/datasets/dali_dataloader.py | 139 ++++
{datasets => code/datasets}/data_interface.py | 0
code/lightning_logs/version_0/cm_test.png | Bin 0 -> 47096 bytes
...nts.out.tfevents.1657535217.dgx2.2080039.0 | Bin 0 -> 4702 bytes
code/lightning_logs/version_0/hparams.yaml | 368 +++++++++
code/lightning_logs/version_1/cm_test.png | Bin 0 -> 45165 bytes
...nts.out.tfevents.1657535625.dgx2.2086189.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_1/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657546166.dgx1.47613.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_10/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657546322.dgx1.48740.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_11/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657546521.dgx1.50053.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_12/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657546918.dgx1.52290.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_13/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657546992.dgx1.53435.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_14/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657547134.dgx1.54703.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_15/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657547198.dgx1.55641.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_16/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657623153.dgx1.41577.0 | Bin 0 -> 4704 bytes
code/lightning_logs/version_17/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657624768.dgx1.72352.0 | Bin 0 -> 4704 bytes
code/lightning_logs/version_18/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657624869.dgx1.76706.0 | Bin 0 -> 4704 bytes
code/lightning_logs/version_19/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657543650.dgx1.33429.0 | Bin 0 -> 4700 bytes
code/lightning_logs/version_2/hparams.yaml | 368 +++++++++
...events.out.tfevents.1657625133.dgx1.5999.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_20/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625248.dgx1.11114.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_21/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625470.dgx1.20071.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_22/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625510.dgx1.22295.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_23/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625570.dgx1.25099.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_24/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625613.dgx1.27343.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_25/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625763.dgx1.33397.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_26/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625819.dgx1.36236.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_27/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625859.dgx1.38333.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_28/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625903.dgx1.40628.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_29/hparams.yaml | 368 +++++++++
code/lightning_logs/version_3/cm_test.png | Bin 0 -> 63231 bytes
...vents.out.tfevents.1657543830.dgx1.34643.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_3/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657625960.dgx1.43463.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_30/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657626120.dgx1.48909.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_31/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657626628.dgx1.65460.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_32/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657626794.dgx1.71384.0 | Bin 0 -> 4699 bytes
code/lightning_logs/version_33/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657545281.dgx1.41143.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_4/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657545407.dgx1.42159.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_5/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657545574.dgx1.43290.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_6/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657545679.dgx1.44154.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_7/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657545837.dgx1.45339.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_8/hparams.yaml | 368 +++++++++
...vents.out.tfevents.1657545985.dgx1.46385.0 | Bin 0 -> 4705 bytes
code/lightning_logs/version_9/hparams.yaml | 368 +++++++++
{models => code/models}/AttMIL.py | 2 +-
code/models/DTFDMIL.py | 109 +++
{models => code/models}/TransMIL.py | 53 +-
code/models/TransformerMIL.py | 129 ++++
{models => code/models}/__init__.py | 0
.../models}/__pycache__/AttMIL.cpython-39.pyc | Bin 1551 -> 1555 bytes
.../models/__pycache__/DTFDMIL.cpython-39.pyc | Bin 0 -> 3969 bytes
.../__pycache__/TransMIL.cpython-39.pyc | Bin 0 -> 3455 bytes
.../__pycache__/TransformerMIL.cpython-39.pyc | Bin 0 -> 3440 bytes
.../__pycache__/__init__.cpython-39.pyc | Bin
.../model_interface.cpython-39.pyc | Bin 0 -> 15830 bytes
.../model_interface_dtfd.cpython-39.pyc | Bin 0 -> 17058 bytes
.../__pycache__/resnet50.cpython-39.pyc | Bin
.../vision_transformer.cpython-39.pyc | Bin
{models => code/models}/model_interface.py | 293 +++++---
code/models/model_interface_dtfd.py | 707 ++++++++++++++++++
{models => code/models}/resnet50.py | 0
{models => code/models}/vision_transformer.py | 0
code/test_visualize.py | 437 +++++++++++
train.py => code/train.py | 30 +-
code/train_loop.py | 496 ++++++++++++
{utils => code/utils}/__init__.py | 0
.../__pycache__/__init__.cpython-39.pyc | Bin
code/utils/__pycache__/utils.cpython-39.pyc | Bin 0 -> 4080 bytes
code/utils/extract_features.py | 37 +
{utils => code/utils}/utils.py | 11 +-
datasets/__init__.py | 2 -
datasets/__pycache__/__init__.cpython-39.pyc | Bin 193 -> 0 bytes
.../custom_jpg_dataloader.cpython-39.pyc | Bin 11037 -> 0 bytes
models/__pycache__/TransMIL.cpython-39.pyc | Bin 3233 -> 0 bytes
.../model_interface.cpython-39.pyc | Bin 14054 -> 0 bytes
test_visualize.py | 148 ----
train_loop.py | 212 ------
utils/__pycache__/utils.cpython-39.pyc | Bin 4005 -> 0 bytes
utils/extract_features.py | 27 -
178 files changed, 15046 insertions(+), 592 deletions(-)
create mode 100644 DeepGraft/DTFDMIL_resnet50_tcmr_viral.yaml
create mode 100644 DeepGraft/TransformerMIL_resnet50_tcmr_viral.yaml
delete mode 100644 MyLoss/__pycache__/loss_factory.cpython-39.pyc
rename {MyBackbone => code/MyBackbone}/__init__.py (100%)
rename {MyBackbone => code/MyBackbone}/backbone_factory.py (99%)
rename {MyLoss => code/MyLoss}/ND_Crossentropy.py (100%)
rename {MyLoss => code/MyLoss}/__init__.py (100%)
rename {MyLoss => code/MyLoss}/__pycache__/ND_Crossentropy.cpython-39.pyc (100%)
rename {MyLoss => code/MyLoss}/__pycache__/__init__.cpython-39.pyc (100%)
rename {MyLoss => code/MyLoss}/__pycache__/boundary_loss.cpython-39.pyc (100%)
rename {MyLoss => code/MyLoss}/__pycache__/dice_loss.cpython-39.pyc (100%)
rename {MyLoss => code/MyLoss}/__pycache__/focal_loss.cpython-39.pyc (100%)
rename {MyLoss => code/MyLoss}/__pycache__/hausdorff.cpython-39.pyc (100%)
create mode 100644 code/MyLoss/__pycache__/loss_factory.cpython-39.pyc
rename {MyLoss => code/MyLoss}/__pycache__/lovasz_loss.cpython-39.pyc (100%)
rename {MyLoss => code/MyLoss}/__pycache__/poly_loss.cpython-39.pyc (100%)
rename {MyLoss => code/MyLoss}/boundary_loss.py (100%)
rename {MyLoss => code/MyLoss}/dice_loss.py (100%)
rename {MyLoss => code/MyLoss}/focal_loss.py (100%)
rename {MyLoss => code/MyLoss}/hausdorff.py (100%)
rename {MyLoss => code/MyLoss}/loss_factory.py (91%)
rename {MyLoss => code/MyLoss}/lovasz_loss.py (100%)
rename {MyLoss => code/MyLoss}/poly_loss.py (100%)
rename {MyOptimizer => code/MyOptimizer}/__init__.py (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/__init__.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/adafactor.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/adahessian.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/adamp.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/adamw.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/lookahead.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/nadam.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/novograd.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/nvnovograd.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/optim_factory.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/radam.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/rmsprop_tf.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/__pycache__/sgdp.cpython-39.pyc (100%)
rename {MyOptimizer => code/MyOptimizer}/adafactor.py (100%)
rename {MyOptimizer => code/MyOptimizer}/adahessian.py (100%)
rename {MyOptimizer => code/MyOptimizer}/adamp.py (100%)
rename {MyOptimizer => code/MyOptimizer}/adamw.py (100%)
rename {MyOptimizer => code/MyOptimizer}/lookahead.py (100%)
rename {MyOptimizer => code/MyOptimizer}/nadam.py (100%)
rename {MyOptimizer => code/MyOptimizer}/novograd.py (100%)
rename {MyOptimizer => code/MyOptimizer}/nvnovograd.py (100%)
rename {MyOptimizer => code/MyOptimizer}/optim_factory.py (100%)
rename {MyOptimizer => code/MyOptimizer}/radam.py (100%)
rename {MyOptimizer => code/MyOptimizer}/rmsprop_tf.py (100%)
rename {MyOptimizer => code/MyOptimizer}/sgdp.py (100%)
create mode 100644 code/__pycache__/test_visualize.cpython-39.pyc
create mode 100644 code/__pycache__/train_loop.cpython-39.pyc
create mode 100644 code/datasets/__init__.py
create mode 100644 code/datasets/__pycache__/__init__.cpython-39.pyc
rename {datasets => code/datasets}/__pycache__/camel_data.cpython-39.pyc (100%)
rename {datasets => code/datasets}/__pycache__/camel_dataloader.cpython-39.pyc (100%)
rename {datasets => code/datasets}/__pycache__/custom_dataloader.cpython-39.pyc (100%)
create mode 100644 code/datasets/__pycache__/custom_jpg_dataloader.cpython-39.pyc
rename {datasets => code/datasets}/__pycache__/data_interface.cpython-39.pyc (100%)
rename {datasets => code/datasets}/camel_data.py (100%)
rename {datasets => code/datasets}/camel_dataloader.py (100%)
rename {datasets => code/datasets}/custom_dataloader.py (100%)
rename {datasets => code/datasets}/custom_jpg_dataloader.py (87%)
create mode 100644 code/datasets/dali_dataloader.py
rename {datasets => code/datasets}/data_interface.py (100%)
create mode 100644 code/lightning_logs/version_0/cm_test.png
create mode 100644 code/lightning_logs/version_0/events.out.tfevents.1657535217.dgx2.2080039.0
create mode 100644 code/lightning_logs/version_0/hparams.yaml
create mode 100644 code/lightning_logs/version_1/cm_test.png
create mode 100644 code/lightning_logs/version_1/events.out.tfevents.1657535625.dgx2.2086189.0
create mode 100644 code/lightning_logs/version_1/hparams.yaml
create mode 100644 code/lightning_logs/version_10/events.out.tfevents.1657546166.dgx1.47613.0
create mode 100644 code/lightning_logs/version_10/hparams.yaml
create mode 100644 code/lightning_logs/version_11/events.out.tfevents.1657546322.dgx1.48740.0
create mode 100644 code/lightning_logs/version_11/hparams.yaml
create mode 100644 code/lightning_logs/version_12/events.out.tfevents.1657546521.dgx1.50053.0
create mode 100644 code/lightning_logs/version_12/hparams.yaml
create mode 100644 code/lightning_logs/version_13/events.out.tfevents.1657546918.dgx1.52290.0
create mode 100644 code/lightning_logs/version_13/hparams.yaml
create mode 100644 code/lightning_logs/version_14/events.out.tfevents.1657546992.dgx1.53435.0
create mode 100644 code/lightning_logs/version_14/hparams.yaml
create mode 100644 code/lightning_logs/version_15/events.out.tfevents.1657547134.dgx1.54703.0
create mode 100644 code/lightning_logs/version_15/hparams.yaml
create mode 100644 code/lightning_logs/version_16/events.out.tfevents.1657547198.dgx1.55641.0
create mode 100644 code/lightning_logs/version_16/hparams.yaml
create mode 100644 code/lightning_logs/version_17/events.out.tfevents.1657623153.dgx1.41577.0
create mode 100644 code/lightning_logs/version_17/hparams.yaml
create mode 100644 code/lightning_logs/version_18/events.out.tfevents.1657624768.dgx1.72352.0
create mode 100644 code/lightning_logs/version_18/hparams.yaml
create mode 100644 code/lightning_logs/version_19/events.out.tfevents.1657624869.dgx1.76706.0
create mode 100644 code/lightning_logs/version_19/hparams.yaml
create mode 100644 code/lightning_logs/version_2/events.out.tfevents.1657543650.dgx1.33429.0
create mode 100644 code/lightning_logs/version_2/hparams.yaml
create mode 100644 code/lightning_logs/version_20/events.out.tfevents.1657625133.dgx1.5999.0
create mode 100644 code/lightning_logs/version_20/hparams.yaml
create mode 100644 code/lightning_logs/version_21/events.out.tfevents.1657625248.dgx1.11114.0
create mode 100644 code/lightning_logs/version_21/hparams.yaml
create mode 100644 code/lightning_logs/version_22/events.out.tfevents.1657625470.dgx1.20071.0
create mode 100644 code/lightning_logs/version_22/hparams.yaml
create mode 100644 code/lightning_logs/version_23/events.out.tfevents.1657625510.dgx1.22295.0
create mode 100644 code/lightning_logs/version_23/hparams.yaml
create mode 100644 code/lightning_logs/version_24/events.out.tfevents.1657625570.dgx1.25099.0
create mode 100644 code/lightning_logs/version_24/hparams.yaml
create mode 100644 code/lightning_logs/version_25/events.out.tfevents.1657625613.dgx1.27343.0
create mode 100644 code/lightning_logs/version_25/hparams.yaml
create mode 100644 code/lightning_logs/version_26/events.out.tfevents.1657625763.dgx1.33397.0
create mode 100644 code/lightning_logs/version_26/hparams.yaml
create mode 100644 code/lightning_logs/version_27/events.out.tfevents.1657625819.dgx1.36236.0
create mode 100644 code/lightning_logs/version_27/hparams.yaml
create mode 100644 code/lightning_logs/version_28/events.out.tfevents.1657625859.dgx1.38333.0
create mode 100644 code/lightning_logs/version_28/hparams.yaml
create mode 100644 code/lightning_logs/version_29/events.out.tfevents.1657625903.dgx1.40628.0
create mode 100644 code/lightning_logs/version_29/hparams.yaml
create mode 100644 code/lightning_logs/version_3/cm_test.png
create mode 100644 code/lightning_logs/version_3/events.out.tfevents.1657543830.dgx1.34643.0
create mode 100644 code/lightning_logs/version_3/hparams.yaml
create mode 100644 code/lightning_logs/version_30/events.out.tfevents.1657625960.dgx1.43463.0
create mode 100644 code/lightning_logs/version_30/hparams.yaml
create mode 100644 code/lightning_logs/version_31/events.out.tfevents.1657626120.dgx1.48909.0
create mode 100644 code/lightning_logs/version_31/hparams.yaml
create mode 100644 code/lightning_logs/version_32/events.out.tfevents.1657626628.dgx1.65460.0
create mode 100644 code/lightning_logs/version_32/hparams.yaml
create mode 100644 code/lightning_logs/version_33/events.out.tfevents.1657626794.dgx1.71384.0
create mode 100644 code/lightning_logs/version_33/hparams.yaml
create mode 100644 code/lightning_logs/version_4/events.out.tfevents.1657545281.dgx1.41143.0
create mode 100644 code/lightning_logs/version_4/hparams.yaml
create mode 100644 code/lightning_logs/version_5/events.out.tfevents.1657545407.dgx1.42159.0
create mode 100644 code/lightning_logs/version_5/hparams.yaml
create mode 100644 code/lightning_logs/version_6/events.out.tfevents.1657545574.dgx1.43290.0
create mode 100644 code/lightning_logs/version_6/hparams.yaml
create mode 100644 code/lightning_logs/version_7/events.out.tfevents.1657545679.dgx1.44154.0
create mode 100644 code/lightning_logs/version_7/hparams.yaml
create mode 100644 code/lightning_logs/version_8/events.out.tfevents.1657545837.dgx1.45339.0
create mode 100644 code/lightning_logs/version_8/hparams.yaml
create mode 100644 code/lightning_logs/version_9/events.out.tfevents.1657545985.dgx1.46385.0
create mode 100644 code/lightning_logs/version_9/hparams.yaml
rename {models => code/models}/AttMIL.py (98%)
create mode 100644 code/models/DTFDMIL.py
rename {models => code/models}/TransMIL.py (70%)
create mode 100644 code/models/TransformerMIL.py
rename {models => code/models}/__init__.py (100%)
rename {models => code/models}/__pycache__/AttMIL.cpython-39.pyc (79%)
create mode 100644 code/models/__pycache__/DTFDMIL.cpython-39.pyc
create mode 100644 code/models/__pycache__/TransMIL.cpython-39.pyc
create mode 100644 code/models/__pycache__/TransformerMIL.cpython-39.pyc
rename {models => code/models}/__pycache__/__init__.cpython-39.pyc (100%)
create mode 100644 code/models/__pycache__/model_interface.cpython-39.pyc
create mode 100644 code/models/__pycache__/model_interface_dtfd.cpython-39.pyc
rename {models => code/models}/__pycache__/resnet50.cpython-39.pyc (100%)
rename {models => code/models}/__pycache__/vision_transformer.cpython-39.pyc (100%)
rename {models => code/models}/model_interface.py (70%)
create mode 100644 code/models/model_interface_dtfd.py
rename {models => code/models}/resnet50.py (100%)
rename {models => code/models}/vision_transformer.py (100%)
create mode 100644 code/test_visualize.py
rename train.py => code/train.py (85%)
create mode 100644 code/train_loop.py
rename {utils => code/utils}/__init__.py (100%)
rename {utils => code/utils}/__pycache__/__init__.cpython-39.pyc (100%)
create mode 100644 code/utils/__pycache__/utils.cpython-39.pyc
create mode 100644 code/utils/extract_features.py
rename {utils => code/utils}/utils.py (95%)
delete mode 100644 datasets/__init__.py
delete mode 100644 datasets/__pycache__/__init__.cpython-39.pyc
delete mode 100644 datasets/__pycache__/custom_jpg_dataloader.cpython-39.pyc
delete mode 100644 models/__pycache__/TransMIL.cpython-39.pyc
delete mode 100644 models/__pycache__/model_interface.cpython-39.pyc
delete mode 100644 test_visualize.py
delete mode 100644 train_loop.py
delete mode 100644 utils/__pycache__/utils.cpython-39.pyc
delete mode 100644 utils/extract_features.py
diff --git a/DeepGraft/AttMIL_simple_no_other.yaml b/DeepGraft/AttMIL_simple_no_other.yaml
index ae90a80..344c204 100644
--- a/DeepGraft/AttMIL_simple_no_other.yaml
+++ b/DeepGraft/AttMIL_simple_no_other.yaml
@@ -16,7 +16,7 @@ General:
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_no_other.json'
fold: 1
nfold: 3
diff --git a/DeepGraft/AttMIL_simple_no_viral.yaml b/DeepGraft/AttMIL_simple_no_viral.yaml
index 37ee074..ac18f9c 100644
--- a/DeepGraft/AttMIL_simple_no_viral.yaml
+++ b/DeepGraft/AttMIL_simple_no_viral.yaml
@@ -6,7 +6,7 @@ General:
precision: 16
multi_gpu_mode: dp
gpus: [3]
- epochs: &epoch 500
+ epochs: &epoch 200
grad_acc: 2
frozen_bn: False
patience: 50
@@ -16,7 +16,7 @@ General:
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_no_viral.json'
fold: 1
nfold: 4
@@ -41,7 +41,7 @@ Optimizer:
opt_eps: null
opt_betas: null
momentum: null
- weight_decay: 0.00001
+ weight_decay: 0.01
Loss:
base_loss: CrossEntropyLoss
diff --git a/DeepGraft/AttMIL_simple_tcmr_viral.yaml b/DeepGraft/AttMIL_simple_tcmr_viral.yaml
index c982d3a..2aa4ed8 100644
--- a/DeepGraft/AttMIL_simple_tcmr_viral.yaml
+++ b/DeepGraft/AttMIL_simple_tcmr_viral.yaml
@@ -6,7 +6,7 @@ General:
precision: 16
multi_gpu_mode: dp
gpus: [3]
- epochs: &epoch 300
+ epochs: &epoch 200
grad_acc: 2
frozen_bn: False
patience: 20
@@ -16,7 +16,7 @@ General:
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
fold: 1
nfold: 3
@@ -34,6 +34,7 @@ Model:
name: AttMIL
n_classes: 2
backbone: simple
+ in_features: 512
Optimizer:
@@ -42,7 +43,7 @@ Optimizer:
opt_eps: null
opt_betas: null
momentum: null
- weight_decay: 0.00001
+ weight_decay: 0.01
Loss:
base_loss: CrossEntropyLoss
diff --git a/DeepGraft/DTFDMIL_resnet50_tcmr_viral.yaml b/DeepGraft/DTFDMIL_resnet50_tcmr_viral.yaml
new file mode 100644
index 0000000..7493b37
--- /dev/null
+++ b/DeepGraft/DTFDMIL_resnet50_tcmr_viral.yaml
@@ -0,0 +1,51 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 200
+ 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/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: DTFDMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
diff --git a/DeepGraft/TransMIL_efficientnet_no_viral.yaml b/DeepGraft/TransMIL_efficientnet_no_viral.yaml
index 98fe377..cffd500 100644
--- a/DeepGraft/TransMIL_efficientnet_no_viral.yaml
+++ b/DeepGraft/TransMIL_efficientnet_no_viral.yaml
@@ -11,15 +11,16 @@ General:
frozen_bn: False
patience: 20
server: test #train #test
- log_path: logs/
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
+ data_dir: '/home/ylan/data/DeepGraft/256_256um/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_no_viral.json'
fold: 1
nfold: 4
+ cross_val: False
train_dataloader:
batch_size: 1
diff --git a/DeepGraft/TransMIL_efficientnet_tcmr_viral.yaml b/DeepGraft/TransMIL_efficientnet_tcmr_viral.yaml
index 5223032..0dc5fa1 100644
--- a/DeepGraft/TransMIL_efficientnet_tcmr_viral.yaml
+++ b/DeepGraft/TransMIL_efficientnet_tcmr_viral.yaml
@@ -6,21 +6,21 @@ General:
precision: 16
multi_gpu_mode: dp
gpus: [0]
- epochs: &epoch 500
+ epochs: &epoch 200
grad_acc: 2
frozen_bn: False
- patience: 200
+ patience: 20
server: train #train #test
- log_path: logs/
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/data/DeepGraft/256_256um/'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um/'
label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
fold: 1
nfold: 3
- cross_val: True
+ cross_val: False
train_dataloader:
batch_size: 1
@@ -35,6 +35,7 @@ Model:
n_classes: 2
backbone: efficientnet
in_features: 512
+ out_features: 512
Optimizer:
@@ -43,7 +44,7 @@ Optimizer:
opt_eps: null
opt_betas: null
momentum: null
- weight_decay: 0.00001
+ weight_decay: 0.01
Loss:
base_loss: CrossEntropyLoss
diff --git a/DeepGraft/TransMIL_resnet18_tcmr_viral.yaml b/DeepGraft/TransMIL_resnet18_tcmr_viral.yaml
index c26e1e9..961f819 100644
--- a/DeepGraft/TransMIL_resnet18_tcmr_viral.yaml
+++ b/DeepGraft/TransMIL_resnet18_tcmr_viral.yaml
@@ -5,21 +5,22 @@ General:
amp_level: O2
precision: 16
multi_gpu_mode: dp
- gpus: [3]
- epochs: &epoch 500
+ gpus: [0]
+ epochs: &epoch 200
grad_acc: 2
frozen_bn: False
patience: 50
server: test #train #test
- log_path: logs/
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
- label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json'
fold: 1
- nfold: 4
+ nfold: 3
+ cross_val: False
train_dataloader:
batch_size: 1
@@ -33,6 +34,8 @@ Model:
name: TransMIL
n_classes: 2
backbone: resnet18
+ in_features: 512
+ out_features: 512
Optimizer:
@@ -41,7 +44,7 @@ Optimizer:
opt_eps: null
opt_betas: null
momentum: null
- weight_decay: 0.00001
+ weight_decay: 0.01
Loss:
base_loss: CrossEntropyLoss
diff --git a/DeepGraft/TransMIL_resnet50_tcmr_viral.yaml b/DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
index f6e4697..df205a2 100644
--- a/DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
+++ b/DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
@@ -6,21 +6,21 @@ General:
precision: 16
multi_gpu_mode: dp
gpus: [0]
- epochs: &epoch 200
+ epochs: &epoch 500
grad_acc: 2
frozen_bn: False
patience: 50
server: test #train #test
- log_path: logs/
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
Data:
dataset_name: custom
data_shuffle: False
- data_dir: '/home/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
- label_file: '/home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json'
+ data_dir: '/home/ylan/data/DeepGraft/224_128um/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
fold: 1
nfold: 3
- cross_val: True
+ cross_val: False
train_dataloader:
batch_size: 1
@@ -34,6 +34,8 @@ Model:
name: TransMIL
n_classes: 2
backbone: resnet50
+ in_features: 512
+ out_features: 512
Optimizer:
@@ -42,8 +44,9 @@ Optimizer:
opt_eps: null
opt_betas: null
momentum: null
- weight_decay: 0.00001
+ weight_decay: 0.01
Loss:
base_loss: CrossEntropyLoss
+ loss_weight: [1., 1.]
diff --git a/DeepGraft/TransformerMIL_resnet50_tcmr_viral.yaml b/DeepGraft/TransformerMIL_resnet50_tcmr_viral.yaml
new file mode 100644
index 0000000..896bef1
--- /dev/null
+++ b/DeepGraft/TransformerMIL_resnet50_tcmr_viral.yaml
@@ -0,0 +1,52 @@
+General:
+ comment:
+ seed: 2021
+ fp16: True
+ amp_level: O2
+ precision: 16
+ multi_gpu_mode: dp
+ gpus: [0]
+ epochs: &epoch 500
+ grad_acc: 2
+ frozen_bn: False
+ patience: 50
+ server: test #train #test
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+
+Data:
+ dataset_name: custom
+ data_shuffle: False
+ data_dir: '/home/ylan/data/DeepGraft/224_128um/'
+ label_file: '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
+ fold: 1
+ nfold: 3
+ cross_val: False
+
+ train_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+ test_dataloader:
+ batch_size: 1
+ num_workers: 8
+
+Model:
+ name: TransformerMIL
+ n_classes: 2
+ backbone: resnet50
+ in_features: 512
+ out_features: 512
+
+
+Optimizer:
+ opt: lookahead_radam
+ lr: 0.0002
+ opt_eps: null
+ opt_betas: null
+ momentum: null
+ weight_decay: 0.01
+
+Loss:
+ base_loss: CrossEntropyLoss
+
+
diff --git a/MyLoss/__pycache__/loss_factory.cpython-39.pyc b/MyLoss/__pycache__/loss_factory.cpython-39.pyc
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diff --git a/README.md b/README.md
index effc0fe..c8594bc 100644
--- a/README.md
+++ b/README.md
@@ -13,3 +13,26 @@ python train.py --stage='test' --config='Camelyon/TransMIL.yaml' --gpus=0 --fol
### Changes Made:
+### Baseline:
+
+lr = 0.0002
+wd = 0.01
+
+| task | main | backbone | train_auc | val_auc | epochs | version |
+|---|---|---|---|---|
+| tcmr_viral | TransMIL | resnet50 | 0.997 | 0.871 | 200 | 4 |
+| | | resnet18 | 0.999 | 0.687 | 200 | 0 |
+| | | efficientnet | 0.99 | 0.76 | 200 | 107 |
+| | DTFD | resnet50 | 0.989 | 0.621 | 200 | 44 |
+| | AttMIL | simple | 0.513 | 0.518 | 200 | 50 |
+
+
+159 28639 0.9222221970558167 0.19437336921691895 0.5906432867050171 0.56540447473526 0.7159091234207153 0.8709122538566589 0.30908203125
+
+### Ablation
+
+image drop out:
+tcmr_viral TCMR efficientnet: version 0
+
+wd incerease:
+tcmr_viral TCMR efficientnet: version 110
\ No newline at end of file
diff --git a/MyBackbone/__init__.py b/code/MyBackbone/__init__.py
similarity index 100%
rename from MyBackbone/__init__.py
rename to code/MyBackbone/__init__.py
diff --git a/MyBackbone/backbone_factory.py b/code/MyBackbone/backbone_factory.py
similarity index 99%
rename from MyBackbone/backbone_factory.py
rename to code/MyBackbone/backbone_factory.py
index ff770e5..31bc17b 100644
--- a/MyBackbone/backbone_factory.py
+++ b/code/MyBackbone/backbone_factory.py
@@ -25,7 +25,6 @@ def init_backbone(**kargs):
resnet18 = models.resnet18(pretrained=True)
modules = list(resnet18.children())[:-1]
# model_ft.fc = nn.Linear(512, out_features)
-
res18 = nn.Sequential(
*modules,
)
diff --git a/MyLoss/ND_Crossentropy.py b/code/MyLoss/ND_Crossentropy.py
similarity index 100%
rename from MyLoss/ND_Crossentropy.py
rename to code/MyLoss/ND_Crossentropy.py
diff --git a/MyLoss/__init__.py b/code/MyLoss/__init__.py
similarity index 100%
rename from MyLoss/__init__.py
rename to code/MyLoss/__init__.py
diff --git a/MyLoss/__pycache__/ND_Crossentropy.cpython-39.pyc b/code/MyLoss/__pycache__/ND_Crossentropy.cpython-39.pyc
similarity index 100%
rename from MyLoss/__pycache__/ND_Crossentropy.cpython-39.pyc
rename to code/MyLoss/__pycache__/ND_Crossentropy.cpython-39.pyc
diff --git a/MyLoss/__pycache__/__init__.cpython-39.pyc b/code/MyLoss/__pycache__/__init__.cpython-39.pyc
similarity index 100%
rename from MyLoss/__pycache__/__init__.cpython-39.pyc
rename to code/MyLoss/__pycache__/__init__.cpython-39.pyc
diff --git a/MyLoss/__pycache__/boundary_loss.cpython-39.pyc b/code/MyLoss/__pycache__/boundary_loss.cpython-39.pyc
similarity index 100%
rename from MyLoss/__pycache__/boundary_loss.cpython-39.pyc
rename to code/MyLoss/__pycache__/boundary_loss.cpython-39.pyc
diff --git a/MyLoss/__pycache__/dice_loss.cpython-39.pyc b/code/MyLoss/__pycache__/dice_loss.cpython-39.pyc
similarity index 100%
rename from MyLoss/__pycache__/dice_loss.cpython-39.pyc
rename to code/MyLoss/__pycache__/dice_loss.cpython-39.pyc
diff --git a/MyLoss/__pycache__/focal_loss.cpython-39.pyc b/code/MyLoss/__pycache__/focal_loss.cpython-39.pyc
similarity index 100%
rename from MyLoss/__pycache__/focal_loss.cpython-39.pyc
rename to code/MyLoss/__pycache__/focal_loss.cpython-39.pyc
diff --git a/MyLoss/__pycache__/hausdorff.cpython-39.pyc b/code/MyLoss/__pycache__/hausdorff.cpython-39.pyc
similarity index 100%
rename from MyLoss/__pycache__/hausdorff.cpython-39.pyc
rename to code/MyLoss/__pycache__/hausdorff.cpython-39.pyc
diff --git a/code/MyLoss/__pycache__/loss_factory.cpython-39.pyc b/code/MyLoss/__pycache__/loss_factory.cpython-39.pyc
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diff --git a/MyLoss/__pycache__/lovasz_loss.cpython-39.pyc b/code/MyLoss/__pycache__/lovasz_loss.cpython-39.pyc
similarity index 100%
rename from MyLoss/__pycache__/lovasz_loss.cpython-39.pyc
rename to code/MyLoss/__pycache__/lovasz_loss.cpython-39.pyc
diff --git a/MyLoss/__pycache__/poly_loss.cpython-39.pyc b/code/MyLoss/__pycache__/poly_loss.cpython-39.pyc
similarity index 100%
rename from MyLoss/__pycache__/poly_loss.cpython-39.pyc
rename to code/MyLoss/__pycache__/poly_loss.cpython-39.pyc
diff --git a/MyLoss/boundary_loss.py b/code/MyLoss/boundary_loss.py
similarity index 100%
rename from MyLoss/boundary_loss.py
rename to code/MyLoss/boundary_loss.py
diff --git a/MyLoss/dice_loss.py b/code/MyLoss/dice_loss.py
similarity index 100%
rename from MyLoss/dice_loss.py
rename to code/MyLoss/dice_loss.py
diff --git a/MyLoss/focal_loss.py b/code/MyLoss/focal_loss.py
similarity index 100%
rename from MyLoss/focal_loss.py
rename to code/MyLoss/focal_loss.py
diff --git a/MyLoss/hausdorff.py b/code/MyLoss/hausdorff.py
similarity index 100%
rename from MyLoss/hausdorff.py
rename to code/MyLoss/hausdorff.py
diff --git a/MyLoss/loss_factory.py b/code/MyLoss/loss_factory.py
similarity index 91%
rename from MyLoss/loss_factory.py
rename to code/MyLoss/loss_factory.py
index f3bdceb..8ff6d18 100755
--- a/MyLoss/loss_factory.py
+++ b/code/MyLoss/loss_factory.py
@@ -19,11 +19,15 @@ from pytorch_toolbelt import losses as L
def create_loss(args, w1=1.0, w2=0.5):
conf_loss = args.base_loss
+ if args.loss_weight:
+ weight = torch.tensor(args.loss_weight)
+ else: weight = None
### MulticlassJaccardLoss(classes=np.arange(11)
# mode = args.base_loss #BINARY_MODE \MULTICLASS_MODE \MULTILABEL_MODE
loss = None
if hasattr(nn, conf_loss):
- loss = getattr(nn, conf_loss)(label_smoothing=0.5)
+ loss = getattr(nn, conf_loss)(weight=weight, label_smoothing=0.5)
+ # loss = getattr(nn, conf_loss)(label_smoothing=0.5)
#binary loss
elif conf_loss == "focal":
loss = L.BinaryFocalLoss()
diff --git a/MyLoss/lovasz_loss.py b/code/MyLoss/lovasz_loss.py
similarity index 100%
rename from MyLoss/lovasz_loss.py
rename to code/MyLoss/lovasz_loss.py
diff --git a/MyLoss/poly_loss.py b/code/MyLoss/poly_loss.py
similarity index 100%
rename from MyLoss/poly_loss.py
rename to code/MyLoss/poly_loss.py
diff --git a/MyOptimizer/__init__.py b/code/MyOptimizer/__init__.py
similarity index 100%
rename from MyOptimizer/__init__.py
rename to code/MyOptimizer/__init__.py
diff --git a/MyOptimizer/__pycache__/__init__.cpython-39.pyc b/code/MyOptimizer/__pycache__/__init__.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/__init__.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/__init__.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/adafactor.cpython-39.pyc b/code/MyOptimizer/__pycache__/adafactor.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/adafactor.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/adafactor.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/adahessian.cpython-39.pyc b/code/MyOptimizer/__pycache__/adahessian.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/adahessian.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/adahessian.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/adamp.cpython-39.pyc b/code/MyOptimizer/__pycache__/adamp.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/adamp.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/adamp.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/adamw.cpython-39.pyc b/code/MyOptimizer/__pycache__/adamw.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/adamw.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/adamw.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/lookahead.cpython-39.pyc b/code/MyOptimizer/__pycache__/lookahead.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/lookahead.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/lookahead.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/nadam.cpython-39.pyc b/code/MyOptimizer/__pycache__/nadam.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/nadam.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/nadam.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/novograd.cpython-39.pyc b/code/MyOptimizer/__pycache__/novograd.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/novograd.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/novograd.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/nvnovograd.cpython-39.pyc b/code/MyOptimizer/__pycache__/nvnovograd.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/nvnovograd.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/nvnovograd.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/optim_factory.cpython-39.pyc b/code/MyOptimizer/__pycache__/optim_factory.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/optim_factory.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/optim_factory.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/radam.cpython-39.pyc b/code/MyOptimizer/__pycache__/radam.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/radam.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/radam.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/rmsprop_tf.cpython-39.pyc b/code/MyOptimizer/__pycache__/rmsprop_tf.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/rmsprop_tf.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/rmsprop_tf.cpython-39.pyc
diff --git a/MyOptimizer/__pycache__/sgdp.cpython-39.pyc b/code/MyOptimizer/__pycache__/sgdp.cpython-39.pyc
similarity index 100%
rename from MyOptimizer/__pycache__/sgdp.cpython-39.pyc
rename to code/MyOptimizer/__pycache__/sgdp.cpython-39.pyc
diff --git a/MyOptimizer/adafactor.py b/code/MyOptimizer/adafactor.py
similarity index 100%
rename from MyOptimizer/adafactor.py
rename to code/MyOptimizer/adafactor.py
diff --git a/MyOptimizer/adahessian.py b/code/MyOptimizer/adahessian.py
similarity index 100%
rename from MyOptimizer/adahessian.py
rename to code/MyOptimizer/adahessian.py
diff --git a/MyOptimizer/adamp.py b/code/MyOptimizer/adamp.py
similarity index 100%
rename from MyOptimizer/adamp.py
rename to code/MyOptimizer/adamp.py
diff --git a/MyOptimizer/adamw.py b/code/MyOptimizer/adamw.py
similarity index 100%
rename from MyOptimizer/adamw.py
rename to code/MyOptimizer/adamw.py
diff --git a/MyOptimizer/lookahead.py b/code/MyOptimizer/lookahead.py
similarity index 100%
rename from MyOptimizer/lookahead.py
rename to code/MyOptimizer/lookahead.py
diff --git a/MyOptimizer/nadam.py b/code/MyOptimizer/nadam.py
similarity index 100%
rename from MyOptimizer/nadam.py
rename to code/MyOptimizer/nadam.py
diff --git a/MyOptimizer/novograd.py b/code/MyOptimizer/novograd.py
similarity index 100%
rename from MyOptimizer/novograd.py
rename to code/MyOptimizer/novograd.py
diff --git a/MyOptimizer/nvnovograd.py b/code/MyOptimizer/nvnovograd.py
similarity index 100%
rename from MyOptimizer/nvnovograd.py
rename to code/MyOptimizer/nvnovograd.py
diff --git a/MyOptimizer/optim_factory.py b/code/MyOptimizer/optim_factory.py
similarity index 100%
rename from MyOptimizer/optim_factory.py
rename to code/MyOptimizer/optim_factory.py
diff --git a/MyOptimizer/radam.py b/code/MyOptimizer/radam.py
similarity index 100%
rename from MyOptimizer/radam.py
rename to code/MyOptimizer/radam.py
diff --git a/MyOptimizer/rmsprop_tf.py b/code/MyOptimizer/rmsprop_tf.py
similarity index 100%
rename from MyOptimizer/rmsprop_tf.py
rename to code/MyOptimizer/rmsprop_tf.py
diff --git a/MyOptimizer/sgdp.py b/code/MyOptimizer/sgdp.py
similarity index 100%
rename from MyOptimizer/sgdp.py
rename to code/MyOptimizer/sgdp.py
diff --git a/code/__pycache__/test_visualize.cpython-39.pyc b/code/__pycache__/test_visualize.cpython-39.pyc
new file mode 100644
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diff --git a/code/datasets/__init__.py b/code/datasets/__init__.py
new file mode 100644
index 0000000..0eb1fe7
--- /dev/null
+++ b/code/datasets/__init__.py
@@ -0,0 +1,3 @@
+
+from .custom_jpg_dataloader import JPGMILDataloader
+from .data_interface import MILDataModule
diff --git a/code/datasets/__pycache__/__init__.cpython-39.pyc b/code/datasets/__pycache__/__init__.cpython-39.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3735aa6b4ec5cd68471afdec6d2068bc6293a2ed
GIT binary patch
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diff --git a/datasets/__pycache__/camel_data.cpython-39.pyc b/code/datasets/__pycache__/camel_data.cpython-39.pyc
similarity index 100%
rename from datasets/__pycache__/camel_data.cpython-39.pyc
rename to code/datasets/__pycache__/camel_data.cpython-39.pyc
diff --git a/datasets/__pycache__/camel_dataloader.cpython-39.pyc b/code/datasets/__pycache__/camel_dataloader.cpython-39.pyc
similarity index 100%
rename from datasets/__pycache__/camel_dataloader.cpython-39.pyc
rename to code/datasets/__pycache__/camel_dataloader.cpython-39.pyc
diff --git a/datasets/__pycache__/custom_dataloader.cpython-39.pyc b/code/datasets/__pycache__/custom_dataloader.cpython-39.pyc
similarity index 100%
rename from datasets/__pycache__/custom_dataloader.cpython-39.pyc
rename to code/datasets/__pycache__/custom_dataloader.cpython-39.pyc
diff --git a/code/datasets/__pycache__/custom_jpg_dataloader.cpython-39.pyc b/code/datasets/__pycache__/custom_jpg_dataloader.cpython-39.pyc
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HcmV?d00001
diff --git a/datasets/__pycache__/data_interface.cpython-39.pyc b/code/datasets/__pycache__/data_interface.cpython-39.pyc
similarity index 100%
rename from datasets/__pycache__/data_interface.cpython-39.pyc
rename to code/datasets/__pycache__/data_interface.cpython-39.pyc
diff --git a/datasets/camel_data.py b/code/datasets/camel_data.py
similarity index 100%
rename from datasets/camel_data.py
rename to code/datasets/camel_data.py
diff --git a/datasets/camel_dataloader.py b/code/datasets/camel_dataloader.py
similarity index 100%
rename from datasets/camel_dataloader.py
rename to code/datasets/camel_dataloader.py
diff --git a/datasets/custom_dataloader.py b/code/datasets/custom_dataloader.py
similarity index 100%
rename from datasets/custom_dataloader.py
rename to code/datasets/custom_dataloader.py
diff --git a/datasets/custom_jpg_dataloader.py b/code/datasets/custom_jpg_dataloader.py
similarity index 87%
rename from datasets/custom_jpg_dataloader.py
rename to code/datasets/custom_jpg_dataloader.py
index 722b275..b47fc7f 100644
--- a/datasets/custom_jpg_dataloader.py
+++ b/code/datasets/custom_jpg_dataloader.py
@@ -25,18 +25,8 @@ class RangeNormalization(object):
return (img / 255.0 - 0.5) / 0.5
class JPGMILDataloader(data.Dataset):
- """Represents an abstract HDF5 dataset. For single H5 container!
- Input params:
- file_path: Path to the folder containing the dataset (one or multiple HDF5 files).
- mode: 'train' or 'test'
- load_data: If True, loads all the data immediately into RAM. Use this if
- the dataset is fits into memory. Otherwise, leave this at false and
- the data will load lazily.
- data_cache_size: Number of HDF5 files that can be cached in the cache (default=3).
-
- """
- def __init__(self, file_path, label_path, mode, n_classes, load_data=False, data_cache_size=10, bag_size=1024):
+ def __init__(self, file_path, label_path, mode, n_classes, load_data=False, data_cache_size=10, max_bag_size=1296):
super().__init__()
self.data_info = []
@@ -49,7 +39,8 @@ class JPGMILDataloader(data.Dataset):
# self.csv_path = csv_path
self.label_path = label_path
self.n_classes = n_classes
- self.bag_size = bag_size
+ self.max_bag_size = max_bag_size
+ self.min_bag_size = 120
self.empty_slides = []
# self.label_file = label_path
recursive = True
@@ -64,7 +55,7 @@ class JPGMILDataloader(data.Dataset):
for cohort in Path(self.file_path).iterdir():
x_complete_path = Path(self.file_path) / cohort / 'BLOCKS' / Path(x)
if x_complete_path.is_dir():
- if len(list(x_complete_path.iterdir())) > 50:
+ if len(list(x_complete_path.iterdir())) > self.min_bag_size:
# print(x_complete_path)
self.slideLabelDict[x] = y
self.files.append(x_complete_path)
@@ -87,7 +78,7 @@ class JPGMILDataloader(data.Dataset):
sometimes5 = lambda aug: iaa.Sometimes(0.9, aug, name="Random5")
self.train_transforms = iaa.Sequential([
- iaa.AddToHueAndSaturation(value=(-13, 13), name="MyHSV"),
+ 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"),
@@ -139,7 +130,7 @@ class JPGMILDataloader(data.Dataset):
def __getitem__(self, index):
# get data
- batch, label, name = self.get_data(index)
+ (batch, batch_names), label, name = self.get_data(index)
out_batch = []
seq_img_d = self.train_transforms.to_deterministic()
@@ -174,7 +165,7 @@ class JPGMILDataloader(data.Dataset):
label = torch.as_tensor(label)
label = torch.nn.functional.one_hot(label, num_classes=self.n_classes)
# print(out_batch)
- return out_batch, label, name #, name_batch
+ return out_batch, label, (name, batch_names) #, name_batch
def __len__(self):
return len(self.data_info)
@@ -194,7 +185,7 @@ class JPGMILDataloader(data.Dataset):
data_info structure.
"""
wsi_batch = []
- tile_names = []
+ name_batch = []
# print(wsi_batch)
# for tile_path in Path(file_path).iterdir():
# print(tile_path)
@@ -206,7 +197,7 @@ class JPGMILDataloader(data.Dataset):
# print(wsi_batch)
wsi_batch.append(img)
- tile_names.append(tile_path.stem)
+ name_batch.append(tile_path.stem)
# if wsi_batch:
wsi_batch = torch.stack(wsi_batch)
@@ -220,8 +211,9 @@ class JPGMILDataloader(data.Dataset):
# if wsi_batch.shape[1:] != torch.Size([3, 256, 256]) and wsi_batch.shape[1:] != torch.Size([256, 256, 3]):
# print(file_path)
# print(wsi_batch.shape)
- # wsi_batch, name_batch = to_fixed_size_bag(wsi_batch, self.bag_size)
- idx = self._add_to_cache(wsi_batch, file_path)
+ if wsi_batch.size(0) > self.max_bag_size:
+ wsi_batch, name_batch, _ = to_fixed_size_bag(wsi_batch, name_batch, self.max_bag_size)
+ idx = self._add_to_cache((wsi_batch,name_batch), file_path)
file_idx = next(i for i,v in enumerate(self.data_info) if v['data_path'] == file_path)
self.data_info[file_idx + idx]['cache_idx'] = idx
@@ -309,25 +301,26 @@ class RandomHueSaturationValue(object):
img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
return img #, lbl
-def to_fixed_size_bag(bag, bag_size: int = 512):
+def to_fixed_size_bag(bag, names, bag_size: int = 512):
#duplicate bag instances unitl
# get up to bag_size elements
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)
+ # 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,:, :, :]])
+ # 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 self_padded, min(bag_size, len(bag))
+ return bag_samples, name_samples, min(bag_size, len(bag))
class RandomHueSaturationValue(object):
@@ -365,23 +358,23 @@ if __name__ == '__main__':
home = Path.cwd().parts[1]
train_csv = f'/{home}/ylan/DeepGraft_project/code/debug_train.csv'
- data_root = f'/{home}/ylan/data/DeepGraft/256_256um'
+ data_root = f'/{home}/ylan/data/DeepGraft/224_128um'
# data_root = f'/{home}/ylan/DeepGraft/dataset/hdf5/256_256um_split/'
# label_path = f'/{home}/ylan/DeepGraft_project/code/split_PAS_bin.json'
label_path = f'/{home}/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
- output_path = f'/{home}/ylan/DeepGraft/dataset/check/256_256um_split/augments'
- os.makedirs(output_path, exist_ok=True)
+ output_dir = f'/{data_root}/debug/augments'
+ os.makedirs(output_dir, exist_ok=True)
n_classes = 2
- dataset = JPGMILDataloader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes, bag_size=20)
+ dataset = JPGMILDataloader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes)
# print(dataset.dataset)
# a = int(len(dataset)* 0.8)
# b = int(len(dataset) - a)
# train_ds, val_ds = torch.utils.data.random_split(dataset, [a, b])
- dl = DataLoader(dataset, None, num_workers=1)
- print(len(dl))
- dl = DataLoader(dataset, None, sampler=ImbalancedDatasetSampler(dataset), num_workers=5)
+ dl = DataLoader(dataset, None, num_workers=1, shuffle=False)
+ # print(len(dl))
+ # dl = DataLoader(dataset, batch_size=1, sampler=ImbalancedDatasetSampler(dataset), num_workers=5)
@@ -394,18 +387,16 @@ if __name__ == '__main__':
label_count = [0] *n_classes
print(len(dl))
for item in dl:
- # if c >=10:
- # break
- bag, label, name = item
- # print(label)
+ if c >= 5:
+ break
+ bag, label, (name, _) = item
label_count[torch.argmax(label)] += 1
# print(name)
# if name == 'RU0248_PASD_jke_PASD_20200201_195900_BIG':
# print(bag)
# print(label)
- c += 1
- print(label_count)
+
# # # print(bag.shape)
# # if bag.shape[1] == 1:
# # print(name)
@@ -423,21 +414,23 @@ if __name__ == '__main__':
# # bag = item[0]
# bag = bag.squeeze()
# original = original.squeeze()
- # for i in range(bag.shape[0]):
- # img = bag[i, :, :, :]
- # img = img.squeeze()
+ output_path = Path(output_dir) / name
+ output_path.mkdir(exist_ok=True)
+ for i in range(bag.shape[0]):
+ img = bag[i, :, :, :]
+ img = img.squeeze()
- # img = ((img-img.min())/(img.max() - img.min())) * 255
- # print(img)
- # # print(img)
- # img = img.numpy().astype(np.uint8).transpose(1,2,0)
+ img = ((img-img.min())/(img.max() - img.min())) * 255
+ # print(img)
+ # print(img)
+ img = img.numpy().astype(np.uint8).transpose(1,2,0)
- # img = Image.fromarray(img)
- # img = img.convert('RGB')
- # img.save(f'{output_path}/{i}.png')
-
+ img = Image.fromarray(img)
+ img = img.convert('RGB')
+ img.save(f'{output_path}/{i}.png')
+ c += 1
# o_img = original[i,:,:,:]
# o_img = o_img.squeeze()
diff --git a/code/datasets/dali_dataloader.py b/code/datasets/dali_dataloader.py
new file mode 100644
index 0000000..0851613
--- /dev/null
+++ b/code/datasets/dali_dataloader.py
@@ -0,0 +1,139 @@
+from nvidia.dali import pipeline_def
+from nvidia.dali.pipeline import Pipeline
+import nvidia.dali.fn as fn
+import nvidia.dali.types as types
+
+from pathlib import Path
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import math
+import json
+import numpy as np
+import cupy as cp
+import torch
+import imageio
+
+batch_size = 10
+home = Path.cwd().parts[1]
+# image_filename = f"/{home}/ylan/data/DeepGraft/224_128um/Aachen_Biopsy_Slides/BLOCKS/"
+
+class ExternalInputIterator(object):
+ def __init__(self, batch_size):
+ self.file_path = f"/{home}/ylan/data/DeepGraft/224_128um/"
+ # self.label_file = f'/{home}/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
+ self.label_path = f'/{home}/ylan/DeepGraft/training_tables/split_Aachen_PAS_tcmr_viral.json'
+ self.batch_size = batch_size
+
+ mode = 'test'
+ # with open(self.images_dir + "file_list.txt", 'r') as f:
+ # self.files = [line.rstrip() for line in f if line != '']
+ self.slideLabelDict = {}
+ self.files = []
+ self.empty_slides = []
+ with open(self.label_path, 'r') as f:
+ temp_slide_label_dict = json.load(f)[mode]
+ for (x, y) in temp_slide_label_dict:
+ x = Path(x).stem
+
+ # x_complete_path = Path(self.file_path)/Path(x)
+ for cohort in Path(self.file_path).iterdir():
+ x_complete_path = Path(self.file_path) / cohort / 'BLOCKS' / Path(x)
+ if x_complete_path.is_dir():
+ if len(list(x_complete_path.iterdir())) > 50:
+ # print(x_complete_path)
+ # self.slideLabelDict[x] = y
+ self.files.append((x_complete_path, y))
+ else: self.empty_slides.append(x_complete_path)
+
+ # shuffle(self.files)
+
+ def __iter__(self):
+ self.i = 0
+ self.n = len(self.files)
+ return self
+
+ def __next__(self):
+ batch = []
+ labels = []
+ file_names = []
+
+ for _ in range(self.batch_size):
+ wsi_batch = []
+ wsi_filename, label = self.files[self.i]
+ # jpeg_filename, label = self.files[self.i].split(' ')
+ name = Path(wsi_filename).stem
+ file_names.append(name)
+ for img_path in Path(wsi_filename).iterdir():
+ # f = open(img, 'rb')
+
+ f = imageio.imread(img_path)
+ img = cp.asarray(f)
+ wsi_batch.append(img.astype(cp.uint8))
+ wsi_batch = cp.stack(wsi_batch)
+ batch.append(wsi_batch)
+ labels.append(cp.array([label], dtype = cp.uint8))
+
+ self.i = (self.i + 1) % self.n
+ # print(batch)
+ # print(labels)
+ return (batch, labels)
+
+
+eii = ExternalInputIterator(batch_size=10)
+
+@pipeline_def()
+def hsv_pipeline(device, hue, saturation, value):
+ # files, labels = fn.readers.file(file_root=image_filename)
+ files, labels = fn.external_source(source=eii, num_outputs=2, dtype=types.UINT8)
+ images = fn.decoders.image(files, device = 'cpu' if device == 'cpu' else 'mixed')
+ converted = fn.hsv(images, hue=hue, saturation=saturation, value=value)
+ return images, converted
+
+def display(outputs, idx, columns=2, captions=None, cpu=True):
+ rows = int(math.ceil(len(outputs) / columns))
+ fig = plt.figure()
+ fig.set_size_inches(16, 6 * rows)
+ gs = gridspec.GridSpec(rows, columns)
+ row = 0
+ col = 0
+ for i, out in enumerate(outputs):
+ plt.subplot(gs[i])
+ plt.axis("off")
+ if captions is not None:
+ plt.title(captions[i])
+ plt.imshow(out.at(idx) if cpu else out.as_cpu().at(idx))
+
+# pipe_cpu = hsv_pipeline(device='cpu', hue=120, saturation=1, value=0.4, batch_size=batch_size, num_threads=1, device_id=0)
+# pipe_cpu.build()
+# cpu_output = pipe_cpu.run()
+
+# display(cpu_output, 3, captions=["Original", "Hue=120, Saturation=1, Value=0.4"])
+
+# pipe_gpu = hsv_pipeline(device='gpu', hue=120, saturation=2, value=1, batch_size=batch_size, num_threads=1, device_id=0)
+# pipe_gpu.build()
+# gpu_output = pipe_gpu.run()
+
+# display(gpu_output, 0, cpu=False, captions=["Original", "Hue=120, Saturation=2, Value=1"])
+
+
+pipe_gpu = Pipeline(batch_size=batch_size, num_threads=2, device_id=0)
+with pipe_gpu:
+ images, labels = fn.external_source(source=eii, num_outputs=2, device="gpu", dtype=types.UINT8)
+ enhance = fn.brightness_contrast(images, contrast=2)
+ pipe_gpu.set_outputs(enhance, labels)
+
+pipe_gpu.build()
+pipe_out_gpu = pipe_gpu.run()
+batch_gpu = pipe_out_gpu[0].as_cpu()
+labels_gpu = pipe_out_gpu[1].as_cpu()
+# file_names = pipe_out_gpu[2].as_cpu()
+
+output_path = f"/{home}/ylan/data/DeepGraft/224_128um/debug/augments/"
+output_path.mkdir(exist_ok=True)
+
+
+img = batch_gpu.at(2)
+print(img.shape)
+print(labels_gpu.at(2))
+plt.axis('off')
+plt.imsave(f'{output_path}/0.jpg', img[0, :, :, :])
\ No newline at end of file
diff --git a/datasets/data_interface.py b/code/datasets/data_interface.py
similarity index 100%
rename from datasets/data_interface.py
rename to code/datasets/data_interface.py
diff --git a/code/lightning_logs/version_0/cm_test.png b/code/lightning_logs/version_0/cm_test.png
new file mode 100644
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diff --git a/code/lightning_logs/version_1/hparams.yaml b/code/lightning_logs/version_1/hparams.yaml
new file mode 100644
index 0000000..3027219
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+++ b/code/lightning_logs/version_1/hparams.yaml
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+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
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+ precision: 16
+ seed: 2021
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+ - !!python/tuple
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+ data_shuffle: false
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+ label_file: /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json
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diff --git a/code/lightning_logs/version_10/events.out.tfevents.1657546166.dgx1.47613.0 b/code/lightning_logs/version_10/events.out.tfevents.1657546166.dgx1.47613.0
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diff --git a/code/lightning_logs/version_11/events.out.tfevents.1657546322.dgx1.48740.0 b/code/lightning_logs/version_11/events.out.tfevents.1657546322.dgx1.48740.0
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diff --git a/code/lightning_logs/version_11/hparams.yaml b/code/lightning_logs/version_11/hparams.yaml
new file mode 100644
index 0000000..385e52b
--- /dev/null
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diff --git a/code/lightning_logs/version_13/events.out.tfevents.1657546918.dgx1.52290.0 b/code/lightning_logs/version_13/events.out.tfevents.1657546918.dgx1.52290.0
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new file mode 100644
index 0000000..385e52b
--- /dev/null
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diff --git a/code/lightning_logs/version_14/hparams.yaml b/code/lightning_logs/version_14/hparams.yaml
new file mode 100644
index 0000000..385e52b
--- /dev/null
+++ b/code/lightning_logs/version_14/hparams.yaml
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diff --git a/code/lightning_logs/version_15/hparams.yaml b/code/lightning_logs/version_15/hparams.yaml
new file mode 100644
index 0000000..385e52b
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diff --git a/code/lightning_logs/version_16/events.out.tfevents.1657547198.dgx1.55641.0 b/code/lightning_logs/version_16/events.out.tfevents.1657547198.dgx1.55641.0
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diff --git a/code/lightning_logs/version_16/hparams.yaml b/code/lightning_logs/version_16/hparams.yaml
new file mode 100644
index 0000000..385e52b
--- /dev/null
+++ b/code/lightning_logs/version_16/hparams.yaml
@@ -0,0 +1,368 @@
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+ - /home/ylan/workspace/TransMIL-DeepGraft/logs/
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+ comment: null
+ epochs: 200
+ fp16: true
+ frozen_bn: false
+ gpus: *id001
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+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
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+ patience: 50
+ precision: 16
+ seed: 2021
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+ data_dir: /home/ylan/data/DeepGraft/224_128um/
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+ dataset_name: custom
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+ label_file: /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_test.json
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diff --git a/code/lightning_logs/version_17/events.out.tfevents.1657623153.dgx1.41577.0 b/code/lightning_logs/version_17/events.out.tfevents.1657623153.dgx1.41577.0
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diff --git a/code/lightning_logs/version_17/hparams.yaml b/code/lightning_logs/version_17/hparams.yaml
new file mode 100644
index 0000000..399847b
--- /dev/null
+++ b/code/lightning_logs/version_17/hparams.yaml
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+- /
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+- workspace
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+- logs
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diff --git a/code/lightning_logs/version_18/hparams.yaml b/code/lightning_logs/version_18/hparams.yaml
new file mode 100644
index 0000000..5d30687
--- /dev/null
+++ b/code/lightning_logs/version_18/hparams.yaml
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+ seed: 2021
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+ label_file: /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_test.json
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+ - !!python/tuple
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diff --git a/code/lightning_logs/version_19/events.out.tfevents.1657624869.dgx1.76706.0 b/code/lightning_logs/version_19/events.out.tfevents.1657624869.dgx1.76706.0
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diff --git a/code/lightning_logs/version_19/hparams.yaml b/code/lightning_logs/version_19/hparams.yaml
new file mode 100644
index 0000000..5d30687
--- /dev/null
+++ b/code/lightning_logs/version_19/hparams.yaml
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new file mode 100644
index 0000000..d8ca6a0
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diff --git a/code/lightning_logs/version_23/events.out.tfevents.1657625510.dgx1.22295.0 b/code/lightning_logs/version_23/events.out.tfevents.1657625510.dgx1.22295.0
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diff --git a/code/lightning_logs/version_23/hparams.yaml b/code/lightning_logs/version_23/hparams.yaml
new file mode 100644
index 0000000..d8ca6a0
--- /dev/null
+++ b/code/lightning_logs/version_23/hparams.yaml
@@ -0,0 +1,368 @@
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+ args:
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+ - comment
+ - null
+ - !!python/tuple
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+ - 2021
+ - !!python/tuple
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+ - true
+ - !!python/tuple
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+ - !!python/tuple
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+ - 16
+ - !!python/tuple
+ - multi_gpu_mode
+ - dp
+ - !!python/tuple
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+ - &id001
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+ - !!python/tuple
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+ - !!python/tuple
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+ - test
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+ - /home/ylan/workspace/TransMIL-DeepGraft/logs/
+ dictitems:
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+ comment: null
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+ fp16: true
+ frozen_bn: false
+ gpus: *id001
+ grad_acc: 2
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+ multi_gpu_mode: dp
+ patience: 50
+ precision: 16
+ seed: 2021
+ server: test
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+ - /home/ylan/data/DeepGraft/224_128um/
+ - !!python/tuple
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+ data_dir: /home/ylan/data/DeepGraft/224_128um/
+ data_shuffle: false
+ dataset_name: custom
+ fold: 0
+ label_file: /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json
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+ - !!python/tuple
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+ - version_4
+ dictitems:
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diff --git a/code/lightning_logs/version_24/events.out.tfevents.1657625570.dgx1.25099.0 b/code/lightning_logs/version_24/events.out.tfevents.1657625570.dgx1.25099.0
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diff --git a/code/lightning_logs/version_24/hparams.yaml b/code/lightning_logs/version_24/hparams.yaml
new file mode 100644
index 0000000..d8ca6a0
--- /dev/null
+++ b/code/lightning_logs/version_24/hparams.yaml
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diff --git a/code/lightning_logs/version_26/events.out.tfevents.1657625763.dgx1.33397.0 b/code/lightning_logs/version_26/events.out.tfevents.1657625763.dgx1.33397.0
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diff --git a/code/lightning_logs/version_26/hparams.yaml b/code/lightning_logs/version_26/hparams.yaml
new file mode 100644
index 0000000..d8ca6a0
--- /dev/null
+++ b/code/lightning_logs/version_26/hparams.yaml
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diff --git a/code/lightning_logs/version_27/events.out.tfevents.1657625819.dgx1.36236.0 b/code/lightning_logs/version_27/events.out.tfevents.1657625819.dgx1.36236.0
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diff --git a/code/lightning_logs/version_27/hparams.yaml b/code/lightning_logs/version_27/hparams.yaml
new file mode 100644
index 0000000..d8ca6a0
--- /dev/null
+++ b/code/lightning_logs/version_27/hparams.yaml
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diff --git a/code/lightning_logs/version_29/events.out.tfevents.1657625903.dgx1.40628.0 b/code/lightning_logs/version_29/events.out.tfevents.1657625903.dgx1.40628.0
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diff --git a/code/lightning_logs/version_29/hparams.yaml b/code/lightning_logs/version_29/hparams.yaml
new file mode 100644
index 0000000..d8ca6a0
--- /dev/null
+++ b/code/lightning_logs/version_29/hparams.yaml
@@ -0,0 +1,368 @@
+backbone: resnet50
+cfg: &id010 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - General
+ - &id002 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - comment
+ - null
+ - !!python/tuple
+ - seed
+ - 2021
+ - !!python/tuple
+ - fp16
+ - true
+ - !!python/tuple
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+ - 16
+ - !!python/tuple
+ - multi_gpu_mode
+ - dp
+ - !!python/tuple
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+ - &id001
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+ - !!python/tuple
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+ - !!python/tuple
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+ - test
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+ - /home/ylan/workspace/TransMIL-DeepGraft/logs/
+ dictitems:
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+ comment: null
+ epochs: 200
+ fp16: true
+ frozen_bn: false
+ gpus: *id001
+ grad_acc: 2
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+ multi_gpu_mode: dp
+ patience: 50
+ precision: 16
+ seed: 2021
+ server: test
+ state: *id002
+ - !!python/tuple
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+ args:
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+ - !!python/tuple
+ - data_shuffle
+ - false
+ - !!python/tuple
+ - data_dir
+ - /home/ylan/data/DeepGraft/224_128um/
+ - !!python/tuple
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+ args:
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+ data_dir: /home/ylan/data/DeepGraft/224_128um/
+ data_shuffle: false
+ dataset_name: custom
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+ label_file: /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json
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+ state: *id005
+ - !!python/tuple
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+ args:
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+ args:
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+ weight_decay: 0.01
+ state: *id007
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+ - !!python/tuple
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+ - !!python/tuple
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+ - !!python/tuple
+ - log_path
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+ - workspace
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+ - lightning_logs
+ - version_4
+ dictitems:
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+ Loss: *id008
+ Model: *id006
+ Optimizer: *id007
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+- /
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diff --git a/code/lightning_logs/version_3/hparams.yaml b/code/lightning_logs/version_3/hparams.yaml
new file mode 100644
index 0000000..a662047
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+ fold: 0
+ label_file: /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_test.json
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+ - !!python/tuple
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diff --git a/code/lightning_logs/version_30/events.out.tfevents.1657625960.dgx1.43463.0 b/code/lightning_logs/version_30/events.out.tfevents.1657625960.dgx1.43463.0
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diff --git a/code/lightning_logs/version_30/hparams.yaml b/code/lightning_logs/version_30/hparams.yaml
new file mode 100644
index 0000000..d8ca6a0
--- /dev/null
+++ b/code/lightning_logs/version_30/hparams.yaml
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diff --git a/code/lightning_logs/version_31/events.out.tfevents.1657626120.dgx1.48909.0 b/code/lightning_logs/version_31/events.out.tfevents.1657626120.dgx1.48909.0
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new file mode 100644
index 0000000..d8ca6a0
--- /dev/null
+++ b/code/lightning_logs/version_31/hparams.yaml
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diff --git a/code/lightning_logs/version_33/events.out.tfevents.1657626794.dgx1.71384.0 b/code/lightning_logs/version_33/events.out.tfevents.1657626794.dgx1.71384.0
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new file mode 100644
index 0000000..d8ca6a0
--- /dev/null
+++ b/code/lightning_logs/version_33/hparams.yaml
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new file mode 100644
index 0000000..a662047
--- /dev/null
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diff --git a/code/lightning_logs/version_6/events.out.tfevents.1657545574.dgx1.43290.0 b/code/lightning_logs/version_6/events.out.tfevents.1657545574.dgx1.43290.0
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index 0000000..385e52b
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new file mode 100644
index 0000000..385e52b
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+++ b/code/lightning_logs/version_7/hparams.yaml
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diff --git a/code/lightning_logs/version_9/events.out.tfevents.1657545985.dgx1.46385.0 b/code/lightning_logs/version_9/events.out.tfevents.1657545985.dgx1.46385.0
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HcmV?d00001
diff --git a/code/lightning_logs/version_9/hparams.yaml b/code/lightning_logs/version_9/hparams.yaml
new file mode 100644
index 0000000..385e52b
--- /dev/null
+++ b/code/lightning_logs/version_9/hparams.yaml
@@ -0,0 +1,368 @@
+backbone: resnet50
+cfg: &id010 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - General
+ - &id002 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - comment
+ - null
+ - !!python/tuple
+ - seed
+ - 2021
+ - !!python/tuple
+ - fp16
+ - true
+ - !!python/tuple
+ - amp_level
+ - O2
+ - !!python/tuple
+ - precision
+ - 16
+ - !!python/tuple
+ - multi_gpu_mode
+ - dp
+ - !!python/tuple
+ - gpus
+ - &id001
+ - 1
+ - !!python/tuple
+ - epochs
+ - 200
+ - !!python/tuple
+ - grad_acc
+ - 2
+ - !!python/tuple
+ - frozen_bn
+ - false
+ - !!python/tuple
+ - patience
+ - 50
+ - !!python/tuple
+ - server
+ - train
+ - !!python/tuple
+ - log_path
+ - /home/ylan/workspace/TransMIL-DeepGraft/logs/
+ dictitems:
+ amp_level: O2
+ comment: null
+ epochs: 200
+ fp16: true
+ frozen_bn: false
+ gpus: *id001
+ grad_acc: 2
+ log_path: /home/ylan/workspace/TransMIL-DeepGraft/logs/
+ multi_gpu_mode: dp
+ patience: 50
+ precision: 16
+ seed: 2021
+ server: train
+ state: *id002
+ - !!python/tuple
+ - Data
+ - &id005 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - dataset_name
+ - custom
+ - !!python/tuple
+ - data_shuffle
+ - false
+ - !!python/tuple
+ - data_dir
+ - /home/ylan/data/DeepGraft/224_128um/
+ - !!python/tuple
+ - label_file
+ - /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_test.json
+ - !!python/tuple
+ - fold
+ - 0
+ - !!python/tuple
+ - nfold
+ - 3
+ - !!python/tuple
+ - cross_val
+ - false
+ - !!python/tuple
+ - train_dataloader
+ - &id003 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - batch_size
+ - 1
+ - !!python/tuple
+ - num_workers
+ - 8
+ dictitems:
+ batch_size: 1
+ num_workers: 8
+ state: *id003
+ - !!python/tuple
+ - test_dataloader
+ - &id004 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - batch_size
+ - 1
+ - !!python/tuple
+ - num_workers
+ - 8
+ dictitems:
+ batch_size: 1
+ num_workers: 8
+ state: *id004
+ - !!python/tuple
+ - bag_size
+ - 1024
+ dictitems:
+ bag_size: 1024
+ cross_val: false
+ data_dir: /home/ylan/data/DeepGraft/224_128um/
+ data_shuffle: false
+ dataset_name: custom
+ fold: 0
+ label_file: /home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral_test.json
+ nfold: 3
+ test_dataloader: *id004
+ train_dataloader: *id003
+ state: *id005
+ - !!python/tuple
+ - Model
+ - &id006 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - name
+ - TransMIL
+ - !!python/tuple
+ - n_classes
+ - 2
+ - !!python/tuple
+ - backbone
+ - resnet50
+ - !!python/tuple
+ - in_features
+ - 512
+ - !!python/tuple
+ - out_features
+ - 512
+ dictitems:
+ backbone: resnet50
+ in_features: 512
+ n_classes: 2
+ name: TransMIL
+ out_features: 512
+ state: *id006
+ - !!python/tuple
+ - Optimizer
+ - &id007 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - opt
+ - lookahead_radam
+ - !!python/tuple
+ - lr
+ - 0.0002
+ - !!python/tuple
+ - opt_eps
+ - null
+ - !!python/tuple
+ - opt_betas
+ - null
+ - !!python/tuple
+ - momentum
+ - null
+ - !!python/tuple
+ - weight_decay
+ - 0.01
+ dictitems:
+ lr: 0.0002
+ momentum: null
+ opt: lookahead_radam
+ opt_betas: null
+ opt_eps: null
+ weight_decay: 0.01
+ state: *id007
+ - !!python/tuple
+ - Loss
+ - &id008 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - base_loss
+ - CrossEntropyLoss
+ dictitems:
+ base_loss: CrossEntropyLoss
+ state: *id008
+ - !!python/tuple
+ - config
+ - ../DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
+ - !!python/tuple
+ - version
+ - 4
+ - !!python/tuple
+ - epoch
+ - '159'
+ - !!python/tuple
+ - log_path
+ - &id009 !!python/object/apply:pathlib.PosixPath
+ - /
+ - home
+ - ylan
+ - workspace
+ - TransMIL-DeepGraft
+ - logs
+ - DeepGraft
+ - TransMIL
+ - tcmr_viral
+ - _resnet50_CrossEntropyLoss
+ - lightning_logs
+ - version_4
+ dictitems:
+ Data: *id005
+ General: *id002
+ Loss: *id008
+ Model: *id006
+ Optimizer: *id007
+ config: ../DeepGraft/TransMIL_resnet50_tcmr_viral.yaml
+ epoch: '159'
+ log_path: *id009
+ version: 4
+ state: *id010
+data: &id013 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - dataset_name
+ - custom
+ - !!python/tuple
+ - data_shuffle
+ - false
+ - !!python/tuple
+ - data_dir
+ - /home/ylan/data/DeepGraft/224_128um/
+ - !!python/tuple
+ - label_file
+ - /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
+ - !!python/tuple
+ - fold
+ - 0
+ - !!python/tuple
+ - nfold
+ - 3
+ - !!python/tuple
+ - cross_val
+ - false
+ - !!python/tuple
+ - train_dataloader
+ - &id011 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - batch_size
+ - 1
+ - !!python/tuple
+ - num_workers
+ - 8
+ dictitems:
+ batch_size: 1
+ num_workers: 8
+ state: *id011
+ - !!python/tuple
+ - test_dataloader
+ - &id012 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - batch_size
+ - 1
+ - !!python/tuple
+ - num_workers
+ - 8
+ dictitems:
+ batch_size: 1
+ num_workers: 8
+ state: *id012
+ - !!python/tuple
+ - bag_size
+ - 1024
+ dictitems:
+ bag_size: 1024
+ cross_val: false
+ data_dir: /home/ylan/data/DeepGraft/224_128um/
+ data_shuffle: false
+ dataset_name: custom
+ fold: 0
+ label_file: /home/ylan/DeepGraft/training_tables/split_bt_PAS_tcmr_viral.json
+ nfold: 3
+ test_dataloader: *id012
+ train_dataloader: *id011
+ state: *id013
+log: !!python/object/apply:pathlib.PosixPath
+- /
+- home
+- ylan
+- workspace
+- TransMIL-DeepGraft
+- logs
+- DeepGraft
+- TransMIL
+- tcmr_viral
+- _resnet50_CrossEntropyLoss
+loss: &id014 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - base_loss
+ - CrossEntropyLoss
+ dictitems:
+ base_loss: CrossEntropyLoss
+ state: *id014
+model: &id015 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - name
+ - TransMIL
+ - !!python/tuple
+ - n_classes
+ - 2
+ - !!python/tuple
+ - backbone
+ - resnet50
+ - !!python/tuple
+ - in_features
+ - 512
+ - !!python/tuple
+ - out_features
+ - 512
+ dictitems:
+ backbone: resnet50
+ in_features: 512
+ n_classes: 2
+ name: TransMIL
+ out_features: 512
+ state: *id015
+optimizer: &id016 !!python/object/new:addict.addict.Dict
+ args:
+ - !!python/tuple
+ - opt
+ - lookahead_radam
+ - !!python/tuple
+ - lr
+ - 0.0002
+ - !!python/tuple
+ - opt_eps
+ - null
+ - !!python/tuple
+ - opt_betas
+ - null
+ - !!python/tuple
+ - momentum
+ - null
+ - !!python/tuple
+ - weight_decay
+ - 0.01
+ dictitems:
+ lr: 0.0002
+ momentum: null
+ opt: lookahead_radam
+ opt_betas: null
+ opt_eps: null
+ weight_decay: 0.01
+ state: *id016
diff --git a/models/AttMIL.py b/code/models/AttMIL.py
similarity index 98%
rename from models/AttMIL.py
rename to code/models/AttMIL.py
index d1e20eb..d4a5938 100644
--- a/models/AttMIL.py
+++ b/code/models/AttMIL.py
@@ -76,4 +76,4 @@ class AttMIL(nn.Module): #gated attention
M = torch.mm(A, H) # KxL
logits = self.classifier(M)
- return logits
\ No newline at end of file
+ return logits, A
\ No newline at end of file
diff --git a/code/models/DTFDMIL.py b/code/models/DTFDMIL.py
new file mode 100644
index 0000000..272c1c7
--- /dev/null
+++ b/code/models/DTFDMIL.py
@@ -0,0 +1,109 @@
+import os
+import logging
+from argparse import ArgumentParser
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+
+import pytorch_lightning as pl
+from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR
+
+class Attention_Gated(nn.Module):
+ def __init__(self, features=512, D=128, K=1):
+ super(Attention_Gated, self).__init__()
+
+ self.L = features
+ self.D = D
+ self.K = K
+
+ self.attention_V = nn.Sequential(
+ nn.Linear(self.L, self.D),
+ nn.Tanh()
+ )
+
+ self.attention_U = nn.Sequential(
+ nn.Linear(self.L, self.D),
+ nn.Sigmoid()
+ )
+
+ self.attention_weights = nn.Linear(self.D, self.K)
+
+ def forward(self, x, isNorm=True):
+ ## x: N x L
+ # print(x.shape)
+ A_V = self.attention_V(x) # NxD
+ A_U = self.attention_U(x) # NxD
+ A = self.attention_weights(A_V * A_U) # NxK
+ A = torch.transpose(A, 1, 0) # KxN
+
+ if isNorm:
+ A = F.softmax(A, dim=1) # softmax over N
+
+ return A ### K x N
+
+class Attention_with_Classifier(nn.Module):
+ def __init__(self, L=512, D=128, K=1, num_cls=2, droprate=0):
+ super(Attention_with_Classifier, self).__init__()
+ self.attention = Attention_Gated(L, D, K)
+ self.classifier = Classifier_1fc(L, num_cls, droprate)
+ def forward(self, x): ## x: N x L
+ AA = self.attention(x) ## K x N
+ afeat = torch.mm(AA, x) ## K x L
+ pred = self.classifier(afeat) ## K x num_cls
+ return pred
+
+class Classifier_1fc(nn.Module):
+ def __init__(self, n_channels, n_classes, droprate=0.0):
+ super(Classifier_1fc, self).__init__()
+ self.fc = nn.Linear(n_channels, n_classes)
+ self.droprate = droprate
+ if self.droprate != 0.0:
+ self.dropout = torch.nn.Dropout(p=self.droprate)
+
+ def forward(self, x):
+
+ if self.droprate != 0.0:
+ x = self.dropout(x)
+ x = self.fc(x)
+ return x
+
+
+class residual_block(nn.Module):
+ def __init__(self, nChn=512):
+ super(residual_block, self).__init__()
+ self.block = nn.Sequential(
+ nn.Linear(nChn, nChn, bias=False),
+ nn.ReLU(inplace=True),
+ nn.Linear(nChn, nChn, bias=False),
+ nn.ReLU(inplace=True),
+ )
+ def forward(self, x):
+ tt = self.block(x)
+ x = x + tt
+ return x
+
+
+class DimReduction(nn.Module):
+ def __init__(self, n_channels, m_dim=512, numLayer_Res=0):
+ super(DimReduction, self).__init__()
+ self.fc1 = nn.Linear(n_channels, m_dim, bias=False)
+ self.relu1 = nn.ReLU(inplace=True)
+ self.numRes = numLayer_Res
+
+ self.resBlocks = []
+ for ii in range(numLayer_Res):
+ self.resBlocks.append(residual_block(m_dim))
+ self.resBlocks = nn.Sequential(*self.resBlocks)
+
+ def forward(self, x):
+
+ x = self.fc1(x)
+ x = self.relu1(x)
+
+ if self.numRes > 0:
+ x = self.resBlocks(x)
+
+ return x
\ No newline at end of file
diff --git a/models/TransMIL.py b/code/models/TransMIL.py
similarity index 70%
rename from models/TransMIL.py
rename to code/models/TransMIL.py
index ca2a1fb..c78599d 100755
--- a/models/TransMIL.py
+++ b/code/models/TransMIL.py
@@ -17,13 +17,15 @@ class TransLayer(nn.Module):
num_landmarks = dim//2, # number of landmarks
pinv_iterations = 6, # number of moore-penrose iterations for approximating pinverse. 6 was recommended by the paper
residual = True, # whether to do an extra residual with the value or not. supposedly faster convergence if turned on
- dropout=0.1
+ dropout=0.7 #0.1
)
def forward(self, x):
- x = x + self.attn(self.norm(x))
+ out, attn = self.attn(self.norm(x), return_attn=True)
+ x = x + out
+ # x = x + self.attn(self.norm(x))
- return x
+ return x, attn
class PPEG(nn.Module):
@@ -44,8 +46,10 @@ class PPEG(nn.Module):
class TransMIL(nn.Module):
- def __init__(self, n_classes, in_features, out_features=384):
+ def __init__(self, n_classes):
super(TransMIL, self).__init__()
+ in_features = 512
+ 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())
@@ -62,42 +66,63 @@ class TransMIL(nn.Module):
h = x.float() #[B, n, 1024]
h = self._fc1(h) #[B, n, 512]
- #---->pad
+ # print('Feature Representation: ', h.shape)
+ #---->duplicate pad
H = h.shape[1]
_H, _W = int(np.ceil(np.sqrt(H))), int(np.ceil(np.sqrt(H)))
add_length = _H * _W - H
h = torch.cat([h, h[:,:add_length,:]],dim = 1) #[B, N, 512]
+
#---->cls_token
B = h.shape[0]
cls_tokens = self.cls_token.expand(B, -1, -1).cuda()
h = torch.cat((cls_tokens, h), dim=1)
+
#---->Translayer x1
- h = self.layer1(h) #[B, N, 512]
+ h, attn1 = self.layer1(h) #[B, N, 512]
+
+ # print('After first TransLayer: ', h.shape)
#---->PPEG
h = self.pos_layer(h, _H, _W) #[B, N, 512]
+ # print('After PPEG: ', h.shape)
#---->Translayer x2
- h = 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
- print(h.shape) #[1, 1025, 512] 1025 = cls_token + 1024
-
- # tokens = h
+
h = self.norm(h)[:,0]
#---->predict
logits = self._fc2(h) #[B, n_classes]
- return logits
+ return logits, attn2
if __name__ == "__main__":
data = torch.randn((1, 6000, 512)).cuda()
- model = TransMIL(n_classes=2, in_features=512).cuda()
+ model = TransMIL(n_classes=2).cuda()
print(model.eval())
- results_dict = model(data)
- print(results_dict)
+ logits, attn = model(data)
+ cls_attention = attn[:,:, 0, :6000]
+ values, indices = torch.max(cls_attention, 1)
+ mean = values.mean()
+ zeros = torch.zeros(values.shape).cuda()
+ filtered = torch.where(values > mean, values, zeros)
+
+ # filter = values > values.mean()
+ # filtered_values = values[filter]
+ # values = np.where(values>values.mean(), values, 0)
+
+ print(filtered.shape)
+
+
+ # values = [v if v > values.mean().item() else 0 for v in values]
+ # print(values)
+ # print(len(values))
+
# logits = results_dict['logits']
# Y_prob = results_dict['Y_prob']
# Y_hat = results_dict['Y_hat']
diff --git a/code/models/TransformerMIL.py b/code/models/TransformerMIL.py
new file mode 100644
index 0000000..ea249ba
--- /dev/null
+++ b/code/models/TransformerMIL.py
@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from nystrom_attention import NystromAttention
+
+
+class TransLayer(nn.Module):
+
+ def __init__(self, norm_layer=nn.LayerNorm, dim=512):
+ super().__init__()
+ self.norm = norm_layer(dim)
+ self.attn = NystromAttention(
+ dim = dim,
+ dim_head = dim//8,
+ heads = 8,
+ num_landmarks = dim//2, # number of landmarks
+ pinv_iterations = 6, # number of moore-penrose iterations for approximating pinverse. 6 was recommended by the paper
+ residual = True, # whether to do an extra residual with the value or not. supposedly faster convergence if turned on
+ dropout=0.25 #0.1
+ )
+
+ def forward(self, x):
+ out, attn = self.attn(self.norm(x), return_attn=True)
+ x = x + out
+ # x = x + self.attn(self.norm(x))
+
+ return x, attn
+
+
+class PPEG(nn.Module):
+ def __init__(self, dim=512):
+ super(PPEG, self).__init__()
+ self.proj = nn.Conv2d(dim, dim, 7, 1, 7//2, groups=dim)
+ self.proj1 = nn.Conv2d(dim, dim, 5, 1, 5//2, groups=dim)
+ self.proj2 = nn.Conv2d(dim, dim, 3, 1, 3//2, groups=dim)
+
+ def forward(self, x, H, W):
+ B, _, C = x.shape
+ cls_token, feat_token = x[:, 0], x[:, 1:]
+ cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
+ x = self.proj(cnn_feat)+cnn_feat+self.proj1(cnn_feat)+self.proj2(cnn_feat)
+ x = x.flatten(2).transpose(1, 2)
+ x = torch.cat((cls_token.unsqueeze(1), x), dim=1)
+ return x
+
+
+class TransformerMIL(nn.Module):
+ def __init__(self, n_classes):
+ super(TransformerMIL, self).__init__()
+ in_features = 512
+ out_features = 512
+ self.pos_layer = PPEG(dim=out_features)
+ self._fc1 = nn.Sequential(nn.Linear(in_features, out_features), nn.GELU())
+ # self._fc1 = nn.Sequential(nn.Linear(1024, 512), nn.ReLU())
+ self.cls_token = nn.Parameter(torch.randn(1, 1, out_features))
+ self.n_classes = n_classes
+ self.layer1 = TransLayer(dim=out_features)
+ self.layer2 = TransLayer(dim=out_features)
+ self.norm = nn.LayerNorm(out_features)
+ self._fc2 = nn.Linear(out_features, self.n_classes)
+
+
+ def forward(self, x): #, **kwargs
+
+ h = x.float() #[B, n, 1024]
+ h = self._fc1(h) #[B, n, 512]
+
+ # print('Feature Representation: ', h.shape)
+ #---->duplicate pad
+ H = h.shape[1]
+ _H, _W = int(np.ceil(np.sqrt(H))), int(np.ceil(np.sqrt(H)))
+ add_length = _H * _W - H
+ h = torch.cat([h, h[:,:add_length,:]],dim = 1) #[B, N, 512]
+
+
+ #---->cls_token
+ B = h.shape[0]
+ cls_tokens = self.cls_token.expand(B, -1, -1).cuda()
+ h = torch.cat((cls_tokens, h), dim=1)
+
+
+ #---->Translayer x1
+ h, attn1 = self.layer1(h) #[B, N, 512]
+
+ # print('After first TransLayer: ', h.shape)
+
+ #---->PPEG
+ # h = self.pos_layer(h, _H, _W) #[B, N, 512]
+ # # print('After PPEG: ', h.shape)
+
+ # #---->Translayer x2
+ # h, attn2 = self.layer2(h) #[B, N, 512]
+
+ # print('After second TransLayer: ', h.shape) #[1, 1025, 512] 1025 = cls_token + 1024
+ #---->cls_token
+
+ h = self.norm(h)[:,0]
+
+ #---->predict
+ logits = self._fc2(h) #[B, n_classes]
+ return logits, attn1
+
+if __name__ == "__main__":
+ data = torch.randn((1, 6000, 512)).cuda()
+ model = TransMIL(n_classes=2).cuda()
+ print(model.eval())
+ logits, attn = model(data)
+ cls_attention = attn[:,:, 0, :6000]
+ values, indices = torch.max(cls_attention, 1)
+ mean = values.mean()
+ zeros = torch.zeros(values.shape).cuda()
+ filtered = torch.where(values > mean, values, zeros)
+
+ # filter = values > values.mean()
+ # filtered_values = values[filter]
+ # values = np.where(values>values.mean(), values, 0)
+
+ print(filtered.shape)
+
+
+ # values = [v if v > values.mean().item() else 0 for v in values]
+ # print(values)
+ # print(len(values))
+
+ # logits = results_dict['logits']
+ # Y_prob = results_dict['Y_prob']
+ # Y_hat = results_dict['Y_hat']
+ # print(F.sigmoid(logits))
diff --git a/models/__init__.py b/code/models/__init__.py
similarity index 100%
rename from models/__init__.py
rename to code/models/__init__.py
diff --git a/models/__pycache__/AttMIL.cpython-39.pyc b/code/models/__pycache__/AttMIL.cpython-39.pyc
similarity index 79%
rename from models/__pycache__/AttMIL.cpython-39.pyc
rename to code/models/__pycache__/AttMIL.cpython-39.pyc
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diff --git a/code/models/__pycache__/model_interface.cpython-39.pyc b/code/models/__pycache__/model_interface.cpython-39.pyc
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diff --git a/models/__pycache__/resnet50.cpython-39.pyc b/code/models/__pycache__/resnet50.cpython-39.pyc
similarity index 100%
rename from models/__pycache__/resnet50.cpython-39.pyc
rename to code/models/__pycache__/resnet50.cpython-39.pyc
diff --git a/models/__pycache__/vision_transformer.cpython-39.pyc b/code/models/__pycache__/vision_transformer.cpython-39.pyc
similarity index 100%
rename from models/__pycache__/vision_transformer.cpython-39.pyc
rename to code/models/__pycache__/vision_transformer.cpython-39.pyc
diff --git a/models/model_interface.py b/code/models/model_interface.py
similarity index 70%
rename from models/model_interface.py
rename to code/models/model_interface.py
index b3a561e..586d80c 100755
--- a/models/model_interface.py
+++ b/code/models/model_interface.py
@@ -1,5 +1,6 @@
import sys
import numpy as np
+import re
import inspect
import importlib
import random
@@ -30,9 +31,10 @@ from torch import optim as optim
#---->
import pytorch_lightning as pl
from .vision_transformer import vit_small
+import torchvision
from torchvision import models
from torchvision.models import resnet
-from transformers import AutoFeatureExtractor, ViTModel
+from transformers import AutoFeatureExtractor, ViTModel, SwinModel
from pytorch_grad_cam import GradCAM, EigenGradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
@@ -52,12 +54,11 @@ class ModelInterface(pl.LightningModule):
# self.loss = LabelSmoothingCrossEntropy(smoothing=0.1)
# self.loss =
# print(self.model)
-
+ self.model_name = model.name
# self.ecam = EigenGradCAM(model = self.model, target_layers = target_layers, use_cuda=True, reshape_transform=self.reshape_transform)
self.optimizer = optimizer
self.n_classes = model.n_classes
- print(self.n_classes)
self.save_path = kargs['log']
if Path(self.save_path).parts[3] == 'tcmr':
temp = list(Path(self.save_path).parts)
@@ -85,7 +86,7 @@ class ModelInterface(pl.LightningModule):
num_classes = self.n_classes)])
else :
- self.AUROC = torchmetrics.AUROC(num_classes=2, average = 'weighted')
+ self.AUROC = torchmetrics.AUROC(num_classes=self.n_classes, average = 'weighted')
metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = 2,
average = 'micro'),
@@ -97,6 +98,7 @@ class ModelInterface(pl.LightningModule):
torchmetrics.Precision(average = 'macro',
num_classes = 2)])
self.PRC = torchmetrics.PrecisionRecallCurve(num_classes = self.n_classes)
+ self.ROC = torchmetrics.ROC(num_classes=self.n_classes)
# 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_')
@@ -112,39 +114,42 @@ class ModelInterface(pl.LightningModule):
self.feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/dino-vitb16')
self.model_ft = ViTModel.from_pretrained('facebook/dino-vitb16')
elif kargs['backbone'] == 'resnet18':
- resnet18 = models.resnet18(pretrained=True)
- modules = list(resnet18.children())[:-1]
- # model_ft.fc = nn.Linear(512, out_features)
-
- res18 = nn.Sequential(
- *modules,
- )
- for param in res18.parameters():
+ self.model_ft = models.resnet18(pretrained=True)
+ # modules = list(resnet18.children())[:-1]
+ for param in self.model_ft.parameters():
param.requires_grad = False
- self.model_ft = nn.Sequential(
- res18,
- nn.AdaptiveAvgPool2d(1),
- View((-1, 512)),
- nn.Linear(512, self.out_features),
- nn.GELU(),
- )
+ self.model_ft.fc = nn.Linear(512, self.out_features)
+
+ # res18 = nn.Sequential(
+ # *modules,
+ # )
+ # for param in res18.parameters():
+ # param.requires_grad = False
+ # self.model_ft = nn.Sequential(
+ # res18,
+ # nn.AdaptiveAvgPool2d(1),
+ # View((-1, 512)),
+ # nn.Linear(512, self.out_features),
+ # nn.GELU(),
+ # )
elif kargs['backbone'] == 'resnet50':
- resnet50 = models.resnet50(pretrained=True)
- # model_ft.fc = nn.Linear(1024, out_features)
- modules = list(resnet50.children())[:-3]
- res50 = nn.Sequential(
- *modules,
- )
- for param in res50.parameters():
+ self.model_ft = models.resnet50(pretrained=True)
+ for param in self.model_ft.parameters():
param.requires_grad = False
- self.model_ft = nn.Sequential(
- res50,
- nn.AdaptiveAvgPool2d(1),
- View((-1, 1024)),
- nn.Linear(1024, self.out_features),
- # nn.GELU()
- )
+ self.model_ft.fc = nn.Linear(2048, self.out_features)
+ # modules = list(resnet50.children())[:-3]
+ # res50 = nn.Sequential(
+ # *modules,
+ # )
+
+ # self.model_ft = nn.Sequential(
+ # res50,
+ # nn.AdaptiveAvgPool2d(1),
+ # View((-1, 1024)),
+ # nn.Linear(1024, self.out_features),
+ # # nn.GELU()
+ # )
elif kargs['backbone'] == 'efficientnet':
efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_widese_b0', pretrained=True)
for param in efficientnet.parameters():
@@ -164,30 +169,22 @@ class ModelInterface(pl.LightningModule):
nn.Conv2d(20, 50, kernel_size=5),
nn.ReLU(),
nn.MaxPool2d(2, stride=2),
- View((-1, 1024)),
- nn.Linear(1024, self.out_features),
+ View((-1, 53*53)),
+ nn.Linear(53*53, self.out_features),
nn.ReLU(),
)
# print(self.model_ft[0].features[-1])
# print(self.model_ft)
- if 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)
def forward(self, x):
-
+ # print(x.shape)
feats = self.model_ft(x).unsqueeze(0)
return self.model(feats)
def step(self, input):
input = input.squeeze(0).float()
- logits = self(input)
+ logits, _ = self(input)
Y_hat = torch.argmax(logits, dim=1)
Y_prob = F.softmax(logits, dim=1)
@@ -195,10 +192,14 @@ class ModelInterface(pl.LightningModule):
return logits, Y_prob, Y_hat
def training_step(self, batch, batch_idx):
- #---->inference
-
input, label, _= batch
+
+ #random image dropout
+ # bag_size = 500
+ # bag_idxs = torch.randperm(input.squeeze(0).shape[0])[:bag_size]
+ # input = input.squeeze(0)[bag_idxs].unsqueeze(0)
+
label = label.float()
logits, Y_prob, Y_hat = self.step(input)
@@ -217,24 +218,31 @@ class ModelInterface(pl.LightningModule):
# Y = int(label[0])
self.data[Y]["count"] += 1
self.data[Y]["correct"] += (int(Y_hat) == Y)
- self.log('loss', loss, prog_bar=True, on_epoch=True, logger=True)
+ self.log('loss', loss, prog_bar=True, on_epoch=True, logger=True, batch_size=1)
if self.current_epoch % 10 == 0:
- grid = torchvision.utils.make_grid(images)
+ # images = input.squeeze()[:10, :, :, :]
+ # for i in range(10):
+ img = input.squeeze(0)[:10, :, :, :]
+ img = (img - torch.min(img)/(torch.max(img)-torch.min(img)))*255.0
+
+ # mg = img.cpu().numpy()
+ grid = torchvision.utils.make_grid(img, normalize=True, value_range=(0, 255), scale_each=True)
+ # grid = img.detach().cpu().numpy()
# log input images
- # self.loggers[0].experiment.add_figure(f'{stage}/input', , self.current_epoch)
+ self.loggers[0].experiment.add_image(f'{self.current_epoch}/input', grid)
- return {'loss': loss, 'Y_prob': Y_prob, 'Y_hat': Y_hat, 'label': label}
+ return {'loss': loss, 'Y_prob': Y_prob, 'Y_hat': Y_hat, 'label': Y}
def training_epoch_end(self, training_step_outputs):
# logits = torch.cat([x['logits'] for x in training_step_outputs], dim = 0)
probs = torch.cat([x['Y_prob'] for x in training_step_outputs])
max_probs = torch.stack([x['Y_hat'] for x in training_step_outputs])
# target = torch.stack([x['label'] for x in training_step_outputs], dim = 0)
- target = torch.cat([x['label'] for x in training_step_outputs])
- target = torch.argmax(target, dim=1)
+ target = torch.stack([x['label'] for x in training_step_outputs])
+ # target = torch.argmax(target, dim=1)
for c in range(self.n_classes):
count = self.data[c]["count"]
correct = self.data[c]["correct"]
@@ -248,9 +256,9 @@ class ModelInterface(pl.LightningModule):
# print('max_probs: ', max_probs)
# print('probs: ', probs)
if self.current_epoch % 10 == 0:
- self.log_confusion_matrix(probs, target, stage='train')
+ self.log_confusion_matrix(max_probs, target, stage='train')
- self.log('Train/auc', self.AUROC(probs, target.squeeze()), prog_bar=True, on_epoch=True, logger=True)
+ self.log('Train/auc', self.AUROC(probs, target), prog_bar=True, on_epoch=True, logger=True)
def validation_step(self, batch, batch_idx):
@@ -266,21 +274,24 @@ class ModelInterface(pl.LightningModule):
self.data[Y]["count"] += 1
self.data[Y]["correct"] += (Y_hat.item() == Y)
- return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : label}
+ return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : Y}
def validation_epoch_end(self, 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.cat([x['label'] for x in val_step_outputs])
- target = torch.argmax(target, dim=1)
+ target = torch.stack([x['label'] for x in val_step_outputs])
+
+ self.log_dict(self.valid_metrics(logits, target),
+ on_epoch = True, logger = 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.squeeze()), prog_bar=True, on_epoch=True, logger=True)
+ self.log('val_auc', self.AUROC(probs, target), prog_bar=True, on_epoch=True, logger=True)
else:
self.log('val_auc', 0.0, prog_bar=True, on_epoch=True, logger=True)
@@ -289,13 +300,16 @@ class ModelInterface(pl.LightningModule):
self.log('val_loss', cross_entropy_torch(logits, target), prog_bar=True, on_epoch=True, logger=True)
+ precision, recall, thresholds = self.PRC(probs, target)
+
+
+
# print(max_probs.squeeze(0).shape)
# print(target.shape)
- self.log_dict(self.valid_metrics(max_probs.squeeze() , target),
- on_epoch = True, logger = True)
+
#----> log confusion matrix
- self.log_confusion_matrix(probs, target, stage='val')
+ self.log_confusion_matrix(max_probs, target, stage='val')
#---->acc log
@@ -306,7 +320,7 @@ class ModelInterface(pl.LightningModule):
acc = None
else:
acc = float(correct) / count
- print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+ print('val class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
#---->random, if shuffle data, change seed
@@ -315,34 +329,73 @@ class ModelInterface(pl.LightningModule):
random.seed(self.count*50)
def test_step(self, batch, batch_idx):
+
torch.set_grad_enabled(True)
- data, label, name = batch
+ 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 = self(data).detach()
+ 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 Topk tiles
+ #----> 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)
- target = [ClassifierOutputTarget(Y)]
data_ft = self.model_ft(data).unsqueeze(0).float()
- # data_ft = self.model_ft(data).unsqueeze(0).float()
- # print(data_ft.shape)
- # print(target)
+ instance_count = data.size(0)
+ target = [ClassifierOutputTarget(Y)]
grayscale_cam = self.cam(input_tensor=data_ft, targets=target)
- # grayscale_ecam = self.ecam(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')
+
- # print(grayscale_cam)
+ # output_path = self.save_path / str(Y.item())
+ # output_path.mkdir(parents=True, exist_ok=True)
+ # img.save(f'{output_path}/{wsi_name}.jpg')
- summed = torch.mean(torch.Tensor(grayscale_cam), dim=2)
- print(summed)
- print(summed.shape)
+
+ #----> 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()
@@ -368,20 +421,33 @@ class ModelInterface(pl.LightningModule):
self.data[Y]["count"] += 1
self.data[Y]["correct"] += (Y_hat.item() == Y)
- 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' : 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
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.cat([x['label'] for x in output_results])
- target = torch.argmax(target, dim=1)
+ 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.squeeze())
- metrics = self.test_metrics(max_probs.squeeze() , target)
+ auc = self.AUROC(probs, target)
+ fpr, tpr, thresholds = self.ROC(probs, target)
+ fpr = fpr.cpu().numpy()
+ tpr = tpr.cpu().numpy()
+
+ 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)
+
+ metrics = self.test_metrics(logits , target)
# metrics = self.test_metrics(max_probs.squeeze() , torch.argmax(target.squeeze(), dim=1))
@@ -390,16 +456,20 @@ class ModelInterface(pl.LightningModule):
# self.log('auc', auc, prog_bar=True, on_epoch=True, logger=True)
#---->get highest scoring patients for each class
- test_path = Path(self.save_path) / 'most_predictive'
+ # 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[0]
- outpath = test_path / str(n) / patient
+ 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]
@@ -408,7 +478,7 @@ class ModelInterface(pl.LightningModule):
tile = tile.astype(np.uint8)
img = Image.fromarray(tile)
- img.save(f'{test_path}/{n}/{patient}/{i}_gradcam.jpg')
+ img.save(f'{outpath}/{i}.jpg')
@@ -448,7 +518,7 @@ class ModelInterface(pl.LightningModule):
#[tp, fp, tn, fn, tp+fn]
- self.log_confusion_matrix(probs, target, stage='test')
+ 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())
@@ -462,8 +532,13 @@ class ModelInterface(pl.LightningModule):
optimizer = create_optimizer(self.optimizer, self.model)
return optimizer
- def reshape_transform(self, tensor, h=32, w=32):
- result = tensor[:, 1:, :].reshape(tensor.size(0), h, w, tensor.size(2))
+ def reshape_transform(self, tensor):
+ # print(tensor.shape)
+ H = tensor.shape[1]
+ _H, _W = int(np.ceil(np.sqrt(H))), int(np.ceil(np.sqrt(H)))
+ add_length = _H * _W - H
+ tensor = torch.cat([tensor, tensor[:,:add_length,:]],dim = 1)
+ result = tensor[:, :, :].reshape(tensor.size(0), _H, _W, tensor.size(2))
result = result.transpose(2,3).transpose(1,2)
# print(result.shape)
return result
@@ -510,24 +585,42 @@ class ModelInterface(pl.LightningModule):
def log_confusion_matrix(self, max_probs, target, stage):
- confmat = self.confusion_matrix(max_probs.squeeze(), target)
+ confmat = self.confusion_matrix(max_probs, target)
print(confmat)
df_cm = pd.DataFrame(confmat.cpu().numpy(), index=range(self.n_classes), columns=range(self.n_classes))
- # plt.figure()
- fig_ = sns.heatmap(df_cm, annot=True, cmap='Spectral').get_figure()
- # plt.close(fig_)
- # plt.savefig(f'{self.save_path}/cm_e{self.current_epoch}')
+ fig_ = sns.heatmap(df_cm, annot=True, fmt='d', cmap='Spectral').get_figure()
+ 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)
+
+ def log_roc_curve(self, probs, target, stage):
+
+ fpr_list, tpr_list, thresholds = self.ROC(probs, target)
+
+ plt.figure(1)
+ if self.n_classes > 2:
+ for i in range(len(fpr_list)):
+ fpr = fpr_list[i].cpu().numpy()
+ tpr = tpr_list[i].cpu().numpy()
+ plt.plot(fpr, tpr, label=f'class_{i}')
+ else:
+ print(fpr_list)
+ fpr = fpr_list.cpu().numpy()
+ tpr = tpr_list.cpu().numpy()
+ plt.plot(fpr, tpr)
+ plt.xlabel('False positive rate')
+ plt.ylabel('True positive rate')
+ plt.title('ROC curve')
+ plt.savefig(f'{self.loggers[0].log_dir}/roc.jpg')
if stage == 'train':
- # print(self.save_path)
- # plt.savefig(f'{self.save_path}/cm_test')
-
- self.loggers[0].experiment.add_figure(f'{stage}/Confusion matrix', fig_, self.current_epoch)
+ self.loggers[0].experiment.add_figure(f'{stage}/ROC', plt, self.current_epoch)
else:
- fig_.savefig(f'{self.save_path}/cm_test.png', dpi=400)
- # plt.close(fig_)
- # self.logger[0].experiment.add_figure('Confusion matrix', fig_, self.current_epoch)
+ plt.savefig(f'{self.loggers[0].log_dir}/roc.jpg', dpi=400)
+
+
class View(nn.Module):
def __init__(self, shape):
@@ -538,8 +631,6 @@ class View(nn.Module):
'''
Reshapes the input according to the shape saved in the view data structure.
'''
- # batch_size = input.size(0)
- # shape = (batch_size, *self.shape)
out = input.view(*self.shape)
return out
diff --git a/code/models/model_interface_dtfd.py b/code/models/model_interface_dtfd.py
new file mode 100644
index 0000000..0b503cc
--- /dev/null
+++ b/code/models/model_interface_dtfd.py
@@ -0,0 +1,707 @@
+import sys
+import numpy as np
+import re
+import inspect
+import importlib
+import random
+import pandas as pd
+import seaborn as sns
+from pathlib import Path
+from matplotlib import pyplot as plt
+import cv2
+from PIL import Image
+
+#---->
+from MyOptimizer import create_optimizer
+from MyLoss import create_loss
+from utils.utils import cross_entropy_torch
+from timm.loss import AsymmetricLossSingleLabel
+from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy, JsdCrossEntropy
+
+#---->
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchmetrics
+from torchmetrics.functional import stat_scores
+from torch import optim as optim
+# from sklearn.metrics import roc_curve, auc, roc_curve_score
+
+
+#---->
+import pytorch_lightning as pl
+from .vision_transformer import vit_small
+import torchvision
+from torchvision import models
+from torchvision.models import resnet
+from transformers import AutoFeatureExtractor, ViTModel
+
+from pytorch_grad_cam import GradCAM, EigenGradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+
+from captum.attr import LayerGradCam
+from models.DTFDMIL import Attention_Gated, Classifier_1fc, DimReduction, Attention_with_Classifier
+
+class ModelInterface_DTFD(pl.LightningModule):
+
+ #---->init
+ def __init__(self, model, loss, optimizer, **kargs):
+ super(ModelInterface_DTFD, self).__init__()
+ self.save_hyperparameters()
+ # self.load_model()
+ self.loss = create_loss(loss)
+ # self.asl = AsymmetricLossSingleLabel()
+ # self.loss = LabelSmoothingCrossEntropy(smoothing=0.1)
+ # self.loss =
+ # print(self.model)
+ self.model_name = model.name
+
+ self.optimizer = optimizer
+ self.n_classes = model.n_classes
+ self.save_path = kargs['log']
+ if Path(self.save_path).parts[3] == 'tcmr':
+ temp = list(Path(self.save_path).parts)
+ temp[3] = 'tcmr_viral'
+ self.save_path = '/'.join(temp)
+
+ #---->acc
+ self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
+ #---->Metrics
+ if self.n_classes > 2:
+ self.AUROC = torchmetrics.AUROC(num_classes = self.n_classes, average = 'weighted')
+
+ metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = self.n_classes,
+ average='micro'),
+ torchmetrics.CohenKappa(num_classes = self.n_classes),
+ torchmetrics.F1Score(num_classes = self.n_classes,
+ average = 'macro'),
+ torchmetrics.Recall(average = 'macro',
+ num_classes = self.n_classes),
+ torchmetrics.Precision(average = 'macro',
+ num_classes = self.n_classes),
+ torchmetrics.Specificity(average = 'macro',
+ num_classes = self.n_classes)])
+
+ else :
+ self.AUROC = torchmetrics.AUROC(num_classes=self.n_classes, average = 'weighted')
+
+ metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = 2,
+ average = 'micro'),
+ torchmetrics.CohenKappa(num_classes = 2),
+ torchmetrics.F1Score(num_classes = 2,
+ average = 'macro'),
+ torchmetrics.Recall(average = 'macro',
+ num_classes = 2),
+ torchmetrics.Precision(average = 'macro',
+ num_classes = 2)])
+ self.PRC = torchmetrics.PrecisionRecallCurve(num_classes = self.n_classes)
+ self.ROC = torchmetrics.ROC(num_classes=self.n_classes)
+ # 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.test_metrics = metrics.clone(prefix = 'test_')
+
+ #--->random
+ self.shuffle = kargs['data'].data_shuffle
+ self.count = 0
+ self.backbone = kargs['backbone']
+
+ self.out_features = 1024
+ if kargs['backbone'] == 'dino':
+ self.feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/dino-vitb16')
+ self.model_ft = ViTModel.from_pretrained('facebook/dino-vitb16')
+ elif kargs['backbone'] == 'resnet18':
+ self.model_ft = models.resnet18(pretrained=True)
+ # modules = list(resnet18.children())[:-1]
+ 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,
+ # )
+ # for param in res18.parameters():
+ # param.requires_grad = False
+ # self.model_ft = nn.Sequential(
+ # res18,
+ # nn.AdaptiveAvgPool2d(1),
+ # View((-1, 512)),
+ # nn.Linear(512, self.out_features),
+ # nn.GELU(),
+ # )
+ elif kargs['backbone'] == 'resnet50':
+
+ self.model_ft = models.resnet50(pretrained=True)
+ for param in self.model_ft.parameters():
+ param.requires_grad = False
+ self.model_ft.fc = nn.Linear(2048, self.out_features)
+
+ # modules = list(resnet50.children())[:-3]
+ # res50 = nn.Sequential(
+ # *modules,
+ # )
+
+ # self.model_ft = nn.Sequential(
+ # res50,
+ # nn.AdaptiveAvgPool2d(1),
+ # View((-1, 1024)),
+ # nn.Linear(1024, self.out_features),
+ # # nn.GELU()
+ # )
+ elif kargs['backbone'] == 'efficientnet':
+ efficientnet = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_efficientnet_widese_b0', pretrained=True)
+ for param in efficientnet.parameters():
+ param.requires_grad = False
+ # efn = list(efficientnet.children())[:-1]
+ efficientnet.classifier.fc = nn.Linear(1280, self.out_features)
+ self.model_ft = nn.Sequential(
+ efficientnet,
+ nn.GELU(),
+ )
+ self.classifier = Classifier_1fc(n_channels=512, n_classes=self.n_classes)
+ self.attention = Attention_Gated(features=512)
+ self.dimreduction = DimReduction(n_channels=self.out_features, m_dim=512)
+ self.attCls = Attention_with_Classifier(L=512, num_cls=self.n_classes)
+ self.trainable_parameters = []
+ self.trainable_parameters += list(self.classifier.parameters())
+ self.trainable_parameters += list(self.attention.parameters())
+ self.trainable_parameters += list(self.dimreduction.parameters())
+
+ # print(self.model_ft[0].features[-1])
+ # print(self.model_ft)
+
+ def forward(self, x, bag_size=120):
+ # print(x.shape)
+ x = x.float()
+ max_pseudo_bags = x.squeeze(0).shape[0] // bag_size
+ max_pseudo_bags = min(8, max_pseudo_bags)
+
+ slide_pseudo_feat = []
+ sub_predictions = []
+
+ input = x.squeeze(0)
+ features = self.model_ft(input) # max_pseudo_bags, 512
+ features = self.dimreduction(features)
+ randomized_idx = torch.randperm(features.shape[0])
+
+
+ for n in range(max_pseudo_bags):
+
+ bag_idxs = randomized_idx[bag_size*n:bag_size*(n+1)] #torch.randperm(x.squeeze(0).shape[0])
+ bag_features = features.squeeze(0)[bag_idxs]
+
+ t1AA = self.attention(bag_features).squeeze(0)
+ # print('features: ', features.shape)
+ # print('t1AA: ', t1AA.shape)
+ t1attFeats = torch.einsum('ns, n->ns', bag_features, t1AA)
+ # print('t1attFeats: ', t1attFeats.shape)
+ t1attFeats_tensor = torch.sum(t1attFeats, dim=0).unsqueeze(0)
+ # print('t1attFeats_tensor: ', t1attFeats_tensor.shape)
+ t1Predict = self.classifier(t1attFeats_tensor)
+ sub_predictions.append(t1Predict)
+
+ patch_pred_logits = get_cam_1d(self.classifier, t1attFeats.unsqueeze(0)).squeeze(0)
+ patch_pred_logits = torch.transpose(patch_pred_logits, 0, 1) ## n x cls
+ # patch_pred_softmax = torch.softmax(patch_pred_logits, dim=1) ## n x cls
+
+ af_inst_feat = t1attFeats_tensor
+ slide_pseudo_feat.append(af_inst_feat)
+
+ slide_pseudo_feat = torch.cat(slide_pseudo_feat, dim=0)
+
+ ## optimization for first tier
+ sub_predictions = torch.cat(sub_predictions, dim=0)
+
+ ## optimization for second tier
+ slide_prediction = self.attCls(slide_pseudo_feat)
+
+ Y_hat = torch.argmax(slide_prediction, dim=1)
+ Y_prob = F.softmax(slide_prediction, dim=1)
+
+
+
+ return sub_predictions, slide_prediction, Y_prob, Y_hat
+
+
+ def training_step(self, batch, batch_idx, optimizer_idx):
+
+ input, label, _= batch
+
+
+ sub_predictions, slide_prediction, Y_prob, Y_hat = self(input)
+
+ # print(sub_predictions.size(0))
+ label = label.float()
+ sub_labels = [label] * sub_predictions.size(0)
+ sub_labels = torch.cat(sub_labels, dim=0)
+
+
+ sub_loss = self.loss(sub_predictions, sub_labels)
+ slide_loss = self.loss(slide_prediction, label)
+
+
+ Y = torch.argmax(label)
+ # Y = int(label[0])
+ self.data[Y]["count"] += 1
+ self.data[Y]["correct"] += (int(Y_hat) == Y)
+ self.log('sub_loss', sub_loss, prog_bar=True, on_epoch=True, logger=True, batch_size=1)
+ self.log('slide_loss', slide_loss, prog_bar=True, on_epoch=True, logger=True, batch_size=1)
+
+ # if self.current_epoch % 10 == 0:
+
+ # # images = input.squeeze()[:10, :, :, :]
+ # # for i in range(10):
+ # img = input.squeeze(0)[:10, :, :, :]
+ # img = (img - torch.min(img)/(torch.max(img)-torch.min(img)))*255.0
+
+ # # mg = img.cpu().numpy()
+ # grid = torchvision.utils.make_grid(img, normalize=True, value_range=(0, 255), scale_each=True)
+ # # grid = img.detach().cpu().numpy()
+ # # log input images
+ # self.loggers[0].experiment.add_image(f'{self.current_epoch}/input', grid)
+
+
+ # print(Y_prob)
+ # print(Y_prob.shape)
+
+ total_loss = (sub_loss + slide_loss)/2
+ # print(sub_predictions)
+ # print(sub_labels)
+ # sub_probs = sub_predictions
+ # sub_targets = torch.argmax(sub_labels, dim=1)
+ # if len(sub_targets.unique()) != 1:
+ # self.log('Train/sub_auc', self.AUROC(sub_predictions, sub_targets), prog_bar=True, on_epoch=True, logger=True)
+
+ # else:
+ # self.log('Train/sub_auc', 0.0, prog_bar=True, on_epoch=True, logger=True)
+
+ return {'loss': total_loss, 'Y_prob': Y_prob.detach(), 'Y_hat': Y_hat.detach(), 'label': Y}
+
+ def training_epoch_end(self, training_step_outputs):
+ # print(training_step_outputs)
+ # for x in training_step_outputs:
+ # print(x)
+ # print(x['Y_prob'])
+ # logits = torch.cat([x['logits'] for x in training_step_outputs], dim = 0)
+ probs = torch.cat([x[0]['Y_prob'] for x in training_step_outputs])
+ max_probs = torch.stack([x[0]['Y_hat'] for x in training_step_outputs])
+ # target = torch.stack([x['label'] for x in training_step_outputs], dim = 0)
+ target = torch.stack([x[0]['label'] for x in training_step_outputs])
+
+ # sub_probs = torch.cat(x[0]['sub_probs'] for x in training_step_outputs)
+ # sub_targets = torch.cat(x[0]['sub_targets'] for x in training_step_outputs)
+ # target = torch.argmax(target, dim=1)
+ for c in range(self.n_classes):
+ count = self.data[c]["count"]
+ correct = self.data[c]["correct"]
+ if count == 0:
+ acc = None
+ else:
+ acc = float(correct) / count
+ print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+ self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
+
+ # print('max_probs: ', max_probs)
+ # print('probs: ', probs)
+ if self.current_epoch % 10 == 0:
+ self.log_confusion_matrix(max_probs, target, stage='train')
+
+ self.log('Train/auc', self.AUROC(probs, target), prog_bar=True, on_epoch=True, logger=True)
+ # self.log('Train/sub_auc', self.AUROC(sub_probs, sub_targets), prog_bar=True, on_epoch=True, logger=True)
+
+ def validation_step(self, batch, batch_idx):
+
+ input, label, _= batch
+ label = label.float()
+
+ sub_predictions, slide_prediction, Y_prob, Y_hat = self(input)
+
+ # print(sub_predictions.size(0))
+ sub_labels = [label] * sub_predictions.size(0)
+
+
+ sub_labels = torch.stack(sub_labels).squeeze()
+ # print(sub_labels.shape)
+ # print(sub_predictions.shape)
+
+ sub_loss = self.loss(sub_predictions, sub_labels)
+ slide_loss = self.loss(slide_prediction, label)
+
+
+ Y = torch.argmax(label)
+
+ #---->acc log
+ # Y = int(label[0][1])
+ Y = torch.argmax(label)
+
+ self.data[Y]["count"] += 1
+ self.data[Y]["correct"] += (Y_hat.item() == Y)
+
+ return {'val_sub_loss': sub_loss, 'val_slide_loss': slide_loss, 'logits' : slide_prediction, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : Y}
+
+
+ def validation_epoch_end(self, 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])
+
+ self.log_dict(self.valid_metrics(logits, target),
+ on_epoch = True, logger = 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)
+ else:
+ self.log('val_auc', 0.0, prog_bar=True, on_epoch=True, logger=True)
+
+
+
+ self.log('val_loss', cross_entropy_torch(logits, target), prog_bar=True, on_epoch=True, logger=True)
+
+
+ precision, recall, thresholds = self.PRC(probs, target)
+
+
+
+ # print(max_probs.squeeze(0).shape)
+ # print(target.shape)
+
+
+ #----> log confusion matrix
+ self.log_confusion_matrix(max_probs, target, stage='val')
+
+
+ #---->acc log
+ for c in range(self.n_classes):
+ count = self.data[c]["count"]
+ correct = self.data[c]["correct"]
+ if count == 0:
+ acc = None
+ else:
+ acc = float(correct) / count
+ print('val class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+ self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
+
+ #---->random, if shuffle data, change seed
+ if self.shuffle == True:
+ 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')
+
+
+ #----> 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()
+
+ # 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)
+
+ #---->acc log
+ Y = torch.argmax(label)
+ self.data[Y]["count"] += 1
+ 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
+
+ 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()
+
+ 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)
+
+ metrics = self.test_metrics(logits , target)
+
+
+ # metrics = self.test_metrics(max_probs.squeeze() , torch.argmax(target.squeeze(), dim=1))
+ metrics['test_auc'] = auc
+
+ # self.log('auc', auc, prog_bar=True, on_epoch=True, logger=True)
+
+ #---->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)
+
+ img.save(f'{outpath}/{i}.jpg')
+
+
+
+ #----->visualize top predictive tiles
+
+
+
+
+ # 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
+ for c in range(self.n_classes):
+ count = self.data[c]["count"]
+ correct = self.data[c]["correct"]
+ if count == 0:
+ acc = None
+ else:
+ acc = float(correct) / count
+ print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+ self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
+
+ #---->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])
+
+ def configure_optimizers(self):
+ # optimizer_ft = optim.Adam(self.model_ft.parameters(), lr=self.optimizer.lr*0.1)
+ optimizer0 = torch.optim.Adam(self.trainable_parameters, lr=1e-4, weight_decay=1e-2)
+ optimizer1 = torch.optim.Adam(self.attCls.parameters(), lr=1e-4, weight_decay=1e-2)
+
+ scheduler0 = torch.optim.lr_scheduler.MultiStepLR(optimizer0, [100], gamma=0.2)
+ scheduler1 = torch.optim.lr_scheduler.MultiStepLR(optimizer1, [100], gamma=0.2)
+ return [optimizer0, optimizer1], [scheduler0, scheduler1]
+
+ def reshape_transform(self, tensor):
+ # print(tensor.shape)
+ H = tensor.shape[1]
+ _H, _W = int(np.ceil(np.sqrt(H))), int(np.ceil(np.sqrt(H)))
+ add_length = _H * _W - H
+ tensor = torch.cat([tensor, tensor[:,:add_length,:]],dim = 1)
+ result = tensor[:, :, :].reshape(tensor.size(0), _H, _W, tensor.size(2))
+ result = result.transpose(2,3).transpose(1,2)
+ # print(result.shape)
+ return result
+
+ def load_model(self):
+ name = self.hparams.model.name
+ # 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 '_' in name:
+ camel_name = ''.join([i.capitalize() for i in name.split('_')])
+ else:
+ camel_name = name
+ try:
+
+ Model = getattr(importlib.import_module(
+ f'models.{name}'), camel_name)
+ except:
+ raise ValueError('Invalid Module File Name or Invalid Class Name!')
+ self.model = self.instancialize(Model)
+ pass
+
+ def instancialize(self, Model, **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.hparams.
+ """
+ class_args = inspect.getargspec(Model.__init__).args[1:]
+ inkeys = self.hparams.model.keys()
+ args1 = {}
+ for arg in class_args:
+ if arg in inkeys:
+ args1[arg] = getattr(self.hparams.model, arg)
+ args1.update(other_args)
+ return Model(**args1)
+
+ def log_image(self, tensor, stage, name):
+
+ 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)
+ self.loggers[0].experiment.add_figure(f'{stage}/{name}', img, self.current_epoch)
+
+
+ def log_confusion_matrix(self, max_probs, target, stage):
+ confmat = self.confusion_matrix(max_probs, target)
+ print(confmat)
+ df_cm = pd.DataFrame(confmat.cpu().numpy(), index=range(self.n_classes), columns=range(self.n_classes))
+ fig_ = sns.heatmap(df_cm, annot=True, fmt='d', cmap='Spectral').get_figure()
+ 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)
+
+ def log_roc_curve(self, probs, target, stage):
+
+ fpr_list, tpr_list, thresholds = self.ROC(probs, target)
+
+ plt.figure(1)
+ if self.n_classes > 2:
+ for i in range(len(fpr_list)):
+ fpr = fpr_list[i].cpu().numpy()
+ tpr = tpr_list[i].cpu().numpy()
+ plt.plot(fpr, tpr, label=f'class_{i}')
+ else:
+ print(fpr_list)
+ fpr = fpr_list.cpu().numpy()
+ tpr = tpr_list.cpu().numpy()
+ plt.plot(fpr, tpr)
+
+ plt.xlabel('False positive rate')
+ plt.ylabel('True positive rate')
+ plt.title('ROC curve')
+ plt.savefig(f'{self.loggers[0].log_dir}/roc.jpg')
+
+ if stage == 'train':
+ self.loggers[0].experiment.add_figure(f'{stage}/ROC', plt, self.current_epoch)
+ else:
+ plt.savefig(f'{self.loggers[0].log_dir}/roc.jpg', dpi=400)
+
+def get_cam_1d(classifier, features):
+ tweight = list(classifier.parameters())[-2]
+ cam_maps = torch.einsum('bgf,cf->bcg', [features, tweight])
+ return cam_maps
+
+class View(nn.Module):
+ def __init__(self, shape):
+ super().__init__()
+ self.shape = shape
+
+ def forward(self, input):
+ '''
+ Reshapes the input according to the shape saved in the view data structure.
+ '''
+ out = input.view(*self.shape)
+ return out
+
diff --git a/models/resnet50.py b/code/models/resnet50.py
similarity index 100%
rename from models/resnet50.py
rename to code/models/resnet50.py
diff --git a/models/vision_transformer.py b/code/models/vision_transformer.py
similarity index 100%
rename from models/vision_transformer.py
rename to code/models/vision_transformer.py
diff --git a/code/test_visualize.py b/code/test_visualize.py
new file mode 100644
index 0000000..82f6cb4
--- /dev/null
+++ b/code/test_visualize.py
@@ -0,0 +1,437 @@
+import argparse
+from pathlib import Path
+import numpy as np
+import glob
+import re
+
+from sklearn.model_selection import KFold
+from scipy.interpolate import griddata
+
+# from datasets.data_interface import DataInterface, MILDataModule, CrossVal_MILDataModule
+from datasets import JPGMILDataloader, MILDataModule
+from models.model_interface import ModelInterface
+import models.vision_transformer as vits
+from utils.utils import *
+
+# pytorch_lightning
+import pytorch_lightning as pl
+from pytorch_lightning import Trainer
+import torch
+
+from pytorch_grad_cam import GradCAM, EigenGradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+
+import cv2
+from PIL import Image
+from matplotlib import pyplot as plt
+import pandas as pd
+
+#--->Setting parameters
+def make_parse():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--stage', default='test', type=str)
+ parser.add_argument('--config', default='DeepGraft/TransMIL.yaml',type=str)
+ parser.add_argument('--version', default=0,type=int)
+ parser.add_argument('--epoch', default='0',type=str)
+ parser.add_argument('--gpus', default = 2, type=int)
+ parser.add_argument('--loss', default = 'CrossEntropyLoss', type=str)
+ parser.add_argument('--fold', default = 0)
+ parser.add_argument('--bag_size', default = 1024, type=int)
+
+ args = parser.parse_args()
+ return args
+
+class custom_test_module(ModelInterface):
+
+ def test_step(self, batch, batch_idx):
+
+ torch.set_grad_enabled(True)
+ input_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)
+ input_data = input_data.squeeze(0).float()
+ logits, attn = 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)
+
+ #----> 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(input_data).unsqueeze(0).float()
+ instance_count = input_data.size(0)
+ target = [ClassifierOutputTarget(Y)]
+ grayscale_cam = self.cam(input_tensor=data_ft, targets=target)
+ grayscale_cam = torch.Tensor(grayscale_cam)[:instance_count, :] #.to(self.device)
+
+ #----------------------------------------------------
+ # 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 = input_data[topk_indices].detach()
+
+ #----------------------------------------------------
+ # Log Correct/Count
+ #----------------------------------------------------
+ Y = torch.argmax(label)
+ self.data[Y]["count"] += 1
+ self.data[Y]["correct"] += (Y_hat.item() == Y)
+
+ #----------------------------------------------------
+ # Tile Level Attention Maps
+ #----------------------------------------------------
+
+ self.save_attention_map(wsi_name, input_data, batch_names, grayscale_cam, target=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
+
+ 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)
+ metrics = self.test_metrics(logits , target)
+
+
+ # metrics = self.test_metrics(max_probs.squeeze() , torch.argmax(target.squeeze(), dim=1))
+ metrics['test_auc'] = auc
+
+ # self.log('auc', auc, prog_bar=True, on_epoch=True, logger=True)
+
+ #---->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)
+
+ img.save(f'{outpath}/{i}.jpg')
+
+ for keys, values in metrics.items():
+ print(f'{keys} = {values}')
+ metrics[keys] = values.cpu().numpy()
+ #---->acc log
+ for c in range(self.n_classes):
+ count = self.data[c]["count"]
+ correct = self.data[c]["correct"]
+ if count == 0:
+ acc = None
+ else:
+ acc = float(correct) / count
+ print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+ self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
+
+
+ # self.log_roc_curve(probs, target, 'test')
+ 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())
+
+ def save_attention_map(self, wsi_name, data, batch_names, grayscale_cam, target):
+
+ 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[0].split('_')
+ coords.append((int(x),int(y)))
+ return coords
+
+ coords = get_coords(batch_names)
+ # temp_data = data.cpu()
+ # print(data.shape)
+ wsi = self.assemble(data, coords).cpu().numpy()
+ # wsi = (wsi-wsi.min())/(wsi.max()-wsi.min())
+ # wsi = wsi
+
+ #--> Get interpolated mask from GradCam
+ W, H = wsi.shape[0], wsi.shape[1]
+
+
+ attention_map = grayscale_cam[:, :, 1].squeeze()
+ attention_map = F.relu(attention_map)
+ # print(attention_map)
+ input_h = 256
+
+ mask = torch.ones(( int(W/input_h), int(H/input_h))).to(self.device)
+
+ for i, (x,y) in enumerate(coords):
+ mask[y][x] = attention_map[i]
+ mask = mask.unsqueeze(0).unsqueeze(0)
+ # mask = torch.stack([mask, mask, mask]).unsqueeze(0)
+
+ mask = F.interpolate(mask, (W,H), mode='bilinear')
+ mask = mask.squeeze(0).permute(1,2,0)
+
+ mask = (mask - mask.min())/(mask.max()-mask.min())
+ mask = mask.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_cam = show_cam_on_image(wsi, mask)
+ wsi_cam = ((wsi_cam-wsi_cam.min())/(wsi_cam.max()-wsi_cam.min()) * 255.0).astype(np.uint8)
+
+ img = Image.fromarray(wsi_cam)
+ img = img.convert('RGB')
+ 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)
+ img = img.convert('RGB')
+ output_path = self.save_path / str(target.item())
+ output_path.mkdir(parents=True, exist_ok=True)
+ img.save(f'{output_path}/{wsi_name}.jpg')
+
+
+ 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
+
+ position_dict = {}
+ assembled = []
+ # for tile in self.predictions:
+ count = 0
+ # max_x = max(coords, key = lambda t: t[0])[0]
+ d = tiles[0,:,:,:].permute(1,2,0).shape
+ print(d)
+ white_value = 0
+ x_max = max([x[0] for x in coords])
+ y_max = max([x[1] for x in coords])
+
+ for i, (x,y) in enumerate(coords):
+
+ # name = n[0]
+ # image = tiles[i,:,:,:].permute(1,2,0)
+
+ # d = image.shape
+ # print(image.min())
+ # print(image.max())
+ # if image.max() > white_value:
+ # white_value = image.max()
+ # # print(image.shape)
+
+ # tile_position = '-'.join(name.split('_')[-2:])
+ # x,y = getPosition(tile_position)
+
+ # y_max = y if y > y_max else y_max
+ if x not in position_dict.keys():
+ position_dict[x] = [(y, i)]
+ else: position_dict[x].append((y, i))
+ # count += 1
+ print(position_dict.keys())
+ x_positions = sorted(position_dict.keys())
+ # print(x_max)
+ # complete_image = torch.zeros([x_max, y_max, 3])
+
+
+ for i in range(x_max+1):
+
+ # if i in position_dict.keys():
+ # print(i)
+ column = [None]*(int(y_max+1))
+ # if len(d) == 3:
+ # empty_tile = torch.zeros(d).to(self.device)
+ # else:
+ # empty_tile = torch.ones(d)
+ empty_tile = torch.ones(d).to(self.device)
+ # print(i)
+ if i in position_dict.keys():
+ # print(i)
+ for j in position_dict[i]:
+ print(j)
+ sample_idx = j[1]
+ print(sample_idx)
+ # img = tiles[sample_idx, :, :, :].permute(1,2,0)
+ column[int(j[0])] = tiles[sample_idx, :, :, :]
+ column = [empty_tile if i is None else i for i in column]
+ print(column)
+ # for c in column:
+ # print(c.shape)
+ # column = torch.vstack(column)
+ # print(column)
+ column = torch.stack(column)
+ assembled.append((i, column))
+
+ assembled = sorted(assembled, key=lambda x: x[0])
+
+ stack = [i[1] for i in assembled]
+ # print(stack)
+ img_compl = torch.hstack(stack)
+ print(img_compl)
+ return img_compl
+
+
+#---->main
+def main(cfg):
+
+ torch.set_num_threads(16)
+
+ #---->Initialize seed
+ pl.seed_everything(cfg.General.seed)
+
+ #---->load loggers
+ # cfg.load_loggers = load_loggers(cfg)
+
+ # print(cfg.load_loggers)
+ # save_path = Path(cfg.load_loggers[0].log_dir)
+
+ #---->load callbacks
+ # cfg.callbacks = load_callbacks(cfg, save_path)
+
+ home = Path.cwd().parts[1]
+ cfg.Data.label_file = '/home/ylan/DeepGraft/training_tables/split_PAS_tcmr_viral.json'
+ cfg.Data.data_dir = '/home/ylan/data/DeepGraft/224_128um/'
+ DataInterface_dict = {
+ 'data_root': cfg.Data.data_dir,
+ 'label_path': cfg.Data.label_file,
+ 'batch_size': cfg.Data.train_dataloader.batch_size,
+ 'num_workers': cfg.Data.train_dataloader.num_workers,
+ 'n_classes': cfg.Model.n_classes,
+ 'backbone': cfg.Model.backbone,
+ 'bag_size': cfg.Data.bag_size,
+ }
+
+ dm = MILDataModule(**DataInterface_dict)
+
+
+ #---->Define Model
+ ModelInterface_dict = {'model': cfg.Model,
+ 'loss': cfg.Loss,
+ 'optimizer': cfg.Optimizer,
+ 'data': cfg.Data,
+ 'log': cfg.log_path,
+ 'backbone': cfg.Model.backbone,
+ }
+ # model = ModelInterface(**ModelInterface_dict)
+ model = custom_test_module(**ModelInterface_dict)
+ # model.save_path = cfg.log_path
+ #---->Instantiate Trainer
+
+ trainer = Trainer(
+ num_sanity_val_steps=0,
+ # logger=cfg.load_loggers,
+ # callbacks=cfg.callbacks,
+ max_epochs= cfg.General.epochs,
+ min_epochs = 200,
+ gpus=cfg.General.gpus,
+ # gpus = [0,2],
+ # strategy='ddp',
+ amp_backend='native',
+ # amp_level=cfg.General.amp_level,
+ precision=cfg.General.precision,
+ accumulate_grad_batches=cfg.General.grad_acc,
+ # fast_dev_run = True,
+
+ # deterministic=True,
+ check_val_every_n_epoch=10,
+ )
+
+ #---->train or test
+ log_path = Path(cfg.log_path) / 'checkpoints'
+ # print(log_path)
+ # log_path = Path('lightning_logs/2/checkpoints')
+ model_paths = list(log_path.glob('*.ckpt'))
+
+ if cfg.epoch == 'last':
+ model_paths = [str(model_path) for model_path in model_paths if f'last' in str(model_path)]
+ else:
+ model_paths = [str(model_path) for model_path in model_paths if f'epoch={cfg.epoch}' in str(model_path)]
+
+ # model_paths = [str(model_path) for model_path in model_paths if f'epoch={cfg.epoch}' in str(model_path)]
+ # model_paths = [f'lightning_logs/0/.ckpt']
+ # model_paths = [f'{log_path}/last.ckpt']
+ if not model_paths:
+ print('No Checkpoints vailable!')
+ for path in model_paths:
+ # with open(f'{log_path}/test_metrics.txt', 'w') as f:
+ # f.write(str(path) + '\n')
+ print(path)
+ new_model = model.load_from_checkpoint(checkpoint_path=path, cfg=cfg)
+ new_model.save_path = Path(cfg.log_path) / 'visualization'
+ trainer.test(model=new_model, datamodule=dm)
+
+ # Top 5 scoring patches for patient
+ # GradCam
+
+
+if __name__ == '__main__':
+
+ args = make_parse()
+ cfg = read_yaml(args.config)
+
+ #---->update
+ cfg.config = args.config
+ cfg.General.gpus = [args.gpus]
+ cfg.General.server = args.stage
+ cfg.Data.fold = args.fold
+ cfg.Loss.base_loss = args.loss
+ cfg.Data.bag_size = args.bag_size
+ cfg.version = args.version
+ cfg.epoch = args.epoch
+
+ config_path = '/'.join(Path(cfg.config).parts[1:])
+ log_path = Path(cfg.General.log_path) / str(Path(config_path).parent)
+
+ Path(cfg.General.log_path).mkdir(exist_ok=True, parents=True)
+ log_name = f'_{cfg.Model.backbone}' + f'_{cfg.Loss.base_loss}'
+ task = '_'.join(Path(cfg.config).name[:-5].split('_')[2:])
+ # task = Path(cfg.config).name[:-5].split('_')[2:][0]
+ cfg.log_path = log_path / f'{cfg.Model.name}' / task / log_name / 'lightning_logs' / f'version_{cfg.version}'
+
+
+
+ #---->main
+ main(cfg)
+
\ No newline at end of file
diff --git a/train.py b/code/train.py
similarity index 85%
rename from train.py
rename to code/train.py
index 5e30394..ddd37e4 100644
--- a/train.py
+++ b/code/train.py
@@ -7,6 +7,7 @@ from sklearn.model_selection import KFold
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
from utils.utils import *
@@ -80,11 +81,14 @@ def main(cfg):
'log': cfg.log_path,
'backbone': cfg.Model.backbone,
}
- model = ModelInterface(**ModelInterface_dict)
+ if cfg.Model.name == 'DTFDMIL':
+ model = ModelInterface_DTFD(**ModelInterface_dict)
+ else:
+ model = ModelInterface(**ModelInterface_dict)
#---->Instantiate Trainer
trainer = Trainer(
- num_sanity_val_steps=0,
+ # num_sanity_val_steps=0,
logger=cfg.load_loggers,
callbacks=cfg.callbacks,
max_epochs= cfg.General.epochs,
@@ -96,11 +100,25 @@ def main(cfg):
# amp_level=cfg.General.amp_level,
precision=cfg.General.precision,
accumulate_grad_batches=cfg.General.grad_acc,
+ gradient_clip_val=0.0,
# fast_dev_run = True,
+ # limit_train_batches=1,
# deterministic=True,
- check_val_every_n_epoch=10,
+ check_val_every_n_epoch=5,
)
+ # print(cfg.log_path)
+ # print(trainer.loggers[0].log_dir)
+ # print(trainer.loggers[1].log_dir)
+ #----> Copy Code
+ copy_path = Path(trainer.loggers[0].log_dir) / 'code'
+ copy_path.mkdir(parents=True, exist_ok=True)
+ copy_origin = '/' / Path('/'.join(cfg.log_path.parts[1:5])) / 'code'
+ # print(copy_origin)
+ shutil.copytree(copy_origin, copy_path, dirs_exist_ok=True)
+
+
+ # print(trainer.loggers[0].log_dir)
#---->train or test
if cfg.resume_training:
@@ -148,7 +166,11 @@ if __name__ == '__main__':
cfg.Data.bag_size = args.bag_size
cfg.version = args.version
- log_path = Path(cfg.General.log_path) / str(Path(cfg.config).parent)
+ config_path = '/'.join(Path(cfg.config).parts[1:])
+ log_path = Path(cfg.General.log_path) / str(Path(config_path).parent)
+ # print(log_path)
+
+
Path(cfg.General.log_path).mkdir(exist_ok=True, parents=True)
log_name = f'_{cfg.Model.backbone}' + f'_{cfg.Loss.base_loss}'
task = '_'.join(Path(cfg.config).name[:-5].split('_')[2:])
diff --git a/code/train_loop.py b/code/train_loop.py
new file mode 100644
index 0000000..d198abd
--- /dev/null
+++ b/code/train_loop.py
@@ -0,0 +1,496 @@
+from pytorch_lightning import LightningModule
+import torch
+import torch.nn.functional as F
+from torchmetrics.classification.accuracy import Accuracy
+import os.path as osp
+from abc import ABC, abstractmethod
+from copy import deepcopy
+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 datasets.data_interface import BaseKFoldDataModule
+from typing import Any, Dict, List, Optional, Type
+import torchmetrics
+import numpy as np
+from PIL import Image
+import cv2
+import re
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from test_visualize import custom_test_module
+from pytorch_grad_cam import GradCAM, EigenGradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pathlib import Path
+
+
+
+class EnsembleVotingModel(LightningModule):
+ def __init__(self, model_cls: Type[LightningModule], checkpoint_paths: List[str], n_classes, log_path) -> None:
+ super().__init__()
+ # Create `num_folds` models with their associated fold weights
+ self.n_classes = n_classes
+ self.log_path = log_path
+ self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
+ self.models = torch.nn.ModuleList([model_cls.load_from_checkpoint(p) for p in checkpoint_paths])
+ self.test_acc = Accuracy()
+ if self.n_classes > 2:
+ self.AUROC = torchmetrics.AUROC(num_classes = self.n_classes, average = 'weighted')
+ metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = self.n_classes,
+ average='micro'),
+ torchmetrics.CohenKappa(num_classes = self.n_classes),
+ torchmetrics.F1Score(num_classes = self.n_classes,
+ average = 'macro'),
+ torchmetrics.Recall(average = 'macro',
+ num_classes = self.n_classes),
+ torchmetrics.Precision(average = 'macro',
+ num_classes = self.n_classes),
+ torchmetrics.Specificity(average = 'macro',
+ num_classes = self.n_classes)])
+
+ else :
+ self.AUROC = torchmetrics.AUROC(num_classes=2, average = 'weighted')
+ metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = 2,
+ average = 'micro'),
+ torchmetrics.CohenKappa(num_classes = 2),
+ torchmetrics.F1Score(num_classes = 2,
+ average = 'macro'),
+ torchmetrics.Recall(average = 'macro',
+ num_classes = 2),
+ torchmetrics.Precision(average = 'macro',
+ num_classes = 2)])
+ self.test_metrics = metrics.clone(prefix = 'test_')
+ self.confusion_matrix = torchmetrics.ConfusionMatrix(num_classes = self.n_classes)
+ 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, :] #.to(self.device)
+
+ #----------------------------------------------------
+ # 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()
+
+ #----------------------------------------------------
+ # Log Correct/Count
+ #----------------------------------------------------
+ Y = torch.argmax(label)
+ self.data[Y]["count"] += 1
+ self.data[Y]["correct"] += (Y_hat.item() == Y)
+
+ #----------------------------------------------------
+ # Tile Level Attention Maps
+ #----------------------------------------------------
+
+ # self.save_attention_map(wsi_name, data, batch_names, grayscale_cam, 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
+
+ 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)
+ metrics = self.test_metrics(logits , target)
+
+
+ # metrics = self.test_metrics(max_probs.squeeze() , torch.argmax(target.squeeze(), dim=1))
+ metrics['test_auc'] = auc
+
+ # self.log('auc', auc, prog_bar=True, on_epoch=True, logger=True)
+
+ #---->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)
+
+ img.save(f'{outpath}/{i}.jpg')
+
+ for keys, values in metrics.items():
+ print(f'{keys} = {values}')
+ metrics[keys] = values.cpu().numpy()
+ #---->acc log
+ for c in range(self.n_classes):
+ count = self.data[c]["count"]
+ correct = self.data[c]["correct"]
+ if count == 0:
+ acc = None
+ else:
+ acc = float(correct) / count
+ print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+ self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
+
+
+ # self.log_roc_curve(probs, target, 'test')
+ 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())
+
+ def save_attention_map(self, wsi_name, data, batch_names, grayscale_cam, target):
+
+ 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[0].split('_')
+
+ coords.append((int(x),int(y)))
+ return coords
+
+
+ coords = get_coords(batch_names)
+ # temp_data = data.cpu()
+ print(data.shape)
+ wsi = self.assemble(data, coords).cpu().numpy()
+ wsi = (wsi-wsi.min())/(wsi.max()-wsi.min())
+ # wsi = wsi
+
+ #--> Get interpolated mask from GradCam
+ W, H = wsi.shape[0], wsi.shape[1]
+
+
+ attention_map = grayscale_cam[:, :, 1].squeeze()
+ attention_map = F.relu(attention_map)
+ # print(attention_map)
+ input_h = 256
+
+ mask = torch.ones(( int(W/input_h), int(H/input_h))).to(self.device)
+
+ for i, (x,y) in enumerate(coords):
+ mask[y][x] = attention_map[i]
+ mask = mask.unsqueeze(0).unsqueeze(0)
+ # mask = torch.stack([mask, mask, mask]).unsqueeze(0)
+
+ mask = F.interpolate(mask, (W,H), mode='bilinear')
+ mask = mask.squeeze(0).permute(1,2,0)
+
+ mask = (mask - mask.min())/(mask.max()-mask.min())
+ mask = mask.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_cam = show_cam_on_image(wsi, mask)
+ wsi_cam = ((wsi_cam-wsi_cam.min())/(wsi_cam.max()-wsi_cam.min()) * 255.0).astype(np.uint8)
+
+ img = Image.fromarray(wsi_cam)
+ img = img.convert('RGB')
+ output_path = self.save_path / str(target)
+ 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)
+ img = img.convert('RGB')
+ output_path = self.save_path / str(target)
+ output_path.mkdir(parents=True, exist_ok=True)
+ img.save(f'{output_path}/{wsi_name}.jpg')
+
+
+ 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
+
+ position_dict = {}
+ assembled = []
+ y_max = 0
+ # for tile in self.predictions:
+ count = 0
+ max_x = max(coords, key = lambda t: t[0])[0]
+ d = 0
+ white_value = 0
+
+ for i, (x,y) in enumerate(coords):
+
+ # name = n[0]
+ image = tiles[i,:,:,:].permute(1,2,0)
+
+ d = image.shape
+ # print(image.min())
+ # print(image.max())
+ # if image.max() > white_value:
+ # white_value = image.max()
+ # # print(image.shape)
+
+ # tile_position = '-'.join(name.split('_')[-2:])
+ # x,y = getPosition(tile_position)
+
+ y_max = y if y > y_max else y_max
+ if x not in position_dict.keys():
+ position_dict[x] = [(y, image)]
+ else: position_dict[x].append((y, image))
+ count += 1
+
+
+ for i in range(max_x+1):
+ column = [None]*(int(y_max+1))
+ # if len(d) == 3:
+ # empty_tile = torch.zeros(d).to(self.device)
+ # else:
+ empty_tile = torch.ones(d)
+ empty_tile = torch.ones(d).to(self.device)
+ if i in position_dict.keys():
+ for j in position_dict[i]:
+ sample = j[1]
+ column[int(j[0])] = sample
+ column = [empty_tile if i is None else i for i in column]
+ # for c in column:
+ # print(c.shape)
+ # column = torch.vstack(column)
+ column = torch.stack(column)
+ assembled.append((i, column))
+
+
+
+ # for key in position_dict.keys():
+ # column = [None]*(int(y_max+1))
+ # # print(key)
+ # for i in position_dict[key]:
+ # sample = i[1]
+ # d = sample.shape
+ # # print(d) # [3,256,256]
+ # if len(d) == 3:
+ # empty_tile = torch.ones(d).to(self.device)
+ # else:
+ # empty_tile = torch.zeros(d).to(self.device)
+ # column[int(i[0])] = sample
+ # column = [empty_tile if i is None else i for i in column]
+ # column = torch.vstack(column)
+ # assembled.append((key, column))
+ # print(len(assembled))
+
+ assembled = sorted(assembled, key=lambda x: x[0])
+
+ stack = [i[1] for i in assembled]
+ # print(stack)
+ img_compl = torch.hstack(stack)
+ return img_compl
+ # def test_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None:
+ # # Compute the averaged predictions over the `num_folds` models.
+ # # print(batch[0].shape)
+ # input, label, _ = batch
+ # label = label.float()
+ # input = input.squeeze(0).float()
+
+
+ # logits = torch.stack([m(input) for m in self.models]).mean(0)
+ # Y_hat = torch.argmax(logits, dim=1)
+ # Y_prob = F.softmax(logits, dim = 1)
+ # # #---->acc log
+ # Y = torch.argmax(label)
+ # self.data[Y]["count"] += 1
+ # self.data[Y]["correct"] += (Y_hat.item() == Y)
+
+ # return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : label}
+
+ # def test_epoch_end(self, output_results):
+ # 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.cat([x['label'] for x in output_results])
+ # target = torch.argmax(target, dim=1)
+
+ # #---->
+ # auc = self.AUROC(probs, target.squeeze())
+ # metrics = self.test_metrics(max_probs.squeeze() , target)
+
+
+ # # metrics = self.test_metrics(max_probs.squeeze() , torch.argmax(target.squeeze(), dim=1))
+ # metrics['test_auc'] = auc
+
+ # # self.log('auc', auc, prog_bar=True, on_epoch=True, logger=True)
+
+ # # print(max_probs.squeeze(0).shape)
+ # # print(target.shape)
+ # # self.log_dict(metrics, logger = True)
+ # for keys, values in metrics.items():
+ # print(f'{keys} = {values}')
+ # metrics[keys] = values.cpu().numpy()
+ # #---->acc log
+ # for c in range(self.n_classes):
+ # count = self.data[c]["count"]
+ # correct = self.data[c]["correct"]
+ # if count == 0:
+ # acc = None
+ # else:
+ # acc = float(correct) / count
+ # print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
+ # self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
+
+ # self.log_confusion_matrix(probs, target, stage='test')
+ # #---->
+ # result = pd.DataFrame([metrics])
+ # result.to_csv(self.log_path / 'result.csv')
+
+
+ def log_confusion_matrix(self, max_probs, target, stage):
+ confmat = self.confusion_matrix(max_probs.squeeze(), target)
+ df_cm = pd.DataFrame(confmat.cpu().numpy(), index=range(self.n_classes), columns=range(self.n_classes))
+ plt.figure()
+ fig_ = sns.heatmap(df_cm, annot=True, cmap='Spectral').get_figure()
+ # plt.close(fig_)
+ # plt.savefig(f'{self.log_path}/cm_e{self.current_epoch}')
+ self.loggers[0].experiment.add_figure(f'{stage}/Confusion matrix', fig_, self.current_epoch)
+
+ if stage == 'test':
+ plt.savefig(f'{self.log_path}/cm_test')
+ plt.close(fig_)
+
+class KFoldLoop(Loop):
+ def __init__(self, num_folds: int, export_path: str, **kargs) -> None:
+ super().__init__()
+ self.num_folds = num_folds
+ self.current_fold: int = 0
+ self.export_path = export_path
+ self.n_classes = kargs["model"].n_classes
+ self.log_path = kargs["log"]
+
+ @property
+ def done(self) -> bool:
+ return self.current_fold >= self.num_folds
+
+ def connect(self, fit_loop: FitLoop) -> None:
+ self.fit_loop = fit_loop
+
+ def reset(self) -> None:
+ """Nothing to reset in this loop."""
+
+ def on_run_start(self, *args: Any, **kwargs: Any) -> None:
+ """Used to call `setup_folds` from the `BaseKFoldDataModule` instance and store the original weights of the
+ model."""
+ assert isinstance(self.trainer.datamodule, BaseKFoldDataModule)
+ self.trainer.datamodule.setup_folds(self.num_folds)
+ self.lightning_module_state_dict = deepcopy(self.trainer.lightning_module.state_dict())
+
+ def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
+ """Used to call `setup_fold_index` from the `BaseKFoldDataModule` instance."""
+ print(f"STARTING FOLD {self.current_fold}")
+ assert isinstance(self.trainer.datamodule, BaseKFoldDataModule)
+ self.trainer.datamodule.setup_fold_index(self.current_fold)
+
+ def advance(self, *args: Any, **kwargs: Any) -> None:
+ """Used to the run a fitting and testing on the current hold."""
+ self._reset_fitting() # requires to reset the tracking stage.
+ self.fit_loop.run()
+
+ self._reset_testing() # requires to reset the tracking stage.
+ self.trainer.test_loop.run()
+ self.current_fold += 1 # increment fold tracking number.
+
+ def on_advance_end(self) -> None:
+ """Used to save the weights of the current fold and reset the LightningModule and its optimizers."""
+ self.trainer.save_checkpoint(osp.join(self.export_path, f"model.{self.current_fold}.pt"))
+ # restore the original weights + optimizers and schedulers.
+ self.trainer.lightning_module.load_state_dict(self.lightning_module_state_dict)
+ self.trainer.strategy.setup_optimizers(self.trainer)
+ self.replace(fit_loop=FitLoop)
+
+ def on_run_end(self) -> None:
+ """Used to compute the performance of the ensemble model on the test set."""
+ checkpoint_paths = [osp.join(self.export_path, f"model.{f_idx + 1}.pt") for f_idx in range(self.num_folds)]
+ voting_model = EnsembleVotingModel(type(self.trainer.lightning_module), checkpoint_paths, n_classes=self.n_classes, log_path=self.log_path)
+ voting_model.trainer = self.trainer
+ # This requires to connect the new model and move it the right device.
+ self.trainer.strategy.connect(voting_model)
+ self.trainer.strategy.model_to_device()
+ self.trainer.test_loop.run()
+
+ def on_save_checkpoint(self) -> Dict[str, int]:
+ return {"current_fold": self.current_fold}
+
+ def on_load_checkpoint(self, state_dict: Dict) -> None:
+ self.current_fold = state_dict["current_fold"]
+
+ def _reset_fitting(self) -> None:
+ self.trainer.reset_train_dataloader()
+ self.trainer.reset_val_dataloader()
+ self.trainer.state.fn = TrainerFn.FITTING
+ self.trainer.training = True
+
+ def _reset_testing(self) -> None:
+ self.trainer.reset_test_dataloader()
+ self.trainer.state.fn = TrainerFn.TESTING
+ self.trainer.testing = True
+
+ def __getattr__(self, key) -> Any:
+ # requires to be overridden as attributes of the wrapped loop are being accessed.
+ if key not in self.__dict__:
+ return getattr(self.fit_loop, key)
+ return self.__dict__[key]
+
+ def __setstate__(self, state: Dict[str, Any]) -> None:
+ self.__dict__.update(state)
\ No newline at end of file
diff --git a/utils/__init__.py b/code/utils/__init__.py
similarity index 100%
rename from utils/__init__.py
rename to code/utils/__init__.py
diff --git a/utils/__pycache__/__init__.cpython-39.pyc b/code/utils/__pycache__/__init__.cpython-39.pyc
similarity index 100%
rename from utils/__pycache__/__init__.cpython-39.pyc
rename to code/utils/__pycache__/__init__.cpython-39.pyc
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/extract_features.py b/code/utils/extract_features.py
new file mode 100644
index 0000000..9ec600e
--- /dev/null
+++ b/code/utils/extract_features.py
@@ -0,0 +1,37 @@
+## Choose Model and extract features from (augmented) image patches and save as .pt file
+
+# from datasets.custom_dataloader import HDF5MILDataloader
+from datasets import JPGMILDataloader
+from torchvision import models
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+def extract_features(input_dir, output_dir, model, batch_size):
+
+
+
+ dataset = JPGMILDataloader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes)
+ model = models.resnet50(pretrained=True)
+ for param in self.model_ft.parameters():
+ param.requires_grad = False
+ self.model_ft.fc = nn.Linear(2048, self.out_features)
+
+ model = model.to(device)
+ model.eval()
+
+
+
+
+
+
+if __name__ == '__main__':
+
+ # input_dir, output_dir
+ # initiate data loader
+ # use data loader to load and augment images
+ # prediction from model
+ # choose save as bag or not (needed?)
+
+ # features = torch.from_numpy(features)
+ # torch.save(features, output_path + '.pt')
+
diff --git a/utils/utils.py b/code/utils/utils.py
similarity index 95%
rename from utils/utils.py
rename to code/utils/utils.py
index 814cf1d..7e992cf 100755
--- a/utils/utils.py
+++ b/code/utils/utils.py
@@ -15,6 +15,7 @@ from pytorch_lightning.loops.base import Loop
from pytorch_lightning.loops.fit_loop import FitLoop
from pytorch_lightning.trainer.states import TrainerFn
from typing import Any, Dict, List, Optional, Type
+import shutil
#---->read yaml
import yaml
@@ -26,6 +27,7 @@ def read_yaml(fpath=None):
#---->load Loggers
from pytorch_lightning import loggers as pl_loggers
+
def load_loggers(cfg):
# log_path = cfg.General.log_path
@@ -40,9 +42,12 @@ def load_loggers(cfg):
tb_logger = pl_loggers.TensorBoardLogger(cfg.log_path,
# version = f'fold{cfg.Data.fold}'
log_graph = True, default_hp_metric = False)
+ # print(tb_logger.version)
+ version = tb_logger.version
#---->CSV
- csv_logger = pl_loggers.CSVLogger(cfg.log_path,
+ csv_logger = pl_loggers.CSVLogger(cfg.log_path, version = version
) # version = f'fold{cfg.Data.fold}',
+ # print(csv_logger.version)
else:
cfg.log_path = Path(cfg.log_path) / f'test'
tb_logger = pl_loggers.TensorBoardLogger(cfg.log_path,
@@ -98,7 +103,7 @@ 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}',
+ filename = '{epoch:02d}-{val_loss:.4f}-{val_auc: .4f}',
verbose = True,
save_last = True,
save_top_k = 1,
@@ -106,7 +111,7 @@ def load_callbacks(cfg, save_path):
save_weights_only = True))
Mycallbacks.append(ModelCheckpoint(monitor = 'val_auc',
dirpath = str(output_path),
- filename = '{epoch:02d}-{val_auc:.4f}',
+ filename = '{epoch:02d}-{val_loss:.4f}-{val_auc:.4f}',
verbose = True,
save_last = True,
save_top_k = 1,
diff --git a/datasets/__init__.py b/datasets/__init__.py
deleted file mode 100644
index e752eeb..0000000
--- a/datasets/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-
-from .data_interface import DataInterface
\ No newline at end of file
diff --git a/datasets/__pycache__/__init__.cpython-39.pyc b/datasets/__pycache__/__init__.cpython-39.pyc
deleted file mode 100644
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diff --git a/test_visualize.py b/test_visualize.py
deleted file mode 100644
index 7ef56d3..0000000
--- a/test_visualize.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import argparse
-from pathlib import Path
-import numpy as np
-import glob
-
-from sklearn.model_selection import KFold
-
-from datasets.data_interface import DataInterface, MILDataModule, CrossVal_MILDataModule
-from models.model_interface import ModelInterface
-import models.vision_transformer as vits
-from utils.utils import *
-
-# pytorch_lightning
-import pytorch_lightning as pl
-from pytorch_lightning import Trainer
-import torch
-from train_loop import KFoldLoop
-
-#--->Setting parameters
-def make_parse():
- parser = argparse.ArgumentParser()
- parser.add_argument('--stage', default='train', type=str)
- parser.add_argument('--config', default='DeepGraft/TransMIL.yaml',type=str)
- parser.add_argument('--version', default=0,type=int)
- parser.add_argument('--epoch', default='0',type=str)
- parser.add_argument('--gpus', default = 2, type=int)
- parser.add_argument('--loss', default = 'CrossEntropyLoss', type=str)
- parser.add_argument('--fold', default = 0)
- parser.add_argument('--bag_size', default = 1024, type=int)
-
- args = parser.parse_args()
- return args
-
-#---->main
-def main(cfg):
-
- torch.set_num_threads(16)
-
- #---->Initialize seed
- pl.seed_everything(cfg.General.seed)
-
- #---->load loggers
- # cfg.load_loggers = load_loggers(cfg)
-
- # print(cfg.load_loggers)
- # save_path = Path(cfg.load_loggers[0].log_dir)
-
- #---->load callbacks
- # cfg.callbacks = load_callbacks(cfg, save_path)
-
- home = Path.cwd().parts[1]
- DataInterface_dict = {
- 'data_root': cfg.Data.data_dir,
- 'label_path': cfg.Data.label_file,
- 'batch_size': cfg.Data.train_dataloader.batch_size,
- 'num_workers': cfg.Data.train_dataloader.num_workers,
- 'n_classes': cfg.Model.n_classes,
- 'backbone': cfg.Model.backbone,
- 'bag_size': cfg.Data.bag_size,
- }
-
- dm = MILDataModule(**DataInterface_dict)
-
-
- #---->Define Model
- ModelInterface_dict = {'model': cfg.Model,
- 'loss': cfg.Loss,
- 'optimizer': cfg.Optimizer,
- 'data': cfg.Data,
- 'log': cfg.log_path,
- 'backbone': cfg.Model.backbone,
- }
- model = ModelInterface(**ModelInterface_dict)
-
- #---->Instantiate Trainer
- trainer = Trainer(
- num_sanity_val_steps=0,
- # logger=cfg.load_loggers,
- # callbacks=cfg.callbacks,
- max_epochs= cfg.General.epochs,
- min_epochs = 200,
- gpus=cfg.General.gpus,
- # gpus = [0,2],
- # strategy='ddp',
- amp_backend='native',
- # amp_level=cfg.General.amp_level,
- precision=cfg.General.precision,
- accumulate_grad_batches=cfg.General.grad_acc,
- # fast_dev_run = True,
-
- # deterministic=True,
- check_val_every_n_epoch=10,
- )
-
- #---->train or test
- log_path = cfg.log_path
- # print(log_path)
- # log_path = Path('lightning_logs/2/checkpoints')
- model_paths = list(log_path.glob('*.ckpt'))
-
- if cfg.epoch == 'last':
- model_paths = [str(model_path) for model_path in model_paths if f'last' in str(model_path)]
- else:
- model_paths = [str(model_path) for model_path in model_paths if f'epoch={cfg.epoch}' in str(model_path)]
-
- # model_paths = [str(model_path) for model_path in model_paths if f'epoch={cfg.epoch}' in str(model_path)]
- # model_paths = [f'lightning_logs/0/.ckpt']
- # model_paths = [f'{log_path}/last.ckpt']
- if not model_paths:
- print('No Checkpoints vailable!')
- for path in model_paths:
- # with open(f'{log_path}/test_metrics.txt', 'w') as f:
- # f.write(str(path) + '\n')
- print(path)
- new_model = model.load_from_checkpoint(checkpoint_path=path, cfg=cfg)
- trainer.test(model=new_model, datamodule=dm)
-
- # Top 5 scoring patches for patient
- # GradCam
-
-
-if __name__ == '__main__':
-
- args = make_parse()
- cfg = read_yaml(args.config)
-
- #---->update
- cfg.config = args.config
- cfg.General.gpus = [args.gpus]
- cfg.General.server = args.stage
- cfg.Data.fold = args.fold
- cfg.Loss.base_loss = args.loss
- cfg.Data.bag_size = args.bag_size
- cfg.version = args.version
- cfg.epoch = args.epoch
-
- log_path = Path(cfg.General.log_path) / str(Path(cfg.config).parent)
- Path(cfg.General.log_path).mkdir(exist_ok=True, parents=True)
- log_name = f'_{cfg.Model.backbone}' + f'_{cfg.Loss.base_loss}'
- task = '_'.join(Path(cfg.config).name[:-5].split('_')[2:])
- # task = Path(cfg.config).name[:-5].split('_')[2:][0]
- cfg.log_path = log_path / f'{cfg.Model.name}' / task / log_name / 'lightning_logs' / f'version_{cfg.version}' / 'checkpoints'
-
-
-
- #---->main
- main(cfg)
-
\ No newline at end of file
diff --git a/train_loop.py b/train_loop.py
deleted file mode 100644
index f923681..0000000
--- a/train_loop.py
+++ /dev/null
@@ -1,212 +0,0 @@
-from pytorch_lightning import LightningModule
-import torch
-import torch.nn.functional as F
-from torchmetrics.classification.accuracy import Accuracy
-import os.path as osp
-from abc import ABC, abstractmethod
-from copy import deepcopy
-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 datasets.data_interface import BaseKFoldDataModule
-from typing import Any, Dict, List, Optional, Type
-import torchmetrics
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-
-
-class EnsembleVotingModel(LightningModule):
- def __init__(self, model_cls: Type[LightningModule], checkpoint_paths: List[str], n_classes, log_path) -> None:
- super().__init__()
- # Create `num_folds` models with their associated fold weights
- self.n_classes = n_classes
- self.log_path = log_path
- self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
- self.models = torch.nn.ModuleList([model_cls.load_from_checkpoint(p) for p in checkpoint_paths])
- self.test_acc = Accuracy()
- if self.n_classes > 2:
- self.AUROC = torchmetrics.AUROC(num_classes = self.n_classes, average = 'weighted')
- metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = self.n_classes,
- average='micro'),
- torchmetrics.CohenKappa(num_classes = self.n_classes),
- torchmetrics.F1Score(num_classes = self.n_classes,
- average = 'macro'),
- torchmetrics.Recall(average = 'macro',
- num_classes = self.n_classes),
- torchmetrics.Precision(average = 'macro',
- num_classes = self.n_classes),
- torchmetrics.Specificity(average = 'macro',
- num_classes = self.n_classes)])
-
- else :
- self.AUROC = torchmetrics.AUROC(num_classes=2, average = 'weighted')
- metrics = torchmetrics.MetricCollection([torchmetrics.Accuracy(num_classes = 2,
- average = 'micro'),
- torchmetrics.CohenKappa(num_classes = 2),
- torchmetrics.F1Score(num_classes = 2,
- average = 'macro'),
- torchmetrics.Recall(average = 'macro',
- num_classes = 2),
- torchmetrics.Precision(average = 'macro',
- num_classes = 2)])
- self.test_metrics = metrics.clone(prefix = 'test_')
- self.confusion_matrix = torchmetrics.ConfusionMatrix(num_classes = self.n_classes)
-
- def test_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None:
- # Compute the averaged predictions over the `num_folds` models.
- # print(batch[0].shape)
- input, label, _ = batch
- label = label.float()
- input = input.squeeze(0).float()
-
-
- logits = torch.stack([m(input) for m in self.models]).mean(0)
- Y_hat = torch.argmax(logits, dim=1)
- Y_prob = F.softmax(logits, dim = 1)
- # #---->acc log
- Y = torch.argmax(label)
- self.data[Y]["count"] += 1
- self.data[Y]["correct"] += (Y_hat.item() == Y)
-
- return {'logits' : logits, 'Y_prob' : Y_prob, 'Y_hat' : Y_hat, 'label' : label}
-
- def test_epoch_end(self, output_results):
- 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.cat([x['label'] for x in output_results])
- target = torch.argmax(target, dim=1)
-
- #---->
- auc = self.AUROC(probs, target.squeeze())
- metrics = self.test_metrics(max_probs.squeeze() , target)
-
-
- # metrics = self.test_metrics(max_probs.squeeze() , torch.argmax(target.squeeze(), dim=1))
- metrics['test_auc'] = auc
-
- # self.log('auc', auc, prog_bar=True, on_epoch=True, logger=True)
-
- # print(max_probs.squeeze(0).shape)
- # print(target.shape)
- # self.log_dict(metrics, logger = True)
- for keys, values in metrics.items():
- print(f'{keys} = {values}')
- metrics[keys] = values.cpu().numpy()
- #---->acc log
- for c in range(self.n_classes):
- count = self.data[c]["count"]
- correct = self.data[c]["correct"]
- if count == 0:
- acc = None
- else:
- acc = float(correct) / count
- print('class {}: acc {}, correct {}/{}'.format(c, acc, correct, count))
- self.data = [{"count": 0, "correct": 0} for i in range(self.n_classes)]
-
- self.log_confusion_matrix(probs, target, stage='test')
- #---->
- result = pd.DataFrame([metrics])
- result.to_csv(self.log_path / 'result.csv')
-
-
- def log_confusion_matrix(self, max_probs, target, stage):
- confmat = self.confusion_matrix(max_probs.squeeze(), target)
- df_cm = pd.DataFrame(confmat.cpu().numpy(), index=range(self.n_classes), columns=range(self.n_classes))
- plt.figure()
- fig_ = sns.heatmap(df_cm, annot=True, cmap='Spectral').get_figure()
- # plt.close(fig_)
- # plt.savefig(f'{self.log_path}/cm_e{self.current_epoch}')
- self.loggers[0].experiment.add_figure(f'{stage}/Confusion matrix', fig_, self.current_epoch)
-
- if stage == 'test':
- plt.savefig(f'{self.log_path}/cm_test')
- plt.close(fig_)
-
-class KFoldLoop(Loop):
- def __init__(self, num_folds: int, export_path: str, **kargs) -> None:
- super().__init__()
- self.num_folds = num_folds
- self.current_fold: int = 0
- self.export_path = export_path
- self.n_classes = kargs["model"].n_classes
- self.log_path = kargs["log"]
-
- @property
- def done(self) -> bool:
- return self.current_fold >= self.num_folds
-
- def connect(self, fit_loop: FitLoop) -> None:
- self.fit_loop = fit_loop
-
- def reset(self) -> None:
- """Nothing to reset in this loop."""
-
- def on_run_start(self, *args: Any, **kwargs: Any) -> None:
- """Used to call `setup_folds` from the `BaseKFoldDataModule` instance and store the original weights of the
- model."""
- assert isinstance(self.trainer.datamodule, BaseKFoldDataModule)
- self.trainer.datamodule.setup_folds(self.num_folds)
- self.lightning_module_state_dict = deepcopy(self.trainer.lightning_module.state_dict())
-
- def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
- """Used to call `setup_fold_index` from the `BaseKFoldDataModule` instance."""
- print(f"STARTING FOLD {self.current_fold}")
- assert isinstance(self.trainer.datamodule, BaseKFoldDataModule)
- self.trainer.datamodule.setup_fold_index(self.current_fold)
-
- def advance(self, *args: Any, **kwargs: Any) -> None:
- """Used to the run a fitting and testing on the current hold."""
- self._reset_fitting() # requires to reset the tracking stage.
- self.fit_loop.run()
-
- self._reset_testing() # requires to reset the tracking stage.
- self.trainer.test_loop.run()
- self.current_fold += 1 # increment fold tracking number.
-
- def on_advance_end(self) -> None:
- """Used to save the weights of the current fold and reset the LightningModule and its optimizers."""
- self.trainer.save_checkpoint(osp.join(self.export_path, f"model.{self.current_fold}.pt"))
- # restore the original weights + optimizers and schedulers.
- self.trainer.lightning_module.load_state_dict(self.lightning_module_state_dict)
- self.trainer.strategy.setup_optimizers(self.trainer)
- self.replace(fit_loop=FitLoop)
-
- def on_run_end(self) -> None:
- """Used to compute the performance of the ensemble model on the test set."""
- checkpoint_paths = [osp.join(self.export_path, f"model.{f_idx + 1}.pt") for f_idx in range(self.num_folds)]
- voting_model = EnsembleVotingModel(type(self.trainer.lightning_module), checkpoint_paths, n_classes=self.n_classes, log_path=self.log_path)
- voting_model.trainer = self.trainer
- # This requires to connect the new model and move it the right device.
- self.trainer.strategy.connect(voting_model)
- self.trainer.strategy.model_to_device()
- self.trainer.test_loop.run()
-
- def on_save_checkpoint(self) -> Dict[str, int]:
- return {"current_fold": self.current_fold}
-
- def on_load_checkpoint(self, state_dict: Dict) -> None:
- self.current_fold = state_dict["current_fold"]
-
- def _reset_fitting(self) -> None:
- self.trainer.reset_train_dataloader()
- self.trainer.reset_val_dataloader()
- self.trainer.state.fn = TrainerFn.FITTING
- self.trainer.training = True
-
- def _reset_testing(self) -> None:
- self.trainer.reset_test_dataloader()
- self.trainer.state.fn = TrainerFn.TESTING
- self.trainer.testing = True
-
- def __getattr__(self, key) -> Any:
- # requires to be overridden as attributes of the wrapped loop are being accessed.
- if key not in self.__dict__:
- return getattr(self.fit_loop, key)
- return self.__dict__[key]
-
- def __setstate__(self, state: Dict[str, Any]) -> None:
- self.__dict__.update(state)
\ No newline at end of file
diff --git a/utils/__pycache__/utils.cpython-39.pyc b/utils/__pycache__/utils.cpython-39.pyc
deleted file mode 100644
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diff --git a/utils/extract_features.py b/utils/extract_features.py
deleted file mode 100644
index fb040a1..0000000
--- a/utils/extract_features.py
+++ /dev/null
@@ -1,27 +0,0 @@
-## Choose Model and extract features from (augmented) image patches and save as .pt file
-
-from datasets.custom_dataloader import HDF5MILDataloader
-
-
-def extract_features(input_dir, output_dir, model, batch_size):
-
-
- dataset = HDF5MILDataloader(data_root, label_path=label_path, mode='train', load_data=False, n_classes=n_classes)
- if model == 'resnet50':
- model = Resnet50_baseline(pretrained = True)
- model = model.to(device)
- model.eval()
-
-
-
-if __name__ == '__main__':
-
- # input_dir, output_dir
- # initiate data loader
- # use data loader to load and augment images
- # prediction from model
- # choose save as bag or not (needed?)
-
- # features = torch.from_numpy(features)
- # torch.save(features, output_path + '.pt')
-
--
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