From b442f837332793e09d165fd524701c31545eb18c Mon Sep 17 00:00:00 2001 From: KumoLiu Date: Thu, 7 Sep 2023 20:20:40 +0800 Subject: [PATCH 1/2] update bundle related notebook Signed-off-by: KumoLiu --- .../pythonic_bundle_access.ipynb | 45 +++++++----- .../endoscopic_inbody_classification.ipynb | 73 ++++++++++++------- 2 files changed, 72 insertions(+), 46 deletions(-) diff --git a/bundle/pythonic_usage_guidance/pythonic_bundle_access.ipynb b/bundle/pythonic_usage_guidance/pythonic_bundle_access.ipynb index c0910e2e9c..eb85593314 100644 --- a/bundle/pythonic_usage_guidance/pythonic_bundle_access.ipynb +++ b/bundle/pythonic_usage_guidance/pythonic_bundle_access.ipynb @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -159,18 +159,18 @@ "url None\n", "remove_prefix monai_\n", "progress True\n", - "2023-09-06 08:44:11,165 - INFO - --- input summary of monai.bundle.scripts.download ---\n", - "2023-09-06 08:44:11,167 - INFO - > name: 'spleen_ct_segmentation'\n", - "2023-09-06 08:44:11,167 - INFO - > bundle_dir: '/workspace/Data'\n", - "2023-09-06 08:44:11,167 - INFO - > source: 'github'\n", - "2023-09-06 08:44:11,167 - INFO - > remove_prefix: 'monai_'\n", - "2023-09-06 08:44:11,168 - INFO - > progress: True\n", - "2023-09-06 08:44:11,168 - INFO - ---\n", + "2023-09-07 12:19:11,083 - INFO - --- input summary of monai.bundle.scripts.download ---\n", + "2023-09-07 12:19:11,086 - INFO - > name: 'spleen_ct_segmentation'\n", + "2023-09-07 12:19:11,088 - INFO - > bundle_dir: '/workspace/Data'\n", + "2023-09-07 12:19:11,089 - INFO - > source: 'github'\n", + "2023-09-07 12:19:11,090 - INFO - > remove_prefix: 'monai_'\n", + "2023-09-07 12:19:11,091 - INFO - > progress: True\n", + "2023-09-07 12:19:11,092 - INFO - ---\n", "\n", "\n", - "2023-09-06 08:44:12,165 - INFO - Expected md5 is None, skip md5 check for file /workspace/Data/spleen_ct_segmentation_v0.5.3.zip.\n", - "2023-09-06 08:44:12,165 - INFO - File exists: /workspace/Data/spleen_ct_segmentation_v0.5.3.zip, skipped downloading.\n", - "2023-09-06 08:44:12,166 - INFO - Writing into directory: /workspace/Data.\n" + "2023-09-07 12:19:11,991 - INFO - Expected md5 is None, skip md5 check for file /workspace/Data/spleen_ct_segmentation_v0.5.3.zip.\n", + "2023-09-07 12:19:11,992 - INFO - File exists: /workspace/Data/spleen_ct_segmentation_v0.5.3.zip, skipped downloading.\n", + "2023-09-07 12:19:11,994 - INFO - Writing into directory: /workspace/Data.\n" ] } ], @@ -198,13 +198,13 @@ "workflow_name None\n", "config_file /workspace/Data/spleen_ct_segmentation/configs/train.json\n", "workflow_type train\n", - "2023-09-06 09:18:47,393 - INFO - --- input summary of monai.bundle.scripts.run ---\n", - "2023-09-06 09:18:47,395 - INFO - > config_file: '/workspace/Data/spleen_ct_segmentation/configs/train.json'\n", - "2023-09-06 09:18:47,396 - INFO - > workflow_type: 'train'\n", - "2023-09-06 09:18:47,397 - INFO - ---\n", + "2023-09-07 12:19:14,769 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2023-09-07 12:19:14,772 - INFO - > config_file: '/workspace/Data/spleen_ct_segmentation/configs/train.json'\n", + "2023-09-07 12:19:14,775 - INFO - > workflow_type: 'train'\n", + "2023-09-07 12:19:14,776 - INFO - ---\n", "\n", "\n", - "2023-09-06 09:18:47,397 - INFO - Setting logging properties based on config: /workspace/Data/spleen_ct_segmentation/configs/logging.conf.\n" + "2023-09-07 12:19:14,778 - INFO - Setting logging properties based on config: /workspace/Data/spleen_ct_segmentation/configs/logging.conf.\n" ] } ], @@ -395,11 +395,19 @@ "metadata": {}, "outputs": [], "source": [ + "# Here we specify `return_state_dict=False` to return an instantiated model only for compatibility, will remove after MONAI v1.5.\n", "# directly get an instantiated network that loaded the weights.\n", - "model = load(name=\"brats_mri_segmentation\", bundle_dir=root_dir, source=\"monaihosting\")\n", + "model = load(name=\"brats_mri_segmentation\", bundle_dir=root_dir, source=\"monaihosting\", return_state_dict=False)\n", "\n", "# directly update the parameters for the model from the bundle.\n", - "model = load(name=\"brats_mri_segmentation\", bundle_dir=root_dir, source=\"monaihosting\", in_channels=3, out_channels=1)\n", + "model = load(\n", + " name=\"brats_mri_segmentation\",\n", + " bundle_dir=root_dir,\n", + " source=\"monaihosting\",\n", + " in_channels=3,\n", + " out_channels=1,\n", + " return_state_dict=False,\n", + ")\n", "\n", "# using `exclude_vars` to filter loading weights.\n", "model = load(\n", @@ -407,6 +415,7 @@ " bundle_dir=root_dir,\n", " source=\"monaihosting\",\n", " copy_model_args={\"exclude_vars\": \"convInit|conv_final\"},\n", + " return_state_dict=False,\n", ")\n", "\n", "# pass model and return an instantiated network that loaded the weights.\n", diff --git a/computer_assisted_intervention/endoscopic_inbody_classification.ipynb b/computer_assisted_intervention/endoscopic_inbody_classification.ipynb index f8e4d71af1..3548778355 100644 --- a/computer_assisted_intervention/endoscopic_inbody_classification.ipynb +++ b/computer_assisted_intervention/endoscopic_inbody_classification.ipynb @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "f5e536dd", "metadata": {}, "outputs": [ @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "495b1db7", "metadata": {}, "outputs": [ @@ -148,9 +148,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "2023-09-04 02:46:58,451 - INFO - Verified 'inbody_outbody_samples.zip', md5: cce8f3beb1fb2e8fc2429e073c927489.\n", - "2023-09-04 02:46:58,453 - INFO - File exists: inbody_outbody_samples.zip, skipped downloading.\n", - "2023-09-04 02:46:58,456 - INFO - Non-empty folder exists in /workspace/Data/endo_cls/inbody_outbody_samples, skipped extracting.\n" + "2023-09-07 12:17:16,497 - INFO - Verified 'inbody_outbody_samples.zip', md5: cce8f3beb1fb2e8fc2429e073c927489.\n", + "2023-09-07 12:17:16,498 - INFO - File exists: inbody_outbody_samples.zip, skipped downloading.\n", + "2023-09-07 12:17:16,501 - INFO - Non-empty folder exists in /workspace/Data/endo_cls/inbody_outbody_samples, skipped extracting.\n" ] } ], @@ -173,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "8d0e9249", "metadata": {}, "outputs": [ @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "c6699a23", "metadata": {}, "outputs": [ @@ -342,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "4784fe9e", "metadata": {}, "outputs": [ @@ -358,41 +358,54 @@ "url None\n", "remove_prefix monai_\n", "progress True\n", - "2023-09-04 02:49:09,215 - INFO - --- input summary of monai.bundle.scripts.download ---\n", - "2023-09-04 02:49:09,216 - INFO - > name: 'endoscopic_inbody_classification'\n", - "2023-09-04 02:49:09,218 - INFO - > bundle_dir: PosixPath('.')\n", - "2023-09-04 02:49:09,218 - INFO - > source: 'github'\n", - "2023-09-04 02:49:09,219 - INFO - > remove_prefix: 'monai_'\n", - "2023-09-04 02:49:09,219 - INFO - > progress: True\n", - "2023-09-04 02:49:09,219 - INFO - ---\n", + "2023-09-07 12:17:23,537 - INFO - --- input summary of monai.bundle.scripts.download ---\n", + "2023-09-07 12:17:23,539 - INFO - > name: 'endoscopic_inbody_classification'\n", + "2023-09-07 12:17:23,540 - INFO - > bundle_dir: PosixPath('.')\n", + "2023-09-07 12:17:23,540 - INFO - > source: 'github'\n", + "2023-09-07 12:17:23,541 - INFO - > remove_prefix: 'monai_'\n", + "2023-09-07 12:17:23,542 - INFO - > progress: True\n", + "2023-09-07 12:17:23,542 - INFO - ---\n", "\n", - "\n", - "2023-09-04 02:49:10,337 - INFO - Expected md5 is None, skip md5 check for file endoscopic_inbody_classification_v0.4.4.zip.\n", - "2023-09-04 02:49:10,338 - INFO - File exists: endoscopic_inbody_classification_v0.4.4.zip, skipped downloading.\n", - "2023-09-04 02:49:10,339 - INFO - Writing into directory: ..\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "endoscopic_inbody_classification_v0.4.4.zip: 185MB [00:09, 21.4MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-09-07 12:17:33,469 - INFO - Downloaded: endoscopic_inbody_classification_v0.4.4.zip\n", + "2023-09-07 12:17:33,469 - INFO - Expected md5 is None, skip md5 check for file endoscopic_inbody_classification_v0.4.4.zip.\n", + "2023-09-07 12:17:33,470 - INFO - Writing into directory: ..\n", "workflow_name None\n", "config_file endoscopic_inbody_classification/configs/train.json\n", "workflow_type train\n", - "2023-09-04 02:49:11,411 - INFO - --- input summary of monai.bundle.scripts.run ---\n", - "2023-09-04 02:49:11,412 - INFO - > config_file: 'endoscopic_inbody_classification/configs/train.json'\n", - "2023-09-04 02:49:11,413 - INFO - > workflow_type: 'train'\n", - "2023-09-04 02:49:11,414 - INFO - ---\n", + "2023-09-07 12:17:34,468 - INFO - --- input summary of monai.bundle.scripts.run ---\n", + "2023-09-07 12:17:34,468 - INFO - > config_file: 'endoscopic_inbody_classification/configs/train.json'\n", + "2023-09-07 12:17:34,469 - INFO - > workflow_type: 'train'\n", + "2023-09-07 12:17:34,469 - INFO - ---\n", "\n", "\n", - "2023-09-04 02:49:11,415 - INFO - Setting logging properties based on config: endoscopic_inbody_classification/configs/logging.conf.\n", - "2023-09-04 02:49:11,692 - INFO - 'dst' model updated: 384 of 384 variables.\n" + "2023-09-07 12:17:34,470 - INFO - Setting logging properties based on config: endoscopic_inbody_classification/configs/logging.conf.\n", + "2023-09-07 12:17:34,701 - INFO - 'dst' model updated: 384 of 384 variables.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 5/5 [00:02<00:00, 2.31it/s]\n" + "100%|██████████| 5/5 [00:02<00:00, 2.25it/s]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -404,7 +417,11 @@ "source": [ "max_epochs = 5\n", "\n", - "pretrained_model = monai.bundle.load(name=\"endoscopic_inbody_classification\", bundle_dir=\"./\")\n", + "pretrained_model = monai.bundle.load(\n", + " name=\"endoscopic_inbody_classification\",\n", + " bundle_dir=\"./\",\n", + " return_state_dict=False\n", + ")\n", "\n", "pretrained_model.train()\n", "losses = []\n", From c16ea8198e4656f4267d239597b3fcba26625b44 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 7 Sep 2023 12:24:58 +0000 Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../endoscopic_inbody_classification.ipynb | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/computer_assisted_intervention/endoscopic_inbody_classification.ipynb b/computer_assisted_intervention/endoscopic_inbody_classification.ipynb index 3548778355..0aa976668f 100644 --- a/computer_assisted_intervention/endoscopic_inbody_classification.ipynb +++ b/computer_assisted_intervention/endoscopic_inbody_classification.ipynb @@ -417,11 +417,7 @@ "source": [ "max_epochs = 5\n", "\n", - "pretrained_model = monai.bundle.load(\n", - " name=\"endoscopic_inbody_classification\",\n", - " bundle_dir=\"./\",\n", - " return_state_dict=False\n", - ")\n", + "pretrained_model = monai.bundle.load(name=\"endoscopic_inbody_classification\", bundle_dir=\"./\", return_state_dict=False)\n", "\n", "pretrained_model.train()\n", "losses = []\n",