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Auto3DSeg CI Test Errors Fix (#961)
Signed-off-by: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com> Fixes #952 . ### Description 1. fix notebook work_dir sharing causing CI fails ### Checks <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [ ] Notebook runs automatically `./runner [-p <regex_pattern>]` Signed-off-by: Mingxin Zheng <18563433+mingxin-zheng@users.noreply.github.com> Signed-off-by: Mingxin Zheng <mingxinz@nvidia.com>
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‎auto3dseg/notebooks/auto3dseg_autorunner_ref_api.ipynb‎

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@@ -78,28 +78,35 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Task09_Spleen.tar: 1.50GB [00:43, 36.7MB/s] \n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2022-09-21 03:23:09,534 - INFO - Expected md5 is None, skip md5 check for file Task05_Prostate.tar.\n",
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"2022-09-21 03:23:09,535 - INFO - File exists: Task05_Prostate.tar, skipped downloading.\n",
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"2022-09-21 03:23:09,536 - INFO - Non-empty folder exists in Task05_Prostate, skipped extracting.\n"
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"2022-09-29 03:49:02,357 - INFO - Downloaded: Task09_Spleen.tar\n",
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"2022-09-29 03:49:02,359 - INFO - Expected md5 is None, skip md5 check for file Task09_Spleen.tar.\n",
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"2022-09-29 03:49:02,359 - INFO - Writing into directory: ./.\n"
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]
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}
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],
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"source": [
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"root = \"./\"\n",
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"work_dir = os.path.join(root, 'auto3dseg_work_dir')\n",
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"work_dir = os.path.join(root, 'ref_api_work_dir')\n",
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"if not os.path.isdir(work_dir):\n",
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" os.makedirs(work_dir)\n",
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"\n",
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"msd_task = \"Task05_Prostate\"\n",
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"msd_task = \"Task09_Spleen\"\n",
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"dataroot = os.path.join(root, msd_task)\n",
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"datalist_file = \"../tasks/msd/Task05_Prostate/msd_task05_prostate_folds.json\"\n",
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"datalist_file = os.path.join(\"..\", \"tasks\", \"msd\", msd_task, \"msd_\" + msd_task.lower() + \"_folds.json\")\n",
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"\n",
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"resource = \"https://msd-for-monai.s3-us-west-2.amazonaws.com/\" + msd_task + \".tar\"\n",
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"compressed_file = os.path.join(root, msd_task + \".tar\")\n",
@@ -116,16 +123,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_src_cfg = {\n",
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" \"name\": \"Task05_Prostate\",\n",
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" \"task\": \"segmentation\",\n",
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" \"modality\": \"MRI\",\n",
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" \"datalist\": datalist_file,\n",
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" \"dataroot\": dataroot,\n",
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" \"name\": msd_task, # optional\n",
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" \"task\": \"segmentation\", # optional\n",
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" \"modality\": \"MRI\", # required\n",
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" \"datalist\": datalist_file, # required\n",
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" \"dataroot\": dataroot, # required\n",
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"}\n",
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"input = os.path.join(work_dir, 'input.yaml')\n",
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"ConfigParser.export_config_file(data_src_cfg, input)"
@@ -152,21 +159,28 @@
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"text": [
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"100%|██████████| 30/30 [00:04<00:00, 6.51it/s]\n"
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"100%|██████████| 40/40 [01:34<00:00, 2.37s/it]"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2022-09-21 03:23:29,492 - WARNING - Data is not completely uniform. MONAI transforms may provide unexpected result\n"
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"2022-09-29 01:43:36,891 - WARNING - Data is not completely uniform. MONAI transforms may provide unexpected result\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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@@ -183,7 +197,7 @@
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"Besides the Python API call, user can also use command line interface (CLI) provided by the user's OS. One example is the following bash commands:\n",
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"\n",
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"```bash\n",
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"python -m monai.apps.auto3dseg DataAnalyzer get_all_case_stats --datalist=\"../tasks/msd/Task05_Prostate/msd_task05_prostate_folds.json\" --dataroot=\"./Task05_Prostate\" --output_path=\"./auto3dseg_work_dir/data_stats.yaml\"\n",
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"python -m monai.apps.auto3dseg DataAnalyzer get_all_case_stats --datalist=\"../tasks/msd/Task09_Spleen/msd_task09_spleen_folds.json\" --dataroot=\"./Task09_Spleen\" --output_path=\"./ref_api_work_dir/data_stats.yaml\"\n",
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"```"
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"cell_type": "code",
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"execution_count": 6,
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"name": "stderr",
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"algo_templates.tar.gz: 100%|██████████| 280k/280k [00:01<00:00, 241kB/s] "
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"algo_templates.tar.gz: 100%|██████████| 280k/280k [00:00<00:00, 607kB/s] "
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"2022-09-21 03:23:38,373 - INFO - Downloaded: /tmp/tmp8t3vbaz0/algo_templates.tar.gz\n",
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"2022-09-21 03:23:38,375 - INFO - Expected md5 is None, skip md5 check for file /tmp/tmp8t3vbaz0/algo_templates.tar.gz.\n",
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"2022-09-21 03:23:38,377 - INFO - Writing into directory: ./auto3dseg_work_dir.\n"
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"2022-09-29 01:43:37,829 - INFO - Downloaded: /tmp/tmpodiia3qq/algo_templates.tar.gz\n",
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"2022-09-29 01:43:37,830 - INFO - Expected md5 is None, skip md5 check for file /tmp/tmpodiia3qq/algo_templates.tar.gz.\n",
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"2022-09-29 01:43:37,830 - INFO - Writing into directory: ./ref_api_work_dir.\n"
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"name": "stdout",
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"text": [
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"2022-09-21 03:23:38,856 - INFO - ./auto3dseg_work_dir/segresnet2d_0\n",
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"2022-09-21 03:23:39,401 - INFO - ./auto3dseg_work_dir/segresnet2d_1\n",
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"2022-09-21 03:23:39,823 - INFO - ./auto3dseg_work_dir/segresnet2d_2\n",
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"2022-09-21 03:23:40,353 - INFO - ./auto3dseg_work_dir/segresnet2d_3\n",
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"2022-09-21 03:23:40,776 - INFO - ./auto3dseg_work_dir/segresnet2d_4\n",
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"2022-09-21 03:23:41,363 - INFO - ./auto3dseg_work_dir/dints_0\n",
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"2022-09-21 03:23:41,846 - INFO - ./auto3dseg_work_dir/dints_1\n",
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"2022-09-21 03:23:42,431 - INFO - ./auto3dseg_work_dir/dints_2\n",
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"2022-09-21 03:23:43,453 - INFO - ./auto3dseg_work_dir/dints_4\n",
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"2022-09-21 03:23:43,868 - INFO - ./auto3dseg_work_dir/swinunetr_0\n",
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"2022-09-21 03:23:44,361 - INFO - ./auto3dseg_work_dir/swinunetr_1\n",
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"2022-09-21 03:23:44,779 - INFO - ./auto3dseg_work_dir/swinunetr_2\n",
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"2022-09-21 03:23:45,297 - INFO - ./auto3dseg_work_dir/swinunetr_3\n",
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"2022-09-21 03:23:45,711 - INFO - ./auto3dseg_work_dir/swinunetr_4\n",
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"2022-09-21 03:23:46,255 - INFO - ./auto3dseg_work_dir/segresnet_0\n",
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"2022-09-21 03:23:48,280 - INFO - ./auto3dseg_work_dir/segresnet_4\n"
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"2022-09-29 01:43:38,651 - INFO - ./ref_api_work_dir/segresnet2d_0\n",
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"2022-09-29 01:43:39,029 - INFO - ./ref_api_work_dir/segresnet2d_1\n",
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"2022-09-29 01:43:39,411 - INFO - ./ref_api_work_dir/segresnet2d_2\n",
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"2022-09-29 01:43:39,916 - INFO - ./ref_api_work_dir/segresnet2d_3\n",
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"2022-09-29 01:43:40,373 - INFO - ./ref_api_work_dir/segresnet2d_4\n",
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"2022-09-29 01:43:41,656 - INFO - ./ref_api_work_dir/dints_0\n",
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"2022-09-29 01:43:42,284 - INFO - ./ref_api_work_dir/dints_1\n",
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"2022-09-29 01:43:42,678 - INFO - ./ref_api_work_dir/dints_2\n",
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"2022-09-29 01:43:43,172 - INFO - ./ref_api_work_dir/dints_3\n",
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"2022-09-29 01:43:43,681 - INFO - ./ref_api_work_dir/dints_4\n",
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"2022-09-29 01:43:44,448 - INFO - ./ref_api_work_dir/swinunetr_0\n",
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"2022-09-29 01:43:44,807 - INFO - ./ref_api_work_dir/swinunetr_1\n",
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"2022-09-29 01:43:45,687 - INFO - ./ref_api_work_dir/swinunetr_3\n",
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"2022-09-29 01:43:48,313 - INFO - ./ref_api_work_dir/swinunetr_4\n",
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"2022-09-29 01:43:48,983 - INFO - ./ref_api_work_dir/segresnet_0\n",
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]
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"cell_type": "code",
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"execution_count": 7,
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"{'num_iterations': 12000, 'num_iterations_per_validation': 6000, 'num_images_per_batch': 2, 'num_epochs': 2000, 'num_warmup_iterations': 6000}\n"
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"{'num_iterations': 32000, 'num_iterations_per_validation': 16000, 'num_images_per_batch': 2, 'num_epochs': 2000, 'num_warmup_iterations': 16000}\n"
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"history = import_bundle_algo_history(work_dir, only_trained=False)\n",
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"for task in history:\n",
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" for name, algo in task.items():\n",
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" for _, algo in task.items():\n",
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" algo.train(train_param) # can use default params by `algo.train()`\n",
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" acc = algo.get_score()\n",
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" algo_to_pickle(algo, template_path=algo.template_path, best_metrics=acc)"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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"version": "3.8.12"
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},
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"vscode": {
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"interpreter": {

‎auto3dseg/notebooks/auto3dseg_hello_world.ipynb‎

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@@ -115,7 +115,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"work_dir = \"./auto3dseg_work_dir\"\n",
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"work_dir = \"./helloworld_work_dir\"\n",
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"if not os.path.isdir(work_dir):\n",
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"cell_type": "markdown",
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"## 2. Test the Auto3Dsseg AutoRunner pipeline\n",
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"## 2. Test the Auto3DSeg AutoRunner pipeline\n",
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"\n",
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"AutoRunner provides an interface to run the Auot3Dseg pipeline. It will automatically configure four different neural networks and perform multi-fold training to achieve the state-of-the-art performance. The module is highly configurable, but only requires minimal user inputs. In this example, we want to complete the piepline in two minutes, so we need to have some customizations\n",
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"\n",

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