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change datasets from task 09 to 04 for all auto3dseg notebooks #997

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Merged
merged 7 commits into from
Oct 18, 2022
Merged

change datasets from task 09 to 04 for all auto3dseg notebooks #997

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18c4efc
change datasets from task 09 to 04 for all auto3dseg notebook
mingxin-zheng Oct 18, 2022
e32df20
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 18, 2022
7e3d84f
undo the workaround
mingxin-zheng Oct 18, 2022
a9e65db
Merge branch 'change_datasets_auto3dseg' of https://github.com/mingxi…
mingxin-zheng Oct 18, 2022
91aeeb0
change epochs from 2000 to 2
mingxin-zheng Oct 18, 2022
2182225
Update README.md
wyli Oct 18, 2022
9e602be
Merge branch 'main' into change_datasets_auto3dseg
wyli Oct 18, 2022
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1 change: 1 addition & 0 deletions auto3dseg/README.md
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Expand Up @@ -43,6 +43,7 @@ We have demonstrated preliminary results of public datasets are described in the
- [INSTANCE22](tasks/instance22)
- [Beyond the Cranial Vault (BTCV) Abdomen Dataset](tasks/btcv)
- Medical Segmentation Decathlon (MSD) Dataset
- [Task04 Task04_Hippocampus](tasks/msd/Task04_Hippocampus)
- [Task05 Prostate](tasks/msd/Task05_Prostate)
- [Task09 Spleen](tasks/msd/Task09_Spleen)

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113 changes: 43 additions & 70 deletions auto3dseg/notebooks/auto3dseg_autorunner_ref_api.ipynb
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Expand Up @@ -24,7 +24,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -40,13 +40,15 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"\n",
"from pathlib import Path\n",
"\n",
"from monai.apps import download_and_extract\n",
"from monai.apps.auto3dseg import (\n",
" DataAnalyzer,\n",
Expand All @@ -69,33 +71,26 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Task09_Spleen.tar: 1.50GB [00:43, 36.7MB/s] \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-09-29 03:49:02,357 - INFO - Downloaded: Task09_Spleen.tar\n",
"2022-09-29 03:49:02,359 - INFO - Expected md5 is None, skip md5 check for file Task09_Spleen.tar.\n",
"2022-09-29 03:49:02,359 - INFO - Writing into directory: ./.\n"
"2022-10-18 08:14:33,395 - INFO - Expected md5 is None, skip md5 check for file Task04_Hippocampus.tar.\n",
"2022-10-18 08:14:33,395 - INFO - File exists: Task04_Hippocampus.tar, skipped downloading.\n",
"2022-10-18 08:14:33,396 - INFO - Non-empty folder exists in Task04_Hippocampus, skipped extracting.\n"
]
}
],
"source": [
"root = \"./\"\n",
"root = str(Path(\".\"))\n",
"work_dir = os.path.join(root, 'ref_api_work_dir')\n",
"if not os.path.isdir(work_dir):\n",
" os.makedirs(work_dir)\n",
"\n",
"msd_task = \"Task09_Spleen\"\n",
"msd_task = \"Task04_Hippocampus\"\n",
"dataroot = os.path.join(root, msd_task)\n",
"datalist_file = os.path.join(\"..\", \"tasks\", \"msd\", msd_task, \"msd_\" + msd_task.lower() + \"_folds.json\")\n",
"\n",
Expand All @@ -114,7 +109,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -157,21 +152,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 40/40 [01:34<00:00, 2.37s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-09-29 01:43:36,891 - WARNING - Data is not completely uniform. MONAI transforms may provide unexpected result\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
"100%|██████████| 30/30 [00:00<00:00, 71.04it/s]\n"
]
}
],
Expand All @@ -188,7 +169,7 @@
"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",
"\n",
"```bash\n",
"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",
"python -m monai.apps.auto3dseg DataAnalyzer get_all_case_stats --datalist=\"../tasks/msd/Task04_Hippocampus/msd_task04_hippocampus_folds.json\" --dataroot=\"./Task04_Hippocampus\" --output_path=\"./ref_api_work_dir/data_stats.yaml\"\n",
"```"
]
},
Expand All @@ -214,16 +195,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
"algo_templates.tar.gz: 100%|██████████| 280k/280k [00:00<00:00, 607kB/s] "
"algo_templates.tar.gz: 296kB [00:01, 247kB/s] "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022-09-29 01:43:37,829 - INFO - Downloaded: /tmp/tmpodiia3qq/algo_templates.tar.gz\n",
"2022-09-29 01:43:37,830 - INFO - Expected md5 is None, skip md5 check for file /tmp/tmpodiia3qq/algo_templates.tar.gz.\n",
"2022-09-29 01:43:37,830 - INFO - Writing into directory: ./ref_api_work_dir.\n"
"2022-10-18 08:14:35,703 - INFO - Downloaded: /tmp/tmps2pt5u7y/algo_templates.tar.gz\n",
"2022-10-18 08:14:35,705 - INFO - Expected md5 is None, skip md5 check for file /tmp/tmps2pt5u7y/algo_templates.tar.gz.\n",
"2022-10-18 08:14:35,706 - INFO - Writing into directory: ./ref_api_work_dir.\n"
]
},
{
Expand All @@ -237,26 +218,26 @@
"name": "stdout",
"output_type": "stream",
"text": [
"2022-09-29 01:43:38,651 - INFO - ./ref_api_work_dir/segresnet2d_0\n",
"2022-09-29 01:43:39,029 - INFO - ./ref_api_work_dir/segresnet2d_1\n",
"2022-09-29 01:43:39,411 - INFO - ./ref_api_work_dir/segresnet2d_2\n",
"2022-09-29 01:43:39,916 - INFO - ./ref_api_work_dir/segresnet2d_3\n",
"2022-09-29 01:43:40,373 - INFO - ./ref_api_work_dir/segresnet2d_4\n",
"2022-09-29 01:43:41,656 - INFO - ./ref_api_work_dir/dints_0\n",
"2022-09-29 01:43:42,284 - INFO - ./ref_api_work_dir/dints_1\n",
"2022-09-29 01:43:42,678 - INFO - ./ref_api_work_dir/dints_2\n",
"2022-09-29 01:43:43,172 - INFO - ./ref_api_work_dir/dints_3\n",
"2022-09-29 01:43:43,681 - INFO - ./ref_api_work_dir/dints_4\n",
"2022-09-29 01:43:44,448 - INFO - ./ref_api_work_dir/swinunetr_0\n",
"2022-09-29 01:43:44,807 - INFO - ./ref_api_work_dir/swinunetr_1\n",
"2022-09-29 01:43:45,177 - INFO - ./ref_api_work_dir/swinunetr_2\n",
"2022-09-29 01:43:45,687 - INFO - ./ref_api_work_dir/swinunetr_3\n",
"2022-09-29 01:43:48,313 - INFO - ./ref_api_work_dir/swinunetr_4\n",
"2022-09-29 01:43:48,983 - INFO - ./ref_api_work_dir/segresnet_0\n",
"2022-09-29 01:43:49,597 - INFO - ./ref_api_work_dir/segresnet_1\n",
"2022-09-29 01:43:50,008 - INFO - ./ref_api_work_dir/segresnet_2\n",
"2022-09-29 01:43:50,393 - INFO - ./ref_api_work_dir/segresnet_3\n",
"2022-09-29 01:43:51,990 - INFO - ./ref_api_work_dir/segresnet_4\n"
"2022-10-18 08:14:36,019 - INFO - ./ref_api_work_dir/segresnet2d_0\n",
"2022-10-18 08:14:36,270 - INFO - ./ref_api_work_dir/segresnet2d_1\n",
"2022-10-18 08:14:36,616 - INFO - ./ref_api_work_dir/segresnet2d_2\n",
"2022-10-18 08:14:36,879 - INFO - ./ref_api_work_dir/segresnet2d_3\n",
"2022-10-18 08:14:37,142 - INFO - ./ref_api_work_dir/segresnet2d_4\n",
"2022-10-18 08:14:37,412 - INFO - ./ref_api_work_dir/dints_0\n",
"2022-10-18 08:14:37,799 - INFO - ./ref_api_work_dir/dints_1\n",
"2022-10-18 08:14:38,071 - INFO - ./ref_api_work_dir/dints_2\n",
"2022-10-18 08:14:38,343 - INFO - ./ref_api_work_dir/dints_3\n",
"2022-10-18 08:14:38,717 - INFO - ./ref_api_work_dir/dints_4\n",
"2022-10-18 08:14:38,975 - INFO - ./ref_api_work_dir/swinunetr_0\n",
"2022-10-18 08:14:39,233 - INFO - ./ref_api_work_dir/swinunetr_1\n",
"2022-10-18 08:14:39,492 - INFO - ./ref_api_work_dir/swinunetr_2\n",
"2022-10-18 08:14:39,849 - INFO - ./ref_api_work_dir/swinunetr_3\n",
"2022-10-18 08:14:40,106 - INFO - ./ref_api_work_dir/swinunetr_4\n",
"2022-10-18 08:14:40,398 - INFO - ./ref_api_work_dir/segresnet_0\n",
"2022-10-18 08:14:40,771 - INFO - ./ref_api_work_dir/segresnet_1\n",
"2022-10-18 08:14:41,042 - INFO - ./ref_api_work_dir/segresnet_2\n",
"2022-10-18 08:14:41,309 - INFO - ./ref_api_work_dir/segresnet_3\n",
"2022-10-18 08:14:41,574 - INFO - ./ref_api_work_dir/segresnet_4\n"
]
}
],
Expand Down Expand Up @@ -318,21 +299,13 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'num_iterations': 32000, 'num_iterations_per_validation': 16000, 'num_images_per_batch': 2, 'num_epochs': 2000, 'num_warmup_iterations': 16000}\n"
]
}
],
"outputs": [],
"source": [
"# The training params are optional. If you don't want to change the default settings, you can use either `train()` or `train({})` in 2.3.2 \n",
"\n",
"max_epochs = 2000\n",
"max_epochs = 2 # change epoch number to 2 to cut down the notebook running time\n",
"\n",
"# safeguard to ensure max_epochs is greater or equal to 2\n",
"max_epochs = max(max_epochs, 2)\n",
Expand Down Expand Up @@ -370,7 +343,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -438,7 +411,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -466,7 +439,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
"version": "3.8.13"
},
"vscode": {
"interpreter": {
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