From fbd795eafdaa8605892485bd542f4cf27fcd6e11 Mon Sep 17 00:00:00 2001 From: Vikash Gupta Date: Wed, 24 Nov 2021 16:14:50 -0500 Subject: [PATCH] Fixed minor dependencies for fresh deployment Signed-off-by: Vikash Gupta --- notebooks/tutorials/03_segmentation_app.ipynb | 319 ++++++++++++++---- notebooks/tutorials/04_mis_tutorial.ipynb | 236 ++++++++----- 2 files changed, 402 insertions(+), 153 deletions(-) diff --git a/notebooks/tutorials/03_segmentation_app.ipynb b/notebooks/tutorials/03_segmentation_app.ipynb index 3270d9f1..37d9ecc7 100644 --- a/notebooks/tutorials/03_segmentation_app.ipynb +++ b/notebooks/tutorials/03_segmentation_app.ipynb @@ -681,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -741,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -838,7 +838,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -878,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -886,50 +886,94 @@ "output_type": "stream", "text": [ "\u001b[34mGoing to initiate execution of operator DICOMDataLoaderOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMDataLoaderOperator \u001b[33m(Process ID: 3885, Operator ID: a345e29f-33ba-44d6-ac64-f4c37edf3eed)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMDataLoaderOperator \u001b[33m(Process ID: 999862, Operator ID: c04e9506-86e0-4675-8f7c-698ede56a3e6)\u001b[39m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2021-11-24 15:29:08,591] [WARNING] (root) - No selection rules given; select all series.\n", + "[2021-11-24 15:29:08,591] [INFO] (root) - Working on study, instance UID: 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213\n", + "[2021-11-24 15:29:08,592] [INFO] (root) - Working on series, instance UID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\u001b[34mDone performing execution of operator DICOMDataLoaderOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSeriesSelectorOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSeriesSelectorOperator \u001b[33m(Process ID: 3885, Operator ID: ad5e3c12-6c1c-42c1-b5ae-f02124e2b4d8)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMSeriesSelectorOperator \u001b[33m(Process ID: 999862, Operator ID: 8cd97116-305b-492f-8d51-13864d3108e2)\u001b[39m\n", + "Working on study, instance UID: 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213\n", + "Working on series, instance UID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763\n", "\u001b[34mDone performing execution of operator DICOMSeriesSelectorOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSeriesToVolumeOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSeriesToVolumeOperator \u001b[33m(Process ID: 3885, Operator ID: 1702a7dd-5cc7-4acc-bbab-d6c5e191471b)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMSeriesToVolumeOperator \u001b[33m(Process ID: 999862, Operator ID: a5ab0fb9-6a54-4fef-9f77-056531cce7e7)\u001b[39m\n", "\u001b[34mDone performing execution of operator DICOMSeriesToVolumeOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator SpleenSegOperator\u001b[39m\n", - "\u001b[32mExecuting operator SpleenSegOperator \u001b[33m(Process ID: 3885, Operator ID: 6222eee5-4a70-424e-b010-2263d41c8ccf)\u001b[39m\n" + "\u001b[32mExecuting operator SpleenSegOperator \u001b[33m(Process ID: 999862, Operator ID: 65c122dd-aca1-4de2-9757-067c4fb5562d)\u001b[39m\n", + "Converted Image object metadata:\n", + "SeriesInstanceUID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763, type \n", + "Modality: CT, type \n", + "SeriesDescription: No series description, type \n", + "PatientPosition: HFS, type \n", + "SeriesNumber: 1, type \n", + "row_pixel_spacing: 1.0, type \n", + "col_pixel_spacing: 1.0, type \n", + "depth_pixel_spacing: 1.0, type \n", + "row_direction_cosine: [-1.0, 0.0, 0.0], type \n", + "col_direction_cosine: [0.0, -1.0, 0.0], type \n", + "depth_direction_cosine: [0.0, 0.0, 1.0], type \n", + "dicom_affine_transform: [[-1. 0. 0. 0.]\n", + " [ 0. -1. 0. 0.]\n", + " [ 0. 0. 1. 0.]\n", + " [ 0. 0. 0. 1.]], type \n", + "nifti_affine_transform: [[ 1. -0. -0. -0.]\n", + " [-0. 1. -0. -0.]\n", + " [ 0. 0. 1. 0.]\n", + " [ 0. 0. 0. 1.]], type \n", + "StudyInstanceUID: 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213, type \n", + "StudyID: SLICER10001, type \n", + "StudyDate: 2019-09-16, type \n", + "StudyTime: 010100.000000, type \n", + "StudyDescription: spleen, type \n", + "AccessionNumber: 1, type \n", + "selection_name: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763, type \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2021-09-23 02:20:17,805] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of DICOM instance datasets in the list: 515\n", - "[2021-09-23 02:20:17,806] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of slices in the numpy image: 515\n", - "[2021-09-23 02:20:17,807] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Labels of the segments: ['Spleen']\n" + "[2021-11-24 15:29:18,697] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of DICOM instance datasets in the list: 515\n", + "[2021-11-24 15:29:18,698] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of slices in the numpy image: 515\n", + "[2021-11-24 15:29:18,698] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Labels of the segments: ['Spleen']\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "file written: /home/gbae/repo/monai-deploy-app-sdk/notebooks/tutorials/output/prediction_output/Img_in_context/Img_in_context_seg.nii.gz.\n", + "file written: /data/Vikash/Tools/RSNA/monai-deploy-app-sdk/notebooks/tutorials/output/prediction_output/1.2.826.0.1.3680043.2.1125.1/1.2.826.0.1.3680043.2.1125.1_seg.nii.gz.\n", "Output Seg image numpy array shaped: (515, 440, 440)\n", "Output Seg image pixel max value: 1\n", "\u001b[34mDone performing execution of operator SpleenSegOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSegmentationWriterOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSegmentationWriterOperator \u001b[33m(Process ID: 3885, Operator ID: d6208465-9e0e-44fa-8b71-938c490b7c04)\u001b[39m\n" + "\u001b[32mExecuting operator DICOMSegmentationWriterOperator \u001b[33m(Process ID: 999862, Operator ID: 5953ac10-35c0-46d1-97ff-a2c2d53fa02c)\u001b[39m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2021-09-23 02:20:19,972] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Unique values in seg image: [0 1]\n", - "[2021-09-23 02:20:21,118] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Saving output file /home/gbae/repo/monai-deploy-app-sdk/notebooks/tutorials/output/dicom_seg-DICOMSEG.dcm\n", - "[2021-09-23 02:20:21,397] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - File saved.\n" + "[2021-11-24 15:29:20,287] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Unique values in seg image: [0 1]\n", + "[2021-11-24 15:29:22,188] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Saving output file /data/Vikash/Tools/RSNA/monai-deploy-app-sdk/notebooks/tutorials/output/dicom_seg-DICOMSEG.dcm\n", + "[2021-11-24 15:29:22,346] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - File saved.\n" ] }, { @@ -970,7 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -987,7 +1031,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1108,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1200,7 +1244,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1221,14 +1265,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "__main__.py app.py spleen_seg_operator.py\n" + "app.py\t__main__.py spleen_seg_operator.py\r\n" ] } ], @@ -1245,7 +1289,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1253,32 +1297,73 @@ "output_type": "stream", "text": [ "\u001b[34mGoing to initiate execution of operator DICOMDataLoaderOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMDataLoaderOperator \u001b[33m(Process ID: 5326, Operator ID: ba037402-5d1d-4133-944b-080397ff5ac5)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMDataLoaderOperator \u001b[33m(Process ID: 1000710, Operator ID: f74b2234-c1bc-461a-81b1-4044c3c53d10)\u001b[39m\n", "\u001b[34mDone performing execution of operator DICOMDataLoaderOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSeriesSelectorOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSeriesSelectorOperator \u001b[33m(Process ID: 5326, Operator ID: 9fe72901-dd8b-4574-8378-7a13fb927d92)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMSeriesSelectorOperator \u001b[33m(Process ID: 1000710, Operator ID: e70b5143-62b8-4bb7-a9a9-87343414189e)\u001b[39m\n", + "[2021-11-24 15:29:44,079] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2021-11-24 15:29:44,079] [INFO] (root) - Searching study, : 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213\n", + " # of series: 1\n", + "[2021-11-24 15:29:44,079] [INFO] (root) - Working on series, instance UID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763\n", + "[2021-11-24 15:29:44,079] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2021-11-24 15:29:44,079] [INFO] (root) - Series attribute value: spleen\n", + "[2021-11-24 15:29:44,080] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 15:29:44,080] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2021-11-24 15:29:44,080] [INFO] (root) - Series attribute value: CT\n", + "[2021-11-24 15:29:44,080] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 15:29:44,080] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2021-11-24 15:29:44,080] [INFO] (root) - Series attribute value: No series description\n", + "[2021-11-24 15:29:44,080] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 15:29:44,080] [INFO] (root) - Selected Series, UID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763\n", "\u001b[34mDone performing execution of operator DICOMSeriesSelectorOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSeriesToVolumeOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSeriesToVolumeOperator \u001b[33m(Process ID: 5326, Operator ID: ea353df5-0177-4286-bd63-0e2456d9f4f0)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMSeriesToVolumeOperator \u001b[33m(Process ID: 1000710, Operator ID: 3ef89a0a-07d4-41e8-9744-c4b7e4020ae6)\u001b[39m\n", "\u001b[34mDone performing execution of operator DICOMSeriesToVolumeOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator SpleenSegOperator\u001b[39m\n", - "\u001b[32mExecuting operator SpleenSegOperator \u001b[33m(Process ID: 5326, Operator ID: a46c7abc-1f90-4009-80d2-0804b23221db)\u001b[39m\n", - "file written: /home/gbae/repo/monai-deploy-app-sdk/notebooks/tutorials/output/prediction_output/Img_in_context/Img_in_context_seg.nii.gz.\n", + "\u001b[32mExecuting operator SpleenSegOperator \u001b[33m(Process ID: 1000710, Operator ID: 1493e6ee-9391-4c76-a721-6b8d46f6e253)\u001b[39m\n", + "Converted Image object metadata:\n", + "SeriesInstanceUID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763, type \n", + "Modality: CT, type \n", + "SeriesDescription: No series description, type \n", + "PatientPosition: HFS, type \n", + "SeriesNumber: 1, type \n", + "row_pixel_spacing: 1.0, type \n", + "col_pixel_spacing: 1.0, type \n", + "depth_pixel_spacing: 1.0, type \n", + "row_direction_cosine: [-1.0, 0.0, 0.0], type \n", + "col_direction_cosine: [0.0, -1.0, 0.0], type \n", + "depth_direction_cosine: [0.0, 0.0, 1.0], type \n", + "dicom_affine_transform: [[-1. 0. 0. 0.]\n", + " [ 0. -1. 0. 0.]\n", + " [ 0. 0. 1. 0.]\n", + " [ 0. 0. 0. 1.]], type \n", + "nifti_affine_transform: [[ 1. -0. -0. -0.]\n", + " [-0. 1. -0. -0.]\n", + " [ 0. 0. 1. 0.]\n", + " [ 0. 0. 0. 1.]], type \n", + "StudyInstanceUID: 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213, type \n", + "StudyID: SLICER10001, type \n", + "StudyDate: 2019-09-16, type \n", + "StudyTime: 010100.000000, type \n", + "StudyDescription: spleen, type \n", + "AccessionNumber: 1, type \n", + "selection_name: CT Series, type \n", + "file written: /data/Vikash/Tools/RSNA/monai-deploy-app-sdk/notebooks/tutorials/output/prediction_output/1.2.826.0.1.3680043.2.1125.1/1.2.826.0.1.3680043.2.1125.1_seg.nii.gz.\n", "Output Seg image numpy array shaped: (515, 440, 440)\n", "Output Seg image pixel max value: 1\n", "\u001b[34mDone performing execution of operator SpleenSegOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSegmentationWriterOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSegmentationWriterOperator \u001b[33m(Process ID: 5326, Operator ID: 7787e305-8bbd-4a7d-ad04-c4a46524358c)\u001b[39m\n", - "[2021-09-23 02:20:31,260] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of DICOM instance datasets in the list: 515\n", - "[2021-09-23 02:20:31,260] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of slices in the numpy image: 515\n", - "[2021-09-23 02:20:31,260] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Labels of the segments: ['Spleen']\n", - "[2021-09-23 02:20:33,355] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Unique values in seg image: [0 1]\n", - "[2021-09-23 02:20:34,659] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Saving output file /home/gbae/repo/monai-deploy-app-sdk/notebooks/tutorials/output/dicom_seg-DICOMSEG.dcm\n", - "[2021-09-23 02:20:34,846] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - File saved.\n", + "\u001b[32mExecuting operator DICOMSegmentationWriterOperator \u001b[33m(Process ID: 1000710, Operator ID: 61e30bdb-2d21-4fad-9a06-ad17c6f308df)\u001b[39m\n", + "[2021-11-24 15:29:55,905] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of DICOM instance datasets in the list: 515\n", + "[2021-11-24 15:29:55,905] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of slices in the numpy image: 515\n", + "[2021-11-24 15:29:55,905] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Labels of the segments: ['Spleen']\n", + "[2021-11-24 15:29:57,442] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Unique values in seg image: [0 1]\n", + "[2021-11-24 15:29:59,170] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Saving output file /data/Vikash/Tools/RSNA/monai-deploy-app-sdk/notebooks/tutorials/output/dicom_seg-DICOMSEG.dcm\n", + "[2021-11-24 15:29:59,323] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - File saved.\n", "\u001b[34mDone performing execution of operator DICOMSegmentationWriterOperator\n", "\u001b[39m\n" ] @@ -1297,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1305,51 +1390,94 @@ "output_type": "stream", "text": [ "\u001b[34mGoing to initiate execution of operator DICOMDataLoaderOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMDataLoaderOperator \u001b[33m(Process ID: 5905, Operator ID: 24d2620a-fdc0-46ce-b87b-f24311e63532)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMDataLoaderOperator \u001b[33m(Process ID: 1002078, Operator ID: a41f4089-5f8f-4ef2-b8d8-b4224c9518e9)\u001b[39m\n", "\u001b[34mDone performing execution of operator DICOMDataLoaderOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSeriesSelectorOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSeriesSelectorOperator \u001b[33m(Process ID: 5905, Operator ID: 5e6f5d1c-6812-4b07-8090-ea05cec57f04)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMSeriesSelectorOperator \u001b[33m(Process ID: 1002078, Operator ID: fc706328-9c42-4ff6-842f-221092170a9a)\u001b[39m\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Searching study, : 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213\n", + " # of series: 1\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Working on series, instance UID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Series attribute value: spleen\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Series attribute value: CT\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Series attribute value: No series description\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 15:31:24,236] [INFO] (root) - Selected Series, UID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763\n", "\u001b[34mDone performing execution of operator DICOMSeriesSelectorOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSeriesToVolumeOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSeriesToVolumeOperator \u001b[33m(Process ID: 5905, Operator ID: 7b51ddcd-ad2c-4cca-ad95-2b9b39cfea8d)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMSeriesToVolumeOperator \u001b[33m(Process ID: 1002078, Operator ID: 3ca4482b-2cc2-4309-ad80-a896b3ea7a82)\u001b[39m\n", "\u001b[34mDone performing execution of operator DICOMSeriesToVolumeOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator SpleenSegOperator\u001b[39m\n", - "\u001b[32mExecuting operator SpleenSegOperator \u001b[33m(Process ID: 5905, Operator ID: 33da2642-2729-46ea-afe0-632e2a1ae5fc)\u001b[39m\n", - "file written: /home/gbae/repo/monai-deploy-app-sdk/notebooks/tutorials/output/prediction_output/Img_in_context/Img_in_context_seg.nii.gz.\n", + "\u001b[32mExecuting operator SpleenSegOperator \u001b[33m(Process ID: 1002078, Operator ID: 7837e92c-5ecf-4a0c-a19d-53a1786d546b)\u001b[39m\n", + "Converted Image object metadata:\n", + "SeriesInstanceUID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763, type \n", + "Modality: CT, type \n", + "SeriesDescription: No series description, type \n", + "PatientPosition: HFS, type \n", + "SeriesNumber: 1, type \n", + "row_pixel_spacing: 1.0, type \n", + "col_pixel_spacing: 1.0, type \n", + "depth_pixel_spacing: 1.0, type \n", + "row_direction_cosine: [-1.0, 0.0, 0.0], type \n", + "col_direction_cosine: [0.0, -1.0, 0.0], type \n", + "depth_direction_cosine: [0.0, 0.0, 1.0], type \n", + "dicom_affine_transform: [[-1. 0. 0. 0.]\n", + " [ 0. -1. 0. 0.]\n", + " [ 0. 0. 1. 0.]\n", + " [ 0. 0. 0. 1.]], type \n", + "nifti_affine_transform: [[ 1. -0. -0. -0.]\n", + " [-0. 1. -0. -0.]\n", + " [ 0. 0. 1. 0.]\n", + " [ 0. 0. 0. 1.]], type \n", + "StudyInstanceUID: 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213, type \n", + "StudyID: SLICER10001, type \n", + "StudyDate: 2019-09-16, type \n", + "StudyTime: 010100.000000, type \n", + "StudyDescription: spleen, type \n", + "AccessionNumber: 1, type \n", + "selection_name: CT Series, type \n", + "file written: /data/Vikash/Tools/RSNA/monai-deploy-app-sdk/notebooks/tutorials/output/prediction_output/1.2.826.0.1.3680043.2.1125.1/1.2.826.0.1.3680043.2.1125.1_seg.nii.gz.\n", "Output Seg image numpy array shaped: (515, 440, 440)\n", "Output Seg image pixel max value: 1\n", "\u001b[34mDone performing execution of operator SpleenSegOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSegmentationWriterOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSegmentationWriterOperator \u001b[33m(Process ID: 5905, Operator ID: a90c464f-496a-4d2e-8d0a-b4a359f5f3ac)\u001b[39m\n", - "[2021-09-23 02:20:44,984] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of DICOM instance datasets in the list: 515\n", - "[2021-09-23 02:20:44,984] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of slices in the numpy image: 515\n", - "[2021-09-23 02:20:44,984] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Labels of the segments: ['Spleen']\n", - "[2021-09-23 02:20:47,101] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Unique values in seg image: [0 1]\n", - "[2021-09-23 02:20:48,312] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Saving output file /home/gbae/repo/monai-deploy-app-sdk/notebooks/tutorials/output/dicom_seg-DICOMSEG.dcm\n", - "[2021-09-23 02:20:48,513] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - File saved.\n", + "\u001b[32mExecuting operator DICOMSegmentationWriterOperator \u001b[33m(Process ID: 1002078, Operator ID: 94777d8c-a82e-42c3-99f5-ac7fdb0dc4dd)\u001b[39m\n", + "[2021-11-24 15:31:34,476] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of DICOM instance datasets in the list: 515\n", + "[2021-11-24 15:31:34,476] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of slices in the numpy image: 515\n", + "[2021-11-24 15:31:34,476] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Labels of the segments: ['Spleen']\n", + "[2021-11-24 15:31:36,032] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Unique values in seg image: [0 1]\n", + "[2021-11-24 15:31:37,810] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Saving output file /data/Vikash/Tools/RSNA/monai-deploy-app-sdk/notebooks/tutorials/output/dicom_seg-DICOMSEG.dcm\n", + "[2021-11-24 15:31:37,964] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - File saved.\n", "\u001b[34mDone performing execution of operator DICOMSegmentationWriterOperator\n", "\u001b[39m\n" ] } ], "source": [ + "import os\n", + "os.environ['MKL_THREADING_LAYER'] = 'GNU'\n", "!monai-deploy exec my_app -i dcm -o output -m model.pt" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dicom_seg-DICOMSEG.dcm\tprediction_output\n" + "dicom_seg-DICOMSEG.dcm\tprediction_output\r\n" ] } ], @@ -1373,7 +1501,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1381,12 +1509,12 @@ "output_type": "stream", "text": [ "Building MONAI Application Package... Done\n", - "[2021-09-23 02:20:50,882] [INFO] (app_packager) - Successfully built my_app:latest\n" + "[2021-11-24 16:01:18,268] [INFO] (app_packager) - Successfully built my_app:latest\n" ] } ], "source": [ - "!monai-deploy package my_app --tag my_app:latest -m model.pt" + "!monai-deploy package -b nvcr.io/nvidia/pytorch:21.11-py3 my_app --tag my_app:latest -m model.pt" ] }, { @@ -1402,14 +1530,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "my_app latest 7176a8fc6df7 6 minutes ago 15.6GB\n" + "my_app latest 4f5025774c06 6 seconds ago 14.9GB\r\n" ] } ], @@ -1428,7 +1556,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1444,36 +1572,76 @@ "\"my_app:latest\" found.\n", "\n", "Reading MONAI App Package manifest...\n", - " > export '/var/run/monai/export/' detected\n", "--> Verifying if \"nvidia-docker\" is installed...\n", "\n", "\u001b[34mGoing to initiate execution of operator DICOMDataLoaderOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMDataLoaderOperator \u001b[33m(Process ID: 1, Operator ID: 8ae35b26-50b3-45e1-8e31-f6ceeabd067e)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMDataLoaderOperator \u001b[33m(Process ID: 1, Operator ID: 441a7274-a2dd-4b01-99c4-170b51c248d7)\u001b[39m\n", "\u001b[34mDone performing execution of operator DICOMDataLoaderOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSeriesSelectorOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSeriesSelectorOperator \u001b[33m(Process ID: 1, Operator ID: 1145bd17-87e6-4ef2-aea4-193bf5b7de2b)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMSeriesSelectorOperator \u001b[33m(Process ID: 1, Operator ID: e5f2533b-cc73-4c7f-9e57-84dca9380776)\u001b[39m\n", + "[2021-11-24 21:01:34,900] [INFO] (root) - Finding series for Selection named: CT Series\n", + "[2021-11-24 21:01:34,900] [INFO] (root) - Searching study, : 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213\n", + " # of series: 1\n", + "[2021-11-24 21:01:34,900] [INFO] (root) - Working on series, instance UID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - On attribute: 'StudyDescription' to match value: '(.*?)'\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - Series attribute value: spleen\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - On attribute: 'Modality' to match value: '(?i)CT'\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - Series attribute value: CT\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - On attribute: 'SeriesDescription' to match value: '(.*?)'\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - Series attribute value: No series description\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - Series attribute string value did not match. Try regEx.\n", + "[2021-11-24 21:01:34,901] [INFO] (root) - Selected Series, UID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763\n", "\u001b[34mDone performing execution of operator DICOMSeriesSelectorOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSeriesToVolumeOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSeriesToVolumeOperator \u001b[33m(Process ID: 1, Operator ID: 514e95ba-fc8b-4134-be5a-9f2f9fd77955)\u001b[39m\n", + "\u001b[32mExecuting operator DICOMSeriesToVolumeOperator \u001b[33m(Process ID: 1, Operator ID: 0cb153e2-f98d-432a-b953-4b9238821062)\u001b[39m\n", "\u001b[34mDone performing execution of operator DICOMSeriesToVolumeOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator SpleenSegOperator\u001b[39m\n", - "\u001b[32mExecuting operator SpleenSegOperator \u001b[33m(Process ID: 1, Operator ID: fd562906-0cdc-43bf-82ec-1de79df8e155)\u001b[39m\n", - "file written: /var/monai/output/prediction_output/Img_in_context/Img_in_context_seg.nii.gz.\n", + "\u001b[32mExecuting operator SpleenSegOperator \u001b[33m(Process ID: 1, Operator ID: fad57270-f46f-46ff-a5c0-88723f1fb717)\u001b[39m\n", + "Converted Image object metadata:\n", + "SeriesInstanceUID: 1.2.826.0.1.3680043.2.1125.1.68102559796966796813942775094416763, type \n", + "Modality: CT, type \n", + "SeriesDescription: No series description, type \n", + "PatientPosition: HFS, type \n", + "SeriesNumber: 1, type \n", + "row_pixel_spacing: 1.0, type \n", + "col_pixel_spacing: 1.0, type \n", + "depth_pixel_spacing: 1.0, type \n", + "row_direction_cosine: [-1.0, 0.0, 0.0], type \n", + "col_direction_cosine: [0.0, -1.0, 0.0], type \n", + "depth_direction_cosine: [0.0, 0.0, 1.0], type \n", + "dicom_affine_transform: [[-1. 0. 0. 0.]\n", + " [ 0. -1. 0. 0.]\n", + " [ 0. 0. 1. 0.]\n", + " [ 0. 0. 0. 1.]], type \n", + "nifti_affine_transform: [[ 1. -0. -0. -0.]\n", + " [-0. 1. -0. -0.]\n", + " [ 0. 0. 1. 0.]\n", + " [ 0. 0. 0. 1.]], type \n", + "StudyInstanceUID: 1.2.826.0.1.3680043.2.1125.1.67295333199898911264201812221946213, type \n", + "StudyID: SLICER10001, type \n", + "StudyDate: 2019-09-16, type \n", + "StudyTime: 010100.000000, type \n", + "StudyDescription: spleen, type \n", + "AccessionNumber: 1, type \n", + "selection_name: CT Series, type \n", + "file written: /var/monai/output/prediction_output/1.2.826.0.1.3680043.2.1125.1/1.2.826.0.1.3680043.2.1125.1_seg.nii.gz.\n", "Output Seg image numpy array shaped: (515, 440, 440)\n", "Output Seg image pixel max value: 1\n", "\u001b[34mDone performing execution of operator SpleenSegOperator\n", "\u001b[39m\n", "\u001b[34mGoing to initiate execution of operator DICOMSegmentationWriterOperator\u001b[39m\n", - "\u001b[32mExecuting operator DICOMSegmentationWriterOperator \u001b[33m(Process ID: 1, Operator ID: 90794f86-fedc-44e8-857a-0ab4cf22f8e4)\u001b[39m\n", - "[2021-09-23 09:21:12,891] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of DICOM instance datasets in the list: 515\n", - "[2021-09-23 09:21:12,891] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of slices in the numpy image: 515\n", - "[2021-09-23 09:21:12,891] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Labels of the segments: ['Spleen']\n", - "[2021-09-23 09:21:15,148] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Unique values in seg image: [0 1]\n", - "[2021-09-23 09:21:16,705] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Saving output file /var/monai/output/dicom_seg-DICOMSEG.dcm\n", - "[2021-09-23 09:21:16,967] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - File saved.\n", + "\u001b[32mExecuting operator DICOMSegmentationWriterOperator \u001b[33m(Process ID: 1, Operator ID: 90f5b1e4-71c0-48a1-91d9-f1d462683c14)\u001b[39m\n", + "[2021-11-24 21:01:48,195] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of DICOM instance datasets in the list: 515\n", + "[2021-11-24 21:01:48,195] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Number of slices in the numpy image: 515\n", + "[2021-11-24 21:01:48,195] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Labels of the segments: ['Spleen']\n", + "[2021-11-24 21:01:49,743] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Unique values in seg image: [0 1]\n", + "[2021-11-24 21:01:51,712] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - Saving output file /var/monai/output/dicom_seg-DICOMSEG.dcm\n", + "[2021-11-24 21:01:51,874] [INFO] (monai.deploy.operators.dicom_seg_writer_operator.DICOMSegWriter) - File saved.\n", "\u001b[34mDone performing execution of operator DICOMSegmentationWriterOperator\n", "\u001b[39m\n" ] @@ -1488,20 +1656,27 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "dicom_seg-DICOMSEG.dcm\tprediction_output\n" + "dicom_seg-DICOMSEG.dcm\tprediction_output\r\n" ] } ], "source": [ "!ls output" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1523,7 +1698,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.6.13" } }, "nbformat": 4, diff --git a/notebooks/tutorials/04_mis_tutorial.ipynb b/notebooks/tutorials/04_mis_tutorial.ipynb index 293a2659..a74b57b7 100644 --- a/notebooks/tutorials/04_mis_tutorial.ipynb +++ b/notebooks/tutorials/04_mis_tutorial.ipynb @@ -24,14 +24,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[?1h\u001b= > export '/var/run/monai/export/' detected\n" + "\u001b[?1h\u001b= > export '/var/run/monai/export/' detected\r\n" ] } ], @@ -50,14 +50,14 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "app.json pkg.json\n" + "app.json pkg.json\r\n" ] } ], @@ -67,29 +67,29 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{\n", - " \"api-version\": \"0.1.0\",\n", - " \"command\": \"python3 -u /opt/monai/app/app.py\",\n", - " \"environment\": {},\n", - " \"input\": {\n", - " \"formats\": [],\n", - " \"path\": \"input\"\n", - " },\n", - " \"output\": {\n", - " \"format\": {},\n", - " \"path\": \"output\"\n", - " },\n", - " \"sdk-version\": \"0.1.1\",\n", - " \"timeout\": 0,\n", - " \"version\": \"0.0.0\",\n", - " \"working-directory\": \"/var/monai/\"\n", + "{\r\n", + " \"api-version\": \"0.1.0\",\r\n", + " \"command\": \"python3 -u /opt/monai/app/app.py\",\r\n", + " \"environment\": {},\r\n", + " \"input\": {\r\n", + " \"formats\": [],\r\n", + " \"path\": \"input\"\r\n", + " },\r\n", + " \"output\": {\r\n", + " \"format\": {},\r\n", + " \"path\": \"output\"\r\n", + " },\r\n", + " \"sdk-version\": \"0.2.0\",\r\n", + " \"timeout\": 0,\r\n", + " \"version\": \"0.0.0\",\r\n", + " \"working-directory\": \"/var/monai/\"\r\n", "}" ] } @@ -100,29 +100,29 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{\n", - " \"api-version\": \"0.1.0\",\n", - " \"application-root\": \"/var/monai/\",\n", - " \"models\": [\n", - " {\n", - " \"name\": \"model-54dd6cdaac290a0e800c0fa627cd60410bb2cea173ac8bb2f4816cc54ea88c90\",\n", - " \"path\": \"/opt/monai/models/model/model.pt\"\n", - " }\n", - " ],\n", - " \"resources\": {\n", - " \"cpu\": 1,\n", - " \"gpu\": 1,\n", - " \"memory\": \"7168Mi\"\n", - " },\n", - " \"sdk-version\": \"0.1.1\",\n", - " \"version\": \"0.0.0\"\n", + "{\r\n", + " \"api-version\": \"0.1.0\",\r\n", + " \"application-root\": \"/var/monai/\",\r\n", + " \"models\": [\r\n", + " {\r\n", + " \"name\": \"model-54dd6cdaac290a0e800c0fa627cd60410bb2cea173ac8bb2f4816cc54ea88c90\",\r\n", + " \"path\": \"/opt/monai/models/model/model.pt\"\r\n", + " }\r\n", + " ],\r\n", + " \"resources\": {\r\n", + " \"cpu\": 1,\r\n", + " \"gpu\": 1,\r\n", + " \"memory\": \"7168Mi\"\r\n", + " },\r\n", + " \"sdk-version\": \"0.2.0\",\r\n", + " \"version\": \"0.0.0\"\r\n", "}" ] } @@ -150,18 +150,37 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Collecting gdown\n", + " Using cached gdown-4.2.0-py3-none-any.whl\n", + "Collecting tqdm\n", + " Using cached tqdm-4.62.3-py2.py3-none-any.whl (76 kB)\n", + "Requirement already satisfied: six in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from gdown) (1.16.0)\n", + "Requirement already satisfied: requests[socks] in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from gdown) (2.12.5)\n", + "Collecting filelock\n", + " Downloading filelock-3.4.0-py3-none-any.whl (9.8 kB)\n", + "Collecting beautifulsoup4\n", + " Downloading beautifulsoup4-4.10.0-py3-none-any.whl (97 kB)\n", + "\u001b[K |████████████████████████████████| 97 kB 1.2 MB/s eta 0:00:011\n", + "\u001b[?25hCollecting soupsieve>1.2\n", + " Downloading soupsieve-2.3.1-py3-none-any.whl (37 kB)\n", + "Collecting PySocks!=1.5.7,>=1.5.6\n", + " Using cached PySocks-1.7.1-py3-none-any.whl (16 kB)\n", + "Installing collected packages: soupsieve, PySocks, tqdm, filelock, beautifulsoup4, gdown\n", + "Successfully installed PySocks-1.7.1 beautifulsoup4-4.10.0 filelock-3.4.0 gdown-4.2.0 soupsieve-2.3.1 tqdm-4.62.3\n", "Downloading...\n", "From: https://drive.google.com/uc?id=12uNO1tyqZh1oFkZH41Osliey7TRm-BBG\n", - "To: /home/kavink/src/work_folder/monai-deploy-app-sdk/notebooks/tutorials/charts.zip\n", - "100%|██████████████████████████████████████| 9.18k/9.18k [00:00<00:00, 10.9MB/s]\n", + "To: /data/Vikash/Tools/RSNA/monai-deploy-app-sdk/notebooks/tutorials/charts.zip\n", + "100%|███████████████████████████████████████| 9.18k/9.18k [00:00<00:00, 284kB/s]\n", "Archive: charts.zip\n", + " creating: charts/\n", + " creating: charts/templates/\n", " inflating: charts/templates/monaiinferenceservice-deployment.yaml \n", " inflating: charts/templates/monaiinferenceservice-service.yaml \n", " inflating: charts/templates/monaiinferenceservice-clusterrole.yaml \n", @@ -176,6 +195,7 @@ } ], "source": [ + "!pip install gdown\n", "!gdown https://drive.google.com/uc?id=12uNO1tyqZh1oFkZH41Osliey7TRm-BBG\n", "!unzip -o charts.zip" ] @@ -204,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -302,19 +322,19 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "NAME: monai-inference-service\n", - "LAST DEPLOYED: Tue Nov 23 16:29:43 2021\n", - "NAMESPACE: default\n", - "STATUS: deployed\n", - "REVISION: 1\n", - "TEST SUITE: None\n" + "NAME: monai-inference-service\r\n", + "LAST DEPLOYED: Wed Nov 24 16:06:57 2021\r\n", + "NAMESPACE: default\r\n", + "STATUS: deployed\r\n", + "REVISION: 1\r\n", + "TEST SUITE: None\r\n" ] } ], @@ -324,15 +344,23 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "NAME READY STATUS RESTARTS AGE\n", - "monai-inference-service-7fcc7f5b69-qmp9s 1/1 Running 0 76s\n" + "NAME READY STATUS RESTARTS AGE\r\n", + "clara-clara-platformapiserver-75bc9b4867-q4jvz 1/1 Running 7 68d\r\n", + "clara-console-6d944c97d5-zsxn2 2/2 Running 10 68d\r\n", + "clara-dicom-adapter-7fb4cc587b-6fn6h 1/1 Running 5 68d\r\n", + "clara-node-monitor-rjb5z 1/1 Running 5 68d\r\n", + "clara-resultsservice-f758699b-lv56q 1/1 Running 5 68d\r\n", + "clara-ui-758d9645b7-zf9mn 1/1 Running 5 68d\r\n", + "clara-workflow-controller-7c66d77f55-557js 1/1 Running 7 68d\r\n", + "fluentd-ffrtf 1/1 Running 7 68d\r\n", + "monai-inference-service-6bd7d8f5df-ngcfz 0/1 ContainerCreating 0 3s\r\n" ] } ], @@ -357,27 +385,25 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n", - "Requirement already satisfied: gdown in /home/kavink/.local/lib/python3.8/site-packages (4.2.0)\n", - "Requirement already satisfied: filelock in /usr/lib/python3/dist-packages (from gdown) (3.0.12)\n", - "Requirement already satisfied: requests[socks] in /home/kavink/.local/lib/python3.8/site-packages (from gdown) (2.25.1)\n", - "Requirement already satisfied: six in /home/kavink/.local/lib/python3.8/site-packages (from gdown) (1.16.0)\n", - "Requirement already satisfied: tqdm in /home/kavink/.local/lib/python3.8/site-packages (from gdown) (4.62.3)\n", - "Requirement already satisfied: beautifulsoup4 in /home/kavink/.local/lib/python3.8/site-packages (from gdown) (4.10.0)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/kavink/.local/lib/python3.8/site-packages (from requests[socks]->gdown) (1.26.5)\n", - "Requirement already satisfied: idna<3,>=2.5 in /home/kavink/.local/lib/python3.8/site-packages (from requests[socks]->gdown) (2.10)\n", - "Requirement already satisfied: chardet<5,>=3.0.2 in /home/kavink/.local/lib/python3.8/site-packages (from requests[socks]->gdown) (4.0.0)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /home/kavink/.local/lib/python3.8/site-packages (from requests[socks]->gdown) (2021.5.30)\n", - "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6; extra == \"socks\" in /home/kavink/.local/lib/python3.8/site-packages (from requests[socks]->gdown) (1.7.1)\n", - "Requirement already satisfied: soupsieve>1.2 in /home/kavink/.local/lib/python3.8/site-packages (from beautifulsoup4->gdown) (2.2.1)\n", - "/bin/bash: gdown: command not found\n", + "Requirement already satisfied: gdown in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (4.2.0)\n", + "Requirement already satisfied: six in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from gdown) (1.16.0)\n", + "Requirement already satisfied: beautifulsoup4 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from gdown) (4.10.0)\n", + "Requirement already satisfied: filelock in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from gdown) (3.4.0)\n", + "Requirement already satisfied: tqdm in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from gdown) (4.62.3)\n", + "Requirement already satisfied: requests[socks] in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from gdown) (2.12.5)\n", + "Requirement already satisfied: soupsieve>1.2 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from beautifulsoup4->gdown) (2.3.1)\n", + "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from requests[socks]->gdown) (1.7.1)\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1uTQsm8omwimBcp_kRXlduWBP2M6cspr1\n", + "To: /data/Vikash/Tools/RSNA/monai-deploy-app-sdk/notebooks/tutorials/ai_spleen_seg_data.zip\n", + "100%|████████████████████████████████████████| 104M/104M [00:20<00:00, 4.97MB/s]\n", "Archive: ai_spleen_seg_data.zip\n", " inflating: dcm/IMG0001.dcm \n", " inflating: dcm/IMG0002.dcm \n", @@ -570,7 +596,13 @@ " inflating: dcm/IMG0189.dcm \n", " inflating: dcm/IMG0190.dcm \n", " inflating: dcm/IMG0191.dcm \n", - " inflating: dcm/IMG0192.dcm \n", + " inflating: dcm/IMG0192.dcm \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " inflating: dcm/IMG0193.dcm \n", " inflating: dcm/IMG0194.dcm \n", " inflating: dcm/IMG0195.dcm \n", @@ -785,7 +817,13 @@ " inflating: dcm/IMG0404.dcm \n", " inflating: dcm/IMG0405.dcm \n", " inflating: dcm/IMG0406.dcm \n", - " inflating: dcm/IMG0407.dcm \n", + " inflating: dcm/IMG0407.dcm \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " inflating: dcm/IMG0408.dcm \n", " inflating: dcm/IMG0409.dcm \n", " inflating: dcm/IMG0410.dcm \n", @@ -939,7 +977,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -948,12 +986,12 @@ "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", - "100 86.6M 100 87283 100 86.5M 3185 3233k 0:00:27 0:00:27 --:--:-- 18523\n" + "100 86.5M 100 1018 100 86.5M 7953 675M --:--:-- --:--:-- --:--:-- 675M\n" ] } ], "source": [ - "!curl -X 'POST' 'http://10.108.206.27:8000/upload/' \\\n", + "!curl -X 'POST' 'http://localhost:8000/upload/' \\\n", " -H 'accept: application/json' \\\n", " -H 'Content-Type: multipart/form-data' \\\n", " -F 'file=@input.zip;type=application/x-zip-compressed' \\\n", @@ -962,16 +1000,20 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Archive: output.zip\n", - " inflating: output/dicom_seg-DICOMSEG.dcm \n", - " inflating: output/prediction_output/1.2.826.0.1.3680043.2.1125.1/1.2.826.0.1.3680043.2.1125.1_seg.nii.gz \n" + "Archive: output.zip\r\n", + " End-of-central-directory signature not found. Either this file is not\r\n", + " a zipfile, or it constitutes one disk of a multi-part archive. In the\r\n", + " latter case the central directory and zipfile comment will be found on\r\n", + " the last disk(s) of this archive.\r\n", + "unzip: cannot find zipfile directory in one of output.zip or\r\n", + " output.zip.zip, and cannot find output.zip.ZIP, period.\r\n" ] } ], @@ -984,9 +1026,33 @@ "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: nilearn in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (0.8.1)\n", + "Requirement already satisfied: matplotlib in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (3.3.4)\n", + "Requirement already satisfied: pandas>=0.24.0 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from nilearn) (1.1.5)\n", + "Requirement already satisfied: numpy>=1.16 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from nilearn) (1.19.2)\n", + "Requirement already satisfied: scikit-learn>=0.21 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from nilearn) (0.24.2)\n", + "Requirement already satisfied: nibabel>=2.5 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from nilearn) (3.2.1)\n", + "Requirement already satisfied: requests>=2 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from nilearn) (2.12.5)\n", + "Requirement already satisfied: joblib>=0.12 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from nilearn) (1.1.0)\n", + "Requirement already satisfied: scipy>=1.2 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from nilearn) (1.5.4)\n", + "Requirement already satisfied: cycler>=0.10 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from matplotlib) (1.3.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from matplotlib) (8.3.1)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from matplotlib) (3.0.4)\n", + "Requirement already satisfied: packaging>=14.3 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from nibabel>=2.5->nilearn) (21.3)\n", + "Requirement already satisfied: pytz>=2017.2 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from pandas>=0.24.0->nilearn) (2021.3)\n", + "Requirement already satisfied: six>=1.5 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib) (1.16.0)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/gupta/miniconda3/envs/monairsna/lib/python3.6/site-packages (from scikit-learn>=0.21->nilearn) (3.0.0)\n" + ] + }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -996,6 +1062,7 @@ } ], "source": [ + "!pip install nilearn matplotlib\n", "def visualize_output(predicted_output: str):\n", " import SimpleITK as sitk\n", " reader = sitk.ImageSeriesReader()\n", @@ -1016,7 +1083,7 @@ " display = plotting.plot_anat(input_image_nib, cut_coords=(123, 162, 387), colorbar=False)\n", " display.add_overlay(image_itk)\n", "\n", - "visualize_output(\"output/prediction_output/1.2.826.0.1.3680043.2.1125.1/1.2.826.0.1.3680043.2.1125.1_seg.nii.gz\")" + "visualize_output(\"./output/prediction_output/1.2.826.0.1.3680043.2.1125.1/1.2.826.0.1.3680043.2.1125.1_seg.nii.gz\")" ] }, { @@ -1028,20 +1095,27 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "release \"monai-inference-service\" uninstalled\n" + "release \"monai-inference-service\" uninstalled\r\n" ] } ], "source": [ "!helm uninstall monai-inference-service" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1063,7 +1137,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.6.13" } }, "nbformat": 4,