diff --git "a/train.ipynb" "b/train.ipynb"
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+++ "b/train.ipynb"
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+{"cells":[{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2023-03-10T14:42:22.020810Z","iopub.status.busy":"2023-03-10T14:42:22.019368Z","iopub.status.idle":"2023-03-10T14:42:37.395475Z","shell.execute_reply":"2023-03-10T14:42:37.393745Z","shell.execute_reply.started":"2023-03-10T14:42:22.020734Z"},"trusted":true},"outputs":[],"source":["from fastkaggle import *\n","from fastai.vision.all import *\n","# from fastai.medical.imaging import *\n","from fastai.data.all import *\n","from tqdm.notebook import tqdm\n","import pydicom\n","from PIL import Image"]},{"cell_type":"code","execution_count":97,"metadata":{},"outputs":[],"source":["train_path = Path(\"data\")\n","bs = 16\n","epochs = 1\n","# arch = 'resnet26d'\n","arch = 'convnext_tiny_in22k'"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>study_id</th>\n","      <th>series_id</th>\n","      <th>image_id</th>\n","      <th>laterality</th>\n","      <th>view_position</th>\n","      <th>height</th>\n","      <th>width</th>\n","      <th>breast_birads</th>\n","      <th>breast_density</th>\n","      <th>finding_categories</th>\n","      <th>finding_birads</th>\n","      <th>xmin</th>\n","      <th>ymin</th>\n","      <th>xmax</th>\n","      <th>ymax</th>\n","      <th>split</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>48575a27b7c992427041a82fa750d3fa</td>\n","      <td>26de4993fa6b8ae50a91c8baf49b92b0</td>\n","      <td>4e3a578fe535ea4f5258d3f7f4419db8</td>\n","      <td>R</td>\n","      <td>CC</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 4</td>\n","      <td>DENSITY C</td>\n","      <td>['Mass']</td>\n","      <td>BI-RADS 4</td>\n","      <td>2355.139893</td>\n","      <td>1731.640015</td>\n","      <td>2482.979980</td>\n","      <td>1852.750000</td>\n","      <td>training</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>48575a27b7c992427041a82fa750d3fa</td>\n","      <td>26de4993fa6b8ae50a91c8baf49b92b0</td>\n","      <td>dac39351b0f3a8c670b7f8dc88029364</td>\n","      <td>R</td>\n","      <td>MLO</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 4</td>\n","      <td>DENSITY C</td>\n","      <td>['Mass']</td>\n","      <td>BI-RADS 4</td>\n","      <td>2386.679932</td>\n","      <td>1240.609985</td>\n","      <td>2501.800049</td>\n","      <td>1354.040039</td>\n","      <td>training</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>75e8e48933289d70b407379a564f8594</td>\n","      <td>853b70e7e6f39133497909d9ca4c756d</td>\n","      <td>c83f780904f25eacb44e9030f32c66e1</td>\n","      <td>R</td>\n","      <td>CC</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 3</td>\n","      <td>DENSITY C</td>\n","      <td>['Global Asymmetry']</td>\n","      <td>BI-RADS 3</td>\n","      <td>2279.179932</td>\n","      <td>1166.510010</td>\n","      <td>2704.439941</td>\n","      <td>2184.260010</td>\n","      <td>training</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>75e8e48933289d70b407379a564f8594</td>\n","      <td>853b70e7e6f39133497909d9ca4c756d</td>\n","      <td>893528bc38a0362928a89364f1b692fd</td>\n","      <td>R</td>\n","      <td>MLO</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 3</td>\n","      <td>DENSITY C</td>\n","      <td>['Global Asymmetry']</td>\n","      <td>BI-RADS 3</td>\n","      <td>1954.270020</td>\n","      <td>1443.640015</td>\n","      <td>2589.760010</td>\n","      <td>2193.810059</td>\n","      <td>training</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>c3487424fee1bdd4515b72dc3fd69813</td>\n","      <td>77619c914263eae44e9099f1ce07192c</td>\n","      <td>318264c881bf12f2c1efe5f93920cc37</td>\n","      <td>R</td>\n","      <td>CC</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 4</td>\n","      <td>DENSITY C</td>\n","      <td>['Architectural Distortion']</td>\n","      <td>BI-RADS 4</td>\n","      <td>2172.300049</td>\n","      <td>1967.410034</td>\n","      <td>2388.699951</td>\n","      <td>2147.159912</td>\n","      <td>training</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>c3487424fee1bdd4515b72dc3fd69813</td>\n","      <td>77619c914263eae44e9099f1ce07192c</td>\n","      <td>adf961b5379855af0293ca526f3d0c51</td>\n","      <td>R</td>\n","      <td>MLO</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 4</td>\n","      <td>DENSITY C</td>\n","      <td>['Architectural Distortion']</td>\n","      <td>BI-RADS 4</td>\n","      <td>2252.379883</td>\n","      <td>2367.070068</td>\n","      <td>2438.060059</td>\n","      <td>2523.429932</td>\n","      <td>training</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>5683854eafabc34f6d854000d2ac6c2d</td>\n","      <td>4ac33111294b83d43537cb8604b0808c</td>\n","      <td>2f944efb1cb9579442df2d7fe6a579b7</td>\n","      <td>L</td>\n","      <td>CC</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 3</td>\n","      <td>DENSITY C</td>\n","      <td>['Mass']</td>\n","      <td>BI-RADS 3</td>\n","      <td>142.899002</td>\n","      <td>2171.810059</td>\n","      <td>439.584991</td>\n","      <td>2403.370117</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>5683854eafabc34f6d854000d2ac6c2d</td>\n","      <td>4ac33111294b83d43537cb8604b0808c</td>\n","      <td>7385e8cf7b29764525c81de4aa1aebe4</td>\n","      <td>L</td>\n","      <td>MLO</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 3</td>\n","      <td>DENSITY C</td>\n","      <td>['Mass']</td>\n","      <td>BI-RADS 3</td>\n","      <td>142.899002</td>\n","      <td>2045.170044</td>\n","      <td>417.876007</td>\n","      <td>2265.879883</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>7c51789da6c462e55bcb95c2a7d437ee</td>\n","      <td>ac4d0771f6d7a7400ab463458f789dbe</td>\n","      <td>f581ef53bb7e61f4575db33eceac8ff8</td>\n","      <td>L</td>\n","      <td>CC</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 4</td>\n","      <td>DENSITY C</td>\n","      <td>['Nipple Retraction', 'Mass']</td>\n","      <td>BI-RADS 4</td>\n","      <td>588.874023</td>\n","      <td>1397.709961</td>\n","      <td>812.362000</td>\n","      <td>1734.719971</td>\n","      <td>training</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>7c51789da6c462e55bcb95c2a7d437ee</td>\n","      <td>ac4d0771f6d7a7400ab463458f789dbe</td>\n","      <td>1142cdfbfe725d62038252693cf064e5</td>\n","      <td>L</td>\n","      <td>MLO</td>\n","      <td>3518</td>\n","      <td>2800</td>\n","      <td>BI-RADS 4</td>\n","      <td>DENSITY C</td>\n","      <td>['Nipple Retraction', 'Mass']</td>\n","      <td>BI-RADS 4</td>\n","      <td>632.033997</td>\n","      <td>1727.099976</td>\n","      <td>930.142029</td>\n","      <td>2190.830078</td>\n","      <td>training</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                           study_id                         series_id  \\\n","0  48575a27b7c992427041a82fa750d3fa  26de4993fa6b8ae50a91c8baf49b92b0   \n","1  48575a27b7c992427041a82fa750d3fa  26de4993fa6b8ae50a91c8baf49b92b0   \n","2  75e8e48933289d70b407379a564f8594  853b70e7e6f39133497909d9ca4c756d   \n","3  75e8e48933289d70b407379a564f8594  853b70e7e6f39133497909d9ca4c756d   \n","4  c3487424fee1bdd4515b72dc3fd69813  77619c914263eae44e9099f1ce07192c   \n","5  c3487424fee1bdd4515b72dc3fd69813  77619c914263eae44e9099f1ce07192c   \n","6  5683854eafabc34f6d854000d2ac6c2d  4ac33111294b83d43537cb8604b0808c   \n","7  5683854eafabc34f6d854000d2ac6c2d  4ac33111294b83d43537cb8604b0808c   \n","8  7c51789da6c462e55bcb95c2a7d437ee  ac4d0771f6d7a7400ab463458f789dbe   \n","9  7c51789da6c462e55bcb95c2a7d437ee  ac4d0771f6d7a7400ab463458f789dbe   \n","\n","                           image_id laterality view_position  height  width  \\\n","0  4e3a578fe535ea4f5258d3f7f4419db8          R            CC    3518   2800   \n","1  dac39351b0f3a8c670b7f8dc88029364          R           MLO    3518   2800   \n","2  c83f780904f25eacb44e9030f32c66e1          R            CC    3518   2800   \n","3  893528bc38a0362928a89364f1b692fd          R           MLO    3518   2800   \n","4  318264c881bf12f2c1efe5f93920cc37          R            CC    3518   2800   \n","5  adf961b5379855af0293ca526f3d0c51          R           MLO    3518   2800   \n","6  2f944efb1cb9579442df2d7fe6a579b7          L            CC    3518   2800   \n","7  7385e8cf7b29764525c81de4aa1aebe4          L           MLO    3518   2800   \n","8  f581ef53bb7e61f4575db33eceac8ff8          L            CC    3518   2800   \n","9  1142cdfbfe725d62038252693cf064e5          L           MLO    3518   2800   \n","\n","  breast_birads breast_density             finding_categories finding_birads  \\\n","0     BI-RADS 4      DENSITY C                       ['Mass']      BI-RADS 4   \n","1     BI-RADS 4      DENSITY C                       ['Mass']      BI-RADS 4   \n","2     BI-RADS 3      DENSITY C           ['Global Asymmetry']      BI-RADS 3   \n","3     BI-RADS 3      DENSITY C           ['Global Asymmetry']      BI-RADS 3   \n","4     BI-RADS 4      DENSITY C   ['Architectural Distortion']      BI-RADS 4   \n","5     BI-RADS 4      DENSITY C   ['Architectural Distortion']      BI-RADS 4   \n","6     BI-RADS 3      DENSITY C                       ['Mass']      BI-RADS 3   \n","7     BI-RADS 3      DENSITY C                       ['Mass']      BI-RADS 3   \n","8     BI-RADS 4      DENSITY C  ['Nipple Retraction', 'Mass']      BI-RADS 4   \n","9     BI-RADS 4      DENSITY C  ['Nipple Retraction', 'Mass']      BI-RADS 4   \n","\n","          xmin         ymin         xmax         ymax     split  \n","0  2355.139893  1731.640015  2482.979980  1852.750000  training  \n","1  2386.679932  1240.609985  2501.800049  1354.040039  training  \n","2  2279.179932  1166.510010  2704.439941  2184.260010  training  \n","3  1954.270020  1443.640015  2589.760010  2193.810059  training  \n","4  2172.300049  1967.410034  2388.699951  2147.159912  training  \n","5  2252.379883  2367.070068  2438.060059  2523.429932  training  \n","6   142.899002  2171.810059   439.584991  2403.370117      test  \n","7   142.899002  2045.170044   417.876007  2265.879883      test  \n","8   588.874023  1397.709961   812.362000  1734.719971  training  \n","9   632.033997  1727.099976   930.142029  2190.830078  training  "]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["annotations = pd.read_csv(path/\"finding_annotations.csv\")\n","annotations.head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Path to the directory containing DICOM files\n","dicom_directory = \"physionet.org/files/vindr-mammo/1.0.0/images\"\n","\n","# Path to the finding_annotations.csv file\n","csv_file_path = \"physionet.org/files/vindr-mammo/1.0.0/finding_annotations.csv\"\n","\n","# Output directory for saving JPG files\n","output_directory = \"data\"\n","\n","# Read the CSV file into a Pandas DataFrame\n","annotations_df = pd.read_csv(csv_file_path)\n","\n","# Iterate through study_id subdirectories\n","for study_id_folder in os.listdir(dicom_directory):\n","    study_id_path = os.path.join(dicom_directory, study_id_folder)\n","    \n","    if os.path.isdir(study_id_path):\n","        # Iterate through image_id DICOM files in each study_id folder\n","        for image_id in os.listdir(study_id_path):\n","            image_id_path = os.path.join(study_id_path, image_id)\n","            \n","            if os.path.isfile(image_id_path) and image_id.endswith(\".dicom\"):\n","                # Get corresponding row from annotations DataFrame\n","                annotation_row = annotations_df[\n","                    (annotations_df[\"study_id\"] == study_id_folder) &\n","                    (annotations_df[\"image_id\"] == image_id[:-6])]\n","                \n","                if not annotation_row.empty:\n","                    finding_birads = annotation_row.iloc[0][\"finding_birads\"]\n","                    \n","                    # Create subdirectory based on finding_birads if it doesn't exist\n","                    output_subdirectory = os.path.join(output_directory, str(finding_birads))\n","                    os.makedirs(output_subdirectory, exist_ok=True)\n","                    \n","                    # Convert DICOM to JPG\n","                    dcm = pydicom.dcmread(image_id_path).pixel_array\n","                    dcm = (dcm - np.min(dcm)) / (np.max(dcm) - np.min(dcm)) * 255\n","                    dcm = dcm.astype(np.uint8)\n","                    img = Image.fromarray(dcm)\n","                    \n","                    # Save the JPG image\n","                    jpg_path = os.path.join(output_subdirectory, f\"{study_id_folder}_{image_id[:-6]}.jpg\")\n","                    img.save(jpg_path)"]},{"cell_type":"code","execution_count":100,"metadata":{},"outputs":[],"source":["dls = ImageDataLoaders.from_folder(train_path, \n","                                   valid_pct=0.2,\n","                                   item_tfms=Resize(192),\n","                                   batch_tfms=aug_transforms(size=128, min_scale=0.75))"]},{"cell_type":"code","execution_count":101,"metadata":{},"outputs":[{"data":{"text/plain":["6865"]},"execution_count":101,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 648x648 with 9 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["dls.show_batch()\n","len(dls.train_ds)"]},{"cell_type":"code","execution_count":102,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.8/site-packages/timm/models/_factory.py:114: UserWarning: Mapping deprecated model name convnext_tiny_in22k to current convnext_tiny.fb_in22k.\n","  model = create_fn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f45e96d3cee142e8802d9cdf38c0503a","version_major":2,"version_minor":0},"text/plain":["Downloading model.safetensors:   0%|          | 0.00/178M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["learn = vision_learner(dls, arch, metrics=error_rate, path='.').to_fp16() "]},{"cell_type":"code","execution_count":103,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py:1402: UserWarning: positional arguments and argument \"destination\" are deprecated. nn.Module.state_dict will not accept them in the future. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.\n","  warnings.warn(\n"]},{"data":{"text/html":["\n","<style>\n","    /* Turns off some styling */\n","    progress {\n","        /* gets rid of default border in Firefox and Opera. */\n","        border: none;\n","        /* Needs to be in here for Safari polyfill so background images work as expected. */\n","        background-size: auto;\n","    }\n","    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n","        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n","    }\n","    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n","        background: #F44336;\n","    }\n","</style>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[],"text/plain":["<IPython.core.display.HTML 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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["learn.lr_find(suggest_funcs=(valley, slide))"]},{"cell_type":"code","execution_count":104,"metadata":{},"outputs":[{"data":{"text/html":["\n","<style>\n","    /* Turns off some styling */\n","    progress {\n","        /* gets rid of default border in Firefox and Opera. */\n","        border: none;\n","        /* Needs to be in here for Safari polyfill so background images work as expected. */\n","        background-size: auto;\n","    }\n","    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n","        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n","    }\n","    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n","        background: #F44336;\n","    }\n","</style>\n"],"text/plain":["<IPython.core.display.HTML 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progress:not([value])::-webkit-progress-bar {\n","        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n","    }\n","    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n","        background: #F44336;\n","    }\n","</style>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>epoch</th>\n","      <th>train_loss</th>\n","      <th>valid_loss</th>\n","      <th>error_rate</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>0</td>\n","      <td>46.436878</td>\n","      <td>nan</td>\n","      <td>0.952214</td>\n","      <td>04:58</td>\n","    </tr>\n","  </tbody>\n","</table>"],"text/plain":["<IPython.core.display.HTML 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