{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport seaborn as sn\nfrom sklearn.model_selection import GroupKFold\nfrom tqdm import tqdm\nimport os\nimport cv2\nimport datetime\nimport time\nimport gc\nimport glob\nimport math\nimport random\nimport skimage.morphology\nimport sys\nimport tensorflow as tf\nimport tifffile\nimport openslide\nfrom PIL import Image\nimport plotly.graph_objs as go\n\nimport zipfile\nimport rasterio\nfrom rasterio.windows import Window\n\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport albumentations as A\nimport cv2\n\n!pip install monai\nimport monai\nfrom monai.inferers import sliding_window_inference\nfrom monai.metrics import DiceMetric\nfrom monai.visualize import plot_2d_or_3d_image\nfrom monai.data import decollate_batch\n\nfrom monai.transforms import (\n    Activations,\n    AddChannel,\n    AsDiscrete,\n    Compose,\n    LoadImage,\n    RandRotate90,\n    RandSpatialCrop,\n    ScaleIntensity,\n    EnsureType,\n)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:16:39.704998Z","iopub.execute_input":"2022-12-02T10:16:39.705452Z","iopub.status.idle":"2022-12-02T10:17:13.613106Z","shell.execute_reply.started":"2022-12-02T10:16:39.705366Z","shell.execute_reply":"2022-12-02T10:17:13.611921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# rle encodding - image encoded data in  a form of string \n# in this dataset string is given so we converted it into images \ndef rle_to_image(rle_mask, image_shape):\n    \"\"\"\n    Converts an rle string to an image represented as a numpy array.\n    Reference: https://www.kaggle.com/paulorzp/rle-functions-run-lenght-encode-decode\n\n    :param rle_mask: string with rle mask.\n    :param image_shape: (width, height) of array to return\n    :return: Image as a numpy array. 1 = mask, 0 = background.\n    \"\"\"\n\n    # Processing\n    s = rle_mask.split()\n    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]\n    starts -= 1\n    ends = starts + lengths\n    image = np.zeros(image_shape[0] * image_shape[1], dtype=np.uint8)\n    for lo, hi in zip(starts, ends):\n        image[lo:hi] = 1\n\n    return image.reshape(image_shape).T","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:13.615461Z","iopub.execute_input":"2022-12-02T10:17:13.616745Z","iopub.status.idle":"2022-12-02T10:17:13.626497Z","shell.execute_reply.started":"2022-12-02T10:17:13.616702Z","shell.execute_reply":"2022-12-02T10:17:13.624370Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Parameters\nbase_path = '../input/hubmap-kidney-segmentation'\n\nplot_full_image = True\n\n# Number of glomeruli to display for each image\nnum_glom_display = 5\n\n# Number of glomberuli to save as tiff files.\nnum_glom_save = 5\n\nglob_scale = 0.25","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:13.628234Z","iopub.execute_input":"2022-12-02T10:17:13.629097Z","iopub.status.idle":"2022-12-02T10:17:13.639005Z","shell.execute_reply.started":"2022-12-02T10:17:13.629059Z","shell.execute_reply":"2022-12-02T10:17:13.638129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Directory Contents\nprint('\\n'.join(os.listdir(base_path)))","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:13.641956Z","iopub.execute_input":"2022-12-02T10:17:13.642306Z","iopub.status.idle":"2022-12-02T10:17:13.658897Z","shell.execute_reply.started":"2022-12-02T10:17:13.642273Z","shell.execute_reply":"2022-12-02T10:17:13.657827Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Training Images\ntrain_files = sorted(glob.glob(os.path.join(base_path, 'train/*.tiff')))\nprint(f'Number of training images: {len(train_files)}')\nprint('\\n'.join(train_files))","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:13.660480Z","iopub.execute_input":"2022-12-02T10:17:13.660916Z","iopub.status.idle":"2022-12-02T10:17:13.685980Z","shell.execute_reply.started":"2022-12-02T10:17:13.660882Z","shell.execute_reply":"2022-12-02T10:17:13.684975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Test Images\ntest_files = sorted(glob.glob(os.path.join(base_path, 'test/*.tiff')))\nprint(f'Number of test images: {len(test_files)}')\nprint('\\n'.join(test_files))","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:13.687492Z","iopub.execute_input":"2022-12-02T10:17:13.687823Z","iopub.status.idle":"2022-12-02T10:17:13.699823Z","shell.execute_reply.started":"2022-12-02T10:17:13.687791Z","shell.execute_reply":"2022-12-02T10:17:13.698735Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Train CSV\n#The masks indicating a glomeruli FTUs are stored in rle format in the train.csv for each training image id.\ndf_train = pd.read_csv(os.path.join(base_path, 'train.csv'))\ndisplay(df_train)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:13.701546Z","iopub.execute_input":"2022-12-02T10:17:13.701907Z","iopub.status.idle":"2022-12-02T10:17:14.208179Z","shell.execute_reply.started":"2022-12-02T10:17:13.701873Z","shell.execute_reply":"2022-12-02T10:17:14.207189Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Sample_Submission.csv\n#The sample_submission.csv files shows the format of the submissions files consisting of the test image id and an rle encoded masks.\ndf_submission = pd.read_csv(os.path.join(base_path,'sample_submission.csv'))\ndisplay(df_submission)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:14.209891Z","iopub.execute_input":"2022-12-02T10:17:14.210303Z","iopub.status.idle":"2022-12-02T10:17:14.228064Z","shell.execute_reply.started":"2022-12-02T10:17:14.210265Z","shell.execute_reply":"2022-12-02T10:17:14.226626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Patient Data\n#HuBMAP-20-dataset_information.csv contains additional information about each image such as image size and anonymized patient data.\ndf_info = pd.read_csv(os.path.join(base_path,'HuBMAP-20-dataset_information.csv'))\ndisplay(df_info)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:14.229181Z","iopub.execute_input":"2022-12-02T10:17:14.229601Z","iopub.status.idle":"2022-12-02T10:17:14.265439Z","shell.execute_reply.started":"2022-12-02T10:17:14.229564Z","shell.execute_reply":"2022-12-02T10:17:14.264533Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# for f in train_files + test_files:\n#     image = tifffile.imread(f)\n#     print(f'Image {f} shape: {image.shape}', flush=True)\n#     del image\n#     gc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:14.269907Z","iopub.execute_input":"2022-12-02T10:17:14.270548Z","iopub.status.idle":"2022-12-02T10:17:14.274887Z","shell.execute_reply.started":"2022-12-02T10:17:14.270520Z","shell.execute_reply":"2022-12-02T10:17:14.273603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#The size of the images varies greatly as well.\nplt.scatter(df_info['width_pixels'], df_info['height_pixels'])\nplt.title('Image Height and Width')\nplt.xlabel('Width')\nplt.ylabel('Height')\nplt.xlim(0, df_info['width_pixels'].max() * 1.1)\nplt.ylim(0, df_info['height_pixels'].max() * 1.1)\nplt.grid()","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:14.278391Z","iopub.execute_input":"2022-12-02T10:17:14.279057Z","iopub.status.idle":"2022-12-02T10:17:14.570180Z","shell.execute_reply.started":"2022-12-02T10:17:14.279023Z","shell.execute_reply":"2022-12-02T10:17:14.569290Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def overlay_image_mask(image, mask, mask_color=(0,255,0), alpha=1.0):\n    im_f= image.astype(np.float32)\n#     if mask.ndim == 2:\n#         mask = np.expand_dims(mask,-1)        \n    mask_col = np.expand_dims(np.array(mask_color)/255.0, axis=(0,1))\n    return (im_f + alpha * mask * (np.mean(0.8 * im_f + 0.2 * 255, axis=2, keepdims=True) * mask_col - im_f)).astype(np.uint8)\n\n\ndef overlay_image_mask_original(image, mask, mask_color=(0,255,0), alpha=1.0):\n    return  np.concatenate((image, overlay_image_mask(image, mask)), axis=1)\n\ndef get_image_id(image_file):\n    return os.path.splitext(os.path.split(image_file)[1])[0]\n\n\ndef read_image(image_file, scale=1.0):\n    image = tifffile.imread(image_file).squeeze()\n    if image.shape[0] == 3:\n        image = np.transpose(image, (1,2,0))\n    \n    orig_shape = image.shape\n    if scale != 1.0:\n        image = cv2.resize(image, (0,0), fx=scale, fy=scale)\n    return image, orig_shape\n\n\ndef read_mask(image_file, image_shape, scale=1.0):\n    image_id = get_image_id(image_file)\n    train_info = df_train.loc[df_train['id'] == image_id]\n    rle = train_info['encoding'].values[0] if len(train_info) > 0 else None\n    if rle is not None:\n        mask = rle_to_image(rle, (image_shape[1], image_shape[0]))\n        if scale != 1.0:\n            mask = cv2.resize(mask, (0,0), fx=scale, fy=scale)\n        return np.expand_dims(mask,-1)\n    else:\n        return None        \n\n    \ndef read_image_mask(image_file, scale=1.0):\n    image, image_shape = read_image(image_file, scale)\n    mask = read_mask(image_file, image_shape, scale)\n    return image, mask\n\n\ndef get_tile(image, mask, x, y, tile_size, scale=1.0):\n    x = round(x * scale)\n    y = round(y * scale)\n    size = int(round(tile_size / 2 * scale))\n    image_s = image[y-size:y+size, x-size:x+size, :] \n    mask_s = mask[y-size:y+size, x-size:x+size, :]\n    return image_s, mask_s\n\n\ndef get_particles(mask, scale=1.0):\n    num, labels, stats, centroids = cv2.connectedComponentsWithStats(mask)\n    df_particles = pd.DataFrame(dict(zip(['x','y','left','top','width','height','area'],\n                               [(centroids[1:,0]) / scale,\n                                (centroids[1:,1]) / scale,\n                                (stats[1:,cv2.CC_STAT_LEFT]) / scale,\n                                (stats[1:,cv2.CC_STAT_TOP]) / scale,\n                                (stats[1:,cv2.CC_STAT_WIDTH]) / scale,\n                                (stats[1:,cv2.CC_STAT_HEIGHT]) / scale,\n                                (stats[1:,cv2.CC_STAT_AREA]) / (scale * scale)])))\n    df_particles.sort_values(['x','y'], inplace=True, ignore_index=True)\n    df_particles['no'] = range(len(df_particles))\n    return df_particles\n\n\ndef analyze_image(image_file):\n    image_id = get_image_id(image_file)\n    image, image_shape = read_image(image_file, glob_scale)\n    mask = read_mask(image_file, image_shape, glob_scale)\n\n    mask_full = read_mask(image_file, image_shape, scale=1.0)\n    df_glom = get_particles(mask_full, scale=1.0)\n    df_glom['id'] = image_id\n    del mask_full\n    gc.collect()\n    \n    info = df_info[df_info['image_file'] == f'{image_id}.tiff']\n    print(f'Image ID:        {image_id:}')\n    print(f'Image Size:      {info[\"width_pixels\"].values[0]} x {info[\"height_pixels\"].values[0]}')\n    print(f'Patient No:      {info[\"patient_number\"].values[0]}')\n    print(f'Sex:             {info[\"sex\"].values[0]}')\n    print(f'Age:             {info[\"age\"].values[0]}')\n    print(f'Race:            {info[\"race\"].values[0]}')\n    print(f'Height:          {info[\"height_centimeters\"].values[0]} cm')\n    print(f'Weight:          {info[\"weight_kilograms\"].values[0]} kg')\n    print(f'BMI:             {info[\"bmi_kg/m^2\"].values[0]} kg/m^2')\n    print(f'Laterality:      {info[\"laterality\"].values[0]}')\n    print(f'Percent Cortex:  {info[\"percent_cortex\"].values[0]} %')\n    print(f'Percent Medulla: {info[\"percent_medulla\"].values[0]} %')\n    \n    # Plot full image\n    if plot_full_image:\n        scale = 0.1\n        image_small = cv2.resize(image, (0,0), fx=scale, fy=scale)\n        mask_small = cv2.resize(mask, (0,0), fx=scale, fy=scale)\n        mask_small = np.expand_dims(mask_small,-1) \n    \n        plt.figure(figsize=(16, 16))\n        plt.imshow(overlay_image_mask(image_small, mask_small))\n        plt.axis('off')\n\n    # Plot glomeruli images\n    fig_cols = 5\n    fig_rows = int(math.ceil(num_glom_display/fig_cols))\n    plt.figure(figsize=(4 * fig_cols, 4 * fig_rows))\n    if num_glom_save > 0 and not os.path.exists(image_id):\n        os.mkdir(image_id)\n    for i in range(min(max(num_glom_display, num_glom_save), len(df_glom))):\n        image_s, mask_s = get_tile(image,mask, df_glom['x'][i], df_glom['y'][i], 1000, scale=glob_scale)\n        ovl = overlay_image_mask(image_s, mask_s)\n        if i < num_glom_display:\n            plt.subplot(fig_rows, fig_cols, i+1)\n            plt.imshow(ovl)\n            plt.axis('off')\n        if i < num_glom_save:\n            cv2.imwrite(f'{image_id}_{i:03}.png', cv2.cvtColor(ovl, cv2.COLOR_RGB2BGR))    \n    \n    del image, mask\n    gc.collect()\n    return df_glom\n\n\ndef plot_glom(df, image_id, glom_no):\n    image, mask = read_image_mask(os.path.join(base_path, f'train/{image_id}.tiff'), scale=glob_scale)\n    glom = df.loc[(df['id'] == image_id) & (df['no'] == glom_no)]\n    im, ma = get_tile(image, mask, glom['x'].iloc[0], glom['y'].iloc[0], 1000, scale=glob_scale)\n    del image, mask\n    gc.collect()\n    plt.figure(figsize=(16,8))\n    plt.imshow(overlay_image_mask_original(im, ma))\n    plt.title(f'Image: {image_id}, Glomeruli No: {glom_no}, Area: {glom[\"area\"].iloc[0]}')","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:14.571829Z","iopub.execute_input":"2022-12-02T10:17:14.572177Z","iopub.status.idle":"2022-12-02T10:17:14.601611Z","shell.execute_reply.started":"2022-12-02T10:17:14.572144Z","shell.execute_reply":"2022-12-02T10:17:14.600455Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_glom = pd.DataFrame()\ndf_glom = df_glom.append(analyze_image(train_files[0]), ignore_index=True)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:14.603316Z","iopub.execute_input":"2022-12-02T10:17:14.603662Z","iopub.status.idle":"2022-12-02T10:17:53.829192Z","shell.execute_reply.started":"2022-12-02T10:17:14.603628Z","shell.execute_reply":"2022-12-02T10:17:53.828093Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_glom = df_glom.append(analyze_image(train_files[1]), ignore_index=True)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:17:53.830613Z","iopub.execute_input":"2022-12-02T10:17:53.831462Z","iopub.status.idle":"2022-12-02T10:19:35.279962Z","shell.execute_reply.started":"2022-12-02T10:17:53.831425Z","shell.execute_reply":"2022-12-02T10:19:35.279153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_info[\"split\"] = \"test\"\ndf_info.loc[df_info[\"image_file\"].isin(os.listdir(os.path.join(base_path, \"train\"))), \"split\"] = \"train\"","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:19:35.281283Z","iopub.execute_input":"2022-12-02T10:19:35.282163Z","iopub.status.idle":"2022-12-02T10:19:35.297273Z","shell.execute_reply.started":"2022-12-02T10:19:35.282129Z","shell.execute_reply":"2022-12-02T10:19:35.296138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_info[\"area\"] = df_info[\"width_pixels\"] * df_info[\"height_pixels\"]","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:19:35.298613Z","iopub.execute_input":"2022-12-02T10:19:35.298960Z","iopub.status.idle":"2022-12-02T10:19:35.305172Z","shell.execute_reply.started":"2022-12-02T10:19:35.298925Z","shell.execute_reply":"2022-12-02T10:19:35.303968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(16, 35))\nplt.subplot(6, 2, 1)\nsn.countplot(x=\"race\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 2)\nsn.countplot(x=\"ethnicity\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 3)\nsn.countplot(x=\"sex\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 4)\nsn.countplot(x=\"laterality\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 5)\nsn.histplot(x=\"age\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 6)\nsn.histplot(x=\"weight_kilograms\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 7)\nsn.histplot(x=\"height_centimeters\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 8)\nsn.histplot(x=\"bmi_kg/m^2\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 9)\nsn.histplot(x=\"percent_cortex\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 10)\nsn.histplot(x=\"percent_medulla\", hue=\"split\", data=df_info)\nplt.subplot(6, 2, 11)\nsn.histplot(x=\"area\", hue=\"split\", data=df_info);","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:19:35.306645Z","iopub.execute_input":"2022-12-02T10:19:35.307165Z","iopub.status.idle":"2022-12-02T10:19:37.286783Z","shell.execute_reply.started":"2022-12-02T10:19:35.307131Z","shell.execute_reply":"2022-12-02T10:19:37.285607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Converting images into tiles","metadata":{}},{"cell_type":"markdown","source":"The images in the train folder have large size to train deep learning model reasonably on them we have to split this **image into tiles** of specific size for example 256X256 .\nFor example if the image is 5000x5000 , we split this images into tiles of size 256x256, we divide 5000/256=19.53 then we ceil it to 20 so we have 20x20 grid with tile size 256x256 . we pad any image to be of size 256x256.\nThe dataset i used for training is 256x256 dataset which is the ouput of spliting the image into grid of tiles of size 256x256\nRefer to this notebook : https://www.kaggle.com/iafoss/256x256-images\n\n","metadata":{}},{"cell_type":"code","source":"# sz = 256   #the size of tiles\n# reduce = 4 #reduce the original images by 4 times \n# MASKS = '../input/hubmap-kidney-segmentation/train.csv'\n# DATA = '../input/hubmap-kidney-segmentation/train/'\n# OUT_TRAIN = 'train.zip'\n# OUT_MASKS = 'masks.zip'","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:36.436794Z","iopub.execute_input":"2022-12-02T10:22:36.437172Z","iopub.status.idle":"2022-12-02T10:22:36.441994Z","shell.execute_reply.started":"2022-12-02T10:22:36.437140Z","shell.execute_reply":"2022-12-02T10:22:36.440843Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# def enc2mask(encs, shape):\n#     img = np.zeros(shape[0]*shape[1], dtype=np.uint8)\n#     for m,enc in enumerate(encs):\n#         if isinstance(enc,np.float64) and np.isnan(enc): continue\n#         s = enc.split()\n#         for i in range(len(s)//2):\n#             start = int(s[2*i]) - 1\n#             length = int(s[2*i+1])\n#             img[start:start+length] = 1 + m\n#     return img.reshape(shape).T\n\n# def mask2enc(mask, n=1):\n#     pixels = mask.T.flatten()\n#     encs = []\n#     for i in range(1,n+1):\n#         p = (pixels == i).astype(np.int8)\n#         if p.sum() == 0: encs.append(np.nan)\n#         else:\n#             p = np.concatenate([[0], p, [0]])\n#             runs = np.where(p[1:] != p[:-1])[0] + 1\n#             runs[1::2] -= runs[::2]\n#             encs.append(' '.join(str(x) for x in runs))\n#     return encs\n\n# df_masks = pd.read_csv(MASKS).set_index('id')\n# df_masks.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:36.839520Z","iopub.execute_input":"2022-12-02T10:22:36.840148Z","iopub.status.idle":"2022-12-02T10:22:36.844745Z","shell.execute_reply.started":"2022-12-02T10:22:36.840116Z","shell.execute_reply":"2022-12-02T10:22:36.843718Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# #one of the new images cannot be loaded into 16GB RAM\n# #using rasterio to load image part by part\n\n# s_th = 40  #saturation blancking threshold\n# p_th = 1000*(sz//256)**2 #threshold for the minimum number of pixels\n\n\n# class HuBMAPDataset(Dataset):\n#     def __init__(self, idx, sz=sz, reduce=reduce, encs=None):\n#         self.data = rasterio.open(os.path.join(DATA,idx+'.tiff'),num_threads='all_cpus')\n#         # some images have issues with their format \n#         # and must be saved correctly before reading with rasterio\n#         if self.data.count != 3:\n#             subdatasets = self.data.subdatasets\n#             self.layers = []\n#             if len(subdatasets) > 0:\n#                 for i, subdataset in enumerate(subdatasets, 0):\n#                     self.layers.append(rasterio.open(subdataset))\n#         self.shape = self.data.shape\n#         self.reduce = reduce\n#         self.sz = reduce*sz\n#         self.pad0 = (self.sz - self.shape[0]%self.sz)%self.sz\n#         self.pad1 = (self.sz - self.shape[1]%self.sz)%self.sz\n#         self.n0max = (self.shape[0] + self.pad0)//self.sz\n#         self.n1max = (self.shape[1] + self.pad1)//self.sz\n#         self.mask = enc2mask(encs,(self.shape[1],self.shape[0])) if encs is not None else None\n        \n#     def __len__(self):\n#         return self.n0max*self.n1max\n    \n#     def __getitem__(self, idx):\n#         # the code below may be a little bit difficult to understand,\n#         # but the thing it does is mapping the original image to\n#         # tiles created with adding padding (like in the previous version of the kernel)\n#         # then the tiles are loaded with rasterio\n#         # n0,n1 - are the x and y index of the tile (idx = n0*self.n1max + n1)\n#         n0,n1 = idx//self.n1max, idx%self.n1max\n#         # x0,y0 - are the coordinates of the lower left corner of the tile in the image\n#         # negative numbers correspond to padding (which must not be loaded)\n#         x0,y0 = -self.pad0//2 + n0*self.sz, -self.pad1//2 + n1*self.sz\n\n#         # make sure that the region to read is within the image\n#         p00,p01 = max(0,x0), min(x0+self.sz,self.shape[0])\n#         p10,p11 = max(0,y0), min(y0+self.sz,self.shape[1])\n#         img = np.zeros((self.sz,self.sz,3),np.uint8)\n#         mask = np.zeros((self.sz,self.sz),np.uint8)\n#         # mapping the loade region to the tile\n#         if self.data.count == 3:\n#             img[(p00-x0):(p01-x0),(p10-y0):(p11-y0)] = np.moveaxis(self.data.read([1,2,3],\n#                 window=Window.from_slices((p00,p01),(p10,p11))), 0, -1)\n#         else:\n#             for i,layer in enumerate(self.layers):\n#                 img[(p00-x0):(p01-x0),(p10-y0):(p11-y0),i] =\\\n#                   layer.read(1,window=Window.from_slices((p00,p01),(p10,p11)))\n#         if self.mask is not None: mask[(p00-x0):(p01-x0),(p10-y0):(p11-y0)] = self.mask[p00:p01,p10:p11]\n        \n#         if self.reduce != 1:\n#             img = cv2.resize(img,(self.sz//reduce,self.sz//reduce),\n#                              interpolation = cv2.INTER_AREA)\n#             mask = cv2.resize(mask,(self.sz//reduce,self.sz//reduce),\n#                              interpolation = cv2.INTER_NEAREST)\n#         #check for empty imges\n#         hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n#         h,s,v = cv2.split(hsv)\n#         #return -1 for empty images\n#         return img, mask, (-1 if (s>s_th).sum() <= p_th or img.sum() <= p_th else idx)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:37.307780Z","iopub.execute_input":"2022-12-02T10:22:37.308417Z","iopub.status.idle":"2022-12-02T10:22:37.320237Z","shell.execute_reply.started":"2022-12-02T10:22:37.308373Z","shell.execute_reply":"2022-12-02T10:22:37.319340Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# x_tot,x2_tot = [],[]\n# with zipfile.ZipFile(OUT_TRAIN, 'w') as img_out,\\\n#  zipfile.ZipFile(OUT_MASKS, 'w') as mask_out:\n#     for index, encs in tqdm(df_masks.iterrows(),total=len(df_masks)):\n#         #image+mask dataset\n#         ds = HuBMAPDataset(index,encs=encs)\n#         for i in range(len(ds)):\n#             im,m,idx = ds[i]\n#             if idx < 0: continue\n                \n#             x_tot.append((im/255.0).reshape(-1,3).mean(0))\n#             x2_tot.append(((im/255.0)**2).reshape(-1,3).mean(0))\n            \n#             #write data   \n#             im = cv2.imencode('.png',cv2.cvtColor(im, cv2.COLOR_RGB2BGR))[1]\n#             img_out.writestr(f'{index}_{idx:04d}.png', im)\n#             m = cv2.imencode('.png',m)[1]\n#             mask_out.writestr(f'{index}_{idx:04d}.png', m)\n        \n# #image stats\n# img_avr =  np.array(x_tot).mean(0)\n# img_std =  np.sqrt(np.array(x2_tot).mean(0) - img_avr**2)\n# print('mean:',img_avr, ', std:', img_std)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:37.713091Z","iopub.execute_input":"2022-12-02T10:22:37.713458Z","iopub.status.idle":"2022-12-02T10:22:37.718653Z","shell.execute_reply.started":"2022-12-02T10:22:37.713427Z","shell.execute_reply":"2022-12-02T10:22:37.717624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# with_mask=False\n# columns, rows = 4,4\n# idx0 = 20\n# fig=plt.figure(figsize=(columns*4, rows*4))\n# with zipfile.ZipFile(OUT_TRAIN, 'r') as img_arch, \\\n#      zipfile.ZipFile(OUT_MASKS, 'r') as msk_arch:\n#     fnames = sorted(img_arch.namelist())[8:]\n#     for i in range(rows):\n#         for j in range(columns):\n#             idx = i+j*columns\n#             img = cv2.imdecode(np.frombuffer(img_arch.read(fnames[idx0+idx]), \n#                                              np.uint8), cv2.IMREAD_COLOR)\n#             img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n#             if with_mask:\n                \n#                 mask = cv2.imdecode(np.frombuffer(msk_arch.read(fnames[idx0+idx]), \n#                                               np.uint8), cv2.IMREAD_GRAYSCALE)\n    \n#             fig.add_subplot(rows, columns, idx+1)\n#             plt.axis('off')\n#             plt.imshow(Image.fromarray(img))\n#             if with_mask:\n                \n#                 plt.imshow(Image.fromarray(mask), alpha=0.2)\n# plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:38.155076Z","iopub.execute_input":"2022-12-02T10:22:38.155770Z","iopub.status.idle":"2022-12-02T10:22:38.162503Z","shell.execute_reply.started":"2022-12-02T10:22:38.155736Z","shell.execute_reply":"2022-12-02T10:22:38.159775Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from monai.apps import download_and_extract\nfrom monai.config import print_config\nfrom monai.data import decollate_batch, DataLoader\nfrom monai.metrics import ROCAUCMetric\nfrom monai.networks.nets import DenseNet121\nfrom monai.transforms import (\n    Activations,\n    EnsureChannelFirst,\n    AsDiscrete,\n    Compose,\n    LoadImage,\n    RandFlip,\n    RandRotate,\n    RandZoom,\n    ScaleIntensity,\n)\nfrom monai.utils import set_determinism\n\nprint_config()","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:38.897424Z","iopub.execute_input":"2022-12-02T10:22:38.898334Z","iopub.status.idle":"2022-12-02T10:22:38.924204Z","shell.execute_reply.started":"2022-12-02T10:22:38.898289Z","shell.execute_reply":"2022-12-02T10:22:38.923468Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# UNET","metadata":{}},{"cell_type":"code","source":"def seed_everything(seed):\n    random.seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True\nseed_everything(42)\nsz = 256  \nreduce = 4\nTH = 0.39 ","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:40.755869Z","iopub.execute_input":"2022-12-02T10:22:40.756240Z","iopub.status.idle":"2022-12-02T10:22:40.765958Z","shell.execute_reply.started":"2022-12-02T10:22:40.756207Z","shell.execute_reply":"2022-12-02T10:22:40.765019Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class HuBMAPDataset(Dataset):\n    def __init__(self, ids, phase):\n        self.ids = ids\n        if phase=='train':\n            self.transform = get_train_transform()\n        else:\n            self.transform = get_val_transform()\n        \n    def __getitem__(self, idx):\n        name = self.ids[idx]\n#         print(name)\n#         /kaggle/input/256x256images/train/0486052bb_0079.png\n        img = cv2.imread(f\"/kaggle/input/256x256images/train/{name}\").astype(\"float32\")[:,:,::-1]\n        img /= 255.\n        mask = cv2.imread(f\"/kaggle/input/256x256images/masks/{name}\")[:,:,0:1]\n\n        transformed = self.transform(image=img, mask=mask)\n        img = transformed['image']\n        mask = transformed['mask']\n        img = img.transpose(2,0,1).astype('float32')\n        mask = mask.transpose(2,0,1).astype('float32')\n        return img, mask\n\n    def __len__(self):\n        return len(self.ids)\n\n        \ndef get_train_transform():\n    return A.Compose([\n        A.HorizontalFlip(),\n            A.OneOf([\n                A.RandomBrightnessContrast(),\n                A.RandomGamma(),\n                A.RandomBrightness(),\n                ], p=0.3),\n            A.OneOf([\n                A.ElasticTransform(alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03),\n                A.GridDistortion(),\n                A.OpticalDistortion(distort_limit=2, shift_limit=0.5),\n                ], p=0.3),\n            A.ShiftScaleRotate(p=0.2),\n            A.Resize(256,256,always_apply=True),\n    ],p=1.)\n\ndef get_val_transform():\n    return A.Compose([\n        A.Resize(256,256,always_apply=True),\n    ],p=1.)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:41.290826Z","iopub.execute_input":"2022-12-02T10:22:41.292061Z","iopub.status.idle":"2022-12-02T10:22:41.304618Z","shell.execute_reply.started":"2022-12-02T10:22:41.292012Z","shell.execute_reply":"2022-12-02T10:22:41.303508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"directory_list = os.listdir('/kaggle/input/256x256images/train')\ndir_df = pd.DataFrame(directory_list, columns=['Image_Paths'])\ndir_df","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:41.694738Z","iopub.execute_input":"2022-12-02T10:22:41.695090Z","iopub.status.idle":"2022-12-02T10:22:41.905215Z","shell.execute_reply.started":"2022-12-02T10:22:41.695059Z","shell.execute_reply":"2022-12-02T10:22:41.904157Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(dir_df)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:42.471477Z","iopub.execute_input":"2022-12-02T10:22:42.473545Z","iopub.status.idle":"2022-12-02T10:22:42.479862Z","shell.execute_reply.started":"2022-12-02T10:22:42.473501Z","shell.execute_reply":"2022-12-02T10:22:42.478701Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def prepare_train_valid_dataloader(df, fold):\n    train_ids = df.loc[~df.Folds.isin(fold), \"Image_Paths\"].values\n    val_ids = df.loc[df.Folds.isin(fold), \"Image_Paths\"].values\n    train_ds = HuBMAPDataset(train_ids, \"train\")\n    val_ds = HuBMAPDataset(val_ids, \"val\")\n    train_loader = DataLoader(train_ds, batch_size=16, pin_memory=True, shuffle=True, num_workers=2)\n    val_loader = DataLoader(val_ds, batch_size=4, pin_memory=True, shuffle=False, num_workers=2)\n    return train_loader, val_loader\n","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:42.939680Z","iopub.execute_input":"2022-12-02T10:22:42.940047Z","iopub.status.idle":"2022-12-02T10:22:42.948798Z","shell.execute_reply.started":"2022-12-02T10:22:42.940019Z","shell.execute_reply":"2022-12-02T10:22:42.947710Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch import nn\nclass HuBMAP(nn.Module):\n    def __init__(self):\n        super(HuBMAP, self).__init__()\n        self.cnn_model =  monai.networks.nets.SegResNetVAE(\n                                                spatial_dims = 2,\n                                                in_channels=3,\n                                                out_channels=1,\n                                                input_image_size=(256,256),\n                                                upsample_mode = \"deconv\")\n        #self.cnn_model.decoder.blocks.append(self.cnn_model.decoder.blocks[-1])\n        #self.cnn_model.decoder.blocks[-2] = self.cnn_model.decoder.blocks[-3]\n    \n    def forward(self, imgs):\n        img_segs = self.cnn_model(imgs)\n        return img_segs","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:43.776191Z","iopub.execute_input":"2022-12-02T10:22:43.777298Z","iopub.status.idle":"2022-12-02T10:22:43.783732Z","shell.execute_reply.started":"2022-12-02T10:22:43.777234Z","shell.execute_reply":"2022-12-02T10:22:43.782747Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:36daabcd-91b3-4650-b1f9-e10dc34e8cf0.png)","metadata":{},"attachments":{"36daabcd-91b3-4650-b1f9-e10dc34e8cf0.png":{"image/png":"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"}}},{"cell_type":"code","source":"from torch import nn\nfrom torch.nn import functional as F\nclass DiceLoss(nn.Module):\n    def __init__(self, weight=None, size_average=True):\n        super(DiceLoss, self).__init__()\n\n    def forward(self, inputs, targets, smooth=1):\n        \n        #comment out if your model contains a sigmoid or equivalent activation layer\n        inputs = F.sigmoid(inputs)       \n        \n        #flatten label and prediction tensors\n        inputs = inputs.view(-1)\n        targets = targets.view(-1)\n        \n        intersection = (inputs * targets).sum()                            \n        dice = (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth)  \n        \n        return dice\n    \n    \n    \nclass DiceBCELoss(nn.Module):\n    # Formula Given above.\n    def __init__(self, weight=None, size_average=True):\n        super(DiceBCELoss, self).__init__()\n    def forward(self, inputs, targets, smooth=1):\n        \n        #comment out if your model contains a sigmoid or equivalent activation layer\n        inputs = F.sigmoid(inputs)       \n        \n        #flatten label and prediction tensors\n        inputs = inputs.view(-1)\n        targets = targets.view(-1)\n        \n        intersection = (inputs * targets).mean()                            \n        dice_loss = 1 - (2.*intersection + smooth)/(inputs.mean() + targets.mean() + smooth)  \n        BCE = F.binary_cross_entropy(inputs, targets, reduction='mean')\n        Dice_BCE = BCE + dice_loss\n        \n        return Dice_BCE.mean()\n# ","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:44.884848Z","iopub.execute_input":"2022-12-02T10:22:44.885789Z","iopub.status.idle":"2022-12-02T10:22:44.896158Z","shell.execute_reply.started":"2022-12-02T10:22:44.885741Z","shell.execute_reply":"2022-12-02T10:22:44.895297Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Rreason for using diceloss functionaddresses the imbalance problem between foreground and background yet overlooks another imbalance between easy and hard examples that also severely affects the training process of a learning model.","metadata":{}},{"cell_type":"code","source":"def HuBMAPLoss(images, targets, model, device):\n    model.to(device)\n    images = images.to(device)\n    targets = targets.to(device)\n    outputs, aux = model(images)\n    criterion = DiceBCELoss()\n    loss = criterion(outputs, targets)\n    return loss, outputs","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:45.894877Z","iopub.execute_input":"2022-12-02T10:22:45.895478Z","iopub.status.idle":"2022-12-02T10:22:45.901339Z","shell.execute_reply.started":"2022-12-02T10:22:45.895443Z","shell.execute_reply":"2022-12-02T10:22:45.900335Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def train_one_epoch(epoch, model, device, optimizer, scheduler, trainloader):\n    model.train()\n    t = time.time()\n    total_loss = 0\n    for step, (images, targets) in enumerate(trainloader):\n        loss, outputs = HuBMAPLoss(images, targets, model, device)\n        loss.backward()\n        if ((step+1)%4==0 or (step+1)==len(trainloader)):\n            optimizer.step()\n            scheduler.step()\n            optimizer.zero_grad()\n        loss = loss.detach().item()\n        total_loss += loss\n        if ((step+1)%10==0 or (step+1)==len(trainloader)):\n            print(\n                    f'epoch {epoch} train step {step+1}/{len(trainloader)}, ' + \\\n                    f'loss: {total_loss/len(trainloader):.4f}, ' + \\\n                    f'time: {(time.time() - t):.4f}', end= '\\r' if (step + 1) != len(trainloader) else '\\n'\n                )\n\n            \n        \ndef valid_one_epoch(epoch, model, device, optimizer, scheduler, validloader):\n    model.eval()\n    t = time.time()\n    total_loss = 0\n    for step, (images, targets) in enumerate(validloader):\n        loss, outputs = HuBMAPLoss(images, targets, model, device)\n        loss = loss.detach().item()\n        total_loss += loss\n        if ((step+1)%4==0 or (step+1)==len(validloader)):\n            scheduler.step(total_loss/len(validloader))\n        if ((step+1)%10==0 or (step+1)==len(validloader)):\n            print(\n                    f'epoch {epoch} valid step {step+1}/{len(validloader)}, ' + \\\n                    f'loss: {total_loss/len(validloader):.4f}, ' + \\\n                    f'time: {(time.time() - t):.4f}', end= '\\r' if (step + 1) != len(validloader) else '\\n'\n                )","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:46.349301Z","iopub.execute_input":"2022-12-02T10:22:46.349857Z","iopub.status.idle":"2022-12-02T10:22:46.362579Z","shell.execute_reply.started":"2022-12-02T10:22:46.349823Z","shell.execute_reply":"2022-12-02T10:22:46.361564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"FOLDS = 5\ngkf = GroupKFold(FOLDS)\ndir_df['Folds'] = 0\nfor fold, (tr_idx, val_idx) in enumerate(gkf.split(dir_df, groups=dir_df[dir_df.columns[0]].values)):\n    dir_df.loc[val_idx, 'Folds'] = fold","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:46.814289Z","iopub.execute_input":"2022-12-02T10:22:46.814863Z","iopub.status.idle":"2022-12-02T10:22:46.859658Z","shell.execute_reply.started":"2022-12-02T10:22:46.814828Z","shell.execute_reply":"2022-12-02T10:22:46.858774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display(dir_df.groupby(\"Folds\").count())","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:47.265584Z","iopub.execute_input":"2022-12-02T10:22:47.266574Z","iopub.status.idle":"2022-12-02T10:22:47.278091Z","shell.execute_reply.started":"2022-12-02T10:22:47.266518Z","shell.execute_reply":"2022-12-02T10:22:47.276904Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import time \nfor fold, (tr_idx, val_idx) in enumerate(gkf.split(dir_df, groups=dir_df[dir_df.columns[0]].values)):\n    if fold>1:\n        break\n    trainloader, validloader = prepare_train_valid_dataloader(dir_df, [fold])\n    if torch.cuda.is_available():\n        print(\"Cuda Available\")\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n#     device = torch.device('cpu')\n    model = HuBMAP().to(device)\n    lr = 5e-5\n    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, gamma=0.001, step_size=1)\n#     num_epochs = 15\n    num_epochs = 1\n    for epoch in range(num_epochs):\n        train_one_epoch(epoch, model, device, optimizer, scheduler, trainloader)\n        with torch.no_grad():\n            valid_one_epoch(epoch, model, device, optimizer, scheduler, validloader)\n    torch.save(model.state_dict(),f'FOLD-{fold}-lr-{lr}-Unet-eff.pth')","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:22:47.750034Z","iopub.execute_input":"2022-12-02T10:22:47.750409Z","iopub.status.idle":"2022-12-02T10:27:24.444036Z","shell.execute_reply.started":"2022-12-02T10:22:47.750377Z","shell.execute_reply":"2022-12-02T10:27:24.442646Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"torch.save(model.state_dict(),f'FOLD-{fold}-lr-{lr}-Unet-eff.pth')","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:27:24.446771Z","iopub.execute_input":"2022-12-02T10:27:24.447164Z","iopub.status.idle":"2022-12-02T10:27:24.487039Z","shell.execute_reply.started":"2022-12-02T10:27:24.447122Z","shell.execute_reply":"2022-12-02T10:27:24.486097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:27:24.488287Z","iopub.execute_input":"2022-12-02T10:27:24.488643Z","iopub.status.idle":"2022-12-02T10:27:25.597832Z","shell.execute_reply.started":"2022-12-02T10:27:24.488608Z","shell.execute_reply":"2022-12-02T10:27:25.596509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = torch.load(\"/kaggle/working/FOLD-0-lr-5e-05-Unet-eff.pth\")","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:27:25.601233Z","iopub.execute_input":"2022-12-02T10:27:25.602023Z","iopub.status.idle":"2022-12-02T10:27:25.641178Z","shell.execute_reply.started":"2022-12-02T10:27:25.601983Z","shell.execute_reply":"2022-12-02T10:27:25.640293Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#dataset ko train and valid mai seprate karke de degaa  \ntrainloader, validloader = prepare_train_valid_dataloader(dir_df, [0])\nfor idx,batch in enumerate(validloader):\n#     print(idx)\n    if idx == 1:\n        img, mask = batch\n        break\n    else:\n        continue","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:27:25.642673Z","iopub.execute_input":"2022-12-02T10:27:25.643040Z","iopub.status.idle":"2022-12-02T10:27:25.969514Z","shell.execute_reply.started":"2022-12-02T10:27:25.643004Z","shell.execute_reply":"2022-12-02T10:27:25.968183Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"! pip install segmentation-models-pytorch\n","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:27:25.971656Z","iopub.execute_input":"2022-12-02T10:27:25.972037Z","iopub.status.idle":"2022-12-02T10:27:42.210748Z","shell.execute_reply.started":"2022-12-02T10:27:25.971997Z","shell.execute_reply":"2022-12-02T10:27:42.209437Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import segmentation_models_pytorch as smp\nmodel_inf_act = smp.Unet('efficientnet-b5', encoder_weights=None, classes=1, activation=\"sigmoid\")\n\nmodel_inf_act.load_state_dict(torch.load(\"/kaggle/working/FOLD-1-lr-5e-05-Unet-eff.pth\"), strict = False)\n\nout = model_inf_act(img)","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:27:42.213025Z","iopub.execute_input":"2022-12-02T10:27:42.213485Z","iopub.status.idle":"2022-12-02T10:27:48.284282Z","shell.execute_reply.started":"2022-12-02T10:27:42.213439Z","shell.execute_reply":"2022-12-02T10:27:48.282873Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(12,16))\nplt.subplot(1,3,1)\nplt.imshow(img[0].T)\nplt.title(\"Original Image\")\n# plt.show()\n\nplt.subplot(1,3,2)\nplt.imshow(mask[0].T, cmap=\"Greys\")\nplt.title(\"Original Mask\")\n# plt.show()\n\nplt.subplot(1,3,3)\nprint(\"Shape \", out[0].detach().numpy().shape)\nfinal_out = out[0].detach().numpy().T\nprint(np.unique(final_out))\nprint(np.mean(final_out))\nplt.imshow((final_out)>0.8, cmap=\"Greys\")\nplt.title(\"Predicted Mask\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:35:41.107206Z","iopub.execute_input":"2022-12-02T10:35:41.107977Z","iopub.status.idle":"2022-12-02T10:35:41.521998Z","shell.execute_reply.started":"2022-12-02T10:35:41.107931Z","shell.execute_reply":"2022-12-02T10:35:41.521129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# SegResNetVAE","metadata":{}},{"cell_type":"code","source":"from torch import nn\nclass HuBMAP(nn.Module):\n    def __init__(self):\n        super(HuBMAP, self).__init__()\n        self.cnn_model =  monai.networks.nets.SegResNetVAE(\n                                                spatial_dims = 2,\n                                                in_channels=3,\n                                                out_channels=1,\n                                                input_image_size=(256,256),\n                                                upsample_mode = \"deconv\")\n        #self.cnn_model.decoder.blocks.append(self.cnn_model.decoder.blocks[-1])\n        #self.cnn_model.decoder.blocks[-2] = self.cnn_model.decoder.blocks[-3]\n    \n    def forward(self, imgs):\n        img_segs = self.cnn_model(imgs)\n        return img_segs","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:28:16.007216Z","iopub.execute_input":"2022-12-02T10:28:16.007935Z","iopub.status.idle":"2022-12-02T10:28:16.014472Z","shell.execute_reply.started":"2022-12-02T10:28:16.007897Z","shell.execute_reply":"2022-12-02T10:28:16.013286Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def HuBMAPLoss(images, targets, model, device):\n    model.to(device)\n    images = images.to(device)\n    targets = targets.to(device)\n    outputs, aux = model(images)\n    criterion = DiceBCELoss()\n    loss = criterion(outputs, targets)\n    return loss, outputs","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:28:16.458113Z","iopub.execute_input":"2022-12-02T10:28:16.458685Z","iopub.status.idle":"2022-12-02T10:28:16.465353Z","shell.execute_reply.started":"2022-12-02T10:28:16.458651Z","shell.execute_reply":"2022-12-02T10:28:16.464161Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def train_one_epoch(epoch, model, device, optimizer, scheduler, trainloader):\n    model.train()\n    t = time.time()\n    total_loss = 0\n    for step, (images, targets) in enumerate(trainloader):\n        loss, outputs = HuBMAPLoss(images, targets, model, device)\n        loss.backward()\n        if ((step+1)%4==0 or (step+1)==len(trainloader)):\n            optimizer.step()\n            scheduler.step()\n            optimizer.zero_grad()\n        loss = loss.detach().item()\n        total_loss += loss\n        if ((step+1)%10==0 or (step+1)==len(trainloader)):\n            print(\n                    f'epoch {epoch} train step {step+1}/{len(trainloader)}, ' + \\\n                    f'loss: {total_loss/len(trainloader):.4f}, ' + \\\n                    f'time: {(time.time() - t):.4f}', end= '\\r' if (step + 1) != len(trainloader) else '\\n'\n                )\ndef valid_one_epoch(epoch, model, device, optimizer, scheduler, validloader):\n    model.eval()\n    t = time.time()\n    total_loss = 0\n    for step, (images, targets) in enumerate(validloader):\n        loss, outputs = HuBMAPLoss(images, targets, model, device)\n        loss = loss.detach().item()\n        total_loss += loss\n        if ((step+1)%4==0 or (step+1)==len(validloader)):\n            scheduler.step(total_loss/len(validloader))\n        if ((step+1)%10==0 or (step+1)==len(validloader)):\n            print(\n                    f'epoch {epoch} valid step {step+1}/{len(validloader)}, ' + \\\n                    f'loss: {total_loss/len(validloader):.4f}, ' + \\\n                    f'time: {(time.time() - t):.4f}', end= '\\r' if (step + 1) != len(validloader) else '\\n'\n                )","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:28:16.876802Z","iopub.execute_input":"2022-12-02T10:28:16.877188Z","iopub.status.idle":"2022-12-02T10:28:16.888798Z","shell.execute_reply.started":"2022-12-02T10:28:16.877153Z","shell.execute_reply":"2022-12-02T10:28:16.887682Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import GroupKFold\nFOLDS = 2\ngkf = GroupKFold(FOLDS)\ndir_df['Folds'] = 0\nfor fold, (tr_idx, val_idx) in enumerate(gkf.split(dir_df, groups=dir_df[dir_df.columns[0]].values)):\n    dir_df.loc[val_idx, 'Folds'] = fold","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:28:17.218316Z","iopub.execute_input":"2022-12-02T10:28:17.219429Z","iopub.status.idle":"2022-12-02T10:28:17.272735Z","shell.execute_reply.started":"2022-12-02T10:28:17.219388Z","shell.execute_reply":"2022-12-02T10:28:17.271776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch.optim import Adam\nimport time \n\nfor fold, (tr_idx, val_idx) in enumerate(gkf.split(dir_df, groups=dir_df[dir_df.columns[0]].values)):\n    if fold>1:\n        break\n    trainloader, validloader = prepare_train_valid_dataloader(dir_df, [fold])\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    model = HuBMAP().to(device)\n    optimizer = Adam(model.parameters(), lr=4e-5)\n    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, gamma=0.1, step_size=30)\n#     num_epochs = 15\n    num_epochs = 2\n    for epoch in range(num_epochs):\n        train_one_epoch(epoch, model, device, optimizer, scheduler, trainloader)\n        with torch.no_grad():\n            valid_one_epoch(epoch, model, device, optimizer, scheduler, validloader)\n    torch.save(model.state_dict(),f'FOLD-{fold}-model.pth')","metadata":{"execution":{"iopub.status.busy":"2022-12-02T10:28:31.298614Z","iopub.execute_input":"2022-12-02T10:28:31.299051Z","iopub.status.idle":"2022-12-02T10:35:41.104767Z","shell.execute_reply.started":"2022-12-02T10:28:31.299017Z","shell.execute_reply":"2022-12-02T10:35:41.103307Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"raw","source":"","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}