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"}}},{"cell_type":"markdown","source":"> I have been working on this solution since the very beginning of the competition and until now. I methodically tested ideas and moved forward very slowly because this is painstaking work, and I also have cerebral palsy and it’s physically difficult for me to write any text.\n> \n> This is not the final version of the code, I don't even know if it fully works (this is one of hundreds of experiments), but I have to publish it because This morning V. Putin attacked my country and I don't know if I can continue my work. Please, treat with understanding my work, translation inaccuracies and errors in the code. Also, I will be grateful if someone trains the model (at least 50 epochs) and add images with network operation in the comments.\n\n\nSome reports: https://wandb.ai/green_wizard/Sartorius%20Segmentation","metadata":{}},{"cell_type":"markdown","source":"This work has achieved less than 0.15 LB, but here's why I find it useful:\n\n- Detectron2, Mask RCNN, Yolo, etc. are not used. MobileNetV2 is used as a backbone, so as not to waste extra time for pretraining, but this is not even a segmentation model in its original form.\n\n- simple segmentation is used. This, of course, is incorrect, but I am not yet experienced enough to implement something original for instance segmentation.\n\n- the model has less than 1M parameters.\n\n- the work is more of a research nature. Of course, I would like to get into the top 100, but very often I chose a more interesting path, rather than an effective one.\n\nThe main disadvantages of my solution, in my opinion:\n\n- It is very difficult for me to express my thoughts in English. Almost everything was translated by Google and rechecked many times, but the text definitely contains typos, incorrectly constructed phrases, etc.\n\n- I'm not good enough at explaining ideas scientifically, with formulas, referring to articles, etc.\n\n- many ideas remained just ideas, many implementation details remained just a good guess. It is important for me to understand why this is so and not otherwise, although I understand that the lack of resources forces me to simply believe that only such a combination works.\n\n\n---\n\n\n\n---\n\n","metadata":{"id":"0wkmgR3C84at"}},{"cell_type":"markdown","source":"From the very beginning of the competition, I decided NOT to use \"normal\" approaches. For example, Mask R-CNN can solve the instance segmentation problem well, but this is very boring and, in my opinion, will not give a deep understanding of anything other than fine-tuning the Mask R-CNN. If you prefer standard and understandable solutions, then you better not read further :)\n\nI will try to explain the main points of my code, the reasons for certain decisions. However, I cannot explain everything in too much detail, so feel free to leave your questions in the comments.\n\nAnd so, let's start with a typical import of all the necessary libraries.","metadata":{"id":"ILP_P-kIBfxc"}},{"cell_type":"code","source":"import os, time\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'\nos.environ['TF_XLA_FLAGS'] = '--tf_xla_auto_jit=2,--xla_gpu_strict_conv_algorithm_picker=false'\nos.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'\nos.environ['TF_GPU_THREAD_COUNT'] = '1'\n\nIS_COLAB = 'COLAB_GPU' in os.environ\nDATASET_PATH = '.' if IS_COLAB else '..'\n\nimport cv2\n\nfrom tensorflow import keras\nimport tensorflow as tf\nimport tensorflow_addons as tfa\nfrom keras import backend as K\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport math, random\nimport albumentations as A\nimport scipy\nimport skimage\n\nif IS_COLAB:\n    from google.colab.patches import cv2_imshow\nelse:\n    def cv2_imshow(img):\n        plt.figure(figsize=(10,10))\n        plt.imshow(img / 255.0)\n        plt.axis('off')\n        plt.show()\n        return\n\nTRAIN_MODEL = not True\nUSE_WANDB = IS_COLAB # only in colab?","metadata":{"id":"jwstCanU8NGy"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are many high-quality EDAs, they describe in detail the nuances of the dataset, analyze each type of cells, and so on, but here's what I think:\n\n- the competition does not require determining the type of cells. Each sample contains only one cell type, so either the training will be unstable or you will need to be very careful in choosing the way to synthesize the combined examples. I do not see reasons for teaching to recognize the type of cells.\n\n- a markup of cell masks is very imprecise and sometimes contains errors.\n\nTaking these into account, I decided to proceed as follows:\n\n- create own classes: core, body and cell boundaries. I just used cv2.distanceTransform for this.\n\n- depending on the distance to the borders of the cells the probability of errors should also change. The inner part of the cells almost always contains the body of the cells, but the boundaries may contain errors. So I decided to generate a pixel-wise weight mask. However, later I found a more interesting approach :)\n\nAlso, from the code below, you can see that my model works with 192 * 192 areas and additionally receives 4 pixels to determine the context. In addition, the model predicts watershed energy, which makes it possible to locate cells more accurately.","metadata":{"id":"1h_EREEeIJc7"}},{"cell_type":"code","source":"train_df = pd.read_csv(os.path.join(DATASET_PATH, 'input/sartorius-cell-instance-segmentation/train.csv'))\n\nCELL_TYPE = train_df['cell_type'].unique().tolist()\nSEG_CLASSES = 1#len(CELL_TYPE)\n\nRAW_INSTANCES = 0\nRAW_OVERLAPS = 1 \n\nLAYER_CELL_CORE = 0\nLAYER_CELL_BODY = 1\nLAYER_CELL_BOUNDS = 2\nINNER_CLASSES = 2 # from 0 to INNER_CLASSES\nTOTAL_CLASSES = INNER_CLASSES + 1\nLAYER_WEIGHTS = TOTAL_CLASSES\nLAYER_WATERSHED = LAYER_WEIGHTS + 1\n\nINNER_CROP = 192\nOUTER_CROP = INNER_CROP + 8\nCROP_PADDING = (OUTER_CROP - INNER_CROP) // 2\n\nBORDER_WIDTH = 2.0\nMAX_INNER_WEIGHT = 5.0\nMIN_WEIGHT = 0.1\nMAX_WEIGHT = 200.0","metadata":{"id":"gw6nsfD7aOJI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"GLOBAL_CONFIG = {\n  \"border_width\": BORDER_WIDTH,\n  'inner_crop': INNER_CROP,\n  'outer_crop': OUTER_CROP,\n\n  'trainer_params': {\n      'microBatchSize': 4,\n      'useEstimatedErrors': True,\n      # can be float or object (i.e. {'min': 0.005, 'max': 0.03})\n      # if object then entropy depend on Estimated Errors (or equal to max)\n      'entropyTerm': {'min': 0.005, 'max': 0.03},\n\n      'regularization': {\n          'active': True,\n          'factor': 1e-4,\n      }\n  },\n\n  'training_params': {\n      \"batch_size\": 4,\n      'epochs': 250,\n      'max_time': -1,\n  },\n\n  'preprocessed channels': 16,\n  'mixer_params': {\n      'blocks': 3,\n      \n      'hiddenDim': None,\n      'hiddenLayers': 3,\n      'dropout': 0.1, 'finalDropout': 0.1,\n      'activation': 'gelu',\n      \n      'mixActivation': 'gelu',\n      'scaleInput': False,\n      'addToInput': True,\n\n      'innerLayersType': 'conv',\n      'convKernelSize': 3,\n  },\n  'global_mixer': True,\n  'mixer_N': 5,\n\n  'output_N': 16,\n  'output_N_last': 16,# TOTAL_CLASSES + 1,\n\n  'latent': {\n      'channels': 4,\n  },\n  'skip_connections': False,\n  'use EEB extra': True,\n  'use WEB extra': True,\n\n  'use_latent_scoring': True,\n  'latent_scoring': {\n      'layers_N': 3,\n      'replicas': 1,\n      'preprocess': ['linear', 'softmax'] # 'linear', 'softmax'\n  },\n}","metadata":{"id":"s5f8cAloRZKA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import hashlib, json\nGLOBAL_CONFIG['config_hash'] = hashlib.sha256(json.dumps(GLOBAL_CONFIG).encode('utf8', errors='ignore')).hexdigest()","metadata":{"id":"AnDNS2FIXcki"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Useful functions and data loaders.","metadata":{"id":"2eQ4EiA1cO9I"}},{"cell_type":"code","source":"def replace_with_dict(ar, dic):\n  # Extract out keys and values\n  k = np.array(list(dic.keys()))\n  v = np.array(list(dic.values()))\n\n  # Get argsort indices\n  sidx = k.argsort()\n\n  ks = k[sidx]\n  vs = v[sidx]\n  return vs[np.searchsorted(ks,ar)]\n\nSEG_COLORS = {\n  i: matplotlib.colors.to_rgb(x) for i, x in enumerate(matplotlib.colors.XKCD_COLORS)\n}\n\ndef labels2image(lbl):\n  fixed = 1 + ((lbl - 1) % (len(SEG_COLORS) - 1))\n  fixed[0==lbl] = 0\n  return replace_with_dict(fixed, SEG_COLORS)","metadata":{"id":"JsSGBEGU7LR1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def rle_decode(mask_rle, shape, color=1):\n    '''\n    mask_rle: run-length as string formated (start length)\n    shape: (height,width) of array to return \n    Returns numpy array, 1 - mask, 0 - background\n    '''\n    s = mask_rle.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    img = np.zeros((shape[0] * shape[1], shape[2]), dtype=np.uint8)\n    for lo, hi in zip(starts, ends):\n        img[lo : hi] = color\n    res = img.reshape(shape)\n    # res = scipy.ndimage.morphology.binary_fill_holes(res)\n    return res\n\ndef bboxBool(np_array):\n    result = np.where(np_array)\n    x1 = np.min(result[0])\n    x2 = np.max(result[0])\n    y1 = np.min(result[1])\n    y2 = np.max(result[1])\n    return x1, y1, x2, y2\n\ndef bbox(np_array):\n    return bboxBool(0 < np_array)\n\ndef build_masks(image_id, bigInstances=False):\n    input_shape = (520, 704)\n    height, width = input_shape\n\n    data = train_df[train_df[\"id\"] == image_id]\n    labels = data[\"annotation\"].tolist()\n    cells = data[\"cell_type\"].tolist()\n\n    mask = np.zeros((height, width, SEG_CLASSES), np.uint8)\n    overlapped = np.zeros((height, width, 1), np.uint8)\n    instances = np.zeros((height, width, 1), np.uint32 if bigInstances else np.uint8)\n    validID = set(range(1, 255))\n    instID = 1\n    for label, cell in zip(labels, cells):\n        ind = CELL_TYPE.index(cell) % SEG_CLASSES\n        cellMask = rle_decode(label, shape=(height, width, 1))\n        x1, y1, x2, y2 = cellBox = bbox(cellMask)\n        mask[x1:x2, y1:y2, ind] += cellMask[x1:x2, y1:y2, 0]\n        overlapped[x1:x2, y1:y2] += cellMask[x1:x2, y1:y2]\n        ######################################\n        # instances\n        x1, y1, x2, y2 = cellBox\n        d = OUTER_CROP\n        x1 = max((0, x1 - d))\n        y1 = max((0, y1 - d))\n        x2 += d\n        y2 += d\n        \n        if not bigInstances:\n            instID = min( list(validID - set(np.unique(instances[x1:x2, y1:y2]))) )\n\n        instances[x1:x2, y1:y2][np.where(0 < cellMask[x1:x2, y1:y2])] = instID\n        instID += 1\n        continue\n    \n    if bigInstances: return instances\n    # overlapped = np.where(overlapped <= 1, 0, 1)\n    \n    return np.dstack((\n        instances.astype(np.uint8), # RAW_INSTANCES\n        overlapped.astype(np.uint8), # RAW_OVERLAPS\n    ))","metadata":{"id":"zB2jn-fEb3r1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Additional mask, so that the network is more concentrated on areas close to the boundaries of the analyzed area.\ndef amplifyMask():\n  x_axis = np.linspace(-1, 1, INNER_CROP)\n  y_axis = np.linspace(-1, 1, INNER_CROP)\n  xx, yy = np.meshgrid(x_axis, y_axis)\n  arr = np.sqrt(xx ** 2 + yy ** 2)\n  arr /= arr.max()\n  return 1 + arr\n\n_ = plt.imshow(amplifyMask())","metadata":{"id":"0bfnptej7Itv","outputId":"83c988e4-fb7b-47c4-cbdd-e92231896d7a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def loadGrayscale(img_path):\n    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n    return img\n    \ndef supervisedSample(path, im_name):\n    img_path = f\"{path}/{im_name}.png\"\n    img = loadGrayscale(img_path)\n    mask = build_masks(im_name)\n    return img, mask\n\ndef unpackInstances(instances, onlyMain=False):\n    HW = instances.shape[0]\n    s = (HW, HW, 1)\n    # build weights map, classes, etc.\n    cores = np.zeros(s, np.float32)\n    borders = np.zeros(s, np.float32)\n    bodies = np.zeros(s, np.float32)\n    weights = np.zeros(s, np.float32)\n    touching = np.zeros(s, np.uint8)\n    EE = np.zeros(s, np.float32)\n        \n    for id in np.unique(instances):\n        if 0 == id: continue\n\n        cellArea = (id == instances)\n        x1, y1, x2, y2 = box = bboxBool(cellArea)\n        d = 2\n        x1 = max((0, x1 - d))\n        y1 = max((0, y1 - d))\n        x2 += d\n        y2 += d\n        \n        cellArea = cellArea[x1:x2, y1:y2]\n        cell = cellArea.astype(np.uint8)\n\n        dist = cv2.distanceTransform(cell, cv2.DIST_L2, 3, dstType=cv2.CV_32F)\n        maxD = dist.max()\n        assert 0 < maxD\n        cellCore = np.logical_and((maxD * (1.0 - 0.3)) < dist, cellArea)\n        innerBorders = np.logical_and(dist <= BORDER_WIDTH, cellArea)\n        # area between core and borders\n        cellBody = np.logical_and(np.logical_not(np.logical_or(cellCore, innerBorders)), cellArea)\n\n        cores[x1:x2, y1:y2][cellCore] = 1\n        bodies[x1:x2, y1:y2][cellBody] = 1\n        borders[x1:x2, y1:y2][innerBorders] = 1\n\n        dist = dist[..., None]\n        EE[x1:x2, y1:y2] = np.maximum(EE[x1:x2, y1:y2], dist / maxD)\n        if not onlyMain:\n            dist = np.clip(dist, 0.0, MAX_INNER_WEIGHT)\n            dist[innerBorders] = maxD\n\n            weights[x1:x2, y1:y2] = np.maximum(weights[x1:x2, y1:y2], dist)\n\n            outterD = cv2.distanceTransform(1 - cell, cv2.DIST_L2, 3, dstType=cv2.CV_32F)\n            msk = np.logical_and(outterD < BORDER_WIDTH, np.logical_not(cellArea))\n            touching[x1:x2, y1:y2][msk] += 1\n        continue\n\n    if not onlyMain:\n        binBg = np.where(0 < instances, 0, 1).astype(np.uint8)\n        bgDist = cv2.distanceTransform(binBg, cv2.DIST_L2, 3, dstType=cv2.CV_32F)[..., None]\n        bgDist = np.clip(bgDist, 0.0, BORDER_WIDTH*2) / BORDER_WIDTH\n        weights = np.maximum(weights, bgDist * MAX_WEIGHT)\n\n        fgDist = cv2.distanceTransform(1 - binBg, cv2.DIST_L2, 3, dstType=cv2.CV_32F)[..., None]\n        weights = weights * (1 + fgDist) # increase importance of inner areas\n        weights = np.clip(weights, MIN_WEIGHT, MAX_WEIGHT)\n        weights[1 < touching] = MAX_WEIGHT\n        \n        weights /= weights.max()\n        weights[1 < touching] = 1.0\n\n    EE = np.power(1.0 - EE, 2)\n    return np.dstack([cores, bodies, borders, weights, EE])\n\ndef unpackSupervisedMasks(masks, amplifyMask=1.0):\n    instances = masks[..., RAW_INSTANCES]\n    # pack instances id's\n    allID = list(sorted(np.unique(instances)))\n    if not(0 in allID): # if cell bigger than crop\n        allID.insert(0, 0)\n    instances = replace_with_dict(instances, {v: i for i, v in enumerate(allID)})\n\n    res = unpackInstances(instances)\n    # center crop\n    c = OUTER_CROP // 2\n    d = INNER_CROP // 2\n    res = res[c-d:c+d, c-d:c+d]\n\n    res[..., LAYER_WEIGHTS] *= amplifyMask\n    return res","metadata":{"id":"G358V096XGJj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class CTrainDataloader(tf.keras.utils.Sequence):\n    def __init__(self, IDs,\n        path='input/sartorius-cell-instance-segmentation/train',\n        batch_size=32,\n        cropsPerSample=1,\n        transform=lambda *x: x,\n        minLabeledArea=0.1,\n    ):\n        self.minLabeledArea = minLabeledArea\n        self.transform = transform\n        self.batch_size = batch_size\n        self.amplifyMask = amplifyMask()\n        \n        path = os.path.join(DATASET_PATH, path)\n        \n        N = len(IDs)\n        X = []\n        Y = []\n        for im_name in IDs:\n          img, mask = supervisedSample(path, im_name)\n          X.append(img[..., None])\n          Y.append(mask)\n          continue\n          \n        self._X = np.array(X, np.uint8)\n        self._Y = np.array(Y, np.uint8)\n        self._indexes = np.arange(math.ceil(N * cropsPerSample / batch_size) * batch_size) % N\n        self.on_epoch_end()\n        return\n\n    def __len__(self):\n        return len(self._indexes) // self.batch_size\n        \n    def __getitem__(self, index):\n        indexes = self._indexes[index*self.batch_size:(index+1)*self.batch_size]\n\n        rawX = self._X[indexes]\n        rawY = self._Y[indexes]\n        HW = np.array(rawY.shape[:2])\n\n        X = []\n        Y = []\n        for i, (sampleX, sampleY) in enumerate(zip(rawX, rawY)):\n          masks = x = None\n          for _ in range(25):\n            x, y = self.transform(sampleX, sampleY)\n            area = (0 < y[..., RAW_INSTANCES]).sum() / np.prod(y.shape[:2])\n            if self.minLabeledArea < area: break\n            continue\n\n          X.append(x)\n          Y.append(unpackSupervisedMasks(y, self.amplifyMask))\n          continue\n\n        return (\n            # np.array(X, np.float32) / 255.0,\n            np.array(X, np.uint8), # save some memory/time by deriving conversion to trainer\n            np.array(Y, np.float32)\n        )\n        \n    def on_epoch_end(self):\n        'Updates indexes after each epoch'\n        np.random.shuffle(self._indexes)\n        return","metadata":{"id":"9LLty_qidwjN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def withAugmentations(augm):\n    def applyAugm(image, mask):\n        tranformed = augm(image=image , mask=mask)\n        tranformed_image = tranformed['image']\n        tranformed_mask = tranformed['mask']\n        return tranformed_image, tranformed_mask\n    return applyAugm","metadata":{"id":"O9rknKqF0Riy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ids = random.choices(train_df['id'].unique().tolist(), k=1)\nids = random.choices(train_df['id'].unique().tolist()[:1], k=1)\ntestGen = CTrainDataloader(\n    ids, cropsPerSample=1,\n    transform=withAugmentations(\n        A.Compose([\n            A.RandomCrop(OUTER_CROP, OUTER_CROP, always_apply=True),\n            A.MultiplicativeNoise(multiplier=(0.9, 1.1)),\n            A.RandomBrightnessContrast(brightness_limit=0.05, contrast_limit=0.05),\n        ])\n    ),\n    batch_size=1\n)\n\ntmp = None\nX, Y = testGen[0]\nprint(X.shape, Y.shape)\nfor x, y in zip(X, Y):\n    fig, axs = plt.subplots(1, 4, figsize=(6 * 4, 5))\n    axs[0].imshow(cv2.cvtColor(x[..., 0], cv2.COLOR_GRAY2BGR))\n    axs[0].set_title('image')\n    axs[0].axis('off')\n    \n    cellMask = y[..., :TOTAL_CLASSES]\n    c = cellMask.argmax(-1) + np.where(0 < cellMask.max(-1), 1, 0)\n    axs[1].imshow(labels2image(c))\n    axs[1].set_title('classes')\n    axs[1].axis('off')\n    \n    # print(y[..., LAYER_WEIGHTS].min(), y[..., LAYER_WEIGHTS].max())\n    axs[2].imshow(y[..., LAYER_WEIGHTS]/y[..., LAYER_WEIGHTS].max())\n    axs[2].set_title('weights')\n    axs[2].axis('off')\n    \n    axs[3].imshow(y[..., LAYER_WATERSHED])\n    axs[3].set_title('Watershed')\n    axs[3].axis('off')\n    # print(np.unique(y[..., :-1].astype(np.int)))\n    # break\n    ","metadata":{"id":"wz0UpVVOm6Hd","outputId":"9ce97efc-fbd2-4036-9bbe-2b35a7474a1e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def cropsFor(W, H, innerCrop=INNER_CROP):\n  padding = (OUTER_CROP - innerCrop) // 2\n  res = []\n  for x in range(padding, W, innerCrop):\n    for y in range(padding, H, innerCrop):\n      x = min((x, W - innerCrop - padding))\n      y = min((y, H - innerCrop - padding))\n      res.append((x, y))\n\n  return sorted(set(res))","metadata":{"id":"6B_tH_gPWXV3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class CTestDataloader(tf.keras.utils.Sequence):\n    def __init__(self, IDs,\n        path='input/sartorius-cell-instance-segmentation/train',\n        batch_size=32,\n    ):\n        self.batch_size = batch_size\n        \n        path = os.path.join(DATASET_PATH, path)\n        X = []\n        Y = []\n        for im_name in IDs:\n            img, mask = supervisedSample(path, im_name)\n            paddedImage = cv2.copyMakeBorder(img, CROP_PADDING, CROP_PADDING, CROP_PADDING, CROP_PADDING, cv2.BORDER_CONSTANT, 0)\n            paddedMask = cv2.copyMakeBorder(mask, CROP_PADDING, CROP_PADDING, CROP_PADDING, CROP_PADDING, cv2.BORDER_CONSTANT, 0)\n            X.append(paddedImage[..., None])\n            Y.append(paddedMask)\n            continue\n          \n        self._X = np.array(X, np.uint8)\n        self._Y = np.array(Y, np.uint8)\n\n        pW, pH = self._X[0].shape[:2]\n        self._crops = cropsFor(pH, pW)\n        N = len(self._X) * len(self._crops)\n        self._indexes = np.arange(math.ceil(N / batch_size) * batch_size) % N\n        return\n\n    def __len__(self):\n        return len(self._indexes) // self.batch_size\n        \n    def __getitem__(self, index):\n        indexes = self._indexes[index*self.batch_size:(index+1)*self.batch_size]\n        \n        X = []\n        Y = []\n        for sample_crop in indexes:\n            sampleID = sample_crop // len(self._crops)\n            rawX = self._X[sampleID]\n            rawY = self._Y[sampleID]\n\n            cropID = sample_crop % len(self._crops)\n            (y, x) = self._crops[cropID]\n\n            cropX = rawX[x-CROP_PADDING:x+INNER_CROP+CROP_PADDING, y-CROP_PADDING:y+INNER_CROP+CROP_PADDING]\n            cropY = rawY[x-CROP_PADDING:x+INNER_CROP+CROP_PADDING, y-CROP_PADDING:y+INNER_CROP+CROP_PADDING]\n            X.append(cropX)\n            Y.append(unpackSupervisedMasks(cropY))\n            continue\n\n        return (\n            np.array(X, np.float32) / 255.0,\n            np.array(Y, np.float32)\n        )","metadata":{"id":"Ei2MdKzEWcoS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ids = random.choices(train_df['id'].unique().tolist(), k=1)\ntestGen = CTestDataloader(ids)\n\ntmp = None\nX, Y = testGen[0]\nprint(X.shape, Y.shape)\nfor x, y in zip(X, Y):\n    tmp = x\n    fig, axs = plt.subplots(1, 4, figsize=(6 * 4, 5))\n    axs[0].imshow(cv2.cvtColor(x[..., 0], cv2.COLOR_GRAY2BGR))\n    axs[0].set_title('image')\n    axs[0].axis('off')\n    \n    cellMask = y[..., :TOTAL_CLASSES]\n    c = cellMask.argmax(-1) + np.where(0 < cellMask.max(-1), 1, 0)\n    axs[1].imshow(labels2image(c))\n    axs[1].set_title('classes')\n    axs[1].axis('off')\n    \n    axs[2].imshow(y[..., LAYER_WEIGHTS]/y[..., LAYER_WEIGHTS].max())\n    axs[2].set_title('weights')\n    axs[2].axis('off')\n    \n    axs[3].imshow(y[..., LAYER_WATERSHED])\n    axs[3].set_title('Watershed')\n    axs[3].axis('off')\n    # print(np.unique(y[..., :-1].astype(np.int)))\n    break\n    ","metadata":{"id":"y2_qvYeThCEN","outputId":"e37f87c0-32eb-4f92-fa3c-6f864563776e"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model and trainer","metadata":{"id":"3MyS58gDgtg_"}},{"cell_type":"markdown","source":"Some basic functions:","metadata":{"id":"gP41wwiDjyWY"}},{"cell_type":"code","source":"@tf.function(jit_compile=True)\ndef channelwise(conv):\n    return tf.keras.backend.permute_dimensions(conv, (0, 3, 1, 2))\n\ndef focalLoss2d(alpha=2.0, beta=4.0):\n    def focal_loss(hm_true, hm_pred, W=1.0):\n        axis = [1, 2]\n\n        eps = K.epsilon()\n        pos_mask = tf.cast(tf.equal(hm_true, 1.0), dtype=tf.float32)\n        neg_mask = tf.cast(tf.less(hm_true, 1.0), dtype=tf.float32)\n        neg_weights = tf.pow(1.0 - hm_true, beta)\n\n        pos_loss = (\n          -tf.math.log(tf.clip_by_value(hm_pred, eps, 1.0 - eps))\n          * tf.math.pow(1.0 + eps - hm_pred, alpha)\n          * pos_mask\n          * W\n        )\n        neg_loss = (\n            -tf.math.log(tf.clip_by_value(1.0 - hm_pred, eps, 1.0 - eps))\n            * tf.math.pow(hm_pred + eps, alpha)\n            * neg_weights\n            * neg_mask\n            * W\n        )\n        tf.assert_equal(tf.shape(pos_loss), tf.shape(hm_true))\n        tf.assert_equal(tf.shape(neg_loss), tf.shape(hm_true))\n\n        num_pos = tf.reduce_sum(pos_mask, axis=axis)\n        pos_loss = tf.reduce_sum(pos_loss, axis=axis)\n        neg_loss = tf.reduce_sum(neg_loss, axis=axis)\n\n        lossNonZero = tf.math.divide_no_nan(pos_loss + neg_loss, num_pos)\n        return tf.where(0 < num_pos, lossNonZero, neg_loss)\n    return focal_loss\n    \n@tf.function(jit_compile=True)\ndef iou_coef(y_true, y_pred, smooth=1, axis=[1, 2, 3]):\n    intersection = K.sum(K.abs(y_true * y_pred), axis=axis)\n    union = K.sum(y_true, axis) + K.sum(y_pred, axis) - intersection\n    iou = K.mean((intersection + smooth) / (union + smooth), axis=0)\n    return iou\n\n@tf.function(jit_compile=True)\ndef JensenShannonDivergence(p, q):\n    m = (p + q) / 2.0\n    return (tf.losses.kl_divergence(p, m) + tf.losses.kl_divergence(q, m)) / 2.0\n\n@tf.function(jit_compile=True)\ndef diceLoss(y_true, y_pred):\n    axes = (1, 2) # [batch dim] + [1, 2] + [classes]\n    numerator = 2. * K.sum(y_pred * y_true, axes)\n    denominator = K.sum(K.square(y_pred) + K.square(y_true), axes)\n    dice = numerator / (denominator + K.epsilon())\n    return 1.0 - dice\n\ndef createTiles(tile_size):\n    def _tile(images):\n        patches = tf.image.extract_patches(\n            images=images,\n            sizes=[1, tile_size, tile_size, 1],\n            strides=[1, tile_size, tile_size, 1],\n            rates=[1, 1, 1, 1],\n            padding=\"VALID\",\n        )\n        patches = tf.reshape(patches, [tf.shape(images)[0], -1, tile_size, tile_size])\n        return tf.transpose(patches, (0, 2, 3, 1))\n      \n    def apply(images):\n        imagesShape = images.get_shape().as_list()\n        batch_size = tf.shape(images)[0]\n        H, W, channels = imagesShape[1:]\n        N = (H // tile_size) ** 2\n\n        inner = _tile(images[:, :-1, :-1, :])\n        bottom = _tile(images[:, :-1, -tile_size:])\n        right = _tile(images[:, -tile_size:, :-1])\n        corner = images[:, -tile_size:, -tile_size:, :]\n        patches = tf.concat([inner, bottom, right, corner], axis=-1)\n        return tf.reshape(patches, [tf.shape(images)[0], tile_size, tile_size, channels * (math.ceil(H / tile_size) * math.ceil(W / tile_size))])\n    return apply\n\n@tf.function(jit_compile=True)\ndef normalizeTensor(x, axis):\n    mean = tf.reduce_mean(x, axis=axis, keepdims=True)\n    std = tf.math.reduce_std(x, axis=axis, keepdims=True)\n    return tf.math.divide_no_nan(x - mean, std)\n    \n@tf.function(jit_compile=True)\ndef normalizeTensorRange(x, axis):\n    Max = tf.reduce_max(x, axis=axis, keepdims=True)\n    Min = tf.reduce_min(x, axis=axis, keepdims=True)\n    return tf.clip_by_value(\n        tf.math.divide_no_nan(x - Min, Max - Min),\n        clip_value_min=0.0, clip_value_max=1.0\n    )\n    \n@tf.function(jit_compile=True)\ndef topKMaskChannelwise(x, k):\n    values, _ = tf.nn.top_k(\n        tf.reshape( tf.transpose(x, (0, 3, 1, 2)), (tf.shape(x)[0], tf.shape(x)[-1], -1)),\n        k=k\n    ) # B x N x k\n    values = values[..., -1][:, None, None, :] # B x N x k => B x 1 x 1 x N\n    return tf.cast(values <= x, tf.float32)\n    \n@tf.function(jit_compile=True)\ndef normAttention(x):\n    x = normalizeTensor(x, axis=(1, 2))\n    l = tf.where(x < 0.0, -x, 0.0)\n    h = tf.where(x > 0.0, x, 0.0)\n    return tf.clip_by_value(\n        normalizeTensorRange(l, axis=(1, 2)) + normalizeTensorRange(h, axis=(1, 2)),\n        clip_value_min=0.0, clip_value_max=1.0\n    )\n    \ndef reduceMinMax(tensors):\n    maxV = minV = tensors[0]\n    for x in tensors[1:]:\n        maxV = tf.maximum(x, maxV)\n        minV = tf.minimum(x, minV)\n        continue\n\n    return [minV, maxV]\n\nclass FakeObject(object):\n    def __init__(self, data):\n        for name, value in data.items():\n            setattr(self, name.replace(' ', '_'), value)\n            continue\n        return\n\ndef SG(x):\n    return tf.keras.layers.Lambda(tf.stop_gradient)(x)\n\ndef flatTensor(x):\n  return tf.reshape(x, (tf.shape(x)[0], -1))","metadata":{"id":"xhJjhCSbJqNn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def layerwiseSoftmax(x):\n    s = tf.shape(x)\n    x = tf.transpose(x, (0, 3, 1, 2))\n    x = tf.reshape(x, (s[0], s[3], -1))\n    x = tf.nn.softmax(x, axis=-1)\n    x = tf.reshape(x, (s[0], s[3], s[1], s[2]))\n    x = tf.transpose(x, (0, 2, 3, 1))\n    x = tf.clip_by_value(x, 0.0, 1.0) # prevent NaN, when all values in layer is same/zero\n    return x\n\nclass LayerwiseSoftmaxLayer(tf.keras.layers.Layer):\n    def call(self, data):\n        return layerwiseSoftmax(data)","metadata":{"id":"RGUL5aB1ORkI"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This layer provides additional information to the network. The network, of course, can learn these transformations, but this will require additional \"capacity\" of the network (in the form of weights and/or layers), therefore, it is better to initially provide the data in a form that the network may find useful.","metadata":{"id":"WVlLBwMglFl_"}},{"cell_type":"code","source":"class PrepareImagesLayer(tf.keras.layers.Layer):\n    def __init__(self, channels=1, activation='tanh', F=None):\n        super().__init__()\n        self._compress = tf.keras.layers.Dense(channels, activation=activation)\n        self._F = F if not(F is None) else lambda x: x\n        return\n\n    def _process(self, images):\n        res = [images]\n        res.append(tf.image.sobel_edges(images)[..., 0, :])\n        for factor in [0.1, 0.5, 2.0, 5.0, 20.0]:\n            img = tf.image.adjust_contrast(images, factor)\n            res.append(img)\n            res.append(tf.image.sobel_edges(img)[..., 0, :])\n            continue\n\n        res = tf.concat(res, axis=-1)\n        res = tf.stop_gradient(res)\n        return self._compress(res)\n\n    def call(self, images):\n        images = self._F(images)\n        return self._process(images)","metadata":{"id":"WVDzI6CRHgOD"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This is probably the strangest part of my code. I was thinking about using MLP-Mixer, but I quickly realized that this is not what I want. MLP-Mixer allows very complex data manipulations, it can have a huge perception field. However, MLP-Mixer only changes the input based on the same data, but I wanted to change it based on \"external\" data as well.\n\nThis block turned out to be so complex, since each of its variants has a very significant effect not only on the final accuracy of the model, but also on the properties of the features. ","metadata":{"id":"OKEqwwngoSeX"}},{"cell_type":"code","source":"@tf.function(jit_compile=True)\ndef relativeSoftmax(x):\n    x = tf.nn.softmax(x, axis=-1)\n    return x - tf.reduce_mean(x, axis=-1, keepdims=True)\n\nclass StrangeMixerLayer(tf.keras.layers.Layer):\n    def __init__(self,\n                 blocks=1,\n                 hiddenDim=None,\n                 hiddenLayers=0,\n                 dropout=0.05, finalDropout=0.05,\n                 activation='gelu',\n                 mixActivation='gelu',\n                 innerLayersType='dense', convKernelSize=1,\n                 scaleInput=False,\n                 addToInput=True\n    ):\n        super().__init__()\n\n        self._dropout = tf.keras.layers.Dropout(dropout, name='dropoutA') if 0.0 < dropout else lambda x: x\n        self._finalDropout = tf.keras.layers.Dropout(finalDropout, name='dropoutB') if 0.0 < finalDropout else lambda x: x\n        self._activation = activation\n        self._hidden = hiddenDim\n        self._blocks = blocks\n        self._scaleInput = scaleInput\n        self._addToInput = addToInput\n        self._hiddenLayers = hiddenLayers\n\n        if 'relativeSoftmax' == mixActivation: # small hack\n            mixActivation = relativeSoftmax\n        self._mixActivation = mixActivation\n\n        self._innerLayersType = innerLayersType.lower()\n        assert self._innerLayersType in ['dense', 'conv']\n        self._convKernelSize = convKernelSize\n        return\n\n    def build(self, input_shape):\n        *_, H, W, N = input_shape\n\n        LKind = None\n        if 'conv' == self._innerLayersType:\n            args = {'activation': self._activation, 'padding': 'same', 'kernel_size': self._convKernelSize}\n            LKind = tf.keras.layers.Conv2D\n\n        if 'dense' == self._innerLayersType:\n            args = {'activation': self._activation}\n            LKind = tf.keras.layers.Dense\n        #####################\n        hidden = self._hidden if not (self._hidden is None) else H\n        self._mix1L = tf.keras.Sequential([\n            # LayerwiseSoftmaxLayer(name='%s/mix1-norm' % self.name),\n            *[LKind(hidden, **args, name='%s/mix1-L%d' % (self.name, ind)) for ind in range(self._hiddenLayers)],\n            LKind(H, **args, name='%s/mix1-last' % self.name)\n        ], name='%s/mix1' % self.name)\n\n        hidden = self._hidden if not (self._hidden is None) else W\n        self._mix2L = tf.keras.Sequential([\n            # LayerwiseSoftmaxLayer(name='%s/mix2-norm' % self.name),\n            *[LKind(hidden, **args, name='%s/mix2-L%d' % (self.name, ind)) for ind in range(self._hiddenLayers)],\n            LKind(W, **args, name='%s/mix2-last' % self.name)\n        ], name='%s/mix2' % self.name)\n\n        hidden = self._hidden if not (self._hidden is None) else N\n        self._mix3 = tf.keras.Sequential([\n            # LayerwiseSoftmaxLayer(name='%s/mix3-norm' % self.name),\n            *[LKind(hidden, **args, name='%s/mix3-L%d' % (self.name, ind)) for ind in range(self._hiddenLayers)],\n            LKind(N, **args, name='%s/mix3-last' % self.name)\n        ], name='%s/mix3' % self.name)\n        \n        self._mix1A = LKind(N, **args, name='%s/mix1-after' % self.name)\n        self._mix2A = LKind(N, **args, name='%s/mix2-after' % self.name)\n\n        args['activation'] = self._mixActivation\n        self._mixF = tf.keras.Sequential([\n            # LayerwiseSoftmaxLayer(name='%s/mixFinal-norm' % self.name),\n            LKind(N, **args, name='%s/mixFinal-last' % self.name)\n        ], name='%s/mixFinal' % self.name)\n        return\n\n    def _mix1(self, x):\n        N = len(tf.shape(x))\n        tail = list(range(N - 3))\n        transposeForward = tail + [N - 1, N - 2, N - 3] # ....HWC => ....CWH\n        transposeBackward = tail + [N - 1, N - 2, N - 3] # ....CWH => ....HWC\n\n        x = tf.transpose(x, transposeForward)\n        x = self._mix1L(x)\n        x = tf.transpose(x, transposeBackward)\n        return self._mix1A(x)\n\n    def _mix2(self, x):\n        N = len(tf.shape(x))\n        tail = list(range(N - 3))\n        transposeForward = tail + [N - 1, N - 3, N - 2] # ....HWC => ....CHW\n        transposeBackward = tail + [N - 2, N - 1, N - 3] # ....CHW => ....HWC\n\n        x = tf.transpose(x, transposeForward)\n        x = self._mix2L(x)\n        x = tf.transpose(x, transposeBackward)\n        return self._mix2A(x)\n\n    def _finalMix(self, A, B, C, mixes):\n        allMix = tf.concat([*mixes], -1)\n        allMix = self._finalDropout(allMix)\n        return self._mixF(allMix)\n        \n    def _apply(self, A, B):\n        C = tf.concat([A, B], axis=-1)\n        mixers = [self._mix1, self._mix2, self._mix3]\n        mixes = [f(self._dropout(C)) for f in mixers]\n        return self._finalMix(A, B, C, mixes)\n\n    @tf.function(jit_compile=True)\n    def call(self, *inputs):\n        A, *other = inputs\n        B = tf.concat(other, axis=-1) if 1 < len(other) else other[0]\n        \n        for i in range(self._blocks):\n            C = self._apply(A, B)\n            if self._scaleInput:\n                C = A * C\n            if self._addToInput:\n                C = A + C\n            A = C\n            continue\n        return A","metadata":{"id":"OWLUr0mlBBaC"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"I use MobileNetV2 as backbone for feature extraction, enriching them with additional information. I do not use the smallest features (block_15_project), since it only increases the final size of the model, slows down the training, but not much improves accuracy.","metadata":{"id":"ZrlfNOAhYTqK"}},{"cell_type":"code","source":"def _featuresProvider(shape):\n    assert np.array_equal(shape, (192, 192, 3))\n    base_model = None\n    if IS_COLAB:\n        base_model = tf.keras.applications.MobileNetV2(input_shape=shape, include_top=False)\n        base_model.save_weights('backbone.h5')\n    else: # kaggle\n        base_model = tf.keras.applications.MobileNetV2(input_shape=shape, include_top=False, weights=None)\n        base_model.load_weights('../input/backbone/backbone.h5')\n    # Use the activations of these layers\n    layer_names = [\n      'block_1_expand_relu',\n      'block_3_expand_relu',\n      'block_5_project',\n      'block_8_project',\n      # 'block_15_project',\n    ]\n    layers = [base_model.get_layer(name).output for name in layer_names]\n    \n    # Create the feature extraction model\n    model = tf.keras.Model(inputs=base_model.input, outputs=layers)\n    model.trainable = False\n    return model\n\n# from tensorflow_examples.models.pix2pix import pix2pix\ndef upsample(filters, size, norm_type='batchnorm', apply_dropout=False):\n    \"\"\"Upsamples an input.\n\n    Conv2DTranspose => Batchnorm => Dropout => Relu\n\n    Args:\n      filters: number of filters\n      size: filter size\n      norm_type: Normalization type; either 'batchnorm' or 'instancenorm'.\n      apply_dropout: If True, adds the dropout layer\n\n    Returns:\n      Upsample Sequential Model\n    \"\"\"\n\n    initializer = tf.random_normal_initializer(0., 0.02)\n\n    result = tf.keras.Sequential()\n    result.add(\n        tf.keras.layers.Conv2DTranspose(filters, size, strides=2,\n                                        padding='same',\n                                        kernel_initializer=initializer,\n                                        use_bias=False))\n\n    if norm_type.lower() == 'batchnorm':\n        result.add(tf.keras.layers.BatchNormalization())\n    elif norm_type.lower() == 'instancenorm':\n        result.add(InstanceNormalization())\n\n    if apply_dropout:\n        result.add(tf.keras.layers.Dropout(0.5)),\n\n    result.add(tf.keras.layers.ReLU())\n    return result\n\ndef PreprocessImageBlock(outputN, extraN, hidden=None):\n    hidden = hidden if not(hidden is None) else outputN\n    images = tf.keras.layers.Input(shape=(INNER_CROP, INNER_CROP, 1))\n    extra = tf.keras.layers.Input(shape=(OUTER_CROP, OUTER_CROP, extraN))\n    \n    featuresNet = _featuresProvider(shape=(INNER_CROP, INNER_CROP, 3))\n    rgb = tf.image.grayscale_to_rgb(images)\n    features = featuresNet((2.0 * rgb) - 1.0)[::-1]\n\n    res = None\n    heatmaps = []\n    for FV in features:\n      resized = tf.keras.layers.Lambda(createTiles(FV.shape[1]))(extra)\n\n      parts = [resized, FV]\n      if not(res is None):\n        parts.append(res)\n      \n      concatenated = res = tf.keras.layers.Concatenate(axis=-1)(parts)\n      heatmaps.append(tf.keras.layers.Conv2D(hidden, 3, padding='same', activation='relu')(\n          tf.keras.layers.Dropout(0.05)(res)\n      ))\n      res = upsample(hidden, 3, apply_dropout=True)(concatenated)\n      continue\n\n    def dummyConv(x):\n        for i in range(3):\n            x = tf.keras.layers.Conv2D(hidden, 3, padding='same', activation='relu')(x)\n        return x\n\n    heatmaps.append(dummyConv(res))\n    heatmaps.append(dummyConv(images))\n    heatmaps.append(dummyConv(\n        tf.keras.layers.Lambda(createTiles(INNER_CROP))(extra)\n    ))\n    combined = tf.keras.layers.Add()(\n        [tf.keras.layers.experimental.preprocessing.Resizing(INNER_CROP, INNER_CROP)(x) for x in heatmaps]\n    )\n    res = tf.keras.layers.Conv2D(outputN, 3, padding='same', activation='relu')(tf.keras.layers.Dropout(0.1)(combined))\n    res = tf.keras.layers.Conv2D(outputN, 1, padding='same', activation='relu')(res)\n    return keras.Model(inputs=[images, extra], outputs=[res])","metadata":{"id":"fTO3QSqvy4SP"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"`HeatmapGenerationBlock` had to be implemented as an autoencoder. You see, I wanted to do most of the work in latent space using the autoregressive approach and `StrangeMixerLayer`, but this requires a lot of memory. In addition, I used something like a latent discriminator and it is not known how it will behave if some of the information goes through skip-connections.\n\nI did several experiments without a discriminator and with skip-connections (making them very noisy (95% dropout) so that most of the information goes through the latent space and `StrangeMixerLayer`). This increased learning speed and accuracy, but I'm more interested in learning something new.","metadata":{"id":"5zaqQmUS31xa"}},{"cell_type":"code","source":"def LatentSpaceEvaluator(latentShape, conditionShape):\n    latent = tf.keras.layers.Input(shape=latentShape)\n    cond = tf.keras.layers.Input(shape=conditionShape)\n    \n    evaluated = latent\n    stages = [evaluated]\n\n    if GLOBAL_CONFIG['global_mixer']:\n        mixer = StrangeMixerLayer(**GLOBAL_CONFIG['mixer_params'])\n    else:\n        mixer = lambda *args: StrangeMixerLayer(**GLOBAL_CONFIG['mixer_params'])(*args)\n        \n    for _ in range(GLOBAL_CONFIG['mixer_N']):\n        evaluated = mixer(evaluated, cond)\n        stages.append(evaluated)\n        continue\n\n    return keras.Model(inputs=[latent, cond], outputs=stages)\n\ndef HGMDecoder(shapeA, pool):\n    outputN = GLOBAL_CONFIG['output_N']\n    useSkips = GLOBAL_CONFIG['skip_connections']\n    \n    decoderInput = tf.keras.layers.Input(shapeA)\n    skipConnections = []\n    decodingHM = None\n    prevState = decoderInput\n    for stepIndex, SCShape in enumerate(pool):\n        decodingHM = prevState\n\n        data = [decodingHM]\n        if useSkips:\n            skipConnections.append(tf.keras.layers.Input(SCShape))\n            # 0.95 - not a typo\n            data.append(tf.keras.layers.Dropout(0.95)(skipConnections[-1]))\n\n        decodingHM = tf.keras.layers.Conv2D(outputN, 3, padding='same', activation='relu')(\n            tf.keras.layers.Concatenate(axis=-1)(data) if 1 < len(data) else data[0]\n        )\n        for _ in range(3):\n            decodingHM = tf.keras.layers.Conv2D(outputN, 3, padding='same', activation='relu')(decodingHM)\n        ########\n        prevState = tf.keras.layers.Conv2DTranspose(outputN, 3, padding='same', activation='relu', strides=2)(\n            tf.keras.layers.Dropout(0.05)(decodingHM)\n        )\n        continue\n\n    decodingHM = tf.keras.layers.Conv2D(\n        GLOBAL_CONFIG.get('output_N_last', outputN), \n        kernel_size=3, padding='same', activation='softmax'\n    )(decodingHM)\n    return tf.keras.Model(inputs=[decoderInput] + skipConnections, outputs=[decodingHM])\n\ndef HeatmapGenerationBlock(inputShape, extraShape):\n    encodedImages = tf.keras.layers.Input(shape=inputShape)\n    extra = tf.keras.layers.Input(shape=extraShape)\n\n    channels = inputShape[-1]\n    encoded = tf.keras.layers.Conv2D(channels, 3, padding='same', activation='relu')(\n        tf.keras.layers.Dropout(0.05)(encodedImages)\n    )\n\n    extraData = extra\n    pool = [tf.keras.layers.Concatenate(axis=-1)([encoded, extraData])]\n    # downsample\n    for _ in range(3):\n        extraData = tf.keras.layers.Conv2D(channels, 3, padding='same', activation='relu', strides=2)(\n            tf.keras.layers.Dropout(0.05)(extraData)\n        )\n        encoded = tf.keras.layers.Conv2D(channels, 3, padding='same', activation='relu', strides=2)(\n            tf.keras.layers.Dropout(0.05)(encoded)\n        )\n        pool.append(tf.keras.layers.Concatenate(axis=-1)([encoded, extraData]))\n        continue\n    # evaluate in latent space\n    latent = tf.keras.layers.Conv2D(GLOBAL_CONFIG['latent']['channels'], 3, padding='same', activation='relu')(encoded)\n    condition = tf.keras.layers.Concatenate(axis=-1)([encoded, extraData])\n\n    evaluator = LatentSpaceEvaluator(latentShape=latent.shape[1:], conditionShape=condition.shape[1:])\n    stages = evaluator([latent, condition])\n    ##############################\n    heatmaps = stages[-1]\n    pool = pool[::-1]\n    decoder = HGMDecoder(heatmaps.shape[1:], [x.shape[1:] for x in pool])\n    decoderData = [heatmaps]\n    skipConnections = []\n    if GLOBAL_CONFIG['skip_connections']:\n        decoderData.extend(pool)\n        skipConnections = pool\n    \n    return(\n        keras.Model(inputs=[encodedImages, extra], outputs={\n            'heatmaps': decoder(decoderData),\n            'latent condition': condition,\n            'latent stages': stages,\n            'skip_connections': skipConnections\n        }),\n        evaluator,\n        decoder\n    )","metadata":{"id":"TBQCPYFp4VcZ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It's finally time to put all the parts of the detector together. Could there be something strange here? Well, I wouldn't be me if not.\n\nAt some point, I asked myself \"Can the network predict where it will make mistakes?\". If we can take a picture and estimate the probability of belonging to a certain class, then we can take features from the inner layer of the network and estimate the probability of errors. Of course, we cannot allow the estimation process to affect the main work, so I stop the flow of gradients. I had a lot of concerns about this idea, but experiments have shown that this approach works quite well.\n\nHow can error estimation be used? In addition to being used in the process of training the network, this information can be used by the network itself. Of course, the network will not be able to correct itself, but this information can be considered a kind of alternative modality for the initial information, which has been confirmed by experiments (alas, with a slightly different architecture, it didn't have that much effect now).\n\nIt is worth noting that I additionally normalize the error estimation (see `normAttention`). Intuition: the further the estimate is from the mean, the more confident the network is in its predictions.\n\nIn general, this solution partially solves the problem of the presence of errors in the dataset. The network learns more slowly, but it picks up fine details better. Estimating errors introduce some inertia into the model/trainer, which can help the network (or can ruin everything).\n\nI did a similar thing with the watershed energy estimation. Although this is part of the main task, it makes no sense to force the main network to solve this problem if the internal representation is enough to solve it. Traditionally, I also use this information in further layers of the network, since there is a chance that it will be useful.","metadata":{"id":"yLwxEoFJbAQs"}},{"cell_type":"code","source":"def detectorModel():\n    images = tf.keras.layers.Input(shape=(OUTER_CROP, OUTER_CROP, 1))\n\n    centerCrop = tf.keras.layers.CenterCrop(INNER_CROP, INNER_CROP)(images)\n    PIL = PrepareImagesLayer(\n        2, activation='relu', \n        F=lambda x: tf.concat([x[:, CROP_PADDING:CROP_PADDING+INNER_CROP, CROP_PADDING:CROP_PADDING+INNER_CROP, :], createTiles(INNER_CROP)(x)], axis=-1)\n    )(images)\n    \n    PIB = PreprocessImageBlock(GLOBAL_CONFIG['output_N'], extraN=1, hidden=20)\n    # PIB.trainable = True\n    PIBRes = PIB([centerCrop, images])\n\n    #################\n    combo = SG(tf.keras.layers.Concatenate(-1)([PIBRes, PIL]))\n    # combo = SG(tf.keras.layers.Concatenate(-1)([PIBRes]))\n    # error estimation branch\n    EEB = tf.keras.layers.Conv2D(8, 3, padding='same', activation='relu')(combo)\n    for _ in range(5):\n        EEB = tf.keras.layers.Conv2D(8, 3, padding='same', activation='relu')(EEB)\n    EEB = tf.keras.layers.Conv2D(1, 1, padding='same', activation='sigmoid')(EEB)\n\n    # watershed energy branch\n    WEB = tf.keras.layers.Conv2D(8, 3, padding='same', activation='relu')(combo)\n    for _ in range(5):\n        WEB = tf.keras.layers.Conv2D(8, 3, padding='same', activation='relu')(WEB)\n    WEB = tf.keras.layers.Conv2D(1, 1, padding='same', activation='sigmoid')(WEB)\n    WEB = 1.0 - WEB # invert\n    #################\n    preprocessed = tf.keras.layers.Conv2D(GLOBAL_CONFIG['preprocessed channels'], 3, padding='same', activation='relu')(\n        tf.keras.layers.Dropout(0.05)(PIBRes)\n    )\n\n    extraData = [PIL]\n    if GLOBAL_CONFIG.get('use EEB extra', True):\n        extraData.append(tf.keras.layers.Lambda(lambda x: tf.stop_gradient(normAttention(x))) (EEB))\n    if GLOBAL_CONFIG.get('use WEB', True):\n        extraData.append(SG(WEB))\n    extra = tf.keras.layers.Concatenate(axis=-1)(extraData)\n\n    HGB, HGBLatentEvaluator, HGBLatentDecoder = HeatmapGenerationBlock(preprocessed.shape[1:], extra.shape[1:])\n    HGBRes = HGB([preprocessed, extra])\n\n    model = keras.Model(\n        inputs=[images],\n        outputs={\n            'errors': EEB,\n            'watershed energy': WEB,\n            **HGBRes\n        }\n    )\n    return model, HGBLatentDecoder\n\ndetectorModel()[0].summary()","metadata":{"id":"5boc7qQZrmNT","outputId":"fb6f28e6-cf23-4ebd-beb5-d150b0febb0f"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"(less than 550.000 trainable parameters) Not a very impressive number of parameters, is it?\n\n\n---\n\n\nTo train the network, I also use a kind of discriminator. I assumed that each \"stage\" of computation in latent space should bring us closer and closer to the answer, especially if it is done in autoregressive manner, so:\n\n- we can try to transfer \"knowledge\" between stages. If it would be possible to \"compress\" the calculations by one step, then the freed stage could further refine the result. Again, this is a strange idea and I doubted it, but the article [\"Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed\"](https://arxiv.org/abs/2101.02388) (hooray, I mentioned at least one article out of hundreds :) ) a bit of confidence it gave me. Of course, the article is about completely different topic, but if transfer knowledge between models of different types is possible, then it is theoretically possible between different stages of computations in the latent space. Shortly I confirmed this by simply adding a loss between the stages, but it's too straightforward way. If we use a learned discriminator, then, theoretically, only important features will be transfered and, most importantly, in a softer mode (not \"only this way and not otherwise\", but in the form \"it is desirable to change such value in such direction\").\n\n- each next stage should bring us closer to the answer, which means we can use them to learn kinda some manifold, which sets the proximity of the latent representation and the correct prediction for a specific input sample. Moreover, in any case, we have a latent representation for each of the stages (otherwise the network could not work), so all that remains is to apply the contrastive loss and train the discriminator (although it is closer to EBM, but not in all aspects).\n\nHowever, a simple discriminator often either finds a too simple solution (for example, based on a range of values) or does not converge at all. I solved the first problem by using a layerwise softmax, which leaves only the relative distribution of values. I had almost no time left for the second problem, so I just added a second discriminator.","metadata":{"id":"tdLoXIjOXBTJ"}},{"cell_type":"code","source":"def _latentScoringModel(shape):\n    l2_term = 1e-4\n    latent = tf.keras.layers.Input(shape)\n    res = latent\n    K = GLOBAL_CONFIG['latent_scoring']['layers_N']\n    for _ in range(3):\n        res = tf.keras.layers.BatchNormalization()(res)\n        for _ in range(K):\n            res = tf.keras.layers.Conv2D(res.shape[-1], 3, padding='same', activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l2_term))(res)\n            res = tf.keras.layers.Dropout(0.2)(res)\n            \n        res = tf.keras.layers.Conv2D(res.shape[-1] // 2, 3, strides=2, padding='same', activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l2_term))(res)\n        continue\n        \n    res = tf.keras.layers.Conv2D(1, 1, padding='same', activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l2_term))(res)\n    score = tf.keras.layers.Dense(1, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l2_term))( tf.keras.layers.Flatten()(res) )\n    model = keras.Model(\n        inputs=[latent],\n        outputs=[score]\n    )\n    return model\n\ndef latentScoringModel(latentShape, condShape):\n    latent = tf.keras.layers.Input(shape=latentShape)\n    cond = tf.keras.layers.Input(shape=condShape)\n\n    X1 = tf.keras.layers.Concatenate(-1)([latent, cond])\n    X2 = tf.keras.layers.Lambda(layerwiseSoftmax)(X1)\n\n    scores = []\n    for _ in range(GLOBAL_CONFIG['latent_scoring']['replicas']):\n        if 'linear' in GLOBAL_CONFIG['latent_scoring']['preprocess']:\n            scores.append(_latentScoringModel(X1.shape[1:])([X1]))\n            \n        if 'softmax' in GLOBAL_CONFIG['latent_scoring']['preprocess']:\n            scores.append(_latentScoringModel(X2.shape[1:])([X2]))\n        continue\n\n    scores = tf.keras.layers.Concatenate(axis=-1)(scores)\n    model = keras.Model(\n        inputs=[latent, cond],\n        outputs=[scores]\n    )\n    return model","metadata":{"id":"YWh4h4PeM0rQ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The basis of the whole solution is the use of consistency between different view samples. This is not only used for predictions but is also part of the training. The time required for each training epoch and memory consumption increase by 8-10 times, but the benefits cover all this.","metadata":{"id":"u0xp2VKgYxFK"}},{"cell_type":"code","source":"def TTA_Not_TF(model, args, training=False, softmax=True, useRotate=False, useFlip=True):\n    args = args if isinstance(args, list) else [args]\n    addedLoss = 0.0\n    #################\n    pred = model(args, training=training)\n    # dict_keys(['errors', 'watershed energy', 'heatmaps', 'latent condition', 'latent stages', 'skip_connections'])\n    res = {nm: [] for nm in pred.keys()}\n    res['added loss'] = 0.0\n\n    def store(data, transformation):\n        applyTransformation = ['errors', 'watershed energy', 'heatmaps']\n        for nm, value in data.items():\n            if nm in applyTransformation:\n                value = transformation(value)\n            res[nm].append(value)\n            continue\n\n        if training:\n            res['added loss'] = res['added loss'] + sum(model.losses)\n        return\n\n    store(pred, lambda x: x)\n    #################\n    def revertFlip(axs): return lambda x: tf.reverse(x, axis=axs)\n    if useFlip:\n        for axs in [[1], [2], [1, 2]]:\n            store(\n                model([tf.reverse(x, axis=axs) for x in args], training=training),\n                revertFlip(axs)\n            )\n            continue\n        #################\n    #################\n    def revertRotate(k): return lambda x: tf.image.rot90(x, k=4-k)\n    if useRotate:\n        for k in [1, 2, 3]:\n            store(\n                model([tf.image.rot90(x, k=k) for x in args], training=training),\n                revertRotate(k)\n            )\n            continue\n        #################\n    #################\n    masks = res['heatmaps']\n    combinedMasks = 1.0\n    for x in masks:\n        combinedMasks = combinedMasks * (1.0 + x)\n\n    if softmax:\n        combinedMasks = tf.nn.softmax(combinedMasks, axis=-1)\n\n    res['added loss'] = res['added loss']  / len(masks)\n    res = {\n        'combined': combinedMasks,\n        'masks': masks,\n        **res\n    }\n    return res\n\n@tf.function\ndef TTA(model, images, softmax=True):\n    return TTA_Not_TF(model, images, softmax=softmax, training=False, useRotate=True, useFlip=True)","metadata":{"id":"OBHyYaSNg4lD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from skimage.feature import peak_local_max\nfrom skimage.segmentation import watershed\nfrom scipy import ndimage\n\ndef extractInstances(EE, classes):\n    meanE = EE.mean()\n    sureBG = np.logical_or(meanE < EE, TOTAL_CLASSES <= classes)\n    sureNBG = ~sureBG\n\n    meanEBg = EE[sureNBG].mean()\n    sureFG = np.logical_and(EE < meanEBg, classes < INNER_CLASSES)\n    \n    sureCoreE = EE.copy()\n    sureCoreE[~sureFG] = 1\n    sureCoreE = -sureCoreE # for peak_local_max\n\n    local_maxima = np.empty(EE.shape, dtype=np.uint8)\n    def f(min_distance):\n        cellSeed = peak_local_max(sureCoreE, min_distance=min_distance)\n\n        local_maxima[:] = 0\n        local_maxima[tuple(cellSeed.T)] = 1\n        markers = ndimage.label(local_maxima, structure=np.ones((3, 3)))[0]\n        \n        return watershed(EE, markers, mask=sureNBG, watershed_line=False, compactness=255)\n    return f\n\ndef extractMasks(watershed_energy, classes):\n    labels = extractInstances(watershed_energy, classes)(min_distance=5)\n    for label in np.unique(labels):\n        if label == 0: continue\n        yield np.where(labels == label, 1, 0)\n        continue\n    return\n\ndef _processPredictions(watershed_energy, classes, min_distances):\n  WE = [[] for _ in min_distances]\n  Classes = [[] for _ in min_distances]\n  for W, C in zip(watershed_energy, classes):\n      instF = extractInstances(W, C)\n      for i, dist in enumerate(min_distances):\n          inst = instF(dist)\n          unpacked = unpackInstances(inst, onlyMain=True)\n          UClasses = unpacked[..., :TOTAL_CLASSES]\n          \n          Classes[i].append(np.dstack((UClasses, 1 - UClasses.argmax(-1)[..., None])))\n          WE[i].append(unpacked[..., LAYER_WATERSHED, None])\n      continue\n\n  res = []\n  for e, c in zip(WE, Classes):\n      res.extend((np.stack(e).astype(np.float32), np.stack(c).astype(np.float32)))\n  return res\n  \ndef processPredictions(watershed_energy, classesProbs, min_distances):\n    res = tf.numpy_function(\n        _processPredictions,\n        inp=[watershed_energy[..., 0], tf.argmax(classesProbs, axis=-1), min_distances],\n        Tout=[tf.float32, tf.float32] * len(min_distances)\n    )\n    return list(zip(res[0::2], res[1::2])) # A, B, C, D -> (A, B), (C, D)","metadata":{"id":"1-snBaQRnmL0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# img = debugImg[None]\n# tta = FakeObject(TTA(model, img))\n\n# min_distances = [1, 2, 3, 4, 5]\n# res = []\n# for WE in reduceMinMax(tta.watershed_energy):\n#     PP = processPredictions(WE, tta.combined, min_distances)\n#     res.extend([w for w, _ in PP])\n\n# N = len(min_distances)\n# for a, b in [[0, N], [N, 2*N], [0, 2*N]]:\n#     img = [cv2.resize(x[0].numpy()*255, None, fx=1.5, fy=1.5) for x in reduceMinMax(res[a:b])]\n#     cv2_imshow(np.concatenate(img, 1))","metadata":{"id":"LI4bTgBPS-Cl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"id":"lYAMwQobBJyt"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"And finally, a trainer for this whole system. It also has enough small nuances, but I will describe only the most unobvious:\n\n- `masksPermutations` does not generate all combinations of masks, as it requires a lot of memory, and the benefit is not so great.\n\n- to get around the memory limitation, the trainer accumulates gradients for the main model. This feature is not used, but there is such an opportunity (again, I did not see any significant benefit).\n\n- the discriminator is updated 8 times more often than the detector, which simplifies the code. It might be better to accumulate its gradients and update it less frequently. Although it seems to me that it would be more useful to have a copy of the discriminator that would be updated by the EMA.\n\n- the signal from the discriminator is scaled to be no more than 30% of the loss. By its nature, the discriminator is constantly increasing the scores, which can, in the end, outweigh the main objective. One could, of course, artificially reduce them, but this can distort the gradients.\n\nThe rest, I think, is clear from the code. You can also notice that there is a reserve for more complex methods, but I had to abandon them due to a lack of time and resources.","metadata":{"id":"iUlfZl1XpLyv"}},{"cell_type":"code","source":"# trainer utils\nimport itertools\n\ndef masksPermutations(masks):\n    res = []\n    res.extend(masks)\n\n    for L in range(2, len(masks)+1):\n        combinations = list(itertools.combinations(masks, L))\n        if 1 < len(combinations):\n            combinations = [combinations[0], combinations[-1]]  \n        for subset in combinations:\n            combined = 1.0\n            for x in subset:\n                combined = combined * (1.0 + x)\n            res.append(tf.nn.softmax(combined, axis=-1))\n            continue\n        continue\n    return res\n\ndef combineLosses(losses):\n    loss = N = 0.0\n    for L in losses:\n        coef = 1.0\n        if isinstance(L, tuple):\n            L, coef = L\n        loss = loss + tf.reduce_mean(L) * coef\n        N = N + coef\n        continue\n    return loss / N\n\n@tf.function(jit_compile=True)\ndef watershed2classesOHE(watershed):\n    axs = (1, 2)\n    fgMask = tf.cast(watershed < tf.reduce_mean(watershed, axis=axs, keepdims=True), tf.float32)\n    # # # # # # # # # # # \n    meanL = tf.math.divide_no_nan(\n        tf.reduce_sum(watershed * fgMask, axis=axs, keepdims=True),\n        tf.reduce_sum(fgMask, axis=axs, keepdims=True),\n    )\n    innerMask = tf.cast(watershed < meanL, tf.float32)\n    bordersMask = (1.0 - innerMask) * fgMask\n    # # # # # # # # # # # \n    meanL = tf.math.divide_no_nan(\n        tf.reduce_sum(watershed * innerMask, axis=axs, keepdims=True),\n        tf.reduce_sum(innerMask, axis=axs, keepdims=True),\n    )\n    \n    coreMask = tf.cast(watershed < meanL, tf.float32)\n    bodyMask = (1.0 - coreMask) * innerMask\n    # # # # # # # # # # # \n    res = tf.concat([coreMask, bodyMask, bordersMask, 1.0 - fgMask], axis=-1)\n    tf.assert_equal(tf.shape(res)[1:], (INNER_CROP, INNER_CROP, TOTAL_CLASSES + 1))\n    return res\n\ndef masks2watershed(masks, power=2.0):\n    OHE = tf.one_hot(tf.argmax(masks, -1), tf.shape(masks)[-1], dtype=masks.dtype)[..., :TOTAL_CLASSES]\n    binMask = tf.cast(0 < tf.reduce_sum(OHE[..., :INNER_CLASSES], axis=-1, keepdims=True), tf.uint8)\n    dist = tfa.image.euclidean_dist_transform(binMask, tf.float32)\n    # mask borders\n    dist = tf.where(0.0 < OHE[..., INNER_CLASSES:], 1.0, dist)\n\n    # dist = tf.clip_by_value(dist, 0.0, 5.0) / 5.0\n    dist = tf.math.divide_no_nan(dist, tf.reduce_max(dist, axis=(1, 2), keepdims=True))\n    return tf.pow(1.0 - dist, power)\n    # a utility function to add weight decay after the model is defined.","metadata":{"id":"iNUfto_QgAqK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class CDetectorTrainer(tf.keras.Model):\n    def __init__(self, detector, latentScoring, tau=0.005, microBatchSize=4, useEstimatedErrors=True, entropyTerm=0.0, LSFraction=0.3, regularization=None, **kwargs):\n        super().__init__(**kwargs)\n        self._regularization = FakeObject(regularization or {'active': False})\n        self._useEstimatedErrors = useEstimatedErrors\n        self._LSFraction = LSFraction\n\n        if not isinstance(entropyTerm, dict):\n            entropyTerm = {'min': float(entropyTerm), 'max': float(entropyTerm)}\n        self._entropyTerm = entropyTerm\n\n        self._latentScoring = None # latentScoring\n        self._useLatentScoring = not(self._latentScoring is None)\n\n        self._detector = detector\n        # self._detectorEMA = detectorModel() # tf.keras.models.clone_model(detector) fails\n        # self._detectorEMA.set_weights(self._detector.get_weights())\n        # self._detectorEMA.trainable = False\n\n        self._masksLoss = focalLoss2d(alpha=2.0, beta=4.0)\n        self._tau = tau\n            \n        self._loss = tf.keras.metrics.Mean(name=\"loss\")\n        self._masksL = tf.keras.metrics.Mean(name=\"masks\")\n        self._EEL = tf.keras.metrics.Mean(name=\"EE\")\n        self._masksConsistencyL = tf.keras.metrics.Mean(name=\"masks consistency\")\n        self._MWConsistencyL = tf.keras.metrics.Mean(name=\"masks-watershed consistency\")\n        self._WatershedL = tf.keras.metrics.Mean(name=\"watershed\")\n        self._latentScores = tf.keras.metrics.Mean(name=\"Scores\")\n        self._LSLoss = tf.keras.metrics.Mean(name=\"Scoring loss\")\n        self._addedLoss = tf.keras.metrics.Mean(name=\"Added loss\")\n        self._PPCoef = tf.keras.metrics.Mean(name=\"PPCoef\")\n        self._regularizationLoss = tf.keras.metrics.Mean(name=\"regularization\")\n\n        self._IoUMetrics = []\n        self._IoU = {}\n        metricNames = ['inner flat', 'flat'] + ['class %d' % i for i in range(TOTAL_CLASSES)]\n        for nm in ['Single', 'Combined', 'Avg']:\n            metric = [tf.keras.metrics.Mean(name=\"IoU %s %s\" % (nm, x)) for x in metricNames]\n            self._IoUMetrics.extend(metric)\n            self._IoU[nm] = metric\n            continue\n\n        self._dummyGradients = None\n        self._microBatchSize = microBatchSize\n        return\n\n    @tf.function\n    def call(self, X, training=False):\n        return self._detector(X, training)\n\n    @tf.function(jit_compile=True)\n    def _emaUpdate(self):\n        for (a, b) in zip(self._detectorEMA.trainable_variables, self._detector.trainable_variables):\n            a.assign(b * self._tau + a * (1 - self._tau))\n        return\n\n    @tf.function(jit_compile=True)\n    def _SVLoss(self, masks, predMasks, W):\n        N = TOTAL_CLASSES\n        baseW = 1.0 # masks[..., LAYER_WEIGHTS, None]\n        masksLoss = self._masksLoss(masks[..., :N], predMasks[..., :N], baseW + W)\n        \n        # calc IoU\n        # convert [0.2, 0, 0.1, ...] => [1.0, 0, 0, ...]\n        OHE = tf.one_hot(tf.argmax(predMasks, -1), tf.shape(predMasks)[-1], dtype=predMasks.dtype)[..., :N]\n\n        iou = {\n            'inner flat': iou_coef(\n                tf.reduce_max(masks[..., :INNER_CLASSES], axis=-1, keepdims=True),\n                tf.reduce_max(OHE[..., :INNER_CLASSES], axis=-1, keepdims=True),\n                axis=(1, 2, 3)\n            ),\n            'flat': iou_coef(\n                tf.reduce_max(masks[..., :N], axis=-1, keepdims=True),\n                tf.reduce_max(OHE[..., :N], axis=-1, keepdims=True),\n                axis=(1, 2, 3)\n            ),\n            'classes': iou_coef(masks[..., :N], OHE, axis=(1, 2))\n        }\n        return (masksLoss, iou)\n      \n    @tf.function(jit_compile=True)\n    def _supervised(self, masks, predMasksAll, W):\n        losses = []\n        IoUAll = []\n        for m in predMasksAll:\n            L, IouMask = self._SVLoss(masks, m, W)\n            losses.append(L)\n            IoUAll.append(IouMask)\n            continue\n\n        return (combineLosses(losses), IoUAll)\n\n    @tf.function(jit_compile=True)\n    def _masksConsistency(self, targetMasks, predMasksAll, errorsWeights):\n        eps = 1e-8\n        tf.assert_equal(tf.shape(errorsWeights), tf.shape(targetMasks)[:3])\n        # forces the model to make a more explicit choice\n        entropyTerm = self._entropyTerm['min'] + (tf.square(1.0 - errorsWeights) * (self._entropyTerm['max'] - self._entropyTerm['min']))\n        errorsWeights = 1.0 + errorsWeights # 0..1 => 1..2\n\n        consistencyLoss = tf.reduce_mean(-tf.reduce_sum(targetMasks * tf.math.log(targetMasks + eps)q, -1) * entropyTerm)\n\n        targetMasks = tf.one_hot(tf.argmax(targetMasks, -1), tf.shape(targetMasks)[-1], dtype=targetMasks.dtype)\n        targetMasks = tf.stop_gradient(targetMasks)\n        for m in predMasksAll:\n            entropyL = tf.reduce_mean(-tf.reduce_sum(m * tf.math.log(m + eps), -1) * entropyTerm)\n            # errors = JensenShannonDivergence(targetMasks, m)\n            errors = -tf.reduce_sum(targetMasks * tf.math.log(m + eps), -1)\n            tf.assert_equal(tf.shape(errors), tf.shape(errorsWeights))\n            mLoss = tf.reduce_mean(errors * errorsWeights)\n            consistencyLoss = consistencyLoss + mLoss + entropyL\n            continue\n\n        return consistencyLoss / len(predMasksAll)\n        \n    @tf.function(jit_compile=False)\n    def _consistencyMaskAndWatershed(self, predictions, CombinedWatershed):\n        losses = []\n        # masks => watershed_energy\n        losses.append( self._WatershedLossWithList(tf.stop_gradient(masks2watershed(predictions.combined)), predictions.watershed_energy) )\n        # watershed_energy => masks\n        classesProb = tf.concat([\n            predictions.combined[..., :TOTAL_CLASSES],\n            tf.reduce_sum(predictions.combined[..., TOTAL_CLASSES:], axis=-1, keepdims=True), # background\n        ], axis=-1)\n        lossF = tf.losses.categorical_crossentropy # focalLoss2d()\n\n        coef = 1.0 / len(CombinedWatershed)\n        for watershed in CombinedWatershed:\n            w2c = watershed2classesOHE(watershed)\n            losses.append(( lossF(tf.stop_gradient(watershed2classesOHE(watershed)), classesProb), coef ))\n            continue\n        return combineLosses(losses)\n\n    @tf.function(jit_compile=True)\n    def _EELoss(self, ytrue, EEAll):\n        lossF = tf.losses.mse\n        return combineLosses([lossF(ytrue, pred) for pred in EEAll])\n\n    @tf.function(jit_compile=True)\n    def _errorsWeights(self, predictions):\n        if not self._useEstimatedErrors:\n            return tf.zeros_like(predictions.errors[0])\n            \n        # _, errorW = reduceMinMax(predictions.errors) # agregrate by max\n        # errorW = tf.reduce_max(errorW, axis=-1, keepdims=True)\n        errorW = sum(predictions.errors) / len(predictions.errors)\n        errorW = normAttention(errorW)\n        # errorW = tf.reduce_max(errorW, axis=-1, keepdims=True)\n\n        return tf.stop_gradient(errorW)\n\n    @tf.function(jit_compile=True)\n    def _estimateErrors(self, GT, predictions):\n        # elementwise error\n        errors = tf.square(GT.masks[..., :TOTAL_CLASSES] - predictions.combined[..., :TOTAL_CLASSES])\n        errors = tf.reduce_sum(errors, axis=-1, keepdims=True)\n        for mask in predictions.masks:\n            diff = tf.square(mask - predictions.combined)\n            errors = errors + tf.reduce_sum(diff, axis=-1, keepdims=True)\n            continue\n        tf.assert_equal(tf.shape(errors)[1:], (INNER_CROP,  INNER_CROP, 1))\n\n        # errors = topKMaskChannelwise(errors, k=24) * errors\n        errors = normalizeTensorRange(errors, axis=(1, 2))\n        return tf.stop_gradient(errors)\n\n    @tf.function(jit_compile=True)\n    def _WatershedLossWithList(self, ytrue, All):\n        lossF = tf.losses.mse # binary_crossentropy\n        return combineLosses([lossF(ytrue, pred) for pred in All])\n\n    @tf.function(jit_compile=True)\n    def _calcLatentLoss(self, predictions):\n        return 0.0\n        losses = []\n        # lossF = JensenShannonDivergence\n        lossF = tf.losses.kl_divergence\n        for stages in predictions.latent_stages:\n            res = stages[-1]\n            target = tf.stop_gradient(tf.nn.softmax(flatTensor(res), axis=-1))\n            for stage in stages[:-1]:\n                losses.append( lossF(target, tf.nn.softmax(flatTensor(stage), axis=-1)) )\n                continue\n        return sum(losses)\n        # return combineLosses(losses)\n        if not self._useLatentScoring: return 0.0\n\n        allScores = []\n        for latents, cond in zip(predictions.latent_stages, predictions.latent_condition):\n            allScores.extend(self._calcScores(\n                cond=tf.stop_gradient(cond), # DON'T change conditional variable\n                latents=latents[:-1],\n                training=False\n            ))\n            continue\n\n        allScores = tf.concat(allScores, axis=0)\n        # allScores = tf.math.log(1.0 + allScores)\n        return tf.reduce_mean(allScores)\n        \n    def _processedWatersheds(self, predictions, MMWatershed):\n        min_distances = [1]#2, 3, 4]\n        processed = [\n            [w for w, _ in processPredictions(x, predictions.combined, min_distances)]\n            for x in MMWatershed\n        ]\n\n        good = []\n        bad  = []\n        for x in [processed[0:1], processed[1:2], processed[0:2]]:\n            minW, maxW = reduceMinMax(sum(x, []))\n            good.append(tf.stop_gradient(minW))\n            bad.append(tf.stop_gradient(maxW))\n            continue\n        return good, bad\n\n    def _WatershedLoss(self, GT, predictions, goodWatersheds, badWatersheds, PPCoef):\n        targets = [sum(predictions.watershed_energy) / len(predictions.watershed_energy)] # predictions.watershed_energy\n        wLosses = []\n        # suppress incorrect waterlines\n        for w in badWatersheds:\n            wLosses.append((\n                self._WatershedLossWithList(GT.watershed_energy, [x + tf.stop_gradient(tf.maximum(0.0, w - x)) for x in targets]),\n                PPCoef / len(badWatersheds)\n            ))\n            continue\n        # pull correct? waterlines\n        for w in goodWatersheds:\n            wLosses.append((\n                self._WatershedLossWithList(w, targets),\n                PPCoef / len(goodWatersheds)\n            ))\n            continue\n\n        return combineLosses([\n            self._WatershedLossWithList(GT.watershed_energy, predictions.watershed_energy), # supervised term\n            # self._WatershedLoss(tf.stop_gradient(MMWatershed[0]), predictions.watershed_energy),\n            *wLosses,\n        ])\n        \n    # @tf.function(jit_compile=True)\n    def _calcLossJitted(self, GT, predictions):\n        errorsWeights = self._errorsWeights(predictions)\n        MMWatersheds = reduceMinMax(predictions.watershed_energy)\n        \n        masksSet = masksPermutations(predictions.masks)\n        (masksLoss, iou) = self._supervised(GT.masks, masksSet, errorsWeights)\n        \n        PPCoef = tf.stop_gradient(iou[-1]['flat']) # combined flat iou\n        PPCoef = (PPCoef ** 2) * tf.math.exp(1.0 + tf.math.sqrt(PPCoef))\n        self._PPCoef.update_state(PPCoef)\n\n        masksConsistencyLoss = self._masksConsistency(predictions.combined, masksSet, errorsWeights[..., 0])\n\n        MWConsistencyLoss = self._consistencyMaskAndWatershed(predictions, MMWatersheds)\n        \n        goodWatersheds, badWatersheds = self._processedWatersheds(predictions, MMWatersheds)\n        WatershedLoss = self._WatershedLoss(GT, predictions, goodWatersheds, badWatersheds, PPCoef)\n\n        # if training:\n        #   EMAMasks =  TTA_Not_TF(self._detectorEMA, images, training=False)[0]\n        #   consistencyLoss = consistencyLoss + self._consistency(tf.stop_gradient(EMAMasks), predMasks)\n\n        EELoss = self._EELoss(self._estimateErrors(GT, predictions), predictions.errors + reduceMinMax(predictions.errors))\n        latentScores = self._calcLatentLoss(predictions) * PPCoef * 1e-3\n        \n        return(\n            masksLoss, masksConsistencyLoss, MWConsistencyLoss, EELoss, WatershedLoss, latentScores, iou\n        )\n\n    def _regularizationTerm(self):\n        if not self._regularization.active: return 0.0\n        if self._regularization.factor <= 0.0: return 0.0\n\n        loss = 0.0\n        for w in self._detector.trainable_variables:\n            if 'kernel' in w.name: # weights\n                loss = loss + tf.reduce_mean(tf.abs(tf.square(w) - 1.0))\n                continue\n            if 'bias' in w.name:\n                loss = loss + tf.reduce_mean(tf.square(w))\n                continue\n            continue\n        return loss * self._regularization.factor\n\n    def _calcLoss(self, data, TTAArgs):\n        pred = FakeObject(TTA_Not_TF(self._detector, data.images, training=True, **TTAArgs))\n        masksLoss, masksConsistencyLoss, MWConsistencyLoss, EELoss, WatershedLoss, latentScores, iou = self._calcLossJitted(data, pred)\n        regLoss = self._regularizationTerm()\n\n        losses = [masksLoss, masksConsistencyLoss, MWConsistencyLoss, EELoss, WatershedLoss, pred.added_loss, regLoss]\n        totalLoss = sum([tf.reduce_mean(x) for x in losses])\n\n        latentScores = tf.reduce_mean(latentScores)\n        LSScale = tf.stop_gradient(tf.math.divide_no_nan(tf.minimum(totalLoss * self._LSFraction, latentScores), latentScores))\n        totalLoss = totalLoss + latentScores * LSScale\n        ############\n        self._addedLoss.update_state(pred.added_loss)\n        self._masksL.update_state(masksLoss)\n        self._masksConsistencyL.update_state(masksConsistencyLoss)\n        self._MWConsistencyL.update_state(MWConsistencyLoss)\n        self._loss.update_state(totalLoss)\n\n        self._updateIOU(iou)\n\n        self._EEL.update_state(EELoss)\n        self._WatershedL.update_state(WatershedLoss)\n        self._latentScores.update_state(latentScores)\n        self._regularizationLoss.update_state(regLoss)\n        return(\n            totalLoss,\n            FakeObject({ # artifacts\n                'latent condition': pred.latent_condition,\n                'latent stages': pred.latent_stages\n            })\n        )\n\n    def _calcScores(self, cond, latents, training):\n        NLatents = len(latents)\n        M = tf.shape(cond)[0]\n        tf.assert_equal(tf.shape(latents[0])[0], M)\n\n        cond = tf.concat([cond] * NLatents, axis=0)\n        cond = tf.stop_gradient(cond) # DON'T change conditional variable\n        latents = tf.concat(latents, axis=0)\n        if training:\n            latents = tf.stop_gradient(latents)\n        \n        pred = self._latentScoring([latents, cond], training=training) # (M*NLatents, 1)\n        modelLosses = sum(self._latentScoring.losses)\n\n        res = []\n        NScores = self._latentScoring.output_shape[-1]\n        for i in range(NScores):\n            s = tf.reshape(pred[..., i], (NLatents, M)) # each row - one stage\n            s = tf.transpose(s, (1, 0)) # each row - stages for same sample\n            s = s[..., ::-1] # reverse, 0 - last stage\n            tf.assert_equal(tf.shape(s), (M, NLatents))\n            res.append(s)\n            continue\n\n        if training: return(res, modelLosses)\n        return res\n\n    def _calcLatentScoringLoss(self, cond, latents):\n        losses = []\n        allScores, modelLosses = self._calcScores(cond, latents, training=True)\n        for scores in allScores:\n            for ai in range(len(latents) - 1):\n                A = scores[..., ai, None]\n                B = scores[..., ai+1:]\n                # loss = tf.math.log(1.0 + tf.exp(A - B))\n                loss = tf.math.softplus(A - B)\n                losses.append(loss)\n                continue\n            continue\n\n        losses = tf.concat(losses, axis=-1)\n        return tf.reduce_mean(losses) + tf.reduce_mean(modelLosses)\n\n    # NOT WORKING @tf.function(jit_compile=True)\n    def _resetGradients(self):\n        if self._dummyGradients is None:\n            self._dummyGradients = [tf.Variable(tf.zeros_like(x), trainable=False) for x in self.trainable_variables]\n        \n        for x in self._dummyGradients: x.assign_sub(x) # x = 0\n        return self._dummyGradients\n\n    def _train(self, data, TTAArgs):\n        (images), (masks) = data\n        gradients = self._resetGradients()\n\n        total = tf.cast(tf.shape(images)[0], tf.float32)\n        for index in tf.range(0, tf.shape(images)[0], self._microBatchSize):\n            batch = FakeObject({\n                'images': tf.cast(images[index:index+self._microBatchSize], tf.float32) / 255.0,\n                'masks' : masks[index:index+self._microBatchSize],\n                'watershed energy': masks[index:index+self._microBatchSize, ..., LAYER_WATERSHED, None],\n            })\n            lossFraction = tf.cast(tf.shape(batch.images)[0], tf.float32) / total\n\n            with tf.GradientTape(watch_accessed_variables=False) as tape:\n                tape.watch(self._detector.trainable_variables)\n                totalLoss, artifacts = self._calcLoss(batch, TTAArgs)\n                totalLoss = totalLoss * lossFraction\n\n            for old, cur in zip(gradients, tape.gradient(totalLoss, self.trainable_variables)):\n                if cur is not None:\n                    old.assign_add(cur)\n                continue\n\n            ############################\n            if not(self._latentScoring is None):\n                TV = self._latentScoring.trainable_variables\n                for latents, cond in zip(artifacts.latent_stages, artifacts.latent_condition):\n                    with tf.GradientTape(watch_accessed_variables=False) as tape:\n                        tape.watch(TV)\n                        totalLoss = self._calcLatentScoringLoss(cond, latents)\n\n                    self._latentScoring.optimizer.apply_gradients(zip( tape.gradient(totalLoss, TV), TV ))\n                    self._LSLoss.update_state(totalLoss)\n                    continue\n            continue\n\n        self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))\n        return\n\n    @tf.function\n    def train_step(self, data):\n        # self._train(data, {'useFlip': True, 'useRotate': False})\n        # self._train(data, {'useFlip': False, 'useRotate': True})\n        self._train(data, {'useFlip': True, 'useRotate': True})\n        # self._emaUpdate()\n        return {x.name: x.result() for x in [\n          self._loss, \n          self._masksL, self._masksConsistencyL,\n          self._MWConsistencyL,\n          self._EEL,\n          self._WatershedL,\n          self._latentScores, self._LSLoss,\n          self._PPCoef,\n          self._addedLoss,\n          self._regularizationLoss,\n          *self._IoUMetrics\n        ]}\n    \n    @tf.function\n    def test_step(self, data):\n        (images), (masks) = data\n        pred = FakeObject(TTA_Not_TF(self._detector, images, training=False))\n\n        masksSet = pred.masks + [pred.combined]\n        (masksLossAll, iou) = self._supervised(masks, masksSet, 0.0)\n        # consistencyLoss = self._consistency(combinedMasks, masksSet)\n        masksLoss = tf.concat(masksLossAll, axis=0)\n        totalLoss = tf.reduce_mean(masksLoss)# + tf.reduce_mean(consistencyLoss)\n        ############\n        self._masksL.update_state(masksLoss)\n        # self._consistencyL.update_state(consistencyLoss)\n        self._loss.update_state(totalLoss)\n        self._updateIOU(iou)\n\n        return {x.name: x.result() for x in [self._loss, *self._IoUMetrics]}\n    \n    def _updateIOU(self, iouAll):\n        def setIoU(nm, stats):\n            metrics = self._IoU[nm]\n            flatStats = [stats['inner flat'], stats['flat']] + [stats['classes'][i] for i in range(TOTAL_CLASSES)]\n            for m, value in zip(metrics, flatStats):\n                m.update_state(value)\n            return\n            \n        setIoU('Single', iouAll[0])\n        setIoU('Combined', iouAll[-1])\n\n        avg = iouAll[0]\n        for iou in iouAll[1:]:\n            for k in avg.keys():\n                avg[k] = avg[k] + iou[k]\n                continue\n            continue\n\n        for k in avg.keys():\n            avg[k] = avg[k] / len(iouAll)\n        setIoU('Avg', avg)\n        return","metadata":{"id":"ugt1qLu413bW"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Callbacks for debugging and for stopping training after 8 hours of training so that there is an hour to generate submissions.","metadata":{"id":"jwwP5E07s6yP"}},{"cell_type":"code","source":"class CVisualizePredictionsCallback(tf.keras.callbacks.Callback):\n  def __init__(self, model, sample, sampleMasks):\n    super().__init__()\n    if not(np.float32 == sample.dtype):\n        sample = sample.astype(np.float32) / 255.0\n    self._params = [model, sample, sampleMasks]\n    self._norm = matplotlib.colors.Normalize(vmin=0.0, vmax=1.0, clip=True)\n    return\n  \n  def on_epoch_end(self, epoch, logs=None):\n    if IS_COLAB and (0 == epoch % 50):\n        from google.colab import output\n        output.clear()\n\n    model, sample, sampleMasks = self._params\n    model.save_weights('latest.h5')\n    fig, axs = plt.subplots(2, 6, figsize=(5*6, 5*2))\n    for axsR in axs:\n      for axsC in axsR:\n        axsC.axis('off')\n\n    axs[0, 0].imshow(cv2.cvtColor(sample[..., 0], cv2.COLOR_GRAY2BGR))\n    \n    rect = matplotlib.patches.Rectangle(((OUTER_CROP - INNER_CROP) // 2, (OUTER_CROP - INNER_CROP) // 2), INNER_CROP, INNER_CROP, linewidth=1, edgecolor='r', facecolor='none')\n    axs[0, 0].add_patch(rect)\n    axs[0, 0].set_title('Image')\n    \n    y = sampleMasks[..., :TOTAL_CLASSES]\n    y = np.where(np.any(0 < y, -1), y.argmax(-1), len(SEG_COLORS) - 1)\n    axs[0, 1].imshow(labels2image(y))\n    axs[0, 1].set_title('GT classes')\n    # # # # # # # # # # # # \n    pred = model([sample[None]])\n    \n    predHM = pred['heatmaps'][0].numpy()\n    axs[0, 2].imshow(labels2image(predHM.argmax(-1)))\n    axs[0, 2].set_title('Pred classes')\n    # # # # # # # # # # # #\n    tta = TTA(model, sample[None])\n    \n    axs[1, 0].imshow(tf.reduce_min(tf.concat(tta['watershed energy'], -1), -1)[0].numpy(), norm=self._norm)\n    axs[1, 0].set_title('Pred watershed TTA min')\n    \n    axs[1, 1].imshow(tf.reduce_max(tf.concat(tta['watershed energy'], -1), -1)[0].numpy(), norm=self._norm)\n    axs[1, 1].set_title('Pred watershed TTA max')\n\n    axs[0, 3].imshow(labels2image(tta['combined'][0].numpy().argmax(-1)))\n    axs[0, 3].set_title('Pred classes TTA')\n    # # # # # # # # # # # #\n    errorW = sum([x for x in tta['errors']]) / len(tta['errors'])\n    axs[0, -1].imshow(normAttention(errorW)[0, ..., 0].numpy(), norm=self._norm)\n    axs[0, -1].set_title('Pred errors TTA normed')\n\n    axs[1, -1].imshow(errorW[0, ..., 0].numpy(), norm=self._norm)\n    axs[1, -1].set_title('Pred errors TTA raw')\n    # # # # # # # # # # # #\n    watershed_energy = tf.reduce_min(tf.concat(tta['watershed energy'], -1), -1, keepdims=True)\n    CEnergy, CClasses = processPredictions(watershed_energy=watershed_energy, classesProbs=tta['combined'], min_distances=[5])[0]\n    axs[1, 2].imshow(CEnergy[0, ..., 0].numpy(), norm=self._norm)\n    axs[1, 2].set_title('Pred processed watershed (5px)')\n\n    axs[1, 3].imshow(labels2image(CClasses[0].numpy().argmax(-1)))\n    axs[1, 3].set_title('Pred processed classes (5px)')\n    # # # # # # # # # # # #\n    plt.show()\n\n    if USE_WANDB:\n        wandb.log({'debug plot': fig}, step=epoch)\n    return\n\nclass CTimeLimitCallback(tf.keras.callbacks.Callback):\n  def __init__(self, startT, maxDuration):\n    super().__init__()\n    self._maxTime = startT + maxDuration\n    return\n  \n  def on_epoch_end(self, epoch, logs=None):\n    if self._maxTime < time.time():\n      self.model.stop_training = True\n    return","metadata":{"id":"nBOa9E6ICFW2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Training (on demand)","metadata":{"id":"-NEqhpFhtEP2"}},{"cell_type":"code","source":"model, decoder = detectorModel()\nmodel.summary()","metadata":{"id":"GNX37J1d5MCi","outputId":"474650e3-5f91-48cd-b88f-68c6f6cb84a0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if TRAIN_MODEL:\n    ids = train_df['id'].unique().tolist()\n    trainIds = ids[:-16]\n    valIds = ids[-16:]\n\n    trainGen = CTrainDataloader(\n        trainIds, cropsPerSample=1,\n        transform=withAugmentations(\n            A.Compose([\n              # A.RandomResizedCrop(OUTER_CROP, OUTER_CROP, scale=(0.025, 0.2), always_apply=True),\n              A.RandomCrop(OUTER_CROP, OUTER_CROP, always_apply=True),\n              A.MultiplicativeNoise(multiplier=(0.9, 1.1)),\n              A.RandomBrightnessContrast(brightness_limit=0.05, contrast_limit=0.05),\n          ])\n        ),\n        batch_size=GLOBAL_CONFIG['training_params']['batch_size']\n    )\n    valGen = CTestDataloader(valIds, batch_size=32)","metadata":{"id":"7-JWlbePbspA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if TRAIN_MODEL:\n    LSM = None\n    if GLOBAL_CONFIG['use_latent_scoring']:\n        LSM = latentScoringModel(\n            latentShape=model.output['latent stages'][-1].shape[1:],\n            condShape=model.output['latent condition'].shape[1:],\n        )\n        LSM.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4))\n\n    trainer = CDetectorTrainer(\n        model, LSM, \n        **GLOBAL_CONFIG['trainer_params']\n    )\n    trainer.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4, clipnorm=1.0))\n    \n    callbacks = []\n    debugImg, debugMask = next(zip(*valGen[3])) # random.choice([x for x in zip(*valGen[random.randint(0, len(valGen) - 1)])])\n    callbacks.append(CVisualizePredictionsCallback(model, debugImg, debugMask))\n    if 0 < GLOBAL_CONFIG['training_params']['max_time']:\n        callbacks.append(CTimeLimitCallback(time.time(), GLOBAL_CONFIG['training_params']['max_time']))\n\n    if USE_WANDB:\n        !wandb login\n        import wandb\n        wandb.init(project=\"Sartorius Segmentation\", entity=\"green_wizard\", config=GLOBAL_CONFIG)\n        callbacks.append(wandb.keras.WandbCallback())\n\n    _ = trainer.fit(\n        trainGen,\n        validation_data=valGen,\n        epochs=GLOBAL_CONFIG['training_params']['epochs'], verbose=2, callbacks=callbacks\n    )\n    model.save_weights('detector.h5')\n    if USE_WANDB: wandb.log_artifact('detector.h5', type='bytes')\nelse:\n    model.load_weights('../input/detector/detector.h5')","metadata":{"id":"ZL8T1h_NINXC","outputId":"9d4a5c01-4391-4b05-d9fa-3264354848a0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Some experiments with the model (only if trained)","metadata":{"id":"KNT5colzuop3"}},{"cell_type":"code","source":"if TRAIN_MODEL:\n    gen = valGen\n    expImg, expMask = random.choice([x for x in zip(*gen[random.randint(0, len(gen) - 1)])])","metadata":{"id":"gwHGSsSku_sH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if TRAIN_MODEL and LSM:\n    res = model([ expImg[None].astype(np.float32) ])\n    latent = res['latent stages'][-1]\n    latentCond = res['latent condition']\n\n    for latent in res['latent stages']:\n        print(LSM([latent, latentCond]))","metadata":{"id":"R9Jg8At_N7cw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if TRAIN_MODEL and LSM:\n    sigma = 0.1\n    opt = tf.optimizers.Adam(learning_rate=1e-2)\n    X = tf.Variable(res['latent stages'][0])\n    for _ in range(5555):\n        with tf.GradientTape(watch_accessed_variables=False) as tape:\n            tape.watch(X)\n            E = LSM([X, latentCond])\n            loss = tf.reduce_mean(E)\n        \n        print(E.numpy())\n        # X.assign_sub(tf.random.uniform(X.shape, -sigma, sigma) + tape.gradient(loss, X)[0])\n        opt.apply_gradients(zip( [tape.gradient(loss, X)], [X] ))\n        continue\n        \n    pred = decoder([X]).numpy()[0]\n    cv2_imshow(labels2image(pred.argmax(-1)) * 255)\n    predB = decoder(res['latent stages'][-1]).numpy()[0]\n    cv2_imshow(np.abs(labels2image(predB.argmax(-1)) - labels2image(pred.argmax(-1))) * 255)\n    print(np.abs(labels2image(predB.argmax(-1)) - labels2image(pred.argmax(-1))).sum())","metadata":{"id":"CX6l4Q5oX8zL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if TRAIN_MODEL:\n  fig, axs = plt.subplots(2, 6, figsize=(5*6, 5*2))\n  Ax = []\n  for axsR in axs:\n    for axsC in axsR:\n      axsC.axis('off')\n      Ax.append(axsC)\n\n  for i, latent in enumerate(res['latent stages']):\n    pred = decoder([latent]).numpy()[0]\n    Ax[i].imshow(labels2image(pred.argmax(-1)))","metadata":{"id":"Jnp8IIvtrcay"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Inference and Submission\n","metadata":{"id":"m5swgtWJ0NAd"}},{"cell_type":"code","source":"def inferImage(model, image, batch_size):\n    innerPadding = 16 # smooths out predictions at borders\n    innerCrop = INNER_CROP - 2 * innerPadding\n    padding = (OUTER_CROP - innerCrop) // 2\n    W, H = image.shape[:2]\n    paddedImage = cv2.copyMakeBorder(image, padding, padding, padding, padding, cv2.BORDER_CONSTANT, 0)\n    pW, pH = paddedImage.shape[:2]\n\n    crops = cropsFor(pH, pW, innerCrop)\n    cropped = np.zeros((batch_size, OUTER_CROP, OUTER_CROP, 1), np.float32)\n    result = np.zeros_like(image, np.int32)\n    EE = np.zeros_like(image, np.float32)\n    for bi in range(0, len(crops), batch_size):\n        subcrops = crops[bi:bi+batch_size]\n        for i, (y, x) in enumerate(subcrops):\n            crop = paddedImage[x-padding:x+innerCrop+padding, y-padding:y+innerCrop+padding, None]\n            cropped[i] = crop\n            continue\n        cropped /= 255.0\n\n        tta = TTA(model, cropped)\n        predictedHM = tta['combined']\n        predictedHM = predictedHM[:len(subcrops)]\n        \n        predictedEE = reduceMinMax(tta['watershed energy'])[0] # min\n        predictedEE = predictedEE[:len(subcrops), ..., 0].numpy()\n\n        predictedHM = tf.argmax(predictedHM, axis=-1).numpy()\n        for (y, x), predHM, predEE in zip(subcrops, predictedHM, predictedEE):\n            result[x-padding:x+innerCrop-padding, y-padding:y+innerCrop-padding] = predHM[innerPadding:-innerPadding, innerPadding:-innerPadding]\n            EE[x-padding:x+innerCrop-padding, y-padding:y+innerCrop-padding] = np.maximum(\n                predEE[innerPadding:-innerPadding, innerPadding:-innerPadding],\n                EE[x-padding:x+innerCrop-padding, y-padding:y+innerCrop-padding]\n            )\n            continue\n        continue\n    return (EE, result)\n\ndef predictMasks(model, image, batch_size=64, debug=False):\n    watershed_energy, classes = inferImage(model, image, batch_size)\n\n    for x in extractMasks(watershed_energy, classes):\n      yield x\n    return\n\ndef predictCellsWithBox(model, image, minArea=10):\n    maskPadding = 5\n    for cellMask in predictMasks(model, image):\n        x1, y1, x2, y2 = cellBox = bbox(cellMask)\n        x1 = max((0, x1 - maskPadding))\n        y1 = max((0, y1 - maskPadding))\n        submask = cellMask[x1:x2+maskPadding, y1:y2+maskPadding]\n        yield submask, (x1, y1, x1 + submask.shape[0], y1 + submask.shape[1])\n        continue\n    return\n\ndef predictCells(model, image, minArea=10):\n    cells = []\n    for cellMask, cellBox in predictCellsWithBox(model, image):\n        cells.append((cellMask, cellBox, cellMask.sum()))\n        continue\n\n    # sort by mask area\n    cells = sorted(cells, key=lambda x: x[-1], reverse=True)\n\n    globalMask = np.zeros_like(image)\n    bigMask = np.zeros_like(image)\n    for cellMask, cellBox, cellArea in cells:\n        x1, y1, x2, y2 = cellBox\n        gsm = globalMask[x1:x2, y1:y2]\n        cellMask[0 < gsm] = 0\n\n        if cellMask.sum() < minArea: continue\n        globalMask[x1:x2, y1:y2] = np.maximum(gsm, cellMask)\n\n        bigMask[:] = 0\n        bigMask[x1:x2, y1:y2] = cellMask\n        yield bigMask\n        continue\n    return","metadata":{"id":"mlDL06-ROlde"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This block of code is for a relatively fair comparison of models. Of course, it's not good to test on training data, but I think it's acceptable in this case.","metadata":{"id":"DqRBpG0bcUCG"}},{"cell_type":"code","source":"def instancesIOU(GT, masks):\n    IoU = []\n    GT = GT.copy()\n    GTMask = np.zeros_like(GT)\n    maskPadding = 5\n    for cellMask in masks:\n        x1, y1, x2, y2 = cellBox = bbox(cellMask)\n        x1 = max((0, x1 - maskPadding))\n        y1 = max((0, y1 - maskPadding))\n        submask = cellMask[x1:x2+maskPadding, y1:y2+maskPadding]\n        submaskGT = GT[x1:x2+maskPadding, y1:y2+maskPadding]\n\n        bestIds = [(id, cnt) for id, cnt in zip(*np.unique(submaskGT[0 < submask], return_counts=True)) if 0 < id]\n        iou = 0.0\n        if bestIds:\n            bestOverlap = sorted(bestIds, key=lambda x: -x[1])[0][0]\n            GTMask[:] = 0\n            GTMask[GT == bestOverlap] = 1\n            # expand masks\n            x1, y1, x2, y2 = cellBox = bbox(GTMask)\n            x1 = max((0, x1 - maskPadding))\n            y1 = max((0, y1 - maskPadding))\n            submask = np.where(0 < cellMask[x1:x2+maskPadding, y1:y2+maskPadding], 1, 0)\n            submaskGT = GTMask[x1:x2+maskPadding, y1:y2+maskPadding, 0]\n            assert submaskGT.shape == submask.shape\n\n            intersection = (submask * submaskGT).sum()\n            union = submaskGT.sum() + submask.sum() - intersection\n            iou = (intersection + 1) / (union + 1)\n            GT[GT == bestOverlap] = 0\n        IoU.append(iou)\n        continue\n    # not detected instances\n    for _ in np.unique(GT[0 < GT]):\n        IoU.append(0.0)\n    return np.mean(IoU)\n\nif IS_COLAB:\n    IOUs = []\n    ids = train_df['id'].unique().tolist()\n    for id in ids:\n        sample = loadGrayscale(f\"{DATASET_PATH}/input/sartorius-cell-instance-segmentation/train/{id}.png\")\n        sampleIou = instancesIOU(\n            build_masks(id, bigInstances=True),\n            predictCells(model, sample)\n        )\n        IOUs.append(sampleIou)\n        print(len(IOUs), sampleIou)\n        continue\n\n    print(', '.join(['%s: %.4f' % (nm, getattr(np, nm)(IOUs)) for nm in ['min', 'max', 'mean', 'std']]))\n    if USE_WANDB:\n        f = plt.figure(1)\n        plt.hist(IOUs, bins=50)\n        f = plt.figure(1, figsize=(12, 12))\n        plt.hist(IOUs, bins=50)\n        plt.xlim(0, 1)\n        plt.ylim(0, 100)\n        f.savefig('iou.png')\n        wandb.log({'IOU Debug': wandb.Image('iou.png')})","metadata":{"id":"IEotAp3raFdR","outputId":"d079eaf8-23c5-4a04-cf28-04ae5d937483"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def rle_encoding(x):\n    dots = np.where(0 < x.flatten())[0]\n    run_lengths = []\n    prev = -2\n    for b in dots:\n        if (b>prev+1): run_lengths.extend((b + 1, 0))\n        run_lengths[-1] += 1\n        prev = b\n    return ' '.join(map(str, run_lengths))","metadata":{"id":"ohNx1gAJosT1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import glob\nids, masks = [], []\nfor fn in glob.glob(f\"{DATASET_PATH}/input/sartorius-cell-instance-segmentation/test/*.*\"):\n    id, _ = os.path.basename(fn).split('.')\n    sample = cv2.imread(fn, cv2.IMREAD_GRAYSCALE)\n    for mask in predictCells(model, sample):\n        ids.append(id)\n        masks.append(rle_encoding(mask))\n    continue\n\nsubmission = pd.DataFrame({'id':ids, 'predicted':masks})\nsubmission.to_csv('submission.csv', index=False)\nsubmission.head()","metadata":{"id":"wPsS1ZTIpt9E"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Ablation study, insights, notes, etc.","metadata":{"id":"ld52FOiL8iGO"}},{"cell_type":"markdown","source":"I decided to use the following settings as a baseline:\n\n- Input size: 200x200\n- Output size: 192x192\n- Output classes/channels: 16 (3 real classes + 13 for different types of the background)\n- Use estimated errors: yes\n- Entropy term: yes (min: 0.005, max: 0.03)\n- Skip connections: no\n- Latent Space Evaluator:\n  - Latent state: 24x24x16\n  - Latent global condition: 24x24x32\n  - Global/shared mixer block: yes\n  - Mixer configuration:\n    - Blocks: 3\n    - Hidden layers: 3 (conv2d with 3x3 kernel)\n    - Hidden layers activation: gelu\n    - Behaves like a simple residual block (final activation: linear. Predicted values added to input data).\n- Latent scoring/discriminator:\n  - Active: yes\n  - Branches: linear/raw input, layerwise softmax.\n  - Replicas: 1 (of each branch type. That is, two discriminators are used in total.)\n\n**CAUTION!** To save resources, in almost all experiments the models were trained for 50 epochs. Also, many experiments were repeated only once, so the obtained data cannot be considered statistically reliable.","metadata":{"id":"Q9zSUUVuA0tl"}},{"cell_type":"markdown","source":"- without EE\n- without Discriminator\n- without entropy\n- without extra info","metadata":{"id":"s9pV34P6ErIP"}},{"cell_type":"code","source":"","metadata":{"id":"rnP7_qZxD_cn"},"execution_count":null,"outputs":[]}]}