{"cells":[{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"!pip install lap\n# Read the dataset description\nimport gzip\n# Read or generate p2h, a dictionary of image name to image id (picture to hash)\nimport pickle\nimport platform\nimport random\n# Suppress annoying stderr output when importing keras.\nimport sys\nfrom lap import lapjv\nfrom math import sqrt\n# Determine the size of each image\nfrom os.path import isfile\n\nimport keras\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import figure\nimport seaborn as sns\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image as pil_image\nfrom imagehash import phash\nfrom keras import backend as K\nfrom keras import regularizers\nfrom keras.engine.topology import Input\nfrom keras.layers import Activation, Add, BatchNormalization, Concatenate, Conv2D, Dense, Flatten, GlobalMaxPooling2D, \\\n    Lambda, MaxPooling2D, Reshape\nfrom keras.models import Model\nfrom keras.optimizers import Adam\nfrom keras.preprocessing.image import img_to_array\nfrom keras.utils import Sequence\nfrom pandas import read_csv\nfrom scipy.ndimage import affine_transform\nfrom tqdm import tqdm_notebook as tqdm\nimport time\n\n%matplotlib inline","execution_count":1,"outputs":[{"output_type":"stream","text":"Collecting lap\n\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bf/64/d9fb6a75b15e783952b2fec6970f033462e67db32dc43dfbb404c14e91c2/lap-0.4.0.tar.gz (1.5MB)\n\u001b[K    100% |████████████████████████████████| 1.5MB 18.6MB/s ta 0:00:01\n\u001b[?25hBuilding wheels for collected packages: lap\n  Building wheel for lap (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Stored in directory: /tmp/.cache/pip/wheels/da/3e/af/eddcd6ffaa27df8d0ddac573758f8953c4e57c64c4c8c8b7d0\nSuccessfully built lap\nInstalling collected packages: lap\nSuccessfully installed lap-0.4.0\n\u001b[33mYou are using pip version 19.0.3, however version 19.1.1 is available.\nYou should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n","name":"stdout"},{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"Data Analysis for the set¶"},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nfig = plt.figure(figsize=(8, 8), dpi=100,facecolor='w', edgecolor='k')\ntrain_imgs = os.listdir(\"../input/humpback-whale-identification/train\")\nfor idx, img in enumerate(np.random.choice(train_imgs, 12)):\n    ax = fig.add_subplot(4, 20//5, idx+1, xticks=[], yticks=[])\n    im = pil_image.open(\"../input/humpback-whale-identification/train/\" + img)\n    plt.imshow(im)","execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x800 with 12 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# load image data\n#image name\ndf = pd.read_csv('../input/humpback-whale-identification/train.csv')\ndf.head()","execution_count":3,"outputs":[{"output_type":"execute_result","execution_count":3,"data":{"text/plain":"           Image         Id\n0  0000e88ab.jpg  w_f48451c\n1  0001f9222.jpg  w_c3d896a\n2  00029d126.jpg  w_20df2c5\n3  00050a15a.jpg  new_whale\n4  0005c1ef8.jpg  new_whale","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Image</th>\n      <th>Id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0000e88ab.jpg</td>\n      <td>w_f48451c</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0001f9222.jpg</td>\n      <td>w_c3d896a</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00029d126.jpg</td>\n      <td>w_20df2c5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00050a15a.jpg</td>\n      <td>new_whale</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0005c1ef8.jpg</td>\n      <td>new_whale</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(f'Training examples: {len(df)}')\nprint(\"Unique whales: \",df['Id'].nunique()) # it includes new_whale as a separate type.\ntraining_pts_per_class = df.groupby('Id').size()\nprint(\"Min example a class can have: \",training_pts_per_class.min())\nprint(\"0.99 quantile: \",training_pts_per_class.quantile(0.99))\nprint(\"Max example a class can have: \\n\",training_pts_per_class.nlargest(2)) ","execution_count":4,"outputs":[{"output_type":"stream","text":"Training examples: 25361\nUnique whales:  5005\nMin example a class can have:  1\n0.99 quantile:  22.0\nMax example a class can have: \n Id\nnew_whale    9664\nw_23a388d      73\ndtype: int64\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = training_pts_per_class.copy()\ndata.loc[data > data.quantile(0.99)] = '22+'\nplt.figure(figsize=(15,10))\nsns.countplot(data.astype('str'))\nplt.title(\"#classes with different number of images\",fontsize=15)\nplt.show()","execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x720 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Based on SNN with limited computing resources¶\nThe basic idea to solve the problem besed on SNN structure is in order to deal with highly unbanlenced dataset -- class 0 has more than a half of the pics in the whole dataset.\n\nWe are just using the pretrained weights from @martinpiotte.\n\nWe keep the basic structure of the Siamese (pretrained) 0.822 kernel, but we change the input as the RGB channels. And for the epochs , we narrow its number to 20 in this kernel for every model, but we use the pretrained weight to do the warm initialization.\n\nWe also get generated bounding boxes from this kernel here which saved as a .csv instead of pickle for readability."},{"metadata":{"trusted":true},"cell_type":"code","source":"TRAIN_DF = '../input/humpback-whale-identification/train.csv'\nSUB_Df = '../input/humpback-whale-identification/sample_submission.csv'\nTRAIN = '../input/humpback-whale-identification/train/'\nTEST = '../input/humpback-whale-identification/test/'\nP2H = '../input/metadata/p2h.pickle'\nP2SIZE = '../input/metadata/p2size.pickle'\nBB_DF = \"../input/metadata/bounding_boxes.csv\"\ntagged = dict([(p, w) for _, p, w in read_csv(TRAIN_DF).to_records()])\nsubmit = [p for _, p, _ in read_csv(SUB_Df).to_records()]\njoin = list(tagged.keys()) + submit","execution_count":6,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Duplicate image identification\nThis part was from the original kernel, seems like in the playground competition dulicated images was a real issue. I don't know the case about this one but I took one for the team and generated the results anyway. I'm such a nice chap."},{"metadata":{"trusted":true},"cell_type":"code","source":"def expand_path(p):\n    if isfile(TRAIN + p):\n        return TRAIN + p\n    if isfile(TEST + p):\n        return TEST + p\n    return p\n\nif isfile(P2SIZE):\n    print(\"P2SIZE exists.\")\n    with open(P2SIZE, 'rb') as f:\n        p2size = pickle.load(f)\nelse:\n    p2size = {}\n    for p in tqdm(join):\n        size = pil_image.open(expand_path(p)).size\n        p2size[p] = size","execution_count":7,"outputs":[{"output_type":"stream","text":"P2SIZE exists.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def match(h1, h2):\n    for p1 in h2ps[h1]:\n        for p2 in h2ps[h2]:\n            i1 = pil_image.open(expand_path(p1))\n            i2 = pil_image.open(expand_path(p2))\n            if i1.mode != i2.mode or i1.size != i2.size: return False\n            a1 = np.array(i1)\n            a1 = a1 - a1.mean()\n            a1 = a1 / sqrt((a1 ** 2).mean())\n            a2 = np.array(i2)\n            a2 = a2 - a2.mean()\n            a2 = a2 / sqrt((a2 ** 2).mean())\n            a = ((a1 - a2) ** 2).mean()\n            if a > 0.1: return False\n    return True\n\n\nif isfile(P2H):\n    print(\"P2H exists.\")\n    with open(P2H, 'rb') as f:\n        p2h = pickle.load(f)\nelse:\n    # Compute phash for each image in the training and test set.\n    p2h = {}\n    for p in tqdm(join):\n        img = pil_image.open(expand_path(p))\n        h = phash(img)\n        p2h[p] = h\n    # Find all images associated with a given phash value.\n    h2ps = {}\n    for p, h in p2h.items():\n        if h not in h2ps: h2ps[h] = []\n        if p not in h2ps[h]: h2ps[h].append(p)\n\n    # Find all distinct phash values\n    hs = list(h2ps.keys())\n\n    # If the images are close enough, associate the two phash values (this is the slow part: n^2 algorithm)\n    h2h = {}\n    for i, h1 in enumerate(tqdm(hs)):\n        for h2 in hs[:i]:\n            if h1 - h2 <= 6 and match(h1, h2):\n                s1 = str(h1)\n                s2 = str(h2)\n                if s1 < s2: s1, s2 = s2, s1\n                h2h[s1] = s2\n    # Group together images with equivalent phash, and replace by string format of phash (faster and more readable)\n    for p, h in p2h.items():\n        h = str(h)\n        if h in h2h: h = h2h[h]\n        p2h[p] = h\n#     with open(P2H, 'wb') as f:\n#         pickle.dump(p2h, f)\n# For each image id, determine the list of pictures\nh2ps = {}\nfor p, h in p2h.items():\n    if h not in h2ps: h2ps[h] = []\n    if p not in h2ps[h]: h2ps[h].append(p)","execution_count":8,"outputs":[{"output_type":"stream","text":"P2H exists.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def show_whale(imgs, per_row=2):\n    n = len(imgs)\n    rows = (n + per_row - 1) // per_row\n    cols = min(per_row, n)\n    fig, axes = plt.subplots(rows, cols, figsize=(24 // per_row * cols, 24 // per_row * rows))\n    for ax in axes.flatten(): ax.axis('off')\n    for i, (img, ax) in enumerate(zip(imgs, axes.flatten())): ax.imshow(img.convert('RGB'))\n        \n\ndef read_raw_image(p):\n    img = pil_image.open(expand_path(p))\n    return img","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# For each images id, select the prefered image\ndef prefer(ps):\n    if len(ps) == 1: return ps[0]\n    best_p = ps[0]\n    best_s = p2size[best_p]\n    for i in range(1, len(ps)):\n        p = ps[i]\n        s = p2size[p]\n        if s[0] * s[1] > best_s[0] * best_s[1]:  # Select the image with highest resolution\n            best_p = p\n            best_s = s\n    return best_p\n\nh2p = {}\nfor h, ps in h2ps.items():\n    h2p[h] = prefer(ps)\nlen(h2p), list(h2p.items())[:5]","execution_count":10,"outputs":[{"output_type":"execute_result","execution_count":10,"data":{"text/plain":"(33317,\n [('d26698c3271c757c', '0000e88ab.jpg'),\n  ('ba8cc231ad489b77', '0001f9222.jpg'),\n  ('bbcad234a52d0f0b', '00029d126.jpg'),\n  ('c09ae7dc09f33a29', '00050a15a.jpg'),\n  ('d02f65ba9f74a08a', '0005c1ef8.jpg')])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Read the bounding box data from the bounding box kernel (see reference above)\np2bb = pd.read_csv(BB_DF).set_index(\"Image\")\n\nold_stderr = sys.stderr\nsys.stderr = open('/dev/null' if platform.system() != 'Windows' else 'nul', 'w')\n\nsys.stderr = old_stderr\n\nimg_shape = (384, 384, 1)  # The image shape used by the model\nanisotropy = 2.15  # The horizontal compression ratio\ncrop_margin = 0.05  # The margin added around the bounding box to compensate for bounding box inaccuracy","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def build_transform(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):\n    \"\"\"\n    Build a transformation matrix with the specified characteristics.\n    \"\"\"\n    rotation = np.deg2rad(rotation)\n    shear = np.deg2rad(shear)\n    rotation_matrix = np.array(\n        [[np.cos(rotation), np.sin(rotation), 0], [-np.sin(rotation), np.cos(rotation), 0], [0, 0, 1]])\n    shift_matrix = np.array([[1, 0, height_shift], [0, 1, width_shift], [0, 0, 1]])\n    shear_matrix = np.array([[1, np.sin(shear), 0], [0, np.cos(shear), 0], [0, 0, 1]])\n    zoom_matrix = np.array([[1.0 / height_zoom, 0, 0], [0, 1.0 / width_zoom, 0], [0, 0, 1]])\n    shift_matrix = np.array([[1, 0, -height_shift], [0, 1, -width_shift], [0, 0, 1]])\n    return np.dot(np.dot(rotation_matrix, shear_matrix), np.dot(zoom_matrix, shift_matrix))","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def read_cropped_image(p, augment):\n    \"\"\"\n    @param p : the name of the picture to read\n    @param augment: True/False if data augmentation should be performed\n    @return a numpy array with the transformed image\n    \"\"\"\n    # If an image id was given, convert to filename\n    if p in h2p:\n        p = h2p[p]\n    size_x, size_y = p2size[p]\n\n    # Determine the region of the original image we want to capture based on the bounding box.\n    row = p2bb.loc[p]\n    x0, y0, x1, y1 = row['x0'], row['y0'], row['x1'], row['y1']\n    dx = x1 - x0\n    dy = y1 - y0\n    x0 -= dx * crop_margin\n    x1 += dx * crop_margin + 1\n    y0 -= dy * crop_margin\n    y1 += dy * crop_margin + 1\n    if x0 < 0:\n        x0 = 0\n    if x1 > size_x:\n        x1 = size_x\n    if y0 < 0:\n        y0 = 0\n    if y1 > size_y:\n        y1 = size_y\n    dx = x1 - x0\n    dy = y1 - y0\n    if dx > dy * anisotropy:\n        dy = 0.5 * (dx / anisotropy - dy)\n        y0 -= dy\n        y1 += dy\n    else:\n        dx = 0.5 * (dy * anisotropy - dx)\n        x0 -= dx\n        x1 += dx\n        \n    # Generate the transformation matrix\n    trans = np.array([[1, 0, -0.5 * img_shape[0]], [0, 1, -0.5 * img_shape[1]], [0, 0, 1]])\n    trans = np.dot(np.array([[(y1 - y0) / img_shape[0], 0, 0], [0, (x1 - x0) / img_shape[1], 0], [0, 0, 1]]), trans)\n    if augment:\n        trans = np.dot(build_transform(\n            random.uniform(-5, 5),\n            random.uniform(-5, 5),\n            random.uniform(0.8, 1.0),\n            random.uniform(0.8, 1.0),\n            random.uniform(-0.05 * (y1 - y0), 0.05 * (y1 - y0)),\n            random.uniform(-0.05 * (x1 - x0), 0.05 * (x1 - x0))\n        ), trans)\n    trans = np.dot(np.array([[1, 0, 0.5 * (y1 + y0)], [0, 1, 0.5 * (x1 + x0)], [0, 0, 1]]), trans)\n\n    # Read the image, transform to black and white and comvert to numpy array\n    img = read_raw_image(p).convert('L')\n    img = img_to_array(img)\n\n    # Apply affine transformation\n    matrix = trans[:2, :2]\n    offset = trans[:2, 2]\n    img = img.reshape(img.shape[:-1])\n    img = affine_transform(img, matrix, offset, output_shape=img_shape[:-1], order=1, mode='constant',\n                           cval=np.average(img))\n    img = img.reshape(img_shape)\n\n    # Normalize to zero mean and unit variance\n    img -= np.mean(img, keepdims=True)\n    img /= np.std(img, keepdims=True) + K.epsilon()\n    return img\n\ndef read_for_training(p):\n    \"\"\"\n    Read and preprocess an image with data augmentation (random transform).\n    \"\"\"\n    return read_cropped_image(p, True)\n\n\ndef read_for_validation(p):\n    \"\"\"\n    Read and preprocess an image without data augmentation (use for testing).\n    \"\"\"\n    return read_cropped_image(p, False)\n\n\np = list(tagged.keys())[312]\nprint(p)","execution_count":13,"outputs":[{"output_type":"stream","text":"031b230c7.jpg\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# # Dependent for SNN Model structure\n# from keras import backend as K\n# from keras import regularizers\n# from keras.applications.xception import Xception\n# from keras.applications.densenet import DenseNet121, DenseNet169\n# from keras.applications.inception_v3 import InceptionV3\n# from keras.applications.mobilenet import MobileNet\n# from keras.applications.resnet50 import ResNet50\n# from keras.applications.nasnet import NASNetMobile\n# from keras.engine.topology import Input\n# from keras.layers import Activation, Add, BatchNormalization, Concatenate, Conv2D, Dense, Flatten, GlobalMaxPooling2D, \\\n#     Lambda, MaxPooling2D, Reshape, GlobalAveragePooling2D\n# from keras.models import Model\n# from keras.optimizers import Adam\n","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Here we only should the self-designed cnn layers, the Pretrained layers like dense121 are well packaged.\ndef subblock(x, filter, **kwargs):\n    x = BatchNormalization()(x)\n    y = x\n    y = Conv2D(filter, (1, 1), activation='relu', **kwargs)(y)  # Reduce the number of features to 'filter'\n    y = BatchNormalization()(y)\n    y = Conv2D(filter, (3, 3), activation='relu', **kwargs)(y)  # Extend the feature field\n    y = BatchNormalization()(y)\n    y = Conv2D(K.int_shape(x)[-1], (1, 1), **kwargs)(y)  # no activation # Restore the number of original features\n    y = Add()([x, y])  # Add the bypass connection\n    y = Activation('relu')(y)\n    return y\n\n\ndef build_model(lr, l2, activation='sigmoid'):\n    ##############\n    # BRANCH MODEL\n    ##############\n    regul = regularizers.l2(l2)\n    optim = Adam(lr=lr)\n    kwargs = {'padding': 'same', 'kernel_regularizer': regul}\n\n    inp = Input(shape=img_shape)  # 384x384x1\n    x = Conv2D(64, (9, 9), strides=2, activation='relu', **kwargs)(inp)\n\n    x = MaxPooling2D((2, 2), strides=(2, 2))(x)  # 96x96x64\n    for _ in range(2):\n        x = BatchNormalization()(x)\n        x = Conv2D(64, (3, 3), activation='relu', **kwargs)(x)\n\n    x = MaxPooling2D((2, 2), strides=(2, 2))(x)  # 48x48x64\n    x = BatchNormalization()(x)\n    x = Conv2D(128, (1, 1), activation='relu', **kwargs)(x)  # 48x48x128\n    for _ in range(4):\n        x = subblock(x, 64, **kwargs)\n\n    x = MaxPooling2D((2, 2), strides=(2, 2))(x)  # 24x24x128\n    x = BatchNormalization()(x)\n    x = Conv2D(256, (1, 1), activation='relu', **kwargs)(x)  # 24x24x256\n    for _ in range(4):\n        x = subblock(x, 64, **kwargs)\n\n    x = MaxPooling2D((2, 2), strides=(2, 2))(x)  # 12x12x256\n    x = BatchNormalization()(x)\n    x = Conv2D(384, (1, 1), activation='relu', **kwargs)(x)  # 12x12x384\n    for _ in range(4):\n        x = subblock(x, 96, **kwargs)\n    x = MaxPooling2D((2, 2), strides=(2, 2))(x)  # 6x6x384\n    x = BatchNormalization()(x)\n    x = Conv2D(512, (1, 1), activation='relu', **kwargs)(x)  # 6x6x512\n    for _ in range(4):\n        x = subblock(x, 128, **kwargs)\n\n    x = GlobalMaxPooling2D()(x)  # 512\n    branch_model = Model(inp, x)\n\n    ############\n    # HEAD MODEL\n    ############\n    mid = 32\n    xa_inp = Input(shape=branch_model.output_shape[1:])\n    xb_inp = Input(shape=branch_model.output_shape[1:])\n    x1 = Lambda(lambda x: x[0] * x[1])([xa_inp, xb_inp])\n    x2 = Lambda(lambda x: x[0] + x[1])([xa_inp, xb_inp])\n    x3 = Lambda(lambda x: K.abs(x[0] - x[1]))([xa_inp, xb_inp])\n    x4 = Lambda(lambda x: K.square(x))(x3)\n    x = Concatenate()([x1, x2, x3, x4])\n    x = Reshape((4, branch_model.output_shape[1], 1), name='reshape1')(x)\n\n    # Per feature NN with shared weight is implemented using CONV2D with appropriate stride.\n    x = Conv2D(mid, (4, 1), activation='relu', padding='valid')(x)\n    x = Reshape((branch_model.output_shape[1], mid, 1))(x)\n    x = Conv2D(1, (1, mid), activation='linear', padding='valid')(x)\n    x = Flatten(name='flatten')(x)\n\n    # Weighted sum implemented as a Dense layer.\n    x = Dense(1, use_bias=True, activation=activation, name='weighted-average')(x)\n    head_model = Model([xa_inp, xb_inp], x, name='head')\n\n    ########################\n    # SIAMESE NEURAL NETWORK\n    ########################\n    # Complete model is constructed by calling the branch model on each input image,\n    # and then the head model on the resulting 512-vectors.\n    img_a = Input(shape=img_shape)\n    img_b = Input(shape=img_shape)\n    xa = branch_model(img_a)\n    xb = branch_model(img_b)\n    x = head_model([xa, xb])\n    model = Model([img_a, img_b], x)\n    model.compile(optim, loss='binary_crossentropy', metrics=['binary_crossentropy', 'acc'])\n    return model, branch_model, head_model\n\nmodel, branch_model, head_model = build_model(64e-5, 0)","execution_count":15,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nColocations handled automatically by placer.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"h2ws = {}\nnew_whale = 'new_whale'\nfor p, w in tagged.items():\n    if w != new_whale:  # Use only identified whales\n        h = p2h[p]\n        if h not in h2ws: h2ws[h] = []\n        if w not in h2ws[h]: h2ws[h].append(w)\nfor h, ws in h2ws.items():\n    if len(ws) > 1:\n        h2ws[h] = sorted(ws)\n\n# For each whale, find the unambiguous images ids.\nw2hs = {}\nfor h, ws in h2ws.items():\n    if len(ws) == 1:  # Use only unambiguous pictures\n        w = ws[0]\n        if w not in w2hs: w2hs[w] = []\n        if h not in w2hs[w]: w2hs[w].append(h)\nfor w, hs in w2hs.items():\n    if len(hs) > 1:\n        w2hs[w] = sorted(hs)","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = []  # A list of training image ids\nfor hs in w2hs.values():\n    if len(hs) > 1:\n        train += hs\nrandom.shuffle(train)\ntrain_set = set(train)\n\nw2ts = {}  # Associate the image ids from train to each whale id.\nfor w, hs in w2hs.items():\n    for h in hs:\n        if h in train_set:\n            if w not in w2ts:\n                w2ts[w] = []\n            if h not in w2ts[w]:\n                w2ts[w].append(h)\nfor w, ts in w2ts.items():\n    w2ts[w] = np.array(ts)\n\nt2i = {}  # The position in train of each training image id\nfor i, t in enumerate(train):\n    t2i[t] = i","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class TrainingData(Sequence):\n    def __init__(self, score, steps=1000, batch_size=32):\n        \"\"\"\n        @param score the cost matrix for the picture matching\n        @param steps the number of epoch we are planning with this score matrix\n        \"\"\"\n        super(TrainingData, self).__init__()\n        self.score = -score  # Maximizing the score is the same as minimuzing -score.\n        self.steps = steps\n        self.batch_size = batch_size\n        for ts in w2ts.values():\n            idxs = [t2i[t] for t in ts]\n            for i in idxs:\n                for j in idxs:\n                    self.score[\n                        i, j] = 10000.0  # Set a large value for matching whales -- eliminates this potential pairing\n        self.on_epoch_end()\n\n    def __getitem__(self, index):\n        start = self.batch_size * index\n        end = min(start + self.batch_size, len(self.match) + len(self.unmatch))\n        size = end - start\n        assert size > 0\n        a = np.zeros((size,) + img_shape, dtype=K.floatx())\n        b = np.zeros((size,) + img_shape, dtype=K.floatx())\n        c = np.zeros((size, 1), dtype=K.floatx())\n        j = start // 2\n        for i in range(0, size, 2):\n            a[i, :, :, :] = read_for_training(self.match[j][0])\n            b[i, :, :, :] = read_for_training(self.match[j][1])\n            c[i, 0] = 1  # This is a match\n            a[i + 1, :, :, :] = read_for_training(self.unmatch[j][0])\n            b[i + 1, :, :, :] = read_for_training(self.unmatch[j][1])\n            c[i + 1, 0] = 0  # Different whales\n            j += 1\n        return [a, b], c\n\n    def on_epoch_end(self):\n        if self.steps <= 0: return  # Skip this on the last epoch.\n        self.steps -= 1\n        self.match = []\n        self.unmatch = []\n        _, _, x = lapjv(self.score)  # Solve the linear assignment problem\n        y = np.arange(len(x), dtype=np.int32)\n\n        # Compute a derangement for matching whales\n        for ts in w2ts.values():\n            d = ts.copy()\n            while True:\n                random.shuffle(d)\n                if not np.any(ts == d): break\n            for ab in zip(ts, d): self.match.append(ab)\n\n        # Construct unmatched whale pairs from the LAP solution.\n        for i, j in zip(x, y):\n            if i == j:\n                print(self.score)\n                print(x)\n                print(y)\n                print(i, j)\n            assert i != j\n            self.unmatch.append((train[i], train[j]))\n\n        # Force a different choice for an eventual next epoch.\n        self.score[x, y] = 10000.0\n        self.score[y, x] = 10000.0\n        random.shuffle(self.match)\n        random.shuffle(self.unmatch)\n        # print(len(self.match), len(train), len(self.unmatch), len(train))\n        assert len(self.match) == len(train) and len(self.unmatch) == len(train)\n\n    def __len__(self):\n        return (len(self.match) + len(self.unmatch) + self.batch_size - 1) // self.batch_size\n\n# Test on a batch of 32 with random costs.\nscore = np.random.random_sample(size=(len(train), len(train)))\ndata = TrainingData(score)\n(a, b), c = data[0]","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# A Keras generator to evaluate only the BRANCH MODEL\nclass FeatureGen(Sequence):\n    def __init__(self, data, batch_size=64, verbose=1):\n        super(FeatureGen, self).__init__()\n        self.data = data\n        self.batch_size = batch_size\n        self.verbose = verbose\n        if self.verbose > 0: self.progress = tqdm(total=len(self), desc='Features')\n\n    def __getitem__(self, index):\n        start = self.batch_size * index\n        size = min(len(self.data) - start, self.batch_size)\n        a = np.zeros((size,) + img_shape, dtype=K.floatx())\n        for i in range(size): a[i, :, :, :] = read_for_validation(self.data[start + i])\n        if self.verbose > 0:\n            self.progress.update()\n            if self.progress.n >= len(self): self.progress.close()\n        return a\n\n    def __len__(self):\n        return (len(self.data) + self.batch_size - 1) // self.batch_size\n\nclass ScoreGen(Sequence):\n    def __init__(self, x, y=None, batch_size=2048, verbose=1):\n        super(ScoreGen, self).__init__()\n        self.x = x\n        self.y = y\n        self.batch_size = batch_size\n        self.verbose = verbose\n        if y is None:\n            self.y = self.x\n            self.ix, self.iy = np.triu_indices(x.shape[0], 1)\n        else:\n            self.iy, self.ix = np.indices((y.shape[0], x.shape[0]))\n            self.ix = self.ix.reshape((self.ix.size,))\n            self.iy = self.iy.reshape((self.iy.size,))\n        self.subbatch = (len(self.x) + self.batch_size - 1) // self.batch_size\n        if self.verbose > 0:\n            self.progress = tqdm(total=len(self), desc='Scores')\n\n    def __getitem__(self, index):\n        start = index * self.batch_size\n        end = min(start + self.batch_size, len(self.ix))\n        a = self.y[self.iy[start:end], :]\n        b = self.x[self.ix[start:end], :]\n        if self.verbose > 0:\n            self.progress.update()\n            if self.progress.n >= len(self): self.progress.close()\n        return [a, b]\n\n    def __len__(self):\n        return (len(self.ix) + self.batch_size - 1) // self.batch_size","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def set_lr(model, lr):\n    K.set_value(model.optimizer.lr, float(lr))\n\n\ndef get_lr(model):\n    return K.get_value(model.optimizer.lr)\n\n\ndef score_reshape(score, x, y=None):\n    \"\"\"\n    Tranformed the packed matrix 'score' into a square matrix.\n    @param score the packed matrix\n    @param x the first image feature tensor\n    @param y the second image feature tensor if different from x\n    @result the square matrix\n    \"\"\"\n    if y is None:\n        # When y is None, score is a packed upper triangular matrix.\n        # Unpack, and transpose to form the symmetrical lower triangular matrix.\n        m = np.zeros((x.shape[0], x.shape[0]), dtype=K.floatx())\n        m[np.triu_indices(x.shape[0], 1)] = score.squeeze()\n        m += m.transpose()\n    else:\n        m = np.zeros((y.shape[0], x.shape[0]), dtype=K.floatx())\n        iy, ix = np.indices((y.shape[0], x.shape[0]))\n        ix = ix.reshape((ix.size,))\n        iy = iy.reshape((iy.size,))\n        m[iy, ix] = score.squeeze()\n    return m\n\n\ndef compute_score(verbose=1):\n    \"\"\"\n    Compute the score matrix by scoring every pictures from the training set against every other picture O(n^2).\n    \"\"\"\n    features = branch_model.predict_generator(FeatureGen(train, verbose=verbose), max_queue_size=12, workers=6,\n                                              verbose=0)\n    score = head_model.predict_generator(ScoreGen(features, verbose=verbose), max_queue_size=12, workers=6, verbose=0)\n    score = score_reshape(score, features)\n    return features, score\ndef make_steps(step, ampl):\n    \"\"\"\n    Perform training epochs\n    @param step Number of epochs to perform\n    @param ampl the K, the randomized component of the score matrix.\n    \"\"\"\n    global w2ts, t2i, steps, features, score, histories\n\n    # shuffle the training pictures\n    random.shuffle(train)\n\n    # Map whale id to the list of associated training picture hash value\n    w2ts = {}\n    for w, hs in w2hs.items():\n        for h in hs:\n            if h in train_set:\n                if w not in w2ts: w2ts[w] = []\n                if h not in w2ts[w]: w2ts[w].append(h)\n    for w, ts in w2ts.items(): w2ts[w] = np.array(ts)\n\n    # Map training picture hash value to index in 'train' array    \n    t2i = {}\n    for i, t in enumerate(train): t2i[t] = i\n\n    # Compute the match score for each picture pair\n    features, score = compute_score()\n\n    # Train the model for 'step' epochs\n    history = model.fit_generator(\n        TrainingData(score + ampl * np.random.random_sample(size=score.shape), steps=step, batch_size=32),\n        initial_epoch=steps, epochs=steps + step, max_queue_size=12, workers=6, verbose=1).history\n    steps += step\n\n    # Collect history data\n    history['epochs'] = steps\n    history['ms'] = np.mean(score)\n    history['lr'] = get_lr(model)\n    print(history['epochs'], history['lr'], history['ms'])\n    histories.append(history)","execution_count":20,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"histories = []\nsteps = 0\n\nif isfile('../input/humpback-whale-identification-model-files/mpiotte-standard.model'):\n    print('pretrained exist.')\n    tmp = keras.models.load_model('../input/humpback-whale-identification-model-files/mpiotte-standard.model')\n    model.set_weights(tmp.get_weights())\nelse:\n    # epoch -> 10\n    make_steps(10, 1000)\n    ampl = 100.0\n    for _ in range(2):\n        print('noise ampl.  = ', ampl)\n        make_steps(5, ampl)\n        ampl = max(1.0, 100 ** -0.1 * ampl)\n        \nmodel.summary()","execution_count":21,"outputs":[{"output_type":"stream","text":"pretrained exist.\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_4 (InputLayer)            (None, 384, 384, 1)  0                                            \n__________________________________________________________________________________________________\ninput_5 (InputLayer)            (None, 384, 384, 1)  0                                            \n__________________________________________________________________________________________________\nmodel_1 (Model)                 (None, 512)          2692096     input_4[0][0]                    \n                                                                 input_5[0][0]                    \n__________________________________________________________________________________________________\nhead (Model)                    (None, 1)            706         model_1[1][0]                    \n                                                                 model_1[2][0]                    \n==================================================================================================\nTotal params: 2,692,802\nTrainable params: 2,675,010\nNon-trainable params: 17,792\n__________________________________________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def prepare_submission(threshold, filename):\n    \"\"\"\n    Generate a Kaggle submission file.\n    @param threshold the score given to 'new_whale'\n    @param filename the submission file name\n    \"\"\"\n    vtop = 0\n    vhigh = 0\n    pos = [0, 0, 0, 0, 0, 0]\n    with open(filename, 'wt', newline='\\n') as f:\n        f.write('Image,Id\\n')\n        for i, p in enumerate(tqdm(submit)):\n            t = []\n            s = set()\n            a = score[i, :]\n            for j in list(reversed(np.argsort(a))):\n                h = known[j]\n                if a[j] < threshold and new_whale not in s:\n                    pos[len(t)] += 1\n                    s.add(new_whale)\n                    t.append(new_whale)\n                    if len(t) == 5: break;\n                for w in h2ws[h]:\n                    assert w != new_whale\n                    if w not in s:\n                        if a[j] > 1.0:\n                            vtop += 1\n                        elif a[j] >= threshold:\n                            vhigh += 1\n                        s.add(w)\n                        t.append(w)\n                        if len(t) == 5: break;\n                if len(t) == 5: break;\n            if new_whale not in s: pos[5] += 1\n            assert len(t) == 5 and len(s) == 5\n            f.write(p + ',' + ' '.join(t[:5]) + '\\n')\n    return vtop, vhigh, pos","execution_count":22,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Find elements from training sets not 'new_whale'\ntic = time.time()\nh2ws = {}\nfor p, w in tagged.items():\n    if w != new_whale:  # Use only identified whales\n        h = p2h[p]\n        if h not in h2ws: h2ws[h] = []\n        if w not in h2ws[h]: h2ws[h].append(w)\nknown = sorted(list(h2ws.keys()))\n\n# Dictionary of picture indices\nh2i = {}\nfor i, h in enumerate(known): h2i[h] = i\n\n# Evaluate the model.\nfknown = branch_model.predict_generator(FeatureGen(known), max_queue_size=20, workers=10, verbose=0)\nfsubmit = branch_model.predict_generator(FeatureGen(submit), max_queue_size=20, workers=10, verbose=0)\nscore = head_model.predict_generator(ScoreGen(fknown, fsubmit), max_queue_size=20, workers=10, verbose=0)\nscore = score_reshape(score, fknown, fsubmit)\n\n# Generate the subsmission file.\nprepare_submission(0.99, 'submission_for_em.csv')\ntoc = time.time()\nprint(\"Submission time: \", (toc - tic) / 60.)\n# Here the submission_for_em.csv, we use other way to get upload it get the leaderboard score.","execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, description='Features', max=246, style=ProgressStyle(description_width='in…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"68bfff5be8d64684b3b207c73015130c"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, description='Features', max=125, style=ProgressStyle(description_width='in…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bb5ce04e35704d7d9ee2d5912b8efb98"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, description='Scores', max=61006, style=ProgressStyle(description_width='in…","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"05fd92f04443425eb0036ab0585efdd8"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=7960), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8ff21f4b816c42aebc167f70efa82a82"}},"metadata":{}},{"output_type":"stream","text":"\nSubmission time:  16.428190914789834\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"By training differrnt rounds of the model, the more we train, the better result we have, but the performance will be worse. For each round, we need nearly 5 hours to finish but the total session of the kernel is 9 hours, so we desided to store each round's submission result and combine them in the last round by emsembling."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Emsembling we only choose the 4 most different results from 4 different model structure to compute the final result \nimport csv\n\nsub_files = ['../input/em-whale/submission_0.csv',\n             '../input/em-whale/submission_7.csv',\n            '../input/em-whale/submission_8.csv',\n            '../input/em-whale/submission_3.csv']\nsub_weight = [0.8500**2,\n              0.8500**2,\n             0.8500**2,\n             0.8500**2]\nHlabel = 'Image' \nHtarget = 'Id'\nnpt = 6\nplace_weights = {}\nfor i in range(npt):\n    place_weights[i] = ( 1 / (i + 1) )\n    \nprint(place_weights)\n\nlg = len(sub_files)\nsub = [None]*lg\nfor i, file in enumerate( sub_files ):\n \n    print(\"Reading {}: w={} - {}\". format(i, sub_weight[i], file))\n    reader = csv.DictReader(open(file,\"r\"))\n    sub[i] = sorted(reader, key=lambda d: str(d[Hlabel]))\n\nout = open(\"submission.csv\", \"w\", newline='')\nwriter = csv.writer(out)\nwriter.writerow([Hlabel,Htarget])\n\nfor p, row in enumerate(sub[0]):\n    target_weight = {}\n    for s in range(lg):\n        row1 = sub[s][p]\n        for ind, trgt in enumerate(row1[Htarget].split(' ')):\n            target_weight[trgt] = target_weight.get(trgt,0) + (place_weights[ind]*sub_weight[s])\n    tops_trgt = sorted(target_weight, key=target_weight.get, reverse=True)[:npt]\n    writer.writerow([row1[Hlabel], \" \".join(tops_trgt)])\nout.close()","execution_count":24,"outputs":[{"output_type":"stream","text":"{0: 1.0, 1: 0.5, 2: 0.3333333333333333, 3: 0.25, 4: 0.2, 5: 0.16666666666666666}\nReading 0: w=0.79227801 - ../input/em-whale/submission_0.csv\nReading 1: w=0.59582961 - ../input/em-whale/submission_7.csv\nReading 2: w=0.6771644099999999 - ../input/em-whale/submission_8.csv\nReading 3: w=0.82864609 - ../input/em-whale/submission_3.csv\n","name":"stdout"}]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}