{"cells":[{"metadata":{},"cell_type":"markdown","source":"  ADIAS kernel - @copyright adias team\n* this notebook is owned by adias team and the team members have the right to edit,change,and customise this kernel.\n\n* the team members may add this kernel to their kaggle profile and remove this header if they wanted to\n","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"Introductory information on Melanoma from  [Anshul Sharma's notebook](https://www.kaggle.com/anshuls235/siim-isic-melanoma-analysis-eda-prediction)","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# <a id='mel'>1. What is Melanoma?</a>\n<a href='#toc'><span class=\"label label-info\">Go back to Table of Contents</span></a>\n## -> Overview\n<img src='https://www.mayoclinic.org/-/media/kcms/gbs/patient-consumer/images/2013/11/15/17/43/ds00190_-ds00439_im04411_mcdc7_melanomathu_jpg.jpg' style=\"width:500px;height:300px;\">\nMelanoma, the most serious type of skin cancer, develops in the cells (melanocytes) that produce melanin — the pigment that gives your skin its color. Melanoma can also form in your eyes and, rarely, inside your body, such as in your nose or throat.\n\nThe exact cause of all melanomas isn't clear, but exposure to ultraviolet (UV) radiation from sunlight or tanning lamps and beds increases your risk of developing melanoma. Limiting your exposure to UV radiation can help reduce your risk of melanoma.\n\nThe risk of melanoma seems to be increasing in people under 40, especially women. Knowing the warning signs of skin cancer can help ensure that cancerous changes are detected and treated before the cancer has spread. Melanoma can be treated successfully if it is detected early.\n## -> Symptoms\nMelanomas can develop anywhere on your body. They most often develop in areas that have had exposure to the sun, such as your back, legs, arms and face.\n\nMelanomas can also occur in areas that don't receive much sun exposure, such as the soles of your feet, palms of your hands and fingernail beds. These hidden melanomas are more common in people with darker skin.\n\nThe first melanoma signs and symptoms often are:\n\nA change in an existing mole\nThe development of a new pigmented or unusual-looking growth on your skin\nMelanoma doesn't always begin as a mole. It can also occur on otherwise normal-appearing skin.\n## -> Causes\n<img src='https://www.mayoclinic.org/-/media/kcms/gbs/patient-consumer/images/2013/11/15/17/40/ds00190_-ds00439_-ds00924_-ds00925_im02400_c7_skincancerthu_jpg.jpg' style=\"width:500px;height:300px;\">\nMelanoma occurs when something goes wrong in the melanin-producing cells (melanocytes) that give color to your skin.\n\nNormally, skin cells develop in a controlled and orderly way — healthy new cells push older cells toward your skin's surface, where they die and eventually fall off. But when some cells develop DNA damage, new cells may begin to grow out of control and can eventually form a mass of cancerous cells.\n\nJust what damages DNA in skin cells and how this leads to melanoma isn't clear. It's likely that a combination of factors, including environmental and genetic factors, causes melanoma. Still, doctors believe exposure to ultraviolet (UV) radiation from the sun and from tanning lamps and beds is the leading cause of melanoma.\n\nUV light doesn't cause all melanomas, especially those that occur in places on your body that don't receive exposure to sunlight. This indicates that other factors may contribute to your risk of melanoma.","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# Start..","execution_count":null},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport seaborn as sns\nsns.set()\n\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objects as go\n\nfrom PIL import Image\nimport pydicom\nfrom skimage.io import imread\n\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler\nfrom sklearn.cluster import KMeans\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import StratifiedKFold\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from matplotlib import rcParams\n\nrcParams['figure.figsize'] = 12,8","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from os import listdir\nlistdir(\"../input/\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"base = \"../input/siim-isic-melanoma-classification/\"\nmodels = \"../input/pytorch-pretrained-image-models/\"\nimagestats = \"../input/siimisic-melanoma-classification-image-stats/\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data = pd.read_csv(base + \"train.csv\")\ntrain_data.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data.dtypes","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"100*train_data.isnull().mean()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data.nunique()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"columns = ['sex', 'anatom_site_general_challenge', 'diagnosis', 'age_approx']\nfor column in columns:\n    uniques = train_data[column].unique().tolist()\n    print(column, ' - ', uniques)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data =  pd.read_csv(base + \"test.csv\")\ntest_data.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Data visualization and EDA","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"## General Info","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install chart_studio\nimport plotly.express as px\nimport chart_studio.plotly as py\nimport plotly.graph_objs as go\nfrom plotly.offline import iplot","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Total number of images in the dataset(train+test)\nprint(\"Total images in Train set: \",train_data['image_name'].count())\nprint(\"Total images in Test set: \",test_data['image_name'].count())","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Basic statistics","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"#### target vs. sex","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"groupped_data = train_data.groupby(['benign_malignant', 'sex']).agg({'target': 'count'})\ngroupped_data['%'] = groupped_data.groupby(level=0).apply(lambda x: 100*x/x.sum())\ngroupped_data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"groupped_data = train_data.groupby(['diagnosis']).agg({'age_approx': 'mean'})\ngroupped_data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"groupped_data = train_data.groupby(['diagnosis', 'anatom_site_general_challenge']).agg({'target': 'count'})\ngroupped_data['%'] = groupped_data.groupby(level=0).apply(lambda x: 100*x/x.sum())\ngroupped_data = groupped_data.drop(['target'], axis=1)\ngroupped_data = groupped_data.reset_index()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"g = sns.barplot(x='anatom_site_general_challenge', y='%', hue = 'diagnosis', data = groupped_data)\ng.legend(loc='upper right')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"groupped_data = train_data.groupby('patient_id').agg({'diagnosis': lambda x: x.nunique()}).reset_index()\n\nids = groupped_data.loc[groupped_data['diagnosis'] > 1, 'patient_id'].unique().tolist()\nprint('Patients with more than one diagnosis', len(set(ids)))\n\ndata = train_data.loc[train_data['patient_id'].isin(ids), ]\nids = data.loc[data['target'] == 1, 'patient_id'].unique().tolist()\nprint('Patients with melanoma and other diagnosis', len(ids))\ndata = data.loc[data['patient_id'].isin(ids), ]\ndata = data.sort_values(by=['patient_id', 'age_approx', 'image_name'])\ndata['d'] = 1","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ids = data['patient_id'].unique().tolist()\nl = []\nk = 0\n\nfor i, id in enumerate(ids):\n    d = data.loc[data['patient_id'] == id, ].copy()\n    d = d.reset_index(drop=True)\n    index = d.loc[d['target'] == 1, ].index.values[0]\n    if index == 0:\n        k = k+1\n        continue\n    else:\n        d = d[d.index < index]\n    groupped_data = d.groupby(['benign_malignant', 'diagnosis']).agg({'target': 'count'})\n    groupped_data['%'] = groupped_data.groupby(level=0).apply(lambda x: 100*x/x.sum())\n    groupped_data = groupped_data.reset_index()\n    l.append(groupped_data)\nprint(k)\nprint(len(l))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"groupped_data = train_data.groupby(['sex', 'diagnosis']).agg({'target': 'count'})\ngroupped_data['%'] = groupped_data.groupby(level=0).apply(lambda x: 100*x/x.sum())\ngroupped_data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"groupped_data = train_data.groupby(['sex', 'diagnosis']).agg({'age_approx': 'mean'})\ngroupped_data","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"#### target vs. age_approx","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.distplot(train_data.loc[train_data['target'] == 0, 'age_approx'], label='Benign')\nsns.distplot(train_data.loc[train_data['target'] == 1, 'age_approx'], label='Malignant')\nplt.legend()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"columns = ['benign_malignant', 'sex', 'target']\ntrain_data[columns].groupby(['benign_malignant', 'sex']).count()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# gender vs target\ntar=train_data.groupby(['target','sex'])['benign_malignant'].count().to_frame().reset_index()\ntar.style.background_gradient(cmap='Reds')  ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.catplot(x='target',y='benign_malignant', hue='sex',data=tar,kind='bar')\nplt.ylabel('Count')\nplt.xlabel('benign:0 vs malignant:1')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# melanoma vs average age\nplt.figure()\ntrain_data.groupby(['benign_malignant']).mean()['age_approx'].plot.bar(x = 'Diagnosis Type', y = 'Average age', rot = 0)\nplt.title('Benign/Malignant vs Average Age')\nplt.xlabel('Diagnosis Outcome')\nplt.ylabel('Average Approx. Age')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"basepath = \"../input/siim-isic-melanoma-classification/\"\nmodelspath = \"../input/pytorch-pretrained-image-models/\"\nimagestatspath = \"../input/siimisic-melanoma-classification-image-stats/\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nexample_files = os.listdir(basepath + \"train/\")[0:2]\nexample_files","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_image_stats = pd.read_csv(imagestatspath +  \"test_image_stats.csv\")\ntest_image_stats.head(1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_test = True","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if plot_test:\n    N = test_image_stats.shape[0]\n    selected_data = test_image_stats\n    my_title = \"Test image statistics\"\nelse:\n    N = train_image_stats.shape[0]\n    selected_data = train_image_stats\n    my_title = \"Train image statistics\"\n\ntrace1 = go.Scatter3d(\n    x=selected_data.img_mean.values[0:N], \n    y=selected_data.img_std.values[0:N],\n    z=selected_data.img_skew.values[0:N],\n    mode='markers',\n    text=selected_data[\"rows\"].values[0:N],\n    marker=dict(\n        color=selected_data[\"columns\"].values[0:N],\n        colorscale = \"Jet\",\n        colorbar=dict(thickness=10, title=\"image columns\", len=0.8),\n        opacity=0.4,\n        size=2\n    )\n)\n\nfigure_data = [trace1]\nlayout = go.Layout(\n    title = my_title,\n    scene = dict(\n        xaxis = dict(title=\"Image mean\"),\n        yaxis = dict(title=\"Image standard deviation\"),\n        zaxis = dict(title=\"Image skewness\"),\n    ),\n    margin=dict(\n        l=0,\n        r=0,\n        b=0,\n        t=0\n    ),\n    showlegend=True\n)\n\nfig = go.Figure(data=figure_data, layout=layout)\npy.iplot(fig, filename='simple-3d-scatter')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Machine Learning/DL","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install efficientnet","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nfrom sklearn import metrics\nfrom sklearn.model_selection import train_test_split\n\nimport tensorflow as tf\nimport tensorflow.keras.layers as L\nfrom kaggle_datasets import KaggleDatasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import auc\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau\nfrom tensorflow.keras.callbacks import CSVLogger\n\nimport numpy as np # the most important library in python\nimport pandas as pd\n\n\nimport efficientnet.keras as efn","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# TPU confugiration","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    print('Running on TPU ', tpu.master())\nexcept ValueError:\n    tpu = None\n\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nelse:\n    # Default distribution strategy in Tensorflow. Works on CPU and single GPU.\n    strategy = tf.distribute.get_strategy()\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## 2-Decoding data","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def decode_image(image):\n    image = tf.image.decode_jpeg(image, channels=3)\n    image = tf.cast(image, tf.float32) / 255.0\n    image = tf.reshape(image, [*IMAGE_SIZE, 3])\n    return image","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## 3-Augmenting the data","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def augmentation_pipeline(image, label):\n    image = tf.image.random_flip_left_right(image)\n    return image, label","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def read_tfrecord(example, labeled):\n    tfrecord_format = {\n        \"image\": tf.io.FixedLenFeature([], tf.string),\n        \"target\": tf.io.FixedLenFeature([], tf.int64)\n    } if labeled else {\n        \"image\": tf.io.FixedLenFeature([], tf.string),\n        \"image_name\": tf.io.FixedLenFeature([], tf.string)\n    }\n    example = tf.io.parse_single_example(example, tfrecord_format)\n    image = decode_image(example['image'])\n    if labeled:\n        label = tf.cast(example['target'], tf.int32)\n        return image, label\n    idnum = example['image_name']\n    return image, idnum","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Loading labeled Tensorflow records","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def read_labeled_tfrecord(example):\n    tfrec_format = {\n        'image'                        : tf.io.FixedLenFeature([], tf.string),\n        'image_name'                   : tf.io.FixedLenFeature([], tf.string),\n        'patient_id'                   : tf.io.FixedLenFeature([], tf.int64),\n        'sex'                          : tf.io.FixedLenFeature([], tf.int64),\n        'age_approx'                   : tf.io.FixedLenFeature([], tf.int64),\n        'anatom_site_general_challenge': tf.io.FixedLenFeature([], tf.int64),\n        'diagnosis'                    : tf.io.FixedLenFeature([], tf.int64),\n        'target'                       : tf.io.FixedLenFeature([], tf.int64)\n    }           \n    example = tf.io.parse_single_example(example, tfrec_format)\n    return example['image'], example['target']\n\n\ndef read_unlabeled_tfrecord(example, return_image_name):\n    tfrec_format = {\n        'image'                        : tf.io.FixedLenFeature([], tf.string),\n        'image_name'                   : tf.io.FixedLenFeature([], tf.string),\n    }\n    example = tf.io.parse_single_example(example, tfrec_format)\n    return example['image'], example['image_name'] if return_image_name else 0\n\n \ndef prepare_image(img, augment=True, dim=256):    \n    img = tf.image.decode_jpeg(img, channels=3)\n    img = tf.cast(img, tf.float32) / 255.0\n    \n    if augment:\n        img = transform(img,DIM=dim)\n        img = tf.image.random_flip_left_right(img)\n        #img = tf.image.random_hue(img, 0.01)\n        img = tf.image.random_saturation(img, 0.7, 1.3)\n        img = tf.image.random_contrast(img, 0.8, 1.2)\n        img = tf.image.random_brightness(img, 0.1)\n                      \n    img = tf.reshape(img, [dim,dim, 3])\n            \n    return img\n\ndef count_data_items(filenames):\n    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) \n         for filename in filenames]\n    return np.sum(n)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## working with hair augmentation","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"class AdvancedHairAugmentation:\n    \"\"\"\n    Impose an image of a hair to the target image\n\n    Args:\n        hairs (int): maximum number of hairs to impose\n        hairs_folder (str): path to the folder with hairs images\n    \"\"\"\n\n    def __init__(self, hairs: int = 5, hairs_folder: str = \"\"):\n        self.hairs = hairs\n        self.hairs_folder = hairs_folder\n\n    def __call__(self, img):\n        \"\"\"\n        Args:\n            img (PIL Image): Image to draw hairs on.\n\n        Returns:\n            PIL Image: Image with drawn hairs.\n        \"\"\"\n        n_hairs = random.randint(0, self.hairs)\n        \n        if not n_hairs:\n            return img\n        \n        height, width, _ = img.shape  # target image width and height\n        hair_images = [im for im in os.listdir(self.hairs_folder) if 'png' in im]\n        \n        for _ in range(n_hairs):\n            hair = cv2.imread(os.path.join(self.hairs_folder, random.choice(hair_images)))\n            hair = cv2.flip(hair, random.choice([-1, 0, 1]))\n            hair = cv2.rotate(hair, random.choice([0, 1, 2]))\n\n            h_height, h_width, _ = hair.shape  # hair image width and height\n            roi_ho = random.randint(0, img.shape[0] - hair.shape[0])\n            roi_wo = random.randint(0, img.shape[1] - hair.shape[1])\n            roi = img[roi_ho:roi_ho + h_height, roi_wo:roi_wo + h_width]\n\n            # Creating a mask and inverse mask\n            img2gray = cv2.cvtColor(hair, cv2.COLOR_BGR2GRAY)\n            ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)\n            mask_inv = cv2.bitwise_not(mask)\n\n            # Now black-out the area of hair in ROI\n            img_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)\n\n            # Take only region of hair from hair image.\n            hair_fg = cv2.bitwise_and(hair, hair, mask=mask)\n\n            # Put hair in ROI and modify the target image\n            dst = cv2.add(img_bg, hair_fg)\n\n            img[roi_ho:roi_ho + h_height, roi_wo:roi_wo + h_width] = dst\n                \n        return img\n\n    def __repr__(self):\n        return f'{self.__class__.__name__}(hairs={self.hairs}, hairs_folder=\"{self.hairs_folder}\")'","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Evaluation","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"### Cross entropy loss\n\n\n$$L_{bce} = - \\sum_{n}^{N} \\sum_{k}^{2} t_{n,k} \\cdot \\log(y_{n,k}) = \\sum_{n}^{N} \\cdot l_{bce}$$\n\n$$l_{bce} = - \\sum_{k}^{2} t_{n,k} \\cdot \\log(y_{n,k}) $$","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_ce_loss():   \n    criterion = torch.nn.CrossEntropyLoss()\n    return criterion","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Weighted cross entropy loss\n\n\n$$L_{bce} = - \\sum_{n}^{N} \\sum_{k}^{2} \\alpha_{k} \\cdot t_{n,k} \\cdot \\log(y_{n,k}) $$","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_wce_loss(train_targets):\n    weights = compute_class_weight(y=train_targets,\n                                   class_weight=\"balanced\",\n                                   classes=np.unique(train_targets))    \n    class_weights = torch.FloatTensor(weights)\n    if device.type==\"cuda\":\n        class_weights = class_weights.cuda()\n    criterion = torch.nn.CrossEntropyLoss(weight=class_weights)\n    return criterion","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Focal entropy loss","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"$$L_{focal} = - \\sum_{n}^{N} \\sum_{k}^{2} \\alpha_{k} \\cdot t_{n,k} \\cdot (1-y_{n,k})^{\\gamma} \\cdot \\log(y_{n,k})$$","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"class MulticlassFocalLoss(torch.nn.Module):\n    \n    def __init__(self, train_targets=None, gamma=2):\n        super(MulticlassFocalLoss, self).__init__()\n        self.gamma = gamma\n        if train_targets is None:\n            self.class_weights = None\n        else:\n            weights = compute_class_weight(y=train_targets,\n                                   class_weight=\"balanced\",\n                                   classes=np.unique(train_targets))    \n            self.class_weights = torch.FloatTensor(weights)\n            if device.type==\"cuda\":\n                self.class_weights = self.class_weights.cuda()\n    \n    def forward(self, input, target):\n        if self.class_weights is None:\n            ce_loss = F.cross_entropy(input, target, reduction='none')\n        else:\n            ce_loss = F.cross_entropy(input, target, reduction='none', weight=self.class_weights)\n        pt = torch.exp(-ce_loss)\n        loss = (1-pt)**self.gamma * ce_loss\n        return torch.mean(loss)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Train Model\n","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"\n# USE VERBOSE=0 for silent, VERBOSE=1 for interactive, VERBOSE=2 for commit\nVERBOSE = 0\nDISPLAY_PLOT = True\n\nskf = KFold(n_splits=FOLDS,shuffle=True,random_state=SEED)\noof_pred = []; oof_tar = []; oof_val = []; oof_names = []; oof_folds = [] \npreds = np.zeros((count_data_items(files_test),1))\n\nfor fold,(idxT,idxV) in enumerate(skf.split(np.arange(15))):\n    \n    # DISPLAY FOLD INFO\n    if DEVICE=='TPU':\n        if tpu: tf.tpu.experimental.initialize_tpu_system(tpu)\n    print('#'*25); print('#### FOLD',fold+1)\n    print('#### Image Size %i with EfficientNet B%i and batch_size %i'%\n          (IMG_SIZES[fold],EFF_NETS[fold],BATCH_SIZES[fold]*REPLICAS))\n    \n    # CREATE TRAIN AND VALIDATION SUBSETS\n    files_train = tf.io.gfile.glob([GCS_PATH[fold] + '/train%.2i*.tfrec'%x for x in idxT])\n    if INC2019[fold]:\n        files_train += tf.io.gfile.glob([GCS_PATH2[fold] + '/train%.2i*.tfrec'%x for x in idxT*2+1])\n        print('#### Using 2019 external data')\n    if INC2018[fold]:\n        files_train += tf.io.gfile.glob([GCS_PATH2[fold] + '/train%.2i*.tfrec'%x for x in idxT*2])\n        print('#### Using 2018+2017 external data')\n    np.random.shuffle(files_train); print('#'*25)\n    files_valid = tf.io.gfile.glob([GCS_PATH[fold] + '/train%.2i*.tfrec'%x for x in idxV])\n    files_test = np.sort(np.array(tf.io.gfile.glob(GCS_PATH[fold] + '/test*.tfrec')))\n    \n    # BUILD MODEL\n    K.clear_session()\n    with strategy.scope():\n        model = build_model(dim=IMG_SIZES[fold],ef=EFF_NETS[fold])\n        \n    # SAVE BEST MODEL EACH FOLD\n    sv = tf.keras.callbacks.ModelCheckpoint(\n        'fold-%i.h5'%fold, monitor='val_loss', verbose=0, save_best_only=True,\n        save_weights_only=True, mode='min', save_freq='epoch')\n   \n    # TRAIN\n    print('Training...')\n    history = model.fit(\n        get_dataset(files_train, augment=True, shuffle=True, repeat=True,\n                dim=IMG_SIZES[fold], batch_size = BATCH_SIZES[fold]), \n        epochs=EPOCHS[fold], callbacks = [sv,get_lr_callback(BATCH_SIZES[fold])], \n        steps_per_epoch=count_data_items(files_train)/BATCH_SIZES[fold]//REPLICAS,\n        validation_data=get_dataset(files_valid,augment=False,shuffle=False,\n                repeat=False,dim=IMG_SIZES[fold]), #class_weight = {0:1,1:2},\n        verbose=VERBOSE\n    )\n    \n    print('Loading best model...')\n    model.load_weights('fold-%i.h5'%fold)\n    \n    # PREDICT OOF USING TTA\n    print('Predicting OOF with TTA...')\n    ds_valid = get_dataset(files_valid,labeled=False,return_image_names=False,augment=True,\n            repeat=True,shuffle=False,dim=IMG_SIZES[fold],batch_size=BATCH_SIZES[fold]*4)\n    ct_valid = count_data_items(files_valid); STEPS = TTA * ct_valid/BATCH_SIZES[fold]/4/REPLICAS\n    pred = model.predict(ds_valid,steps=STEPS,verbose=VERBOSE)[:TTA*ct_valid,] \n    oof_pred.append( np.mean(pred.reshape((ct_valid,TTA),order='F'),axis=1) )                 \n    #oof_pred.append(model.predict(get_dataset(files_valid,dim=IMG_SIZES[fold]),verbose=1))\n    \n    # GET OOF TARGETS AND NAMES\n    ds_valid = get_dataset(files_valid, augment=False, repeat=False, dim=IMG_SIZES[fold],\n            labeled=True, return_image_names=True)\n    oof_tar.append( np.array([target.numpy() for img, target in iter(ds_valid.unbatch())]) )\n    oof_folds.append( np.ones_like(oof_tar[-1],dtype='int8')*fold )\n    ds = get_dataset(files_valid, augment=False, repeat=False, dim=IMG_SIZES[fold],\n                labeled=False, return_image_names=True)\n    oof_names.append( np.array([img_name.numpy().decode(\"utf-8\") for img, img_name in iter(ds.unbatch())]))\n    \n    # PREDICT TEST USING TTA\n    print('Predicting Test with TTA...')\n    ds_test = get_dataset(files_test,labeled=False,return_image_names=False,augment=True,\n            repeat=True,shuffle=False,dim=IMG_SIZES[fold],batch_size=BATCH_SIZES[fold]*4)\n    ct_test = count_data_items(files_test); STEPS = TTA * ct_test/BATCH_SIZES[fold]/4/REPLICAS\n    pred = model.predict(ds_test,steps=STEPS,verbose=VERBOSE)[:TTA*ct_test,] \n    preds[:,0] += np.mean(pred.reshape((ct_test,TTA),order='F'),axis=1) * WGTS[fold]\n    \n    # REPORT RESULTS\n    auc = roc_auc_score(oof_tar[-1],oof_pred[-1])\n    oof_val.append(np.max( history.history['val_auc'] ))\n    print('#### FOLD %i OOF AUC without TTA = %.3f, with TTA = %.3f'%(fold+1,oof_val[-1],auc))\n    \n    # PLOT TRAINING\n    if DISPLAY_PLOT:\n        plt.figure(figsize=(15,5))\n        plt.plot(np.arange(EPOCHS[fold]),history.history['auc'],'-o',label='Train AUC',color='#ff7f0e')\n        plt.plot(np.arange(EPOCHS[fold]),history.history['val_auc'],'-o',label='Val AUC',color='#1f77b4')\n        x = np.argmax( history.history['val_auc'] ); y = np.max( history.history['val_auc'] )\n        xdist = plt.xlim()[1] - plt.xlim()[0]; ydist = plt.ylim()[1] - plt.ylim()[0]\n        plt.scatter(x,y,s=200,color='#1f77b4'); plt.text(x-0.03*xdist,y-0.13*ydist,'max auc\\n%.2f'%y,size=14)\n        plt.ylabel('AUC',size=14); plt.xlabel('Epoch',size=14)\n        plt.legend(loc=2)\n        plt2 = plt.gca().twinx()\n        plt2.plot(np.arange(EPOCHS[fold]),history.history['loss'],'-o',label='Train Loss',color='#2ca02c')\n        plt2.plot(np.arange(EPOCHS[fold]),history.history['val_loss'],'-o',label='Val Loss',color='#d62728')\n        x = np.argmin( history.history['val_loss'] ); y = np.min( history.history['val_loss'] )\n        ydist = plt.ylim()[1] - plt.ylim()[0]\n        plt.scatter(x,y,s=200,color='#d62728'); plt.text(x-0.03*xdist,y+0.05*ydist,'min loss',size=14)\n        plt.ylabel('Loss',size=14)\n        plt.title('FOLD %i - Image Size %i, EfficientNet B%i, inc2019=%i, inc2018=%i'%\n                (fold+1,IMG_SIZES[fold],EFF_NETS[fold],INC2019[fold],INC2018[fold]),size=18)\n        plt.legend(loc=3)\n        plt.show()  \n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}},"execution_count":null},{"metadata":{},"cell_type":"markdown","source":"# Final submission","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nfrom matplotlib import pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"adias_submission = pd.DataFrame(dict(image_name=image_names, target=preds[:,0]))\nadias_submission = submission.sort_values('image_name') \nadias_submission.to_csv('adias_submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"adias_submission.head()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}