{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Introduction\n\nThis kernel is used for inference purposes on the ResNet-50 pretrained model on old and new data set. The model and preprocessing steps are present [here](https://www.kaggle.com/vikasmalhotra08/aptos19-resnet-trained-with-old-and-new-data)\n\nAs a recap, I trained a ResNet on both old (2015) and new (2019) competition dataset on my machine as kernels lead to memory overflow.\n\nI am importing the pre-trained model weights from [here](https://www.kaggle.com/vikasmalhotra08/resnet50trainedwithaptosolddataset)"},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nprint(os.listdir(\"../input\"))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport os, sys\nimport matplotlib.pyplot as plt\nimport skimage.io\n\nfrom skimage.transform import resize\nfrom imgaug import augmenters as iaa\nfrom tqdm import tqdm\nimport PIL \n\nfrom PIL import Image, ImageOps\nimport cv2\n\nfrom sklearn.utils import class_weight, shuffle\nfrom keras.losses import binary_crossentropy\n\nfrom keras.applications.resnet50 import preprocess_input\nimport keras.backend as K\n\nimport tensorflow as tf\nfrom sklearn.metrics import f1_score, fbeta_score\nfrom keras.utils import Sequence\n\nfrom keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\n\nWORKERS = 8\nCHANNEL = 3\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n#IMG_SIZE = 512\nNUM_CLASSES = 5\nSEED = 77\nTRAIN_NUM = 1000\n\n","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"df_test  = pd.read_csv(\"../input/aptos2019-blindness-detection/test.csv\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_test.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Model Prep"},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport skimage.io\nfrom skimage.transform import resize\nfrom imgaug import augmenters as iaa\nfrom tqdm import tqdm\nimport PIL\nfrom PIL import Image, ImageOps\nimport cv2\nfrom sklearn.utils import class_weight, shuffle\nfrom keras.losses import binary_crossentropy, categorical_crossentropy\nfrom keras.applications.resnet50 import preprocess_input\nimport keras.backend as K\nimport tensorflow as tf\nfrom sklearn.metrics import f1_score, fbeta_score, cohen_kappa_score, accuracy_score\nfrom keras.utils import Sequence\nfrom keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\n\nWORKERS = 12\nCHANNEL = 3\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nSIZE = 300\nNUM_CLASSES = 5","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.legacy import interfaces\nfrom keras.optimizers import Optimizer\nfrom keras import backend as K\n\n\nclass AdamAccumulate_v1(Optimizer):\n    def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999,\n                 epsilon=None, decay=0., amsgrad=False, accum_iters=20, **kwargs):\n        super(AdamAccumulate, self).__init__(**kwargs)\n        with K.name_scope(self.__class__.__name__):\n            self.iterations = K.variable(0, dtype='int64', name='iterations')\n            self.effective_iterations = K.variable(0, dtype='int64', name='effective_iterations')\n\n            self.lr = K.variable(lr, name='lr')\n            self.beta_1 = K.variable(beta_1, name='beta_1')\n            self.beta_2 = K.variable(beta_2, name='beta_2')\n            self.decay = K.variable(decay, name='decay')\n        if epsilon is None:\n            epsilon = K.epsilon()\n        self.epsilon = epsilon\n        self.initial_decay = decay\n        self.amsgrad = amsgrad\n        self.accum_iters = K.variable(accum_iters, dtype='int64')\n\n    @interfaces.legacy_get_updates_support\n    def get_updates(self, loss, params):\n        grads = self.get_gradients(loss, params)\n\n        self.updates = [K.update(self.iterations, self.iterations + 1)]\n\n        flag = K.equal(self.iterations % self.accum_iters, self.accum_iters - 1)\n        flag = K.cast(flag, K.floatx())\n\n        self.updates.append(K.update(self.effective_iterations,\n                                     self.effective_iterations + K.cast(flag, 'int64')))\n\n        lr = self.lr\n        if self.initial_decay > 0:\n            lr = lr * (1. / (1. + self.decay * K.cast(self.effective_iterations,\n                                                      K.dtype(self.decay))))\n\n        t = K.cast(self.effective_iterations, K.floatx()) + 1\n\n        lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /\n                     (1. - K.pow(self.beta_1, t)))\n\n        ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n        vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n        gs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n\n        if self.amsgrad:\n            vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n        else:\n            vhats = [K.zeros(1) for _ in params]\n        self.weights = [self.iterations] + ms + vs + vhats\n\n        for p, g, m, v, vhat, gg in zip(params, grads, ms, vs, vhats, gs):\n\n            gg_t = (1 - flag) * (gg + g)\n            m_t = (self.beta_1 * m) + (1. - self.beta_1) * (gg + flag * g) / K.cast(self.accum_iters, K.floatx())\n            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(\n                (gg + flag * g) / K.cast(self.accum_iters, K.floatx()))\n\n            if self.amsgrad:\n                vhat_t = K.maximum(vhat, v_t)\n                p_t = p - flag * lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)\n                self.updates.append(K.update(vhat, vhat_t))\n            else:\n                p_t = p - flag * lr_t * m_t / (K.sqrt(v_t) + self.epsilon)\n\n            self.updates.append((m, flag * m_t + (1 - flag) * m))\n            self.updates.append((v, flag * v_t + (1 - flag) * v))\n            self.updates.append((gg, gg_t))\n            new_p = p_t\n\n            # Apply constraints.\n            if getattr(p, 'constraint', None) is not None:\n                new_p = p.constraint(new_p)\n\n            self.updates.append(K.update(p, new_p))\n        return self.updates\n\n    def get_config(self):\n        config = {'lr': float(K.get_value(self.lr)),\n                  'beta_1': float(K.get_value(self.beta_1)),\n                  'beta_2': float(K.get_value(self.beta_2)),\n                  'decay': float(K.get_value(self.decay)),\n                  'epsilon': self.epsilon,\n                  'amsgrad': self.amsgrad}\n        base_config = super(AdamAccumulate, self).get_config()\n        return dict(list(base_config.items()) + list(config.items()))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class AdamAccumulate(Optimizer):\n    def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999,\n                 epsilon=None, decay=0., amsgrad=False, accum_iters=2, **kwargs):\n        if accum_iters < 1:\n            raise ValueError('accum_iters must be >= 1')\n        super(AdamAccumulate, self).__init__(**kwargs)\n        with K.name_scope(self.__class__.__name__):\n            self.iterations = K.variable(0, dtype='int64', name='iterations')\n            self.lr = K.variable(lr, name='lr')\n            self.beta_1 = K.variable(beta_1, name='beta_1')\n            self.beta_2 = K.variable(beta_2, name='beta_2')\n            self.decay = K.variable(decay, name='decay')\n        if epsilon is None:\n            epsilon = K.epsilon()\n        self.epsilon = epsilon\n        self.initial_decay = decay\n        self.amsgrad = amsgrad\n        self.accum_iters = K.variable(accum_iters, K.dtype(self.iterations))\n        self.accum_iters_float = K.cast(self.accum_iters, K.floatx())\n\n    @interfaces.legacy_get_updates_support\n    def get_updates(self, loss, params):\n        grads = self.get_gradients(loss, params)\n        self.updates = [K.update_add(self.iterations, 1)]\n\n        lr = self.lr\n\n        completed_updates = K.cast(K.tf.floor(self.iterations / self.accum_iters), K.floatx())\n\n        if self.initial_decay > 0:\n            lr = lr * (1. / (1. + self.decay * completed_updates))\n\n        t = completed_updates + 1\n\n        lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) / (1. - K.pow(self.beta_1, t)))\n\n        # self.iterations incremented after processing a batch\n        # batch:              1 2 3 4 5 6 7 8 9\n        # self.iterations:    0 1 2 3 4 5 6 7 8\n        # update_switch = 1:        x       x    (if accum_iters=4)\n        update_switch = K.equal((self.iterations + 1) % self.accum_iters, 0)\n        update_switch = K.cast(update_switch, K.floatx())\n\n        ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n        vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n        gs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n\n        if self.amsgrad:\n            vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]\n        else:\n            vhats = [K.zeros(1) for _ in params]\n\n        self.weights = [self.iterations] + ms + vs + vhats\n\n        for p, g, m, v, vhat, tg in zip(params, grads, ms, vs, vhats, gs):\n\n            sum_grad = tg + g\n            avg_grad = sum_grad / self.accum_iters_float\n\n            m_t = (self.beta_1 * m) + (1. - self.beta_1) * avg_grad\n            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(avg_grad)\n\n            if self.amsgrad:\n                vhat_t = K.maximum(vhat, v_t)\n                p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)\n                self.updates.append(K.update(vhat, (1 - update_switch) * vhat + update_switch * vhat_t))\n            else:\n                p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)\n\n            self.updates.append(K.update(m, (1 - update_switch) * m + update_switch * m_t))\n            self.updates.append(K.update(v, (1 - update_switch) * v + update_switch * v_t))\n            self.updates.append(K.update(tg, (1 - update_switch) * sum_grad))\n            new_p = p_t\n\n            # Apply constraints.\n            if getattr(p, 'constraint', None) is not None:\n                new_p = p.constraint(new_p)\n\n            self.updates.append(K.update(p, (1 - update_switch) * p + update_switch * new_p))\n        return self.updates\n\n    def get_config(self):\n        config = {'lr': float(K.get_value(self.lr)),\n                  'beta_1': float(K.get_value(self.beta_1)),\n                  'beta_2': float(K.get_value(self.beta_2)),\n                  'decay': float(K.get_value(self.decay)),\n                  'epsilon': self.epsilon,\n                  'amsgrad': self.amsgrad}\n        base_config = super(AdamAccumulate, self).get_config()\n        return dict(list(base_config.items()) + list(config.items()))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class My_Generator(Sequence):\n\n    def __init__(self, image_filenames, labels, batch_size, mix=False, is_train=False):\n        self.image_filenames, self.labels = image_filenames, labels\n        self.batch_size = batch_size\n        self.is_train = is_train\n        if(self.is_train):\n            self.on_epoch_end()\n        self.is_mix = mix\n\n    def __len__(self):\n        return int(np.ceil(len(self.image_filenames) / float(self.batch_size)))\n\n    def __getitem__(self, idx):\n        \n        batch_x = self.image_filenames[idx * self.batch_size:(idx + 1) * self.batch_size]\n        batch_y = self.labels[idx * self.batch_size:(idx + 1) * self.batch_size]\n\n        if(self.is_train):\n            return self.train_generate(batch_x, batch_y)\n        return self.valid_generate(batch_x, batch_y)\n    \n    def on_epoch_end(self):\n        if(self.is_train):\n            self.image_filenames, self.labels = shuffle(self.image_filenames, self.labels)\n    \n    def mix_up(self, x, y):\n        lam = np.random.beta(0.2, 0.4)\n        ori_index = np.arange(int(len(x)))\n        index_array = np.arange(int(len(x)))\n        np.random.shuffle(index_array)        \n        \n        mixed_x = lam * x[ori_index] + (1 - lam) * x[index_array]\n        mixed_y = lam * y[ori_index] + (1 - lam) * y[index_array]\n        \n        return mixed_x, mixed_y\n    \n    def train_generate(self, batch_x, batch_y):\n        batch_images = []\n        for (sample, label) in zip(batch_x, batch_y):\n            path = f\"{sample}\"\n            \n            image = cv2.imread(path)\n            #path=f\"../data/older_data/diabetic-retinopathy-resized/resized_train/{sample}.jpeg\"\n            image = load_ben_color(path,sigmaX=30)            \n            batch_images.append(cv2.resize(image, (SIZE,SIZE), interpolation=cv2.INTER_CUBIC))\n\n        batch_images = np.array(batch_images, np.float32) / 255\n        batch_y = np.array(batch_y, np.float32)\n        \n        if(self.is_mix):\n            batch_images, batch_y = self.mix_up(batch_images, batch_y)\n            \n        return batch_images, batch_y\n    \n    def valid_generate(self, batch_x, batch_y):\n        batch_images = []\n        for (sample, label) in zip(batch_x, batch_y):\n            path = f\"{sample}\"\n            \n            image = cv2.imread(path)\n            #path=f\"../data/older_data/diabetic-retinopathy-resized/resized_train/{sample}.jpeg\"\n            image = load_ben_color(path,sigmaX=30)\n\n                \n            batch_images.append(cv2.resize(image, (SIZE,SIZE), interpolation=cv2.INTER_CUBIC))\n\n        batch_images = np.array(batch_images, np.float32) / 255\n        batch_y = np.array(batch_y, np.float32)\n        return batch_images, batch_y","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential, load_model\nfrom keras.layers import (Activation, Dropout, Flatten, Dense, GlobalMaxPooling2D,\n                          BatchNormalization, Input, Conv2D, GlobalAveragePooling2D)\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.callbacks import ModelCheckpoint\nfrom keras import metrics\nfrom keras.optimizers import Adam \nfrom keras import backend as K\nimport keras\nfrom keras.models import Model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"function = \"softmax\"\ndef create_model(input_shape, n_out):\n    input_tensor = Input(shape=input_shape)\n    base_model = ResNet50(include_top=False,\n                   weights=None,\n                   input_tensor=input_tensor)\n    base_model.load_weights('../input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')\n    x = GlobalAveragePooling2D()(base_model.output)\n    x = Dropout(0.5)(x)\n    x = Dense(1024, activation='relu')(x)\n    x = Dropout(0.5)(x)\n    final_output = Dense(n_out, activation=function, name='final_output')(x)\n    model = Model(input_tensor, final_output)\n    \n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# create callbacks list\nfrom keras.callbacks import (ModelCheckpoint, LearningRateScheduler,\n                             EarlyStopping, ReduceLROnPlateau,CSVLogger)\n\nepochs = 30; batch_size = 32\ncheckpoint = ModelCheckpoint('../working/Resnet50.h5', monitor='val_loss', verbose=1, \n                             save_best_only=True, mode='min', save_weights_only = True)\nreduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=4, \n                                   verbose=1, mode='min', epsilon=0.0001)\nearly = EarlyStopping(monitor=\"val_loss\", \n                      mode=\"min\", \n                      patience=9)\n\ncsv_logger = CSVLogger(filename='../working/training_log.csv',\n                       separator=',',\n                       append=True)\n\nmodel = create_model(\n    input_shape=(SIZE,SIZE,3), \n    n_out=NUM_CLASSES)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Prediction"},{"metadata":{"trusted":true},"cell_type":"code","source":"submit = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')\n# model.load_weights('../working/Resnet50.h5')\nmodel.load_weights('../input/resnet50trainedwithaptosolddataset/Resnet50_bestqwk.h5')\npredicted = []","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss='categorical_crossentropy',\n            # loss=kappa_loss,\n            # loss='binary_crossentropy',\n            # optimizer=Adam(lr=1e-4),\n            optimizer=AdamAccumulate(lr=1e-4, accum_iters=2),\n            metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def crop_image1(img,tol=7):\n    # img is image data\n    # tol  is tolerance\n        \n    mask = img>tol\n    return img[np.ix_(mask.any(1),mask.any(0))]\n\ndef crop_image_from_gray(img,tol=7):\n    if img.ndim ==2:\n        mask = img>tol\n        return img[np.ix_(mask.any(1),mask.any(0))]\n    elif img.ndim==3:\n        gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n        mask = gray_img>tol\n        \n        check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n        if (check_shape == 0): # image is too dark so that we crop out everything,\n            return img # return original image\n        else:\n            img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n            img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n            img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n    #         print(img1.shape,img2.shape,img3.shape)\n            img = np.stack([img1,img2,img3],axis=-1)\n    #         print(img.shape)\n        return img","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def load_ben_color(path, sigmaX=10 ):\n    try:\n        image = cv2.imread(path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        image = crop_image_from_gray(image)\n        image = cv2.resize(image, (SIZE, SIZE))\n        image=cv2.addWeighted ( image,4, cv2.GaussianBlur( image , (0,0) , sigmaX) ,-4 ,128)\n\n        return image\n    except cv2.error as e:\n        print(e)\n        print(path)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i, name in tqdm(enumerate(submit['id_code'])):\n    path = os.path.join('../input/aptos2019-blindness-detection/test_images/', name+'.png')\n    image = cv2.imread(path)\n    image = load_ben_color(path,sigmaX=30)\n    image = cv2.resize(image, (SIZE,SIZE), interpolation=cv2.INTER_CUBIC)\n    score_predict = model.predict((image[np.newaxis])/255)\n    label_predict = np.argmax(score_predict)\n    # label_predict = score_predict.astype(int).sum() - 1\n    predicted.append(str(label_predict))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submit['diagnosis'] = predicted\nsubmit.to_csv('submission.csv', index=False)\nsubmit.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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}