transfer_learning_EfficientNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image classification via fine-tuning with EfficientNet\n",
    "\n",
    "from: https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/\n",
    "\n",
    "STEPS into fine tunning a pretrained model:\n",
    "1. Select pretrained model and add custom layers on the top\n",
    "2. Freeze the base\n",
    "3. Train the custom layers first\n",
    "    - !!!! You must train the added layers first and only then unfreze the base (step 4). Otherwise the error propagating could be too large\n",
    "4. Unfreeze some base layers (few top layers, in order to adjust abstract features to our case in had)\n",
    "    - !!!! Do not unfreeze BatchNormalization layers\n",
    "5. Jointly train the whole network \n",
    "    - !!! You should consider lowering the learning rate in order to avoid destroying generic features too much (we want to adapt them to our case only)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "import cv2 as cv\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras.applications import vgg16, EfficientNetB4\n",
    "    # EfficientNetB4: \n",
    "        # This model takes input images of shape (224, 224, 3), \n",
    "        # and the input data should range [0, 255]. \n",
    "        # Normalization is included as part of the model.\n",
    "\n",
    "from tensorflow.keras.models import Model,load_model\n",
    "\n",
    "from tensorflow.keras.layers import Input, Conv2D, Conv2DTranspose\n",
    "from tensorflow.keras.layers import AveragePooling2D, MaxPooling2D\n",
    "from tensorflow.keras.layers import UpSampling2D, LeakyReLU\n",
    "from tensorflow.keras.layers import concatenate, Dropout, Concatenate\n",
    "from tensorflow.keras.layers import BatchNormalization,Activation\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint\n",
    "import pandas as pd\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.model_selection import train_test_split\n",
    "from numpy.random import seed\n",
    "seed(123)\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(123)\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "IMAGE_RES = (256,512) # H x W"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.generator_old import DataGenerator\n",
    "from scripts.helper_functions import get_classes\n",
    "from scripts.labels import labels\n",
    "\n",
    "# Parameters\n",
    "df_train = pd.DataFrame({'xPathLabel': ['/00 data/sample/aachen_000006_000019_leftImg8bit.png', \n",
    "                                   '/00 data/sample/aachen_000007_000019_leftImg8bit.png',\n",
    "                                   '/00 data/sample/aachen_000008_000019_leftImg8bit.png'],                                 \n",
    "                        'yPathLabel' : ['/00 data/sample/aachen_000006_000019_gtFine_color.png', \n",
    "                                    '/00 data/sample/aachen_000007_000019_gtFine_color.png',\n",
    "                                    '/00 data/sample/aachen_000008_000019_gtFine_color.png']\n",
    "                        })\n",
    "\n",
    "\n",
    "data_dir = os.getcwd()\n",
    "batch_size_min = min(1, len(df_train))\n",
    "\n",
    "params = {'targetSize': (IMAGE_RES[1], IMAGE_RES[0]),\n",
    "          'batchSize': batch_size_min,\n",
    "          'augment': 1,\n",
    "          'nbChannels': 3,\n",
    "          'shuffle': True}\n",
    "\n",
    "class_labels = get_classes(labels)\n",
    "trainGen = DataGenerator(data_dir=data_dir,\n",
    "                         data=df_train,\n",
    "                         xPathLabel='xPathLabel',\n",
    "                         yPathLabel='yPathLabel',\n",
    "                         classes=class_labels,\n",
    "                         **params)\n",
    "\n",
    "testGen = DataGenerator(data_dir=data_dir,\n",
    "                        data=df_train,\n",
    "                        xPathLabel='xPathLabel',\n",
    "                        yPathLabel='yPathLabel',\n",
    "                        classes=class_labels,\n",
    "                        **params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper func for block creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_block(tensor, nfilters, size=3, padding='same', \n",
    "               initializer=\"he_normal\", nb_blocks=1):\n",
    "\n",
    "    #add at least one, and if nb_blocks > 1 add others\n",
    "    x = Conv2D(filters=nfilters, kernel_size=(size, size), \n",
    "                padding=padding, kernel_initializer=initializer\n",
    "                )(tensor)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "\n",
    "    for i in range(nb_blocks-1):\n",
    "        x = Conv2D(filters=nfilters, kernel_size=(size, size), \n",
    "                    padding=padding, kernel_initializer=initializer\n",
    "                    )(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation(\"relu\")(x)\n",
    "    return x\n",
    "\n",
    "def deconv_block(tensor, residual, nfilters, size=3, \n",
    "                 padding='same', strides=(2, 2), nb_conv_blocks=2):\n",
    "\n",
    "    y = Conv2DTranspose(nfilters, kernel_size=(size, size), \n",
    "                        strides=strides, padding=padding\n",
    "                        )(tensor)\n",
    "    y = concatenate([y, residual], axis=3)\n",
    "    y = conv_block(y, nfilters, nb_blocks=nb_conv_blocks)\n",
    "    return y\n",
    "\n",
    "def get_multiple_outputs(model, only_first_blocks = True):\n",
    "    # get all layers until the last one in the list (included)\n",
    "    # define outputs for skip connections\n",
    "    encoder_output_ids=[0,                           # 256 x 512 x 3\n",
    "                        \"block2a_expand_activation\", # 128 x 256 x 144\n",
    "                        \"block3a_expand_activation\", # 64 x 128 x 192 \n",
    "                        \"block4a_expand_activation\", # 32 x 64 x 336\n",
    "                        \"block6a_expand_activation\", # 16 x 32 x 960\n",
    "                        ]\n",
    "\n",
    "    if only_first_blocks == True:\n",
    "      outputs = []\n",
    "      # add first layer and then add layers in the list, names\n",
    "      outputs.append(model.layers[0].output)\n",
    "\n",
    "      for id in encoder_output_ids[1:]:\n",
    "        outputs.append(model.get_layer(id).output)\n",
    "  \n",
    "      return Model(model.input, outputs)\n",
    "    else:\n",
    "      # get the whole model\n",
    "      return Model(model.input, model.output)\n",
    "\n",
    "def build_encoder():\n",
    "  # use EfficientNet as encoder\n",
    "    input_shape = (*IMAGE_RES, 3)\n",
    "    inputs = Input(shape=input_shape)\n",
    "\n",
    "    base_model = EfficientNetB4(include_top=False, \n",
    "                              weights='imagenet', \n",
    "                              # input_shape=input_shape\n",
    "                              input_tensor=inputs\n",
    "                              )\n",
    "\n",
    "    only_first_blocks = True #if false, get entire model\n",
    "    if only_first_blocks == True:\n",
    "      encoder = get_multiple_outputs(base_model, only_first_blocks)\n",
    "    else:\n",
    "      encoder = get_multiple_outputs(base_model, only_first_blocks)\n",
    "\n",
    "    return encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EfficientNet Model Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_EfficientNet(num_classes=8, fine_tuning=False):\n",
    "    CREATE_BOTTLENECK = True\n",
    "\n",
    "    # down\n",
    "    # input_layer = Input(shape=(*IMAGE_RES, 3), name='image_input')\n",
    "\n",
    "    encoder = build_encoder()\n",
    "    NB_UNFREEZE = len(encoder.layers)//5 #20% of the base network\n",
    "\n",
    "    # x = encoder(input_layer)\n",
    "\n",
    "    # freeze all the layers of the base model\n",
    "    for layer in encoder.layers:\n",
    "        layer.trainable = False\n",
    "\n",
    "    if fine_tuning == True: # unfreeze some to adapt some generic features\n",
    "        for layer in encoder.layers[-NB_UNFREEZE:]:\n",
    "            # do not unfreeze Normalization layers, \n",
    "            # see here https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/\n",
    "            if not isinstance(layer, BatchNormalization):\n",
    "                    layer.trainable =  True\n",
    "\n",
    "    conv1_residual = encoder.outputs[0] # 256 512   3\n",
    "    conv2_residual = encoder.outputs[1] # 128 256 144\n",
    "    conv3_residual = encoder.outputs[2] #  64 128 192\n",
    "    conv4_residual = encoder.outputs[3] #  32  64 336\n",
    "    # last layer\n",
    "    conv5_residual = encoder.outputs[4] #  16  32 960\n",
    "    last_layer_shape = conv5_residual.shape[-3:]\n",
    "    last_layer_nb_filters = conv5_residual.shape[-1]\n",
    "\n",
    "    if CREATE_BOTTLENECK == True:\n",
    "        bottleneck = conv_block(conv5_residual, last_layer_nb_filters)\n",
    "    else:\n",
    "        bottleneck = conv5_residual\n",
    "\n",
    "    # up\n",
    "    deconv6 = deconv_block(bottleneck, residual=conv4_residual, nfilters=960)\n",
    "    # deconv6 = Dropout(0.5)(deconv6)\n",
    "    deconv7 = deconv_block(deconv6, residual=conv3_residual, nfilters=336)\n",
    "    # deconv7 = Dropout(0.5)(deconv7) \n",
    "    deconv8 = deconv_block(deconv7, residual=conv2_residual, nfilters=192)\n",
    "    deconv9 = deconv_block(deconv8, residual=conv1_residual, nfilters=144)\n",
    "    \n",
    "    # output\n",
    "    output_layer = Conv2D(filters=num_classes, kernel_size=(1, 1))(deconv9)\n",
    "    output_layer = BatchNormalization()(output_layer)\n",
    "    # output_layer = Reshape((img_height*img_width, nclasses), input_shape=(img_height, img_width, nclasses))(output_layer)\n",
    "    output_layer = Activation('softmax')(output_layer)\n",
    "    \n",
    "\n",
    "    model = Model(inputs=encoder.input, outputs=output_layer, name='my_Unet')\n",
    "    # model = Model(inputs=encoder.input, outputs=x, name='my_Unet')\n",
    "    return model  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Model_ = my_EfficientNet(num_classes=8, fine_tuning=False)\n",
    "\n",
    "if True==False:\n",
    "    Model_.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "365\n"
     ]
    }
   ],
   "source": [
    "print(len(Model_.layers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_Unet\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 256, 512, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "rescaling (Rescaling)           (None, 256, 512, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "normalization (Normalization)   (None, 256, 512, 3)  7           rescaling[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv_pad (ZeroPadding2D)   (None, 257, 513, 3)  0           normalization[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "stem_conv (Conv2D)              (None, 128, 256, 48) 1296        stem_conv_pad[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "stem_bn (BatchNormalization)    (None, 128, 256, 48) 192         stem_conv[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "stem_activation (Activation)    (None, 128, 256, 48) 0           stem_bn[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1a_dwconv (DepthwiseConv2D (None, 128, 256, 48) 432         stem_activation[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1a_bn (BatchNormalization) (None, 128, 256, 48) 192         block1a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1a_activation (Activation) (None, 128, 256, 48) 0           block1a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_squeeze (GlobalAvera (None, 48)           0           block1a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reshape (Reshape)    (None, 1, 1, 48)     0           block1a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_reduce (Conv2D)      (None, 1, 1, 12)     588         block1a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_expand (Conv2D)      (None, 1, 1, 48)     624         block1a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_se_excite (Multiply)    (None, 128, 256, 48) 0           block1a_activation[0][0]         \n",
      "                                                                 block1a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_conv (Conv2D)   (None, 128, 256, 24) 1152        block1a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1a_project_bn (BatchNormal (None, 128, 256, 24) 96          block1a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block1b_dwconv (DepthwiseConv2D (None, 128, 256, 24) 216         block1a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_bn (BatchNormalization) (None, 128, 256, 24) 96          block1b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block1b_activation (Activation) (None, 128, 256, 24) 0           block1b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_squeeze (GlobalAvera (None, 24)           0           block1b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_reshape (Reshape)    (None, 1, 1, 24)     0           block1b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_reduce (Conv2D)      (None, 1, 1, 6)      150         block1b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_expand (Conv2D)      (None, 1, 1, 24)     168         block1b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_se_excite (Multiply)    (None, 128, 256, 24) 0           block1b_activation[0][0]         \n",
      "                                                                 block1b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_project_conv (Conv2D)   (None, 128, 256, 24) 576         block1b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block1b_project_bn (BatchNormal (None, 128, 256, 24) 96          block1b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block1b_drop (Dropout)          (None, 128, 256, 24) 0           block1b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block1b_add (Add)               (None, 128, 256, 24) 0           block1b_drop[0][0]               \n",
      "                                                                 block1a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_conv (Conv2D)    (None, 128, 256, 144 3456        block1b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_bn (BatchNormali (None, 128, 256, 144 576         block2a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2a_expand_activation (Acti (None, 128, 256, 144 0           block2a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv_pad (ZeroPadding (None, 129, 257, 144 0           block2a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2a_dwconv (DepthwiseConv2D (None, 64, 128, 144) 1296        block2a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_bn (BatchNormalization) (None, 64, 128, 144) 576         block2a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2a_activation (Activation) (None, 64, 128, 144) 0           block2a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_squeeze (GlobalAvera (None, 144)          0           block2a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_se_excite (Multiply)    (None, 64, 128, 144) 0           block2a_activation[0][0]         \n",
      "                                                                 block2a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_conv (Conv2D)   (None, 64, 128, 32)  4608        block2a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2a_project_bn (BatchNormal (None, 64, 128, 32)  128         block2a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_conv (Conv2D)    (None, 64, 128, 192) 6144        block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_bn (BatchNormali (None, 64, 128, 192) 768         block2b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2b_expand_activation (Acti (None, 64, 128, 192) 0           block2b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_dwconv (DepthwiseConv2D (None, 64, 128, 192) 1728        block2b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2b_bn (BatchNormalization) (None, 64, 128, 192) 768         block2b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2b_activation (Activation) (None, 64, 128, 192) 0           block2b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_squeeze (GlobalAvera (None, 192)          0           block2b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reshape (Reshape)    (None, 1, 1, 192)    0           block2b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_reduce (Conv2D)      (None, 1, 1, 8)      1544        block2b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_expand (Conv2D)      (None, 1, 1, 192)    1728        block2b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_se_excite (Multiply)    (None, 64, 128, 192) 0           block2b_activation[0][0]         \n",
      "                                                                 block2b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_conv (Conv2D)   (None, 64, 128, 32)  6144        block2b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2b_project_bn (BatchNormal (None, 64, 128, 32)  128         block2b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2b_drop (Dropout)          (None, 64, 128, 32)  0           block2b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2b_add (Add)               (None, 64, 128, 32)  0           block2b_drop[0][0]               \n",
      "                                                                 block2a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_conv (Conv2D)    (None, 64, 128, 192) 6144        block2b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_bn (BatchNormali (None, 64, 128, 192) 768         block2c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2c_expand_activation (Acti (None, 64, 128, 192) 0           block2c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_dwconv (DepthwiseConv2D (None, 64, 128, 192) 1728        block2c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2c_bn (BatchNormalization) (None, 64, 128, 192) 768         block2c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2c_activation (Activation) (None, 64, 128, 192) 0           block2c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_squeeze (GlobalAvera (None, 192)          0           block2c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_reshape (Reshape)    (None, 1, 1, 192)    0           block2c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_reduce (Conv2D)      (None, 1, 1, 8)      1544        block2c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_expand (Conv2D)      (None, 1, 1, 192)    1728        block2c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_se_excite (Multiply)    (None, 64, 128, 192) 0           block2c_activation[0][0]         \n",
      "                                                                 block2c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_project_conv (Conv2D)   (None, 64, 128, 32)  6144        block2c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2c_project_bn (BatchNormal (None, 64, 128, 32)  128         block2c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2c_drop (Dropout)          (None, 64, 128, 32)  0           block2c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2c_add (Add)               (None, 64, 128, 32)  0           block2c_drop[0][0]               \n",
      "                                                                 block2b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2d_expand_conv (Conv2D)    (None, 64, 128, 192) 6144        block2c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2d_expand_bn (BatchNormali (None, 64, 128, 192) 768         block2d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2d_expand_activation (Acti (None, 64, 128, 192) 0           block2d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2d_dwconv (DepthwiseConv2D (None, 64, 128, 192) 1728        block2d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block2d_bn (BatchNormalization) (None, 64, 128, 192) 768         block2d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block2d_activation (Activation) (None, 64, 128, 192) 0           block2d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_squeeze (GlobalAvera (None, 192)          0           block2d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_reshape (Reshape)    (None, 1, 1, 192)    0           block2d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_reduce (Conv2D)      (None, 1, 1, 8)      1544        block2d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_expand (Conv2D)      (None, 1, 1, 192)    1728        block2d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2d_se_excite (Multiply)    (None, 64, 128, 192) 0           block2d_activation[0][0]         \n",
      "                                                                 block2d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2d_project_conv (Conv2D)   (None, 64, 128, 32)  6144        block2d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block2d_project_bn (BatchNormal (None, 64, 128, 32)  128         block2d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block2d_drop (Dropout)          (None, 64, 128, 32)  0           block2d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2d_add (Add)               (None, 64, 128, 32)  0           block2d_drop[0][0]               \n",
      "                                                                 block2c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_conv (Conv2D)    (None, 64, 128, 192) 6144        block2d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_bn (BatchNormali (None, 64, 128, 192) 768         block3a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3a_expand_activation (Acti (None, 64, 128, 192) 0           block3a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv_pad (ZeroPadding (None, 67, 131, 192) 0           block3a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3a_dwconv (DepthwiseConv2D (None, 32, 64, 192)  4800        block3a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_bn (BatchNormalization) (None, 32, 64, 192)  768         block3a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3a_activation (Activation) (None, 32, 64, 192)  0           block3a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_squeeze (GlobalAvera (None, 192)          0           block3a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reshape (Reshape)    (None, 1, 1, 192)    0           block3a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_reduce (Conv2D)      (None, 1, 1, 8)      1544        block3a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_expand (Conv2D)      (None, 1, 1, 192)    1728        block3a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_se_excite (Multiply)    (None, 32, 64, 192)  0           block3a_activation[0][0]         \n",
      "                                                                 block3a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_conv (Conv2D)   (None, 32, 64, 56)   10752       block3a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3a_project_bn (BatchNormal (None, 32, 64, 56)   224         block3a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_conv (Conv2D)    (None, 32, 64, 336)  18816       block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_bn (BatchNormali (None, 32, 64, 336)  1344        block3b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3b_expand_activation (Acti (None, 32, 64, 336)  0           block3b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_dwconv (DepthwiseConv2D (None, 32, 64, 336)  8400        block3b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3b_bn (BatchNormalization) (None, 32, 64, 336)  1344        block3b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3b_activation (Activation) (None, 32, 64, 336)  0           block3b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_squeeze (GlobalAvera (None, 336)          0           block3b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reshape (Reshape)    (None, 1, 1, 336)    0           block3b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_reduce (Conv2D)      (None, 1, 1, 14)     4718        block3b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_expand (Conv2D)      (None, 1, 1, 336)    5040        block3b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_se_excite (Multiply)    (None, 32, 64, 336)  0           block3b_activation[0][0]         \n",
      "                                                                 block3b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_conv (Conv2D)   (None, 32, 64, 56)   18816       block3b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3b_project_bn (BatchNormal (None, 32, 64, 56)   224         block3b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3b_drop (Dropout)          (None, 32, 64, 56)   0           block3b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3b_add (Add)               (None, 32, 64, 56)   0           block3b_drop[0][0]               \n",
      "                                                                 block3a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_conv (Conv2D)    (None, 32, 64, 336)  18816       block3b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_bn (BatchNormali (None, 32, 64, 336)  1344        block3c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3c_expand_activation (Acti (None, 32, 64, 336)  0           block3c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_dwconv (DepthwiseConv2D (None, 32, 64, 336)  8400        block3c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3c_bn (BatchNormalization) (None, 32, 64, 336)  1344        block3c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3c_activation (Activation) (None, 32, 64, 336)  0           block3c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_squeeze (GlobalAvera (None, 336)          0           block3c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_reshape (Reshape)    (None, 1, 1, 336)    0           block3c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_reduce (Conv2D)      (None, 1, 1, 14)     4718        block3c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_expand (Conv2D)      (None, 1, 1, 336)    5040        block3c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_se_excite (Multiply)    (None, 32, 64, 336)  0           block3c_activation[0][0]         \n",
      "                                                                 block3c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_project_conv (Conv2D)   (None, 32, 64, 56)   18816       block3c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3c_project_bn (BatchNormal (None, 32, 64, 56)   224         block3c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3c_drop (Dropout)          (None, 32, 64, 56)   0           block3c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3c_add (Add)               (None, 32, 64, 56)   0           block3c_drop[0][0]               \n",
      "                                                                 block3b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3d_expand_conv (Conv2D)    (None, 32, 64, 336)  18816       block3c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3d_expand_bn (BatchNormali (None, 32, 64, 336)  1344        block3d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3d_expand_activation (Acti (None, 32, 64, 336)  0           block3d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3d_dwconv (DepthwiseConv2D (None, 32, 64, 336)  8400        block3d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block3d_bn (BatchNormalization) (None, 32, 64, 336)  1344        block3d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block3d_activation (Activation) (None, 32, 64, 336)  0           block3d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_squeeze (GlobalAvera (None, 336)          0           block3d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_reshape (Reshape)    (None, 1, 1, 336)    0           block3d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_reduce (Conv2D)      (None, 1, 1, 14)     4718        block3d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_expand (Conv2D)      (None, 1, 1, 336)    5040        block3d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3d_se_excite (Multiply)    (None, 32, 64, 336)  0           block3d_activation[0][0]         \n",
      "                                                                 block3d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3d_project_conv (Conv2D)   (None, 32, 64, 56)   18816       block3d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block3d_project_bn (BatchNormal (None, 32, 64, 56)   224         block3d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block3d_drop (Dropout)          (None, 32, 64, 56)   0           block3d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3d_add (Add)               (None, 32, 64, 56)   0           block3d_drop[0][0]               \n",
      "                                                                 block3c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_conv (Conv2D)    (None, 32, 64, 336)  18816       block3d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_bn (BatchNormali (None, 32, 64, 336)  1344        block4a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4a_expand_activation (Acti (None, 32, 64, 336)  0           block4a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv_pad (ZeroPadding (None, 33, 65, 336)  0           block4a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4a_dwconv (DepthwiseConv2D (None, 16, 32, 336)  3024        block4a_dwconv_pad[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_bn (BatchNormalization) (None, 16, 32, 336)  1344        block4a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4a_activation (Activation) (None, 16, 32, 336)  0           block4a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_squeeze (GlobalAvera (None, 336)          0           block4a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reshape (Reshape)    (None, 1, 1, 336)    0           block4a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_reduce (Conv2D)      (None, 1, 1, 14)     4718        block4a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_expand (Conv2D)      (None, 1, 1, 336)    5040        block4a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_se_excite (Multiply)    (None, 16, 32, 336)  0           block4a_activation[0][0]         \n",
      "                                                                 block4a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_conv (Conv2D)   (None, 16, 32, 112)  37632       block4a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4a_project_bn (BatchNormal (None, 16, 32, 112)  448         block4a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_conv (Conv2D)    (None, 16, 32, 672)  75264       block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_bn (BatchNormali (None, 16, 32, 672)  2688        block4b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4b_expand_activation (Acti (None, 16, 32, 672)  0           block4b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_dwconv (DepthwiseConv2D (None, 16, 32, 672)  6048        block4b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4b_bn (BatchNormalization) (None, 16, 32, 672)  2688        block4b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4b_activation (Activation) (None, 16, 32, 672)  0           block4b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_squeeze (GlobalAvera (None, 672)          0           block4b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block4b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block4b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block4b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_se_excite (Multiply)    (None, 16, 32, 672)  0           block4b_activation[0][0]         \n",
      "                                                                 block4b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_conv (Conv2D)   (None, 16, 32, 112)  75264       block4b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4b_project_bn (BatchNormal (None, 16, 32, 112)  448         block4b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4b_drop (Dropout)          (None, 16, 32, 112)  0           block4b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4b_add (Add)               (None, 16, 32, 112)  0           block4b_drop[0][0]               \n",
      "                                                                 block4a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_conv (Conv2D)    (None, 16, 32, 672)  75264       block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_bn (BatchNormali (None, 16, 32, 672)  2688        block4c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4c_expand_activation (Acti (None, 16, 32, 672)  0           block4c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_dwconv (DepthwiseConv2D (None, 16, 32, 672)  6048        block4c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4c_bn (BatchNormalization) (None, 16, 32, 672)  2688        block4c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4c_activation (Activation) (None, 16, 32, 672)  0           block4c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_squeeze (GlobalAvera (None, 672)          0           block4c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block4c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block4c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block4c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_se_excite (Multiply)    (None, 16, 32, 672)  0           block4c_activation[0][0]         \n",
      "                                                                 block4c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_conv (Conv2D)   (None, 16, 32, 112)  75264       block4c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4c_project_bn (BatchNormal (None, 16, 32, 112)  448         block4c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4c_drop (Dropout)          (None, 16, 32, 112)  0           block4c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4c_add (Add)               (None, 16, 32, 112)  0           block4c_drop[0][0]               \n",
      "                                                                 block4b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_conv (Conv2D)    (None, 16, 32, 672)  75264       block4c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_bn (BatchNormali (None, 16, 32, 672)  2688        block4d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4d_expand_activation (Acti (None, 16, 32, 672)  0           block4d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_dwconv (DepthwiseConv2D (None, 16, 32, 672)  6048        block4d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4d_bn (BatchNormalization) (None, 16, 32, 672)  2688        block4d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4d_activation (Activation) (None, 16, 32, 672)  0           block4d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_squeeze (GlobalAvera (None, 672)          0           block4d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_reshape (Reshape)    (None, 1, 1, 672)    0           block4d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block4d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block4d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_se_excite (Multiply)    (None, 16, 32, 672)  0           block4d_activation[0][0]         \n",
      "                                                                 block4d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_project_conv (Conv2D)   (None, 16, 32, 112)  75264       block4d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4d_project_bn (BatchNormal (None, 16, 32, 112)  448         block4d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4d_drop (Dropout)          (None, 16, 32, 112)  0           block4d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4d_add (Add)               (None, 16, 32, 112)  0           block4d_drop[0][0]               \n",
      "                                                                 block4c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4e_expand_conv (Conv2D)    (None, 16, 32, 672)  75264       block4d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4e_expand_bn (BatchNormali (None, 16, 32, 672)  2688        block4e_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4e_expand_activation (Acti (None, 16, 32, 672)  0           block4e_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4e_dwconv (DepthwiseConv2D (None, 16, 32, 672)  6048        block4e_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4e_bn (BatchNormalization) (None, 16, 32, 672)  2688        block4e_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4e_activation (Activation) (None, 16, 32, 672)  0           block4e_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_squeeze (GlobalAvera (None, 672)          0           block4e_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_reshape (Reshape)    (None, 1, 1, 672)    0           block4e_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block4e_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block4e_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4e_se_excite (Multiply)    (None, 16, 32, 672)  0           block4e_activation[0][0]         \n",
      "                                                                 block4e_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4e_project_conv (Conv2D)   (None, 16, 32, 112)  75264       block4e_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4e_project_bn (BatchNormal (None, 16, 32, 112)  448         block4e_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4e_drop (Dropout)          (None, 16, 32, 112)  0           block4e_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4e_add (Add)               (None, 16, 32, 112)  0           block4e_drop[0][0]               \n",
      "                                                                 block4d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4f_expand_conv (Conv2D)    (None, 16, 32, 672)  75264       block4e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4f_expand_bn (BatchNormali (None, 16, 32, 672)  2688        block4f_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4f_expand_activation (Acti (None, 16, 32, 672)  0           block4f_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4f_dwconv (DepthwiseConv2D (None, 16, 32, 672)  6048        block4f_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block4f_bn (BatchNormalization) (None, 16, 32, 672)  2688        block4f_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block4f_activation (Activation) (None, 16, 32, 672)  0           block4f_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_squeeze (GlobalAvera (None, 672)          0           block4f_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_reshape (Reshape)    (None, 1, 1, 672)    0           block4f_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block4f_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block4f_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4f_se_excite (Multiply)    (None, 16, 32, 672)  0           block4f_activation[0][0]         \n",
      "                                                                 block4f_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4f_project_conv (Conv2D)   (None, 16, 32, 112)  75264       block4f_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block4f_project_bn (BatchNormal (None, 16, 32, 112)  448         block4f_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block4f_drop (Dropout)          (None, 16, 32, 112)  0           block4f_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4f_add (Add)               (None, 16, 32, 112)  0           block4f_drop[0][0]               \n",
      "                                                                 block4e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_conv (Conv2D)    (None, 16, 32, 672)  75264       block4f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_bn (BatchNormali (None, 16, 32, 672)  2688        block5a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5a_expand_activation (Acti (None, 16, 32, 672)  0           block5a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_dwconv (DepthwiseConv2D (None, 16, 32, 672)  16800       block5a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5a_bn (BatchNormalization) (None, 16, 32, 672)  2688        block5a_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5a_activation (Activation) (None, 16, 32, 672)  0           block5a_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_squeeze (GlobalAvera (None, 672)          0           block5a_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5a_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5a_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5a_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_se_excite (Multiply)    (None, 16, 32, 672)  0           block5a_activation[0][0]         \n",
      "                                                                 block5a_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_conv (Conv2D)   (None, 16, 32, 160)  107520      block5a_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5a_project_bn (BatchNormal (None, 16, 32, 160)  640         block5a_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_conv (Conv2D)    (None, 16, 32, 960)  153600      block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_bn (BatchNormali (None, 16, 32, 960)  3840        block5b_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5b_expand_activation (Acti (None, 16, 32, 960)  0           block5b_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_dwconv (DepthwiseConv2D (None, 16, 32, 960)  24000       block5b_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5b_bn (BatchNormalization) (None, 16, 32, 960)  3840        block5b_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5b_activation (Activation) (None, 16, 32, 960)  0           block5b_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_squeeze (GlobalAvera (None, 960)          0           block5b_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reshape (Reshape)    (None, 1, 1, 960)    0           block5b_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block5b_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block5b_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_se_excite (Multiply)    (None, 16, 32, 960)  0           block5b_activation[0][0]         \n",
      "                                                                 block5b_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_conv (Conv2D)   (None, 16, 32, 160)  153600      block5b_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5b_project_bn (BatchNormal (None, 16, 32, 160)  640         block5b_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5b_drop (Dropout)          (None, 16, 32, 160)  0           block5b_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5b_add (Add)               (None, 16, 32, 160)  0           block5b_drop[0][0]               \n",
      "                                                                 block5a_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_conv (Conv2D)    (None, 16, 32, 960)  153600      block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_bn (BatchNormali (None, 16, 32, 960)  3840        block5c_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5c_expand_activation (Acti (None, 16, 32, 960)  0           block5c_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_dwconv (DepthwiseConv2D (None, 16, 32, 960)  24000       block5c_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5c_bn (BatchNormalization) (None, 16, 32, 960)  3840        block5c_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5c_activation (Activation) (None, 16, 32, 960)  0           block5c_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_squeeze (GlobalAvera (None, 960)          0           block5c_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reshape (Reshape)    (None, 1, 1, 960)    0           block5c_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block5c_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block5c_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_se_excite (Multiply)    (None, 16, 32, 960)  0           block5c_activation[0][0]         \n",
      "                                                                 block5c_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_conv (Conv2D)   (None, 16, 32, 160)  153600      block5c_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5c_project_bn (BatchNormal (None, 16, 32, 160)  640         block5c_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5c_drop (Dropout)          (None, 16, 32, 160)  0           block5c_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5c_add (Add)               (None, 16, 32, 160)  0           block5c_drop[0][0]               \n",
      "                                                                 block5b_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_conv (Conv2D)    (None, 16, 32, 960)  153600      block5c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_bn (BatchNormali (None, 16, 32, 960)  3840        block5d_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5d_expand_activation (Acti (None, 16, 32, 960)  0           block5d_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_dwconv (DepthwiseConv2D (None, 16, 32, 960)  24000       block5d_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5d_bn (BatchNormalization) (None, 16, 32, 960)  3840        block5d_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5d_activation (Activation) (None, 16, 32, 960)  0           block5d_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_squeeze (GlobalAvera (None, 960)          0           block5d_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_reshape (Reshape)    (None, 1, 1, 960)    0           block5d_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block5d_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block5d_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_se_excite (Multiply)    (None, 16, 32, 960)  0           block5d_activation[0][0]         \n",
      "                                                                 block5d_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_project_conv (Conv2D)   (None, 16, 32, 160)  153600      block5d_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5d_project_bn (BatchNormal (None, 16, 32, 160)  640         block5d_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5d_drop (Dropout)          (None, 16, 32, 160)  0           block5d_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5d_add (Add)               (None, 16, 32, 160)  0           block5d_drop[0][0]               \n",
      "                                                                 block5c_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5e_expand_conv (Conv2D)    (None, 16, 32, 960)  153600      block5d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5e_expand_bn (BatchNormali (None, 16, 32, 960)  3840        block5e_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5e_expand_activation (Acti (None, 16, 32, 960)  0           block5e_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5e_dwconv (DepthwiseConv2D (None, 16, 32, 960)  24000       block5e_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5e_bn (BatchNormalization) (None, 16, 32, 960)  3840        block5e_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5e_activation (Activation) (None, 16, 32, 960)  0           block5e_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_squeeze (GlobalAvera (None, 960)          0           block5e_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_reshape (Reshape)    (None, 1, 1, 960)    0           block5e_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block5e_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block5e_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5e_se_excite (Multiply)    (None, 16, 32, 960)  0           block5e_activation[0][0]         \n",
      "                                                                 block5e_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5e_project_conv (Conv2D)   (None, 16, 32, 160)  153600      block5e_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5e_project_bn (BatchNormal (None, 16, 32, 160)  640         block5e_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5e_drop (Dropout)          (None, 16, 32, 160)  0           block5e_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5e_add (Add)               (None, 16, 32, 160)  0           block5e_drop[0][0]               \n",
      "                                                                 block5d_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5f_expand_conv (Conv2D)    (None, 16, 32, 960)  153600      block5e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5f_expand_bn (BatchNormali (None, 16, 32, 960)  3840        block5f_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5f_expand_activation (Acti (None, 16, 32, 960)  0           block5f_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5f_dwconv (DepthwiseConv2D (None, 16, 32, 960)  24000       block5f_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "block5f_bn (BatchNormalization) (None, 16, 32, 960)  3840        block5f_dwconv[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block5f_activation (Activation) (None, 16, 32, 960)  0           block5f_bn[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_squeeze (GlobalAvera (None, 960)          0           block5f_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_reshape (Reshape)    (None, 1, 1, 960)    0           block5f_se_squeeze[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_reduce (Conv2D)      (None, 1, 1, 40)     38440       block5f_se_reshape[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_expand (Conv2D)      (None, 1, 1, 960)    39360       block5f_se_reduce[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5f_se_excite (Multiply)    (None, 16, 32, 960)  0           block5f_activation[0][0]         \n",
      "                                                                 block5f_se_expand[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5f_project_conv (Conv2D)   (None, 16, 32, 160)  153600      block5f_se_excite[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block5f_project_bn (BatchNormal (None, 16, 32, 160)  640         block5f_project_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block5f_drop (Dropout)          (None, 16, 32, 160)  0           block5f_project_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5f_add (Add)               (None, 16, 32, 160)  0           block5f_drop[0][0]               \n",
      "                                                                 block5e_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_conv (Conv2D)    (None, 16, 32, 960)  153600      block5f_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_bn (BatchNormali (None, 16, 32, 960)  3840        block6a_expand_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6a_expand_activation (Acti (None, 16, 32, 960)  0           block6a_expand_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 16, 32, 960)  8295360     block6a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 16, 32, 960)  3840        conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 16, 32, 960)  0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose (Conv2DTranspo (None, 32, 64, 960)  8295360     activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 32, 64, 1296) 0           conv2d_transpose[0][0]           \n",
      "                                                                 block4a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 32, 64, 960)  11198400    concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 32, 64, 960)  3840        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 32, 64, 960)  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 32, 64, 960)  8295360     activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 32, 64, 960)  3840        conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 32, 64, 960)  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 64, 128, 336) 2903376     activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 64, 128, 528) 0           conv2d_transpose_1[0][0]         \n",
      "                                                                 block3a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 64, 128, 336) 1597008     concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 64, 128, 336) 1344        conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 64, 128, 336) 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 64, 128, 336) 1016400     activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 64, 128, 336) 1344        conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 64, 128, 336) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 128, 256, 192 580800      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 128, 256, 336 0           conv2d_transpose_2[0][0]         \n",
      "                                                                 block2a_expand_activation[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 128, 256, 192 580800      concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 128, 256, 192 768         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 128, 256, 192 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 128, 256, 192 331968      activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 128, 256, 192 768         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 128, 256, 192 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 256, 512, 144 248976      activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 256, 512, 147 0           conv2d_transpose_3[0][0]         \n",
      "                                                                 input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 256, 512, 144 190656      concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 256, 512, 144 576         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 256, 512, 144 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 256, 512, 144 186768      activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 256, 512, 144 576         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 256, 512, 144 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 256, 512, 8)  1160        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 256, 512, 8)  32          conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 256, 512, 8)  0           batch_normalization_9[0][0]      \n",
      "==================================================================================================\n",
      "Total params: 47,581,407\n",
      "Trainable params: 43,730,856\n",
      "Non-trainable params: 3,850,551\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    " Model_.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Failed to import pydot. You must `pip install pydot` and install graphviz (https://graphviz.gitlab.io/download/), ', 'for `pydotprint` to work.')\n"
     ]
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(Model_, \n",
    "    to_file=\"model.png\",\n",
    "    dpi=96,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.keras.utils import plot_model\n",
    "# from IPython.display import Image\n",
    "\n",
    "# plot_model(Model_,to_file='model_.png',show_shapes=True, show_layer_names=True)\n",
    "# Image(retina=True, filename='model_.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model fine tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1fec9939760>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # test fine tuning\n",
    "# from scripts.model_and_metrics import my_EfficientNet\n",
    "# import tensorflow as tf\n",
    "\n",
    "# Model_ = my_EfficientNet(num_classes=8, fine_tuning=True)\n",
    "# load checkpoint file inside checkpoint folder\n",
    "# Model_.load_weights('model_azure/checkpoint/checkpoint')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.model_and_metrics import DiceLossCls, DiceMetric\n",
    "\n",
    "Model_.compile(optimizer='rmsprop', \n",
    "                loss = DiceLossCls(), \n",
    "                metrics = [DiceMetric(num_classes=8)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = tf.keras.callbacks.ModelCheckpoint(filepath=\"outputs/checkpoint\", \n",
    "                              monitor='loss', \n",
    "                              save_best_only=True,\n",
    "                              mode = 'auto',\n",
    "                              save_weights_only=True)\n",
    "                                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "3/3 [==============================] - 24s 9s/step - loss: 0.3751 - F1DiceMetric: 0.6297 - val_loss: 0.3684 - val_F1DiceMetric: 0.6516\n",
      "Epoch 2/2\n",
      "3/3 [==============================] - 30s 10s/step - loss: 0.2944 - F1DiceMetric: 0.6651 - val_loss: 0.3264 - val_F1DiceMetric: 0.5536\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1fec972a340>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.config.run_functions_eagerly(True)\n",
    "\n",
    "\n",
    "EPOCHS = 2\n",
    "Model_.fit(trainGen.dataset, \n",
    "            validation_data=testGen.dataset,\n",
    "            # use_multiprocessing=True,\n",
    "            # workers=16,\n",
    "            callbacks=[callbacks],\n",
    "            epochs=EPOCHS,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x1febfd2d1c0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model_.load_weights('outputs/checkpoint')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from scripts.helper_functions import get_classes, form_2D_label\n",
    "from scripts.labels import labels\n",
    "\n",
    "from scripts.helper_functions import label_to_mask, visualize\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "mounted_drive = 'C:/Users/marko/Desktop/P8_dataset_small/'\n",
    "class_labels = get_classes(labels)\n",
    "\n",
    "x_img_path = 'images/val/frankfurt_000000_002196_leftImg8bit.png'\n",
    "y_img_path =  'ground_truth/val/frankfurt_000000_002196_gtFine_color.png'\n",
    "\n",
    "\n",
    "x_img = Image.open(mounted_drive + x_img_path).convert('RGB')\n",
    "x_image = x_img.resize(IMAGE_RES[::-1])\n",
    "x_array = np.array(x_image)\n",
    "\n",
    "\n",
    "y_img = Image.open(mounted_drive + y_img_path).convert('RGB')\n",
    "y_image = y_img.resize(IMAGE_RES[::-1])\n",
    "y_array = np.array(y_image)\n",
    "img_array_2D = form_2D_label(y_array, class_labels[0])\n",
    "y_cat = to_categorical(img_array_2D, num_classes=8)\n",
    "\n",
    "test_image = x_array\n",
    "\n",
    "mask = Model_.predict(np.expand_dims(test_image, 0))[0]\n",
    "mask = cv.resize(mask,IMAGE_RES[::-1])\n",
    "\n",
    "visualize(cv.resize(test_image,IMAGE_RES[::-1]), \n",
    "          cv.resize(mask,IMAGE_RES[::-1]), \n",
    "          cv.resize(test_image,IMAGE_RES[::-1]), \n",
    "          cv.resize(y_array,IMAGE_RES[::-1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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