# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# SE-ResNet (50/101/152) v1.0
# Paper: https://arxiv.org/pdf/1709.01507.pdf

import tensorflow as tf
from tensorflow.keras import Input, Model
from tensorflow.keras.layers import ZeroPadding2D, Conv2D, MaxPooling2D, BatchNormalization, ReLU
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Reshape, Multiply, Add

def stem(inputs):
    """ Construct the Stem Convolutional Group 
        inputs : the input vector
    """
    # The 224x224 images are zero padded (black - no signal) to be 230x230 images prior to the first convolution
    x = ZeroPadding2D(padding=(3, 3))(inputs)
    
    # First Convolutional layer which uses a large (coarse) filter 
    x = Conv2D(64, (7, 7), strides=(2, 2), padding='valid', use_bias=False, kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)
    x = ReLU()(x)
    
    # Pooled feature maps will be reduced by 75%
    x = ZeroPadding2D(padding=(1, 1))(x)
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)
    return x

def learner(x, groups, ratio):
    """ Construct the Learner
        x     : input to the learner
	groups: list of groups: number of filters and blocks
        ratio : amount of filter reduction in squeeze
    """
    # First Residual Block Group (not strided)
    n_filters, n_blocks = groups.pop(0)
    x = group(x, n_filters, n_blocks, ratio, strides=(1, 1))

    # Remaining Residual Block Groups (strided)
    for n_filters, n_blocks in groups:
    	x = group(x, n_filters, n_blocks, ratio)
    return x	

def group(x, n_filters, n_blocks, ratio, strides=(2, 2)):
    """ Construct the Squeeze-Excite Group
	x        : input to the group
        n_blocks : number of blocks
	n_filters: number of filters
        ratio    : amount of filter reduction during squeeze
        strides  : whether projection block is strided
    """
    # first block uses linear projection to match the doubling of filters between groups
    x = projection_block(x, n_filters, strides=strides, ratio=ratio)

    # remaining blocks use identity link
    for _ in range(n_blocks-1):
        x = identity_block(x, n_filters, ratio=ratio)
    return x

def squeeze_excite_block(x, ratio=16):
    """ Create a Squeeze and Excite block
        x    : input to the block
        ratio : amount of filter reduction during squeeze
    """  
    # Remember the input
    shortcut = x
    
    # Get the number of filters on the input
    filters = x.shape[-1]

    # Squeeze (dimensionality reduction)
    # Do global average pooling across the filters, which will output a 1D vector
    x = GlobalAveragePooling2D()(x)
    
    # Reshape into 1x1 feature maps (1x1xC)
    x = Reshape((1, 1, filters))(x)
    
    # Reduce the number of filters (1x1xC/r)
    x = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(x)

    # Excitation (dimensionality restoration)
    # Restore the number of filters (1x1xC)
    x = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(x)

    # Scale - multiply the squeeze/excitation output with the input (WxHxC)
    x = Multiply()([shortcut, x])
    return x
    
def identity_block(x, n_filters, ratio=16):
    """ Create a Bottleneck Residual Block with Identity Link
        x        : input into the block
        n_filters: number of filters
        ratio    : amount of filter reduction during squeeze
    """
    # Save input vector (feature maps) for the identity link
    shortcut = x
    
    ## Construct the 1x1, 3x3, 1x1 residual block (fig 3c)

    # Dimensionality reduction
    x = Conv2D(n_filters, (1, 1), strides=(1, 1), use_bias=False, kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)
    x = ReLU()(x)

    # Bottleneck layer
    x = Conv2D(n_filters, (3, 3), strides=(1, 1), padding="same", use_bias=False, kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)
    x = ReLU()(x)

    # Dimensionality restoration - increase the number of output filters by 4X
    x = Conv2D(n_filters * 4, (1, 1), strides=(1, 1), use_bias=False, kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)
    
    # Pass the output through the squeeze and excitation block
    x = squeeze_excite_block(x, ratio)
    
    # Add the identity link (input) to the output of the residual block
    x = Add()([shortcut, x])
    x = ReLU()(x)
    return x

def projection_block(x, n_filters, strides=(2,2), ratio=16):
    """ Create Bottleneck Residual Block with Projection Shortcut
        Increase the number of filters by 4X
        x        : input into the block
        n_filters: number of filters
        strides  : whether entry convolution is strided (i.e., (2, 2) vs (1, 1))
        ratio    : amount of filter reduction during squeeze
    """
    # Construct the projection shortcut
    # Increase filters by 4X to match shape when added to output of block
    shortcut = Conv2D(4 * n_filters, (1, 1), strides=strides, use_bias=False, kernel_initializer='he_normal')(x)
    shortcut = BatchNormalization()(shortcut)

    ## Construct the 1x1, 3x3, 1x1 residual block (fig 3c)

    # Dimensionality reduction
    # Feature pooling when strides=(2, 2)
    x = Conv2D(n_filters, (1, 1), strides=strides, use_bias=False, kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)
    x = ReLU()(x)

    # Bottleneck layer
    x = Conv2D(n_filters, (3, 3), strides=(1, 1), padding='same', use_bias=False, kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)
    x = ReLU()(x)

    # Dimensionality restoration - increase the number of filters by 4X
    x = Conv2D(4 * n_filters, (1, 1), strides=(1, 1), use_bias=False, kernel_initializer='he_normal')(x)
    x = BatchNormalization()(x)

    # Pass the output through the squeeze and excitation block
    x = squeeze_excite_block(x, ratio)

    # Add the projection shortcut link to the output of the residual block
    x = Add()([x, shortcut])
    x = ReLU()(x)
    return x

def classifier(x, n_classes):
  """ Create the Classifier Group 
      x         : input to the classifier
      n_classes : number of output classes
  """
  # Pool at the end of all the convolutional residual blocks
  x = GlobalAveragePooling2D()(x)

  # Final Dense Outputting Layer for the outputs
  outputs = Dense(n_classes, activation='softmax', kernel_initializer='he_normal')(x)
  return outputs

# Meta-parameter: # Meta-parameter: list of groups: filter size and number of blocks
groups = { 50 : [ (64, 3), (128, 4), (256, 6),  (512, 3) ],		# SE-ResNet50
           101: [ (64, 3), (128, 4), (256, 23), (512, 3) ],		# SE-ResNet101
           152: [ (64, 3), (128, 8), (256, 36), (512, 3) ]		# SE-ResNet152
         }

# Meta-parameter: Amount of filter reduction in squeeze operation
ratio = 16
   
# The input tensor
inputs = Input(shape=(224, 224, 3))

# The Stem Group
x = stem(inputs)

# The Learnet
x = learner(x, groups[50], ratio)

# The Classifier for 1000 classes
outputs = classifier(x, 1000)

# Instantiate the Model
model = Model(inputs, outputs)

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