ResNet34_keras dropout

def basic_identity_block(filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut. # Arguments kernel_size: default 3, the kernel size of middle conv layer at main path filters: list of integers, the filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names # Returns Output tensor for the block. """
def layer(input_tensor):
conv_params = get_conv_params()
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name = handle_block_names(stage, block)
x = BatchNormalization(name=bn_name + '1', **bn_params)(input_tensor)
x = Activation('relu', name=relu_name + '1')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), name=conv_name + '1', **conv_params)(x)
x = BatchNormalization(name=bn_name + '2', **bn_params)(x)
x = Activation('relu', name=relu_name + '2')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), name=conv_name + '2', **conv_params)(x)
x = Add()([x, input_tensor])
return x
return layer
def basic_conv_block(filters, stage, block, strides=(2, 2)):
"""The identity block is the block that has no conv layer at shortcut. # Arguments input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main path filters: list of integers, the filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names # Returns Output tensor for the block. """
def layer(input_tensor):
conv_params = get_conv_params()
bn_params = get_bn_params()
conv_name, bn_name, relu_name, sc_name = handle_block_names(stage, block)
x = BatchNormalization(name=bn_name + '1', **bn_params)(input_tensor)
x = Activation('relu', name=relu_name + '1')(x)
shortcut = x
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), strides=strides, name=conv_name + '1', **conv_params)(x)
x = BatchNormalization(name=bn_name + '2', **bn_params)(x)
x = Activation('relu', name=relu_name + '2')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(filters, (3, 3), name=conv_name + '2', **conv_params)(x)
shortcut = Conv2D(filters, (1, 1), name=sc_name, strides=strides, **conv_params)(shortcut)
x = Add()([x, shortcut])
return x
return layer

def build_resnet(
repetitions=(2, 2, 2, 2),
include_top=True,
input_tensor=None,
input_shape=None,
classes=1000,
block_type='usual'):
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=224,
min_size=197,
data_format='channels_last',
require_flatten=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape, name='data')
else:
if not K.is_keras_tensor(input_tensor):
img_input = Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# get parameters for model layers
no_scale_bn_params = get_bn_params(scale=False)
bn_params = get_bn_params()
conv_params = get_conv_params()
init_filters = 64
if block_type == 'basic':
conv_block = basic_conv_block
identity_block = basic_identity_block
else:
conv_block = usual_conv_block
identity_block = usual_identity_block
# renet bottom
x = BatchNormalization(name='bn_data', **no_scale_bn_params)(img_input)
x = ZeroPadding2D(padding=(3, 3))(x)
x = Conv2D(init_filters, (7, 7), strides=(2, 2), name='conv0', **conv_params)(x)
x = BatchNormalization(name='bn0', **bn_params)(x)
x = Activation('relu', name='relu0')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='valid', name='pooling0')(x)
# resnet body repetitions = (3,4,6,3)
for stage, rep in enumerate(repetitions):
for block in range(rep):
# print(block)
filters = init_filters * (2**stage) 
# first block of first stage without strides because we have maxpooling before
if block == 0 and stage == 0:
# x = conv_block(filters, stage, block, strides=(1, 1))(x)
x = identity_block(filters, stage, block)(x)
continue 
elif block == 0:
x = conv_block(filters, stage, block, strides=(2, 2))(x)  
else:
x = identity_block(filters, stage, block)(x)          
x = BatchNormalization(name='bn1', **bn_params)(x)
x = Activation('relu', name='relu1')(x)
# resnet top
if include_top:
x = GlobalAveragePooling2D(name='pool1')(x)
x = Dense(classes, name='fc1')(x)
x = Activation('softmax', name='softmax')(x)
# Ensure that the model takes into account any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = get_source_inputs(input_tensor)
else:
inputs = img_input      
# Create model.
model = Model(inputs, x)
return model
def ResNet34(input_shape, input_tensor=None, weights=None, classes=1000, include_top=True):
model = build_resnet(input_tensor=input_tensor,
input_shape=input_shape,
repetitions=(3, 4, 6, 3),
classes=classes,
include_top=include_top,
block_type='basic')
model.name = 'resnet34'
if weights:
load_model_weights(weights_collection, model, weights, classes, include_top)
return model

def build_unet(backbone, classes, skip_connection_layers,
decoder_filters=(256,128,64,32,16),
upsample_rates=(2,2,2,2,2),
n_upsample_blocks=5,
block_type='upsampling',
activation='sigmoid',
use_batchnorm=True):
input = backbone.input
x = backbone.output
if block_type == 'transpose':
up_block = Transpose2D_block
else:
up_block = Upsample2D_block
# convert layer names to indices
skip_connection_idx = ([get_layer_number(backbone, l) if isinstance(l, str) else l
for l in skip_connection_layers])
# print(skip_connection_idx) [128, 73, 36, 5]
for i in range(n_upsample_blocks):
# print(i)
# check if there is a skip connection
skip_connection = None
if i < len(skip_connection_idx):
skip_connection = backbone.layers[skip_connection_idx[i]].output
# print(backbone.layers[skip_connection_idx[i]])
# <keras.layers.core.Activation object at 0x00000164CC562A20>
upsample_rate = to_tuple(upsample_rates[i])
x = up_block(decoder_filters[i], i, upsample_rate=upsample_rate,
skip=skip_connection, use_batchnorm=use_batchnorm)(x)
x = Conv2D(classes, (3,3), padding='same', name='final_conv')(x)
x = Activation(activation, name=activation)(x)
model = Model(input, x)
return model