You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
548 lines
22 KiB
Python
548 lines
22 KiB
Python
import tensorflow as tf
|
|
from tensorflow import keras
|
|
from tensorflow.keras.models import *
|
|
from tensorflow.keras.layers import *
|
|
from tensorflow.keras import layers
|
|
from tensorflow.keras.regularizers import l2
|
|
|
|
mlp_head_units = [2048, 1024]
|
|
projection_dim = 64
|
|
transformer_layers = 8
|
|
num_heads = 4
|
|
resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
|
|
IMAGE_ORDERING = 'channels_last'
|
|
MERGE_AXIS = -1
|
|
|
|
transformer_units = [
|
|
projection_dim * 2,
|
|
projection_dim,
|
|
] # Size of the transformer layers
|
|
def mlp(x, hidden_units, dropout_rate):
|
|
for units in hidden_units:
|
|
x = layers.Dense(units, activation=tf.nn.gelu)(x)
|
|
x = layers.Dropout(dropout_rate)(x)
|
|
return x
|
|
|
|
|
|
class Patches(layers.Layer):
|
|
def __init__(self, patch_size):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
|
|
super(Patches, self).__init__()
|
|
self.patch_size = patch_size
|
|
|
|
def call(self, images):
|
|
print(tf.shape(images)[1],'images')
|
|
print(self.patch_size,'self.patch_size')
|
|
batch_size = tf.shape(images)[0]
|
|
patches = tf.image.extract_patches(
|
|
images=images,
|
|
sizes=[1, self.patch_size, self.patch_size, 1],
|
|
strides=[1, self.patch_size, self.patch_size, 1],
|
|
rates=[1, 1, 1, 1],
|
|
padding="VALID",
|
|
)
|
|
patch_dims = patches.shape[-1]
|
|
print(patches.shape,patch_dims,'patch_dims')
|
|
patches = tf.reshape(patches, [batch_size, -1, patch_dims])
|
|
return patches
|
|
def get_config(self):
|
|
|
|
config = super().get_config().copy()
|
|
config.update({
|
|
'patch_size': self.patch_size,
|
|
})
|
|
return config
|
|
|
|
class PatchEncoder(layers.Layer):
|
|
def __init__(self, num_patches, projection_dim):
|
|
super(PatchEncoder, self).__init__()
|
|
self.num_patches = num_patches
|
|
self.projection = layers.Dense(units=projection_dim)
|
|
self.position_embedding = layers.Embedding(
|
|
input_dim=num_patches, output_dim=projection_dim
|
|
)
|
|
|
|
def call(self, patch):
|
|
positions = tf.range(start=0, limit=self.num_patches, delta=1)
|
|
encoded = self.projection(patch) + self.position_embedding(positions)
|
|
return encoded
|
|
def get_config(self):
|
|
|
|
config = super().get_config().copy()
|
|
config.update({
|
|
'num_patches': self.num_patches,
|
|
'projection': self.projection,
|
|
'position_embedding': self.position_embedding,
|
|
})
|
|
return config
|
|
|
|
|
|
def one_side_pad(x):
|
|
x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
|
|
if IMAGE_ORDERING == 'channels_first':
|
|
x = Lambda(lambda x: x[:, :, :-1, :-1])(x)
|
|
elif IMAGE_ORDERING == 'channels_last':
|
|
x = Lambda(lambda x: x[:, :-1, :-1, :])(x)
|
|
return x
|
|
|
|
|
|
def identity_block(input_tensor, kernel_size, filters, stage, block):
|
|
"""The identity block is the block that has no conv layer at shortcut.
|
|
# Arguments
|
|
input_tensor: input tensor
|
|
kernel_size: defualt 3, the kernel size of middle conv layer at main path
|
|
filters: list of integers, the filterss 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.
|
|
"""
|
|
filters1, filters2, filters3 = filters
|
|
|
|
if IMAGE_ORDERING == 'channels_last':
|
|
bn_axis = 3
|
|
else:
|
|
bn_axis = 1
|
|
|
|
conv_name_base = 'res' + str(stage) + block + '_branch'
|
|
bn_name_base = 'bn' + str(stage) + block + '_branch'
|
|
|
|
x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2a')(input_tensor)
|
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
|
|
x = Activation('relu')(x)
|
|
|
|
x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING,
|
|
padding='same', name=conv_name_base + '2b')(x)
|
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
|
|
x = Activation('relu')(x)
|
|
|
|
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
|
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
|
|
|
|
x = layers.add([x, input_tensor])
|
|
x = Activation('relu')(x)
|
|
return x
|
|
|
|
|
|
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
|
|
"""conv_block is the block that has a conv layer at shortcut
|
|
# Arguments
|
|
input_tensor: input tensor
|
|
kernel_size: defualt 3, the kernel size of middle conv layer at main path
|
|
filters: list of integers, the filterss 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.
|
|
Note that from stage 3, the first conv layer at main path is with strides=(2,2)
|
|
And the shortcut should have strides=(2,2) as well
|
|
"""
|
|
filters1, filters2, filters3 = filters
|
|
|
|
if IMAGE_ORDERING == 'channels_last':
|
|
bn_axis = 3
|
|
else:
|
|
bn_axis = 1
|
|
|
|
conv_name_base = 'res' + str(stage) + block + '_branch'
|
|
bn_name_base = 'bn' + str(stage) + block + '_branch'
|
|
|
|
x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
|
|
name=conv_name_base + '2a')(input_tensor)
|
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
|
|
x = Activation('relu')(x)
|
|
|
|
x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING, padding='same',
|
|
name=conv_name_base + '2b')(x)
|
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
|
|
x = Activation('relu')(x)
|
|
|
|
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
|
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
|
|
|
|
shortcut = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
|
|
name=conv_name_base + '1')(input_tensor)
|
|
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
|
|
|
|
x = layers.add([x, shortcut])
|
|
x = Activation('relu')(x)
|
|
return x
|
|
|
|
|
|
def resnet50_unet_light(n_classes, input_height=224, input_width=224, taks="segmentation", weight_decay=1e-6, pretraining=False):
|
|
assert input_height % 32 == 0
|
|
assert input_width % 32 == 0
|
|
|
|
img_input = Input(shape=(input_height, input_width, 3))
|
|
|
|
if IMAGE_ORDERING == 'channels_last':
|
|
bn_axis = 3
|
|
else:
|
|
bn_axis = 1
|
|
|
|
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
|
|
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
|
|
name='conv1')(x)
|
|
f1 = x
|
|
|
|
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
|
|
x = Activation('relu')(x)
|
|
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
|
|
|
|
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
|
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
|
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
|
|
f2 = one_side_pad(x)
|
|
|
|
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
|
|
f3 = x
|
|
|
|
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
|
|
f4 = x
|
|
|
|
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
|
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
|
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
|
|
f5 = x
|
|
|
|
if pretraining:
|
|
model = Model(img_input, x).load_weights(resnet50_Weights_path)
|
|
|
|
v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5)
|
|
v512_2048 = (BatchNormalization(axis=bn_axis))(v512_2048)
|
|
v512_2048 = Activation('relu')(v512_2048)
|
|
|
|
v512_1024 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f4)
|
|
v512_1024 = (BatchNormalization(axis=bn_axis))(v512_1024)
|
|
v512_1024 = Activation('relu')(v512_1024)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v512_2048)
|
|
o = (concatenate([o, v512_1024], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, f3], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, f2], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, f1], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, img_input], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
|
|
if task == "segmentation":
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = (Activation('softmax'))(o)
|
|
else:
|
|
o = (Activation('sigmoid'))(o)
|
|
|
|
model = Model(img_input, o)
|
|
return model
|
|
|
|
|
|
def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
|
|
assert input_height % 32 == 0
|
|
assert input_width % 32 == 0
|
|
|
|
img_input = Input(shape=(input_height, input_width, 3))
|
|
|
|
if IMAGE_ORDERING == 'channels_last':
|
|
bn_axis = 3
|
|
else:
|
|
bn_axis = 1
|
|
|
|
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
|
|
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
|
|
name='conv1')(x)
|
|
f1 = x
|
|
|
|
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
|
|
x = Activation('relu')(x)
|
|
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
|
|
|
|
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
|
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
|
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
|
|
f2 = one_side_pad(x)
|
|
|
|
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
|
|
f3 = x
|
|
|
|
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
|
|
f4 = x
|
|
|
|
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
|
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
|
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
|
|
f5 = x
|
|
|
|
if pretraining:
|
|
Model(img_input, x).load_weights(resnet50_Weights_path)
|
|
|
|
v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(
|
|
f5)
|
|
v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
|
|
v1024_2048 = Activation('relu')(v1024_2048)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
|
|
o = (concatenate([o, f4], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, f3], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, f2], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, f1], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, img_input], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
|
|
if task == "segmentation":
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = (Activation('softmax'))(o)
|
|
else:
|
|
o = (Activation('sigmoid'))(o)
|
|
|
|
model = Model(img_input, o)
|
|
|
|
return model
|
|
|
|
|
|
def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
|
|
inputs = layers.Input(shape=(input_height, input_width, 3))
|
|
IMAGE_ORDERING = 'channels_last'
|
|
bn_axis=3
|
|
|
|
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(inputs)
|
|
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
|
|
f1 = x
|
|
|
|
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
|
|
x = Activation('relu')(x)
|
|
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
|
|
|
|
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
|
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
|
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
|
|
f2 = one_side_pad(x)
|
|
|
|
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
|
|
f3 = x
|
|
|
|
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
|
|
f4 = x
|
|
|
|
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
|
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
|
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
|
|
f5 = x
|
|
|
|
if pretraining:
|
|
model = Model(inputs, x).load_weights(resnet50_Weights_path)
|
|
|
|
num_patches = x.shape[1]*x.shape[2]
|
|
patches = Patches(patch_size)(x)
|
|
# Encode patches.
|
|
encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)
|
|
|
|
for _ in range(transformer_layers):
|
|
# Layer normalization 1.
|
|
x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
|
|
# Create a multi-head attention layer.
|
|
attention_output = layers.MultiHeadAttention(
|
|
num_heads=num_heads, key_dim=projection_dim, dropout=0.1
|
|
)(x1, x1)
|
|
# Skip connection 1.
|
|
x2 = layers.Add()([attention_output, encoded_patches])
|
|
# Layer normalization 2.
|
|
x3 = layers.LayerNormalization(epsilon=1e-6)(x2)
|
|
# MLP.
|
|
x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)
|
|
# Skip connection 2.
|
|
encoded_patches = layers.Add()([x3, x2])
|
|
|
|
encoded_patches = tf.reshape(encoded_patches, [-1, x.shape[1], x.shape[2], 64])
|
|
|
|
v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(encoded_patches)
|
|
v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
|
|
v1024_2048 = Activation('relu')(v1024_2048)
|
|
|
|
o = (UpSampling2D( (2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
|
|
o = (concatenate([o, f4],axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o ,f3], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, f2], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, f1], axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
|
o = (concatenate([o, inputs],axis=MERGE_AXIS))
|
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
|
o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = Activation('relu')(o)
|
|
|
|
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(o)
|
|
if task == "segmentation":
|
|
o = (BatchNormalization(axis=bn_axis))(o)
|
|
o = (Activation('softmax'))(o)
|
|
else:
|
|
o = (Activation('sigmoid'))(o)
|
|
|
|
model = Model(inputs=inputs, outputs=o)
|
|
|
|
return model
|
|
|
|
def resnet50_classifier(n_classes,input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
|
|
include_top=True
|
|
assert input_height%32 == 0
|
|
assert input_width%32 == 0
|
|
|
|
|
|
img_input = Input(shape=(input_height,input_width , 3 ))
|
|
|
|
if IMAGE_ORDERING == 'channels_last':
|
|
bn_axis = 3
|
|
else:
|
|
bn_axis = 1
|
|
|
|
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
|
|
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
|
|
f1 = x
|
|
|
|
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
|
|
x = Activation('relu')(x)
|
|
x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x)
|
|
|
|
|
|
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
|
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
|
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
|
|
f2 = one_side_pad(x )
|
|
|
|
|
|
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
|
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
|
|
f3 = x
|
|
|
|
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
|
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
|
|
f4 = x
|
|
|
|
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
|
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
|
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
|
|
f5 = x
|
|
|
|
if pretraining:
|
|
Model(img_input, x).load_weights(resnet50_Weights_path)
|
|
|
|
x = AveragePooling2D((7, 7), name='avg_pool')(x)
|
|
x = Flatten()(x)
|
|
|
|
##
|
|
x = Dense(256, activation='relu', name='fc512')(x)
|
|
x=Dropout(0.2)(x)
|
|
##
|
|
x = Dense(n_classes, activation='softmax', name='fc1000')(x)
|
|
model = Model(img_input, x)
|
|
|
|
|
|
|
|
|
|
return model
|