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