mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-26 23:34:13 +01:00
Merge 2e0c1868e0 into 2e0fb64dcb
This commit is contained in:
Konstantin Baierer
GitHub
commit
56402aeba2
1 changed files with 116 additions and 663 deletions
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@ -1,117 +1,12 @@
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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.models import Model
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from tensorflow.keras.layers import Conv2D, Concatenate, ZeroPadding2D, BatchNormalization, Activation, MaxPooling2D, UpSampling2D, Input, Layer
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from tensorflow.keras import layers
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from tensorflow.keras.regularizers import l2
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import tensorflow as tf
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##mlp_head_units = [512, 256]#[2048, 1024]
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###projection_dim = 64
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##transformer_layers = 2#8
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##num_heads = 1#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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resnet50_Weights_path='./pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
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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_x, patch_size_y):#__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_x = patch_size_x
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self.patch_size_y = patch_size_y
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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_y, self.patch_size_x, 1],
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strides=[1, self.patch_size_y, self.patch_size_x, 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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patch_dims = tf.shape(patches)[-1]
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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_x': self.patch_size_x,
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'patch_size_y': self.patch_size_y,
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})
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return config
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class Patches_old(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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def identity_block(input_tensor, kernel_size, filters, stage, block, data_format='channels_last'):
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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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@ -124,24 +19,21 @@ def identity_block(input_tensor, kernel_size, filters, stage, 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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bn_axis = 3 if data_format == 'channels_last' else 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 = Conv2D(filters1, (1, 1) , data_format=data_format , 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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x = Conv2D(filters2, kernel_size , data_format=data_format ,
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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 = Conv2D(filters3 , (1, 1), data_format=data_format , 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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@ -149,7 +41,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
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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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def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), data_format='channels_last'):
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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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@ -164,28 +56,25 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
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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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bn_axis = 3 if data_format == 'channels_last' else 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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x = Conv2D(filters1, (1, 1) , data_format=data_format, 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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x = Conv2D(filters2, kernel_size , data_format=data_format, 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 = Conv2D(filters3, (1, 1), data_format=data_format, 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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shortcut = Conv2D(filters3, (1, 1), data_format=data_format, 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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@ -194,567 +83,131 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
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return x
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def resnet50_unet_light(n_classes, input_height=224, input_width=224, taks="segmentation", 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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class PadMultiple(Layer):
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def __init__(self, mods, data_format='channels_last'):
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super().__init__()
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self.mods = mods
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self.data_format = data_format
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img_input = Input(shape=(input_height, input_width, 3))
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def call(self, x):
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h, w = self.mods
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padding = (
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[(0,0), (0, -tf.shape(x)[1] % h), (0, -tf.shape(x)[2] % w), (0,0)] if self.data_format == 'channels_last'
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else [(0,0), (0,0), (0, -tf.shape(x)[1] % h), (0, -tf.shape(x)[2] % w)])
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return tf.pad(x, padding)
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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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class CutTo(Layer):
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def __init__(self, data_format='channels_last'):
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super().__init__()
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self.data_format = data_format
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def call(self, inputs):
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h, w = (1, 2) if self.data_format == 'channels_last' else (2,4)
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h, w = tf.shape(inputs[1])[h], tf.shape(inputs[1])[w]
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return inputs[0][:, :h, :w] if self.data_format == 'channels_last' else inputs[0][:, :, :h, :w]
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def resnet50_unet(n_classes, input_height=None, input_width=None, weight_decay=1e-6, pretraining=False, last_activation='softmax', skip_last_batchnorm=False, light_version=False, data_format='channels_last'):
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""" Returns a U-NET model using the keras functional API. """
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img_input = Input(shape=(input_height, input_width, 3 ))
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padded_to_multiple = PadMultiple((32,32))(img_input)
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bn_axis = 3 if data_format == 'channels_last' else 1
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merge_axis = 3 if data_format == 'channels_last' else 1
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x = ZeroPadding2D((3, 3), data_format=data_format)(padded_to_multiple)
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x = Conv2D(64, (7, 7), data_format=data_format, 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 = MaxPooling2D((3, 3) , data_format=data_format , 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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x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), data_format=data_format)
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', data_format=data_format)
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x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', data_format=data_format)
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f2 = ZeroPadding2D(((1,0), (1,0)), data_format=data_format)(x)
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x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', data_format=data_format)
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', data_format=data_format)
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', data_format=data_format)
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x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', data_format=data_format)
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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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x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', data_format=data_format)
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', data_format=data_format)
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', data_format=data_format)
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', data_format=data_format)
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', data_format=data_format)
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x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', data_format=data_format)
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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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if task == "segmentation":
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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')
|
||||
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', data_format=data_format)
|
||||
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', data_format=data_format)
|
||||
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', data_format=data_format)
|
||||
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)
|
||||
if light_version:
|
||||
v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=data_format, kernel_regularizer=l2(weight_decay))(f5)
|
||||
v512_2048 = BatchNormalization(axis=bn_axis)(v512_2048)
|
||||
v512_2048 = Activation('relu')(v512_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)
|
||||
v512_1024 = Conv2D(512, (1, 1), padding='same', data_format=data_format, kernel_regularizer=l2(weight_decay))(f4)
|
||||
v512_1024 = BatchNormalization(axis=bn_axis)(v512_1024)
|
||||
v512_1024 = Activation('relu')(v512_1024)
|
||||
x, c = v512_2048, v512_1024 # continuation and concatenation layers
|
||||
else:
|
||||
o = (Activation('sigmoid'))(o)
|
||||
v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=data_format, kernel_regularizer=l2(weight_decay))(f5)
|
||||
v1024_2048 = BatchNormalization(axis=bn_axis)(v1024_2048)
|
||||
v1024_2048 = Activation('relu')(v1024_2048)
|
||||
x, c = v1024_2048, f4 # continuation and concatenation layers
|
||||
|
||||
model = Model(img_input, o)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, mlp_head_units=None, transformer_layers=8, num_heads =4, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
|
||||
if mlp_head_units is None:
|
||||
mlp_head_units = [128, 64]
|
||||
inputs = layers.Input(shape=(input_height, input_width, 3))
|
||||
|
||||
#transformer_units = [
|
||||
#projection_dim * 2,
|
||||
#projection_dim,
|
||||
#] # Size of the transformer layers
|
||||
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]
|
||||
|
||||
#patch_size_y = input_height / x.shape[1]
|
||||
#patch_size_x = input_width / x.shape[2]
|
||||
#patch_size = patch_size_x * patch_size_y
|
||||
patches = Patches(patch_size_x, patch_size_y)(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=mlp_head_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] , int( projection_dim / (patch_size_x * patch_size_y) )])
|
||||
|
||||
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 = UpSampling2D((2,2), data_format=data_format)(x)
|
||||
o = Concatenate(axis=merge_axis)([o ,c])
|
||||
o = ZeroPadding2D( (1,1), data_format=data_format)(o)
|
||||
o = Conv2D(512, (3, 3), padding='valid', data_format=data_format, 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 = UpSampling2D( (2,2), data_format=data_format)(o)
|
||||
o = Concatenate(axis=merge_axis)([ o ,f3])
|
||||
o = ZeroPadding2D( (1,1), data_format=data_format)(o)
|
||||
o = Conv2D( 256, (3, 3), padding='valid', data_format=data_format, 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 = UpSampling2D( (2,2), data_format=data_format)(o)
|
||||
o = Concatenate(axis=merge_axis)([o,f2])
|
||||
o = ZeroPadding2D((1,1) , data_format=data_format)(o)
|
||||
o = Conv2D( 128 , (3, 3), padding='valid', data_format=data_format, 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 = UpSampling2D( (2,2), data_format=data_format)(o)
|
||||
o = Concatenate(axis=merge_axis)([o,f1])
|
||||
o = ZeroPadding2D((1,1) , data_format=data_format)(o)
|
||||
o = Conv2D( 64 , (3, 3), padding='valid', data_format=data_format, 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 = UpSampling2D( (2,2), data_format=data_format)(o)
|
||||
o = Concatenate(axis=merge_axis)([o, padded_to_multiple])
|
||||
o = ZeroPadding2D((1,1) , data_format=data_format)(o)
|
||||
o = Conv2D(32, (3, 3), padding='valid', data_format=data_format, 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)
|
||||
o = Conv2D(n_classes, (1, 1), padding='same', data_format=data_format, kernel_regularizer=l2(weight_decay))(o)
|
||||
if not skip_last_batchnorm:
|
||||
o = BatchNormalization(axis=bn_axis)(o)
|
||||
|
||||
model = Model(inputs=inputs, outputs=o)
|
||||
o = Activation(last_activation)(o)
|
||||
o = CutTo()([o, img_input])
|
||||
|
||||
return model
|
||||
|
||||
def vit_resnet50_unet_transformer_before_cnn(n_classes, patch_size_x, patch_size_y, num_patches, mlp_head_units=None, transformer_layers=8, num_heads =4, projection_dim = 64, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
|
||||
if mlp_head_units is None:
|
||||
mlp_head_units = [128, 64]
|
||||
inputs = layers.Input(shape=(input_height, input_width, 3))
|
||||
|
||||
##transformer_units = [
|
||||
##projection_dim * 2,
|
||||
##projection_dim,
|
||||
##] # Size of the transformer layers
|
||||
IMAGE_ORDERING = 'channels_last'
|
||||
bn_axis=3
|
||||
|
||||
patches = Patches(patch_size_x, patch_size_y)(inputs)
|
||||
# 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=mlp_head_units, dropout_rate=0.1)
|
||||
# Skip connection 2.
|
||||
encoded_patches = layers.Add()([x3, x2])
|
||||
|
||||
encoded_patches = tf.reshape(encoded_patches, [-1, input_height, input_width , int( projection_dim / (patch_size_x * patch_size_y) )])
|
||||
|
||||
encoded_patches = Conv2D(3, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay), name='convinput')(encoded_patches)
|
||||
|
||||
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(encoded_patches)
|
||||
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(encoded_patches, x).load_weights(resnet50_Weights_path)
|
||||
|
||||
v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(x)
|
||||
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
|
||||
|
||||
def machine_based_reading_order_model(n_classes,input_height=224,input_width=224,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
|
||||
|
||||
x1 = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
|
||||
x1 = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x1)
|
||||
|
||||
x1 = BatchNormalization(axis=bn_axis, name='bn_conv1')(x1)
|
||||
x1 = Activation('relu')(x1)
|
||||
x1 = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(x1)
|
||||
|
||||
x1 = conv_block(x1, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
|
||||
x1 = identity_block(x1, 3, [64, 64, 256], stage=2, block='b')
|
||||
x1 = identity_block(x1, 3, [64, 64, 256], stage=2, block='c')
|
||||
|
||||
x1 = conv_block(x1, 3, [128, 128, 512], stage=3, block='a')
|
||||
x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='b')
|
||||
x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='c')
|
||||
x1 = identity_block(x1, 3, [128, 128, 512], stage=3, block='d')
|
||||
|
||||
x1 = conv_block(x1, 3, [256, 256, 1024], stage=4, block='a')
|
||||
x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='b')
|
||||
x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='c')
|
||||
x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='d')
|
||||
x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='e')
|
||||
x1 = identity_block(x1, 3, [256, 256, 1024], stage=4, block='f')
|
||||
|
||||
x1 = conv_block(x1, 3, [512, 512, 2048], stage=5, block='a')
|
||||
x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='b')
|
||||
x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='c')
|
||||
|
||||
if pretraining:
|
||||
Model(img_input , x1).load_weights(resnet50_Weights_path)
|
||||
|
||||
x1 = AveragePooling2D((7, 7), name='avg_pool1')(x1)
|
||||
flattened = Flatten()(x1)
|
||||
|
||||
o = Dense(256, activation='relu', name='fc512')(flattened)
|
||||
o=Dropout(0.2)(o)
|
||||
|
||||
o = Dense(256, activation='relu', name='fc512a')(o)
|
||||
o=Dropout(0.2)(o)
|
||||
|
||||
o = Dense(n_classes, activation='sigmoid', name='fc1000')(o)
|
||||
model = Model(img_input , o)
|
||||
|
||||
return model
|
||||
return Model(img_input , o)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue