first working update of branch

This commit is contained in:
vahidrezanezhad 2024-04-16 01:00:48 +02:00
parent 02b1436f39
commit d27647a0f1
4 changed files with 452 additions and 151 deletions

179
models.py
View file

@ -1,13 +1,81 @@
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':
@ -292,3 +360,114 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, weight_decay=1e-
model = Model(img_input, o)
return model
def vit_resnet50_unet(n_classes,patch_size, num_patches, input_height=224,input_width=224,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 = keras.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)
o = (BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(o)
model = keras.Model(inputs=inputs, outputs=o)
return model