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https://github.com/qurator-spk/eynollah.git
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training/models.py: make imports explicit
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kba
parent
38c028c6b5
commit
3a73ccca2e
2 changed files with 51 additions and 32 deletions
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@ -58,8 +58,6 @@ source = ["eynollah"]
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[tool.ruff]
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line-length = 120
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# TODO: Reenable and fix after release v0.6.0
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exclude = ['src/eynollah/training']
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[tool.ruff.lint]
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ignore = [
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@ -1,9 +1,29 @@
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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 import layers
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from tensorflow.keras.regularizers import l2
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from keras.layers import (
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Activation,
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Add,
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AveragePooling2D,
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BatchNormalization,
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Conv2D,
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Dense,
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Dropout,
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Embedding,
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Flatten,
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Input,
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Lambda,
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Layer,
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LayerNormalization,
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MaxPooling2D,
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MultiHeadAttention,
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UpSampling2D,
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ZeroPadding2D,
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add,
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concatenate
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)
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from keras.models import Model
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import tensorflow as tf
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# from keras import layers, models
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from keras.regularizers import l2
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##mlp_head_units = [512, 256]#[2048, 1024]
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###projection_dim = 64
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@ -15,13 +35,13 @@ MERGE_AXIS = -1
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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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x = Dense(units, activation=tf.nn.gelu)(x)
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x = Dropout(dropout_rate)(x)
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return x
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class Patches(layers.Layer):
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class Patches(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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super().__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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@ -49,9 +69,9 @@ class Patches(layers.Layer):
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})
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return config
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class Patches_old(layers.Layer):
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class Patches_old(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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super().__init__()
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self.patch_size = patch_size
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def call(self, images):
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@ -69,8 +89,8 @@ class Patches_old(layers.Layer):
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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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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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@ -78,12 +98,12 @@ class Patches_old(layers.Layer):
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return config
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class PatchEncoder(layers.Layer):
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class PatchEncoder(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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self.projection = Dense(units=projection_dim)
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self.position_embedding = Embedding(
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input_dim=num_patches, output_dim=projection_dim
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)
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@ -144,7 +164,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
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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 = add([x, input_tensor])
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x = Activation('relu')(x)
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return x
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@ -189,12 +209,12 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
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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 = 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, taks="segmentation", weight_decay=1e-6, pretraining=False):
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def resnet50_unet_light(n_classes, input_height=224, input_width=224, task="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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@ -397,7 +417,7 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentati
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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):
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if mlp_head_units is None:
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mlp_head_units = [128, 64]
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inputs = layers.Input(shape=(input_height, input_width, 3))
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inputs = Input(shape=(input_height, input_width, 3))
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#transformer_units = [
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#projection_dim * 2,
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@ -452,20 +472,21 @@ def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, mlp_he
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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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x1 = 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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attention_output = 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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x2 = 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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x3 = LayerNormalization(epsilon=1e-6)(x2)
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# MLP.
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x3 = mlp(x3, hidden_units=mlp_head_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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encoded_patches = Add()([x3, x2])
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assert isinstance(x, Layer)
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encoded_patches = tf.reshape(encoded_patches, [-1, x.shape[1], x.shape[2] , int( projection_dim / (patch_size_x * patch_size_y) )])
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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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@ -521,7 +542,7 @@ def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, mlp_he
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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):
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if mlp_head_units is None:
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mlp_head_units = [128, 64]
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inputs = layers.Input(shape=(input_height, input_width, 3))
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inputs = Input(shape=(input_height, input_width, 3))
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##transformer_units = [
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##projection_dim * 2,
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@ -536,19 +557,19 @@ def vit_resnet50_unet_transformer_before_cnn(n_classes, patch_size_x, patch_size
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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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x1 = 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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attention_output = 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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x2 = 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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x3 = LayerNormalization(epsilon=1e-6)(x2)
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# MLP.
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x3 = mlp(x3, hidden_units=mlp_head_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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encoded_patches = Add()([x3, x2])
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encoded_patches = tf.reshape(encoded_patches, [-1, input_height, input_width , int( projection_dim / (patch_size_x * patch_size_y) )])
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