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training/models.py: make imports explicit

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kba 2025-10-17 13:45:14 +02:00
parent 38c028c6b5
commit 3a73ccca2e
2 changed files with 51 additions and 32 deletions
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2
pyproject.toml
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@ -58,8 +58,6 @@ source = ["eynollah"]
[tool.ruff] [tool.ruff]
line-length = 120 line-length = 120
# TODO: Reenable and fix after release v0.6.0
exclude = ['src/eynollah/training']
[tool.ruff.lint] [tool.ruff.lint]
ignore = [ ignore = [

81
src/eynollah/training/models.py
View file

@ -1,9 +1,29 @@
import tensorflow as tf
from tensorflow import keras from tensorflow import keras
from tensorflow.keras.models import * from keras.layers import (
from tensorflow.keras.layers import * Activation,
from tensorflow.keras import layers Add,
from tensorflow.keras.regularizers import l2 AveragePooling2D,
BatchNormalization,
Conv2D,
Dense,
Dropout,
Embedding,
Flatten,
Input,
Lambda,
Layer,
LayerNormalization,
MaxPooling2D,
MultiHeadAttention,
UpSampling2D,
ZeroPadding2D,
add,
concatenate
)
from keras.models import Model
import tensorflow as tf
# from keras import layers, models
from keras.regularizers import l2
##mlp_head_units = [512, 256]#[2048, 1024] ##mlp_head_units = [512, 256]#[2048, 1024]
###projection_dim = 64 ###projection_dim = 64
@ -15,13 +35,13 @@ MERGE_AXIS = -1
def mlp(x, hidden_units, dropout_rate): def mlp(x, hidden_units, dropout_rate):
for units in hidden_units: for units in hidden_units:
x = layers.Dense(units, activation=tf.nn.gelu)(x) x = Dense(units, activation=tf.nn.gelu)(x)
x = layers.Dropout(dropout_rate)(x) x = Dropout(dropout_rate)(x)
return x return x
class Patches(layers.Layer): class Patches(Layer):
def __init__(self, patch_size_x, patch_size_y):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs): def __init__(self, patch_size_x, patch_size_y):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
super(Patches, self).__init__() super().__init__()
self.patch_size_x = patch_size_x self.patch_size_x = patch_size_x
self.patch_size_y = patch_size_y self.patch_size_y = patch_size_y
@ -49,9 +69,9 @@ class Patches(layers.Layer):
}) })
return config return config
class Patches_old(layers.Layer): class Patches_old(Layer):
def __init__(self, patch_size):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs): def __init__(self, patch_size):#__init__(self, **kwargs):#:__init__(self, patch_size):#__init__(self, **kwargs):
super(Patches, self).__init__() super().__init__()
self.patch_size = patch_size self.patch_size = patch_size
def call(self, images): def call(self, images):
@ -69,8 +89,8 @@ class Patches_old(layers.Layer):
#print(patches.shape,patch_dims,'patch_dims') #print(patches.shape,patch_dims,'patch_dims')
patches = tf.reshape(patches, [batch_size, -1, patch_dims]) patches = tf.reshape(patches, [batch_size, -1, patch_dims])
return patches return patches
def get_config(self):
def get_config(self):
config = super().get_config().copy() config = super().get_config().copy()
config.update({ config.update({
'patch_size': self.patch_size, 'patch_size': self.patch_size,
@ -78,12 +98,12 @@ class Patches_old(layers.Layer):
return config return config
class PatchEncoder(layers.Layer): class PatchEncoder(Layer):
def __init__(self, num_patches, projection_dim): def __init__(self, num_patches, projection_dim):
super(PatchEncoder, self).__init__() super(PatchEncoder, self).__init__()
self.num_patches = num_patches self.num_patches = num_patches
self.projection = layers.Dense(units=projection_dim) self.projection = Dense(units=projection_dim)
self.position_embedding = layers.Embedding( self.position_embedding = Embedding(
input_dim=num_patches, output_dim=projection_dim input_dim=num_patches, output_dim=projection_dim
) )
@ -144,7 +164,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x) x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
x = layers.add([x, input_tensor]) x = add([x, input_tensor])
x = Activation('relu')(x) x = Activation('relu')(x)
return x return x
@ -189,12 +209,12 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
name=conv_name_base + '1')(input_tensor) name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
x = layers.add([x, shortcut]) x = add([x, shortcut])
x = Activation('relu')(x) x = Activation('relu')(x)
return x return x
def resnet50_unet_light(n_classes, input_height=224, input_width=224, taks="segmentation", weight_decay=1e-6, pretraining=False): def resnet50_unet_light(n_classes, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
assert input_height % 32 == 0 assert input_height % 32 == 0
assert input_width % 32 == 0 assert input_width % 32 == 0
@ -397,7 +417,7 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentati
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): 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: if mlp_head_units is None:
mlp_head_units = [128, 64] mlp_head_units = [128, 64]
inputs = layers.Input(shape=(input_height, input_width, 3)) inputs = Input(shape=(input_height, input_width, 3))
#transformer_units = [ #transformer_units = [
#projection_dim * 2, #projection_dim * 2,
@ -452,20 +472,21 @@ def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, mlp_he
for _ in range(transformer_layers): for _ in range(transformer_layers):
# Layer normalization 1. # Layer normalization 1.
x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) x1 = LayerNormalization(epsilon=1e-6)(encoded_patches)
# Create a multi-head attention layer. # Create a multi-head attention layer.
attention_output = layers.MultiHeadAttention( attention_output = MultiHeadAttention(
num_heads=num_heads, key_dim=projection_dim, dropout=0.1 num_heads=num_heads, key_dim=projection_dim, dropout=0.1
)(x1, x1) )(x1, x1)
# Skip connection 1. # Skip connection 1.
x2 = layers.Add()([attention_output, encoded_patches]) x2 = Add()([attention_output, encoded_patches])
# Layer normalization 2. # Layer normalization 2.
x3 = layers.LayerNormalization(epsilon=1e-6)(x2) x3 = LayerNormalization(epsilon=1e-6)(x2)
# MLP. # MLP.
x3 = mlp(x3, hidden_units=mlp_head_units, dropout_rate=0.1) x3 = mlp(x3, hidden_units=mlp_head_units, dropout_rate=0.1)
# Skip connection 2. # Skip connection 2.
encoded_patches = layers.Add()([x3, x2]) encoded_patches = Add()([x3, x2])
assert isinstance(x, Layer)
encoded_patches = tf.reshape(encoded_patches, [-1, x.shape[1], x.shape[2] , int( projection_dim / (patch_size_x * patch_size_y) )]) 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 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(encoded_patches)
@ -521,7 +542,7 @@ def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, mlp_he
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): 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: if mlp_head_units is None:
mlp_head_units = [128, 64] mlp_head_units = [128, 64]
inputs = layers.Input(shape=(input_height, input_width, 3)) inputs = Input(shape=(input_height, input_width, 3))
##transformer_units = [ ##transformer_units = [
##projection_dim * 2, ##projection_dim * 2,
@ -536,19 +557,19 @@ def vit_resnet50_unet_transformer_before_cnn(n_classes, patch_size_x, patch_size
for _ in range(transformer_layers): for _ in range(transformer_layers):
# Layer normalization 1. # Layer normalization 1.
x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) x1 = LayerNormalization(epsilon=1e-6)(encoded_patches)
# Create a multi-head attention layer. # Create a multi-head attention layer.
attention_output = layers.MultiHeadAttention( attention_output = MultiHeadAttention(
num_heads=num_heads, key_dim=projection_dim, dropout=0.1 num_heads=num_heads, key_dim=projection_dim, dropout=0.1
)(x1, x1) )(x1, x1)
# Skip connection 1. # Skip connection 1.
x2 = layers.Add()([attention_output, encoded_patches]) x2 = Add()([attention_output, encoded_patches])
# Layer normalization 2. # Layer normalization 2.
x3 = layers.LayerNormalization(epsilon=1e-6)(x2) x3 = LayerNormalization(epsilon=1e-6)(x2)
# MLP. # MLP.
x3 = mlp(x3, hidden_units=mlp_head_units, dropout_rate=0.1) x3 = mlp(x3, hidden_units=mlp_head_units, dropout_rate=0.1)
# Skip connection 2. # Skip connection 2.
encoded_patches = layers.Add()([x3, x2]) encoded_patches = 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 = tf.reshape(encoded_patches, [-1, input_height, input_width , int( projection_dim / (patch_size_x * patch_size_y) )])