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fix Patches/PatchEncoder (make configurable again)

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This commit is contained in:
Robert Sachunsky 2026-02-08 01:06:57 +01:00
parent 2492c257c6
commit ea285124ce
2 changed files with 26 additions and 48 deletions
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52
src/eynollah/patch_encoder.py
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@ -3,52 +3,44 @@ os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
import tensorflow as tf import tensorflow as tf
from tensorflow.keras import layers from tensorflow.keras import layers
projection_dim = 64
patch_size = 1
num_patches =21*21#14*14#28*28#14*14#28*28
class PatchEncoder(layers.Layer): class PatchEncoder(layers.Layer):
def __init__(self): # 441=21*21 # 14*14 # 28*28
def __init__(self, num_patches=441, projection_dim=64):
super().__init__() super().__init__()
self.projection = layers.Dense(units=projection_dim) self.num_patches = num_patches
self.position_embedding = layers.Embedding(input_dim=num_patches, output_dim=projection_dim) self.projection_dim = projection_dim
self.projection = layers.Dense(self.projection_dim)
self.position_embedding = layers.Embedding(self.num_patches, self.projection_dim)
def call(self, patch): def call(self, patch):
positions = tf.range(start=0, limit=num_patches, delta=1) positions = tf.range(start=0, limit=self.num_patches, delta=1)
encoded = self.projection(patch) + self.position_embedding(positions) return self.projection(patch) + self.position_embedding(positions)
return encoded
def get_config(self): def get_config(self):
config = super().get_config().copy() return dict(num_patches=self.num_patches,
config.update({ projection_dim=self.projection_dim,
'num_patches': num_patches, **super().get_config())
'projection': self.projection,
'position_embedding': self.position_embedding,
})
return config
class Patches(layers.Layer): class Patches(layers.Layer):
def __init__(self, **kwargs): def __init__(self, patch_size_x=1, patch_size_y=1):
super(Patches, self).__init__() super().__init__()
self.patch_size = patch_size self.patch_size_x = patch_size_x
self.patch_size_y = patch_size_y
def call(self, images): def call(self, images):
batch_size = tf.shape(images)[0] batch_size = tf.shape(images)[0]
patches = tf.image.extract_patches( patches = tf.image.extract_patches(
images=images, images=images,
sizes=[1, self.patch_size, self.patch_size, 1], sizes=[1, self.patch_size_y, self.patch_size_x, 1],
strides=[1, self.patch_size, self.patch_size, 1], strides=[1, self.patch_size_y, self.patch_size_x, 1],
rates=[1, 1, 1, 1], rates=[1, 1, 1, 1],
padding="VALID", padding="VALID",
) )
patch_dims = patches.shape[-1] patch_dims = patches.shape[-1]
patches = tf.reshape(patches, [batch_size, -1, patch_dims]) return tf.reshape(patches, [batch_size, -1, patch_dims])
return patches
def get_config(self):
config = super().get_config().copy() def get_config(self):
config.update({ return dict(patch_size_x=self.patch_size_x,
'patch_size': self.patch_size, patch_size_y=self.patch_size_y,
}) **super().get_config())
return config

22
src/eynollah/training/models.py
View file

@ -423,16 +423,9 @@ def vit_resnet50_unet(num_patches,
#num_patches = x.shape[1]*x.shape[2] #num_patches = x.shape[1]*x.shape[2]
# rs: fixme patch size not configurable anymore... patches = Patches(transformer_patchsize_x, transformer_patchsize_y)(x)
#patches = Patches(transformer_patchsize_x, transformer_patchsize_y)(inputs)
patches = Patches()(x)
assert transformer_patchsize_x == transformer_patchsize_y == 1
# Encode patches. # Encode patches.
# rs: fixme num patches and dim not configurable anymore... encoded_patches = PatchEncoder(num_patches, transformer_projection_dim)(patches)
#encoded_patches = PatchEncoder(num_patches, transformer_projection_dim)(patches)
encoded_patches = PatchEncoder()(patches)
assert num_patches == 21 * 21
assert transformer_projection_dim == 64
for _ in range(transformer_layers): for _ in range(transformer_layers):
# Layer normalization 1. # Layer normalization 1.
@ -530,16 +523,9 @@ def vit_resnet50_unet_transformer_before_cnn(num_patches,
IMAGE_ORDERING = 'channels_last' IMAGE_ORDERING = 'channels_last'
bn_axis=3 bn_axis=3
# rs: fixme patch size not configurable anymore... patches = Patches(transformer_patchsize_x, transformer_patchsize_y)(inputs)
#patches = Patches(transformer_patchsize_x, transformer_patchsize_y)(inputs)
patches = Patches()(inputs)
assert transformer_patchsize_x == transformer_patchsize_y == 1
# Encode patches. # Encode patches.
# rs: fixme num patches and dim not configurable anymore... encoded_patches = PatchEncoder(num_patches, transformer_projection_dim)(patches)
#encoded_patches = PatchEncoder(num_patches, transformer_projection_dim)(patches)
encoded_patches = PatchEncoder()(patches)
assert num_patches == 21 * 21
assert transformer_projection_dim == 64
for _ in range(transformer_layers): for _ in range(transformer_layers):
# Layer normalization 1. # Layer normalization 1.