qurator-spk/sbb_pixelwise_segmentation
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updating train.py nontransformer backend

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vahidrezanezhad 2024-06-10 22:15:30 +02:00
parent 815e5a1d35
commit 41a0e15e79
2 changed files with 18 additions and 7 deletions
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13
models.py
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@ -30,8 +30,8 @@ class Patches(layers.Layer):
self.patch_size = patch_size
def call(self, images):
print(tf.shape(images)[1],'images')
print(self.patch_size,'self.patch_size')
#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,
@ -41,7 +41,7 @@ class Patches(layers.Layer):
padding="VALID",
)
patch_dims = patches.shape[-1]
print(patches.shape,patch_dims,'patch_dims')
#print(patches.shape,patch_dims,'patch_dims')
patches = tf.reshape(patches, [batch_size, -1, patch_dims])
return patches
def get_config(self):
@ -52,6 +52,7 @@ class Patches(layers.Layer):
})
return config
class PatchEncoder(layers.Layer):
def __init__(self, num_patches, projection_dim):
super(PatchEncoder, self).__init__()
@ -408,7 +409,11 @@ def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, inpu
if pretraining:
model = Model(inputs, x).load_weights(resnet50_Weights_path)
num_patches = x.shape[1]*x.shape[2]
#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)
# Encode patches.
encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)

12
train.py
View file

@ -97,8 +97,6 @@ def run(_config, n_classes, n_epochs, input_height,
pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name):
if task == "segmentation" or task == "enhancement":
num_patches = transformer_num_patches_xy[0]*transformer_num_patches_xy[1]
if data_is_provided:
dir_train_flowing = os.path.join(dir_output, 'train')
dir_eval_flowing = os.path.join(dir_output, 'eval')
@ -213,7 +211,15 @@ def run(_config, n_classes, n_epochs, input_height,
index_start = 0
if backbone_type=='nontransformer':
model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining)
elif backbone_type=='nontransformer':
elif backbone_type=='transformer':
num_patches = transformer_num_patches_xy[0]*transformer_num_patches_xy[1]
if not (num_patches == (input_width / 32) * (input_height / 32)):
print("Error: transformer num patches error. Parameter transformer_num_patches_xy should be set to (input_width/32) = {} and (input_height/32) = {}".format(int(input_width / 32), int(input_height / 32)) )
sys.exit(1)
if not (transformer_patchsize == 1):
print("Error: transformer patchsize error. Parameter transformer_patchsizeshould set to 1" )
sys.exit(1)
model = vit_resnet50_unet(n_classes, transformer_patchsize, num_patches, input_height, input_width, task, weight_decay, pretraining)
#if you want to see the model structure just uncomment model summary.