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https://github.com/qurator-spk/sbb_pixelwise_segmentation.git
synced 2025-06-09 11:50:04 +02:00
updating train.py nontransformer backend
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parent
815e5a1d35
commit
41a0e15e79
2 changed files with 18 additions and 7 deletions
13
models.py
13
models.py
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@ -30,8 +30,8 @@ class Patches(layers.Layer):
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self.patch_size = patch_size
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def call(self, images):
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print(tf.shape(images)[1],'images')
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print(self.patch_size,'self.patch_size')
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#print(tf.shape(images)[1],'images')
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#print(self.patch_size,'self.patch_size')
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batch_size = tf.shape(images)[0]
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patches = tf.image.extract_patches(
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images=images,
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@ -41,7 +41,7 @@ class Patches(layers.Layer):
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padding="VALID",
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)
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patch_dims = patches.shape[-1]
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print(patches.shape,patch_dims,'patch_dims')
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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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@ -52,6 +52,7 @@ class Patches(layers.Layer):
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})
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return config
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class PatchEncoder(layers.Layer):
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def __init__(self, num_patches, projection_dim):
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super(PatchEncoder, self).__init__()
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@ -408,7 +409,11 @@ def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, inpu
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if pretraining:
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model = Model(inputs, x).load_weights(resnet50_Weights_path)
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num_patches = x.shape[1]*x.shape[2]
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#num_patches = x.shape[1]*x.shape[2]
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#patch_size_y = input_height / x.shape[1]
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#patch_size_x = input_width / x.shape[2]
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#patch_size = patch_size_x * patch_size_y
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patches = Patches(patch_size)(x)
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# Encode patches.
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encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)
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12
train.py
12
train.py
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@ -97,8 +97,6 @@ def run(_config, n_classes, n_epochs, input_height,
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pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name):
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if task == "segmentation" or task == "enhancement":
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num_patches = transformer_num_patches_xy[0]*transformer_num_patches_xy[1]
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if data_is_provided:
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dir_train_flowing = os.path.join(dir_output, 'train')
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dir_eval_flowing = os.path.join(dir_output, 'eval')
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@ -213,7 +211,15 @@ def run(_config, n_classes, n_epochs, input_height,
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index_start = 0
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if backbone_type=='nontransformer':
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model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining)
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elif backbone_type=='nontransformer':
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elif backbone_type=='transformer':
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num_patches = transformer_num_patches_xy[0]*transformer_num_patches_xy[1]
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if not (num_patches == (input_width / 32) * (input_height / 32)):
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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)) )
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sys.exit(1)
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if not (transformer_patchsize == 1):
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print("Error: transformer patchsize error. Parameter transformer_patchsizeshould set to 1" )
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sys.exit(1)
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model = vit_resnet50_unet(n_classes, transformer_patchsize, num_patches, input_height, input_width, task, weight_decay, pretraining)
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#if you want to see the model structure just uncomment model summary.
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