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training.models.vit_resnet50_unet: re-use IMAGE_ORDERING
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1 changed files with 8 additions and 12 deletions
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@ -372,12 +372,10 @@ def vit_resnet50_unet(num_patches,
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transformer_mlp_head_units = [128, 64]
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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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#projection_dim,
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#] # Size of the transformer layers
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IMAGE_ORDERING = 'channels_last'
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if IMAGE_ORDERING == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(inputs)
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x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
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@ -508,12 +506,10 @@ def vit_resnet50_unet_transformer_before_cnn(num_patches,
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transformer_mlp_head_units = [128, 64]
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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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##projection_dim,
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##] # Size of the transformer layers
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IMAGE_ORDERING = 'channels_last'
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if IMAGE_ORDERING == 'channels_last':
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bn_axis = 3
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else:
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bn_axis = 1
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patches = Patches(transformer_patchsize_x, transformer_patchsize_y)(inputs)
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# Encode patches.
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