qurator-spk/sbb_pixelwise_segmentation
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Code Issues Releases Wiki Activity

transformer patch size is dynamic now.

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vahidrezanezhad 2024-06-12 13:26:27 +02:00
parent 2aa216e388
commit f1fd74c7eb
3 changed files with 75 additions and 30 deletions
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30
train.py
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@ -70,8 +70,10 @@ def config_params():
brightness = None # Brighten image for augmentation.
flip_index = None # Flip image for augmentation.
continue_training = False # Set to true if you would like to continue training an already trained a model.
transformer_patchsize = None # Patch size of vision transformer patches.
transformer_patchsize_x = None # Patch size of vision transformer patches.
transformer_patchsize_y = None
transformer_num_patches_xy = None # Number of patches for vision transformer.
transformer_projection_dim = 64 # Transformer projection dimension
index_start = 0 # Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3.
dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model.
is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false.
@ -92,7 +94,7 @@ def run(_config, n_classes, n_epochs, input_height,
brightening, binarization, blur_k, scales, degrade_scales,
brightness, dir_train, data_is_provided, scaling_bluring,
scaling_brightness, scaling_binarization, rotation, rotation_not_90,
thetha, scaling_flip, continue_training, transformer_patchsize,
thetha, scaling_flip, continue_training, transformer_projection_dim, transformer_patchsize_x, transformer_patchsize_y,
transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name):
@ -212,15 +214,27 @@ def run(_config, n_classes, n_epochs, input_height,
if backbone_type=='nontransformer':
model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining)
elif backbone_type=='transformer':
num_patches = transformer_num_patches_xy[0]*transformer_num_patches_xy[1]
num_patches_x = transformer_num_patches_xy[0]
num_patches_y = transformer_num_patches_xy[1]
num_patches = num_patches_x * num_patches_y
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)) )
##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)
if (input_height != (num_patches_y * transformer_patchsize_y * 32) ):
print("Error: transformer_patchsize_y or transformer_num_patches_xy height value error . input_height should be equal to ( transformer_num_patches_xy height value * transformer_patchsize_y * 32)")
sys.exit(1)
if not (transformer_patchsize == 1):
print("Error: transformer patchsize error. Parameter transformer_patchsizeshould set to 1" )
if (input_width != (num_patches_x * transformer_patchsize_x * 32) ):
print("Error: transformer_patchsize_x or transformer_num_patches_xy width value error . input_width should be equal to ( transformer_num_patches_xy width value * transformer_patchsize_x * 32)")
sys.exit(1)
model = vit_resnet50_unet(n_classes, transformer_patchsize, num_patches, input_height, input_width, task, weight_decay, pretraining)
if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
print("Error: transformer_projection_dim error. The remainder when parameter transformer_projection_dim is divided by (transformer_patchsize_y*transformer_patchsize_x) should be zero")
sys.exit(1)
model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
#if you want to see the model structure just uncomment model summary.
#model.summary()