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remove redundant parentheses

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This commit is contained in:
cneud 2025-10-01 00:38:01 +02:00
parent f2f93e0251
commit 91d2a74ac9
10 changed files with 29 additions and 29 deletions
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10
train/train.py
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@ -269,10 +269,10 @@ def run(_config, n_classes, n_epochs, input_height,
num_patches = num_patches_x * num_patches_y
if transformer_cnn_first:
if (input_height != (num_patches_y * transformer_patchsize_y * 32) ):
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 (input_width != (num_patches_x * transformer_patchsize_x * 32) ):
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)
if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
@ -282,10 +282,10 @@ def run(_config, n_classes, n_epochs, input_height,
model = vit_resnet50_unet(n_classes, transformer_patchsize_x, transformer_patchsize_y, num_patches, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_projection_dim, input_height, input_width, task, weight_decay, pretraining)
else:
if (input_height != (num_patches_y * transformer_patchsize_y) ):
if input_height != (num_patches_y * transformer_patchsize_y):
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)")
sys.exit(1)
if (input_width != (num_patches_x * transformer_patchsize_x) ):
if input_width != (num_patches_x * transformer_patchsize_x):
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)")
sys.exit(1)
if (transformer_projection_dim % (transformer_patchsize_y * transformer_patchsize_x)) != 0:
@ -297,7 +297,7 @@ def run(_config, n_classes, n_epochs, input_height,
model.summary()
if (task == "segmentation" or task == "binarization"):
if task == "segmentation" or task == "binarization":
if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy',
optimizer=Adam(learning_rate=learning_rate), metrics=['accuracy'])