ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() about watermark-removal

[VIkill33]

You can just comment line 42 out

[Bellman281]

I have got the same problem on Colab:
num_gpus_per_job: 1
num_cpus_per_job: 4
num_hosts_per_job: 1
memory_per_job: 32
gpu_type: nvidia-tesla-p100
name: places2_gated_conv_v100
model_restore:
dataset: celebahq
random_crop: False
val: False
log_dir: logs/full_model_celeba_hq_256
gan: sngan
gan_loss_alpha: 1
gan_with_mask: True
discounted_mask: True
random_seed: False
padding: SAME
train_spe: 4000
max_iters: 100000000
viz_max_out: 10
val_psteps: 2000
data_flist:
celebahq: ['data/celeba_hq/train_shuffled.flist', 'data/celeba_hq/validation_static_view.flist']
celeba: ['data/celeba/train_shuffled.flist', 'data/celeba/validation_static_view.flist']
places2: ['data/places2/train_shuffled.flist', 'data/places2/validation_static_view.flist']
imagenet: ['data/imagenet/train_shuffled.flist', 'data/imagenet/validation_static_view.flist']
static_view_size: 30
img_shapes: [256, 256, 3]
height: 128
width: 128
max_delta_height: 32
max_delta_width: 32
batch_size: 16
vertical_margin: 0
horizontal_margin: 0
ae_loss: True
l1_loss: True
l1_loss_alpha: 1.0
guided: False
edge_threshold: 0.6

image size: (682, 1024, 3)
mask image size: (683, 1024, 3)
(682, 1024, 3)

[askie]

Traceback (most recent call last):
File "main.py", line 32, in
input_image = preprocess_image(image, args.watermark_type)
File "/Users/askie/wwwroot/watermark-removal/preprocess_image.py", line 42, in preprocess_image
assert image.shape == preprocessed_mask_image
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

[hallejuyahaha]

i meet this question too

[beybin]

preprocess_image.py line 42 -> assert image.shape == preprocessed_mask_image.shape

[sovrinbloc]

As @VIkill33 said, you can just comment it out. That worked for me. the assert image.shape == preprocessed_mask_image.shape is a check that apparently doesn't need to be there because it works fine without it.

[shahmustafa]

Can anybody comment, on why I am getting this kind of response from model