An Error in train or test `RuntimeError: output with shape [1, 256, 256] doesn't match the broadcast shape [3, 256, 256]` about bicyclegan HOT 13 CLOSED

Loading model bicycle_gan
Traceback (most recent call last):
  File "./test.py", line 32, in <module>
    for i, data in enumerate(islice(dataset, opt.num_test)):
  File "/root/gans/BicycleGAN/data/__init__.py", line 90, in __iter__
    for i, data in enumerate(self.dataloader):
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 637, in __next__
    return self._process_next_batch(batch)
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 658, in _process_next_batch
    raise batch.exc_type(batch.exc_msg)
RuntimeError: Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 138, in <listcomp>
    samples = collate_fn([dataset[i] for i in batch_indices])
  File "/root/gans/BicycleGAN/data/aligned_dataset.py", line 53, in __getitem__
    A = A_transform(A)
  File "/usr/local/lib/python3.5/dist-packages/torchvision/transforms/transforms.py", line 60, in __call__
    img = t(img)
  File "/usr/local/lib/python3.5/dist-packages/torchvision/transforms/transforms.py", line 163, in __call__
    return F.normalize(tensor, self.mean, self.std, self.inplace)
  File "/usr/local/lib/python3.5/dist-packages/torchvision/transforms/functional.py", line 208, in normalize
    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])
RuntimeError: output with shape [1, 256, 256] doesn't match the broadcast shape [3, 256, 256]

--display_id
1
--dataroot
D:\dataset\edges2shoes\edges2shoes
--name
edges2shoes_bicycle_gan
--model
bicycle_gan
--direction
AtoB
--checkpoints_dir
../checkpoints/edges2shoes/
--load_size
256
--crop_size
256
--nz
8
--input_nc
1
--niter
30
--niter_decay
30
--use_dropout

set -ex
# models
RESULTS_DIR='./results/edges2shoes'
G_PATH='./pretrained_models/edges2shoes_net_G.pth'
E_PATH='./pretrained_models/edges2shoes_net_E.pth'

# dataset
CLASS='edges2shoes'
DIRECTION='AtoB' # from domain A to domain B
LOAD_SIZE=256 # scale images to this size
CROP_SIZE=256 # then crop to this size
INPUT_NC=1  # number of channels in the input image

# misc
GPU_ID=0   # gpu id
NUM_TEST=10 # number of input images duirng test
NUM_SAMPLES=10 # number of samples per input images

# command
CUDA_VISIBLE_DEVICES=${GPU_ID} python ./test.py \
  --dataroot ./datasets/${CLASS} \
  --results_dir ${RESULTS_DIR} \
  --checkpoints_dir ./pretrained_models/ \
  --name ${CLASS} \
  --direction ${DIRECTION} \
  --load_size ${LOAD_SIZE} \
  --crop_size ${CROP_SIZE} \
  --input_nc ${INPUT_NC} \
  --num_test ${NUM_TEST} \
  --n_samples ${NUM_SAMPLES} \
  --center_crop \
  --no_flip