Traceback (most recent call last):


 ".\train-Copy1.py", line 180, in <module>

val_percent=args.val / 100)


 ".\train-Copy1.py", line 78, in train_net

masks_pred = net(imgs)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "C:\Users\piyalong\Pytorch-UNet\unet\unet_model.py", line 32, in forward

x5 = self.down4(x4)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "C:\Users\piyalong\Pytorch-UNet\unet\unet_parts.py", line 39, in forward

return self.maxpool_conv(x)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\container.py", line 117, in forward

input = module(input)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "C:\Users\piyalong\Pytorch-UNet\unet\unet_parts.py", line 25, in forward

return self.double_conv(x)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\container.py", line 117, in forward

input = module(input)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl

result = self.forward(*input, **kwargs)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\modules\batchnorm.py", line 136, in forward

self.weight, self.bias, bn_training, exponential_average_factor, self.eps)


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\functional.py", line 2012, in batch_norm

_verify_batch_size(input.size())


 "C:\Users\piyalong\.conda\envs\TAMIDS2\lib\site-packages\torch\nn\functional.py", line 1995, in _verify_batch_size

raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 512, 1, 1])
Traceback (most recent call last):


 "/tmp/Bazel.runfiles_rBHpvo/runfiles/com_google_deepvariant/deepvariant/make_examples.py", line 1235, in <module>

tf.app.run()


 "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run

_sys.exit(main(argv))


 "/tmp/Bazel.runfiles_rBHpvo/runfiles/com_google_deepvariant/deepvariant/make_examples.py", line 1186, in main

options = default_options(add_flags=True, flags_obj=FLAGS)


 "/tmp/Bazel.runfiles_rBHpvo/runfiles/com_google_deepvariant/deepvariant/make_examples.py", line 315, in default_options

with sam.SamReader(flags_obj.reads) as sam_reader:


 "/tmp/Bazel.runfiles_rBHpvo/runfiles/com_google_deepvariant/third_party/nucleus/io/genomics_reader.py", line 216, in __init__

self._reader = self._native_reader(input_path, **kwargs)


 "/tmp/Bazel.runfiles_rBHpvo/runfiles/com_google_deepvariant/third_party/nucleus/io/sam.py", line 255, in _native_reader

return NativeSamReader(input_path, **kwargs)


 "/tmp/Bazel.runfiles_rBHpvo/runfiles/com_google_deepvariant/third_party/nucleus/io/sam.py", line 232, in __init__

use_original_base_quality_scores=use_original_base_quality_scores)
ValueError: Not found: Could not open /home/chenyangwang600/training-case-study/input/data/BGISEQ_PE100_NA12878.sorted.bam
