Hi, @rohithreddy024
I met this error when run main.py.
tezro@tezro:~/Deep07/Visual-Attention-Pytorch$ python3 main.py
Namespace(batch_size=32, epochs=101, k=2, lr=0.0001, n_c=120, n_glimpses=1, n_jobs=4, n_samples=20, num_workers=4, resume_training=False, rnn_hidden=2048, start_size=2, std_dev=0.2, task='train', valid_size=0.3)
/home/tezro/.local/lib/python3.8/site-packages/torch/nn/functional.py:1628: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Traceback (most recent call last):
File "main.py", line 278, in
training()
File "main.py", line 187, in training
la, lb, lr = train_model(x_batch, labels, info)
File "main.py", line 121, in train_model
_, _, _, _, output = my_model(l, hc1, cv, info, last = True)
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 161, in forward
outputs = self.parallel_apply(replicas, inputs, kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 171, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
output.reraise()
File "/home/tezro/.local/lib/python3.8/site-packages/torch/_utils.py", line 428, in reraise
raise self.exc_type(msg)
TypeError: Caught TypeError in replica 0 on device 0.
Original Traceback (most recent call last):
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
output = module(*input, **kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/tezro/Deep07/Visual-Attention-Pytorch/models.py", line 166, in forward
g = self.glimpse(l_prev, info) #Extract glimpse based on tuple
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/tezro/Deep07/Visual-Attention-Pytorch/models.py", line 68, in forward
phi = retina(l_prev, info, self.opt, self.training) #Extracts high, medium and low resolution patches corresponding to a location centre; Also compressed to 96x96 size
File "/home/tezro/Deep07/Visual-Attention-Pytorch/helper_functions.py", line 64, in retina
phi.append(extract_patches_batch(l, size))
File "/home/tezro/Deep07/Visual-Attention-Pytorch/helper_functions.py", line 54, in extract_patches_batch
patches = Parallel(n_jobs=opt.n_jobs, backend="threading")(
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/parallel.py", line 1061, in call
self.retrieve()
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/parallel.py", line 940, in retrieve
self._output.extend(job.get(timeout=self.timeout))
File "/usr/lib/python3.8/multiprocessing/pool.py", line 771, in get
raise self._value
File "/usr/lib/python3.8/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 595, in call
return self.func(*args, **kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/parallel.py", line 262, in call
return [func(*args, **kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/parallel.py", line 262, in
return [func(*args, **kwargs)
File "/home/tezro/Deep07/Visual-Attention-Pytorch/helper_functions.py", line 34, in get_patch
img, imgsize = get_image(opt, info[i], istrain) #Get context image
File "/home/tezro/Deep07/Visual-Attention-Pytorch/helper_functions.py", line 23, in get_image
img = img.crop((from_w, from_h, from_w + size, from_h + size))
File "/usr/local/lib/python3.8/dist-packages/PIL/Image.py", line 1128, in crop
return self._new(self._crop(self.im, box))
File "/usr/local/lib/python3.8/dist-packages/PIL/Image.py", line 1142, in _crop
x0, y0, x1, y1 = map(int, map(round, box))
TypeError: type Tensor doesn't define round method
What's wrong to me?
Thanks.
@bemoregt.
Hi, @rohithreddy024
I met this error when run main.py.
tezro@tezro:~/Deep07/Visual-Attention-Pytorch$ python3 main.py
Namespace(batch_size=32, epochs=101, k=2, lr=0.0001, n_c=120, n_glimpses=1, n_jobs=4, n_samples=20, num_workers=4, resume_training=False, rnn_hidden=2048, start_size=2, std_dev=0.2, task='train', valid_size=0.3)
/home/tezro/.local/lib/python3.8/site-packages/torch/nn/functional.py:1628: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Traceback (most recent call last):
File "main.py", line 278, in
training()
File "main.py", line 187, in training
la, lb, lr = train_model(x_batch, labels, info)
File "main.py", line 121, in train_model
_, _, _, _, output = my_model(l, hc1, cv, info, last = True)
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 161, in forward
outputs = self.parallel_apply(replicas, inputs, kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 171, in parallel_apply
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
output.reraise()
File "/home/tezro/.local/lib/python3.8/site-packages/torch/_utils.py", line 428, in reraise
raise self.exc_type(msg)
TypeError: Caught TypeError in replica 0 on device 0.
Original Traceback (most recent call last):
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
output = module(*input, **kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/tezro/Deep07/Visual-Attention-Pytorch/models.py", line 166, in forward
g = self.glimpse(l_prev, info) #Extract glimpse based on tuple
File "/home/tezro/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/tezro/Deep07/Visual-Attention-Pytorch/models.py", line 68, in forward
phi = retina(l_prev, info, self.opt, self.training) #Extracts high, medium and low resolution patches corresponding to a location centre; Also compressed to 96x96 size
File "/home/tezro/Deep07/Visual-Attention-Pytorch/helper_functions.py", line 64, in retina
phi.append(extract_patches_batch(l, size))
File "/home/tezro/Deep07/Visual-Attention-Pytorch/helper_functions.py", line 54, in extract_patches_batch
patches = Parallel(n_jobs=opt.n_jobs, backend="threading")(
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/parallel.py", line 1061, in call
self.retrieve()
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/parallel.py", line 940, in retrieve
self._output.extend(job.get(timeout=self.timeout))
File "/usr/lib/python3.8/multiprocessing/pool.py", line 771, in get
raise self._value
File "/usr/lib/python3.8/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 595, in call
return self.func(*args, **kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/parallel.py", line 262, in call
return [func(*args, **kwargs)
File "/home/tezro/.local/lib/python3.8/site-packages/joblib/parallel.py", line 262, in
return [func(*args, **kwargs)
File "/home/tezro/Deep07/Visual-Attention-Pytorch/helper_functions.py", line 34, in get_patch
img, imgsize = get_image(opt, info[i], istrain) #Get context image
File "/home/tezro/Deep07/Visual-Attention-Pytorch/helper_functions.py", line 23, in get_image
img = img.crop((from_w, from_h, from_w + size, from_h + size))
File "/usr/local/lib/python3.8/dist-packages/PIL/Image.py", line 1128, in crop
return self._new(self._crop(self.im, box))
File "/usr/local/lib/python3.8/dist-packages/PIL/Image.py", line 1142, in _crop
x0, y0, x1, y1 = map(int, map(round, box))
TypeError: type Tensor doesn't define round method
What's wrong to me?
Thanks.
@bemoregt.