RuntimeError: uniform_ expects to return a [from, to) range, but found from=1 > to=0.02
afung-git opened this issue · comments
When I try to load the model I get the following error. Any insight into how I can fix this would be of great help. I am relatively new to deep learning.
initialization method [xavier]
RuntimeError Traceback (most recent call last)
in
2 state_dict = torch_load_model(BASE_MODEL + 'x2face_model.pth')
3
----> 4 model = UnwrappedFaceWeightedAverage(output_num_channels=2, input_num_channels=3, inner_nc=128)
5 model.load_state_dict(state_dict['state_dict'])
6 model = model.to(device)
E:\Alberto_MSR_WORK\UnwrapMosaic\UnwrappedFace.py in init(self, output_num_channels, input_num_channels, inner_nc)
19 super(UnwrappedFaceWeightedAverage, self).init()
20
---> 21 self.pix2pixUnwrapped = Pix2PixModel(3)
22
23 self.pix2pixSampler = NoSkipPix2PixModel(input_num_channels, output_num_channels, inner_nc=inner_nc)
E:\Alberto_MSR_WORK\UnwrapMosaic\SkipNet.py in init(self, output_nc, input_nc)
183
184 gpu = []
--> 185 if torch.cuda.is_available():
186 gpu = [0]
187 self.netG = define_G(input_nc, output_nc, 64, 'unet_256', 'batch', False, 'xavier', gpu)
E:\Alberto_MSR_WORK\UnwrapMosaic\SkipNet.py in define_G(input_nc, output_nc, ngf, which_model_netG, norm, use_dropout, init_type, gpu_ids)
163 else:
164 raise NotImplementedError('Generator model name [%s] is not recognized' % which_model_netG)
--> 165 if len(gpu_ids) > 0:
166 netG.cuda()
167 init_weights(netG, init_type=init_type)
E:\Alberto_MSR_WORK\UnwrapMosaic\SkipNet.py in init_weights(net, init_type)
107 print('initialization method [%s]' % init_type)
108 if init_type == 'normal':
--> 109 net.apply(weights_init_normal)
110 elif init_type == 'xavier':
111 net.apply(weights_init_xavier)
d:\users\alberto\miniconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py in apply(self, fn)
291 """
292 for module in self.children():
--> 293 module.apply(fn)
294 fn(self)
295 return self
d:\users\alberto\miniconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py in apply(self, fn)
291 """
292 for module in self.children():
--> 293 module.apply(fn)
294 fn(self)
295 return self
d:\users\alberto\miniconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py in apply(self, fn)
291 """
292 for module in self.children():
--> 293 module.apply(fn)
294 fn(self)
295 return self
d:\users\alberto\miniconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py in apply(self, fn)
291 """
292 for module in self.children():
--> 293 module.apply(fn)
294 fn(self)
295 return self
d:\users\alberto\miniconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py in apply(self, fn)
292 for module in self.children():
293 module.apply(fn)
--> 294 fn(self)
295 return self
296
E:\Alberto_MSR_WORK\UnwrapMosaic\SkipNet.py in weights_init_xavier(m)
74 init.xavier_normal(m.weight.data, gain=1)
75 elif classname.find('BatchNorm2d') != -1:
---> 76 init.uniform(m.weight.data, 1.0, 0.02)
77 init.constant(m.bias.data, 0.0)
78
d:\users\alberto\miniconda3\envs\pytorch\lib\site-packages\torch\nn\init.py in deprecated_init(*args, **kwargs)
434 warnings.warn("nn.init.{} is now deprecated in favor of nn.init.{}."
435 .format(old_name, new_name), stacklevel=2)
--> 436 return meth(*args, **kwargs)
437
438 deprecated_init.doc = r"""
d:\users\alberto\miniconda3\envs\pytorch\lib\site-packages\torch\nn\init.py in uniform_(tensor, a, b)
86 >>> nn.init.uniform_(w)
87 """
---> 88 return no_grad_uniform(tensor, a, b)
89
90
d:\users\alberto\miniconda3\envs\pytorch\lib\site-packages\torch\nn\init.py in no_grad_uniform(tensor, a, b)
12 def no_grad_uniform(tensor, a, b):
13 with torch.no_grad():
---> 14 return tensor.uniform_(a, b)
15
16
RuntimeError: uniform_ expects to return a [from, to) range, but found from=1 > to=0.02
I assume it's a pytorch version issue.
I assume it's a pytorch version issue.
The same issue.
Would you mind provide a valid download link for installing torch==0.2.0_4 correctly? I do not find any satisfied version on: https://pytorch.org/get-started/previous-versions/
All of the Linux binaries are compiled with CUDA 7.5, but my machine is built with CUDA-10.0. It seems it is impossible to inference it.
Thanks.
I assume it's a pytorch version issue.
It is indeed a pytorch version. If you are using a newer version of pytorch just change the order of the ranges in your uniform function from init.uniform(m.weight.data, 1.0, 0.02) ==> init.uniform(m.weight.data, 0.02, 1.0).