How to inference on cpu?

[Bru-Souza]

dear, is it possible to inference the model without gpu? How to do it?

[mrT23]

transform the model first using the InplacABN_to_ABN fuse_bn2d_bn1d_abn functions in: https://github.com/Alibaba-MIIL/Solving_ImageNet/blob/7eb8e3b17f595eb42b7e80f70ef799b9af0f43a8/kd/kd_utils.py#L21

then it will be suitable also for cpu

[victoic]

Hello! I'm encountering the same issue with inference on cpu.

model_test = InplacABN_to_ABN(model_test)
model_test = fuse_bn_recursively(model_test)
model_test = model.cpu().eval()

After transforming the model I encounter the following error, as if the model remains in cuda. Even more strangely, it seems to happen at an F.pad function of the antialiasing class.

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RuntimeError Traceback (most recent call last)

in () 42 print("Will show!") 43 show_img = True ---> 44 pred = infer(args, model_test, classes_list, gt, show_img) 45 conf_mat[gt][pred]+=1 46 #print(pred, gt, pred == gt)

13 frames

in infer(args, model, classes_list, gt, show_img) 32 tensor_img = torch.from_numpy(np_img).permute(2, 0, 1).float() / 255.0 # HWC to CHW 33 tensor_batch = torch.unsqueeze(tensor_img, 0).cpu() # float16 inference ---> 34 output = torch.squeeze(torch.sigmoid(model(tensor_batch))) 35 np_output = output.cpu().detach().numpy() 36

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []

/content/ML_Decoder/src_files/models/tresnet/tresnet.py in forward(self, x) 202 203 def forward(self, x): --> 204 x = self.body(x) 205 self.embeddings = self.global_pool(x) 206 logits = self.head(self.embeddings)

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input) 137 def forward(self, input): 138 for module in self: --> 139 input = module(input) 140 return input 141

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input) 137 def forward(self, input): 138 for module in self: --> 139 input = module(input) 140 return input 141

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []

/content/ML_Decoder/src_files/models/tresnet/tresnet.py in forward(self, x) 118 119 out = self.conv1(x) --> 120 out = self.conv2(out) 121 if self.se is not None: out = self.se(out) 122

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py in forward(self, input) 137 def forward(self, input): 138 for module in self: --> 139 input = module(input) 140 return input 141

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, *kwargs) 1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1050 or _global_forward_hooks or _global_forward_pre_hooks): -> 1051 return forward_call(input, **kwargs) 1052 # Do not call functions when jit is used 1053 full_backward_hooks, non_full_backward_hooks = [], []

/content/ML_Decoder/src_files/models/tresnet/layers/anti_aliasing.py in forward(self, x) 16 17 def forward(self, x): ---> 18 return self.op(x) 19 20

/content/ML_Decoder/src_files/models/tresnet/layers/anti_aliasing.py in call(self, input) 38 self.filt.to(input.device) 39 self.filt = self.filt.float() ---> 40 input_pad = F.pad(input, (1, 1, 1, 1), 'reflect') 41 return F.conv2d(input_pad, self.filt, stride=2, padding=0, groups=input.shape[1]) 42

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same or input should be a MKLDNN tensor and weight is a dense tensor