about input channels of vitstr

I am reproducing the vit-tiny model but have a problem when testing on III5k.It prompts unmatched number of channels.Then i find the input images are RGB mode but the input channels of vitstr is just one.Why is this problem?I'm looking forward to your reply. Thank you

RuntimeError Traceback (most recent call last) /tmp/ipykernel_53850/3528011160.py in 296 opt.num_gpu = torch.cuda.device_count() 297 --> 298 test(opt)

/tmp/ipykernel_53850/3528011160.py in test(opt) 253 with torch.nograd(): 254 --> 255 , accuracy_by_best_model, norm_ED_by_bestmodel, , , , infer_time, length_of_data = validation( 256 model, criterion, test_loader, converter, opt) 257 print(f'{accuracy_by_best_model:0.3f}')

/tmp/ipykernel_53850/3528011160.py in validation(model, criterion, evaluation_loader, converter, opt) 39 start_time = time.time() 40 ---> 41 preds = model(image, seqlen=converter.batch_max_length,istrain=False) 42 , preds_index = preds.topk(1, dim=-1, largest=True, sorted=True) 43 forward_time = time.time() - start_time

~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, kwargs) 725 result = self._slow_forward(*input, *kwargs) 726 else: --> 727 result = self.forward(input, kwargs) 728 for hook in itertools.chain( 729 _global_forward_hooks.values(),

~/model.py in forward(self, input, is_train, seqlen) 44 45 """ Prediction stage """ ---> 46 prediction = self.vitstr(input, seqlen=seqlen) 47 48 return prediction

~/modules/Vistr.py in forward(self, x, seqlen) 73 74 def forward(self, x, seqlen: int =25): ---> 75 x = self.forward_features(x) 76 x = x[:, :seqlen] 77

~/modules/Vistr.py in forward_features(self, x) 59 def forward_features(self, x): 60 B = x.shape[0] ---> 61 x = self.patch_embed(x) 62 63 cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks

~/miniconda3/lib/python3.8/site-packages/timm/models/layers/patch_embed.py in forward(self, x) 33 _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") 34 _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") ---> 35 x = self.proj(x) 36 if self.flatten: 37 x = x.flatten(2).transpose(1, 2) # BCHW -> BNC

~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py in forward(self, input) 421 422 def forward(self, input: Tensor) -> Tensor: --> 423 return self._conv_forward(input, self.weight) 424 425 class Conv3d(_ConvNd):

~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight) 417 weight, self.bias, self.stride, 418 _pair(0), self.dilation, self.groups) --> 419 return F.conv2d(input, weight, self.bias, self.stride, 420 self.padding, self.dilation, self.groups) 421

RuntimeError: Given groups=1, weight of size [192, 1, 16, 16], expected input[16, 3, 224, 224] to have 1 channels, but got 3 channels instead

roatienza / deep-text-recognition-benchmark

about input channels of vitstr #28