about input channels of vitstr

[Name-Lessx]

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.no_grad():
254
--> 255 _, accuracy_by_best_model, norm_ED_by_best_model, _, _, _, 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,is_train=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

~/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(),

~/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/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(),

~/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/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(),

~/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]

vitstr loads rgb but converts it to grayscale before processing.

deep-text-recognition-benchmark/dataset.py

Line 210 in ea0d077

img = Image.open(buf).convert('L')

There is an option to use an rgb image as is:

deep-text-recognition-benchmark/dataset.py

Line 207 in ea0d077

if self.opt.rgb: