Hybrid ViT fails in the constructor for image size = 200

[pgagarinov]

vit = VisionTransformer(CONFIGS['R50-ViT-B_16'], zero_head=False, img_size=200)

leads to "float division by zero" exception:

---

ZeroDivisionError Traceback (most recent call last)
in
----> 1 vit = VisionTransformer(CONFIGS['R50-ViT-B_16'], zero_head=False, img_size=200)

ViT-pytorch/models/modeling.py in init(self, config, img_size, num_classes, zero_head, vis)
267 self.classifier = config.classifier
268
--> 269 self.transformer = Transformer(config, img_size, vis)
270 self.head = Linear(config.hidden_size, num_classes)
271

ViT-pytorch/models/modeling.py in init(self, config, img_size, vis)
251 def init(self, config, img_size, vis):
252 super(Transformer, self).init()
--> 253 self.embeddings = Embeddings(config, img_size=img_size)
254 self.encoder = Encoder(config, vis)
255

ViT-pytorch/models/modeling.py in init(self, config, img_size, in_channels)
144 width_factor=config.resnet.width_factor)
145 in_channels = self.hybrid_model.width * 16
--> 146 self.patch_embeddings = Conv2d(in_channels=in_channels,
147 out_channels=config.hidden_size,
148 kernel_size=patch_size,

~/.conda/envs/ml-devenv2/lib/python3.8/site-packages/torch/nn/modules/conv.py in init(self, in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode)
408 padding = _pair(padding)
409 dilation = _pair(dilation)
--> 410 super(Conv2d, self).init(
411 in_channels, out_channels, kernel_size, stride, padding, dilation,
412 False, _pair(0), groups, bias, padding_mode)

~/.conda/envs/ml-devenv2/lib/python3.8/site-packages/torch/nn/modules/conv.py in init(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias, padding_mode)
81 else:
82 self.register_parameter('bias', None)
---> 83 self.reset_parameters()
84
85 def reset_parameters(self) -> None:

~/.conda/envs/ml-devenv2/lib/python3.8/site-packages/torch/nn/modules/conv.py in reset_parameters(self)
84
85 def reset_parameters(self) -> None:
---> 86 init.kaiming_uniform_(self.weight, a=math.sqrt(5))
87 if self.bias is not None:
88 fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)

~/.conda/envs/ml-devenv2/lib/python3.8/site-packages/torch/nn/init.py in kaiming_uniform_(tensor, a, mode, nonlinearity)
379 fan = _calculate_correct_fan(tensor, mode)
380 gain = calculate_gain(nonlinearity, a)
--> 381 std = gain / math.sqrt(fan)
382 bound = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation
383 with torch.no_grad():

ZeroDivisionError: float division by zero