Changed the video to a mp4 and it started working but now I get this:
['E:/testfps/Take_30fps.mp4']
FPS: 30000/1001
FPS Eval: 29.97002997002997
G:/60fps/Take_30fps
Using Benchmark: True
Batch Size: -1
Input FPS: 29.97002997002997
Use all GPUS: False
Scale: 1.0
Render Mode: 0
Interpolations: 2X
Use Smooth: 0
Use Alpha: 0
Use YUV: 0
Encode: libx264
Using Half-Precision: True
Loading Data
Using Model: 3_1
Selected auto batch size, testing a good batch size.
Resolution: 7680x3840
Setting new batch size to 1
Resolution: 7680x3840
RunTime: 95.317333
Total Frames: 2857
0%| | 4/2857 [00:02<26:36, 1.79it/s, file=File 2]Exception ignored in thread started by: <function queue_model at 0x000002302F3E9940>
Traceback (most recent call last):
File "my_DAIN_class.py", line 407, in queue_model
File "my_DAIN_class.py", line 135, in make_inference
File "model\RIFE_HDv3.py", line 391, in inference
File "model\RIFE_HDv3.py", line 298, in predict
File "torch\nn\modules\module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript, serialized code (most recent call last):
File "code/__torch__/model/RIFE_HDv3.py", line 20, in forward
flow0 = torch.mul(_1, 0.5)
f1 = __torch__.model.warplayer.warp(x1, flow0, )
x2 = (self.conv2).forward(x1, )
~~~~~~~~~~~~~~~~~~~ <--- HERE
_2 = _0(flow0, None, 0.5, "bilinear", False, True, )
flow1 = torch.mul(_2, 0.5)
File "code/__torch__/model/RIFE_HDv3/___torch_mangle_27.py", line 11, in forward
x: Tensor) -> Tensor:
x0 = (self.conv1).forward(x, )
return (self.conv2).forward(x0, )
~~~~~~~~~~~~~~~~~~~ <--- HERE
File "code/__torch__/torch/nn/modules/container/___torch_mangle_26.py", line 12, in forward
_0 = getattr(self, "0")
_1 = getattr(self, "1")
input0 = (_0).forward(input, )
~~~~~~~~~~~ <--- HERE
return (_1).forward(input0, )
def __len__(self: __torch__.torch.nn.modules.container.___torch_mangle_26.Sequential) -> int:
File "code/__torch__/torch/nn/modules/conv/___torch_mangle_25.py", line 21, in forward
def forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_25.Conv2d,
input: Tensor) -> Tensor:
_0 = (self)._conv_forward(input, self.weight, self.bias, )
~~~~~~~~~~~~~~~~~~~ <--- HERE
return _0
def _conv_forward(self: __torch__.torch.nn.modules.conv.___torch_mangle_25.Conv2d,
File "code/__torch__/torch/nn/modules/conv/___torch_mangle_25.py", line 27, in _conv_forward
weight: Tensor,
bias: Optional[Tensor]) -> Tensor:
_1 = torch.conv2d(input, weight, bias, [1, 1], [1, 1], [1, 1])
~~~~~~~~~~~~ <--- HERE
return _1
Traceback of TorchScript, original code (most recent call last):
File "C:\Users\Gabriel\Downloads\torch19\lib\site-packages\torch\nn\modules\container.py", line 139, in forward
def forward(self, input):
for module in self:
input = module(input)
~~~~~~ <--- HERE
return input
File "C:\Users\Gabriel\Downloads\torch19\lib\site-packages\torch\nn\modules\conv.py", line 443, in forward
def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight, self.bias)
~~~~~~~~~~~~~~~~~~ <--- HERE
File "C:\Users\Gabriel\Downloads\torch19\lib\site-packages\torch\nn\modules\conv.py", line 439, in _conv_forward
weight, bias, self.stride,
_pair(0), self.dilation, self.groups)
return F.conv2d(input, weight, bias, self.stride,
~~~~~~~~ <--- HERE
self.padding, self.dilation, self.groups)
RuntimeError: CUDA out of memory. Tried to allocate 7.91 GiB (GPU 0; 24.00 GiB total capacity; 2.66 GiB already allocated; 18.15 GiB free; 3.82 GiB reserved in total by PyTorch)