Caching examples at: '/home/user/app/gradio_cached_examples/15'
FRAME 1: tensor([[[ 78,  78,  78,  ..., 210, 210, 210],
         [ 78,  79,  79,  ..., 210, 210, 209],
         [ 78,  79,  80,  ..., 210, 210, 210],
         ...,
         [ 81,  81,  82,  ...,  50,  50,  50],
         [ 85,  83,  80,  ...,  50,  50,  49],
         [ 86,  84,  79,  ...,  50,  50,  50]],

        [[ 85,  85,  85,  ..., 210, 210, 210],
         [ 85,  86,  86,  ..., 210, 210, 209],
         [ 85,  86,  87,  ..., 210, 210, 210],
         ...,
         [ 54,  54,  55,  ...,  39,  39,  39],
         [ 58,  56,  53,  ...,  39,  39,  38],
         [ 59,  57,  52,  ...,  39,  39,  39]],

        [[ 93,  93,  93,  ..., 208, 208, 208],
         [ 93,  94,  94,  ..., 208, 208, 207],
         [ 93,  94,  95,  ..., 208, 208, 208],
         ...,
         [ 45,  45,  46,  ...,  35,  35,  35],
         [ 49,  47,  42,  ...,  35,  35,  34],
         [ 50,  48,  43,  ...,  35,  35,  35]]], dtype=torch.uint8)
FRAME 1: tensor([[[ 78,  78,  78,  ..., 210, 210, 210],
         [ 78,  79,  79,  ..., 210, 210, 209],
         [ 78,  79,  80,  ..., 210, 210, 210],
         ...,
         [ 80,  83,  86,  ...,  49,  50,  50],
         [ 81,  85,  83,  ...,  50,  50,  50],
         [ 81,  85,  83,  ...,  50,  50,  50]],

        [[ 85,  85,  85,  ..., 210, 210, 210],
         [ 85,  86,  86,  ..., 210, 210, 209],
         [ 85,  86,  87,  ..., 210, 210, 210],
         ...,
         [ 53,  56,  59,  ...,  38,  39,  39],
         [ 54,  58,  56,  ...,  39,  39,  39],
         [ 54,  58,  56,  ...,  39,  39,  39]],

        [[ 93,  93,  93,  ..., 208, 208, 208],
         [ 93,  94,  94,  ..., 208, 208, 207],
         [ 93,  94,  95,  ..., 208, 208, 208],
         ...,
         [ 44,  47,  52,  ...,  34,  35,  35],
         [ 45,  49,  47,  ...,  35,  35,  35],
         [ 45,  49,  47,  ...,  35,  35,  35]]], dtype=torch.uint8)
FRAME AFTER stack: tensor([[[[ 78,  78,  78,  ..., 210, 210, 210],
          [ 78,  79,  79,  ..., 210, 210, 209],
          [ 78,  79,  80,  ..., 210, 210, 210],
          ...,
          [ 81,  81,  82,  ...,  50,  50,  50],
          [ 85,  83,  80,  ...,  50,  50,  49],
          [ 86,  84,  79,  ...,  50,  50,  50]],

         [[ 85,  85,  85,  ..., 210, 210, 210],
          [ 85,  86,  86,  ..., 210, 210, 209],
          [ 85,  86,  87,  ..., 210, 210, 210],
          ...,
          [ 54,  54,  55,  ...,  39,  39,  39],
          [ 58,  56,  53,  ...,  39,  39,  38],
          [ 59,  57,  52,  ...,  39,  39,  39]],

         [[ 93,  93,  93,  ..., 208, 208, 208],
          [ 93,  94,  94,  ..., 208, 208, 207],
          [ 93,  94,  95,  ..., 208, 208, 208],
          ...,
          [ 45,  45,  46,  ...,  35,  35,  35],
          [ 49,  47,  42,  ...,  35,  35,  34],
          [ 50,  48,  43,  ...,  35,  35,  35]]]], dtype=torch.uint8)
shape = torch.Size([1, 3, 520, 960]), dtype = torch.float32
Downloading: "https://download.pytorch.org/models/raft_large_C_T_SKHT_V2-ff5fadd5.pth" to /home/user/.cache/torch/hub/checkpoints/raft_large_C_T_SKHT_V2-ff5fadd5.pth
list_of_flows type = <class 'list'>
list_of_flows length = 12 = number of iterations of the model
predicted_flows dtype = torch.float32
predicted_flows shape = torch.Size([1, 2, 520, 960]) = (N, 2, H, W)
predicted_flows min = -6.072243690490723, predicted_flows max = 3.367666006088257
predicted flow dtype = torch.float32
predicted flow shape = torch.Size([2, 520, 960])
Traceback (most recent call last):
  File "app.py", line 179, in <module>
    gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="frame 1"), gr.Image(source="upload", type="filepath", label="frame 2")], outputs=[gr.Image(label="flow image"), gr.Files(label="flow file")], title="RAFT Optical Flow", description=description, examples=examples).launch(debug=True)
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/gradio/interface.py", line 484, in __init__
    self.render_examples()
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/gradio/interface.py", line 785, in render_examples
    self.examples_handler = Examples(
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/gradio/helpers.py", line 69, in create_examples
    utils.synchronize_async(examples_obj.create)
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/gradio/utils.py", line 412, in synchronize_async
    return fsspec.asyn.sync(fsspec.asyn.get_loop(), func, *args, **kwargs)
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/fsspec/asyn.py", line 100, in sync
    raise return_result
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/fsspec/asyn.py", line 55, in _runner
    result[0] = await coro
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/gradio/helpers.py", line 273, in create
    await self.cache()
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/gradio/helpers.py", line 314, in cache
    cache_logger.flag(output)
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/gradio/flagging.py", line 221, in flag
    component.deserialize(
  File "/home/user/.pyenv/versions/3.8.9/lib/python3.8/site-packages/gradio/serializing.py", line 181, in deserialize
    raise ValueError(
ValueError: A FileSerializable component cannot only deserialize a string or a dict, not a: <class 'list'>