Multi-GPU Parallelism:ERROR:get_image_embedding

[bupianlizhugui]

from typing import List
from PIL import Image
import torch
import numpy as np
from uform import get_model, Modality
import torch.nn as nn
def get_image_embedding(images: List[Image.Image]):
preprocessed = processor_image(images)
embedding = model_image_parallel.forward(preprocessed)
return embedding.detach().cpu().numpy()

def get_text_embedding(texts: List[str]):
preprocessed = processor_text(texts)
embedding = model_text_parallel.forward(preprocessed)
return embedding.detach().cpu().numpy()

from transformers.image_utils import load_image
image1 = load_image("http://img.ltwebstatic.com/images2_pi/2019/03/29/15538517971773429449.jpg")
image2 = load_image("http://img.ltwebstatic.com/images3_pi/2024/04/16/21/17132411401deebc2f549346ffcb2bd626c3afc9b7.jpg")

ERROR:

TypeError Traceback (most recent call last)
Cell In[56], line 1
----> 1 get_image_embedding(image2)

Cell In[50], line 8, in get_image_embedding(images)
7 def get_image_embedding(images: List[Image.Image]):
----> 8 preprocessed = processor_image(images)
9 embedding = model_image_parallel.forward(preprocessed)
10 return embedding.detach().cpu().numpy()

File /opt/conda/envs/uform/lib/python3.10/site-packages/torch/nn/modules/module.py:1553, in Module._wrapped_call_impl(self, *args, **kwargs)
1551 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1552 else:
-> 1553 return self._call_impl(*args, **kwargs)

File /opt/conda/envs/uform/lib/python3.10/site-packages/torch/nn/modules/module.py:1562, in Module._call_impl(self, *args, **kwargs)
1557 # If we don't have any hooks, we want to skip the rest of the logic in
1558 # this function, and just call forward.
1559 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1560 or _global_backward_pre_hooks or _global_backward_hooks
1561 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1562 return forward_call(*args, **kwargs)
1564 try:
1565 result = None

File /opt/conda/envs/uform/lib/python3.10/site-packages/uform/torch_encoders.py:387, in ImageEncoder.forward(self, x, return_features)
384 if _is_on_gpu(self) and not x.is_cuda:
385 x = x.cuda()
--> 387 features = self.forward_features(x)
388 embeddings = self.forward_embedding(features)
389 return_features = return_features if return_features is not None else self.return_features

File /opt/conda/envs/uform/lib/python3.10/site-packages/uform/torch_encoders.py:360, in ImageEncoder.forward_features(self, x)
359 def forward_features(self, x: Union[Tensor, dict]) -> Tensor:
--> 360 x = self.patch_embed(x).flatten(start_dim=2).transpose(2, 1)
361 x = x + self.pos_embed
362 special_tokens = [self.cls_token.expand(x.shape[0], -1, -1)]

File /opt/conda/envs/uform/lib/python3.10/site-packages/torch/nn/modules/module.py:1553, in Module._wrapped_call_impl(self, *args, **kwargs)
1551 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1552 else:
-> 1553 return self._call_impl(*args, **kwargs)

File /opt/conda/envs/uform/lib/python3.10/site-packages/torch/nn/modules/module.py:1562, in Module._call_impl(self, *args, **kwargs)
1557 # If we don't have any hooks, we want to skip the rest of the logic in
1558 # this function, and just call forward.
1559 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1560 or _global_backward_pre_hooks or _global_backward_hooks
1561 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1562 return forward_call(*args, **kwargs)
1564 try:
1565 result = None

File /opt/conda/envs/uform/lib/python3.10/site-packages/torch/nn/modules/conv.py:458, in Conv2d.forward(self, input)
457 def forward(self, input: Tensor) -> Tensor:
--> 458 return self._conv_forward(input, self.weight, self.bias)

File /opt/conda/envs/uform/lib/python3.10/site-packages/torch/nn/modules/conv.py:454, in Conv2d._conv_forward(self, input, weight, bias)
450 if self.padding_mode != 'zeros':
451 return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
452 weight, bias, self.stride,
453 _pair(0), self.dilation, self.groups)
--> 454 return F.conv2d(input, weight, bias, self.stride,
455 self.padding, self.dilation, self.groups)

TypeError: conv2d() received an invalid combination of arguments - got (Image, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:

• (Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, tuple of ints padding = 0, tuple of ints dilation = 1, int groups = 1)
  didn't match because some of the arguments have invalid types: (!Image!, !Parameter!, !Parameter!, !tuple of (int, int)!, !tuple of (int, int)!, !tuple of (int, int)!, !int!)
• (Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, str padding = "valid", tuple of ints dilation = 1, int groups = 1)
  didn't match because some of the arguments have invalid types: (!Image!, !Parameter!, !Parameter!, !tuple of (int, int)!, !tuple of (int, int)!, !tuple of (int, int)!, !int!)