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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,96 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| from typing import Optional | ||
|
|
||
| import torch | ||
| import torch.nn.functional as F | ||
| from torch import nn | ||
| from transformers.configuration_utils import PretrainedConfig | ||
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| class InternVisionEmbeddings(nn.Module): | ||
| """mono vision.""" | ||
|
|
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| def __init__(self, | ||
| config: PretrainedConfig, | ||
| dtype: torch.dtype = None, | ||
| device: torch.device = None): | ||
| super().__init__() | ||
| self.config = config | ||
| self.embed_dim = config.hidden_size | ||
| self.image_size = config.image_size | ||
| self.patch_size = config.patch_size | ||
|
|
||
| self.class_embedding = nn.Parameter( | ||
| torch.empty(1, 1, self.embed_dim, dtype=dtype, device=device), ) | ||
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| self.patch_embedding = nn.Conv2d(in_channels=3, | ||
| out_channels=self.embed_dim, | ||
| kernel_size=self.patch_size, | ||
| stride=self.patch_size, | ||
| dtype=dtype, | ||
| device=device) | ||
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| self.num_patches = (self.image_size // self.patch_size)**2 | ||
| self.num_positions = self.num_patches + 1 | ||
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| self.position_embedding = nn.Parameter( | ||
| torch.empty(1, | ||
| self.num_positions, | ||
| self.embed_dim, | ||
| dtype=dtype, | ||
| device=device)) | ||
|
|
||
| def _get_pos_embed(self, pos_embed, H, W): | ||
| target_dtype = pos_embed.dtype | ||
| pos_embed = pos_embed.float().reshape( | ||
| 1, self.image_size // self.patch_size, | ||
| self.image_size // self.patch_size, -1).permute(0, 3, 1, 2) | ||
| pos_embed = F.interpolate(pos_embed, | ||
| size=(H, W), | ||
| mode='bicubic', | ||
| align_corners=False) | ||
| pos_embed = pos_embed.reshape(1, -1, H * W).permute(0, 2, | ||
| 1).to(target_dtype) | ||
| return pos_embed | ||
|
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| def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: | ||
| target_dtype = self.patch_embedding.weight.dtype | ||
| patch_embeds = self.patch_embedding( | ||
| pixel_values) # shape = [*, channel, width, height] | ||
| batch_size, _, height, width = patch_embeds.shape | ||
| patch_embeds = patch_embeds.flatten(2).transpose(1, 2) | ||
| class_embeds = self.class_embedding.expand(batch_size, 1, | ||
| -1).to(target_dtype) | ||
| embeddings = torch.cat([class_embeds, patch_embeds], dim=1) | ||
| position_embedding = torch.cat([ | ||
| self.position_embedding[:, :1, :], | ||
| self._get_pos_embed(self.position_embedding[:, 1:, :], height, | ||
| width) | ||
| ], | ||
| dim=1) | ||
| embeddings = embeddings + position_embedding.to(target_dtype) | ||
| return embeddings | ||
|
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||
|
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| class InternVisionPatchModel(nn.Module): | ||
| """mono vision.""" | ||
|
|
||
| def __init__(self, | ||
| config: PretrainedConfig, | ||
| dtype: torch.dtype = None, | ||
| device: torch.device = None): | ||
| super().__init__() | ||
| self.config = config | ||
| self.embeddings = InternVisionEmbeddings(config, | ||
| dtype=dtype, | ||
| device=device) | ||
|
|
||
| def forward( | ||
| self, | ||
| pixel_values: Optional[torch.FloatTensor] = None, | ||
| ): | ||
| if len(pixel_values.shape) != 4: | ||
| raise ValueError(f'wrong pixel_values size: {pixel_values.shape}') | ||
|
|
||
| hidden_states = self.embeddings(pixel_values)[:, 1:] | ||
| return hidden_states |
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