81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
# Copyright (c) 2023, Zexin He
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch.nn as nn
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from transformers import ViTImageProcessor
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from einops import rearrange, repeat
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from .dino import ViTModel
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class DinoWrapper(nn.Module):
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"""
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Dino v1 wrapper using huggingface transformer implementation.
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"""
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def __init__(self, model_name: str, freeze: bool = True):
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super().__init__()
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self.model, self.processor = self._build_dino(model_name)
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self.camera_embedder = nn.Sequential(
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nn.Linear(16, self.model.config.hidden_size, bias=True),
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nn.SiLU(),
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nn.Linear(self.model.config.hidden_size, self.model.config.hidden_size, bias=True)
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)
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if freeze:
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self._freeze()
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def forward(self, image, camera):
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# image: [B, N, C, H, W]
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# camera: [B, N, D]
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# RGB image with [0,1] scale and properly sized
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if image.ndim == 5:
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image = rearrange(image, 'b n c h w -> (b n) c h w')
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dtype = image.dtype
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inputs = self.processor(
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images=image.float(),
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return_tensors="pt",
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do_rescale=False,
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do_resize=False,
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).to(self.model.device).to(dtype)
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# embed camera
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N = camera.shape[1]
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camera_embeddings = self.camera_embedder(camera)
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camera_embeddings = rearrange(camera_embeddings, 'b n d -> (b n) d')
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embeddings = camera_embeddings
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# This resampling of positional embedding uses bicubic interpolation
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outputs = self.model(**inputs, adaln_input=embeddings, interpolate_pos_encoding=True)
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last_hidden_states = outputs.last_hidden_state
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return last_hidden_states
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def _freeze(self):
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print(f"======== Freezing DinoWrapper ========")
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self.model.eval()
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for name, param in self.model.named_parameters():
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param.requires_grad = False
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@staticmethod
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def _build_dino(model_name: str, proxy_error_retries: int = 3, proxy_error_cooldown: int = 5):
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import requests
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try:
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model = ViTModel.from_pretrained(model_name, add_pooling_layer=False)
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processor = ViTImageProcessor.from_pretrained(model_name)
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return model, processor
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except requests.exceptions.ProxyError as err:
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if proxy_error_retries > 0:
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print(f"Huggingface ProxyError: Retrying in {proxy_error_cooldown} seconds...")
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import time
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time.sleep(proxy_error_cooldown)
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return DinoWrapper._build_dino(model_name, proxy_error_retries - 1, proxy_error_cooldown)
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else:
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raise err
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