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