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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
@staticmethod
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
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