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Zero
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# Copyright (c) 2023-2024, 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
import torch.nn as nn
from accelerate.logging import get_logger
logger = get_logger(__name__)
class DPTHead(nn.Module):
def __init__(
self,
in_channels,
inner_channels,
use_clstoken=False,
out_channel=1024,
):
super(DPTHead, self).__init__()
self.use_clstoken = use_clstoken
self.projects = nn.ModuleList([
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channel,
kernel_size=1,
stride=1,
padding=0,
) for out_channel in inner_channels
])
if use_clstoken:
self.readout_projects = nn.ModuleList()
for _ in range(len(self.projects)):
self.readout_projects.append(
nn.Sequential(
nn.Linear(2 * in_channels, in_channels),
nn.GELU()))
self.output_conv = nn.Conv2d(sum(inner_channels) , out_channel, kernel_size=1, stride=1, padding=0)
def forward(self, out_features, patch_h, patch_w):
out = []
for i, x in enumerate(out_features):
if self.use_clstoken:
x, cls_token = x[0], x[1]
readout = cls_token.unsqueeze(1).expand_as(x)
x = self.readout_projects[i](torch.cat((x, readout), -1))
else:
x = x[0]
x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
x = self.projects[i](x)
out.append(x)
fusion_feats = torch.cat(out, dim=1)
fusion_feats = self.output_conv(fusion_feats)
return fusion_feats
class Dinov2FusionWrapper(nn.Module):
"""
Dinov2FusionWrapper using original implementation, hacked with modulation.
"""
def __init__(self, model_name: str, modulation_dim: int = None, freeze: bool = True, encoder_feat_dim: int = 384):
super().__init__()
self.modulation_dim = modulation_dim
self.model = self._build_dinov2(model_name, modulation_dim=modulation_dim)
self.intermediate_layer_idx_info = {
'dinov2_vits14_reg': [2, 5, 8, 11],
'dinov2_vitb14_reg': [2, 5, 8, 11],
'dinov2_vitl14_reg': [4, 11, 17, 23],
'dinov2_vitg14_reg': [9, 19, 29, 39]
}
self.intermediate_layer_idx = self.intermediate_layer_idx_info[model_name]
self.fusion_head = DPTHead(in_channels=self.model.embed_dim,
inner_channels=[self.model.embed_dim] * 4,
out_channel=encoder_feat_dim)
if freeze:
if modulation_dim is not None:
raise ValueError("Modulated Dinov2 requires training, freezing is not allowed.")
self._freeze()
def _freeze(self):
# logger.warning(f"======== Freezing Dinov2FusionWrapper ========")
self.model.eval()
for name, param in self.model.named_parameters():
param.requires_grad = False
@staticmethod
def _build_dinov2(model_name: str, modulation_dim: int = None, pretrained: bool = True):
from importlib import import_module
dinov2_hub = import_module(".dinov2.hub.backbones", package=__package__)
model_fn = getattr(dinov2_hub, model_name)
# logger.debug(f"Modulation dim for Dinov2 is {modulation_dim}.")
model = model_fn(modulation_dim=modulation_dim, pretrained=pretrained)
return model
@torch.compile
def forward(self, image: torch.Tensor, mod: torch.Tensor = None):
# image: [N, C, H, W]
# mod: [N, D] or None
# RGB image with [0,1] scale and properly sized
patch_h, patch_w = image.shape[-2] // self.model.patch_size, image.shape[-1] // self.model.patch_size
features = self.model.get_intermediate_layers(image, self.intermediate_layer_idx, return_class_token=True)
out_local = self.fusion_head(features, patch_h, patch_w)
out_global = None
if out_global is not None:
ret = torch.cat([out_local.permute(0, 2, 3, 1).flatten(1, 2), out_global.unsqueeze(1)], dim=1)
else:
ret = out_local.permute(0, 2, 3, 1).flatten(1, 2)
return ret
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