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# Copyright 2023 Google LLC
#
# 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
#
# http://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
from torch import nn
import torch.nn.functional as F
import torch.distributed.nn
import torch.distributed as dist
from torch.nn.init import trunc_normal_
from torch.nn.utils import weight_norm
import models_dinov2
from models_IB import IF_Module
import math
class MetaArch(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
student_model_dict = dict()
teacher_model_dict = dict()
import_student = getattr(models_dinov2, cfg.target_model)
student = import_student(img_size=224,
patch_size=cfg.patch_size,
init_values=1.0,
ffn_layer='mlp',
block_chunks=0,
num_register_tokens=0,
interpolate_antialias=False,
interpolate_offset=0.1)
embed_dim = student.embed_dim
if cfg.teacher_model == 'vit_base':
teacher_backbone = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14_lc')
elif cfg.teacher_model == 'vit_small':
teacher_backbone = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14_lc')
elif cfg.teacher_model == 'vit_large':
teacher_backbone = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14_lc')
elif cfg.teacher_model == 'vit_giant':
teacher_backbone = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14_lc')
teacher_backbone.eval()
student_model_dict['backbone'] = student
teacher_model_dict['backbone'] = teacher_backbone.backbone
self.embed_dim = embed_dim
# initialize parameters and checks
self.total_n_global_crops = cfg.batch_size
self.student = nn.ModuleDict(student_model_dict)
self.teacher = nn.ModuleDict(teacher_model_dict)
teacher_embed_dim = teacher_backbone.backbone.embed_dim
self.ibot_head = nn.Sequential(
nn.LayerNorm(embed_dim),
nn.Linear(embed_dim, teacher_embed_dim))
self.token_head = nn.Sequential(
nn.LayerNorm(embed_dim),
nn.Linear(embed_dim, teacher_embed_dim))
self.fea_head = nn.Sequential(
nn.LayerNorm(embed_dim),
nn.Linear(embed_dim, teacher_embed_dim))
self.soft_criterion = torch.nn.MSELoss()
self.info_bottleneck = IF_Module(embed_dim=embed_dim, num_heads=12, mlp_ratio=4, depth=4)
for param in self.teacher.backbone.parameters():
param.requires_grad = False
def cal_bpp(self, image, unmask_likelihood, mask_likelihood):
b, _, h, w = image.size()
num_pixels = b * h * w
log_unmask_likelihoods = torch.log(unmask_likelihood)
log_mask_likelihoods = torch.log(mask_likelihood)
bpp = (log_unmask_likelihoods.sum() + log_mask_likelihoods.sum()) / (-math.log(2) * num_pixels * 1.5)
return bpp
def forward(self, inputs):
global_crops = inputs["collated_global_crops"]
masks = inputs["collated_masks"]
mask_indices_list = inputs["mask_indices_list"]
n_masked_patches = mask_indices_list.shape[0]
upperbound = inputs["upperbound"]
n_global_crops = 1
# compute teacher output
# @torch.no_grad()
def compute_teacher_output():
with torch.no_grad():
teacher_backbone_output_dict = self.teacher.backbone(global_crops, is_training=True)
teacher_cls_tokens = teacher_backbone_output_dict["x_norm_clstoken"]
teacher_patch_tokens = teacher_backbone_output_dict["x_norm_patchtokens"]
_dim = teacher_patch_tokens.shape[-1]
# mask teacher patch tokens
buffer_tensor_teacher = teacher_patch_tokens.new_zeros(upperbound, _dim)
torch.index_select(
teacher_patch_tokens.flatten(0, 1),
dim=0,
index=mask_indices_list,
out=buffer_tensor_teacher[:n_masked_patches],
)
teacher_patch_tokens_masked = buffer_tensor_teacher[:n_masked_patches]
return teacher_cls_tokens, teacher_patch_tokens, teacher_patch_tokens_masked
# get the teacher outputs
(
teacher_cls_tokens,
teacher_patch_tokens,
teacher_patch_tokens_masked
) = compute_teacher_output()
cur_masks = masks if self.cfg.mask_probability > 0 else None
student_backbone_output_dict, student_backbone_output_dict_unmask = self.student.backbone(
[global_crops, global_crops], masks=[cur_masks, None], is_training=True
)
student_cls_token_unmask = student_backbone_output_dict_unmask["x_norm_clstoken"]
student_patch_tokens_unmask = student_backbone_output_dict_unmask["x_norm_patchtokens"]
student_patch_tokens = student_backbone_output_dict["x_norm_patchtokens"]
# calculate bitrate
student_patch_tokens_unmask, unmask_likelihood = self.info_bottleneck(student_patch_tokens_unmask, is_training=True)
student_patch_tokens, mask_likelihood = self.info_bottleneck(student_patch_tokens, is_training=True)
bpp = self.cal_bpp(global_crops, unmask_likelihood, mask_likelihood)
# mask student patch tokens
_dim = student_patch_tokens.shape[-1]
buffer_tensor_student = student_patch_tokens.new_zeros(upperbound, _dim)
buffer_tensor_student[:n_masked_patches].copy_(
torch.index_select(student_patch_tokens.flatten(0, 1),
dim=0,
index=mask_indices_list)
)
## projection head
student_patch_tokens_unmask = self.fea_head(student_patch_tokens_unmask)
student_cls_token_unmask = self.token_head(student_cls_token_unmask)
tokens_after_head = self.ibot_head(buffer_tensor_student)
student_patch_tokens_masked = tokens_after_head[:n_masked_patches]
## token objective
distillation_loss_token = self.soft_criterion(student_cls_token_unmask, teacher_cls_tokens)
## fea objective
student_whole_fea = torch.cat((student_cls_token_unmask.unsqueeze(1),student_patch_tokens_unmask),dim=1)
teacher_whole_fea = torch.cat((teacher_cls_tokens.unsqueeze(1),teacher_patch_tokens),dim=1)
distillation_loss_fea = self.soft_criterion(student_whole_fea, teacher_whole_fea)
## patch objective
patch_loss = self.soft_criterion(student_patch_tokens_masked, teacher_patch_tokens_masked)
# coefficient
token_loss = self.cfg.lambda_token * distillation_loss_token
fea_loss = self.cfg.lambda_fea * distillation_loss_fea
patch_loss_weighted = self.cfg.lambda_patch * patch_loss
# print(f"self.cfg: {self.cfg}")
# print(f"self.cfg.lambda_token: {self.cfg.lambda_token}, self.cfg.lambda_fea: {self.cfg.lambda_fea}, self.cfg.lambda_patch: {self.cfg.lambda_patch}")
# compute the total loss
total_loss = patch_loss_weighted + fea_loss + token_loss + 0.48 * bpp
# task_loss = patch_loss + fea_loss + token_loss
task_loss = patch_loss + distillation_loss_fea + distillation_loss_token
# return the final loss dict
loss_dict = {"bpp_loss": bpp,
"patch_loss": patch_loss,
"fea_loss": distillation_loss_fea,
"token_loss": token_loss,
"loss": total_loss,
"task_loss": task_loss,
}
return loss_dict |