DiffICM / 4_ControlModule /models_proteus_dinov2.py

code of stage1 & 3, remove large files

1633fcc about 2 years ago

8.26 kB

# 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

	# 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