src/training/declip2.py

from typing import List
import torch
import torch.nn.functional as F
import torch
from torch.cuda.amp import autocast
from torchvision.ops import roi_align
# ! declip2只加入了region loss，没有加入sd smoothing
class DeCLIP2:
    def __call__(self, batch, student, teacher, vfm_model, args):
        losses = {}
        context_weight = args.loss_context_weight
        content_weight = args.loss_content_weight
        region_weight= args.loss_region_weight
        if args.distributed:
            student = student.module
        dtype_map = {"bf16": torch.bfloat16, "amp": torch.float16}
        input_dtype = dtype_map.get(args.precision, torch.float32)
        images, normed_boxes, image_crops, vfm_image = prepare_inputs(batch, args.device, input_dtype)
        loss_ensemble = self.intra_image_distill(student, teacher, vfm_model, images, normed_boxes, image_crops, vfm_image, args)
        loss_context, loss_content ,loss_region= loss_ensemble[0], loss_ensemble[1], loss_ensemble[2]
        losses.update({"loss_context":loss_context * context_weight})
        losses.update({"loss_content":loss_content * content_weight})
        losses.update({"loss_region":loss_region * region_weight})
        return losses, len(images)

    def intra_image_distill(self, student, teacher, vfm_model, images, normed_boxes, image_crops, vfm_image,args):
        roi_size=(3, 3)
        B = images.shape[0]
        rois_list = []
        crops_list = []
        for bboxes_per_image, crops_per_image in zip(normed_boxes, image_crops):
            valid = bboxes_per_image[:, -1] > 0.5
            rois_list.append(bboxes_per_image[valid, :4])
            crops_list.append(crops_per_image[valid])
        image_crops = torch.cat(crops_list)
        student_roi_features, context = student.encode_pseudo_boxes(images, rois_list, normalize=True, mode = args.mode, size=roi_size)

        with torch.no_grad():
            teacher_crop_features = teacher.encode_image(image_crops, normalize=True)
            intra_vfm_feats = extract_vfm_features(vfm_model, vfm_image,args)  # bs,768, h,w
            vfm_roi_features= extract_roi_features(intra_vfm_feats,rois_list,normalize=True)
            intra_vfm_feats = F.normalize(intra_vfm_feats,dim=1).flatten(start_dim=-2)
            intra_vfm_corr = torch.einsum('bci,bcj->bij', intra_vfm_feats, intra_vfm_feats)

        student_intra_corr = compute_student_intra_image_similarity(images.shape[0], context, args)
        loss_context =  context_loss(student_intra_corr, intra_vfm_corr)
        # loss_content = 1.0 - (student_roi_features * teacher_crop_features).sum(-1).mean()
        loss_content = soft_content_distill_loss(student_roi_features,teacher_crop_features)
        loss_region= region_scd_loss(student_roi_features,vfm_roi_features)
        return loss_context, loss_content,loss_region
    
def context_loss(student_corr, teacher_corr, teacher_temp=1.0, student_temp=1.0):
    student_log_prob = F.log_softmax(student_corr / student_temp, dim=-1)
    with torch.no_grad():
        teacher_prob = F.softmax(teacher_corr / teacher_temp, dim=-1)
    kl_loss=F.kl_div(student_log_prob, teacher_prob, reduction='batchmean')* (teacher_temp*student_temp)
    return kl_loss


def soft_content_distill_loss(student_roi_features, teacher_crop_features, T=1.0):
    sim = torch.einsum('bpc,bc->bp', student_roi_features, teacher_crop_features)
    weights = F.softmax(sim / T, dim=1)
    weighted_student = (student_roi_features * weights.unsqueeze(-1)).sum(dim=1)
    weighted_student = F.normalize(weighted_student, dim=-1)
    cosine_similarity = (weighted_student * teacher_crop_features).sum(dim=-1)
    loss = 1.0 - cosine_similarity.mean()                              
    return loss

def region_scd_loss(student_roi_features, intra_vfm_roi_feats, T_teacher=1.0, T_student=1.0):
    with torch.no_grad():
        intra_vfm_roi_feats=intra_vfm_roi_feats.transpose(-2,-1).contiguous() # bs, L, C
        teacher_corr=torch.einsum('bic,bjc->bij', intra_vfm_roi_feats, intra_vfm_roi_feats) / T_teacher
        teacher_prob = F.softmax(teacher_corr, dim=-1)
    student_corr=torch.einsum('bic,bjc->bij', student_roi_features, student_roi_features) / T_student
    student_log_prob = F.log_softmax(student_corr, dim=-1) 
    loss = F.kl_div(student_log_prob, teacher_prob, reduction='batchmean') * (T_teacher * T_student)
    return loss


def extract_roi_features(x, normed_boxes, size=(3,3), normalize=False):
    """
    x:(bs,c,h,w)
    """
    def _denormalize_boxes(normed_boxes, x):
        h, w = x.shape[-2:]
        denormed_boxes = []
        for boxes in normed_boxes:
            new_boxes = boxes.clone()   # FIXME: do not change the value in normed_boxes!
            new_boxes[:, [0, 2]] *= w
            new_boxes[:, [1, 3]] *= h
            denormed_boxes.append(new_boxes)
        return denormed_boxes
    if size==(1, 1):
        roi_feats=roi_align(x, _denormalize_boxes(normed_boxes, x), size, 1.0, -1, True)[..., 0, 0]
    else:
        roi_feats=roi_align(x, _denormalize_boxes(normed_boxes, x), size, 1.0, -1, True).flatten(start_dim=-2)
    if normalize:
        roi_feats=F.normalize(roi_feats,dim=1)
    return roi_feats


def prepare_inputs(batch, device, dtype):
    """
    将输入批次中的数据加载到设备，并转换为指定数据类型。
    """
    images, normed_boxes, image_crops, vfm_image = batch
    images = images.to(device=device, dtype=dtype, non_blocking=True)
    normed_boxes = normed_boxes.to(device=device, dtype=dtype, non_blocking=True)
    image_crops = image_crops.to(device=device, dtype=dtype, non_blocking=True)
    vfm_image = vfm_image.to(device=device, dtype=dtype, non_blocking=True)
    return images, normed_boxes, image_crops, vfm_image


def extract_vfm_features(vfm_model, image, args):
    """
    从 VFM 模型中提取特征，并对其进行归一化。
    """
    if "dinov2" in args.use_vfm or "sd_dino" in args.use_vfm or "sam_dino" in args.use_vfm:
        vfm_feats = vfm_model.get_intermediate_layers(image, reshape=True)[0]
    elif 'sam' in args.use_vfm:
        vfm_feats = vfm_model.image_encoder(image)
    elif 'dino' in args.use_vfm:
        feat = vfm_model.get_intermediate_layers(image)[0]
        nb_im = feat.shape[0]
        patch_size = vfm_model.patch_embed.patch_size
        I, J = image[0].shape[-2] // patch_size, image[0].shape[-2] // patch_size
        vfm_feats = feat[:, 1:, :].reshape(nb_im, I, J, -1).permute(0, 3, 1, 2)
    else:
        raise NotImplementedError(f"VFM mode {args.use_vfm} is not implemented.")
    return vfm_feats

def compute_student_intra_image_similarity(B, context, args):

    N, _ = context[0].shape[1:] if isinstance(context, tuple) else context.shape[1:]
    if args.mode in ["qq_vfm_distill", "kk_vfm_distill", "vv_vfm_distill", "sanity_check"]:
        context = context.transpose(0, 1).contiguous().view(N, B, -1).transpose(0, 1)
        context = F.normalize(context, dim=-1).transpose(-2, -1)
        student_context_similarity = torch.einsum("b c m, b c n -> b m n", context, context)

    elif args.mode == "csa_vfm_distill":
        q_feature, k_feature = context
        q_feature = q_feature.transpose(0, 1).contiguous().view(N, B, -1).transpose(0, 1)
        k_feature = k_feature.transpose(0, 1).contiguous().view(N, B, -1).transpose(0, 1)
        q_feature = F.normalize(q_feature, dim=-1).transpose(-2, -1)
        k_feature = F.normalize(k_feature, dim=-1).transpose(-2, -1)
        student_context_similarity = (torch.einsum("b c m, b c n -> b m n", q_feature, q_feature) +
                                       torch.einsum("b c m, b c n -> b m n", k_feature, k_feature)) / 2.0
    elif args.mode == "all_vfm_distill":
        q_feature, k_feature, v_feature = context
        features = [q_feature, k_feature, v_feature]
        similarities = []
        for feature in features:
            feature = feature.transpose(0, 1).contiguous().view(N, B, -1).transpose(0, 1)
            feature = F.normalize(feature, dim=-1).transpose(-2, -1)
            similarities.append(torch.einsum("b c m, b c n -> b m n", feature, feature))
        student_context_similarity = sum(similarities) / len(features)

    else:
        raise NotImplementedError(f"Mode '{args.mode}' is not implemented.")

    return student_context_similarity