downstream/ProxyCLIP_TPAMI/tinyclip_segmentor.py

import math
import os
import torch
import torch.nn as nn
import sys

from training.file_utils import pt_load
sys.path.append("..")
from clipself.src.open_clip.tiny_clip.factory import create_model, get_tokenizer
from prompts.imagenet_template import openai_imagenet_template, sub_imagenet_template
from mmseg.models.segmentors import BaseSegmentor
from mmengine.structures import PixelData
from mmseg.registry import MODELS
import torch.nn.functional as F
from mmseg.models.data_preprocessor import SegDataPreProcessor
from segment_anything import sam_model_registry
from myutils import UnNormalize, visualize_ade20k, visualize_cityscapes, visualize_coco_stuff, visualize_voc_context59
from torchvision import transforms

@MODELS.register_module()
class TinyCLIPSegmentation(BaseSegmentor):
    def __init__(self, clip_type,
                        name_path,
                        vfm,
                        checkpoint, 
                        mode,
                        device=torch.device('cuda:0'),
                        prob_thd=0.0, logit_scale=40, slide_stride=112, slide_crop=336):
        data_preprocessor = SegDataPreProcessor(
            mean=[122.771, 116.746, 104.094],
            std=[68.501, 66.632, 70.323],
            bgr_to_rgb=True)
        super().__init__(data_preprocessor=data_preprocessor)
        
        # 使用tiny_clip的factory创建模型
        # tiny_clip的create_model可以直接接受checkpoint路径作为pretrained参数
        # 注意：tiny_clip的factory支持precision="fp32"或"fp16"，不支持"amp"
        if checkpoint and os.path.exists(checkpoint):
            # 如果checkpoint是文件路径，直接使用
            self.clip = create_model(
                clip_type,
                pretrained=checkpoint,
                precision="fp32",  # 使用fp32，与CLIPselfSegmentation保持一致
                device=device,
                cache_dir=None)
        else:
            # 如果没有checkpoint，创建空模型
            self.clip = create_model(
                clip_type,
                pretrained="",
                precision="fp32",
                device=device,
                cache_dir=None)
        
        self.tokenizer = get_tokenizer(model_name=clip_type)
     
        self.clip.eval().to(device)
        query_words, self.query_idx = get_cls_idx(name_path)
        self.num_queries = len(query_words)
        self.num_classes = max(self.query_idx) + 1
        self.query_idx = torch.Tensor(self.query_idx).to(torch.int64).to(device)
        self.mode=mode
        query_features = []
        with torch.no_grad():
            for qw in query_words:
                query = self.tokenizer([temp(qw) for temp in openai_imagenet_template]).to(device)
                feature = self.clip.encode_text(query)
                feature /= feature.norm(dim=-1, keepdim=True)
                feature = feature.mean(dim=0)
                feature /= feature.norm()
                query_features.append(feature.unsqueeze(0))
        self.query_features = torch.cat(query_features, dim=0).detach()
        self.dtype = self.query_features.dtype
        self.logit_scale = logit_scale
        self.prob_thd = prob_thd
        self.slide_stride = slide_stride
        self.slide_crop = slide_crop
        # begin vfm #
        self.vfm=vfm
        if vfm:
            if vfm=="sam":
                self.vfm_model = sam_model_registry["vit_b"](checkpoint="sam_ckpts/sam_vit_b_01ec64.pth")
            elif vfm=="dino": 
                self.vfm_model = torch.hub.load('facebookresearch/dino:main', 'dino_vitb8')
            else:
                self.vfm_model = torch.hub.load('facebookresearch/dinov2:main', 'dinov2_vitb14_reg')
            self.vfm_model = self.vfm_model.half()
            for p in self.vfm_model.parameters():
                p.requires_grad = False
            self.vfm_model.eval().to(device)
            self.unnorm = UnNormalize([0.48145466, 0.4578275, 0.40821073], [0.26862954, 0.26130258, 0.27577711])
            self.norm = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        else:
            self.vfm_model=None
        # end vfm #
    @torch.no_grad()
    def forward_feature(self, img, logit_size=None,):
        if type(img) == list:
            img = img[0]
        if self.vfm:
            imgs_norm = [self.norm(self.unnorm(img[i])) for i in range(len(img))]
            imgs_norm = torch.stack(imgs_norm, dim=0)
            imgs_norm = imgs_norm.half()
            if self.vfm=="sam":
                imgs_norm = F.interpolate(imgs_norm, size=(1024, 1024), mode='bilinear', align_corners=False)
                ex_feats = self.vfm_model.image_encoder(imgs_norm)
            elif self.vfm == 'dinov2':
                patch_size = self.vfm_model.patch_embed.patch_size
                I, J = imgs_norm.shape[-2] // patch_size[0], imgs_norm.shape[-2] // patch_size[1]
                imgs_norm = F.interpolate(imgs_norm, size=(896, 896), mode='bilinear', align_corners=False)
                ex_feats = self.vfm_model.get_intermediate_layers(imgs_norm, reshape=True)[0]
            else:
                imgs_norm = F.interpolate(imgs_norm, size=(512, 512), mode='bilinear', align_corners=False)
                feat = self.vfm_model.get_intermediate_layers(imgs_norm)[0]
                nb_im = feat.shape[0]
                patch_size = self.vfm_model.patch_embed.patch_size
                I, J = imgs_norm[0].shape[-2] // patch_size, imgs_norm[0].shape[-2] // patch_size
                ex_feats = feat[:, 1:, :].reshape(nb_im, I, J, -1).permute(0, 3, 1, 2)
            image_features = self.clip.encode_dense(img,
                                                    normalize=True,
                                                    keep_shape=False,
                                                    mode=self.mode,
                                                    ) # bs, N, C
        else:
            image_features = self.clip.encode_dense(img,
                                                    normalize=True,
                                                    keep_shape=False,
                                                    mode=self.mode,
                                                    ) # bs, N, C
        # Calculate h, w from image size and patch_size instead of sqrt(N)
        # This handles cases where N is not a perfect square due to padding
        clip_token_size = (
            img.shape[-2] // self.clip.visual.patch_size[0],
            img.shape[-1] // self.clip.visual.patch_size[1],
        )
        h, w = clip_token_size
        logits = image_features @ self.query_features.T
        logits = logits.permute(0, 2, 1).reshape(-1, logits.shape[-1], h, w)
        if logit_size == None:
            logits = nn.functional.interpolate(logits, size=img.shape[-2:], mode='bilinear')
        else:
            logits = nn.functional.interpolate(logits, size=logit_size, mode='bilinear')
        return logits
    
    def predict(self, inputs, data_samples):
        if data_samples is not None:
            batch_img_metas = [data_sample.metainfo for data_sample in data_samples]
        else:
            batch_img_metas = [
                                dict(
                                      ori_shape=inputs.shape[2:],
                                      img_shape=inputs.shape[2:],
                                      pad_shape=inputs.shape[2:],
                                      padding_size=[0, 0, 0, 0])
                              ] * inputs.shape[0]
        ori_shape=batch_img_metas[0]['ori_shape']
        resize_shape=batch_img_metas[0]['resize_shape']
        img_shape=batch_img_metas[0]['img_shape']
        if self.slide_crop > 0:
            seg_logits = self.forward_slide(inputs, batch_img_metas, self.slide_stride, self.slide_crop)
        else:
            seg_logits = self.forward_feature(inputs,img_shape)
            seg_logits=seg_logits[:,:,:resize_shape[0],:resize_shape[1]]
            seg_logits = nn.functional.interpolate(seg_logits, size=ori_shape, mode='bilinear')
        result=self.postprocess_result(seg_logits, data_samples)
        # visualize_voc_context59(batch_img_metas,result)
        # visualize_ade20k(batch_img_metas,result)
        # visualize_coco_stuff(batch_img_metas,result)
        # visualize_cityscapes(batch_img_metas,result)
        return result


    def forward_slide(self, img, img_metas, stride=112, crop_size=224):
        """Inference by sliding-window with overlap.
        If h_crop > h_img or w_crop > w_img, the small patch will be used to
        decode without padding.
        """
        if type(img) == list:
            img = img[0].unsqueeze(0)
        if type(stride) == int:
            stride = (stride, stride)
        if type(crop_size) == int:
            crop_size = (crop_size, crop_size)
        h_stride, w_stride = stride
        h_crop, w_crop = crop_size
        batch_size, _, h_img, w_img = img.shape
        out_channels = self.num_queries
        h_grids = max(h_img - h_crop + h_stride - 1, 0) // h_stride + 1
        w_grids = max(w_img - w_crop + w_stride - 1, 0) // w_stride + 1
        preds = img.new_zeros((batch_size, out_channels, h_img, w_img))
        count_mat = img.new_zeros((batch_size, 1, h_img, w_img))
        for h_idx in range(h_grids):
            for w_idx in range(w_grids):
                y1 = h_idx * h_stride
                x1 = w_idx * w_stride
                y2 = min(y1 + h_crop, h_img)
                x2 = min(x1 + w_crop, w_img)
                y1 = max(y2 - h_crop, 0)
                x1 = max(x2 - w_crop, 0)
                crop_img = img[:, :, y1:y2, x1:x2]
                # pad image when (image_size % patch_size != 0)
                H, W = crop_img.shape[2:]  # original image shape
                # Use CLIP patch_size for padding calculation
                clip_patch_size = self.clip.visual.patch_size[0] if hasattr(self.clip.visual, 'patch_size') else 16
                pad = self.compute_padsize(H, W, clip_patch_size)
                if any(pad):
                    crop_img = nn.functional.pad(crop_img, pad)  # zero padding
                crop_seg_logit = self.forward_feature(crop_img).detach()
                torch.cuda.empty_cache()
                # mask cutting for padded image
                if any(pad):
                    l, t = pad[0], pad[2]
                    crop_seg_logit = crop_seg_logit[:, :, t:t + H, l:l + W]
                preds += nn.functional.pad(crop_seg_logit,
                                           (int(x1), int(preds.shape[3] - x2), int(y1),
                                            int(preds.shape[2] - y2)))
                count_mat[:, :, y1:y2, x1:x2] += 1
        assert (count_mat == 0).sum() == 0
        preds = preds / count_mat
        img_size = img_metas[0]['ori_shape'][:2]
        logits = nn.functional.interpolate(preds, size=img_size, mode='bilinear')
        return logits

    
    
    def compute_padsize(self, H: int, W: int, patch_size: int):
        l, r, t, b = 0, 0, 0, 0
        if W % patch_size:
            lr = patch_size - (W % patch_size)
            l = lr // 2
            r = lr - l

        if H % patch_size:
            tb = patch_size - (H % patch_size)
            t = tb // 2
            b = tb - t

        return l, r, t, b
    
    def postprocess_result(self, seg_logits, data_samples):
        batch_size = seg_logits.shape[0]
        for i in range(batch_size):
            seg_logits = seg_logits[i] * self.logit_scale
            seg_logits = seg_logits.softmax(0)  # n_queries * w * h

            num_cls, num_queries = max(self.query_idx) + 1, len(self.query_idx)
            if num_cls != num_queries:
                seg_logits = seg_logits.unsqueeze(0)
                cls_index = nn.functional.one_hot(self.query_idx)
                cls_index = cls_index.T.view(num_cls, num_queries, 1, 1)
                seg_logits = (seg_logits * cls_index).max(1)[0]

            seg_pred = seg_logits.argmax(0, keepdim=True)
            seg_pred[seg_logits.max(0, keepdim=True)[0] < self.prob_thd] = 0
            if data_samples is None:
                return seg_pred
            else:
                data_samples[i].set_data({
                    'seg_logits':
                        PixelData(**{'data': seg_logits}),
                    'pred_sem_seg':
                        PixelData(**{'data': seg_pred})
                })
        return data_samples


    def _forward(data_samples):
        """
        """

    def inference(self, img, batch_img_metas):
        """
        """

    def encode_decode(self, inputs, batch_img_metas):
        """
        """

    def extract_feat(self, inputs):
        """
        """

    def loss(self, inputs, data_samples):
        """
        """

def get_cls_idx(path):
    with open(path, 'r') as f:
        name_sets = f.readlines()
    num_cls = len(name_sets)

    class_names, class_indices = [], []
    for idx in range(num_cls):
        names_i = name_sets[idx].split('; ')
        class_names += names_i
        class_indices += [idx for _ in range(len(names_i))]
    class_names = [item.replace('\n', '') for item in class_names]
    return class_names, class_indices