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import torch.nn as nn
import sys
sys.path.append("..")
from prompts.imagenet_template import openai_imagenet_template, sub_imagenet_template
from mmseg.models.segmentors import BaseSegmentor
from mmseg.models.data_preprocessor import SegDataPreProcessor
from mmengine.structures import PixelData
from mmseg.registry import MODELS
from torchvision import transforms
import torch.nn.functional as F
from einops import rearrange
from open_clip import create_model, tokenizer
from segment_anything import sam_model_registry
from myutils import UnNormalize
@MODELS.register_module()
class ProxyCLIPSegmentation(BaseSegmentor):
def __init__(self, clip_type, model_type, vfm_model, name_path, checkpoint=None, device=torch.device('cuda'),
prob_thd=0.0, logit_scale=40, beta=1.2, gamma=3.0, 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)
self.clip = create_model(model_type, pretrained=clip_type, precision='fp16')
self.clip.eval().to(device)
self.tokenizer = tokenizer.tokenize
self.vfm_model = vfm_model
sam_ckpts = {
"sam-B": "/mnt/SSD8T/home/wjj/code/ProxyCLIP/sam_ckpts/sam_vit_b_01ec64.pth",
"sam-L": "/mnt/SSD8T/home/wjj/code/ProxyCLIP/sam_ckpts/sam_vit_l_0b3195.pth",
}
dinov2_ckpts = {
"dinov2-L": "dinov2_vitl14_reg",
"dinov2-B": "dinov2_vitb14_reg",
"dinov2-B-noreg": "dinov2_vitb14",
"dinov2-L-noreg": "dinov2_vitl14",
}
dino_ckpts = {
"dino-B-8": "dino_vitb8",
"dino-B-16": "dino_vitb16",
}
vfm = None
if vfm_model.startswith("dinov2"):
if vfm_model in dinov2_ckpts:
model_name = dinov2_ckpts[vfm_model]
hub_path = '/mnt/SSD8T/home/wjj/.cache/torch/hub/facebookresearch_dinov2_main'
try:
vfm = torch.hub.load(hub_path, model_name, source='local').half()
except Exception as e:
raise RuntimeError(f"Failed to load DINOv2 model '{vfm_model}': {e}")
else:
raise NotImplementedError(f"VLM model '{vfm_model}' not supported under DINOv2 category.")
elif vfm_model.startswith("dino"):
if vfm_model in dino_ckpts:
model_name = dino_ckpts[vfm_model]
hub_path = '/mnt/SSD8T/home/wjj/.cache/torch/hub/facebookresearch_dino_main'
try:
vfm = torch.hub.load(hub_path, model_name, source='local').half()
except Exception as e:
raise RuntimeError(f"Failed to load DINO model '{vfm_model}': {e}")
else:
raise NotImplementedError(f"VLM model '{vfm_model}' not supported under DINO category.")
elif vfm_model.startswith("sam"):
if vfm_model in sam_ckpts:
vit_type = "vit_b" if "B" in vfm_model else "vit_l"
checkpoint_path = sam_ckpts[vfm_model]
try:
vfm = sam_model_registry[vit_type](checkpoint=checkpoint_path).half()
except Exception as e:
raise RuntimeError(f"Failed to load SAM model '{vfm_model}' with checkpoint '{checkpoint_path}': {e}")
else:
# 为了向后兼容，如果只传入 'sam'，默认使用 sam-B
if vfm_model == 'sam':
vfm = sam_model_registry["vit_b"](checkpoint=sam_ckpts["sam-B"]).half()
else:
raise NotImplementedError(f"VLM model '{vfm_model}' not supported under SAM category.")
else:
raise NotImplementedError(f"VLM model '{vfm_model}' not supported.")
for p in vfm.parameters():
p.requires_grad = False
self.vfm = vfm.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])
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)
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
self.beta = beta
self.gamma = gamma
@torch.no_grad()
def forward_feature(self, img, logit_size=None):
if type(img) == list:
img = img[0]
clip_token_size = img.shape[-2] // self.clip.visual.patch_size[0], img.shape[-1] // self.clip.visual.patch_size[1]
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_model.startswith('sam'):
patch_size = self.vfm.image_encoder.patch_embed.proj.kernel_size
imgs_norm = F.interpolate(imgs_norm, size=(1024, 1024), mode='bilinear', align_corners=False)
I, J = imgs_norm.shape[-2] // patch_size[0], imgs_norm.shape[-2] // patch_size[1]
ex_feats = self.vfm.image_encoder(imgs_norm)
elif self.vfm_model.startswith('dino') and not self.vfm_model.startswith('dinov2'):
feat_out = {}
def hook_fn_forward_qkv(module, input, output):
feat_out["qkv"] = output
self.vfm._modules["blocks"][-1]._modules["attn"]._modules["qkv"].register_forward_hook(
hook_fn_forward_qkv)
# Forward pass in the model
feat = self.vfm.get_intermediate_layers(imgs_norm)[0]
nb_im = feat.shape[0] # Batch size
nb_tokens = feat.shape[1] # Number of tokens
nh = self.vfm.blocks[0].attn.num_heads # Number of heads
qkv = (
feat_out["qkv"]
.reshape(nb_im, nb_tokens, 3, nh, -1 // nh)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv[0], qkv[1], qkv[2]
k = k.transpose(1, 2).reshape(nb_im, nb_tokens, -1)[:, 1:, :]
q = q.transpose(1, 2).reshape(nb_im, nb_tokens, -1)[:, 1:, :]
v = v.transpose(1, 2).reshape(nb_im, nb_tokens, -1)[:, 1:, :]
patch_size = self.vfm.patch_embed.patch_size
I, J = imgs_norm[0].shape[-2] // patch_size, imgs_norm[0].shape[-2] // patch_size
# ex_feats = q.reshape(nb_im, I, J, -1).permute(0, 3, 1, 2)
# ex_feats = k.reshape(nb_im, I, J, -1).permute(0, 3, 1, 2)
# ex_feats = v.reshape(nb_im, I, J, -1).permute(0, 3, 1, 2)
ex_feats = feat[:, 1:, :].reshape(nb_im, I, J, -1).permute(0, 3, 1, 2)
elif self.vfm_model.startswith('dinov2'):
patch_size = self.vfm.patch_embed.patch_size
I, J = imgs_norm.shape[-2] // patch_size[0], imgs_norm.shape[-2] // patch_size[1]
ex_feats = self.vfm.get_intermediate_layers(imgs_norm, reshape=True)[0]
elif self.vfm_model == 'mae':
patch_size = self.vfm.patch_embed.patch_size
imgs_norm = F.interpolate(imgs_norm, size=(self.slide_crop, self.slide_crop), mode='bilinear', align_corners=False)
I, J = imgs_norm.shape[-2] // patch_size[0], imgs_norm.shape[-2] // patch_size[1]
image_feat = self.vfm.forward_features(imgs_norm)
ex_feats = rearrange(image_feat, 'b (h w) c -> b c h w', h=I, w=J)
else:
I, J = clip_token_size
ex_feats = None
image_features = self.clip.encode_image(img.half(),
external_feats=ex_feats,
beta=self.beta,
gamma=self.gamma)
image_features /= image_features.norm(dim=-1, keepdim=True)
logits = image_features @ self.query_features.T
logits = logits.permute(0, 2, 1).reshape(-1, logits.shape[-1], I, J)
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 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
pad = self.compute_padsize(H, W, 56)
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 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]
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, batch_img_metas[0]['ori_shape'])
return self.postprocess_result(seg_logits, data_samples)
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 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 _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 |