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# Copyright (c) Ye Liu. Licensed under the BSD 3-Clause License.
import math
import clip
import torch
import torch.nn as nn
import torch.nn.functional as F
from nncore.nn import MODELS, build_loss, build_model
from .generator import PointGenerator
_CLIP_ARCHS = {
'ViT-B/32': (768, 512, 50),
'ViT-B/16': (768, 512, 197),
'ViT-L/14': (1024, 768, 50),
'ViT-L/14-336px': (1024, 768, 577)
}
@MODELS.register()
class R2Tuning(nn.Module):
def __init__(self,
arch='ViT-B/32',
init=True,
dims=256,
strides=(1, 2, 4, 8),
buffer_size=1024,
max_num_moment=50,
merge_cls_sal=True,
adapter_cfg=None,
pyramid_cfg=None,
pooling_cfg=None,
class_head_cfg=None,
coord_head_cfg=None,
loss_cfg=None):
super(R2Tuning, self).__init__()
if init:
self.clip, _ = clip.load(arch, device='cpu')
for param in self.clip.parameters():
param.requires_grad = False
self.cfg = _CLIP_ARCHS[arch]
self.adapter = build_model(adapter_cfg, dims, self.cfg[:2])
self.pyramid = build_model(pyramid_cfg, dims, strides)
self.pooling = build_model(pooling_cfg, dims)
self.class_head = build_model(class_head_cfg, dims, 1)
self.coord_head = build_model(coord_head_cfg, dims, 2)
self.generator = PointGenerator(strides, buffer_size)
self.coef = nn.Parameter(torch.ones(len(strides)))
self.loss = build_loss(loss_cfg)
self.max_num_moment = max_num_moment
self.merge_cls_sal = merge_cls_sal
def train(self, mode=True):
super(R2Tuning, self).train(mode=mode)
if hasattr(self, 'clip'):
self.clip.eval()
@torch.no_grad
def clip_video_tower(self, video):
video = video.type(self.clip.dtype)
video = self.clip.visual.conv1(video)
video = video.reshape(video.size(0), video.size(1), -1).permute(0, 2, 1)
c_emb = video.new_zeros(video.size(0), 1, video.size(-1))
c_emb = self.clip.visual.class_embedding.to(video.dtype) + c_emb
video = torch.cat((c_emb, video), dim=1)
video = video + self.clip.visual.positional_embedding.to(video.dtype)
video = self.clip.visual.ln_pre(video).permute(1, 0, 2)
emb = [video]
for blk in self.clip.visual.transformer.resblocks:
emb.append(blk(emb[-1]))
video = torch.stack([e.permute(1, 0, 2) for e in emb])
return video
@torch.no_grad
def clip_query_tower(self, query):
query = self.clip.token_embedding(query).type(self.clip.dtype)
query = query + self.clip.positional_embedding.type(self.clip.dtype)
query = query.permute(1, 0, 2)
emb = [query]
for blk in self.clip.transformer.resblocks:
emb.append(blk(emb[-1]))
query = torch.stack([e.permute(1, 0, 2) for e in emb])
return query
def forward(self, data, mode='test'):
video, query = data['video'], data['query']
if hasattr(self, 'clip'):
video_msk = torch.where(video[:, :, 0].isfinite(), 1, 0)
query_msk = torch.where(query == 0, 0, 1)
video[~video.isfinite()] = 0
(b, t), d = video.size()[:2], int(math.sqrt(video.size(2) / 3))
video = video.view(b * t, 3, d, d)
video_emb = self.clip_video_tower(video)
query_emb = self.clip_query_tower(query)
n, _, p, c = video_emb.size()
video_emb = video_emb.view(n, b, t, p, c)
else:
video_msk = torch.where(video[:, :, 0].isfinite(), 1, 0)
query_msk = torch.where(query[:, :, 0].isfinite(), 1, 0)
video[~video.isfinite()] = 0
query[~query.isfinite()] = 0
(b, t), l = video.size()[:2], query.size(1)
video = video.view(b, t, -1, self.cfg[2], self.cfg[0]).permute(2, 0, 1, 3, 4)
query = query.view(b, l, -1, self.cfg[1]).permute(2, 0, 1, 3)
video_emb = video.float()
query_emb = query.float()
# video_emb: N * B * T * P * C
# query_emb: N * B * L * C
video_emb, query_emb, coll_v, coll_q = self.adapter(video_emb, query_emb,
video_msk, query_msk)
pymid, pymid_msk = self.pyramid(video_emb, video_msk, return_mask=mode != 'test')
point = self.generator(pymid)
with torch.autocast('cuda', enabled=False):
video_emb = video_emb.float()
query_emb = self.pooling(query_emb.float(), query_msk)
out_class = [self.class_head(e.float()) for e in pymid]
out_class = torch.cat(out_class, dim=1)
if self.coord_head is not None:
out_coord = [
self.coord_head(e.float()).exp() * self.coef[i]
for i, e in enumerate(pymid)
]
out_coord = torch.cat(out_coord, dim=1)
else:
out_coord = None
output = dict(_avg_factor=b)
if mode != 'test':
data['coll_v'] = [e.float() for e in coll_v]
data['coll_q'] = [self.pooling(e.float(), query_msk) for e in coll_q]
data['point'] = point
data['video_emb'] = video_emb
data['query_emb'] = query_emb
data['video_msk'] = video_msk
data['pymid_msk'] = pymid_msk
data['out_class'] = out_class
data['out_coord'] = out_coord
output = self.loss(data, output)
if mode != 'train':
assert b == 1, 'batch size larger than 1 is not supported for inference'
out_class = out_class.sigmoid()
out_score = F.cosine_similarity(video_emb, query_emb, dim=-1)
output['_out'] = dict(label=data.get('label', [None])[0])
pyd_shape = [e.size(1) for e in pymid]
pyd_class = out_class[0, :, 0].split(pyd_shape)
saliency = []
for shape, score in zip(pyd_shape, pyd_class):
if t >= shape:
score = score.repeat_interleave(int(t / shape))
postfix = score[-1:].repeat(t - score.size(0))
score = torch.cat((score, postfix))
else:
scale = int(shape / t)
score = F.max_pool1d(score.unsqueeze(0), scale, stride=scale)[0]
saliency.append(score)
saliency = torch.stack(saliency).amax(dim=0)
if self.merge_cls_sal:
saliency *= out_score[0]
output['_out']['saliency'] = saliency
if self.coord_head is not None:
boundary = out_coord[0]
boundary[:, 0] *= -1
boundary *= point[:, 3, None].repeat(1, 2)
boundary += point[:, 0, None].repeat(1, 2)
boundary /= data['fps'][0]
boundary = torch.cat((boundary, out_class[0]), dim=-1)
_, inds = out_class[0, :, 0].sort(descending=True)
boundary = boundary[inds[:self.max_num_moment]]
output['_out']['boundary'] = boundary
return output
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