File size: 4,694 Bytes
8e29a6e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import torch.nn as nn
import torch
from .head import VQAHead_cls,VARHead,VQAHead
from .backbone.blip import MyBLIP as BLIP
class VideoTextAlignmentModel(nn.Module):
def __init__(
self,
backbone_size="divided",
backbone_preserve_keys="fragments,resize",
multi=False,
layer=-1,
backbone=dict(
resize={"window_size": (4, 4, 4)}, fragments={"window_size": (4, 4, 4)}
),
divide_head=False,
head_type='VQAhead_cls',
vqa_head=dict(in_channels=768),
var=False,
use_tn=False,
model_path=None,
):
self.backbone_preserve_keys = backbone_preserve_keys.split(",")
self.multi = multi
self.layer = layer
super().__init__()
for key, hypers in backbone.items():
if key not in self.backbone_preserve_keys:
continue
if backbone_size == "divided":
t_backbone_size = hypers["type"]
else:
t_backbone_size = backbone_size
assert t_backbone_size == "blip"
type = hypers["blip_type"]
b = BLIP(type, model_path)
setattr(self, key + "_backbone", b)
if divide_head:
for key in backbone:
pre_pool = False # if key == "technical" else True
if key not in self.backbone_preserve_keys:
continue
in_channel = 768
b = VQAHead_cls(pre_pool=pre_pool, in_channels=in_channel, **vqa_head)
setattr(self, key + "_head", b)
else:
if var:
self.vqa_head = VARHead(**vqa_head)
else:
self.vqa_head = VQAHead(**vqa_head)
def forward(
self,
vclips,
prompts=None,
inference=True,
return_pooled_feats=False,
return_raw_feats=False,
reduce_scores=False,
pooled=False,
**kwargs
):
# import pdb;pdb.set_trace()
assert (return_pooled_feats & return_raw_feats) == False, "Please only choose one kind of features to return"
if inference:
self.eval()
with torch.no_grad():
scores = []
feats = {}
for key in self.backbone_preserve_keys:
feat = getattr(self, key.split("_")[0] + "_backbone")(
vclips[key], prompts
)
if hasattr(self, key.split("_")[0] + "_head"):
scores += [getattr(self, key.split("_")[0] + "_head")(feat)[0]]
else:
scores += [getattr(self, "vqa_head")(feat)]
if return_pooled_feats:
feats[key] = feat
if return_raw_feats:
feats[key] = feat
if reduce_scores:
if len(scores) > 1:
scores = reduce(lambda x, y: x + y, scores)
else:
scores = scores[0]
if pooled:
scores = torch.mean(scores, (1, 2, 3, 4))
self.train()
if return_pooled_feats or return_raw_feats:
return scores, feats
return scores
else:
self.train()
scores = []
feats = {}
for key in vclips:
feat = getattr(self, key.split("_")[0] + "_backbone")(
vclips[key], prompts
)
if hasattr(self, key.split("_")[0] + "_head"):
scores += [getattr(self, key.split("_")[0] + "_head")(feat)[0]]
else:
scores += [getattr(self, "vqa_head")(feat)]
if return_pooled_feats:
feats[key] = feat.mean((-3, -2, -1))
if reduce_scores:
if len(scores) > 1:
scores = reduce(lambda x, y: x + y, scores)
else:
scores = scores[0]
if pooled:
# print(scores.shape)
scores = torch.mean(scores, (1, 2, 3, 4))
# print(scores.shape)
if return_pooled_feats:
return scores, feats
return scores
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