-
Notifications
You must be signed in to change notification settings - Fork 8
/
eval_retrieval_task_specific.py
244 lines (210 loc) · 9.27 KB
/
eval_retrieval_task_specific.py
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
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
from utils.lib import *
from utils.args import get_args
from main_retrieval_task_specific import LAVENDER_Retrieval_TS
from dataset import Dataset_Base
class Dataset_RetrievalTsEval(Dataset_Base):
def __init__(self, args, split):
super().__init__(args, split, size_frame=args.size_frame)
self.img_tsv_path = f'{args.data_dir}/img_{args.dataset}.tsv'
self.id2lineidx = pickle.load(open(
f'{args.data_dir}/img_{args.dataset}.id2lineidx.pkl', 'rb'))
self.txt = json.load(
open(f'{args.data_dir}/txt_{args.task}.json',
'r'))[split]
self.gt_txt2vid = {
idx: item["video"] for idx, item in enumerate(self.txt)}
def __len__(self):
return len(self.txt)
def get_clips_with_temporal_sampling(self, list_of_b):
max_size_frame = len(list_of_b)
list_of_sampled_videos = []
if max_size_frame == 1 or self.size_frame == max_size_frame:
list_of_sampled_videos.append(
self.get_img_or_video(list_of_b).unsqueeze(0))
return T.cat(list_of_sampled_videos, dim=0)
if max_size_frame < self.size_frame:
print(f"Error in size_frame",
f"\tasked for {size_frame} from {max_size_frame} frames")
size_frame = min(self.size_frame, max_size_frame)
size_clips = int(math.ceil(max_size_frame / size_frame))
if self.args.multi_clip_testing:
for sampled_start in range(size_clips):
sampled_end = min(
sampled_start + (size_frame - 1) * size_clips,
max_size_frame - 1)
sampled_index = self.sampling(
sampled_start, sampled_end, size_frame)
sampled_video = [list_of_b[i] for i in sampled_index]
sampled_video = self.get_img_or_video(sampled_video)
list_of_sampled_videos.append(sampled_video.unsqueeze(0))
else:
# uniformly sample frames
sampled_index = self.sampling(
0, max_size_frame - 1, size_frame)
sampled_video = [list_of_b[i] for i in sampled_index]
sampled_video = self.get_img_or_video(sampled_video)
list_of_sampled_videos.append(sampled_video.unsqueeze(0))
list_of_sampled_videos = T.cat(list_of_sampled_videos, dim=0)
return list_of_sampled_videos
def get_img_or_video(self, bufs):
img = []
for b in bufs:
single_img = self.str2img(b)
if self.args.img_transform == ["vid_rand_crop"]:
vis_transform = "vid_center_crop"
img.append(single_img)
else:
if self.args.img_transform == ["pad_resize"]:
vis_transform = "pad_resize"
single_img = self.pad_resize(single_img)
else:
vis_transform = "img_center_crop"
single_img = self.img_center_crop(single_img)
img.append(single_img.unsqueeze(0))
if vis_transform == "vid_center_crop":
img = self.vid_center_crop(img)
else:
img = T.cat(img, dim=0)
return img
def __getitem__(self, idx):
item = self.txt[idx]
video_id = item['video']
lineidx = self.id2lineidx[video_id]
b = self.seek_img_tsv(lineidx)[2:]
img = self.get_clips_with_temporal_sampling(b)
raw_txt = item['caption']
if isinstance(raw_txt, list):
assert self.split != "train"
raw_txt = " ".join(raw_txt)
txt, mask = self.str2txt(raw_txt)
return img, txt, mask, idx, item['video']
class Dataset_Product(T.utils.data.Dataset):
def __init__(self, featv, featt):
super().__init__()
self.vids = list(set([item["video"] for key, item in featv.items()]))
self.vid2idx = {v: i for i, v in enumerate(self.vids)}
self.tids = list(set([item["tid"] for key, item in featt.items()]))
self.tid2idx = {t: i for i, t in enumerate(self.tids)}
self.lst = [[featt[p], featv[q]] for p in featt for q in featv]
def __len__(self):
return len(self.lst)
def __getitem__(self, idx):
p, q = self.lst[idx]
return [p['feat_txt'], p['mask_txt'],
p['tid'],
q['feat_img'], q['mask_img'],
q['video']] # (p->text, q->video)
class LAVENDER_RetrievalTsEval(LAVENDER_Retrieval_TS):
def __init__(self, args, tokzr=None):
super().__init__(args, tokzr)
def forward(
self, typ,
img=None, txt=None, mask=None,
feat_img=None, mask_img=None, feat_txt=None, mask_txt=None):
if typ == 'feat':
_B, _Clips, _T, _C, _H, _W = img.shape
img = img.view(-1, _T, _C, _H, _W)
feat_img, mask_img, feat_txt, mask_txt = self.go_feat(
img, txt, mask)
_hidden_size = feat_img.shape[-1]
mean_mask_img = mask_img.view(_B, _Clips, -1)
mean_feat_img = feat_img.view(_B, _Clips, -1, _hidden_size)
mean_feat_img = mean_feat_img.mean(dim=1)
mean_mask_img = mean_mask_img[:, 0, :]
return mean_feat_img, mean_mask_img, feat_txt, mask_txt
elif typ == 'cross':
out, _ = self.go_cross(
feat_img, mask_img, feat_txt, mask_txt)
out = self.fc(out[:, feat_img.shape[1], :]).squeeze()
return out
if __name__ == '__main__':
args = get_args(distributed=False)
args.use_checkpoint = False
args.num_gpus = T.cuda.device_count()
if args.multi_clip_testing:
args.size_batch = 10*args.num_gpus
else:
args.size_batch = 100*args.num_gpus
assert os.path.exists(args.path_ckpt)
print(args)
ds_ret = Dataset_RetrievalTsEval(args, "test")
log = {}
model = T.nn.DataParallel(
LAVENDER_RetrievalTsEval(args, ds_ret.tokzr).cuda())
model.module.load_ckpt(args.path_ckpt)
model.eval()
for split in ['test']: # ['val', 'test']:
ds_ret = Dataset_RetrievalTsEval(args, split)
dl = T.utils.data.DataLoader(
ds_ret,
batch_size=args.size_batch, shuffle=False,
num_workers=args.n_workers, pin_memory=True,
worker_init_fn=ds_ret.read_tsv)
featv = {}
featt = {}
gt_txt2vid = ds_ret.gt_txt2vid
for img, txt, mask, tid, vid in tqdm(dl, ascii=True):
with T.no_grad():
feat_img, mask_img, feat_txt, mask_txt = model(
typ='feat', img=img.cuda(), txt=txt.cuda(),
mask=mask.cuda())
for t, v, f_i, m_i, f_t, m_t in zip(
tid, vid, *[
d.data.cpu().numpy()
for d in [feat_img, mask_img, feat_txt, mask_txt]]
):
if v not in featv:
featv[v] = {
'video': v, 'feat_img': f_i,
'mask_img': m_i}
featt[t] = {
'tid': t, 'feat_txt': f_t, 'mask_txt': m_t}
ds = Dataset_Product(featv, featt)
dl = T.utils.data.DataLoader(ds,
batch_size=args.size_batch,
shuffle=False,
num_workers=args.n_workers,
pin_memory=True)
print(f"number of videos: {len(ds.vid2idx)}")
print(f"number of queires (by text): {len(ds.tid2idx)}")
print(f"number of queries (before gathering rank): {len(ds_ret)}")
rank = {}
for (feat_txt, mask_txt, tid, feat_img,
mask_img, vid) in tqdm(dl, ascii=True):
with T.no_grad():
out = model(typ='cross', feat_img=feat_img,
mask_img=mask_img,
feat_txt=feat_txt, mask_txt=mask_txt)
out = T.sigmoid(out).data.cpu().numpy()
for tid_, vid_, o in zip(tid, vid, out):
i_v = ds.vid2idx[vid_]
i_v, o = int(i_v), float(o)
tid_ = tid_.item()
if tid_ not in rank:
rank[tid_] = []
rank[tid_].append([i_v, o])
res = {'r@1': 0, 'r@5': 0, 'r@10': 0, 'median': []}
print(f"number of queries (after gathering rank): {len(rank)}")
for tid_ in rank:
tmp = sorted(rank[tid_], key=lambda d: -d[1])
gt_iv = ds.vid2idx[gt_txt2vid[tid_]]
p = [d[0] for d in tmp].index(gt_iv)+1
if p <= 1:
res['r@1'] += 1.0/len(rank)
if p <= 5:
res['r@5'] += 1.0/len(rank)
if p <= 10:
res['r@10'] += 1.0/len(rank)
res['median'].append(p)
res['median'] = int(np.median(res['median']))
res = {key: f"{val*100:.2f}"
if key != 'median'
else f"{val}"
for key, val in res.items()}
log[split] = res
print(split, res)
path_ckpt_dir = os.path.dirname(args.path_ckpt)
filename, _ = os.path.splitext(args.path_ckpt.split("/")[-1])
log_path = f"{path_ckpt_dir}/{filename}_results.json"
log["multi_clip_testing"] = args.multi_clip_testing
json.dump(log, open(log_path, "w"))