def test():
summary = defaultdict(float)
f.eval()
gt_dict = np.load(args.test_path + '/gt_dict.npy',
allow_pickle=True).tolist()
category_dict = gt_dict[args.domain]
videos = list(category_dict.keys())
total_features = np.load(os.path.join(args.test_path, "feature",
"{}_audio.npy".format(args.domain)),
allow_pickle=True).tolist()
with torch.no_grad():
for v in tqdm(videos):
features = total_features[v]
scores = []
for feature in features:
x = feature["features"]
logging.info("size of input: {}".format(x.shape))
if x.shape != (20, 23):
if np.size(x) < 20 * 23:
x = np.pad(x, ((0, 20 - x.shape[0]),
(0, 23 - x.shape[1])),
mode="constant")
else:
x = x[0:20, 0:23]
x = torch.unsqueeze(torch.unsqueeze(torch.Tensor(x), dim=0),
dim=0).to(device)
scores.append(f(x).item())
summary[v] = scores
machine_summary = tools.clip2frame(summary)
return evaluate(machine_summary, category_dict, args.topk_mAP)
def test():
summary = defaultdict(float)
f.eval()
gt_dict = np.load(args.test_path + '/gt_' + args.dataset + '.npy',
allow_pickle=True).tolist()
category_dict = gt_dict[args.domain]
videos = list(category_dict.keys())
video_features = np.load(args.test_path + '/feature/' + args.domain +
'_1s.npy',
allow_pickle=True).tolist()
audio_features = np.load(args.test_path + '/feature/' + args.domain +
'_audio_edited_nopost.npy',
allow_pickle=True).tolist()
# pdb.set_trace()
with torch.no_grad():
for ve in tqdm(videos):
label = category_dict[ve]
prefix = ve.split('.')[0]
vfs = video_features[prefix]
afs = audio_features[prefix]
scores = []
for vf, af in zip(vfs, afs):
vseg = vf['segment']
aseg = af['segment']
visual = torch.Tensor(vf['features']).to(device).view(
-1, args.feature_dim).contiguous()
audio = torch.Tensor(af['features']).to(device).view(
-1, args.audio_dim).contiguous()
scores.append(f(visual, audio)[0].item())
summary[ve] = scores
mechine_summary = clip2frame(summary)
mAP, pre, recall = evaluate(mechine_summary, category_dict, args.topk_mAP)
return mAP, pre, recall
def TVsumOrCoSumtest():
summary = defaultdict(float)
f.eval()
gt_dict = np.load(args.test_path + '/gt_' + args.dataset + '.npy',
allow_pickle=True).tolist()
category_dict = gt_dict[args.domain]
videos = list(category_dict.keys())
total_features = np.load(args.test_path + '/feature/' + args.domain +
'.npy',
allow_pickle=True).tolist()
with torch.no_grad():
for ve in tqdm(videos):
label = category_dict[ve]
feature = total_features[ve]
scores = []
for feat in feature:
segment = feat['segment']
scores.append(
f(torch.Tensor(feat['features']).to(device).view(
1, -1)).item())
summary[ve] = scores
mechine_summary = clip2frame(summary)
mAP, pre, recall = TVsumOrCoSumEvaluate(mechine_summary, category_dict,
args.topk_mAP)
return mAP, pre, recall
def test():
summary = defaultdict(float)
f.eval()
gt_dict = np.load(args.test_path+'/gt_'+args.dataset+'.npy',allow_pickle=True).tolist()
category_dict = gt_dict[args.domain]
videos = list(category_dict.keys())
video_features = np.load(args.test_path+'/feature/'+args.domain+'_1s.npy',allow_pickle=True ).tolist()
audio_features = np.load(args.test_path+'/feature/'+args.domain+'_audio_edited_nopost.npy',allow_pickle=True ).tolist()
# pdb.set_trace()
with torch.no_grad():
for ve in tqdm(videos):
label = category_dict[ve]
prefix = ve.split('.')[0]
vfs = video_features[prefix]
afs = audio_features[prefix]
scores = []
length = len(vfs) if len(vfs)<len(afs) else len(afs)
alen = len(afs)
vlen = len(vfs)
for idx in range(length):
vx = []
if idx<int(args.temporalN/2):
vx+=([[0]*args.feature_dim for i in range(int(args.temporalN/2)-idx)])
vx+=([vfs[i]['features'] for i in range(idx+1)])
else:
vx+=([vfs[i]['features'] for i in range(idx-int(args.temporalN/2),idx+1)])
if idx>=vlen-int(args.temporalN/2):
vx+=([vfs[i]['features'] for i in range(idx+1,vlen)])
vx+=([[0]*args.feature_dim for i in range(idx - (vlen-int(args.temporalN/2))+1)])
else:
vx+=([vfs[i]['features'] for i in range(idx+1,idx+int(args.temporalN/2)+1)])
ax = []
if idx<int(args.temporalN/2):
ax+=([[0]*args.audio_dim for i in range(int(args.temporalN/2)-idx)])
ax+=([afs[i]['features'] for i in range(idx+1)])
else:
ax+=([afs[i]['features'] for i in range(idx-int(args.temporalN/2),idx+1)])
if idx>=alen-int(args.temporalN/2):
ax+=([afs[i]['features'] for i in range(idx+1,alen)])
ax+=([[0]*args.audio_dim for i in range(idx - (alen-int(args.temporalN/2))+1)])
else:
ax+=([afs[i]['features'] for i in range(idx+1,idx+int(args.temporalN/2)+1)])
# pdb.set_trace()
vx = torch.tensor(vx).to(device).unsqueeze(0).unsqueeze(0)
ax = torch.tensor(ax).to(device).unsqueeze(0).unsqueeze(0)
# vseg = vf['segment']
# aseg = af['segment']
# visual = torch.Tensor(vf['features']).to(device).view(-1,args.feature_dim).contiguous()
# audio = torch.Tensor(af['features']).to(device).view(-1,args.audio_dim).contiguous()
scores.append(f(vx,ax)[0].item())
summary[ve] = scores
mechine_summary = clip2frame(summary)
mAP,pre,recall = evaluate(mechine_summary,category_dict,args.topk_mAP)
return mAP,pre,recall
def test():
summary = defaultdict(float)
f.eval()
gt_dict = np.load(args.test_path + '/gt_' + args.dataset + '.npy',
allow_pickle=True).tolist()
category_dict = gt_dict[args.domain]
videos = list(category_dict.keys())
video_features = np.load(args.test_path + '/feature/' + args.domain +
'_1s.npy',
allow_pickle=True).tolist()
audio_features = np.load(args.test_path + '/feature/' + args.domain +
'_audio_edited_nopost.npy',
allow_pickle=True).tolist()
with torch.no_grad():
for ve in tqdm(videos):
label = category_dict[ve]
prefix = ve.split('.')[0]
vfs = video_features[prefix]
afs = audio_features[prefix]
scores = []
vfeat = []
afeat = []
for vf, af in zip(vfs, afs):
vfeat.append(vf['features'])
afeat.append(af['features'])
if (len(afeat) == 0):
continue
vfeat = torch.Tensor(vfeat).to(device).unsqueeze(0)
afeat = torch.Tensor(afeat).to(device).unsqueeze(0)
scores, logits = f(vfeat, afeat)
# pdb.set_trace()
summary[ve] = scores.cpu().numpy().reshape(-1).tolist()
mechine_summary = clip2frame(summary)
mAP, pre, recall = evaluate(mechine_summary, category_dict, args.topk_mAP)
return mAP, pre, recall
def test():
summary = defaultdict(float)
f.eval()
# test_feature_path = args.test_path+'/feature/'+args.domain
gt_dict = np.load(args.test_path+'/gt_dict.npy',allow_pickle=True).tolist()
# pdb.set_trace()
category_dict = gt_dict[args.domain]
videos = list(category_dict.keys())
total_features = np.load(args.test_path+'/feature/'+args.domain+'.npy',allow_pickle=True ).tolist()
with torch.no_grad():
for ve in tqdm(videos):
label = category_dict[ve]
# feature = np.load(test_feature_path+'/'+ve+'.npy').tolist()
feature = total_features[ve]
scores = []
for feat in feature:
segment = feat['segment']
scores.append(f(torch.Tensor(feat['features']).to(device).view(1,-1)).item())
summary[ve] = scores
# gt_domain_dict =
mechine_summary = clip2frame(summary)
ret = evaluate(mechine_summary,category_dict,args.topk_mAP)
print(ret)
print('aaaa')
