def test(split, model, criterion, loader, epoch, args):
model.eval()
loss = 0.
_ids = []
predictions = []
with torch.no_grad():
bar = progressbar.ProgressBar(max_value=len(loader))
for idx, data_batch in enumerate(loader):
_ids.extend(data_batch['_id'])
subj_batch_var = data_batch['subject']['embedding'].cuda()
obj_batch_var = data_batch['object']['embedding'].cuda()
predicate = data_batch['predicate'].cuda()
if args.model == 'drnet':
img = data_batch['bbox_img'].cuda()
mask_batch_var = data_batch['bbox_mask'].cuda()
output = model(subj_batch_var, obj_batch_var, img,
mask_batch_var, predicate)
elif args.model == 'vtranse':
img = data_batch['full_img'].cuda()
ts_batch_var = data_batch['subject']['t'].cuda()
to_batch_var = data_batch['object']['t'].cuda()
bboxs_batch_var = data_batch['subject']['bbox'].cuda()
bboxo_batch_var = data_batch['object']['bbox'].cuda()
output = model(subj_batch_var, obj_batch_var, img,
ts_batch_var, to_batch_var, bboxs_batch_var,
bboxo_batch_var, predicate)
elif args.model == 'vipcnn' or args.model == 'pprfcn':
img = data_batch['full_img'].cuda()
bbox_s = data_batch['subject']['bbox'].cuda()
bbox_o = data_batch['object']['bbox'].cuda()
output = model(img, bbox_s, bbox_o, predicate)
predictions.append(output)
if 'label' in data_batch:
label_batch_var = torch.squeeze(data_batch['label']).cuda()
loss_batch_var = criterion(output, label_batch_var)
loss_batch = loss_batch_var.item()
loss += (len(data_batch['label']) * loss_batch)
bar.update(idx)
if epoch is None:
epoch = 'test'
predictions = [v.item() for v in torch.cat(predictions)]
if split == 'valid':
pred_file = os.path.join(args.log_dir,
'predictions/pred_%02d.pickle' % epoch)
else:
pred_file = os.path.join(args.log_dir,
'predictions/pred_test.pickle')
pickle.dump((_ids, predictions), open(pred_file, 'wb'))
accs = accuracies(pred_file, args.datapath, split, split == 'test',
args)
return loss, accs
def test(split, model, criterion, loader, epoch, args):
'validate/test the model'
model.eval()
loss = 0.
acc = 0.
_ids = []
predictions = []
with torch.no_grad():
bar = progressbar.ProgressBar(max_value=len(loader))
for idx, data_batch in enumerate(loader):
_ids.extend(data_batch['_id'])
subj_batch_var = data_batch['subject']['bbox']
obj_batch_var = data_batch['object']['bbox']
predi_batch_var = data_batch['predicate']
if torch.cuda.is_available():
subj_batch_var = subj_batch_var.cuda()
obj_batch_var = obj_batch_var.cuda()
predi_batch_var = predi_batch_var.cuda()
output_var = model(subj_batch_var, obj_batch_var, predi_batch_var)
predictions.append(output_var.detach())
if 'label' in data_batch:
label_batch_var = torch.squeeze(data_batch['label']).view(
-1, 1)
if torch.cuda.is_available():
label_batch_var = label_batch_var.cuda()
loss_batch_var = criterion(output_var, label_batch_var)
loss_batch = loss_batch_var.item()
loss += (len(data_batch['label']) * loss_batch)
bar.update(idx)
if loss != 0:
loss /= len(_ids)
predictions = [v.item() for v in torch.cat(predictions)]
if split == 'valid':
pred_file = os.path.join(args.log_dir,
'predictions/pred_%02d.pickle' % epoch)
else:
pred_file = os.path.join(args.log_dir, 'predictions/pred_test.pickle')
pickle.dump((_ids, predictions), open(pred_file, 'wb'))
accs = accuracies(pred_file, args.datapath, split, False, args)
return loss, accs
def test(split, model, criterion, loader, epoch, args):
"validate/test the model"
model.eval()
loss = 0.0
acc = 0.0
_ids = []
predictions = []
with torch.no_grad():
bar = progressbar.ProgressBar(max_value=len(loader))
for idx, data_batch in enumerate(loader):
_ids.extend(data_batch["_id"])
subj_batch_var = data_batch["subject"]["bbox"]
obj_batch_var = data_batch["object"]["bbox"]
predi_batch_var = data_batch["predicate"]
if torch.cuda.is_available():
subj_batch_var = subj_batch_var.cuda()
obj_batch_var = obj_batch_var.cuda()
predi_batch_var = predi_batch_var.cuda()
output_var = model(subj_batch_var, obj_batch_var, predi_batch_var)
predictions.append(output_var.detach())
if "label" in data_batch:
label_batch_var = torch.squeeze(data_batch["label"]).view(-1, 1)
if torch.cuda.is_available():
label_batch_var = label_batch_var.cuda()
loss_batch_var = criterion(output_var, label_batch_var)
loss_batch = loss_batch_var.item()
loss += len(data_batch["label"]) * loss_batch
bar.update(idx)
if loss != 0:
loss /= len(_ids)
predictions = [v.item() for v in torch.cat(predictions)]
if split == "valid":
pred_file = os.path.join(args.log_dir, "predictions/pred_%02d.pickle" % epoch)
else:
pred_file = os.path.join(args.log_dir, "predictions/pred_test.pickle")
pickle.dump((_ids, predictions), open(pred_file, "wb"))
accs = accuracies(pred_file, args.datapath, split, False, args)
return loss, accs
def test(split, model, criterion, loader, epoch, args):
model.eval()
loss = 0.0
_ids = []
predictions = []
with torch.no_grad():
bar = progressbar.ProgressBar(max_value=len(loader))
for idx, data_batch in enumerate(loader):
_ids.extend(data_batch["_id"])
subj_batch_var = data_batch["subject"]["embedding"].cuda()
obj_batch_var = data_batch["object"]["embedding"].cuda()
predicate = data_batch["predicate"].cuda()
if args.model == "drnet":
img = data_batch["bbox_img"].cuda()
mask_batch_var = data_batch["bbox_mask"].cuda()
output = model(subj_batch_var, obj_batch_var, img,
mask_batch_var, predicate)
elif args.model == "vtranse":
img = data_batch["full_img"].cuda()
ts_batch_var = data_batch["subject"]["t"].cuda()
to_batch_var = data_batch["object"]["t"].cuda()
bboxs_batch_var = data_batch["subject"]["bbox"].cuda()
bboxo_batch_var = data_batch["object"]["bbox"].cuda()
output = model(
subj_batch_var,
obj_batch_var,
img,
ts_batch_var,
to_batch_var,
bboxs_batch_var,
bboxo_batch_var,
predicate,
)
elif args.model == "vipcnn" or args.model == "pprfcn":
img = data_batch["full_img"].cuda()
bbox_s = data_batch["subject"]["bbox"].cuda()
bbox_o = data_batch["object"]["bbox"].cuda()
output = model(img, bbox_s, bbox_o, predicate)
predictions.append(output)
if "label" in data_batch:
label_batch_var = torch.squeeze(data_batch["label"]).cuda()
loss_batch_var = criterion(output, label_batch_var)
loss_batch = loss_batch_var.item()
loss += len(data_batch["label"]) * loss_batch
bar.update(idx)
if epoch is None:
epoch = "test"
predictions = [v.item() for v in torch.cat(predictions)]
if split == "valid":
pred_file = os.path.join(args.log_dir,
"predictions/pred_%02d.pickle" % epoch)
else:
pred_file = os.path.join(args.log_dir,
"predictions/pred_test.pickle")
pickle.dump((_ids, predictions), open(pred_file, "wb"))
accs = accuracies(pred_file, args.datapath, split, split == "test",
args)
return loss, accs