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import torch
from tqdm import tqdm
from utils.data_loader import DenseCapDataset, DataLoaderPFG
from model.evaluator import DenseCapEvaluator
def quality_check(model, dataset, idx_to_token, device, max_iter=-1):
model.to(device)
data_loader = DataLoaderPFG(dataset, batch_size=1, shuffle=False, num_workers=1,
pin_memory=True, collate_fn=DenseCapDataset.collate_fn)
print('[quality check]')
for i, (img, targets, info) in enumerate(data_loader):
img = [img_tensor.to(device) for img_tensor in img]
targets = [{k: v.to(device) for k, v in target.items()} for target in targets]
with torch.no_grad():
model.eval()
model.return_features = False
detections = model(img)
for j in range(len(targets)):
print('<{}>'.format(info[j]['file_name']))
print('=== ground truth ===')
for box, cap, cap_len in zip(targets[j]['boxes'], targets[j]['caps'], targets[j]['caps_len']):
print('box:', box.tolist())
print('len:', cap_len.item())
print('cap:', ' '.join(idx_to_token[idx] for idx in cap.tolist() if idx_to_token[idx] != '<pad>'))
print('-'*20)
print('=== predict ===')
for box, cap, score in zip(detections[j]['boxes'], detections[j]['caps'], detections[j]['scores']):
print('box:', [round(c, 2) for c in box.tolist()])
print('score:', round(score.item(), 2))
print('cap:', ' '.join(idx_to_token[idx] for idx in cap.tolist() if idx_to_token[idx] != '<pad>'))
print('-'*20)
if i >= max_iter > 0:
break
def quantity_check(model, dataset, idx_to_token, device, max_iter=-1, verbose=True):
model.to(device)
data_loader = DataLoaderPFG(dataset, batch_size=4, shuffle=False, num_workers=2,
pin_memory=True, collate_fn=DenseCapDataset.collate_fn)
evaluator = DenseCapEvaluator(list(model.roi_heads.box_describer.special_idx.keys()))
print('[quantity check]')
for i, (img, targets, info) in tqdm(enumerate(data_loader), total=len(data_loader)):
img = [img_tensor.to(device) for img_tensor in img]
targets = [{k: v.to(device) for k, v in target.items()} for target in targets]
with torch.no_grad():
model.eval()
model.return_features = False
detections = model(img)
for j in range(len(targets)):
scores = detections[j]['scores']
boxes = detections[j]['boxes']
text = [' '.join(idx_to_token[idx] for idx in cap.tolist() if idx_to_token[idx] != '<pad>')
for cap in detections[j]['caps']]
target_boxes = targets[j]['boxes']
target_text = [' '.join(idx_to_token[idx] for idx in cap.tolist() if idx_to_token[idx] != '<pad>')
for cap in targets[j]['caps']]
img_id = info[j]['file_name']
evaluator.add_result(scores, boxes, text, target_boxes, target_text, img_id)
if i >= max_iter > 0:
break
results = evaluator.evaluate(verbose)
if verbose:
print('MAP: {:.3f} DET_MAP: {:.3f}'.format(results['map'], results['detmap']))
return results
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