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validate.py
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/
validate.py
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import torch
import numpy as np
from tqdm import tqdm
import argparse
import os, sys
import json
import pickle
from termcolor import colored
from DataLoader import VideoQADataLoader
from utils import todevice
import model.HCRN as HCRN
from config import cfg, cfg_from_file
def validate(cfg, model, data, device, write_preds=False):
model.eval()
print('validating...')
total_acc, count = 0.0, 0
all_preds = []
gts = []
v_ids = []
q_ids = []
with torch.no_grad():
for batch in tqdm(data, total=len(data)):
video_ids, question_ids, answers, *batch_input = [todevice(x, device) for x in batch]
if cfg.train.batch_size == 1:
answers = answers.to(device)
else:
answers = answers.to(device).squeeze()
batch_size = answers.size(0)
logits = model(*batch_input).to(device)
if cfg.dataset.question_type in ['action', 'transition']:
preds = torch.argmax(logits.view(batch_size, 5), dim=1)
agreeings = (preds == answers)
elif cfg.dataset.question_type == 'count':
answers = answers.unsqueeze(-1)
preds = (logits + 0.5).long().clamp(min=1, max=10)
batch_mse = (preds - answers) ** 2
else:
preds = logits.detach().argmax(1)
agreeings = (preds == answers)
if write_preds:
if cfg.dataset.question_type not in ['action', 'transition', 'count']:
preds = logits.argmax(1)
if cfg.dataset.question_type in ['action', 'transition']:
answer_vocab = data.vocab['question_answer_idx_to_token']
else:
answer_vocab = data.vocab['answer_idx_to_token']
for predict in preds:
if cfg.dataset.question_type in ['count', 'transition', 'action']:
all_preds.append(predict.item())
else:
all_preds.append(answer_vocab[predict.item()])
for gt in answers:
if cfg.dataset.question_type in ['count', 'transition', 'action']:
gts.append(gt.item())
else:
gts.append(answer_vocab[gt.item()])
for id in video_ids:
v_ids.append(id.cpu().numpy())
for ques_id in question_ids:
q_ids.append(ques_id)
if cfg.dataset.question_type == 'count':
total_acc += batch_mse.float().sum().item()
count += answers.size(0)
else:
total_acc += agreeings.float().sum().item()
count += answers.size(0)
acc = total_acc / count
if not write_preds:
return acc
else:
return acc, all_preds, gts, v_ids, q_ids
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', dest='cfg_file', help='optional config file', default='tgif_qa_action.yml', type=str)
args = parser.parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
assert cfg.dataset.name in ['tgif-qa', 'msrvtt-qa', 'msvd-qa']
assert cfg.dataset.question_type in ['frameqa', 'count', 'transition', 'action', 'none']
# check if the data folder exists
assert os.path.exists(cfg.dataset.data_dir)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
cfg.dataset.save_dir = os.path.join(cfg.dataset.save_dir, cfg.exp_name)
ckpt = os.path.join(cfg.dataset.save_dir, 'ckpt', 'model.pt')
assert os.path.exists(ckpt)
# load pretrained model
loaded = torch.load(ckpt, map_location='cpu')
model_kwargs = loaded['model_kwargs']
if cfg.dataset.name == 'tgif-qa':
cfg.dataset.test_question_pt = os.path.join(cfg.dataset.data_dir,
cfg.dataset.test_question_pt.format(cfg.dataset.name, cfg.dataset.question_type))
cfg.dataset.vocab_json = os.path.join(cfg.dataset.data_dir, cfg.dataset.vocab_json.format(cfg.dataset.name, cfg.dataset.question_type))
cfg.dataset.appearance_feat = os.path.join(cfg.dataset.data_dir, cfg.dataset.appearance_feat.format(cfg.dataset.name, cfg.dataset.question_type))
cfg.dataset.motion_feat = os.path.join(cfg.dataset.data_dir, cfg.dataset.motion_feat.format(cfg.dataset.name, cfg.dataset.question_type))
else:
cfg.dataset.question_type = 'none'
cfg.dataset.appearance_feat = '{}_appearance_feat.h5'
cfg.dataset.motion_feat = '{}_motion_feat.h5'
cfg.dataset.vocab_json = '{}_vocab.json'
cfg.dataset.test_question_pt = '{}_test_questions.pt'
cfg.dataset.test_question_pt = os.path.join(cfg.dataset.data_dir,
cfg.dataset.test_question_pt.format(cfg.dataset.name))
cfg.dataset.vocab_json = os.path.join(cfg.dataset.data_dir, cfg.dataset.vocab_json.format(cfg.dataset.name))
cfg.dataset.appearance_feat = os.path.join(cfg.dataset.data_dir, cfg.dataset.appearance_feat.format(cfg.dataset.name))
cfg.dataset.motion_feat = os.path.join(cfg.dataset.data_dir, cfg.dataset.motion_feat.format(cfg.dataset.name))
test_loader_kwargs = {
'question_type': cfg.dataset.question_type,
'question_pt': cfg.dataset.test_question_pt,
'vocab_json': cfg.dataset.vocab_json,
'appearance_feat': cfg.dataset.appearance_feat,
'motion_feat': cfg.dataset.motion_feat,
'test_num': cfg.test.test_num,
'batch_size': cfg.train.batch_size,
'num_workers': cfg.num_workers,
'shuffle': False
}
test_loader = VideoQADataLoader(**test_loader_kwargs)
model_kwargs.update({'vocab': test_loader.vocab})
model = HCRN.HCRNNetwork(**model_kwargs).to(device)
model.load_state_dict(loaded['state_dict'])
if cfg.test.write_preds:
acc, preds, gts, v_ids, q_ids = validate(cfg, model, test_loader, device, cfg.test.write_preds)
sys.stdout.write('~~~~~~ Test Accuracy: {test_acc} ~~~~~~~\n'.format(
test_acc=colored("{:.4f}".format(acc), "red", attrs=['bold'])))
sys.stdout.flush()
# write predictions for visualization purposes
output_dir = os.path.join(cfg.dataset.save_dir, 'preds')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
else:
assert os.path.isdir(output_dir)
preds_file = os.path.join(output_dir, "test_preds.json")
if cfg.dataset.question_type in ['action', 'transition']: \
# Find groundtruth questions and corresponding answer candidates
vocab = test_loader.vocab['question_answer_idx_to_token']
dict = {}
with open(cfg.dataset.test_question_pt, 'rb') as f:
obj = pickle.load(f)
questions = obj['questions']
org_v_ids = obj['video_ids']
org_v_names = obj['video_names']
org_q_ids = obj['question_id']
ans_candidates = obj['ans_candidates']
for idx in range(len(org_q_ids)):
dict[str(org_q_ids[idx])] = [org_v_names[idx], questions[idx], ans_candidates[idx]]
instances = [
{'video_id': video_id, 'question_id': q_id, 'video_name': dict[str(q_id)][0], 'question': [vocab[word.item()] for word in dict[str(q_id)][1] if word != 0],
'answer': answer,
'prediction': pred} for video_id, q_id, answer, pred in
zip(np.hstack(v_ids).tolist(), np.hstack(q_ids).tolist(), gts, preds)]
# write preditions to json file
with open(preds_file, 'w') as f:
json.dump(instances, f)
sys.stdout.write('Display 10 samples...\n')
# Display 10 samples
for idx in range(10):
print('Video name: {}'.format(dict[str(q_ids[idx].item())][0]))
cur_question = [vocab[word.item()] for word in dict[str(q_ids[idx].item())][1] if word != 0]
print('Question: ' + ' '.join(cur_question) + '?')
all_answer_cands = dict[str(q_ids[idx].item())][2]
for cand_id in range(len(all_answer_cands)):
cur_answer_cands = [vocab[word.item()] for word in all_answer_cands[cand_id] if word
!= 0]
print('({}): '.format(cand_id) + ' '.join(cur_answer_cands))
print('Prediction: {}'.format(preds[idx]))
print('Groundtruth: {}'.format(gts[idx]))
else:
vocab = test_loader.vocab['question_idx_to_token']
dict = {}
with open(cfg.dataset.test_question_pt, 'rb') as f:
obj = pickle.load(f)
questions = obj['questions']
org_v_ids = obj['video_ids']
org_v_names = obj['video_names']
org_q_ids = obj['question_id']
for idx in range(len(org_q_ids)):
dict[str(org_q_ids[idx])] = [org_v_names[idx], questions[idx]]
instances = [
{'video_id': video_id, 'question_id': q_id, 'video_name': str(dict[str(q_id)][0]), 'question': [vocab[word.item()] for word in dict[str(q_id)][1] if word != 0],
'answer': answer,
'prediction': pred} for video_id, q_id, answer, pred in
zip(np.hstack(v_ids).tolist(), np.hstack(q_ids).tolist(), gts, preds)]
# write preditions to json file
with open(preds_file, 'w') as f:
json.dump(instances, f)
sys.stdout.write('Display 10 samples...\n')
# Display 10 examples
for idx in range(10):
print('Video name: {}'.format(dict[str(q_ids[idx].item())][0]))
cur_question = [vocab[word.item()] for word in dict[str(q_ids[idx].item())][1] if word != 0]
print('Question: ' + ' '.join(cur_question) + '?')
print('Prediction: {}'.format(preds[idx]))
print('Groundtruth: {}'.format(gts[idx]))
else:
acc = validate(cfg, model, test_loader, device, cfg.test.write_preds)
sys.stdout.write('~~~~~~ Test Accuracy: {test_acc} ~~~~~~~\n'.format(
test_acc=colored("{:.4f}".format(acc), "red", attrs=['bold'])))
sys.stdout.flush()