def main():
config = configparser.ConfigParser()
config.read('config.ini', 'UTF-8')
dataset_type = config.get('dataset', 'type')
logger.info('loading {}'.format(dataset_type))
if dataset_type == 'mpii':
_, test_set = get_mpii_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'images'),
annotations=config.get(dataset_type, 'annotations'),
train_size=config.getfloat(dataset_type, 'train_size'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
seed=config.getint('training_param', 'seed'),
)
elif dataset_type == 'coco':
# 已经将原来的图片换成固定大小
test_set = get_coco_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'val_images'),
annotations=config.get(dataset_type, 'val_annotations'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
)
else:
raise Exception('Unknown dataset {}'.format(dataset_type))
model = create_model(config)
## 生成用于计算mAP的gt_KPs、pred_KPs, gt_bbox
mAP = [[], [], []]
# 测试多张图片
for i in range(30):
# pdb()
idx = random.choice(range(len(test_set)))
image = test_set.get_example(idx)['image']
gt_kps = test_set.get_example(idx)['keypoints']
# coco person mpii head
gt_bboxs = test_set.get_example(idx)['bbox']
humans = estimate(model,
image.astype(np.float32))
mAP[0].append(gt_kps)
mAP[1].append(humans)
mAP[2].append(gt_bboxs)
pil_image = Image.fromarray(image.transpose(1, 2, 0).astype(np.uint8))
pil_image = draw_humans(
keypoint_names=model.keypoint_names,
edges=model.edges,
pil_image=pil_image,
humans=humans
)
pil_image.save('results/result{}.png'.format(i), 'PNG')
gene_json(mAP)
def predict(args):
config = load_config(args)
detection_thresh = config.getfloat('predict', 'detection_thresh')
min_num_keypoints = config.getint('predict', 'min_num_keypoints')
dataset_type = config.get('dataset', 'type')
logger.info('loading {}'.format(dataset_type))
if dataset_type == 'mpii':
_, test_set = get_mpii_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'images'),
annotations=config.get(dataset_type, 'annotations'),
train_size=config.getfloat(dataset_type, 'train_size'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
seed=config.getint('training_param', 'seed'),
)
elif dataset_type == 'coco':
test_set = get_coco_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'val_images'),
annotations=config.get(dataset_type, 'val_annotations'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
)
else:
raise Exception('Unknown dataset {}'.format(dataset_type))
model = create_model(args, config)
# choose specific image
idx = random.choice(range(len(test_set)))
idx = 50
image = test_set.get_example(idx)['image']
humans = estimate(
model,
image.astype(np.float32),
detection_thresh,
min_num_keypoints,
)
pil_image = Image.fromarray(image.transpose(1, 2, 0).astype(np.uint8))
pil_image = draw_humans(keypoint_names=model.keypoint_names,
edges=model.edges,
pil_image=pil_image,
humans=humans,
visbbox=config.getboolean('predict', 'visbbox'))
#pil_image.save('result.png', 'PNG')
pil_image.save(
'result_' + 'X'.join((str(_.insize[0]), str(_.insize[1]))) + '_idx_' +
str(idx) + '_time_' + str(round(inference_time, 3)) + 's.png', 'PNG')
def main():
config = configparser.ConfigParser()
config.read('config.ini', 'UTF-8')
dataset_type = config.get('dataset', 'type')
logger.info('loading {}'.format(dataset_type))
if dataset_type == 'mpii':
_, test_set = get_mpii_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'images'),
annotations=config.get(dataset_type, 'annotations'),
train_size=config.getfloat(dataset_type, 'train_size'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
seed=config.getint('training_param', 'seed'),
)
elif dataset_type == 'coco':
test_set = get_coco_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'val_images'),
annotations=config.get(dataset_type, 'val_annotations'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
)
else:
raise Exception('Unknown dataset {}'.format(dataset_type))
model = create_model(config)
idx = random.choice(range(len(test_set)))
image = test_set.get_example(idx)['image']
humans = estimate(model,
image.astype(np.float32))
pil_image = Image.fromarray(image.transpose(1, 2, 0).astype(np.uint8))
pil_image = draw_humans(
keypoint_names=model.keypoint_names,
edges=model.edges,
pil_image=pil_image,
humans=humans
)
pil_image.save('result.png', 'PNG')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', type=str, default='config.ini')
parser.add_argument('--resume')
parser.add_argument('--plot_samples', type=int, default=0)
args = parser.parse_args()
config = configparser.ConfigParser()
config.read(args.config_path, 'UTF-8')
chainer.global_config.autotune = True
chainer.cuda.set_max_workspace_size(11388608)
# create result dir and copy file
logger.info('> store file to result dir %s', config.get('result', 'dir'))
save_files(config.get('result', 'dir'))
logger.info('> set up devices')
devices = setup_devices(config.get('training_param', 'gpus'))
set_random_seed(devices, config.getint('training_param', 'seed'))
logger.info('> get dataset')
dataset_type = config.get('dataset', 'type')
if dataset_type == 'coco':
# force to set `use_cache = False`
train_set = get_coco_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'train_images'),
annotations=config.get(dataset_type, 'train_annotations'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
use_cache=False,
do_augmentation=True,
)
test_set = get_coco_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'val_images'),
annotations=config.get(dataset_type, 'val_annotations'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
use_cache=False,
)
elif dataset_type == 'mpii':
train_set, test_set = get_mpii_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'images'),
annotations=config.get(dataset_type, 'annotations'),
train_size=config.getfloat(dataset_type, 'train_size'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
use_cache=config.getboolean(dataset_type, 'use_cache'),
seed=config.getint('training_param', 'seed'),
)
else:
raise Exception('Unknown dataset {}'.format(dataset_type))
logger.info('dataset type: %s', dataset_type)
logger.info('training images: %d', len(train_set))
logger.info('validation images: %d', len(test_set))
if args.plot_samples > 0:
for i in range(args.plot_samples):
data = train_set[i]
visualize.plot('train-{}.png'.format(i), data['image'],
data['keypoints'], data['bbox'], data['is_labeled'],
data['edges'])
data = test_set[i]
visualize.plot('val-{}.png'.format(i), data['image'],
data['keypoints'], data['bbox'], data['is_labeled'],
data['edges'])
logger.info('> load model')
model = create_model(config, train_set)
logger.info('> transform dataset')
train_set = TransformDataset(train_set, model.encode)
test_set = TransformDataset(test_set, model.encode)
logger.info('> create iterators')
train_iter = chainer.iterators.MultiprocessIterator(
train_set,
config.getint('training_param', 'batchsize'),
n_processes=config.getint('training_param', 'num_process'))
test_iter = chainer.iterators.SerialIterator(test_set,
config.getint(
'training_param',
'batchsize'),
repeat=False,
shuffle=False)
logger.info('> setup optimizer')
optimizer = chainer.optimizers.MomentumSGD()
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0005))
logger.info('> setup trainer')
updater = training.updaters.ParallelUpdater(train_iter,
optimizer,
devices=devices)
trainer = training.Trainer(
updater, (config.getint('training_param', 'train_iter'), 'iteration'),
config.get('result', 'dir'))
logger.info('> setup extensions')
trainer.extend(extensions.LinearShift(
'lr',
value_range=(config.getfloat('training_param', 'learning_rate'), 0),
time_range=(0, config.getint('training_param', 'train_iter'))),
trigger=(1, 'iteration'))
trainer.extend(
extensions.Evaluator(test_iter, model, device=devices['main']))
if extensions.PlotReport.available():
trainer.extend(
extensions.PlotReport([
'main/loss',
'validation/main/loss',
],
'epoch',
file_name='loss.png'))
trainer.extend(extensions.LogReport())
trainer.extend(extensions.observe_lr())
trainer.extend(
extensions.PrintReport([
'epoch',
'elapsed_time',
'lr',
'main/loss',
'validation/main/loss',
'main/loss_resp',
'validation/main/loss_resp',
'main/loss_iou',
'validation/main/loss_iou',
'main/loss_coor',
'validation/main/loss_coor',
'main/loss_size',
'validation/main/loss_size',
'main/loss_limb',
'validation/main/loss_limb',
]))
trainer.extend(extensions.ProgressBar())
trainer.extend(
extensions.snapshot(filename='best_snapshot'),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
trainer.extend(
extensions.snapshot_object(model, filename='bestmodel.npz'),
trigger=training.triggers.MinValueTrigger('validation/main/loss'))
if args.resume:
serializers.load_npz(args.resume, trainer)
logger.info('> start training')
trainer.run()
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-m",
"--modelname",
help="model full name",
default='',
dest='modelName')
parser.add_argument("-n",
"--testnum",
help="the number of test image",
type=int,
default=1000,
dest='test_num')
args = parser.parse_args()
modelName = args.modelName
config = configparser.ConfigParser()
config.read('config.ini', 'UTF-8')
dataset_type = config.get('dataset', 'type')
logger.info('loading {}'.format(dataset_type))
if dataset_type == 'mpii':
_, test_set = get_mpii_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'images'),
annotations=config.get(dataset_type, 'annotations'),
train_size=config.getfloat(dataset_type, 'train_size'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
seed=config.getint('training_param', 'seed'),
)
elif dataset_type == 'coco':
test_set = get_coco_dataset(
insize=parse_size(config.get('model_param', 'insize')),
image_root=config.get(dataset_type, 'val_images'),
annotations=config.get(dataset_type, 'val_annotations'),
min_num_keypoints=config.getint(dataset_type, 'min_num_keypoints'),
)
else:
raise Exception('Unknown dataset {}'.format(dataset_type))
model = create_model(config, modelName)
## 生成用于计算pck_object的gt_KPs、 gt_bboxs, human(pred_KPs, pred_bboxs) is _visible
pck_object = [[], [], [], []]
modelName = modelName if modelName else 'trained/bestmodel.npz'
test_num = args.test_num
print('model name: {}\t test image number: {}'.format(modelName, test_num))
# 测试多张图片
for i in tqdm(range(test_num)):
idx = random.choice(range(len(test_set)))
image = test_set.get_example(idx)['image']
gt_kps = test_set.get_example(idx)['keypoints']
gt_bboxs = test_set.get_example(idx)['bbox'] # (left down point, w, h)
is_visible = test_set.get_example(idx)['is_visible'] #
# include pred_KPs, pred_bbox
humans = estimate(model, image.astype(np.float32), 0.15)
pck_object[0].append(gt_kps)
pck_object[1].append(humans)
pck_object[2].append(gt_bboxs)
pck_object[3].append(is_visible)
mylog.info('model name: {}\t test image number: {}'.format(
modelName, test_num))
evaluation(config, pck_object)