def main(spec, num_samples, pool):
checkpoint_dir = os.path.join(CHECKPOINT_ROOT, spec)
model_type, model_args, dataset_names = spec_util.parse_setup_spec(spec)
if model_type == 'VAE':
model = vae.VAE(model_args)
trainer = vae.Trainer(model, beta=4.)
trainer.cuda()
models.load_checkpoint(trainer, checkpoint_dir)
model.eval()
sample_latent = model.sample_latent(num_samples)
sample_imgs = model.dec(sample_latent)
elif model_type in ['GAN', 'GANmc']:
model = gan.GAN(model_args)
trainer = gan.Trainer(model)
trainer.cuda()
models.load_checkpoint(trainer, checkpoint_dir)
model.eval()
sample_imgs = model(num_samples)
else:
raise ValueError(f"Invalid model type: {model_type}")
print(f"Loaded model {checkpoint_dir}. Measuring samples...")
sample_imgs_np = sample_imgs.detach().cpu().squeeze().numpy()
sample_metrics = measure.measure_batch(sample_imgs_np, pool=pool)
os.makedirs(METRICS_ROOT, exist_ok=True)
metrics_path = os.path.join(METRICS_ROOT, f"{spec}_metrics.csv")
sample_metrics.to_csv(metrics_path, index_label='index')
print(f"Morphometrics saved to {metrics_path}")
def main():
opt = get_opt()
print(opt)
print("GMM: Start to %s, named: %s!" % (opt.stage, "GMM"))
# dataset setup
dataset = Dataset(opt, "GMM")
dataset_loader = DataLoader(opt, dataset)
model = GMM(opt)
if opt.stage == 'train':
if not opt.checkpoint == '' and os.path.exists(opt.checkpoint):
load_checkpoint(model, opt.checkpoint)
train_gmm(opt, dataset_loader, model)
save_checkpoint(
model, os.path.join(opt.checkpoint_dir, opt.name,
'gmm_trained.pth'))
elif opt.stage == 'test':
load_checkpoint(model, opt.checkpoint)
with torch.no_grad():
test_gmm(opt, dataset_loader, model)
else:
raise NotImplementedError('Please input train or test stage')
print('Finished %s stage, named: %s!' % (opt.datamode, opt.name))
def load_model(model_name='dpn131', checkpoint='../models/model_best.pth.tar', device='cpu'):
model = create_model(model_name, num_classes=NUM_CLASS, in_chans=3, pretrained=False)
load_checkpoint(model, checkpoint)
model = model.to(device)
model.eval()
return model
def load_gan(spec):
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
checkpoint_dir = os.path.join(CHECKPOINT_ROOT, spec)
device = torch.device('cuda')
gan = InfoGAN(*latent_dims)
trainer = Trainer(gan).to(device)
load_checkpoint(trainer, checkpoint_dir)
gan.eval()
return gan
def load_gan(spec):
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
checkpoint_dir = os.path.join(CHECKPOINT_ROOT, spec)
device = torch.device('cuda')
gan = InfoGAN(*latent_dims)
trainer = Trainer(gan).to(device)
load_checkpoint(trainer, checkpoint_dir)
gan.eval()
return gan
def main(use_cuda: bool, data_dirs: Union[str, Sequence[str]], weights: Optional[Sequence[Number]],
ckpt_root: str, latent_dim: int, num_epochs: int,
batch_size: int, save: bool, resume: bool, plot: bool):
device = torch.device('cuda' if use_cuda else 'cpu')
if isinstance(data_dirs, str):
data_dirs = [data_dirs]
dataset_names = [os.path.split(data_dir)[-1] for data_dir in data_dirs]
ckpt_name = spec_util.format_setup_spec('VAE', latent_dim, dataset_names)
print(f"Training {ckpt_name}...")
ckpt_dir = None if ckpt_root is None else os.path.join(ckpt_root, ckpt_name)
train_set = data_util.get_dataset(data_dirs, weights, train=True)
test_set = data_util.get_dataset(data_dirs, weights, train=False)
test_batch_size = 32
dl_kwargs = dict(num_workers=1, pin_memory=True) if use_cuda else {}
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, **dl_kwargs)
test_loader = DataLoader(test_set, batch_size=test_batch_size, shuffle=True, **dl_kwargs)
num_batches = len(train_loader.dataset) // train_loader.batch_size
model = vae.VAE(latent_dim)
trainer = vae.Trainer(model, beta=4.)
trainer.to(device)
test_iterator = iter(test_loader)
start_epoch = -1
if resume:
try:
start_epoch = load_checkpoint(trainer, ckpt_dir)
if plot:
test(model, next(test_iterator)[0])
except ValueError:
print(f"No checkpoint to resume from in {ckpt_dir}")
except FileNotFoundError:
print(f"Invalid checkpoint directory: {ckpt_dir}")
elif save:
if os.path.exists(ckpt_dir):
print(f"Clearing existing checkpoints in {ckpt_dir}")
for filename in os.listdir(ckpt_dir):
os.remove(os.path.join(ckpt_dir, filename))
for epoch in range(start_epoch + 1, num_epochs):
trainer.train()
for batch_idx, (data, _) in enumerate(train_loader):
verbose = batch_idx % 10 == 0
if verbose:
print(f"[{epoch}/{num_epochs}: {batch_idx:3d}/{num_batches:3d}] ", end='')
real_data = data.to(device).unsqueeze(1).float() / 255.
trainer.step(real_data, verbose)
if save:
save_checkpoint(trainer, ckpt_dir, epoch)
if plot:
test(model, next(test_iterator)[0])
def main(checkpoint_dir, pcorr_dir=None):
spec = os.path.split(checkpoint_dir)[-1]
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
device = torch.device('cuda')
gan = infogan.InfoGAN(*latent_dims)
trainer = infogan.Trainer(gan).to(device)
load_checkpoint(trainer, checkpoint_dir)
gan.eval()
dataset_name = SPEC_TO_DATASET['+'.join(dataset_names)]
data_dirs = [os.path.join(DATA_ROOT, dataset_name)]
test_metrics, test_images, test_labels, test_which = load_test_data(
data_dirs)
print(test_metrics.head())
idx = np.random.permutation(10000) #[:1000]
X = torch.from_numpy(
test_images[idx]).float().unsqueeze(1).to(device) / 255.
cols = ['length', 'thickness', 'slant', 'width', 'height']
test_cols = cols[:]
test_hrule = None
if 'swel+frac' in spec:
add_swel_frac(data_dirs[0], test_metrics)
test_cols += ['swel', 'frac']
test_hrule = len(cols)
if pcorr_dir is None:
pcorr_dir = checkpoint_dir
os.makedirs(pcorr_dir, exist_ok=True)
process(gan, X, test_metrics.loc[idx], test_cols, pcorr_dir, spec, 'test',
test_hrule)
X_ = gan(1000).detach()
with multiprocessing.Pool() as pool:
sample_metrics = measure.measure_batch(X_.cpu().squeeze().numpy(),
pool=pool)
sample_hrule = None
process(gan, X_, sample_metrics, cols, pcorr_dir, spec, 'sample',
sample_hrule)
def main():
opt = get_opt()
print(opt)
print("TOM: Start to %s, named: %s!" % (opt.datamode, opt.name))
# Dataset setup
dataset = Dataset(opt, "TOM")
data_loader = DataLoader(opt, dataset)
model = UnetGenerator(25, 4, 6, ngf=64, norm_layer=nn.InstanceNorm2d)
if opt.datamode == 'train':
if not opt.checkpoint =='' and os.path.exists(opt.checkpoint):
load_checkpoint(model, opt.checkpoint)
train_tom(opt, data_loader, model)
save_checkpoint(model, os.path.join(opt.checkpoint_dir, opt.name, 'tom_trained.pth'))
elif opt.datamode == 'test':
load_checkpoint(model, opt.checkpoint)
with torch.no_grad():
test_tom(opt, data_loader, model)
else:
raise NotImplementedError('Please input train or test stage')
print('Finished test %s, named: %s!' % (opt.stage, opt.name))
def train(args):
cfg_from_file(args.cfg)
cfg.WORKERS = args.num_workers
pprint.pprint(cfg)
# set the seed manually
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# define outputer
outputer_train = Outputer(args.output_dir, cfg.IMAGETEXT.PRINT_EVERY,
cfg.IMAGETEXT.SAVE_EVERY)
outputer_val = Outputer(args.output_dir, cfg.IMAGETEXT.PRINT_EVERY,
cfg.IMAGETEXT.SAVE_EVERY)
# define the dataset
split_dir, bshuffle = 'train', True
# Get data loader
imsize = cfg.TREE.BASE_SIZE * (2**(cfg.TREE.BRANCH_NUM - 1))
train_transform = transforms.Compose([
transforms.Scale(int(imsize * 76 / 64)),
transforms.RandomCrop(imsize),
])
val_transform = transforms.Compose([
transforms.Scale(int(imsize * 76 / 64)),
transforms.CenterCrop(imsize),
])
if args.dataset == 'bird':
train_dataset = ImageTextDataset(args.data_dir,
split_dir,
transform=train_transform,
sample_type='train')
val_dataset = ImageTextDataset(args.data_dir,
'val',
transform=val_transform,
sample_type='val')
elif args.dataset == 'coco':
train_dataset = CaptionDataset(args.data_dir,
split_dir,
transform=train_transform,
sample_type='train',
coco_data_json=args.coco_data_json)
val_dataset = CaptionDataset(args.data_dir,
'val',
transform=val_transform,
sample_type='val',
coco_data_json=args.coco_data_json)
else:
raise NotImplementedError
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.IMAGETEXT.BATCH_SIZE,
shuffle=bshuffle,
num_workers=int(cfg.WORKERS))
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
batch_size=cfg.IMAGETEXT.BATCH_SIZE,
shuffle=False,
num_workers=1)
# define the model and optimizer
if args.raw_checkpoint != '':
encoder, decoder = load_raw_checkpoint(args.raw_checkpoint)
else:
encoder = Encoder()
decoder = DecoderWithAttention(
attention_dim=cfg.IMAGETEXT.ATTENTION_DIM,
embed_dim=cfg.IMAGETEXT.EMBED_DIM,
decoder_dim=cfg.IMAGETEXT.DECODER_DIM,
vocab_size=train_dataset.n_words)
# load checkpoint
if cfg.IMAGETEXT.CHECKPOINT != '':
outputer_val.log("load model from: {}".format(
cfg.IMAGETEXT.CHECKPOINT))
encoder, decoder = load_checkpoint(encoder, decoder,
cfg.IMAGETEXT.CHECKPOINT)
encoder.fine_tune(False)
# to cuda
encoder = encoder.cuda()
decoder = decoder.cuda()
loss_func = torch.nn.CrossEntropyLoss()
if args.eval: # eval only
outputer_val.log("only eval the model...")
assert cfg.IMAGETEXT.CHECKPOINT != ''
val_rtn_dict, outputer_val = validate_one_epoch(
0, val_dataloader, encoder, decoder, loss_func, outputer_val)
outputer_val.log("\n[valid]: {}\n".format(dict2str(val_rtn_dict)))
return
# define optimizer
optimizer_encoder = torch.optim.Adam(encoder.parameters(),
lr=cfg.IMAGETEXT.ENCODER_LR)
optimizer_decoder = torch.optim.Adam(decoder.parameters(),
lr=cfg.IMAGETEXT.DECODER_LR)
encoder_lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer_encoder, step_size=10, gamma=cfg.IMAGETEXT.LR_GAMMA)
decoder_lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer_decoder, step_size=10, gamma=cfg.IMAGETEXT.LR_GAMMA)
print("train the model...")
for epoch_idx in range(cfg.IMAGETEXT.EPOCH):
# val_rtn_dict, outputer_val = validate_one_epoch(epoch_idx, val_dataloader, encoder,
# decoder, loss_func, outputer_val)
# outputer_val.log("\n[valid] epoch: {}, {}".format(epoch_idx, dict2str(val_rtn_dict)))
train_rtn_dict, outputer_train = train_one_epoch(
epoch_idx, train_dataloader, encoder, decoder, optimizer_encoder,
optimizer_decoder, loss_func, outputer_train)
# adjust lr scheduler
encoder_lr_scheduler.step()
decoder_lr_scheduler.step()
outputer_train.log("\n[train] epoch: {}, {}\n".format(
epoch_idx, dict2str(train_rtn_dict)))
val_rtn_dict, outputer_val = validate_one_epoch(
epoch_idx, val_dataloader, encoder, decoder, loss_func,
outputer_val)
outputer_val.log("\n[valid] epoch: {}, {}\n".format(
epoch_idx, dict2str(val_rtn_dict)))
outputer_val.save_step({
"encoder": encoder.state_dict(),
"decoder": decoder.state_dict()
})
outputer_val.save({
"encoder": encoder.state_dict(),
"decoder": decoder.state_dict()
})
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Evaluate the Robustness of a Detector : prepare_seed : {:}'.format(
args.rand_seed))
prepare_seed(args.rand_seed)
assert args.init_model is not None and Path(
args.init_model).exists(), 'invalid initial model path : {:}'.format(
args.init_model)
checkpoint = load_checkpoint(args.init_model)
xargs = checkpoint['args']
eval_func = procedures[xargs.procedure]
logger = prepare_logger(args)
if xargs.use_gray == False:
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
else:
mean_fill = (0.5, )
normalize = transforms.Normalize(mean=[mean_fill[0]], std=[0.5])
robust_component = [
transforms.ToTensor(), normalize,
transforms.PreCrop(xargs.pre_crop_expand)
]
robust_component += [
transforms.RandomTrans(args.robust_scale, args.robust_offset,
args.robust_rotate, args.robust_iters,
args.robust_cache_dir, True)
]
robust_transform = transforms.Compose3V(robust_component)
logger.log('--- arguments --- : {:}'.format(args))
logger.log('robust_transform : {:}'.format(robust_transform))
recover = xvision.transforms2v.ToPILImage(normalize)
model_config = load_configure(xargs.model_config, logger)
shape = (xargs.height, xargs.width)
logger.log('Model : {:} $$$$ Shape : {:}'.format(model_config, shape))
# Evaluation Dataloader
assert args.eval_lists is not None and len(
args.eval_lists) > 0, 'invalid args.eval_lists : {:}'.format(
args.eval_lists)
eval_loaders = []
for eval_list in args.eval_lists:
eval_data = RobustDataset(robust_transform, xargs.sigma,
model_config.downsample, xargs.heatmap_type,
shape, xargs.use_gray, xargs.data_indicator)
if xargs.x68to49:
eval_data.load_list(eval_list, 68, xargs.boxindicator, True)
convert68to49(eval_data)
else:
eval_data.load_list(eval_list, xargs.num_pts, xargs.boxindicator,
True)
eval_data.get_normalization_distance(None, True)
if hasattr(xargs, 'batch_size'):
batch_size = xargs.batch_size
elif hasattr(xargs, 'i_batch_size') and xargs.i_batch_size > 0:
batch_size = xargs.i_batch_size
elif hasattr(xargs, 'v_batch_size') and xargs.v_batch_size > 0:
batch_size = xargs.v_batch_size
else:
raise ValueError(
'can not find batch size information in xargs : {:}'.format(
xargs))
eval_loader = torch.utils.data.DataLoader(eval_data,
batch_size=batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append(eval_loader)
# define the detection network
detector = obtain_pro_model(model_config, xargs.num_pts, xargs.sigma,
xargs.use_gray)
assert model_config.downsample == detector.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, detector.downsample)
logger.log("=> detector :\n {:}".format(detector))
logger.log("=> Net-Parameters : {:} MB".format(
count_parameters_in_MB(detector)))
for i, eval_loader in enumerate(eval_loaders):
logger.log('The [{:2d}/{:2d}]-th testing-data = {:}'.format(
i, len(eval_loaders), eval_loader.dataset))
logger.log('basic-arguments : {:}\n'.format(xargs))
logger.log('xoxox-arguments : {:}\n'.format(args))
detector.load_state_dict(remove_module_dict(checkpoint['detector']))
detector = detector.cuda()
for ieval, loader in enumerate(eval_loaders):
errors, valids, meta = eval_func(detector, loader, args.print_freq,
logger)
logger.log(
'[{:2d}/{:02d}] eval-data : error : mean={:.3f}, std={:.3f}'.
format(ieval, len(eval_loaders), np.mean(errors), np.std(errors)))
logger.log(
'[{:2d}/{:02d}] eval-data : valid : mean={:.3f}, std={:.3f}'.
format(ieval, len(eval_loaders), np.mean(valids), np.std(valids)))
nme, auc, pck_curves = meta.compute_mse(loader.dataset.dataset_name,
logger)
logger.close()
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
temporal_main, eval_all = procedures['{:}-train'.format(
args.procedure)], procedures['{:}-test'.format(args.procedure)]
logger = prepare_logger(args)
# General Data Argumentation
normalize, train_transform, eval_transform, robust_transform = prepare_data_augmentation(
transforms, args)
recover = transforms.ToPILImage(normalize)
args.tensor2imageF = recover
assert (args.scale_min +
args.scale_max) / 2 == 1, 'The scale is not ok : {:} ~ {:}'.format(
args.scale_min, args.scale_max)
# Model Configure Load
model_config = load_configure(args.model_config, logger)
sbr_config = load_configure(args.sbr_config, logger)
shape = (args.height, args.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(
model_config, args.sigma, shape))
logger.log('--> SBR Configuration : {:}\n'.format(sbr_config))
# Training Dataset
train_data = VDataset(eval_transform, args.sigma, model_config.downsample, args.heatmap_type, shape, args.use_gray, args.mean_point, \
args.data_indicator, sbr_config, transforms.ToPILImage(normalize, 'cv2gray'))
train_data.load_list(args.train_lists, args.num_pts, args.boxindicator,
args.normalizeL, True)
# Evaluation Dataloader
assert len(
args.eval_ilists) == 1, 'invalid length of eval_ilists : {:}'.format(
len(eval_ilists))
eval_data = IDataset(eval_transform, args.sigma, model_config.downsample,
args.heatmap_type, shape, args.use_gray,
args.mean_point, args.data_indicator)
eval_data.load_list(args.eval_ilists[0], args.num_pts, args.boxindicator,
args.normalizeL, True)
if args.x68to49:
assert args.num_pts == 68, 'args.num_pts is not 68 vs. {:}'.format(
args.num_pts)
if train_data is not None: train_data = convert68to49(train_data)
eval_data = convert68to49(eval_data)
args.num_pts = 49
# define the temporal model (accelerated SBR)
net = obtain_pro_temporal(model_config, sbr_config, args.num_pts,
args.sigma, args.use_gray)
assert model_config.downsample == net.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, net.downsample)
logger.log("=> network :\n {}".format(net))
logger.log('Training-data : {:}'.format(train_data))
logger.log('Evaluate-data : {:}'.format(eval_data))
logger.log('arguments : {:}'.format(args))
opt_config = load_configure(args.opt_config, logger)
optimizer, scheduler, criterion = obtain_optimizer(net.parameters(),
opt_config, logger)
logger.log('criterion : {:}'.format(criterion))
net, criterion = net.cuda(), criterion.cuda()
net = torch.nn.DataParallel(net)
last_info = logger.last_info()
try:
last_checkpoint = load_checkpoint(args.init_model)
checkpoint = remove_module_dict(last_checkpoint['state_dict'], False)
net.module.detector.load_state_dict(checkpoint)
except:
last_checkpoint = load_checkpoint(args.init_model)
net.load_state_dict(last_checkpoint['state_dict'])
detector = torch.nn.DataParallel(net.module.detector)
logger.log("=> initialize the detector : {:}".format(args.init_model))
net.eval()
detector.eval()
logger.log('SBR Config : {:}'.format(sbr_config))
save_xdir = logger.path('meta')
type_error = 0
random.seed(111)
index_list = list(range(len(train_data)))
random.shuffle(index_list)
#selected_list = index_list[: min(200, len(index_list))]
selected_list = [
7260, 11506, 39952, 75196, 51614, 41061, 37747, 41355, 47875
]
for iidx, i in enumerate(selected_list):
frames, Fflows, Bflows, targets, masks, normpoints, transthetas, meanthetas, image_index, nopoints, shapes, is_images = train_data[
i]
frames, Fflows, Bflows, is_images = frames.unsqueeze(
0), Fflows.unsqueeze(0), Bflows.unsqueeze(0), is_images.unsqueeze(
0)
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, Sequence, PTS, H/Down, W/Down]
if args.procedure == 'heatmap':
batch_heatmaps, batch_locs, batch_scos, batch_past2now, batch_future2now, batch_FBcheck = net(
frames, Fflows, Bflows, is_images)
else:
batch_locs, batch_past2now, batch_future2now, batch_FBcheck = net(
frames, Fflows, Bflows, is_images)
(batch_size, frame_length, C, H,
W), num_pts, annotate_index = frames.size(
), args.num_pts, train_data.video_L
batch_locs = batch_locs.cpu()[:, :, :num_pts]
video_mask = masks.unsqueeze(0)[:, :num_pts]
batch_past2now = batch_past2now.cpu()[:, :, :num_pts]
batch_future2now = batch_future2now.cpu()[:, :, :num_pts]
batch_FBcheck = batch_FBcheck[:, :num_pts].cpu()
FB_check_oks = FB_communication(criterion, batch_locs, batch_past2now,
batch_future2now, batch_FBcheck,
video_mask, sbr_config)
# locations
norm_past_det_locs = torch.cat(
(batch_locs[0, annotate_index - 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_noww_det_locs = torch.cat(
(batch_locs[0, annotate_index, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_next_det_locs = torch.cat(
(batch_locs[0, annotate_index + 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_next_locs = torch.cat(
(batch_past2now[0, annotate_index, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_past_locs = torch.cat(
(batch_future2now[0, annotate_index - 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[:2, :]
norm_past_det_locs = torch.mm(transtheta, norm_past_det_locs)
norm_noww_det_locs = torch.mm(transtheta, norm_noww_det_locs)
norm_next_det_locs = torch.mm(transtheta, norm_next_det_locs)
norm_next_locs = torch.mm(transtheta, norm_next_locs)
norm_past_locs = torch.mm(transtheta, norm_past_locs)
real_past_det_locs = denormalize_points(shapes.tolist(),
norm_past_det_locs)
real_noww_det_locs = denormalize_points(shapes.tolist(),
norm_noww_det_locs)
real_next_det_locs = denormalize_points(shapes.tolist(),
norm_next_det_locs)
real_next_locs = denormalize_points(shapes.tolist(), norm_next_locs)
real_past_locs = denormalize_points(shapes.tolist(), norm_past_locs)
gt_noww_points = train_data.labels[image_index.item()].get_points()
FB_check_oks = FB_check_oks[:num_pts].squeeze()
#import pdb; pdb.set_trace()
if FB_check_oks.sum().item() > 2:
point_index = FB_check_oks.nonzero().squeeze().tolist()
something_wrong = False
for pidx in point_index:
real_now_det_loc = real_noww_det_locs[:, pidx]
real_pst_det_loc = real_past_det_locs[:, pidx]
real_net_det_loc = real_next_det_locs[:, pidx]
real_nex_loc = real_next_locs[:, pidx]
real_pst_loc = real_next_locs[:, pidx]
grdt_now_loc = gt_noww_points[:2, pidx]
#if torch.abs(real_now_loc - grdt_now_loc).max() > 5:
# something_wrong = True
#if torch.abs(real_nex_loc - grdt_nex_loc).max() > 5:
# something_wrong = True
#if something_wrong == True:
if True:
[image_past, image_noww,
image_next] = train_data.datas[image_index.item()]
try:
crop_box = train_data.labels[
image_index.item()].get_box().tolist()
#crop_box = [crop_box[0]-20, crop_box[1]-20, crop_box[2]+20, crop_box[3]+20]
except:
crop_box = False
RED, GREEN, BLUE = (255, 0, 0), (0, 255, 0), (0, 0, 255)
colors = [
GREEN if _i in point_index else RED
for _i in range(num_pts)
]
if crop_box != False or True:
I_past_det = draw_image_by_points(image_past,
real_past_det_locs[:], 3,
colors, crop_box,
(400, 500))
I_noww_det = draw_image_by_points(image_noww,
real_noww_det_locs[:], 3,
colors, crop_box,
(400, 500))
I_next_det = draw_image_by_points(image_next,
real_next_det_locs[:], 3,
colors, crop_box,
(400, 500))
I_next = draw_image_by_points(image_next,
real_next_locs[:], 3, colors,
crop_box, (400, 500))
I_past = draw_image_by_points(image_past,
real_past_locs[:], 3, colors,
crop_box, (400, 500))
I_past.save(
str(save_xdir / '{:05d}-v1-a-pastt.png'.format(i)))
I_noww_det.save(
str(save_xdir / '{:05d}-v1-b-curre.png'.format(i)))
I_next.save(
str(save_xdir / '{:05d}-v1-c-nextt.png'.format(i)))
I_past_det.save(
str(save_xdir / '{:05d}-v1-det-a-past.png'.format(i)))
I_noww_det.save(
str(save_xdir / '{:05d}-v1-det-b-curr.png'.format(i)))
I_next_det.save(
str(save_xdir / '{:05d}-v1-det-c-next.png'.format(i)))
#[image_past, image_noww, image_next] = train_data.datas[image_index.item()]
#image_noww = draw_image_by_points(image_noww, real_noww_locs[:], 2, colors, False, False)
#image_next = draw_image_by_points(image_next, real_next_locs[:], 2, colors, False, False)
#image_past = draw_image_by_points(image_past, real_past_locs[:], 2, colors, False, False)
#image_noww.save( str(save_xdir / '{:05d}-v2-b-curre.png'.format(i)) )
#image_next.save( str(save_xdir / '{:05d}-v2-c-nextt.png'.format(i)) )
#image_past.save( str(save_xdir / '{:05d}-v2-a-pastt.png'.format(i)) )
#type_error += 1
logger.log(
'Handle {:05d}/{:05d} :: {:05d}, ok-points={:.3f}, wrong data={:}'.
format(iidx, len(selected_list), i,
FB_check_oks.float().mean().item(), type_error))
save_xx_dir = save_xdir.parent / 'image-data'
save_xx_dir.mkdir(parents=True, exist_ok=True)
selected_list = [100, 115, 200, 300, 400] + list(range(200, 220))
for iidx, i in enumerate(selected_list):
inputs, targets, masks, normpoints, transthetas, meanthetas, image_index, nopoints, shapes = eval_data[
i]
inputs = inputs.unsqueeze(0)
(batch_size, C, H, W), num_pts = inputs.size(), args.num_pts
_, _, batch_locs, batch_scos = detector(inputs) # inputs
batch_locs, batch_scos = batch_locs.cpu(), batch_scos.cpu()
norm_locs = normalize_points((H, W),
batch_locs[0, :num_pts].transpose(1, 0))
norm_det_locs = torch.cat((norm_locs, torch.ones(1, num_pts)), dim=0)
norm_det_locs = torch.mm(transthetas[:2, :], norm_det_locs)
real_det_locs = denormalize_points(shapes.tolist(), norm_det_locs)
gt_now_points = eval_data.labels[image_index.item()].get_points()
image_now = eval_data.datas[image_index.item()]
crop_box = eval_data.labels[image_index.item()].get_box().tolist()
RED, GREEN, BLUE = (255, 0, 0), (0, 255, 0), (0, 0, 255)
Gcolors = [GREEN for _ in range(num_pts)]
points = torch.cat((real_det_locs, gt_now_points[:2]), dim=1)
colors = [GREEN
for _ in range(num_pts)] + [BLUE for _ in range(num_pts)]
image = draw_image_by_points(image_now, real_det_locs, 3, Gcolors,
crop_box, (400, 500))
image.save(str(save_xx_dir / '{:05d}-crop.png'.format(i)))
image = draw_image_by_points(image_now, points, 3, colors, False,
False)
#image = draw_image_by_points(image_now, real_det_locs, 3, colors , False, False)
image.save(str(save_xx_dir / '{:05d}-orig.png'.format(i)))
logger.log('Finish drawing : {:}'.format(save_xdir))
logger.log('Finish drawing : {:}'.format(save_xx_dir))
logger.close()
type=str,
default=random_flower,
help='Path to image')
parser.add_argument('--checkpoint',
type=str,
default='checkpoint.pth',
help='Path to checkpoint')
parser.add_argument('--topk',
type=int,
default=5,
help='Top N Classes and Probabilities')
parser.add_argument('--json',
type=str,
default='cat_to_name.json',
help='class_to_name json file')
parser.add_argument('--gpu', type=str, default='cuda', help='GPU or CPU')
arg, unknown = parser.parse_known_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class_name = open_json(arg.json)
model = load_checkpoint(arg.checkpoint)
checkpoint = torch.load(arg.checkpoint)
image = process_image(arg.image_dir)
probs, classes = predict(random_flower, model)
prediction_test(class_name, classes, probs, random_folder)
else:
raise Exception("Dataset {} undefined".format(opts.dataset))
train_dataloader = get_dataloader(dataset=train_data,
opts=opts,
collate_fxn=lambda x: torch.cat(x))
# Setup the model and optimizer
model = models.ValueNet(opts)
model_dict = {"value_model": model}
if opts.cuda == 1:
to_cuda([model, lfn])
optimizer = models.get_optim(model.parameters(), opts)
# Load checkpoint if it exists
if opts.checkpoint != "":
models.load_checkpoint(model_dict, optimizer, opts.checkpoint, opts)
else:
models.check_and_print_opts(opts, None)
# Run the train loop
for i in range(opts.nepoch):
train_loss, train_error = train_epoch(model, lfn, optimizer,
train_dataloader, opts)
pretty_log("train loss {:<5.4f} error {:<5.2f} {}".format(
train_loss, train_error * 100, i))
if opts.checkpoint != "":
metadata = {
"epoch": i,
"train_loss": train_loss,
"train_error": train_error
}
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads( args.workers )
print ('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
temporal_main, eval_all = procedures['{:}-train'.format(args.procedure)], procedures['{:}-test'.format(args.procedure)]
logger = prepare_logger(args)
# General Data Argumentation
normalize, train_transform, eval_transform, robust_transform = prepare_data_augmentation(transforms, args)
recover = transforms.ToPILImage(normalize)
args.tensor2imageF = recover
assert (args.scale_min+args.scale_max) / 2 == 1, 'The scale is not ok : {:} ~ {:}'.format(args.scale_min, args.scale_max)
# Model Configure Load
model_config = load_configure(args.model_config, logger)
sbr_config = load_configure(args.sbr_config, logger)
shape = (args.height, args.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(model_config, args.sigma, shape))
logger.log('--> SBR Configuration : {:}\n'.format(sbr_config))
# Training Dataset
train_data = VDataset(train_transform, args.sigma, model_config.downsample, args.heatmap_type, shape, args.use_gray, args.mean_point, \
args.data_indicator, sbr_config, transforms.ToPILImage(normalize, 'cv2gray'))
train_data.load_list(args.train_lists, args.num_pts, args.boxindicator, args.normalizeL, True)
batch_sampler = SbrBatchSampler(train_data, args.i_batch_size, args.v_batch_size, args.sbr_sampler_use_vid)
train_loader = torch.utils.data.DataLoader(train_data, batch_sampler=batch_sampler, num_workers=args.workers, pin_memory=True)
# Evaluation Dataloader
eval_loaders = []
if args.eval_ilists is not None:
for eval_ilist in args.eval_ilists:
eval_idata = IDataset(eval_transform, args.sigma, model_config.downsample, args.heatmap_type, shape, args.use_gray, args.mean_point, args.data_indicator)
eval_idata.load_list(eval_ilist, args.num_pts, args.boxindicator, args.normalizeL, True)
eval_iloader = torch.utils.data.DataLoader(eval_idata, batch_size=args.i_batch_size+args.v_batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
eval_loaders.append((eval_iloader, False))
if args.eval_vlists is not None:
for eval_vlist in args.eval_vlists:
eval_vdata = IDataset(eval_transform, args.sigma, model_config.downsample, args.heatmap_type, shape, args.use_gray, args.mean_point, args.data_indicator)
eval_vdata.load_list(eval_vlist, args.num_pts, args.boxindicator, args.normalizeL, True)
eval_vloader = torch.utils.data.DataLoader(eval_vdata, batch_size=args.i_batch_size+args.v_batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
eval_loaders.append((eval_vloader, True))
# from 68 points to 49 points, removing the face contour
if args.x68to49:
assert args.num_pts == 68, 'args.num_pts is not 68 vs. {:}'.format(args.num_pts)
if train_data is not None: train_data = convert68to49( train_data )
for eval_loader, is_video in eval_loaders:
convert68to49( eval_loader.dataset )
args.num_pts = 49
# define the temporal model (accelerated SBR)
net = obtain_pro_temporal(model_config, sbr_config, args.num_pts, args.sigma, args.use_gray)
assert model_config.downsample == net.downsample, 'downsample is not correct : {:} vs {:}'.format(model_config.downsample, net.downsample)
logger.log("=> network :\n {}".format(net))
logger.log('Training-data : {:}'.format(train_data))
for i, eval_loader in enumerate(eval_loaders):
eval_loader, is_video = eval_loader
logger.log('The [{:2d}/{:2d}]-th testing-data [{:}] = {:}'.format(i, len(eval_loaders), 'video' if is_video else 'image', eval_loader.dataset))
logger.log('arguments : {:}'.format(args))
opt_config = load_configure(args.opt_config, logger)
if hasattr(net, 'specify_parameter'): net_param_dict = net.specify_parameter(opt_config.LR, opt_config.weight_decay)
else : net_param_dict = net.parameters()
optimizer, scheduler, criterion = obtain_optimizer(net_param_dict, opt_config, logger)
logger.log('criterion : {:}'.format(criterion))
net, criterion = net.cuda(), criterion.cuda()
net = torch.nn.DataParallel(net)
last_info = logger.last_info()
if last_info.exists():
logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info))
last_info = torch.load(last_info)
start_epoch = last_info['epoch'] + 1
checkpoint = torch.load(last_info['last_checkpoint'])
test_accuracies = checkpoint['test_accuracies']
assert last_info['epoch'] == checkpoint['epoch'], 'Last-Info is not right {:} vs {:}'.format(last_info, checkpoint['epoch'])
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
logger.log("=> load-ok checkpoint '{:}' (epoch {:}) done" .format(logger.last_info(), checkpoint['epoch']))
elif args.init_model is not None:
last_checkpoint = load_checkpoint(args.init_model)
checkpoint = remove_module_dict(last_checkpoint['state_dict'], False)
net.module.detector.load_state_dict( checkpoint )
logger.log("=> initialize the detector : {:}".format(args.init_model))
start_epoch, test_accuracies = 0, {'best': 10000}
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, test_accuracies = 0, {'best': 10000}
detector = torch.nn.DataParallel(net.module.detector)
if args.skip_first_eval == False:
logger.log('===>>> First Time Evaluation')
eval_results, eval_metas = eval_all(args, eval_loaders, detector, criterion, 'Before-Training', logger, opt_config, None)
save_path = save_checkpoint(eval_metas, logger.path('meta') / '{:}-first.pth'.format(model_config.arch), logger)
logger.log('===>>> Before Training : {:}'.format(eval_results))
# Main Training and Evaluation Loop
start_time = time.time()
epoch_time = AverageMeter()
for epoch in range(start_epoch, opt_config.epochs):
need_time = convert_secs2time(epoch_time.avg * (opt_config.epochs-epoch), True)
epoch_str = 'epoch-{:03d}-{:03d}'.format(epoch, opt_config.epochs)
LRs = scheduler.get_lr()
logger.log('\n==>>{:s} [{:s}], [{:s}], LR : [{:.5f} ~ {:.5f}], Config : {:}'.format(time_string(), epoch_str, need_time, min(LRs), max(LRs), opt_config))
# train for one epoch
train_loss, train_nme = temporal_main(args, train_loader, net, criterion, optimizer, epoch_str, logger, opt_config, sbr_config, epoch>=sbr_config.start, 'train')
scheduler.step()
# log the results
logger.log('==>>{:s} Train [{:}] Average Loss = {:.6f}, NME = {:.2f}'.format(time_string(), epoch_str, train_loss, train_nme*100))
save_path = save_checkpoint({
'epoch': epoch,
'args' : deepcopy(args),
'arch' : model_config.arch,
'detector' : detector.state_dict(),
'test_accuracies': test_accuracies,
'state_dict': net.state_dict(),
'scheduler' : scheduler.state_dict(),
'optimizer' : optimizer.state_dict(),
}, logger.path('model') / 'ckp-seed-{:}-last-{:}.pth'.format(args.rand_seed, model_config.arch), logger)
last_info = save_checkpoint({
'epoch': epoch,
'last_checkpoint': save_path,
}, logger.last_info(), logger)
if (args.eval_freq is None) or (epoch+1 == opt_config.epochs) or (epoch%args.eval_freq == 0):
if epoch+1 == opt_config.epochs: _robust_transform = robust_transform
else : _robust_transform = None
logger.log('')
eval_results, eval_metas = eval_all(args, eval_loaders, detector, criterion, epoch_str, logger, opt_config, _robust_transform)
# check whether it is the best and save with copyfile(src, dst)
try:
cur_eval_nme = float( eval_results.split('NME = ')[1].split(' ')[0] )
except:
cur_eval_nme = 1e9
test_accuracies[epoch] = cur_eval_nme
if test_accuracies['best'] > cur_eval_nme: # find the lowest error
dest_path = logger.path('model') / 'ckp-seed-{:}-best-{:}.pth'.format(args.rand_seed, model_config.arch)
copyfile(save_path, dest_path)
logger.log('==>> find lowest error = {:}, save into {:}'.format(cur_eval_nme, dest_path))
meta_save_path = save_checkpoint(eval_metas, logger.path('meta') / '{:}-{:}.pth'.format(model_config.arch, epoch_str), logger)
logger.log('==>> evaluation results : {:}'.format(eval_results))
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
logger.log('Final checkpoint into {:}'.format(logger.last_info()))
logger.close()
def main():
cfg, args = _parse_args()
torch.manual_seed(args.seed)
output_base = cfg.OUTPUT_DIR if len(cfg.OUTPUT_DIR) > 0 else './output'
exp_name = '-'.join([
datetime.now().strftime("%Y%m%d-%H%M%S"), cfg.MODEL.ARCHITECTURE,
str(cfg.INPUT.IMG_SIZE)
])
output_dir = get_outdir(output_base, exp_name)
with open(os.path.join(output_dir, 'config.yaml'), 'w',
encoding='utf-8') as file_writer:
# cfg.dump(stream=file_writer, default_flow_style=False, indent=2, allow_unicode=True)
file_writer.write(pyaml.dump(cfg))
logger = setup_logger(file_name=os.path.join(output_dir, 'train.log'),
control_log=False,
log_level='INFO')
# create model
model = create_model(cfg.MODEL.ARCHITECTURE,
num_classes=cfg.MODEL.NUM_CLASSES,
pretrained=True,
in_chans=cfg.INPUT.IN_CHANNELS,
drop_rate=cfg.MODEL.DROP_RATE,
drop_connect_rate=cfg.MODEL.DROP_CONNECT,
global_pool=cfg.MODEL.GLOBAL_POOL)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
gpu_list = list(map(int, args.gpu.split(',')))
device = 'cuda'
if len(gpu_list) == 1:
model.cuda()
torch.backends.cudnn.benchmark = True
elif len(gpu_list) > 1:
model = nn.DataParallel(model, device_ids=gpu_list)
model = convert_model(model).cuda()
torch.backends.cudnn.benchmark = True
else:
device = 'cpu'
logger.info('device: {}, gpu_list: {}'.format(device, gpu_list))
optimizer = create_optimizer(cfg, model)
# optionally initialize from a checkpoint
if args.initial_checkpoint and os.path.isfile(args.initial_checkpoint):
load_checkpoint(model, args.initial_checkpoint)
# optionally resume from a checkpoint
resume_state = None
resume_epoch = None
if args.resume and os.path.isfile(args.resume):
resume_state, resume_epoch = resume_checkpoint(model, args.resume)
if resume_state and not args.no_resume_opt:
if 'optimizer' in resume_state:
optimizer.load_state_dict(resume_state['optimizer'])
logger.info('Restoring optimizer state from [{}]'.format(
args.resume))
start_epoch = 0
if args.start_epoch is not None:
start_epoch = args.start_epoch
elif resume_epoch is not None:
start_epoch = resume_epoch
model_ema = None
if cfg.SOLVER.EMA:
# Important to create EMA model after cuda()
model_ema = ModelEma(model,
decay=cfg.SOLVER.EMA_DECAY,
device=device,
resume=args.resume)
lr_scheduler, num_epochs = create_scheduler(cfg, optimizer)
if lr_scheduler is not None and start_epoch > 0:
lr_scheduler.step(start_epoch)
# summary
print('=' * 60)
print(cfg)
print('=' * 60)
print(model)
print('=' * 60)
summary(model, (3, cfg.INPUT.IMG_SIZE, cfg.INPUT.IMG_SIZE))
# dataset
dataset_train = Dataset(cfg.DATASETS.TRAIN)
dataset_valid = Dataset(cfg.DATASETS.TEST)
train_loader = create_loader(dataset_train, cfg, is_training=True)
valid_loader = create_loader(dataset_valid, cfg, is_training=False)
# loss function
if cfg.SOLVER.LABEL_SMOOTHING > 0:
train_loss_fn = LabelSmoothingCrossEntropy(
smoothing=cfg.SOLVER.LABEL_SMOOTHING).to(device)
validate_loss_fn = nn.CrossEntropyLoss().to(device)
else:
train_loss_fn = nn.CrossEntropyLoss().to(device)
validate_loss_fn = train_loss_fn
eval_metric = cfg.SOLVER.EVAL_METRIC
best_metric = None
best_epoch = None
saver = CheckpointSaver(
checkpoint_dir=output_dir,
recovery_dir=output_dir,
decreasing=True if eval_metric == 'loss' else False)
try:
for epoch in range(start_epoch, num_epochs):
train_metrics = train_epoch(epoch,
model,
train_loader,
optimizer,
train_loss_fn,
cfg,
logger,
lr_scheduler=lr_scheduler,
saver=saver,
device=device,
model_ema=model_ema)
eval_metrics = validate(epoch, model, valid_loader,
validate_loss_fn, cfg, logger)
if model_ema is not None:
ema_eval_metrics = validate(epoch, model_ema.ema, valid_loader,
validate_loss_fn, cfg, logger)
eval_metrics = ema_eval_metrics
if lr_scheduler is not None:
# step LR for next epoch
lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])
update_summary(epoch,
train_metrics,
eval_metrics,
os.path.join(output_dir, 'summary.csv'),
write_header=best_metric is None)
if saver is not None:
# save proper checkpoint with eval metric
save_metric = eval_metrics[eval_metric]
best_metric, best_epoch = saver.save_checkpoint(
model,
optimizer,
cfg,
epoch=epoch,
model_ema=model_ema,
metric=save_metric)
except KeyboardInterrupt:
pass
if best_metric is not None:
logger.info('*** Best metric: {0} (epoch {1})'.format(
best_metric, best_epoch))
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
checkpoint = load_checkpoint(args.init_model)
xargs = checkpoint['args']
logger.log('Previous args : {:}'.format(xargs))
# General Data Augmentation
if xargs.use_gray == False:
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
else:
mean_fill = (0.5, )
normalize = transforms.Normalize(mean=[mean_fill[0]], std=[0.5])
eval_transform = transforms.Compose2V([transforms.ToTensor(), normalize, \
transforms.PreCrop(xargs.pre_crop_expand), \
transforms.CenterCrop(xargs.crop_max)])
# Model Configure Load
model_config = load_configure(xargs.model_config, logger)
shape = (xargs.height, xargs.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(
model_config, xargs.sigma, shape))
# Evaluation Dataloader
eval_loaders = []
if args.eval_ilists is not None:
for eval_ilist in args.eval_ilists:
eval_idata = EvalDataset(eval_transform, xargs.sigma,
model_config.downsample,
xargs.heatmap_type, shape, xargs.use_gray,
xargs.data_indicator)
eval_idata.load_list(eval_ilist, args.num_pts, xargs.boxindicator,
xargs.normalizeL, True)
eval_iloader = torch.utils.data.DataLoader(
eval_idata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_iloader, False))
if args.eval_vlists is not None:
for eval_vlist in args.eval_vlists:
eval_vdata = EvalDataset(eval_transform, xargs.sigma,
model_config.downsample,
xargs.heatmap_type, shape, xargs.use_gray,
xargs.data_indicator)
eval_vdata.load_list(eval_vlist, args.num_pts, xargs.boxindicator,
xargs.normalizeL, True)
eval_vloader = torch.utils.data.DataLoader(
eval_vdata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_vloader, True))
# define the detector
detector = obtain_pro_model(model_config, xargs.num_pts, xargs.sigma,
xargs.use_gray)
assert model_config.downsample == detector.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, detector.downsample)
logger.log("=> detector :\n {:}".format(detector))
logger.log("=> Net-Parameters : {:} MB".format(
count_parameters_in_MB(detector)))
logger.log('=> Eval-Transform : {:}'.format(eval_transform))
detector = detector.cuda()
net = torch.nn.DataParallel(detector)
net.eval()
net.load_state_dict(checkpoint['detector'])
cpu = torch.device('cpu')
assert len(args.use_stable) == 2
for iLOADER, (loader, is_video) in enumerate(eval_loaders):
logger.log(
'{:} The [{:2d}/{:2d}]-th test set [{:}] = {:} with {:} batches.'.
format(time_string(), iLOADER, len(eval_loaders),
'video' if is_video else 'image', loader.dataset,
len(loader)))
with torch.no_grad():
all_points, all_results, all_image_ps = [], [], []
for i, (inputs, targets, masks, normpoints, transthetas,
image_index, nopoints, shapes) in enumerate(loader):
image_index = image_index.squeeze(1).tolist()
(batch_size, C, H, W), num_pts = inputs.size(), xargs.num_pts
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, C, H, W]
if xargs.procedure == 'heatmap':
batch_features, batch_heatmaps, batch_locs, batch_scos = net(
inputs)
batch_locs = batch_locs[:, :-1, :]
else:
batch_locs = net(inputs)
batch_locs = batch_locs.detach().to(cpu)
# evaluate the training data
for ibatch, (imgidx,
nopoint) in enumerate(zip(image_index, nopoints)):
if xargs.procedure == 'heatmap':
norm_locs = normalize_points(
(H, W), batch_locs[ibatch].transpose(1, 0))
norm_locs = torch.cat(
(norm_locs, torch.ones(1, num_pts)), dim=0)
else:
norm_locs = torch.cat((batch_locs[ibatch].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[ibatch][:2, :]
norm_locs = torch.mm(transtheta, norm_locs)
real_locs = denormalize_points(shapes[ibatch].tolist(),
norm_locs)
#real_locs = torch.cat((real_locs, batch_scos[ibatch].permute(1,0)), dim=0)
real_locs = torch.cat((real_locs, torch.ones(1, num_pts)),
dim=0)
xpoints = loader.dataset.labels[imgidx].get_points().numpy(
)
image_path = loader.dataset.datas[imgidx]
# put into the list
all_points.append(torch.from_numpy(xpoints))
all_results.append(real_locs)
all_image_ps.append(image_path)
total = len(all_points)
logger.log(
'{:} The [{:2d}/{:2d}]-th test set finishes evaluation : {:} frames/images'
.format(time_string(), iLOADER, len(eval_loaders), total))
"""
if args.use_stable[0] > 0:
save_dir = Path( osp.join(args.save_path, '{:}-X-{:03d}'.format(args.model_name, iLOADER)) )
save_dir.mkdir(parents=True, exist_ok=True)
wrap_parallel = WrapParallel(save_dir, all_image_ps, all_results, all_points, 180, (255, 0, 0))
wrap_loader = torch.utils.data.DataLoader(wrap_parallel, batch_size=args.workers, shuffle=False, num_workers=args.workers, pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader): _ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -framerate 30 {:}.avi'.format(save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system( cmd )
if args.use_stable[1] > 0:
save_dir = Path( osp.join(args.save_path, '{:}-Y-{:03d}'.format(args.model_name, iLOADER)) )
save_dir.mkdir(parents=True, exist_ok=True)
Xpredictions, Xgts = torch.stack(all_results), torch.stack(all_points)
new_preds = fc_solve(Xgts, Xpredictions, is_cuda=True)
wrap_parallel = WrapParallel(save_dir, all_image_ps, new_preds, all_points, 180, (0, 0, 255))
wrap_loader = torch.utils.data.DataLoader(wrap_parallel, batch_size=args.workers, shuffle=False, num_workers=args.workers, pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader): _ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -framerate 30 {:}.avi'.format(save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system( cmd )
"""
Xpredictions, Xgts = torch.stack(all_results), torch.stack(all_points)
save_path = Path(
osp.join(args.save_path,
'{:}-result-{:03d}.pth'.format(args.model_name, iLOADER)))
torch.save(
{
'paths': all_image_ps,
'ground-truths': Xgts,
'predictions': all_results
}, save_path)
logger.log('{:} save into {:}'.format(time_string(), save_path))
if False:
new_preds = fc_solve_v2(Xgts, Xpredictions, is_cuda=True)
# create the dir
save_dir = Path(
osp.join(args.save_path,
'{:}-T-{:03d}'.format(args.model_name, iLOADER)))
save_dir.mkdir(parents=True, exist_ok=True)
wrap_parallel = WrapParallelV2(save_dir, all_image_ps, Xgts,
all_results, new_preds, all_points,
180, [args.model_name, 'SRT'])
wrap_parallel[0]
wrap_loader = torch.utils.data.DataLoader(wrap_parallel,
batch_size=args.workers,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader):
_ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -vb 5000k {:}.avi'.format(
save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system(cmd)
logger.close()
return
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
basic_main, eval_all = procedures['{:}-train'.format(
args.procedure)], procedures['{:}-test'.format(args.procedure)]
logger = prepare_logger(args)
# General Data Augmentation
normalize, train_transform, eval_transform, robust_transform = prepare_data_augmentation(
transforms, args)
#data_cache = get_path2image( args.shared_img_cache )
data_cache = None
recover = transforms.ToPILImage(normalize)
args.tensor2imageF = recover
assert (args.scale_min +
args.scale_max) / 2 == 1, 'The scale is not ok : {:} ~ {:}'.format(
args.scale_min, args.scale_max)
logger.log('robust_transform : {:}'.format(robust_transform))
# Model Configure Load
model_config = load_configure(args.model_config, logger)
shape = (args.height, args.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(
model_config, args.sigma, shape))
# Training Dataset
if args.train_lists:
train_data = Dataset(train_transform, args.sigma,
model_config.downsample, args.heatmap_type, shape,
args.use_gray, args.mean_point,
args.data_indicator, data_cache)
safex_data = Dataset(eval_transform, args.sigma,
model_config.downsample, args.heatmap_type, shape,
args.use_gray, args.mean_point,
args.data_indicator, data_cache)
train_data.set_cutout(args.cutout_length)
safex_data.set_cutout(args.cutout_length)
train_data.load_list(args.train_lists, args.num_pts, args.boxindicator,
args.normalizeL, True)
safex_data.load_list(args.train_lists, args.num_pts, args.boxindicator,
args.normalizeL, True)
if args.sampler is None:
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True,
pin_memory=True)
safex_loader = torch.utils.data.DataLoader(
safex_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True,
pin_memory=True)
else:
train_sampler = SpecialBatchSampler(train_data, args.batch_size,
args.sampler)
safex_sampler = SpecialBatchSampler(safex_data, args.batch_size,
args.sampler)
logger.log('Training-sampler : {:}'.format(train_sampler))
train_loader = torch.utils.data.DataLoader(
train_data,
batch_sampler=train_sampler,
num_workers=args.workers,
pin_memory=True)
safex_loader = torch.utils.data.DataLoader(
safex_data,
batch_sampler=safex_sampler,
num_workers=args.workers,
pin_memory=True)
logger.log('Training-data : {:}'.format(train_data))
else:
train_data, safex_loader = None, None
#train_data[0]
# Evaluation Dataloader
eval_loaders = []
if args.eval_ilists is not None:
for eval_ilist in args.eval_ilists:
eval_idata = Dataset(eval_transform, args.sigma,
model_config.downsample, args.heatmap_type,
shape, args.use_gray, args.mean_point,
args.data_indicator, data_cache)
eval_idata.load_list(eval_ilist, args.num_pts, args.boxindicator,
args.normalizeL, True)
eval_iloader = torch.utils.data.DataLoader(
eval_idata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_iloader, False))
if args.eval_vlists is not None:
for eval_vlist in args.eval_vlists:
eval_vdata = Dataset(eval_transform, args.sigma,
model_config.downsample, args.heatmap_type,
shape, args.use_gray, args.mean_point,
args.data_indicator, data_cache)
eval_vdata.load_list(eval_vlist, args.num_pts, args.boxindicator,
args.normalizeL, True)
eval_vloader = torch.utils.data.DataLoader(
eval_vdata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_vloader, True))
# from 68 points to 49 points, removing the face contour
if args.x68to49:
assert args.num_pts == 68, 'args.num_pts is not 68 vs. {:}'.format(
args.num_pts)
if train_data is not None: train_data = convert68to49(train_data)
for eval_loader, is_video in eval_loaders:
convert68to49(eval_loader.dataset)
args.num_pts = 49
# define the detector
detector = obtain_pro_model(model_config, args.num_pts, args.sigma,
args.use_gray)
assert model_config.downsample == detector.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, detector.downsample)
logger.log("=> detector :\n {:}".format(detector))
logger.log("=> Net-Parameters : {:} MB".format(
count_parameters_in_MB(detector)))
for i, eval_loader in enumerate(eval_loaders):
eval_loader, is_video = eval_loader
logger.log('The [{:2d}/{:2d}]-th testing-data [{:}] = {:}'.format(
i, len(eval_loaders), 'video' if is_video else 'image',
eval_loader.dataset))
logger.log('arguments : {:}\n'.format(args))
logger.log('train_transform : {:}'.format(train_transform))
logger.log('eval_transform : {:}'.format(eval_transform))
opt_config = load_configure(args.opt_config, logger)
if hasattr(detector, 'specify_parameter'):
net_param_dict = detector.specify_parameter(opt_config.LR,
opt_config.weight_decay)
else:
net_param_dict = detector.parameters()
optimizer, scheduler, criterion = obtain_optimizer(net_param_dict,
opt_config, logger)
logger.log('criterion : {:}'.format(criterion))
detector, criterion = detector.cuda(), criterion.cuda()
net = torch.nn.DataParallel(detector)
last_info = logger.last_info()
if last_info.exists():
logger.log("=> loading checkpoint of the last-info '{:}' start".format(
last_info))
last_info = torch.load(last_info)
start_epoch = last_info['epoch'] + 1
checkpoint = torch.load(last_info['last_checkpoint'])
assert last_info['epoch'] == checkpoint[
'epoch'], 'Last-Info is not right {:} vs {:}'.format(
last_info, checkpoint['epoch'])
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
logger.log("=> load-ok checkpoint '{:}' (epoch {:}) done".format(
logger.last_info(), checkpoint['epoch']))
elif args.init_model is not None:
last_checkpoint = load_checkpoint(args.init_model)
net.load_state_dict(last_checkpoint['detector'])
logger.log("=> initialize the detector : {:}".format(args.init_model))
start_epoch = 0
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch = 0
if args.eval_once is not None:
logger.log("=> only evaluate the model once")
#if safex_loader is not None:
# safe_results, safe_metas = eval_all(args, [(safex_loader, False)], net, criterion, 'eval-once-train', logger, opt_config, robust_transform)
# logger.log('-'*50 + ' evaluate the training set')
#import pdb; pdb.set_trace()
eval_results, eval_metas = eval_all(args, eval_loaders, net, criterion,
'eval-once', logger, opt_config,
robust_transform)
all_predictions = [eval_meta.predictions for eval_meta in eval_metas]
torch.save(
all_predictions,
osp.join(args.save_path,
'{:}-predictions.pth'.format(args.eval_once)))
logger.log('==>> evaluation results : {:}'.format(eval_results))
logger.log('==>> configuration : {:}'.format(model_config))
logger.close()
return
# Main Training and Evaluation Loop
start_time = time.time()
epoch_time = AverageMeter()
for epoch in range(start_epoch, opt_config.epochs):
need_time = convert_secs2time(
epoch_time.avg * (opt_config.epochs - epoch), True)
epoch_str = 'epoch-{:03d}-{:03d}'.format(epoch, opt_config.epochs)
LRs = scheduler.get_lr()
logger.log(
'\n==>>{:s} [{:s}], [{:s}], LR : [{:.5f} ~ {:.5f}], Config : {:}'.
format(time_string(), epoch_str, need_time, min(LRs), max(LRs),
opt_config))
# train for one epoch
train_loss, train_meta, train_nme = basic_main(args, train_loader, net,
criterion, optimizer,
epoch_str, logger,
opt_config, 'train')
scheduler.step()
# log the results
logger.log(
'==>>{:s} Train [{:}] Average Loss = {:.6f}, NME = {:.2f}'.format(
time_string(), epoch_str, train_loss, train_nme * 100))
save_path = save_checkpoint(
{
'epoch': epoch,
'args': deepcopy(args),
'arch': model_config.arch,
'detector': net.state_dict(),
'state_dict': net.state_dict(),
'scheduler': scheduler.state_dict(),
'optimizer': optimizer.state_dict(),
},
logger.path('model') /
'seed-{:}-{:}.pth'.format(args.rand_seed, model_config.arch),
logger)
last_info = save_checkpoint(
{
'epoch': epoch,
'args': deepcopy(args),
'last_checkpoint': save_path,
}, logger.last_info(), logger)
if (args.eval_freq is None) or (epoch + 1 == opt_config.epochs) or (
epoch % args.eval_freq == 0):
if epoch + 1 == opt_config.epochs:
_robust_transform = robust_transform
else:
_robust_transform = None
logger.log('')
eval_results, eval_metas = eval_all(args, eval_loaders, net,
criterion, epoch_str, logger,
opt_config, _robust_transform)
#save_path = save_checkpoint(eval_metas, logger.path('meta') / '{:}-{:}.pth'.format(model_config.arch, epoch_str), logger)
save_path = save_checkpoint(
eval_metas,
logger.path('meta') /
'seed-{:}-{:}.pth'.format(args.rand_seed, model_config.arch),
logger)
logger.log(
'==>> evaluation results : {:}\n==>> save evaluation results into {:}.'
.format(eval_results, save_path))
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
logger.log('Final checkpoint into {:}'.format(logger.last_info()))
logger.close()
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
temporal_main, eval_all = procedures['{:}-train'.format(
args.procedure)], procedures['{:}-test'.format(args.procedure)]
logger = prepare_logger(args)
# General Data Argumentation
normalize, train_transform, eval_transform, robust_transform = prepare_data_augmentation(
transforms, args)
recover = transforms.ToPILImage(normalize)
args.tensor2imageF = recover
assert (args.scale_min +
args.scale_max) / 2 == 1, 'The scale is not ok : {:} ~ {:}'.format(
args.scale_min, args.scale_max)
# Model Configure Load
model_config = load_configure(args.model_config, logger)
sbr_config = load_configure(args.sbr_config, logger)
shape = (args.height, args.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(
model_config, args.sigma, shape))
logger.log('--> SBR Configuration : {:}\n'.format(sbr_config))
# Training Dataset
train_data = VDataset(eval_transform, args.sigma, model_config.downsample, args.heatmap_type, shape, args.use_gray, args.mean_point, \
args.data_indicator, sbr_config, transforms.ToPILImage(normalize, 'cv2gray'))
train_data.load_list(args.train_lists, args.num_pts, args.boxindicator,
args.normalizeL, True)
if args.x68to49:
assert args.num_pts == 68, 'args.num_pts is not 68 vs. {:}'.format(
args.num_pts)
if train_data is not None: train_data = convert68to49(train_data)
args.num_pts = 49
# define the temporal model (accelerated SBR)
net = obtain_pro_temporal(model_config, sbr_config, args.num_pts,
args.sigma, args.use_gray)
assert model_config.downsample == net.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, net.downsample)
logger.log("=> network :\n {}".format(net))
logger.log('Training-data : {:}'.format(train_data))
logger.log('arguments : {:}'.format(args))
opt_config = load_configure(args.opt_config, logger)
optimizer, scheduler, criterion = obtain_optimizer(net.parameters(),
opt_config, logger)
logger.log('criterion : {:}'.format(criterion))
net, criterion = net.cuda(), criterion.cuda()
net = torch.nn.DataParallel(net)
last_info = logger.last_info()
try:
last_checkpoint = load_checkpoint(args.init_model)
checkpoint = remove_module_dict(last_checkpoint['state_dict'], False)
net.module.detector.load_state_dict(checkpoint)
except:
last_checkpoint = load_checkpoint(args.init_model)
net.load_state_dict(last_checkpoint['state_dict'])
detector = torch.nn.DataParallel(net.module.detector)
logger.log("=> initialize the detector : {:}".format(args.init_model))
net.eval()
detector.eval()
logger.log('SBR Config : {:}'.format(sbr_config))
save_xdir = logger.path('meta')
random.seed(111)
index_list = list(range(len(train_data)))
random.shuffle(index_list)
#selected_list = index_list[: min(200, len(index_list))]
#selected_list = [7260, 11506, 39952, 75196, 51614, 41061, 37747, 41355]
#for iidx, i in enumerate(selected_list):
index_list.remove(47875)
selected_list = [47875] + index_list
save_xdir = logger.path('meta')
type_error_1, type_error_2, type_error, misses = 0, 0, 0, 0
type_error_pts, total_pts = 0, 0
for iidx, i in enumerate(selected_list):
frames, Fflows, Bflows, targets, masks, normpoints, transthetas, meanthetas, image_index, nopoints, shapes, is_images = train_data[
i]
frames, Fflows, Bflows, is_images = frames.unsqueeze(
0), Fflows.unsqueeze(0), Bflows.unsqueeze(0), is_images.unsqueeze(
0)
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, Sequence, PTS, H/Down, W/Down]
with torch.no_grad():
if args.procedure == 'heatmap':
batch_heatmaps, batch_locs, batch_scos, batch_past2now, batch_future2now, batch_FBcheck = net(
frames, Fflows, Bflows, is_images)
else:
batch_locs, batch_past2now, batch_future2now, batch_FBcheck = net(
frames, Fflows, Bflows, is_images)
(batch_size, frame_length, C, H,
W), num_pts, annotate_index = frames.size(
), args.num_pts, train_data.video_L
batch_locs = batch_locs.cpu()[:, :, :num_pts]
video_mask = masks.unsqueeze(0)[:, :num_pts]
batch_past2now = batch_past2now.cpu()[:, :, :num_pts]
batch_future2now = batch_future2now.cpu()[:, :, :num_pts]
batch_FBcheck = batch_FBcheck[:, :num_pts].cpu()
FB_check_oks = FB_communication(criterion, batch_locs, batch_past2now,
batch_future2now, batch_FBcheck,
video_mask, sbr_config)
# locations
norm_past_det_locs = torch.cat(
(batch_locs[0, annotate_index - 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_noww_det_locs = torch.cat(
(batch_locs[0, annotate_index, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_next_det_locs = torch.cat(
(batch_locs[0, annotate_index + 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_next_locs = torch.cat(
(batch_past2now[0, annotate_index, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_past_locs = torch.cat(
(batch_future2now[0, annotate_index - 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[:2, :]
norm_past_det_locs = torch.mm(transtheta, norm_past_det_locs)
norm_noww_det_locs = torch.mm(transtheta, norm_noww_det_locs)
norm_next_det_locs = torch.mm(transtheta, norm_next_det_locs)
norm_next_locs = torch.mm(transtheta, norm_next_locs)
norm_past_locs = torch.mm(transtheta, norm_past_locs)
real_past_det_locs = denormalize_points(shapes.tolist(),
norm_past_det_locs)
real_noww_det_locs = denormalize_points(shapes.tolist(),
norm_noww_det_locs)
real_next_det_locs = denormalize_points(shapes.tolist(),
norm_next_det_locs)
real_next_locs = denormalize_points(shapes.tolist(), norm_next_locs)
real_past_locs = denormalize_points(shapes.tolist(), norm_past_locs)
gt_noww_points = train_data.labels[image_index.item()].get_points()
gt_past_points = train_data.find_index(
train_data.datas[image_index.item()][annotate_index - 1])
gt_next_points = train_data.find_index(
train_data.datas[image_index.item()][annotate_index + 1])
FB_check_oks = FB_check_oks[:num_pts].squeeze()
#import pdb; pdb.set_trace()
if FB_check_oks.sum().item() > 2:
# type 1 error : detection at both (t) and (t-1) is wrong, while pass the check
is_type_1, (T_wrong, T_total) = check_is_1st_error(
[real_past_det_locs, real_noww_det_locs, real_next_det_locs],
[gt_past_points, gt_noww_points, gt_next_points], FB_check_oks,
shapes)
# type 2 error : detection at frame t is ok, while tracking are wrong and frame at (t-1) is wrong:
spec_index, is_type_2 = check_is_2nd_error(
real_noww_det_locs, gt_noww_points,
[real_past_locs, real_next_locs],
[gt_past_points, gt_next_points], FB_check_oks, shapes)
type_error_1 += is_type_1
type_error_2 += is_type_2
type_error += is_type_1 or is_type_2
type_error_pts, total_pts = type_error_pts + T_wrong, total_pts + T_total
if is_type_2:
RED, GREEN, BLUE = (255, 0, 0), (0, 255, 0), (0, 0, 255)
[image_past, image_noww,
image_next] = train_data.datas[image_index.item()]
crop_box = train_data.labels[
image_index.item()].get_box().tolist()
point_index = FB_check_oks.nonzero().squeeze().tolist()
colors = [
GREEN if _i in point_index else RED
for _i in range(num_pts)
] + [BLUE for _i in range(num_pts)]
I_past_det = draw_image_by_points(
image_past,
torch.cat((real_past_det_locs, gt_past_points[:2]), dim=1),
3, colors, crop_box, (400, 500))
I_noww_det = draw_image_by_points(
image_noww,
torch.cat((real_noww_det_locs, gt_noww_points[:2]), dim=1),
3, colors, crop_box, (400, 500))
I_next_det = draw_image_by_points(
image_next,
torch.cat((real_next_det_locs, gt_next_points[:2]), dim=1),
3, colors, crop_box, (400, 500))
I_past = draw_image_by_points(
image_past,
torch.cat((real_past_locs, gt_past_points[:2]), dim=1), 3,
colors, crop_box, (400, 500))
I_next = draw_image_by_points(
image_next,
torch.cat((real_next_locs, gt_next_points[:2]), dim=1), 3,
colors, crop_box, (400, 500))
###
I_past.save(str(save_xdir / '{:05d}-v1-a-pastt.png'.format(i)))
I_noww_det.save(
str(save_xdir / '{:05d}-v1-b-curre.png'.format(i)))
I_next.save(str(save_xdir / '{:05d}-v1-c-nextt.png'.format(i)))
I_past_det.save(
str(save_xdir / '{:05d}-v1-det-a-past.png'.format(i)))
I_noww_det.save(
str(save_xdir / '{:05d}-v1-det-b-curr.png'.format(i)))
I_next_det.save(
str(save_xdir / '{:05d}-v1-det-c-next.png'.format(i)))
logger.log('TYPE-ERROR : {:}, landmark-index : {:}'.format(
i, spec_index))
else:
misses += 1
string = 'Handle {:05d}/{:05d} :: {:05d}'.format(
iidx, len(selected_list), i)
string += ', error-1 : {:} ({:.2f}%), error-2 : {:} ({:.2f}%)'.format(
type_error_1, type_error_1 * 100.0 / (iidx + 1), type_error_2,
type_error_2 * 100.0 / (iidx + 1))
string += ', error : {:} ({:.2f}%), miss : {:}'.format(
type_error, type_error * 100.0 / (iidx + 1), misses)
string += ', final-error : {:05d} / {:05d} = {:.2f}%'.format(
type_error_pts, total_pts, type_error_pts * 100.0 / total_pts)
logger.log(string)
def main(args=None):
if args is None:
args = sys.argv[1:]
args = parse_args(args)
print('--------------Arguments----------------')
print('data_type : ', args.data_type)
print('learning_rate : ', args.learning_rate)
print('validation : ', args.validation)
print('epochs : ', args.epochs)
print('keep_train : ', args.keep_train)
print('pretrain_imagenet : ', args.pretrain_imagenet)
print('train_batch_size : ', args.train_batch_size)
print('val_batch_size : ', args.val_batch_size)
print('dropouts : ', args.dropouts)
print('weighted_loss : ', args.weighted_loss)
print('---------------------------------------')
mean = nml_cfg.mean
std = nml_cfg.std
# Make snapshot directory
tools.directoryMake(path_cfg.snapshot_root_path)
train_dir = os.path.join(path_cfg.data_root_path, 'train', args.data_type)
train_dir = os.path.join(path_cfg.data_root_path, args.data_type)
val_dir = os.path.join(path_cfg.data_root_path, 'val', args.data_type)
# Make Train data_loader
train_data = ds.ImageFolder(
train_dir,
transforms.Compose([
transforms.Resize(299),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
#transforms.RandomVerticalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std)
]))
num_of_class = len(os.listdir(train_dir))
train_loader = data.DataLoader(train_data,
batch_size=args.train_batch_size,
shuffle=True,
drop_last=False)
# Make Validation data_loader
if args.validation:
val_data = ds.ImageFolder(
val_dir,
transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean, std)]))
num_of_val_class = len(os.listdir(val_dir))
val_loader = data.DataLoader(val_data,
batch_size=args.val_batch_size,
shuffle=False,
drop_last=False)
print('----------------Data-------------------')
print('num_of_class : ', num_of_class)
print('num_of_images : ', len(train_data))
print('---------------------------------------\n\n')
class_list = train_data.classes
# Make Weight
weight = make_weight(train_dir, class_list, args.weighted_loss)
for model_idx, model_name in enumerate(model_name_list):
save_model_name = model_name + '_' + args.data_type
CNN_model, CNN_optimizer, CNN_criterion, CNN_scheduler = model_setter(
model_name,
weight,
learning_rate=args.learning_rate,
output_size=num_of_class,
usePretrained=args.pretrain_imagenet,
dropouts=args.dropouts)
if args.keep_train:
CNN_model = models.load_checkpoint(CNN_model, save_model_name)
best_prec = 0
for epoch in range(args.epochs):
prec = train(train_loader, CNN_model, CNN_criterion, CNN_optimizer,
epoch)
if args.validation:
prec = val(val_loader, CNN_model, CNN_criterion)
# Learning rate scheduler
CNN_scheduler.step()
# Model weight will be saved based on it's validation performance
is_best = prec > best_prec
best_prec = max(prec, best_prec)
models.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': CNN_model.state_dict(),
'best_prec1': best_prec,
}, is_best, save_model_name)
print('Best Performance : ', best_prec)
print('\n\n\n')
