def train_cresi(config, paths, fn_mapping, image_suffix, folds_file_loc,
save_path, log_path, num_channels=3, logger=None, num_workers=0):
#t0 = time.time()
print('paths', len(paths), paths)
ds = ReadingImageProvider(RawImageType, paths, fn_mapping,
image_suffix=image_suffix, num_channels=num_channels)
if logger:
logger.info("len ds: {}".format(len(ds)))
logger.info("folds_file_loc: {}".format(folds_file_loc))
logger.info("save_path: {}".format(save_path))
else:
print("len ds:", len(ds))
print("folds_file_loc:", folds_file_loc)
print("save_path:", save_path)
folds = get_csv_folds(folds_file_loc, ds.im_names)
print(folds)
for fold, (train_idx, val_idx) in enumerate(folds):
if args.fold is not None and int(args.fold) != fold:
continue
if logger:
logger.info("num workers: {}".format(num_workers))
logger.info("fold: {}".format(fold))
# logger.info("(train_idx, val_idx):", (train_idx, val_idx))
logger.info("len(train_idx): {}".format(len(train_idx)))
logger.info("len(val_idx): {}".format(len(val_idx)))
if config.num_channels == 3:
transforms = get_flips_colors_augmentation()
else:
# can't do hsv rescaling with multiband imagery, so skip this part
transforms = get_flips_shifts_augmentation()
train(ds, fold, train_idx, val_idx, config, save_path, log_path,
num_workers=num_workers, transforms=transforms )
path_masks_train = '/wdata/train/masks_binned'
path_images_train = '/wdata/train/psms'
paths = {
'masks': path_masks_train,
'images': path_images_train
}
fn_mapping = {
'masks': lambda name: os.path.splitext(name)[0].replace('PS-MS', 'PS-RGB') + '.tif' #'.png'
}
ds = ReadingImageProvider(RawImageType, paths, fn_mapping,
image_suffix='', num_channels=8)
train_idx = np.arange(len(os.listdir(paths['images'])))
transforms = get_flips_shifts_augmentation()
train_loader = TrainDataset(ds, train_idx, transforms=transforms)
b = 15
gt = time.time()
for m in range(2):
st = time.time()
for i, t in enumerate(train_loader):
if i == b:
break
print('Time to iterate:', time.time() - gt, 'seconds')