logger = laia.common.logging.get_logger("laia.egs.washington.train_phoc")
laia.common.logging.get_logger("laia.hooks.conditions.multiple_of").setLevel(
laia.common.logging.WARNING)
if __name__ == "__main__":
add_defaults(
"gpu",
"max_epochs",
"max_updates",
"train_samples_per_epoch",
"valid_samples_per_epoch",
"seed",
"train_path",
# Override default values for these arguments, but use the
# same help/checks:
learning_rate=0.0001,
momentum=0.9,
num_rolling_checkpoints=5,
iterations_per_update=10,
save_checkpoint_interval=5,
show_progress_bar=True,
use_distortions=True,
weight_l2_penalty=0.00005,
)
add_argument("--load_checkpoint",
type=str,
help="Path to the checkpoint to load.")
add_argument("--continue_epoch", type=int)
add_argument(
"--phoc_levels",
for img_id, output in zip(img_ids, outputs):
output = output.cpu()
print(img_id, file=fileout)
for t in range(output.size(0)):
for k in range(output.size(1)):
print(
"{:d}\t{:d}\t{:d}\t0,{:.10g},{:d}".format(
t, t + 1, k + 1, -float(output[t, k]), k + 1),
file=fileout,
)
print(output.size(0), file=fileout)
print("", file=fileout)
if __name__ == "__main__":
add_defaults("gpu")
add_argument(
"--image_sequencer",
type=str,
default="avgpool-16",
help="Average adaptive pooling of the images before the LSTM layers",
)
add_argument("--lstm_hidden_size", type=int, default=128)
add_argument("--lstm_num_layers", type=int, default=1)
add_argument("--add_softmax", action="store_true")
add_argument("syms", help="Symbols table mapping from strings to integers")
add_argument("img_dir", help="Directory containing word images")
add_argument("gt_file", help="")
add_argument("checkpoint", help="")
add_argument("output", type=argparse.FileType("w"))
args = args()
x = np.asarray(x, dtype=np.float32)
x = dortmund_distort(x / 255.0)
if x.shape != 3:
x = np.expand_dims(x, axis=-1)
x = np.transpose(x, (2, 0, 1))
return torch.from_numpy(x)
if __name__ == "__main__":
import matplotlib.pyplot as plt
import laia.random
from laia.data import TextImageFromTextTableDataset, ImageDataLoader
from laia.plugins.arguments import add_argument, add_defaults, args
add_defaults("seed")
add_argument("--num_images",
type=int,
help="Show only this number of images")
add_argument("--shuffle",
action="store_true",
help="Shuffle the list of images")
add_argument("img_dir", help="Directory containing images")
add_argument("txt_table", help="Transcriptions of each image")
args = args()
laia.random.manual_seed(args.seed)
dataset = TextImageFromTextTableDataset(
args.txt_table, args.img_dir, img_transform=DortmundImageToTensor())
dataset_loader = ImageDataLoader(dataset=dataset,
image_channels=1,