for t in range(output.size(0)):
for k in range(output.size(1)):
print('{:.10g}'.format(float(output[t, k])),
file=fileout,
end=' ')
print('', file=fileout)
if add_boundary_blank:
print('{:.10g}'.format(0.0), file=fileout, end=' ')
for k in range(1, output.size(1)):
print('{:.10g}'.format(-1e30), file=fileout, end=' ')
print('', file=fileout)
print(']', file=fileout)
if __name__ == '__main__':
add_defaults('gpu')
add_argument('--adaptive_pool_height',
type=int,
default=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('--add_boundary_blank', 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()
#!/usr/bin/env python
from __future__ import absolute_import
import argparse
import torch.nn as nn
from laia.models.htr.gated_crnn import GatedCRNN
from laia.common import ModelSaver
from laia.common.arguments import add_argument, args, add_defaults
from laia.common.arguments_types import NumberInClosedRange, TupleList, \
str2bool
from laia.utils import SymbolsTable
if __name__ == '__main__':
add_defaults('train_path')
add_argument('num_input_channels',
type=NumberInClosedRange(int, vmin=1),
help='Number of channels of the input images')
add_argument('syms',
type=argparse.FileType('r'),
help='Symbols table mapping from strings to integers')
add_argument('--cnn_num_features',
type=NumberInClosedRange(int, vmin=1),
nargs='+',
default=[8, 16, 32, 64, 128],
help='Number of features in each convolutional layer')
add_argument('--cnn_kernel_size',
type=TupleList(int, dimensions=2),
nargs='+',
default=[3, (2, 4), 3, (2, 4), 3],
from laia.engine.phoc_engine_wrapper import PHOCEngineWrapper
from laia.hooks import Hook, HookList, action
from laia.hooks.conditions import GEqThan, Highest
from laia.losses.dortmund_bce_loss import DortmundBCELoss
from laia.common.arguments import add_argument, add_defaults, args
from laia.utils.dortmund_image_to_tensor import DortmundImageToTensor
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,
iterations_per_update=10,
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',
type=int,
default=[1, 2, 3, 4, 5],
nargs='+',
from laia.engine.feeders import ImageFeeder, ItemFeeder
from tqdm import tqdm
import pickle
import editdistance
def cer_score_dict(decoded, target, ids):
cer_dict = {}
for ref, hyp, x in zip(target, decoded, ids):
cer_dict[x] = editdistance.eval(ref, hyp) / len(ref)
return cer_dict
if __name__ == "__main__":
add_defaults("batch_size", "gpu", "train_path", logging_level="WARNING")
add_argument(
"--syms",
type=argparse.FileType("r"),
help="Symbols table mapping from strings to integers",
)
add_argument("--img_dirs",
type=str,
nargs="+",
help="Directory containing word images")
add_argument(
"--txt_table",
type=argparse.FileType("r"),
help="Character transcriptions of each image.",
)
add_argument(
for t in range(output.size(0)):
for k in range(output.size(1)):
print("{:.10g}".format(float(output[t, k])),
file=fileout,
end=" ")
print("", file=fileout)
if add_boundary_blank:
print("{:.10g}".format(0.0), file=fileout, end=" ")
for k in range(1, output.size(1)):
print("{:.10g}".format(-1e30), file=fileout, end=" ")
print("", 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("--add_boundary_blank", 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"))
from laia.hooks.conditions import Lowest, MultipleOf, GEqThan
from laia.common.arguments import add_argument, args, add_defaults
from laia.common.loader import (TrainerLoader, ModelLoader,
TrainerCheckpointLoader, ModelCheckpointLoader)
from laia.common.saver import (TrainerSaver, CheckpointSaver, RollingSaver,
ModelCheckpointSaver, TrainerCheckpointSaver)
from laia.utils import SymbolsTable, ImageToTensor, TextToTensor
from laia.utils.dortmund_image_to_tensor import DortmundImageToTensor
from torch.optim import SGD
if __name__ == '__main__':
add_defaults('batch_size',
'learning_rate',
'gpu',
'max_epochs',
'train_path',
'train_samples_per_epoch',
'iterations_per_update',
momentum=0.9,
show_progress_bar=True)
add_argument('fixed_height',
type=int,
help='Resize images to this fixed height size')
add_argument('syms',
type=argparse.FileType('r'),
help='Symbols table mapping from strings to integers')
add_argument('img_dir', help='Directory containing word images')
add_argument('tr_txt_table',
type=argparse.FileType('r'),
help='Character transcriptions of each training image')
add_argument('va_txt_table',
