def default_trainer_params():
p = CalamariTestScenario.default_trainer_params()
with open(
os.path.join(this_dir, "data", "line_generation_config",
"text_gen_params.json")) as f:
text_gen_params = TextGeneratorParams.from_dict(json.load(f))
with open(
os.path.join(this_dir, "data", "line_generation_config",
"line_gen_params.json")) as f:
line_gen_params = LineGeneratorParams.from_dict(json.load(f))
p.codec.include_files = os.path.join(this_dir, "data",
"line_generation_config",
"whilelist.txt")
p.codec.auto_compute = False
p.gen.train = GeneratedLineDatasetParams(
lines_per_epoch=10,
preload=False,
text_generator=text_gen_params,
line_generator=line_gen_params,
)
p.gen.val = GeneratedLineDatasetParams(
lines_per_epoch=10,
preload=False,
text_generator=text_gen_params,
line_generator=line_gen_params,
)
p.gen.setup.val.batch_size = 1
p.gen.setup.val.num_processes = 1
p.gen.setup.train.batch_size = 1
p.gen.setup.train.num_processes = 1
p.epochs = 1
p.scenario.data.pre_proc.run_parallel = False
p.gen.__post_init__()
p.scenario.data.__post_init__()
p.scenario.__post_init__()
p.__post_init__()
return p
canvas = canvas[cut_top:, :]
return np.pad(canvas, ((3, 3), (3, 3)),
"constant",
constant_values=255)
def _draw(self, text, font: Font = None):
if font is None:
font = random.choice(self.fonts)
return font.draw(text, 0.5)
if __name__ == "__main__":
import matplotlib.pyplot as plt
params = LineGeneratorParams()
params.font_size = 32
params.min_script_offset = -0.5
params.max_script_offset = 0.5
params.fonts.extend(["Junicode.ttf", "DejaVuSerif.ttf"])
line_gen = LineGenerator(params)
plt.imshow(
line_gen.draw([
Word("test", Script.NORMAL, 0, FontVariantType.NORMAL),
Word("12345", Script.SUB, 0, FontVariantType.NORMAL),
Word(" ", Script.NORMAL, 0, FontVariantType.NORMAL),
Word("norm", Script.NORMAL, 0, FontVariantType.BOLD),
Word("top", Script.SUPER, 0, FontVariantType.BOLD),
Word("sub", Script.SUB, 0, FontVariantType.BOLD_ITALICS),
Word("norm", Script.NORMAL, 1, FontVariantType.BOLD),
Word("top", Script.SUPER, 1, FontVariantType.BOLD),
def run(args):
# local imports (to prevent tensorflow from being imported to early)
from calamari_ocr.ocr.scenario import Scenario
from calamari_ocr.ocr.dataset.data import Data
# check if loading a json file
if len(args.files) == 1 and args.files[0].endswith("json"):
with open(args.files[0], 'r') as f:
json_args = json.load(f)
for key, value in json_args.items():
if key == 'dataset' or key == 'validation_dataset':
setattr(args, key, DataSetType.from_string(value))
else:
setattr(args, key, value)
check_train_args(args)
if args.output_dir is not None:
args.output_dir = os.path.abspath(args.output_dir)
setup_log(args.output_dir, append=False)
# parse whitelist
whitelist = args.whitelist
if len(whitelist) == 1:
whitelist = list(whitelist[0])
whitelist_files = glob_all(args.whitelist_files)
for f in whitelist_files:
with open(f) as txt:
whitelist += list(txt.read())
if args.gt_extension is None:
args.gt_extension = DataSetType.gt_extension(args.dataset)
if args.validation_extension is None:
args.validation_extension = DataSetType.gt_extension(
args.validation_dataset)
if args.dataset == DataSetType.GENERATED_LINE or args.validation_dataset == DataSetType.GENERATED_LINE:
if args.text_generator_params is not None:
with open(args.text_generator_params, 'r') as f:
args.text_generator_params = TextGeneratorParams.from_json(
f.read())
else:
args.text_generator_params = TextGeneratorParams()
if args.line_generator_params is not None:
with open(args.line_generator_params, 'r') as f:
args.line_generator_params = LineGeneratorParams.from_json(
f.read())
else:
args.line_generator_params = LineGeneratorParams()
dataset_args = FileDataReaderArgs(
line_generator_params=args.line_generator_params,
text_generator_params=args.text_generator_params,
pad=args.dataset_pad,
text_index=args.pagexml_text_index,
)
params: TrainerParams = Scenario.default_trainer_params()
# =================================================================================================================
# Data Params
data_params: DataParams = params.scenario_params.data_params
data_params.train = PipelineParams(
type=args.dataset,
skip_invalid=not args.no_skip_invalid_gt,
remove_invalid=True,
files=args.files,
text_files=args.text_files,
gt_extension=args.gt_extension,
data_reader_args=dataset_args,
batch_size=args.batch_size,
num_processes=args.num_threads,
)
if args.validation_split_ratio:
if args.validation is not None:
raise ValueError("Set either validation_split_ratio or validation")
if not 0 < args.validation_split_ratio < 1:
raise ValueError("validation_split_ratio must be in (0, 1)")
# resolve all files so we can split them
data_params.train = data_params.train.prepare_for_mode(
PipelineMode.Training)
n = int(args.validation_split_ratio * len(data_params.train.files))
logger.info(
f"Splitting training and validation files with ratio {args.validation_split_ratio}: "
f"{n}/{len(data_params.train.files) - n} for validation/training.")
indices = list(range(len(data_params.train.files)))
shuffle(indices)
all_files = data_params.train.files
all_text_files = data_params.train.text_files
# split train and val img/gt files. Use train settings
data_params.train.files = [all_files[i] for i in indices[:n]]
if all_text_files is not None:
assert (len(all_text_files) == len(all_files))
data_params.train.text_files = [
all_text_files[i] for i in indices[:n]
]
data_params.val = PipelineParams(
type=args.dataset,
skip_invalid=not args.no_skip_invalid_gt,
remove_invalid=True,
files=[all_files[i] for i in indices[n:]],
text_files=[all_text_files[i] for i in indices[n:]]
if data_params.train.text_files is not None else None,
gt_extension=args.gt_extension,
data_reader_args=dataset_args,
batch_size=args.batch_size,
num_processes=args.num_threads,
)
elif args.validation:
data_params.val = PipelineParams(
type=args.validation_dataset,
files=args.validation,
text_files=args.validation_text_files,
skip_invalid=not args.no_skip_invalid_gt,
gt_extension=args.validation_extension,
data_reader_args=dataset_args,
batch_size=args.batch_size,
num_processes=args.num_threads,
)
else:
data_params.val = None
data_params.pre_processors_ = SamplePipelineParams(run_parallel=True)
data_params.post_processors_.run_parallel = SamplePipelineParams(
run_parallel=False,
sample_processors=[
DataProcessorFactoryParams(ReshapeOutputsProcessor.__name__),
DataProcessorFactoryParams(CTCDecoderProcessor.__name__),
])
for p in args.data_preprocessing:
p_p = Data.data_processor_factory().processors[p].default_params()
if 'pad' in p_p:
p_p['pad'] = args.pad
data_params.pre_processors_.sample_processors.append(
DataProcessorFactoryParams(p, INPUT_PROCESSOR, p_p))
# Text pre processing (reading)
data_params.pre_processors_.sample_processors.extend([
DataProcessorFactoryParams(
TextNormalizer.__name__, TARGETS_PROCESSOR,
{'unicode_normalization': args.text_normalization}),
DataProcessorFactoryParams(
TextRegularizer.__name__, TARGETS_PROCESSOR, {
'replacements':
default_text_regularizer_replacements(args.text_regularization)
}),
DataProcessorFactoryParams(StripTextProcessor.__name__,
TARGETS_PROCESSOR)
])
# Text post processing (prediction)
data_params.post_processors_.sample_processors.extend([
DataProcessorFactoryParams(
TextNormalizer.__name__, TARGETS_PROCESSOR,
{'unicode_normalization': args.text_normalization}),
DataProcessorFactoryParams(
TextRegularizer.__name__, TARGETS_PROCESSOR, {
'replacements':
default_text_regularizer_replacements(args.text_regularization)
}),
DataProcessorFactoryParams(StripTextProcessor.__name__,
TARGETS_PROCESSOR)
])
if args.bidi_dir:
data_params.pre_processors_.sample_processors.append(
DataProcessorFactoryParams(BidiTextProcessor.__name__,
TARGETS_PROCESSOR,
{'bidi_direction': args.bidi_dir}))
data_params.post_processors_.sample_processors.append(
DataProcessorFactoryParams(BidiTextProcessor.__name__,
TARGETS_PROCESSOR,
{'bidi_direction': args.bidi_dir}))
data_params.pre_processors_.sample_processors.extend([
DataProcessorFactoryParams(AugmentationProcessor.__name__,
{PipelineMode.Training},
{'augmenter_type': 'simple'}),
DataProcessorFactoryParams(PrepareSampleProcessor.__name__,
INPUT_PROCESSOR),
])
data_params.data_aug_params = DataAugmentationAmount.from_factor(
args.n_augmentations)
data_params.line_height_ = args.line_height
# =================================================================================================================
# Trainer Params
params.calc_ema = args.ema_weights
params.verbose = args.train_verbose
params.force_eager = args.debug
params.skip_model_load_test = not args.debug
params.scenario_params.debug_graph_construction = args.debug
params.epochs = args.epochs
params.samples_per_epoch = int(
args.samples_per_epoch) if args.samples_per_epoch >= 1 else -1
params.scale_epoch_size = abs(
args.samples_per_epoch) if args.samples_per_epoch < 1 else 1
params.skip_load_model_test = True
params.scenario_params.export_frozen = False
params.checkpoint_save_freq_ = args.checkpoint_frequency if args.checkpoint_frequency >= 0 else args.early_stopping_frequency
params.checkpoint_dir = args.output_dir
params.test_every_n = args.display
params.skip_invalid_gt = not args.no_skip_invalid_gt
params.data_aug_retrain_on_original = not args.only_train_on_augmented
params.use_training_as_validation = args.use_train_as_val
if args.seed > 0:
params.random_seed = args.seed
params.optimizer_params.clip_grad = args.gradient_clipping_norm
params.codec_whitelist = whitelist
params.keep_loaded_codec = args.keep_loaded_codec
params.preload_training = not args.train_data_on_the_fly
params.preload_validation = not args.validation_data_on_the_fly
params.warmstart_params.model = args.weights
params.auto_compute_codec = not args.no_auto_compute_codec
params.progress_bar = not args.no_progress_bars
params.early_stopping_params.frequency = args.early_stopping_frequency
params.early_stopping_params.upper_threshold = 0.9
params.early_stopping_params.lower_threshold = 1.0 - args.early_stopping_at_accuracy
params.early_stopping_params.n_to_go = args.early_stopping_nbest
params.early_stopping_params.best_model_name = ''
params.early_stopping_params.best_model_output_dir = args.early_stopping_best_model_output_dir
params.scenario_params.default_serve_dir_ = f'{args.early_stopping_best_model_prefix}.ckpt.h5'
params.scenario_params.trainer_params_filename_ = f'{args.early_stopping_best_model_prefix}.ckpt.json'
# =================================================================================================================
# Model params
params_from_definition_string(args.network, params)
params.scenario_params.model_params.ensemble = args.ensemble
params.scenario_params.model_params.masking_mode = args.masking_mode
scenario = Scenario(params.scenario_params)
trainer = scenario.create_trainer(params)
trainer.train()