def apply(self, sample: Sample) -> Sample:
inputs = sample.inputs
outputs = sample.outputs
assert(inputs['img_len'].shape == (1,))
assert(inputs['meta'].shape == (1,))
inputs = inputs.copy()
outputs = outputs.copy()
inputs['img_len'] = inputs['img_len'][0]
inputs['meta'] = inputs['meta'][0]
def reshape_outputs(suffix):
out_len = 'out_len' + suffix
if out_len in outputs and outputs[out_len].shape == (1,):
outputs[out_len] = outputs[out_len][0]
for name in {'logits', 'softmax', 'blank_last_logits', 'blank_last_softmax'}:
name += suffix
if name in outputs:
outputs[name] = outputs[name][:outputs[out_len]]
reshape_outputs('')
for i in range(self.params.ensemble_):
reshape_outputs(f"_{i}")
return sample.new_inputs(inputs).new_outputs(outputs)
def apply(self, sample: Sample) -> Sample:
# data augmentation
if not self.params.data_aug_params.no_augs() \
and sample.inputs is not None \
and self.data_augmenter \
and np.random.rand() <= self.params.data_aug_params.to_rel():
line, text = self.augment(sample.inputs, sample.targets,
sample.meta)
return sample.new_inputs(line).new_targets(text)
return sample
def apply(self, sample: Sample) -> Sample:
inputs = sample.inputs
outputs = sample.outputs
assert (inputs['img_len'].shape == (1, ))
assert (inputs['meta'].shape == (1, ))
inputs = inputs.copy()
outputs = outputs.copy()
inputs['img_len'] = inputs['img_len'][0]
inputs['meta'] = inputs['meta'][0]
if 'out_len' in outputs and outputs['out_len'].shape == (1, ):
outputs['out_len'] = outputs['out_len'][0]
for name in {
'logits', 'softmax', 'blank_last_logits', 'blank_last_softmax'
}:
if name in outputs:
outputs[name] = outputs[name][:outputs['out_len']]
return sample.new_inputs(inputs).new_outputs(outputs)
def apply(self, sample: Sample) -> Sample:
codec = self.params.codec
# final preparation
text = np.array(
codec.encode(sample.targets) if sample.targets else np.zeros(
(0, ), dtype='int32'))
line = sample.inputs
# gray or binary input, add missing axis
if len(line.shape) == 2:
line = np.expand_dims(line, axis=-1)
if line.shape[-1] != self.params.input_channels:
raise ValueError(
f"Expected {self.params.input_channels} channels but got {line.shape[-1]}. Shape of input {line.shape}"
)
if self.mode in {PipelineMode.Training, PipelineMode.Evaluation
} and not self.is_valid_line(
text,
len(line) // self.params.downscale_factor_):
# skip longer outputs than inputs (also in evaluation due to loss computation)
logger.warning(
f"Skipping line with longer outputs than inputs (id={sample.meta['id']})"
)
return sample.new_invalid()
if self.mode in {PipelineMode.Training, PipelineMode.Evaluation
} and len(text) == 0:
logger.warning(
f"Skipping empty line with empty GT (id={sample.meta['id']})")
return sample.new_invalid()
return sample.new_inputs({
'img': line.astype(np.uint8),
'img_len': [len(line)],
'meta': [json.dumps(sample.meta)]
}).new_targets({
'gt': text,
'gt_len': [len(text)],
'fold_id': [sample.meta.get('fold_id', -1)]
})
def apply(self, sample: Sample) -> Sample:
return sample.new_inputs(self._apply_single(sample.inputs,
sample.meta))