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| # Copyright (C) 2021-2024, Mindee. | |
| # This program is licensed under the Apache License 2.0. | |
| # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details. | |
| from typing import Any, List | |
| from doctr.file_utils import is_tf_available, is_torch_available | |
| from .. import detection | |
| from ..detection.fast import reparameterize | |
| from ..preprocessor import PreProcessor | |
| from .predictor import DetectionPredictor | |
| __all__ = ["detection_predictor"] | |
| ARCHS: List[str] | |
| if is_tf_available(): | |
| ARCHS = [ | |
| "db_resnet50", | |
| "db_mobilenet_v3_large", | |
| "linknet_resnet18", | |
| "linknet_resnet34", | |
| "linknet_resnet50", | |
| "fast_tiny", | |
| "fast_small", | |
| "fast_base", | |
| ] | |
| elif is_torch_available(): | |
| ARCHS = [ | |
| "db_resnet34", | |
| "db_resnet50", | |
| "db_mobilenet_v3_large", | |
| "linknet_resnet18", | |
| "linknet_resnet34", | |
| "linknet_resnet50", | |
| "fast_tiny", | |
| "fast_small", | |
| "fast_base", | |
| ] | |
| def _predictor(arch: Any, pretrained: bool, assume_straight_pages: bool = True, **kwargs: Any) -> DetectionPredictor: | |
| if isinstance(arch, str): | |
| if arch not in ARCHS: | |
| raise ValueError(f"unknown architecture '{arch}'") | |
| _model = detection.__dict__[arch]( | |
| pretrained=pretrained, | |
| pretrained_backbone=kwargs.get("pretrained_backbone", True), | |
| assume_straight_pages=assume_straight_pages, | |
| ) | |
| # Reparameterize FAST models by default to lower inference latency and memory usage | |
| if isinstance(_model, detection.FAST): | |
| _model = reparameterize(_model) | |
| else: | |
| if not isinstance(arch, (detection.DBNet, detection.LinkNet, detection.FAST)): | |
| raise ValueError(f"unknown architecture: {type(arch)}") | |
| _model = arch | |
| _model.assume_straight_pages = assume_straight_pages | |
| kwargs.pop("pretrained_backbone", None) | |
| kwargs["mean"] = kwargs.get("mean", _model.cfg["mean"]) | |
| kwargs["std"] = kwargs.get("std", _model.cfg["std"]) | |
| kwargs["batch_size"] = kwargs.get("batch_size", 2) | |
| predictor = DetectionPredictor( | |
| PreProcessor(_model.cfg["input_shape"][:-1] if is_tf_available() else _model.cfg["input_shape"][1:], **kwargs), | |
| _model, | |
| ) | |
| return predictor | |
| def detection_predictor( | |
| arch: Any = "fast_base", | |
| pretrained: bool = False, | |
| assume_straight_pages: bool = True, | |
| **kwargs: Any, | |
| ) -> DetectionPredictor: | |
| """Text detection architecture. | |
| >>> import numpy as np | |
| >>> from doctr.models import detection_predictor | |
| >>> model = detection_predictor(arch='db_resnet50', pretrained=True) | |
| >>> input_page = (255 * np.random.rand(600, 800, 3)).astype(np.uint8) | |
| >>> out = model([input_page]) | |
| Args: | |
| ---- | |
| arch: name of the architecture or model itself to use (e.g. 'db_resnet50') | |
| pretrained: If True, returns a model pre-trained on our text detection dataset | |
| assume_straight_pages: If True, fit straight boxes to the page | |
| **kwargs: optional keyword arguments passed to the architecture | |
| Returns: | |
| ------- | |
| Detection predictor | |
| """ | |
| return _predictor(arch, pretrained, assume_straight_pages, **kwargs) | |