Detection Model TextNet (TF+PT)

Untitled.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

doctr/models/artefacts/face.py

@@ -28,9 +28,7 @@ def __init__(
     ) -> None:
         self.n_faces = n_faces
         # Instantiate classifier
-        self.detector = cv2.CascadeClassifier(
-            cv2.data.haarcascades + "haarcascade_frontalface_default.xml"  # type: ignore[attr-defined]
-        )
+        self.detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
     def extra_repr(self) -> str:
         return f"n_faces={self.n_faces}"

doctr/models/classification/__init__.py

@@ -4,3 +4,4 @@
 from .magc_resnet import *
 from .vit import *
 from .zoo import *
+from .textnet_fast import *

doctr/models/classification/textnet_fast/__init__.py

@@ -0,0 +1,6 @@
+from doctr.file_utils import is_tf_available, is_torch_available
+
+if is_tf_available():
+    from .tensorflow import *
+elif is_torch_available():
+    from .pytorch import *

doctr/models/classification/textnet_fast/pytorch.py

@@ -0,0 +1,327 @@
+# Copyright (C) 2021-2023, 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 copy import deepcopy
+from typing import Any, Dict, List, Optional, Union
+
+import torch.nn as nn
+
+from doctr.datasets import VOCABS
+from doctr.models.modules.layers.pytorch import RepConvLayer
+from doctr.models.utils.pytorch import conv_sequence_pt as conv_sequence
+from doctr.models.utils.pytorch import (
+    fuse_module,
+    rep_model_convert,
+    rep_model_convert_deploy,
+    rep_model_unconvert,
+    unfuse_module,
+)
+
+from ...utils import load_pretrained_params
+
+__all__ = ["textnetfast_tiny", "textnetfast_small", "textnetfast_base"]
+
+default_cfgs: Dict[str, Dict[str, Any]] = {
+    "textnetfast_tiny": {
+        "input_shape": (3, 32, 32),
+        "classes": list(VOCABS["french"]),
+        "url": None,
+    },
+    "textnetfast_small": {
+        "input_shape": (3, 32, 32),
+        "classes": list(VOCABS["french"]),
+        "url": None,
+    },
+    "textnetfast_base": {
+        "input_shape": (3, 32, 32),
+        "classes": list(VOCABS["french"]),
+        "url": None,
+    },
+}
+
+
+class TextNetFast(nn.Sequential):
+    """Implements TextNet architecture from `"FAST: Faster Arbitrarily-Shaped Text Detector with
+    Minimalist Kernel Representation" <https://arxiv.org/abs/2111.02394>`_.
+    Implementation based on the official Pytorch implementation: <https://github.com/czczup/FAST>`_.
+
+    Args:
+        stage1 (Dict[str, Union[int, List[int]]]): Configuration for stage 1
+        stage2 (Dict[str, Union[int, List[int]]]): Configuration for stage 2
+        stage3 (Dict[str, Union[int, List[int]]]): Configuration for stage 3
+        stage4 (Dict[str, Union[int, List[int]]]): Configuration for stage 4
+        include_top (bool, optional): Whether to include the classifier head. Defaults to True.
+        num_classes (int, optional): Number of output classes. Defaults to 1000.
+        cfg (Optional[Dict[str, Any]], optional): Additional configuration. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        stage1: List[Dict[str, Union[int, List[int]]]],
+        stage2: List[Dict[str, Union[int, List[int]]]],
+        stage3: List[Dict[str, Union[int, List[int]]]],
+        stage4: List[Dict[str, Union[int, List[int]]]],
+        include_top: bool = True,
+        num_classes: int = 1000,
+        cfg: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        _layers: List[Any]
+        super().__init__()
+        first_conv = conv_sequence(in_channels=3, out_channels=64, relu=True, bn=True, kernel_size=3, stride=2)
+        self.first_conv = nn.Sequential(*first_conv)
+        _layers = [self.first_conv]
+
+        for stage in [stage1, stage2, stage3, stage4]:
+            self.stage_ = nn.Sequential(*[RepConvLayer(**params) for params in stage])  # type: ignore[arg-type]
+            _layers.extend([self.stage_])
+
+        if include_top:
+            classif_block = [
+                nn.AdaptiveAvgPool2d(1),
+                nn.Flatten(1),
+                nn.Linear(512, num_classes, bias=True),
+            ]
+            classif_block_ = nn.Sequential(*nn.ModuleList(classif_block))
+            _layers.extend([classif_block_])
+
+        super().__init__(*_layers)
+        self.cfg = cfg
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def eval(self, mode=False):
+        self = rep_model_convert(self)
+        self = fuse_module(self)
+        for param in self.parameters():
+            param.requires_grad = mode
+        self.training = mode
+        return self
+
+    def train(self, mode=True):
+        self = unfuse_module(self)
+        self = rep_model_unconvert(self)
+        for param in self.parameters():
+            param.requires_grad = mode
+        self.training = mode
+        return self
+
+    def test(self, mode=False):
+        self = rep_model_convert_deploy(self)
+        self = fuse_module(self)
+        for param in self.parameters():
+            param.requires_grad = mode
+        self.training = mode
+        return self
+
+
+def _textnetfast(
+    arch: str,
+    pretrained: bool,
+    arch_fn,
+    ignore_keys: Optional[List[str]] = None,
+    **kwargs: Any,
+) -> TextNetFast:
+    kwargs["num_classes"] = kwargs.get("num_classes", len(default_cfgs[arch]["classes"]))
+    kwargs["classes"] = kwargs.get("classes", default_cfgs[arch]["classes"])
+
+    _cfg = deepcopy(default_cfgs[arch])
+    _cfg["num_classes"] = kwargs["num_classes"]
+    _cfg["classes"] = kwargs["classes"]
+    kwargs.pop("classes")
+
+    # Build the model
+    model = arch_fn(**kwargs)
+    # Load pretrained parameters
+    if pretrained:
+        # The number of classes is not the same as the number of classes in the pretrained model =>
+        # remove the last layer weights
+        _ignore_keys = ignore_keys if kwargs["num_classes"] != len(default_cfgs[arch]["classes"]) else None
+        load_pretrained_params(model, default_cfgs[arch]["url"], ignore_keys=_ignore_keys)
+
+    model.cfg = _cfg
+
+    return model
+
+
+def textnetfast_tiny(pretrained: bool = False, **kwargs: Any) -> TextNetFast:
+    """Implements TextNet architecture from `"FAST: Faster Arbitrarily-Shaped Text Detector with
+    Minimalist Kernel Representation" <https://arxiv.org/abs/2111.02394>`_.
+    Implementation based on the official Pytorch implementation: <https://github.com/czczup/FAST>`_.
+
+    >>> import torch
+    >>> from doctr.models import textnetfast_tiny
+    >>> model = textnetfast_tiny(pretrained=False)
+    >>> input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32)
+    >>> out = model(input_tensor)
+
+    Args:
+        pretrained: boolean, True if model is pretrained
+
+    Returns:
+        A TextNet model
+    """
+
+    return _textnetfast(
+        "textnetfast_tiny",
+        pretrained,
+        TextNetFast,
+        stage1=[
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+        ],
+        stage2=[
+            {"in_channels": 64, "out_channels": 128, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 1], "stride": 1},
+        ],
+        stage3=[
+            {"in_channels": 128, "out_channels": 256, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [1, 3], "stride": 1},
+        ],
+        stage4=[
+            {"in_channels": 256, "out_channels": 512, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [3, 3], "stride": 1},
+        ],
+        ignore_keys=["4.3.weight", "4.3.bias"],
+        **kwargs,
+    )
+
+
+def textnetfast_small(pretrained: bool = False, **kwargs: Any) -> TextNetFast:
+    """Implements TextNet architecture from `"FAST: Faster Arbitrarily-Shaped Text Detector with
+    Minimalist Kernel Representation" <https://arxiv.org/abs/2111.02394>`_.
+    Implementation based on the official Pytorch implementation: <https://github.com/czczup/FAST>`_.
+
+    >>> import torch
+    >>> from doctr.models import textnetfast_small
+    >>> model = textnetfast_small(pretrained=False)
+    >>> input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32)
+    >>> out = model(input_tensor)
+
+    Args:
+        pretrained: boolean, True if model is pretrained
+
+    Returns:
+        A TextNetFast model
+    """
+
+    return _textnetfast(
+        "textnetfast_small",
+        pretrained,
+        TextNetFast,
+        stage1=[
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 2},
+        ],
+        stage2=[
+            {"in_channels": 64, "out_channels": 128, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+        ],
+        stage3=[
+            {"in_channels": 128, "out_channels": 256, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 3], "stride": 1},
+        ],
+        stage4=[
+            {"in_channels": 256, "out_channels": 512, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [3, 1], "stride": 1},
+        ],
+        ignore_keys=["4.3.weight", "4.3.bias"],
+        **kwargs,
+    )
+
+
+def textnetfast_base(pretrained: bool = False, **kwargs: Any) -> TextNetFast:
+    """Implements TextNet architecture from `"FAST: Faster Arbitrarily-Shaped Text Detector with
+    Minimalist Kernel Representation" <https://arxiv.org/abs/2111.02394>`_.
+    Implementation based on the official Pytorch implementation: <https://github.com/czczup/FAST>`_.
+
+    >>> import torch
+    >>> from doctr.models import textnetfast_base
+    >>> model = textnetfast_base(pretrained=False)
+    >>> input_tensor = torch.rand((1, 3, 512, 512), dtype=torch.float32)
+    >>> out = model(input_tensor)
+
+    Args:
+        pretrained: boolean, True if model is pretrained
+
+    Returns:
+        A TextNetFast model
+    """
+
+    return _textnetfast(
+        "textnetfast_base",
+        pretrained,
+        TextNetFast,
+        stage1=[
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 64, "out_channels": 64, "kernel_size": [3, 3], "stride": 1},
+        ],
+        stage2=[
+            {"in_channels": 64, "out_channels": 128, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 128, "out_channels": 128, "kernel_size": [3, 3], "stride": 1},
+        ],
+        stage3=[
+            {"in_channels": 128, "out_channels": 256, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 3], "stride": 1},
+            {"in_channels": 256, "out_channels": 256, "kernel_size": [3, 1], "stride": 1},
+        ],
+        stage4=[
+            {"in_channels": 256, "out_channels": 512, "kernel_size": [3, 3], "stride": 2},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [1, 3], "stride": 1},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [3, 1], "stride": 1},
+            {"in_channels": 512, "out_channels": 512, "kernel_size": [1, 3], "stride": 1},
+        ],
+        ignore_keys=["4.3.weight", "4.3.bias"],
+        **kwargs,
+    )

doctr/models/classification/zoo.py

@@ -27,6 +27,9 @@
     "vgg16_bn_r",
     "vit_s",
     "vit_b",
+    "textnetfast_tiny",
+    "textnetfast_small",
+    "textnetfast_base",
 ]
 ORIENTATION_ARCHS: List[str] = ["mobilenet_v3_small_orientation"]
 

doctr/models/detection/__init__.py

@@ -1,3 +1,4 @@
 from .differentiable_binarization import *
 from .linknet import *
 from .zoo import *
+from .fast import *

doctr/models/detection/fast/__init__.py

@@ -0,0 +1,6 @@
+from doctr.file_utils import is_tf_available, is_torch_available
+
+if is_tf_available():
+    from .tensorflow import *
+elif is_torch_available():
+    from .pytorch import *  # type: ignore[assignment]