[PT / TF] Add TextNet - FAST backbone (mindee#1425)

docs/source/modules/models.rst

@@ -33,6 +33,12 @@ doctr.models.classification
 
 .. autofunction:: doctr.models.classification.vit_b
 
+.. autofunction:: doctr.models.classification.textnet_tiny
+
+.. autofunction:: doctr.models.classification.textnet_small
+
+.. autofunction:: doctr.models.classification.textnet_base
+
 .. autofunction:: doctr.models.classification.crop_orientation_predictor
 
 

doctr/models/classification/__init__.py

@@ -3,4 +3,5 @@
 from .vgg import *
 from .magc_resnet import *
 from .vit import *
+from .textnet import *
 from .zoo import *

doctr/models/classification/textnet/__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]

doctr/models/classification/textnet/pytorch.py

@@ -0,0 +1,275 @@
+# 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 copy import deepcopy
+from typing import Any, Dict, List, Optional, Tuple
+
+from torch import nn
+
+from doctr.datasets import VOCABS
+
+from ...modules.layers.pytorch import FASTConvLayer
+from ...utils import conv_sequence_pt, load_pretrained_params
+
+__all__ = ["textnet_tiny", "textnet_small", "textnet_base"]
+
+default_cfgs: Dict[str, Dict[str, Any]] = {
+    "textnet_tiny": {
+        "mean": (0.694, 0.695, 0.693),
+        "std": (0.299, 0.296, 0.301),
+        "input_shape": (3, 32, 32),
+        "classes": list(VOCABS["french"]),
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_tiny-c23a1b9a.pt&src=0",
+    },
+    "textnet_small": {
+        "mean": (0.694, 0.695, 0.693),
+        "std": (0.299, 0.296, 0.301),
+        "input_shape": (3, 32, 32),
+        "classes": list(VOCABS["french"]),
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_small-775169f7.pt&src=0",
+    },
+    "textnet_base": {
+        "mean": (0.694, 0.695, 0.693),
+        "std": (0.299, 0.296, 0.301),
+        "input_shape": (3, 32, 32),
+        "classes": list(VOCABS["french"]),
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_base-6121c044.pt&src=0",
+    },
+}
+
+
+class TextNet(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:
+    ----
+        stages (List[Dict[str, List[int]]]): List of dictionaries containing the parameters of each stage.
+        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,
+        stages: List[Dict[str, List[int]]],
+        input_shape: Tuple[int, int, int] = (3, 32, 32),
+        num_classes: int = 1000,
+        include_top: bool = True,
+        cfg: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        _layers: List[nn.Module] = [
+            *conv_sequence_pt(
+                in_channels=3, out_channels=64, relu=True, bn=True, kernel_size=3, stride=2, padding=(1, 1)
+            ),
+            nn.Sequential(*[
+                nn.Sequential(*[
+                    FASTConvLayer(**params)  # type: ignore[arg-type]
+                    for params in [{key: stage[key][i] for key in stage} for i in range(len(stage["in_channels"]))]
+                ])
+                for stage in stages
+            ]),
+        ]
+
+        if include_top:
+            _layers.append(
+                nn.Sequential(
+                    nn.AdaptiveAvgPool2d(1),
+                    nn.Flatten(1),
+                    nn.Linear(stages[-1]["out_channels"][-1], num_classes),
+                )
+            )
+
+        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 _textnet(
+    arch: str,
+    pretrained: bool,
+    ignore_keys: Optional[List[str]] = None,
+    **kwargs: Any,
+) -> TextNet:
+    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 = TextNet(**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 textnet_tiny(pretrained: bool = False, **kwargs: Any) -> TextNet:
+    """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 textnet_tiny
+    >>> model = textnet_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
+        **kwargs: keyword arguments of the TextNet architecture
+
+    Returns:
+    -------
+        A textnet tiny model
+    """
+    return _textnet(
+        "textnet_tiny",
+        pretrained,
+        stages=[
+            {"in_channels": [64] * 3, "out_channels": [64] * 3, "kernel_size": [(3, 3)] * 3, "stride": [1, 2, 1]},
+            {
+                "in_channels": [64, 128, 128, 128],
+                "out_channels": [128] * 4,
+                "kernel_size": [(3, 3), (1, 3), (3, 3), (3, 1)],
+                "stride": [2, 1, 1, 1],
+            },
+            {
+                "in_channels": [128, 256, 256, 256],
+                "out_channels": [256] * 4,
+                "kernel_size": [(3, 3), (3, 3), (3, 1), (1, 3)],
+                "stride": [2, 1, 1, 1],
+            },
+            {
+                "in_channels": [256, 512, 512, 512],
+                "out_channels": [512] * 4,
+                "kernel_size": [(3, 3), (3, 1), (1, 3), (3, 3)],
+                "stride": [2, 1, 1, 1],
+            },
+        ],
+        ignore_keys=["4.2.weight", "4.2.bias"],
+        **kwargs,
+    )
+
+
+def textnet_small(pretrained: bool = False, **kwargs: Any) -> TextNet:
+    """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 textnet_small
+    >>> model = textnet_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
+        **kwargs: keyword arguments of the TextNet architecture
+
+    Returns:
+    -------
+        A TextNet small model
+    """
+    return _textnet(
+        "textnet_small",
+        pretrained,
+        stages=[
+            {"in_channels": [64] * 2, "out_channels": [64] * 2, "kernel_size": [(3, 3)] * 2, "stride": [1, 2]},
+            {
+                "in_channels": [64, 128, 128, 128, 128, 128, 128, 128],
+                "out_channels": [128] * 8,
+                "kernel_size": [(3, 3), (1, 3), (3, 3), (3, 1), (3, 3), (3, 1), (1, 3), (3, 3)],
+                "stride": [2, 1, 1, 1, 1, 1, 1, 1],
+            },
+            {
+                "in_channels": [128, 256, 256, 256, 256, 256, 256, 256],
+                "out_channels": [256] * 8,
+                "kernel_size": [(3, 3), (3, 3), (1, 3), (3, 1), (3, 3), (1, 3), (3, 1), (3, 3)],
+                "stride": [2, 1, 1, 1, 1, 1, 1, 1],
+            },
+            {
+                "in_channels": [256, 512, 512, 512, 512],
+                "out_channels": [512] * 5,
+                "kernel_size": [(3, 3), (3, 1), (1, 3), (1, 3), (3, 1)],
+                "stride": [2, 1, 1, 1, 1],
+            },
+        ],
+        ignore_keys=["4.2.weight", "4.2.bias"],
+        **kwargs,
+    )
+
+
+def textnet_base(pretrained: bool = False, **kwargs: Any) -> TextNet:
+    """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 textnet_base
+    >>> model = textnet_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
+        **kwargs: keyword arguments of the TextNet architecture
+
+    Returns:
+    -------
+        A TextNet base model
+    """
+    return _textnet(
+        "textnet_base",
+        pretrained,
+        stages=[
+            {
+                "in_channels": [64] * 10,
+                "out_channels": [64] * 10,
+                "kernel_size": [(3, 3), (3, 3), (3, 1), (3, 3), (3, 1), (3, 3), (3, 3), (1, 3), (3, 3), (3, 3)],
+                "stride": [1, 2, 1, 1, 1, 1, 1, 1, 1, 1],
+            },
+            {
+                "in_channels": [64, 128, 128, 128, 128, 128, 128, 128, 128, 128],
+                "out_channels": [128] * 10,
+                "kernel_size": [(3, 3), (1, 3), (3, 3), (3, 1), (3, 3), (3, 3), (3, 1), (3, 1), (3, 3), (3, 3)],
+                "stride": [2, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+            },
+            {
+                "in_channels": [128, 256, 256, 256, 256, 256, 256, 256],
+                "out_channels": [256] * 8,
+                "kernel_size": [(3, 3), (3, 3), (3, 3), (1, 3), (3, 3), (3, 1), (3, 3), (3, 1)],
+                "stride": [2, 1, 1, 1, 1, 1, 1, 1],
+            },
+            {
+                "in_channels": [256, 512, 512, 512, 512],
+                "out_channels": [512] * 5,
+                "kernel_size": [(3, 3), (1, 3), (3, 1), (3, 1), (1, 3)],
+                "stride": [2, 1, 1, 1, 1],
+            },
+        ],
+        ignore_keys=["4.2.weight", "4.2.bias"],
+        **kwargs,
+    )