Source code for doctr.models.classification.mobilenet.tensorflow

# Copyright (C) 2021-2022, Mindee.

# This program is licensed under the Apache License version 2.
# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.

# Greatly inspired by https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenetv3.py

from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple, Union

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

from ....datasets import VOCABS
from ...utils import conv_sequence, load_pretrained_params

__all__ = ["MobileNetV3", "mobilenet_v3_small", "mobilenet_v3_small_r", "mobilenet_v3_large",
           "mobilenet_v3_large_r", "mobilenet_v3_small_orientation"]


default_cfgs: Dict[str, Dict[str, Any]] = {
    'mobilenet_v3_large': {
        'mean': (0.694, 0.695, 0.693),
        'std': (0.299, 0.296, 0.301),
        'input_shape': (32, 32, 3),
        'classes': list(VOCABS['french']),
        'url': 'https://github.com/mindee/doctr/releases/download/v0.4.1/mobilenet_v3_large-47d25d7e.zip',
    },
    'mobilenet_v3_large_r': {
        'mean': (0.694, 0.695, 0.693),
        'std': (0.299, 0.296, 0.301),
        'input_shape': (32, 32, 3),
        'classes': list(VOCABS['french']),
        'url': 'https://github.com/mindee/doctr/releases/download/v0.4.1/mobilenet_v3_large_r-a108e192.zip',
    },
    'mobilenet_v3_small': {
        'mean': (0.694, 0.695, 0.693),
        'std': (0.299, 0.296, 0.301),
        'input_shape': (32, 32, 3),
        'classes': list(VOCABS['french']),
        'url': 'https://github.com/mindee/doctr/releases/download/v0.4.1/mobilenet_v3_small-8a32c32c.zip',
    },
    'mobilenet_v3_small_r': {
        'mean': (0.694, 0.695, 0.693),
        'std': (0.299, 0.296, 0.301),
        'input_shape': (32, 32, 3),
        'classes': list(VOCABS['french']),
        'url': 'https://github.com/mindee/doctr/releases/download/v0.4.1/mobilenet_v3_small_r-3d61452e.zip',
    },
    'mobilenet_v3_small_orientation': {
        'mean': (0.694, 0.695, 0.693),
        'std': (0.299, 0.296, 0.301),
        'input_shape': (128, 128, 3),
        'classes': [0, 90, 180, 270],
        'url': 'https://github.com/mindee/doctr/releases/download/v0.4.1/classif_mobilenet_v3_small-1ea8db03.zip',
    },
}


def hard_swish(x: tf.Tensor) -> tf.Tensor:
    return x * tf.nn.relu6(x + 3.) / 6.0


def _make_divisible(v: float, divisor: int, min_value: Optional[int] = None) -> int:
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class SqueezeExcitation(Sequential):
    """Squeeze and Excitation.
    """
    def __init__(self, chan: int, squeeze_factor: int = 4) -> None:
        super().__init__(
            [
                layers.GlobalAveragePooling2D(),
                layers.Dense(chan // squeeze_factor, activation='relu'),
                layers.Dense(chan, activation='hard_sigmoid'),
                layers.Reshape((1, 1, chan))
            ]
        )

    def call(self, inputs: tf.Tensor, **kwargs: Any) -> tf.Tensor:
        x = super().call(inputs, **kwargs)
        x = tf.math.multiply(inputs, x)
        return x


class InvertedResidualConfig:
    def __init__(
        self,
        input_channels: int,
        kernel: int,
        expanded_channels: int,
        out_channels: int,
        use_se: bool,
        activation: str,
        stride: Union[int, Tuple[int, int]],
        width_mult: float = 1,
    ) -> None:
        self.input_channels = self.adjust_channels(input_channels, width_mult)
        self.kernel = kernel
        self.expanded_channels = self.adjust_channels(expanded_channels, width_mult)
        self.out_channels = self.adjust_channels(out_channels, width_mult)
        self.use_se = use_se
        self.use_hs = activation == "HS"
        self.stride = stride

    @staticmethod
    def adjust_channels(channels: int, width_mult: float):
        return _make_divisible(channels * width_mult, 8)


class InvertedResidual(layers.Layer):
    """InvertedResidual for mobilenet

    Args:
        conf: configuration object for inverted residual
    """
    def __init__(
        self,
        conf: InvertedResidualConfig,
        **kwargs: Any,
    ) -> None:
        _kwargs = {'input_shape': kwargs.pop('input_shape')} if isinstance(kwargs.get('input_shape'), tuple) else {}
        super().__init__(**kwargs)

        act_fn = hard_swish if conf.use_hs else tf.nn.relu

        _is_s1 = (isinstance(conf.stride, tuple) and conf.stride == (1, 1)) or conf.stride == 1
        self.use_res_connect = _is_s1 and conf.input_channels == conf.out_channels

        _layers = []
        # expand
        if conf.expanded_channels != conf.input_channels:
            _layers.extend(conv_sequence(conf.expanded_channels, act_fn, kernel_size=1, bn=True, **_kwargs))

        # depth-wise
        _layers.extend(conv_sequence(
            conf.expanded_channels, act_fn, kernel_size=conf.kernel, strides=conf.stride, bn=True,
            groups=conf.expanded_channels,
        ))

        if conf.use_se:
            _layers.append(SqueezeExcitation(conf.expanded_channels))

        # project
        _layers.extend(conv_sequence(
            conf.out_channels, None, kernel_size=1, bn=True,
        ))

        self.block = Sequential(_layers)

    def call(
        self,
        inputs: tf.Tensor,
        **kwargs: Any,
    ) -> tf.Tensor:

        out = self.block(inputs, **kwargs)
        if self.use_res_connect:
            out = tf.add(out, inputs)

        return out


class MobileNetV3(Sequential):
    """Implements MobileNetV3, inspired from both:
    <https://github.com/xiaochus/MobileNetV3/tree/master/model>`_.
    and <https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv3.html>`_.
    """

    def __init__(
        self,
        layout: List[InvertedResidualConfig],
        include_top: bool = True,
        head_chans: int = 1024,
        num_classes: int = 1000,
        cfg: Optional[Dict[str, Any]] = None,
        input_shape: Optional[Tuple[int, int, int]] = None,
    ) -> None:

        _layers = [
            Sequential(conv_sequence(layout[0].input_channels, hard_swish, True, kernel_size=3, strides=2,
                       input_shape=input_shape), name="stem")
        ]

        for idx, conf in enumerate(layout):
            _layers.append(
                InvertedResidual(conf, name=f"inverted_{idx}"),
            )

        _layers.append(
            Sequential(
                conv_sequence(6 * layout[-1].out_channels, hard_swish, True, kernel_size=1),
                name="final_block"
            )
        )

        if include_top:
            _layers.extend([
                layers.GlobalAveragePooling2D(),
                layers.Dense(head_chans, activation=hard_swish),
                layers.Dropout(0.2),
                layers.Dense(num_classes),
            ])

        super().__init__(_layers)
        self.cfg = cfg


def _mobilenet_v3(
    arch: str,
    pretrained: bool,
    rect_strides: bool = False,
    **kwargs: Any
) -> MobileNetV3:
    _cfg = deepcopy(default_cfgs[arch])
    _cfg['input_shape'] = kwargs.get('input_shape', default_cfgs[arch]['input_shape'])
    _cfg['num_classes'] = kwargs.get('num_classes', len(default_cfgs[arch]['classes']))

    # cf. Table 1 & 2 of the paper
    if arch.startswith("mobilenet_v3_small"):
        inverted_residual_setting = [
            InvertedResidualConfig(16, 3, 16, 16, True, "RE", 2),  # C1
            InvertedResidualConfig(16, 3, 72, 24, False, "RE", (2, 1) if rect_strides else 2),  # C2
            InvertedResidualConfig(24, 3, 88, 24, False, "RE", 1),
            InvertedResidualConfig(24, 5, 96, 40, True, "HS", (2, 1) if rect_strides else 2),  # C3
            InvertedResidualConfig(40, 5, 240, 40, True, "HS", 1),
            InvertedResidualConfig(40, 5, 240, 40, True, "HS", 1),
            InvertedResidualConfig(40, 5, 120, 48, True, "HS", 1),
            InvertedResidualConfig(48, 5, 144, 48, True, "HS", 1),
            InvertedResidualConfig(48, 5, 288, 96, True, "HS", (2, 1) if rect_strides else 2),  # C4
            InvertedResidualConfig(96, 5, 576, 96, True, "HS", 1),
            InvertedResidualConfig(96, 5, 576, 96, True, "HS", 1),
        ]
        head_chans = 1024
    else:
        inverted_residual_setting = [
            InvertedResidualConfig(16, 3, 16, 16, False, "RE", 1),
            InvertedResidualConfig(16, 3, 64, 24, False, "RE", 2),  # C1
            InvertedResidualConfig(24, 3, 72, 24, False, "RE", 1),
            InvertedResidualConfig(24, 5, 72, 40, True, "RE", (2, 1) if rect_strides else 2),  # C2
            InvertedResidualConfig(40, 5, 120, 40, True, "RE", 1),
            InvertedResidualConfig(40, 5, 120, 40, True, "RE", 1),
            InvertedResidualConfig(40, 3, 240, 80, False, "HS", (2, 1) if rect_strides else 2),  # C3
            InvertedResidualConfig(80, 3, 200, 80, False, "HS", 1),
            InvertedResidualConfig(80, 3, 184, 80, False, "HS", 1),
            InvertedResidualConfig(80, 3, 184, 80, False, "HS", 1),
            InvertedResidualConfig(80, 3, 480, 112, True, "HS", 1),
            InvertedResidualConfig(112, 3, 672, 112, True, "HS", 1),
            InvertedResidualConfig(112, 5, 672, 160, True, "HS", (2, 1) if rect_strides else 2),  # C4
            InvertedResidualConfig(160, 5, 960, 160, True, "HS", 1),
            InvertedResidualConfig(160, 5, 960, 160, True, "HS", 1),
        ]
        head_chans = 1280

    kwargs['num_classes'] = _cfg['num_classes']
    kwargs['input_shape'] = _cfg['input_shape']

    # Build the model
    model = MobileNetV3(
        inverted_residual_setting,
        head_chans=head_chans,
        cfg=_cfg,
        **kwargs,
    )
    # Load pretrained parameters
    if pretrained:
        load_pretrained_params(model, default_cfgs[arch]['url'])

    return model


[docs]def mobilenet_v3_small(pretrained: bool = False, **kwargs: Any) -> MobileNetV3: """MobileNetV3-Small architecture as described in `"Searching for MobileNetV3", <https://arxiv.org/pdf/1905.02244.pdf>`_. >>> import tensorflow as tf >>> from doctr.models import mobilenet_v3_small >>> model = mobilenet_v3_small(pretrained=False) >>> input_tensor = tf.random.uniform(shape=[1, 512, 512, 3], maxval=1, dtype=tf.float32) >>> out = model(input_tensor) Args: pretrained: boolean, True if model is pretrained Returns: a keras.Model """ return _mobilenet_v3('mobilenet_v3_small', pretrained, False, **kwargs)
[docs]def mobilenet_v3_small_r(pretrained: bool = False, **kwargs: Any) -> MobileNetV3: """MobileNetV3-Small architecture as described in `"Searching for MobileNetV3", <https://arxiv.org/pdf/1905.02244.pdf>`_, with rectangular pooling. >>> import tensorflow as tf >>> from doctr.models import mobilenet_v3_small_r >>> model = mobilenet_v3_small_r(pretrained=False) >>> input_tensor = tf.random.uniform(shape=[1, 512, 512, 3], maxval=1, dtype=tf.float32) >>> out = model(input_tensor) Args: pretrained: boolean, True if model is pretrained Returns: a keras.Model """ return _mobilenet_v3('mobilenet_v3_small_r', pretrained, True, **kwargs)
[docs]def mobilenet_v3_large(pretrained: bool = False, **kwargs: Any) -> MobileNetV3: """MobileNetV3-Large architecture as described in `"Searching for MobileNetV3", <https://arxiv.org/pdf/1905.02244.pdf>`_. >>> import tensorflow as tf >>> from doctr.models import mobilenet_v3_large >>> model = mobilenet_v3_large(pretrained=False) >>> input_tensor = tf.random.uniform(shape=[1, 512, 512, 3], maxval=1, dtype=tf.float32) >>> out = model(input_tensor) Args: pretrained: boolean, True if model is pretrained Returns: a keras.Model """ return _mobilenet_v3('mobilenet_v3_large', pretrained, False, **kwargs)
[docs]def mobilenet_v3_large_r(pretrained: bool = False, **kwargs: Any) -> MobileNetV3: """MobileNetV3-Large architecture as described in `"Searching for MobileNetV3", <https://arxiv.org/pdf/1905.02244.pdf>`_. >>> import tensorflow as tf >>> from doctr.models import mobilenet_v3_large_r >>> model = mobilenet_v3_large_r(pretrained=False) >>> input_tensor = tf.random.uniform(shape=[1, 512, 512, 3], maxval=1, dtype=tf.float32) >>> out = model(input_tensor) Args: pretrained: boolean, True if model is pretrained Returns: a keras.Model """ return _mobilenet_v3('mobilenet_v3_large_r', pretrained, True, **kwargs)
[docs]def mobilenet_v3_small_orientation(pretrained: bool = False, **kwargs: Any) -> MobileNetV3: """MobileNetV3-Small architecture as described in `"Searching for MobileNetV3", <https://arxiv.org/pdf/1905.02244.pdf>`_. >>> import tensorflow as tf >>> from doctr.models import mobilenet_v3_small_orientation >>> model = mobilenet_v3_small_orientation(pretrained=False) >>> input_tensor = tf.random.uniform(shape=[1, 512, 512, 3], maxval=1, dtype=tf.float32) >>> out = model(input_tensor) Args: pretrained: boolean, True if model is pretrained Returns: a keras.Model """ return _mobilenet_v3('mobilenet_v3_small_orientation', pretrained, include_top=True, **kwargs)