# 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, Callable, Dict, List, Optional, Tuple
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Sequential
from doctr.datasets import VOCABS
from ...utils import conv_sequence, load_pretrained_params
__all__ = ["ResNet", "resnet18", "resnet31", "resnet34", "resnet50", "resnet34_wide"]
default_cfgs: Dict[str, Dict[str, Any]] = {
"resnet18": {
"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://doctr-static.mindee.com/models?id=v0.4.1/resnet18-d4634669.zip&src=0",
},
"resnet31": {
"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://doctr-static.mindee.com/models?id=v0.5.0/resnet31-5a47a60b.zip&src=0",
},
"resnet34": {
"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://doctr-static.mindee.com/models?id=v0.5.0/resnet34-5dcc97ca.zip&src=0",
},
"resnet50": {
"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://doctr-static.mindee.com/models?id=v0.5.0/resnet50-e75e4cdf.zip&src=0",
},
"resnet34_wide": {
"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://doctr-static.mindee.com/models?id=v0.5.0/resnet34_wide-c1271816.zip&src=0",
},
}
class ResnetBlock(layers.Layer):
"""Implements a resnet31 block with shortcut
Args:
----
conv_shortcut: Use of shortcut
output_channels: number of channels to use in Conv2D
kernel_size: size of square kernels
strides: strides to use in the first convolution of the block
"""
def __init__(self, output_channels: int, conv_shortcut: bool, strides: int = 1, **kwargs) -> None:
super().__init__(**kwargs)
if conv_shortcut:
self.shortcut = Sequential([
layers.Conv2D(
filters=output_channels,
strides=strides,
padding="same",
kernel_size=1,
use_bias=False,
kernel_initializer="he_normal",
),
layers.BatchNormalization(),
])
else:
self.shortcut = layers.Lambda(lambda x: x)
self.conv_block = Sequential(self.conv_resnetblock(output_channels, 3, strides))
self.act = layers.Activation("relu")
@staticmethod
def conv_resnetblock(
output_channels: int,
kernel_size: int,
strides: int = 1,
) -> List[layers.Layer]:
return [
*conv_sequence(output_channels, "relu", bn=True, strides=strides, kernel_size=kernel_size),
*conv_sequence(output_channels, None, bn=True, kernel_size=kernel_size),
]
def call(self, inputs: tf.Tensor) -> tf.Tensor:
clone = self.shortcut(inputs)
conv_out = self.conv_block(inputs)
out = self.act(clone + conv_out)
return out
def resnet_stage(
num_blocks: int, out_channels: int, shortcut: bool = False, downsample: bool = False
) -> List[layers.Layer]:
_layers: List[layers.Layer] = [ResnetBlock(out_channels, conv_shortcut=shortcut, strides=2 if downsample else 1)]
for _ in range(1, num_blocks):
_layers.append(ResnetBlock(out_channels, conv_shortcut=False))
return _layers
class ResNet(Sequential):
"""Implements a ResNet architecture
Args:
----
num_blocks: number of resnet block in each stage
output_channels: number of channels in each stage
stage_downsample: whether the first residual block of a stage should downsample
stage_conv: whether to add a conv_sequence after each stage
stage_pooling: pooling to add after each stage (if None, no pooling)
origin_stem: whether to use the orginal ResNet stem or ResNet-31's
stem_channels: number of output channels of the stem convolutions
attn_module: attention module to use in each stage
include_top: whether the classifier head should be instantiated
num_classes: number of output classes
input_shape: shape of inputs
"""
def __init__(
self,
num_blocks: List[int],
output_channels: List[int],
stage_downsample: List[bool],
stage_conv: List[bool],
stage_pooling: List[Optional[Tuple[int, int]]],
origin_stem: bool = True,
stem_channels: int = 64,
attn_module: Optional[Callable[[int], layers.Layer]] = None,
include_top: bool = True,
num_classes: int = 1000,
cfg: Optional[Dict[str, Any]] = None,
input_shape: Optional[Tuple[int, int, int]] = None,
) -> None:
inplanes = stem_channels
if origin_stem:
_layers = [
*conv_sequence(inplanes, "relu", True, kernel_size=7, strides=2, input_shape=input_shape),
layers.MaxPool2D(pool_size=(3, 3), strides=2, padding="same"),
]
else:
_layers = [
*conv_sequence(inplanes // 2, "relu", True, kernel_size=3, input_shape=input_shape),
*conv_sequence(inplanes, "relu", True, kernel_size=3),
layers.MaxPool2D(pool_size=2, strides=2, padding="valid"),
]
for n_blocks, out_chan, down, conv, pool in zip(
num_blocks, output_channels, stage_downsample, stage_conv, stage_pooling
):
_layers.extend(resnet_stage(n_blocks, out_chan, out_chan != inplanes, down))
if attn_module is not None:
_layers.append(attn_module(out_chan))
if conv:
_layers.extend(conv_sequence(out_chan, activation="relu", bn=True, kernel_size=3))
if pool:
_layers.append(layers.MaxPool2D(pool_size=pool, strides=pool, padding="valid"))
inplanes = out_chan
if include_top:
_layers.extend([
layers.GlobalAveragePooling2D(),
layers.Dense(num_classes),
])
super().__init__(_layers)
self.cfg = cfg
def _resnet(
arch: str,
pretrained: bool,
num_blocks: List[int],
output_channels: List[int],
stage_downsample: List[bool],
stage_conv: List[bool],
stage_pooling: List[Optional[Tuple[int, int]]],
origin_stem: bool = True,
**kwargs: Any,
) -> ResNet:
kwargs["num_classes"] = kwargs.get("num_classes", len(default_cfgs[arch]["classes"]))
kwargs["input_shape"] = kwargs.get("input_shape", default_cfgs[arch]["input_shape"])
kwargs["classes"] = kwargs.get("classes", default_cfgs[arch]["classes"])
_cfg = deepcopy(default_cfgs[arch])
_cfg["num_classes"] = kwargs["num_classes"]
_cfg["classes"] = kwargs["classes"]
_cfg["input_shape"] = kwargs["input_shape"]
kwargs.pop("classes")
# Build the model
model = ResNet(
num_blocks, output_channels, stage_downsample, stage_conv, stage_pooling, origin_stem, cfg=_cfg, **kwargs
)
# Load pretrained parameters
if pretrained:
load_pretrained_params(model, default_cfgs[arch]["url"])
return model
def resnet18(pretrained: bool = False, **kwargs: Any) -> ResNet:
"""Resnet-18 architecture as described in `"Deep Residual Learning for Image Recognition",
<https://arxiv.org/pdf/1512.03385.pdf>`_.
>>> import tensorflow as tf
>>> from doctr.models import resnet18
>>> model = resnet18(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
**kwargs: keyword arguments of the ResNet architecture
Returns:
-------
A classification model
"""
return _resnet(
"resnet18",
pretrained,
[2, 2, 2, 2],
[64, 128, 256, 512],
[False, True, True, True],
[False] * 4,
[None] * 4,
True,
**kwargs,
)
def resnet31(pretrained: bool = False, **kwargs: Any) -> ResNet:
"""Resnet31 architecture with rectangular pooling windows as described in
`"Show, Attend and Read:A Simple and Strong Baseline for Irregular Text Recognition",
<https://arxiv.org/pdf/1811.00751.pdf>`_. Downsizing: (H, W) --> (H/8, W/4)
>>> import tensorflow as tf
>>> from doctr.models import resnet31
>>> model = resnet31(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
**kwargs: keyword arguments of the ResNet architecture
Returns:
-------
A classification model
"""
return _resnet(
"resnet31",
pretrained,
[1, 2, 5, 3],
[256, 256, 512, 512],
[False] * 4,
[True] * 4,
[(2, 2), (2, 1), None, None],
False,
stem_channels=128,
**kwargs,
)
def resnet34(pretrained: bool = False, **kwargs: Any) -> ResNet:
"""Resnet-34 architecture as described in `"Deep Residual Learning for Image Recognition",
<https://arxiv.org/pdf/1512.03385.pdf>`_.
>>> import tensorflow as tf
>>> from doctr.models import resnet34
>>> model = resnet34(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
**kwargs: keyword arguments of the ResNet architecture
Returns:
-------
A classification model
"""
return _resnet(
"resnet34",
pretrained,
[3, 4, 6, 3],
[64, 128, 256, 512],
[False, True, True, True],
[False] * 4,
[None] * 4,
True,
**kwargs,
)
def resnet50(pretrained: bool = False, **kwargs: Any) -> ResNet:
"""Resnet-50 architecture as described in `"Deep Residual Learning for Image Recognition",
<https://arxiv.org/pdf/1512.03385.pdf>`_.
>>> import tensorflow as tf
>>> from doctr.models import resnet50
>>> model = resnet50(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
**kwargs: keyword arguments of the ResNet architecture
Returns:
-------
A classification model
"""
kwargs["num_classes"] = kwargs.get("num_classes", len(default_cfgs["resnet50"]["classes"]))
kwargs["input_shape"] = kwargs.get("input_shape", default_cfgs["resnet50"]["input_shape"])
kwargs["classes"] = kwargs.get("classes", default_cfgs["resnet50"]["classes"])
_cfg = deepcopy(default_cfgs["resnet50"])
_cfg["num_classes"] = kwargs["num_classes"]
_cfg["classes"] = kwargs["classes"]
_cfg["input_shape"] = kwargs["input_shape"]
kwargs.pop("classes")
model = ResNet50(
weights=None,
include_top=True,
pooling=True,
input_shape=kwargs["input_shape"],
classes=kwargs["num_classes"],
classifier_activation=None,
)
model.cfg = _cfg
# Load pretrained parameters
if pretrained:
load_pretrained_params(model, default_cfgs["resnet50"]["url"])
return model
def resnet34_wide(pretrained: bool = False, **kwargs: Any) -> ResNet:
"""Resnet-34 architecture as described in `"Deep Residual Learning for Image Recognition",
<https://arxiv.org/pdf/1512.03385.pdf>`_ with twice as many output channels for each stage.
>>> import tensorflow as tf
>>> from doctr.models import resnet34_wide
>>> model = resnet34_wide(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
**kwargs: keyword arguments of the ResNet architecture
Returns:
-------
A classification model
"""
return _resnet(
"resnet34_wide",
pretrained,
[3, 4, 6, 3],
[128, 256, 512, 1024],
[False, True, True, True],
[False] * 4,
[None] * 4,
True,
stem_channels=128,
**kwargs,
)