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ppocr/modeling/backbones/rec_efficientb3_pren.py
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ppocr/modeling/backbones/rec_efficientb3_pren.py
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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Code is refer from:
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https://github.com/RuijieJ/pren/blob/main/Nets/EfficientNet.py
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import re
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import collections
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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__all__ = ["EfficientNetb3_PREN"]
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GlobalParams = collections.namedtuple(
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"GlobalParams",
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[
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"batch_norm_momentum",
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"batch_norm_epsilon",
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"dropout_rate",
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"num_classes",
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"width_coefficient",
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"depth_coefficient",
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"depth_divisor",
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"min_depth",
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"drop_connect_rate",
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"image_size",
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],
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)
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BlockArgs = collections.namedtuple(
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"BlockArgs",
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[
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"kernel_size",
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"num_repeat",
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"input_filters",
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"output_filters",
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"expand_ratio",
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"id_skip",
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"stride",
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"se_ratio",
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],
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)
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class BlockDecoder:
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@staticmethod
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def _decode_block_string(block_string):
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assert isinstance(block_string, str)
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ops = block_string.split("_")
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options = {}
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for op in ops:
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splits = re.split(r"(\d.*)", op)
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if len(splits) >= 2:
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key, value = splits[:2]
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options[key] = value
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assert ("s" in options and len(options["s"]) == 1) or (
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len(options["s"]) == 2 and options["s"][0] == options["s"][1]
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)
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return BlockArgs(
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kernel_size=int(options["k"]),
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num_repeat=int(options["r"]),
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input_filters=int(options["i"]),
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output_filters=int(options["o"]),
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expand_ratio=int(options["e"]),
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id_skip=("noskip" not in block_string),
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se_ratio=float(options["se"]) if "se" in options else None,
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stride=[int(options["s"][0])],
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)
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@staticmethod
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def decode(string_list):
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assert isinstance(string_list, list)
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blocks_args = []
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for block_string in string_list:
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blocks_args.append(BlockDecoder._decode_block_string(block_string))
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return blocks_args
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def efficientnet(
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width_coefficient=None,
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depth_coefficient=None,
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dropout_rate=0.2,
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drop_connect_rate=0.2,
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image_size=None,
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num_classes=1000,
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):
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blocks_args = [
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"r1_k3_s11_e1_i32_o16_se0.25",
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"r2_k3_s22_e6_i16_o24_se0.25",
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"r2_k5_s22_e6_i24_o40_se0.25",
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"r3_k3_s22_e6_i40_o80_se0.25",
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"r3_k5_s11_e6_i80_o112_se0.25",
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"r4_k5_s22_e6_i112_o192_se0.25",
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"r1_k3_s11_e6_i192_o320_se0.25",
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]
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blocks_args = BlockDecoder.decode(blocks_args)
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global_params = GlobalParams(
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batch_norm_momentum=0.99,
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batch_norm_epsilon=1e-3,
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dropout_rate=dropout_rate,
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drop_connect_rate=drop_connect_rate,
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num_classes=num_classes,
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width_coefficient=width_coefficient,
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depth_coefficient=depth_coefficient,
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depth_divisor=8,
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min_depth=None,
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image_size=image_size,
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)
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return blocks_args, global_params
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class EffUtils:
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@staticmethod
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def round_filters(filters, global_params):
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"""Calculate and round number of filters based on depth multiplier."""
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multiplier = global_params.width_coefficient
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if not multiplier:
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return filters
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divisor = global_params.depth_divisor
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min_depth = global_params.min_depth
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filters *= multiplier
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min_depth = min_depth or divisor
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new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
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if new_filters < 0.9 * filters:
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new_filters += divisor
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return int(new_filters)
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@staticmethod
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def round_repeats(repeats, global_params):
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"""Round number of filters based on depth multiplier."""
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multiplier = global_params.depth_coefficient
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if not multiplier:
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return repeats
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return int(math.ceil(multiplier * repeats))
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class MbConvBlock(nn.Layer):
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def __init__(self, block_args):
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super(MbConvBlock, self).__init__()
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self._block_args = block_args
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self.has_se = (self._block_args.se_ratio is not None) and (
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0 < self._block_args.se_ratio <= 1
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)
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self.id_skip = block_args.id_skip
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# expansion phase
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self.inp = self._block_args.input_filters
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oup = self._block_args.input_filters * self._block_args.expand_ratio
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if self._block_args.expand_ratio != 1:
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self._expand_conv = nn.Conv2D(self.inp, oup, 1, bias_attr=False)
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self._bn0 = nn.BatchNorm(oup)
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# depthwise conv phase
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k = self._block_args.kernel_size
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s = self._block_args.stride
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if isinstance(s, list):
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s = s[0]
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self._depthwise_conv = nn.Conv2D(
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oup,
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oup,
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groups=oup,
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kernel_size=k,
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stride=s,
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padding="same",
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bias_attr=False,
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)
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self._bn1 = nn.BatchNorm(oup)
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# squeeze and excitation layer, if desired
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if self.has_se:
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num_squeezed_channels = max(
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1, int(self._block_args.input_filters * self._block_args.se_ratio)
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)
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self._se_reduce = nn.Conv2D(oup, num_squeezed_channels, 1)
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self._se_expand = nn.Conv2D(num_squeezed_channels, oup, 1)
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# output phase and some util class
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self.final_oup = self._block_args.output_filters
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self._project_conv = nn.Conv2D(oup, self.final_oup, 1, bias_attr=False)
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self._bn2 = nn.BatchNorm(self.final_oup)
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self._swish = nn.Swish()
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def _drop_connect(self, inputs, p, training):
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if not training:
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return inputs
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batch_size = inputs.shape[0]
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keep_prob = 1 - p
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random_tensor = keep_prob
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random_tensor += paddle.rand([batch_size, 1, 1, 1], dtype=inputs.dtype)
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random_tensor = paddle.to_tensor(random_tensor, place=inputs.place)
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binary_tensor = paddle.floor(random_tensor)
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output = inputs / keep_prob * binary_tensor
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return output
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def forward(self, inputs, drop_connect_rate=None):
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# expansion and depthwise conv
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x = inputs
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if self._block_args.expand_ratio != 1:
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x = self._swish(self._bn0(self._expand_conv(inputs)))
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x = self._swish(self._bn1(self._depthwise_conv(x)))
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# squeeze and excitation
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if self.has_se:
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x_squeezed = F.adaptive_avg_pool2d(x, 1)
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x_squeezed = self._se_expand(self._swish(self._se_reduce(x_squeezed)))
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x = F.sigmoid(x_squeezed) * x
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x = self._bn2(self._project_conv(x))
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# skip connection and drop connect
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if self.id_skip and self._block_args.stride == 1 and self.inp == self.final_oup:
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if drop_connect_rate:
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x = self._drop_connect(x, p=drop_connect_rate, training=self.training)
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x = x + inputs
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return x
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class EfficientNetb3_PREN(nn.Layer):
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def __init__(self, in_channels):
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super(EfficientNetb3_PREN, self).__init__()
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"""
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the fllowing are efficientnetb3's superparams,
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they means efficientnetb3 network's width, depth, resolution and
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dropout respectively, to fit for text recognition task, the resolution
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here is changed from 300 to 64.
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"""
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w, d, s, p = 1.2, 1.4, 64, 0.3
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self._blocks_args, self._global_params = efficientnet(
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width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s
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)
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self.out_channels = []
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# stem
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out_channels = EffUtils.round_filters(32, self._global_params)
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self._conv_stem = nn.Conv2D(
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in_channels, out_channels, 3, 2, padding="same", bias_attr=False
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)
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self._bn0 = nn.BatchNorm(out_channels)
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# build blocks
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self._blocks = []
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# to extract three feature maps for fpn based on efficientnetb3 backbone
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self._concerned_block_idxes = [7, 17, 25]
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_concerned_idx = 0
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for i, block_args in enumerate(self._blocks_args):
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block_args = block_args._replace(
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input_filters=EffUtils.round_filters(
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block_args.input_filters, self._global_params
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),
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output_filters=EffUtils.round_filters(
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block_args.output_filters, self._global_params
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),
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num_repeat=EffUtils.round_repeats(
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block_args.num_repeat, self._global_params
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),
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)
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self._blocks.append(self.add_sublayer(f"{i}-0", MbConvBlock(block_args)))
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_concerned_idx += 1
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if _concerned_idx in self._concerned_block_idxes:
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self.out_channels.append(block_args.output_filters)
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if block_args.num_repeat > 1:
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block_args = block_args._replace(
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input_filters=block_args.output_filters, stride=1
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)
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for j in range(block_args.num_repeat - 1):
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self._blocks.append(
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self.add_sublayer(f"{i}-{j+1}", MbConvBlock(block_args))
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)
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_concerned_idx += 1
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if _concerned_idx in self._concerned_block_idxes:
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self.out_channels.append(block_args.output_filters)
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self._swish = nn.Swish()
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def forward(self, inputs):
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outs = []
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x = self._swish(self._bn0(self._conv_stem(inputs)))
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for idx, block in enumerate(self._blocks):
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drop_connect_rate = self._global_params.drop_connect_rate
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if drop_connect_rate:
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drop_connect_rate *= float(idx) / len(self._blocks)
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x = block(x, drop_connect_rate=drop_connect_rate)
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if idx in self._concerned_block_idxes:
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outs.append(x)
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return outs
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