fix PREN export and infer (#7833)
littletomatodonkey
GitHub
parent
077196f3cb
commit
eeef62b3c6
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@ -21,124 +21,165 @@ from __future__ import division
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from __future__ import print_function
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import math
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from collections import namedtuple
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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']
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GlobalParams = collections.namedtuple('GlobalParams', [
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'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate', 'num_classes',
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'width_coefficient', 'depth_coefficient', 'depth_divisor', 'min_depth',
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'drop_connect_rate', 'image_size'
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])
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class EffB3Params:
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@staticmethod
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def get_global_params():
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"""
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The fllowing are efficientnetb3's arch superparams, but to fit for scene
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text recognition task, the resolution(image_size) here is changed
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from 300 to 64.
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"""
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GlobalParams = namedtuple('GlobalParams', [
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'drop_connect_rate', 'width_coefficient', 'depth_coefficient',
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'depth_divisor', 'image_size'
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])
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global_params = GlobalParams(
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drop_connect_rate=0.3,
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width_coefficient=1.2,
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depth_coefficient=1.4,
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depth_divisor=8,
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image_size=64)
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return global_params
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@staticmethod
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def get_block_params():
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BlockParams = namedtuple('BlockParams', [
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BlockArgs = collections.namedtuple('BlockArgs', [
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'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
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'expand_ratio', 'id_skip', 'se_ratio', 'stride'
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])
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block_params = [
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BlockParams(3, 1, 32, 16, 1, True, 0.25, 1),
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BlockParams(3, 2, 16, 24, 6, True, 0.25, 2),
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BlockParams(5, 2, 24, 40, 6, True, 0.25, 2),
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BlockParams(3, 3, 40, 80, 6, True, 0.25, 2),
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BlockParams(5, 3, 80, 112, 6, True, 0.25, 1),
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BlockParams(5, 4, 112, 192, 6, True, 0.25, 2),
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BlockParams(3, 1, 192, 320, 6, True, 0.25, 1)
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'expand_ratio', 'id_skip', 'stride', 'se_ratio'
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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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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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@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(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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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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return block_params
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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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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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""" 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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new_filters = int(filters + divisor / 2) // divisor * divisor
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min_depth = min_depth or divisor
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new_filters = max(min_depth,
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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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""" 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 ConvBlock(nn.Layer):
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def __init__(self, block_params):
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super(ConvBlock, self).__init__()
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self.block_args = block_params
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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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self.id_skip = block_params.id_skip
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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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self.id_skip = block_args.id_skip
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# expansion phase
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self.input_filters = self.block_args.input_filters
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output_filters = \
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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(
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self.input_filters, output_filters, 1, bias_attr=False)
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self.bn0 = nn.BatchNorm(output_filters)
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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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self.depthwise_conv = nn.Conv2D(
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output_filters,
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output_filters,
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groups=output_filters,
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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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self.bn1 = nn.BatchNorm(output_filters)
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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(1,
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int(self.block_args.input_filters *
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self.block_args.se_ratio))
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self.se_reduce = nn.Conv2D(output_filters, num_squeezed_channels, 1)
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self.se_expand = nn.Conv2D(num_squeezed_channels, output_filters, 1)
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int(self._block_args.input_filters *
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self._block_args.se_ratio))
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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
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self.final_oup = self.block_args.output_filters
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self.project_conv = nn.Conv2D(
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output_filters, 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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# 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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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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@ -151,22 +192,23 @@ class ConvBlock(nn.Layer):
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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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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_squeezed = self._se_expand(
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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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x = self._bn2(self._project_conv(x))
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# skip conntection and drop connect
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if self.id_skip and self.block_args.stride == 1 and \
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self.input_filters == self.final_oup:
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if self.id_skip and self._block_args.stride == 1 and \
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self.inp == self.final_oup:
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if drop_connect_rate:
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x = self.drop_connect(
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x = self._drop_connect(
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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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@ -175,54 +217,63 @@ class ConvBlock(nn.Layer):
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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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self.blocks_params = EffB3Params.get_block_params()
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self.global_params = EffB3Params.get_global_params()
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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,
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depth_coefficient=d,
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dropout_rate=p,
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image_size=s)
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self.out_channels = []
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# stem
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stem_channels = EffUtils.round_filters(32, self.global_params)
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self.conv_stem = nn.Conv2D(
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in_channels, stem_channels, 3, 2, padding='same', bias_attr=False)
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self.bn0 = nn.BatchNorm(stem_channels)
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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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self._bn0 = nn.BatchNorm(out_channels)
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self.blocks = []
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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_params in enumerate(self.blocks_params):
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block_params = block_params._replace(
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input_filters=EffUtils.round_filters(block_params.input_filters,
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self.global_params),
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output_filters=EffUtils.round_filters(
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block_params.output_filters, self.global_params),
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num_repeat=EffUtils.round_repeats(block_params.num_repeat,
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self.global_params))
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self.blocks.append(
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self.add_sublayer("{}-0".format(i), ConvBlock(block_params)))
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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_params.output_filters)
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if block_params.num_repeat > 1:
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block_params = block_params._replace(
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input_filters=block_params.output_filters, stride=1)
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for j in range(block_params.num_repeat - 1):
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self.blocks.append(
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self.add_sublayer('{}-{}'.format(i, j + 1),
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ConvBlock(block_params)))
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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_params.output_filters)
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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(block_args.input_filters,
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self._global_params),
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output_filters=EffUtils.round_filters(block_args.output_filters,
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self._global_params),
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num_repeat=EffUtils.round_repeats(block_args.num_repeat,
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self._global_params))
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self._blocks.append(
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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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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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_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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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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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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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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if idx in self._concerned_block_idxes:
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outs.append(x)
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return outs
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@ -562,6 +562,7 @@ class PRENLabelDecode(BaseRecLabelDecode):
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return result_list
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def __call__(self, preds, label=None, *args, **kwargs):
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if isinstance(preds, paddle.Tensor):
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preds = preds.numpy()
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preds_idx = preds.argmax(axis=2)
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preds_prob = preds.max(axis=2)
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@ -77,7 +77,7 @@ def export_single_model(model,
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elif arch_config["algorithm"] == "PREN":
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other_shape = [
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paddle.static.InputSpec(
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shape=[None, 3, 64, 512], dtype="float32"),
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shape=[None, 3, 64, 256], dtype="float32"),
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]
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model = to_static(model, input_spec=other_shape)
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elif arch_config["model_type"] == "sr":
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@ -100,6 +100,8 @@ class TextRecognizer(object):
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"use_space_char": args.use_space_char,
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"rm_symbol": True
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}
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elif self.rec_algorithm == "PREN":
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postprocess_params = {'name': 'PRENLabelDecode'}
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self.postprocess_op = build_post_process(postprocess_params)
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self.predictor, self.input_tensor, self.output_tensors, self.config = \
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utility.create_predictor(args, 'rec', logger)
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@ -384,7 +386,7 @@ class TextRecognizer(object):
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self.rec_image_shape)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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elif self.rec_algorithm == "VisionLAN":
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elif self.rec_algorithm in ["VisionLAN", "PREN"]:
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norm_img = self.resize_norm_img_vl(img_list[indices[ino]],
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self.rec_image_shape)
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norm_img = norm_img[np.newaxis, :]
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