229 lines
8.7 KiB
Python
229 lines
8.7 KiB
Python
# 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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from collections import namedtuple
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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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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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'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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]
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return block_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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filters *= multiplier
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new_filters = 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 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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# 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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# 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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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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# 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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# 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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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 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 drop_connect_rate:
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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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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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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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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.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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