978 lines
31 KiB
Python
978 lines
31 KiB
Python
# copyright (c) 2021 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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# Code was based on https://github.com/lukemelas/EfficientNet-PyTorch
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# reference: https://arxiv.org/abs/1905.11946
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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import math
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import collections
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import re
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import copy
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"EfficientNetB0_small":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams",
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"EfficientNetB0":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams",
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"EfficientNetB1":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams",
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"EfficientNetB2":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams",
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"EfficientNetB3":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams",
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"EfficientNetB4":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams",
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"EfficientNetB5":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams",
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"EfficientNetB6":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams",
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"EfficientNetB7":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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GlobalParams = collections.namedtuple('GlobalParams', [
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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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])
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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', 'stride', 'se_ratio'
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])
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GlobalParams.__new__.__defaults__ = (None, ) * len(GlobalParams._fields)
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BlockArgs.__new__.__defaults__ = (None, ) * len(BlockArgs._fields)
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def efficientnet_params(model_name):
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""" Map EfficientNet model name to parameter coefficients. """
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params_dict = {
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# Coefficients: width,depth,resolution,dropout
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'efficientnet-b0': (1.0, 1.0, 224, 0.2),
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'efficientnet-b1': (1.0, 1.1, 240, 0.2),
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'efficientnet-b2': (1.1, 1.2, 260, 0.3),
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'efficientnet-b3': (1.2, 1.4, 300, 0.3),
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'efficientnet-b4': (1.4, 1.8, 380, 0.4),
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'efficientnet-b5': (1.6, 2.2, 456, 0.4),
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'efficientnet-b6': (1.8, 2.6, 528, 0.5),
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'efficientnet-b7': (2.0, 3.1, 600, 0.5),
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}
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return params_dict[model_name]
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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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""" Get block arguments according to parameter and coefficients. """
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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=1000,
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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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return blocks_args, global_params
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def get_model_params(model_name, override_params):
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""" Get the block args and global params for a given model """
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if model_name.startswith('efficientnet'):
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w, d, _, p = efficientnet_params(model_name)
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blocks_args, global_params = efficientnet(
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width_coefficient=w, depth_coefficient=d, dropout_rate=p)
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else:
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raise NotImplementedError('model name is not pre-defined: %s' %
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model_name)
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if override_params:
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global_params = global_params._replace(**override_params)
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return blocks_args, global_params
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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,
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int(filters + divisor / 2) // divisor * divisor)
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if new_filters < 0.9 * filters: # prevent rounding by more than 10%
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new_filters += divisor
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return int(new_filters)
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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 BlockDecoder(object):
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"""
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Block Decoder, straight from the official TensorFlow repository.
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"""
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@staticmethod
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def _decode_block_string(block_string):
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""" Gets a block through a string notation of arguments. """
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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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# Check stride
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cond_1 = ('s' in options and len(options['s']) == 1)
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cond_2 = ((len(options['s']) == 2) and
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(options['s'][0] == options['s'][1]))
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assert (cond_1 or cond_2)
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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 _encode_block_string(block):
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"""Encodes a block to a string."""
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args = [
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'r%d' % block.num_repeat, 'k%d' % block.kernel_size, 's%d%d' %
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(block.strides[0], block.strides[1]), 'e%s' % block.expand_ratio,
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'i%d' % block.input_filters, 'o%d' % block.output_filters
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]
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if 0 < block.se_ratio <= 1:
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args.append('se%s' % block.se_ratio)
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if block.id_skip is False:
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args.append('noskip')
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return '_'.join(args)
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@staticmethod
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def decode(string_list):
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"""
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Decode a list of string notations to specify blocks in the network.
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string_list: list of strings, each string is a notation of block
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return
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list of BlockArgs namedtuples of block args
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"""
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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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@staticmethod
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def encode(blocks_args):
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"""
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Encodes a list of BlockArgs to a list of strings.
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:param blocks_args: a list of BlockArgs namedtuples of block args
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:return: a list of strings, each string is a notation of block
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"""
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block_strings = []
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for block in blocks_args:
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block_strings.append(BlockDecoder._encode_block_string(block))
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return block_strings
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def initial_type(name, use_bias=False):
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param_attr = ParamAttr(name=name + "_weights")
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if use_bias:
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bias_attr = ParamAttr(name=name + "_offset")
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else:
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bias_attr = False
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return param_attr, bias_attr
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def init_batch_norm_layer(name="batch_norm"):
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param_attr = ParamAttr(name=name + "_scale")
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bias_attr = ParamAttr(name=name + "_offset")
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return param_attr, bias_attr
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def init_fc_layer(name="fc"):
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param_attr = ParamAttr(name=name + "_weights")
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bias_attr = ParamAttr(name=name + "_offset")
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return param_attr, bias_attr
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def cal_padding(img_size, stride, filter_size, dilation=1):
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"""Calculate padding size."""
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if img_size % stride == 0:
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out_size = max(filter_size - stride, 0)
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else:
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out_size = max(filter_size - (img_size % stride), 0)
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return out_size // 2, out_size - out_size // 2
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inp_shape = {
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"b0_small": [224, 112, 112, 56, 28, 14, 14, 7],
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"b0": [224, 112, 112, 56, 28, 14, 14, 7],
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"b1": [240, 120, 120, 60, 30, 15, 15, 8],
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"b2": [260, 130, 130, 65, 33, 17, 17, 9],
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"b3": [300, 150, 150, 75, 38, 19, 19, 10],
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"b4": [380, 190, 190, 95, 48, 24, 24, 12],
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"b5": [456, 228, 228, 114, 57, 29, 29, 15],
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"b6": [528, 264, 264, 132, 66, 33, 33, 17],
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"b7": [600, 300, 300, 150, 75, 38, 38, 19]
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}
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def _drop_connect(inputs, prob, is_test):
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if is_test:
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output = inputs
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else:
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keep_prob = 1.0 - prob
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inputs_shape = paddle.shape(inputs)
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random_tensor = keep_prob + paddle.rand(
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shape=[inputs_shape[0], 1, 1, 1])
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binary_tensor = paddle.floor(random_tensor)
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output = paddle.multiply(inputs, binary_tensor) / keep_prob
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return output
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class Conv2ds(nn.Layer):
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def __init__(self,
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input_channels,
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output_channels,
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filter_size,
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stride=1,
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padding=0,
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groups=None,
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name="conv2d",
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act=None,
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use_bias=False,
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padding_type=None,
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model_name=None,
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cur_stage=None):
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super(Conv2ds, self).__init__()
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assert act in [None, "swish", "sigmoid"]
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self.act = act
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param_attr, bias_attr = initial_type(name=name, use_bias=use_bias)
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def get_padding(filter_size, stride=1, dilation=1):
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padding = ((stride - 1) + dilation * (filter_size - 1)) // 2
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return padding
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inps = 1 if model_name == None and cur_stage == None else inp_shape[
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model_name][cur_stage]
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self.need_crop = False
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if padding_type == "SAME":
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top_padding, bottom_padding = cal_padding(inps, stride,
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filter_size)
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left_padding, right_padding = cal_padding(inps, stride,
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filter_size)
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height_padding = bottom_padding
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width_padding = right_padding
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if top_padding != bottom_padding or left_padding != right_padding:
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height_padding = top_padding + stride
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width_padding = left_padding + stride
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self.need_crop = True
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padding = [height_padding, width_padding]
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elif padding_type == "VALID":
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height_padding = 0
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width_padding = 0
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padding = [height_padding, width_padding]
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elif padding_type == "DYNAMIC":
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padding = get_padding(filter_size, stride)
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else:
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padding = padding_type
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groups = 1 if groups is None else groups
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self._conv = Conv2D(
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input_channels,
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output_channels,
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filter_size,
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groups=groups,
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stride=stride,
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# act=act,
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padding=padding,
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weight_attr=param_attr,
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bias_attr=bias_attr)
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def forward(self, inputs):
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x = self._conv(inputs)
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if self.act == "swish":
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x = F.swish(x)
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elif self.act == "sigmoid":
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x = F.sigmoid(x)
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if self.need_crop:
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x = x[:, :, 1:, 1:]
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return x
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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input_channels,
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filter_size,
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output_channels,
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stride=1,
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num_groups=1,
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padding_type="SAME",
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conv_act=None,
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bn_act="swish",
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use_bn=True,
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use_bias=False,
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name=None,
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conv_name=None,
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bn_name=None,
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model_name=None,
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cur_stage=None):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2ds(
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input_channels=input_channels,
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output_channels=output_channels,
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filter_size=filter_size,
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stride=stride,
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groups=num_groups,
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act=conv_act,
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padding_type=padding_type,
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name=conv_name,
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use_bias=use_bias,
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model_name=model_name,
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cur_stage=cur_stage)
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self.use_bn = use_bn
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if use_bn is True:
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bn_name = name + bn_name
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param_attr, bias_attr = init_batch_norm_layer(bn_name)
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self._bn = BatchNorm(
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num_channels=output_channels,
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act=bn_act,
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momentum=0.99,
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epsilon=0.001,
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=bn_name + "_variance",
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param_attr=param_attr,
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bias_attr=bias_attr)
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def forward(self, inputs):
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if self.use_bn:
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x = self._conv(inputs)
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x = self._bn(x)
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return x
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else:
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return self._conv(inputs)
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class ExpandConvNorm(nn.Layer):
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def __init__(self,
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input_channels,
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block_args,
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padding_type,
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name=None,
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model_name=None,
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cur_stage=None):
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super(ExpandConvNorm, self).__init__()
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self.oup = block_args.input_filters * block_args.expand_ratio
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self.expand_ratio = block_args.expand_ratio
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if self.expand_ratio != 1:
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self._conv = ConvBNLayer(
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input_channels,
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1,
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self.oup,
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bn_act=None,
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padding_type=padding_type,
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name=name,
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conv_name=name + "_expand_conv",
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bn_name="_bn0",
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model_name=model_name,
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cur_stage=cur_stage)
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def forward(self, inputs):
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if self.expand_ratio != 1:
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return self._conv(inputs)
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else:
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return inputs
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class DepthwiseConvNorm(nn.Layer):
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def __init__(self,
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input_channels,
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block_args,
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padding_type,
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name=None,
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model_name=None,
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cur_stage=None):
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super(DepthwiseConvNorm, self).__init__()
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self.k = block_args.kernel_size
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self.s = block_args.stride
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if isinstance(self.s, list) or isinstance(self.s, tuple):
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self.s = self.s[0]
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oup = block_args.input_filters * block_args.expand_ratio
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self._conv = ConvBNLayer(
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input_channels,
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self.k,
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oup,
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self.s,
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num_groups=input_channels,
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bn_act=None,
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padding_type=padding_type,
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name=name,
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conv_name=name + "_depthwise_conv",
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bn_name="_bn1",
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model_name=model_name,
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cur_stage=cur_stage)
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def forward(self, inputs):
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return self._conv(inputs)
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class ProjectConvNorm(nn.Layer):
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def __init__(self,
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input_channels,
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block_args,
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padding_type,
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name=None,
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model_name=None,
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cur_stage=None):
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super(ProjectConvNorm, self).__init__()
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final_oup = block_args.output_filters
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self._conv = ConvBNLayer(
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input_channels,
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1,
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final_oup,
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bn_act=None,
|
|
padding_type=padding_type,
|
|
name=name,
|
|
conv_name=name + "_project_conv",
|
|
bn_name="_bn2",
|
|
model_name=model_name,
|
|
cur_stage=cur_stage)
|
|
|
|
def forward(self, inputs):
|
|
return self._conv(inputs)
|
|
|
|
|
|
class SEBlock(nn.Layer):
|
|
def __init__(self,
|
|
input_channels,
|
|
num_squeezed_channels,
|
|
oup,
|
|
padding_type,
|
|
name=None,
|
|
model_name=None,
|
|
cur_stage=None):
|
|
super(SEBlock, self).__init__()
|
|
|
|
self._pool = AdaptiveAvgPool2D(1)
|
|
self._conv1 = Conv2ds(
|
|
input_channels,
|
|
num_squeezed_channels,
|
|
1,
|
|
use_bias=True,
|
|
padding_type=padding_type,
|
|
act="swish",
|
|
name=name + "_se_reduce")
|
|
|
|
self._conv2 = Conv2ds(
|
|
num_squeezed_channels,
|
|
oup,
|
|
1,
|
|
act="sigmoid",
|
|
use_bias=True,
|
|
padding_type=padding_type,
|
|
name=name + "_se_expand")
|
|
|
|
def forward(self, inputs):
|
|
x = self._pool(inputs)
|
|
x = self._conv1(x)
|
|
x = self._conv2(x)
|
|
out = paddle.multiply(inputs, x)
|
|
return out
|
|
|
|
|
|
class MbConvBlock(nn.Layer):
|
|
def __init__(self,
|
|
input_channels,
|
|
block_args,
|
|
padding_type,
|
|
use_se,
|
|
name=None,
|
|
drop_connect_rate=None,
|
|
model_name=None,
|
|
cur_stage=None):
|
|
super(MbConvBlock, self).__init__()
|
|
|
|
oup = block_args.input_filters * block_args.expand_ratio
|
|
self.block_args = block_args
|
|
self.has_se = use_se and (block_args.se_ratio is not None) and (
|
|
0 < block_args.se_ratio <= 1)
|
|
self.id_skip = block_args.id_skip
|
|
self.expand_ratio = block_args.expand_ratio
|
|
self.drop_connect_rate = drop_connect_rate
|
|
|
|
if self.expand_ratio != 1:
|
|
self._ecn = ExpandConvNorm(
|
|
input_channels,
|
|
block_args,
|
|
padding_type=padding_type,
|
|
name=name,
|
|
model_name=model_name,
|
|
cur_stage=cur_stage)
|
|
|
|
self._dcn = DepthwiseConvNorm(
|
|
input_channels * block_args.expand_ratio,
|
|
block_args,
|
|
padding_type=padding_type,
|
|
name=name,
|
|
model_name=model_name,
|
|
cur_stage=cur_stage)
|
|
|
|
if self.has_se:
|
|
num_squeezed_channels = max(
|
|
1, int(block_args.input_filters * block_args.se_ratio))
|
|
self._se = SEBlock(
|
|
input_channels * block_args.expand_ratio,
|
|
num_squeezed_channels,
|
|
oup,
|
|
padding_type=padding_type,
|
|
name=name,
|
|
model_name=model_name,
|
|
cur_stage=cur_stage)
|
|
|
|
self._pcn = ProjectConvNorm(
|
|
input_channels * block_args.expand_ratio,
|
|
block_args,
|
|
padding_type=padding_type,
|
|
name=name,
|
|
model_name=model_name,
|
|
cur_stage=cur_stage)
|
|
|
|
def forward(self, inputs):
|
|
x = inputs
|
|
if self.expand_ratio != 1:
|
|
x = self._ecn(x)
|
|
x = F.swish(x)
|
|
|
|
x = self._dcn(x)
|
|
x = F.swish(x)
|
|
if self.has_se:
|
|
x = self._se(x)
|
|
x = self._pcn(x)
|
|
|
|
if self.id_skip and \
|
|
self.block_args.stride == 1 and \
|
|
self.block_args.input_filters == self.block_args.output_filters:
|
|
if self.drop_connect_rate:
|
|
x = _drop_connect(x, self.drop_connect_rate, not self.training)
|
|
x = paddle.add(x, inputs)
|
|
return x
|
|
|
|
|
|
class ConvStemNorm(nn.Layer):
|
|
def __init__(self,
|
|
input_channels,
|
|
padding_type,
|
|
_global_params,
|
|
name=None,
|
|
model_name=None,
|
|
cur_stage=None):
|
|
super(ConvStemNorm, self).__init__()
|
|
|
|
output_channels = round_filters(32, _global_params)
|
|
self._conv = ConvBNLayer(
|
|
input_channels,
|
|
filter_size=3,
|
|
output_channels=output_channels,
|
|
stride=2,
|
|
bn_act=None,
|
|
padding_type=padding_type,
|
|
name="",
|
|
conv_name="_conv_stem",
|
|
bn_name="_bn0",
|
|
model_name=model_name,
|
|
cur_stage=cur_stage)
|
|
|
|
def forward(self, inputs):
|
|
return self._conv(inputs)
|
|
|
|
|
|
class ExtractFeatures(nn.Layer):
|
|
def __init__(self,
|
|
input_channels,
|
|
_block_args,
|
|
_global_params,
|
|
padding_type,
|
|
use_se,
|
|
model_name=None):
|
|
super(ExtractFeatures, self).__init__()
|
|
|
|
self._global_params = _global_params
|
|
|
|
self._conv_stem = ConvStemNorm(
|
|
input_channels,
|
|
padding_type=padding_type,
|
|
_global_params=_global_params,
|
|
model_name=model_name,
|
|
cur_stage=0)
|
|
|
|
self.block_args_copy = copy.deepcopy(_block_args)
|
|
idx = 0
|
|
block_size = 0
|
|
for block_arg in self.block_args_copy:
|
|
block_arg = block_arg._replace(
|
|
input_filters=round_filters(block_arg.input_filters,
|
|
_global_params),
|
|
output_filters=round_filters(block_arg.output_filters,
|
|
_global_params),
|
|
num_repeat=round_repeats(block_arg.num_repeat, _global_params))
|
|
block_size += 1
|
|
for _ in range(block_arg.num_repeat - 1):
|
|
block_size += 1
|
|
|
|
self.conv_seq = []
|
|
cur_stage = 1
|
|
for block_args in _block_args:
|
|
block_args = block_args._replace(
|
|
input_filters=round_filters(block_args.input_filters,
|
|
_global_params),
|
|
output_filters=round_filters(block_args.output_filters,
|
|
_global_params),
|
|
num_repeat=round_repeats(block_args.num_repeat,
|
|
_global_params))
|
|
|
|
drop_connect_rate = self._global_params.drop_connect_rate
|
|
if drop_connect_rate:
|
|
drop_connect_rate *= float(idx) / block_size
|
|
|
|
_mc_block = self.add_sublayer(
|
|
"_blocks." + str(idx) + ".",
|
|
MbConvBlock(
|
|
block_args.input_filters,
|
|
block_args=block_args,
|
|
padding_type=padding_type,
|
|
use_se=use_se,
|
|
name="_blocks." + str(idx) + ".",
|
|
drop_connect_rate=drop_connect_rate,
|
|
model_name=model_name,
|
|
cur_stage=cur_stage))
|
|
self.conv_seq.append(_mc_block)
|
|
idx += 1
|
|
if block_args.num_repeat > 1:
|
|
block_args = block_args._replace(
|
|
input_filters=block_args.output_filters, stride=1)
|
|
for _ in range(block_args.num_repeat - 1):
|
|
drop_connect_rate = self._global_params.drop_connect_rate
|
|
if drop_connect_rate:
|
|
drop_connect_rate *= float(idx) / block_size
|
|
_mc_block = self.add_sublayer(
|
|
"block." + str(idx) + ".",
|
|
MbConvBlock(
|
|
block_args.input_filters,
|
|
block_args,
|
|
padding_type=padding_type,
|
|
use_se=use_se,
|
|
name="_blocks." + str(idx) + ".",
|
|
drop_connect_rate=drop_connect_rate,
|
|
model_name=model_name,
|
|
cur_stage=cur_stage))
|
|
self.conv_seq.append(_mc_block)
|
|
idx += 1
|
|
cur_stage += 1
|
|
|
|
def forward(self, inputs):
|
|
x = self._conv_stem(inputs)
|
|
x = F.swish(x)
|
|
for _mc_block in self.conv_seq:
|
|
x = _mc_block(x)
|
|
return x
|
|
|
|
|
|
class EfficientNet(nn.Layer):
|
|
def __init__(self,
|
|
name="b0",
|
|
padding_type="SAME",
|
|
override_params=None,
|
|
use_se=True,
|
|
class_num=1000):
|
|
super(EfficientNet, self).__init__()
|
|
|
|
model_name = 'efficientnet-' + name
|
|
self.name = name
|
|
self._block_args, self._global_params = get_model_params(
|
|
model_name, override_params)
|
|
self.padding_type = padding_type
|
|
self.use_se = use_se
|
|
|
|
self._ef = ExtractFeatures(
|
|
3,
|
|
self._block_args,
|
|
self._global_params,
|
|
self.padding_type,
|
|
self.use_se,
|
|
model_name=self.name)
|
|
|
|
output_channels = round_filters(1280, self._global_params)
|
|
if name == "b0_small" or name == "b0" or name == "b1":
|
|
oup = 320
|
|
elif name == "b2":
|
|
oup = 352
|
|
elif name == "b3":
|
|
oup = 384
|
|
elif name == "b4":
|
|
oup = 448
|
|
elif name == "b5":
|
|
oup = 512
|
|
elif name == "b6":
|
|
oup = 576
|
|
elif name == "b7":
|
|
oup = 640
|
|
self._conv = ConvBNLayer(
|
|
oup,
|
|
1,
|
|
output_channels,
|
|
bn_act="swish",
|
|
padding_type=self.padding_type,
|
|
name="",
|
|
conv_name="_conv_head",
|
|
bn_name="_bn1",
|
|
model_name=self.name,
|
|
cur_stage=7)
|
|
self._pool = AdaptiveAvgPool2D(1)
|
|
|
|
if self._global_params.dropout_rate:
|
|
self._drop = Dropout(
|
|
p=self._global_params.dropout_rate, mode="upscale_in_train")
|
|
|
|
param_attr, bias_attr = init_fc_layer("_fc")
|
|
self._fc = Linear(
|
|
output_channels,
|
|
class_num,
|
|
weight_attr=param_attr,
|
|
bias_attr=bias_attr)
|
|
|
|
def forward(self, inputs):
|
|
x = self._ef(inputs)
|
|
x = self._conv(x)
|
|
x = self._pool(x)
|
|
if self._global_params.dropout_rate:
|
|
x = self._drop(x)
|
|
x = paddle.squeeze(x, axis=[2, 3])
|
|
x = self._fc(x)
|
|
return x
|
|
|
|
|
|
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
|
|
if pretrained is False:
|
|
pass
|
|
elif pretrained is True:
|
|
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
|
|
elif isinstance(pretrained, str):
|
|
load_dygraph_pretrain(model, pretrained)
|
|
else:
|
|
raise RuntimeError(
|
|
"pretrained type is not available. Please use `string` or `boolean` type."
|
|
)
|
|
|
|
|
|
def EfficientNetB0_small(padding_type='DYNAMIC',
|
|
override_params=None,
|
|
use_se=False,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b0',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB0_small"])
|
|
return model
|
|
|
|
|
|
def EfficientNetB0(padding_type='SAME',
|
|
override_params=None,
|
|
use_se=True,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b0',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB0"])
|
|
return model
|
|
|
|
|
|
def EfficientNetB1(padding_type='SAME',
|
|
override_params=None,
|
|
use_se=True,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b1',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB1"])
|
|
return model
|
|
|
|
|
|
def EfficientNetB2(padding_type='SAME',
|
|
override_params=None,
|
|
use_se=True,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b2',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB2"])
|
|
return model
|
|
|
|
|
|
def EfficientNetB3(padding_type='SAME',
|
|
override_params=None,
|
|
use_se=True,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b3',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB3"])
|
|
return model
|
|
|
|
|
|
def EfficientNetB4(padding_type='SAME',
|
|
override_params=None,
|
|
use_se=True,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b4',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB4"])
|
|
return model
|
|
|
|
|
|
def EfficientNetB5(padding_type='SAME',
|
|
override_params=None,
|
|
use_se=True,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b5',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB5"])
|
|
return model
|
|
|
|
|
|
def EfficientNetB6(padding_type='SAME',
|
|
override_params=None,
|
|
use_se=True,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b6',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB6"])
|
|
return model
|
|
|
|
|
|
def EfficientNetB7(padding_type='SAME',
|
|
override_params=None,
|
|
use_se=True,
|
|
pretrained=False,
|
|
use_ssld=False,
|
|
**kwargs):
|
|
model = EfficientNet(
|
|
name='b7',
|
|
padding_type=padding_type,
|
|
override_params=override_params,
|
|
use_se=use_se,
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB7"])
|
|
return model
|