forked from PulseFocusPlatform/PulseFocusPlatform
208 lines
6.6 KiB
Python
208 lines
6.6 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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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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from paddle import fluid
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from paddle.fluid.param_attr import ParamAttr
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from ppdet.core.workspace import register
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__all__ = ['VGG']
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@register
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class VGG(object):
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"""
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VGG, see https://arxiv.org/abs/1409.1556
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Args:
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depth (int): the VGG net depth (16 or 19)
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normalizations (list): params list of init scale in l2 norm, skip init
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scale if param is -1.
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with_extra_blocks (bool): whether or not extra blocks should be added
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extra_block_filters (list): in each extra block, params:
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[in_channel, out_channel, padding_size, stride_size, filter_size]
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"""
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def __init__(self,
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depth=16,
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with_extra_blocks=False,
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normalizations=[20., -1, -1, -1, -1, -1],
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extra_block_filters=[[256, 512, 1, 2, 3], [128, 256, 1, 2, 3],
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[128, 256, 0, 1, 3],
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[128, 256, 0, 1, 3]]):
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assert depth in [16, 19], \
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"depth {} not in [16, 19]"
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self.depth = depth
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self.depth_cfg = {16: [2, 2, 3, 3, 3], 19: [2, 2, 4, 4, 4]}
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self.with_extra_blocks = with_extra_blocks
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self.normalizations = normalizations
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self.extra_block_filters = extra_block_filters
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def __call__(self, input):
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layers = []
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layers += self._vgg_block(input)
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if not self.with_extra_blocks:
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return layers[-1]
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layers += self._add_extras_block(layers[-1])
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norm_cfg = self.normalizations
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for k, v in enumerate(layers):
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if not norm_cfg[k] == -1:
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layers[k] = self._l2_norm_scale(v, init_scale=norm_cfg[k])
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return layers
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def _vgg_block(self, input):
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nums = self.depth_cfg[self.depth]
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vgg_base = [64, 128, 256, 512, 512]
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conv = input
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layers = []
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for k, v in enumerate(vgg_base):
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conv = self._conv_block(
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conv, v, nums[k], name="conv{}_".format(k + 1))
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layers.append(conv)
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if k == 4:
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conv = self._pooling_block(conv, 3, 1, pool_padding=1)
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else:
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conv = self._pooling_block(conv, 2, 2)
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fc6 = self._conv_layer(conv, 1024, 3, 1, 6, dilation=6, name="fc6")
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fc7 = self._conv_layer(fc6, 1024, 1, 1, 0, name="fc7")
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return [layers[3], fc7]
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def _add_extras_block(self, input):
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cfg = self.extra_block_filters
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conv = input
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layers = []
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for k, v in enumerate(cfg):
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assert len(v) == 5, "extra_block_filters size not fix"
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conv = self._extra_block(
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conv,
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v[0],
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v[1],
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v[2],
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v[3],
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v[4],
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name="conv{}_".format(6 + k))
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layers.append(conv)
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return layers
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def _conv_block(self, input, num_filter, groups, name=None):
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conv = input
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for i in range(groups):
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conv = self._conv_layer(
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input=conv,
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num_filters=num_filter,
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filter_size=3,
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stride=1,
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padding=1,
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act='relu',
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name=name + str(i + 1))
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return conv
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def _extra_block(self,
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input,
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num_filters1,
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num_filters2,
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padding_size,
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stride_size,
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filter_size,
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name=None):
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# 1x1 conv
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conv_1 = self._conv_layer(
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input=input,
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num_filters=int(num_filters1),
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filter_size=1,
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stride=1,
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act='relu',
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padding=0,
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name=name + "1")
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# 3x3 conv
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conv_2 = self._conv_layer(
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input=conv_1,
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num_filters=int(num_filters2),
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filter_size=filter_size,
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stride=stride_size,
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act='relu',
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padding=padding_size,
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name=name + "2")
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return conv_2
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def _conv_layer(self,
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input,
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num_filters,
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filter_size,
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stride,
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padding,
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dilation=1,
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act='relu',
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use_cudnn=True,
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name=None):
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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act=act,
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use_cudnn=use_cudnn,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=ParamAttr(name=name + "_biases"),
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name=name + '.conv2d.output.1')
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return conv
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def _pooling_block(self,
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conv,
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pool_size,
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pool_stride,
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pool_padding=0,
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ceil_mode=True):
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pool = fluid.layers.pool2d(
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input=conv,
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pool_size=pool_size,
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pool_type='max',
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pool_stride=pool_stride,
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pool_padding=pool_padding,
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ceil_mode=ceil_mode)
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return pool
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def _l2_norm_scale(self, input, init_scale=1.0, channel_shared=False):
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from paddle.fluid.layer_helper import LayerHelper
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from paddle.fluid.initializer import Constant
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helper = LayerHelper("Scale")
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l2_norm = fluid.layers.l2_normalize(
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input, axis=1) # l2 norm along channel
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shape = [1] if channel_shared else [input.shape[1]]
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scale = helper.create_parameter(
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attr=helper.param_attr,
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shape=shape,
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dtype=input.dtype,
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default_initializer=Constant(init_scale))
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out = fluid.layers.elementwise_mul(
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x=l2_norm,
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y=scale,
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axis=-1 if channel_shared else 1,
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name="conv4_3_norm_scale")
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return out
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