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  • 大小: 20KB
    文件類型: .py
    金幣: 1
    下載: 0 次
    發布日期: 2021-06-03
  • 語言: Python
  • 標簽: CNN??ResNet??

資源簡介

Attention-CNN 注意力機制細膩度圖片分類。 ResNet改造

資源截圖

代碼片段和文件信息

import?mxnet?as?mx
import?proposal
import?proposal_target
from?rcnn.config?import?config

eps?=?2e-5
use_global_stats?=?True
workspace?=?512
res_deps?=?{‘50‘:?(3?4?6?3)?‘101‘:?(3?4?23?3)?‘152‘:?(3?8?36?3)?‘200‘:?(3?24?36?3)}
units?=?res_deps[‘101‘]
filter_list?=?[256?512?1024?2048]


def?residual_unit(data?num_filter?stride?dim_match?name):
????bn1?=?mx.sym.BatchNorm(data=data?fix_gamma=False?eps=eps?use_global_stats=use_global_stats?name=name?+?‘_bn1‘)
????act1?=?mx.sym.Activation(data=bn1?act_type=‘relu‘?name=name?+?‘_relu1‘)
????conv1?=?mx.sym.Convolution(data=act1?num_filter=int(num_filter?*?0.25)?kernel=(1?1)?stride=(1?1)?pad=(0?0)
???????????????????????????????no_bias=True?workspace=workspace?name=name?+?‘_conv1‘)
????bn2?=?mx.sym.BatchNorm(data=conv1?fix_gamma=False?eps=eps?use_global_stats=use_global_stats?name=name?+?‘_bn2‘)
????act2?=?mx.sym.Activation(data=bn2?act_type=‘relu‘?name=name?+?‘_relu2‘)
????conv2?=?mx.sym.Convolution(data=act2?num_filter=int(num_filter?*?0.25)?kernel=(3?3)?stride=stride?pad=(1?1)
???????????????????????????????no_bias=True?workspace=workspace?name=name?+?‘_conv2‘)
????bn3?=?mx.sym.BatchNorm(data=conv2?fix_gamma=False?eps=eps?use_global_stats=use_global_stats?name=name?+?‘_bn3‘)
????act3?=?mx.sym.Activation(data=bn3?act_type=‘relu‘?name=name?+?‘_relu3‘)
????conv3?=?mx.sym.Convolution(data=act3?num_filter=num_filter?kernel=(1?1)?stride=(1?1)?pad=(0?0)?no_bias=True
???????????????????????????????workspace=workspace?name=name?+?‘_conv3‘)
????if?dim_match:
????????shortcut?=?data
????else:
????????shortcut?=?mx.sym.Convolution(data=act1?num_filter=num_filter?kernel=(1?1)?stride=stride?no_bias=True
??????????????????????????????????????workspace=workspace?name=name?+?‘_sc‘)
????sum?=?mx.sym.ElementWiseSum(*[conv3?shortcut]?name=name?+?‘_plus‘)
????return?sum


def?get_resnet_conv(data):
????#?res1
????data_bn?=?mx.sym.BatchNorm(data=data?fix_gamma=True?eps=eps?use_global_stats=use_global_stats?name=‘bn_data‘)
????conv0?=?mx.sym.Convolution(data=data_bn?num_filter=64?kernel=(7?7)?stride=(2?2)?pad=(3?3)
???????????????????????????????no_bias=True?name=“conv0“?workspace=workspace)
????bn0?=?mx.sym.BatchNorm(data=conv0?fix_gamma=False?eps=eps?use_global_stats=use_global_stats?name=‘bn0‘)
????relu0?=?mx.sym.Activation(data=bn0?act_type=‘relu‘?name=‘relu0‘)
????pool0?=?mx.symbol.Pooling(data=relu0?kernel=(3?3)?stride=(2?2)?pad=(1?1)?pool_type=‘max‘?name=‘pool0‘)

????#?res2
????unit?=?residual_unit(data=pool0?num_filter=filter_list[0]?stride=(1?1)?dim_match=False?name=‘stage1_unit1‘)
????for?i?in?range(2?units[0]?+?1):
????????unit?=?residual_unit(data=unit?num_filter=filter_list[0]?stride=(1?1)?dim_match=True?name=‘stage1_unit%s‘?%?i)

????#?res3
????unit?=?residual_unit(data=unit?num_filter=filter_list[1]?stride=(2?2)?dim_match=False?name=‘stage2_unit1‘)
????for?i?in?range(2?units[1]?+?1):
??????

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