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225 lines
7.2 KiB
Python
225 lines
7.2 KiB
Python
import sys
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import os
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import mxnet as mx
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import symbol_utils
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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from config import config
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def Act(data, act_type, name):
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#ignore param act_type, set it in this function
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if act_type == 'prelu':
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body = mx.sym.LeakyReLU(data=data, act_type='prelu', name=name)
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else:
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body = mx.sym.Activation(data=data, act_type=act_type, name=name)
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return body
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def Conv(data,
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num_filter=1,
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kernel=(1, 1),
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stride=(1, 1),
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pad=(0, 0),
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num_group=1,
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name=None,
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suffix=''):
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conv = mx.sym.Convolution(data=data,
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num_filter=num_filter,
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kernel=kernel,
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num_group=num_group,
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stride=stride,
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pad=pad,
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no_bias=True,
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name='%s%s_conv2d' % (name, suffix))
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bn = mx.sym.BatchNorm(data=conv,
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name='%s%s_batchnorm' % (name, suffix),
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fix_gamma=False,
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momentum=config.bn_mom)
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act = Act(data=bn,
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act_type=config.net_act,
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name='%s%s_relu' % (name, suffix))
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return act
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def Linear(data,
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num_filter=1,
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kernel=(1, 1),
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stride=(1, 1),
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pad=(0, 0),
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num_group=1,
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name=None,
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suffix=''):
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conv = mx.sym.Convolution(data=data,
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num_filter=num_filter,
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kernel=kernel,
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num_group=num_group,
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stride=stride,
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pad=pad,
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no_bias=True,
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name='%s%s_conv2d' % (name, suffix))
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bn = mx.sym.BatchNorm(data=conv,
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name='%s%s_batchnorm' % (name, suffix),
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fix_gamma=False,
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momentum=config.bn_mom)
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return bn
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def ConvOnly(data,
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num_filter=1,
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kernel=(1, 1),
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stride=(1, 1),
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pad=(0, 0),
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num_group=1,
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name=None,
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suffix=''):
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conv = mx.sym.Convolution(data=data,
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num_filter=num_filter,
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kernel=kernel,
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num_group=num_group,
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stride=stride,
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pad=pad,
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no_bias=True,
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name='%s%s_conv2d' % (name, suffix))
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return conv
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def DResidual(data,
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num_out=1,
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kernel=(3, 3),
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stride=(2, 2),
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pad=(1, 1),
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num_group=1,
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name=None,
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suffix=''):
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conv = Conv(data=data,
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num_filter=num_group,
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kernel=(1, 1),
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pad=(0, 0),
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stride=(1, 1),
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name='%s%s_conv_sep' % (name, suffix))
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conv_dw = Conv(data=conv,
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num_filter=num_group,
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num_group=num_group,
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kernel=kernel,
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pad=pad,
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stride=stride,
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name='%s%s_conv_dw' % (name, suffix))
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proj = Linear(data=conv_dw,
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num_filter=num_out,
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kernel=(1, 1),
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pad=(0, 0),
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stride=(1, 1),
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name='%s%s_conv_proj' % (name, suffix))
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return proj
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def Residual(data,
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num_block=1,
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num_out=1,
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kernel=(3, 3),
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stride=(1, 1),
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pad=(1, 1),
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num_group=1,
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name=None,
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suffix=''):
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identity = data
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for i in range(num_block):
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shortcut = identity
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conv = DResidual(data=identity,
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num_out=num_out,
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kernel=kernel,
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stride=stride,
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pad=pad,
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num_group=num_group,
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name='%s%s_block' % (name, suffix),
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suffix='%d' % i)
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identity = conv + shortcut
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return identity
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def get_symbol():
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num_classes = config.emb_size
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print('in_network', config)
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fc_type = config.net_output
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data = mx.symbol.Variable(name="data")
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data = data - 127.5
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data = data * 0.0078125
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blocks = config.net_blocks
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conv_1 = Conv(data,
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num_filter=64,
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kernel=(3, 3),
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pad=(1, 1),
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stride=(2, 2),
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name="conv_1")
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if blocks[0] == 1:
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conv_2_dw = Conv(conv_1,
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num_group=64,
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num_filter=64,
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kernel=(3, 3),
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pad=(1, 1),
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stride=(1, 1),
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name="conv_2_dw")
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else:
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conv_2_dw = Residual(conv_1,
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num_block=blocks[0],
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num_out=64,
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kernel=(3, 3),
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stride=(1, 1),
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pad=(1, 1),
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num_group=64,
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name="res_2")
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conv_23 = DResidual(conv_2_dw,
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num_out=64,
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kernel=(3, 3),
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stride=(2, 2),
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pad=(1, 1),
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num_group=128,
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name="dconv_23")
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conv_3 = Residual(conv_23,
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num_block=blocks[1],
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num_out=64,
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kernel=(3, 3),
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stride=(1, 1),
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pad=(1, 1),
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num_group=128,
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name="res_3")
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conv_34 = DResidual(conv_3,
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num_out=128,
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kernel=(3, 3),
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stride=(2, 2),
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pad=(1, 1),
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num_group=256,
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name="dconv_34")
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conv_4 = Residual(conv_34,
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num_block=blocks[2],
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num_out=128,
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kernel=(3, 3),
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stride=(1, 1),
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pad=(1, 1),
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num_group=256,
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name="res_4")
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conv_45 = DResidual(conv_4,
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num_out=128,
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kernel=(3, 3),
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stride=(2, 2),
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pad=(1, 1),
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num_group=512,
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name="dconv_45")
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conv_5 = Residual(conv_45,
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num_block=blocks[3],
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num_out=128,
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kernel=(3, 3),
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stride=(1, 1),
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pad=(1, 1),
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num_group=256,
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name="res_5")
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conv_6_sep = Conv(conv_5,
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num_filter=512,
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kernel=(1, 1),
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pad=(0, 0),
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stride=(1, 1),
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name="conv_6sep")
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fc1 = symbol_utils.get_fc1(conv_6_sep, num_classes, fc_type)
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return fc1
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