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inception-v3.py
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inception-v3.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
Inception V3, suitable for images with around 299 x 299
Reference:
Szegedy, Christian, et al. "Rethinking the Inception Architecture for Computer Vision." arXiv preprint arXiv:1512.00567 (2015).
"""
import mxnet as mx
import numpy as np
def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
act = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix))
return act
def Inception7A(data,
num_1x1,
num_3x3_red, num_3x3_1, num_3x3_2,
num_5x5_red, num_5x5,
pool, proj,
name):
tower_1x1 = Conv(data, num_1x1, name=('%s_conv' % name))
tower_5x5 = Conv(data, num_5x5_red, name=('%s_tower' % name), suffix='_conv')
tower_5x5 = Conv(tower_5x5, num_5x5, kernel=(5, 5), pad=(2, 2), name=('%s_tower' % name), suffix='_conv_1')
tower_3x3 = Conv(data, num_3x3_red, name=('%s_tower_1' % name), suffix='_conv')
tower_3x3 = Conv(tower_3x3, num_3x3_1, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name), suffix='_conv_1')
tower_3x3 = Conv(tower_3x3, num_3x3_2, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name), suffix='_conv_2')
pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(pooling, proj, name=('%s_tower_2' % name), suffix='_conv')
concat = mx.sym.Concat(*[tower_1x1, tower_5x5, tower_3x3, cproj], name='ch_concat_%s_chconcat' % name)
return concat
# First Downsample
def Inception7B(data,
num_3x3,
num_d3x3_red, num_d3x3_1, num_d3x3_2,
pool,
name):
tower_3x3 = Conv(data, num_3x3, kernel=(3, 3), pad=(0, 0), stride=(2, 2), name=('%s_conv' % name))
tower_d3x3 = Conv(data, num_d3x3_red, name=('%s_tower' % name), suffix='_conv')
tower_d3x3 = Conv(tower_d3x3, num_d3x3_1, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=('%s_tower' % name), suffix='_conv_1')
tower_d3x3 = Conv(tower_d3x3, num_d3x3_2, kernel=(3, 3), pad=(0, 0), stride=(2, 2), name=('%s_tower' % name), suffix='_conv_2')
pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pad=(0,0), pool_type="max", name=('max_pool_%s_pool' % name))
concat = mx.sym.Concat(*[tower_3x3, tower_d3x3, pooling], name='ch_concat_%s_chconcat' % name)
return concat
def Inception7C(data,
num_1x1,
num_d7_red, num_d7_1, num_d7_2,
num_q7_red, num_q7_1, num_q7_2, num_q7_3, num_q7_4,
pool, proj,
name):
tower_1x1 = Conv(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_conv' % name))
tower_d7 = Conv(data=data, num_filter=num_d7_red, name=('%s_tower' % name), suffix='_conv')
tower_d7 = Conv(data=tower_d7, num_filter=num_d7_1, kernel=(1, 7), pad=(0, 3), name=('%s_tower' % name), suffix='_conv_1')
tower_d7 = Conv(data=tower_d7, num_filter=num_d7_2, kernel=(7, 1), pad=(3, 0), name=('%s_tower' % name), suffix='_conv_2')
tower_q7 = Conv(data=data, num_filter=num_q7_red, name=('%s_tower_1' % name), suffix='_conv')
tower_q7 = Conv(data=tower_q7, num_filter=num_q7_1, kernel=(7, 1), pad=(3, 0), name=('%s_tower_1' % name), suffix='_conv_1')
tower_q7 = Conv(data=tower_q7, num_filter=num_q7_2, kernel=(1, 7), pad=(0, 3), name=('%s_tower_1' % name), suffix='_conv_2')
tower_q7 = Conv(data=tower_q7, num_filter=num_q7_3, kernel=(7, 1), pad=(3, 0), name=('%s_tower_1' % name), suffix='_conv_3')
tower_q7 = Conv(data=tower_q7, num_filter=num_q7_4, kernel=(1, 7), pad=(0, 3), name=('%s_tower_1' % name), suffix='_conv_4')
pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_tower_2' % name), suffix='_conv')
# concat
concat = mx.sym.Concat(*[tower_1x1, tower_d7, tower_q7, cproj], name='ch_concat_%s_chconcat' % name)
return concat
def Inception7D(data,
num_3x3_red, num_3x3,
num_d7_3x3_red, num_d7_1, num_d7_2, num_d7_3x3,
pool,
name):
tower_3x3 = Conv(data=data, num_filter=num_3x3_red, name=('%s_tower' % name), suffix='_conv')
tower_3x3 = Conv(data=tower_3x3, num_filter=num_3x3, kernel=(3, 3), pad=(0,0), stride=(2, 2), name=('%s_tower' % name), suffix='_conv_1')
tower_d7_3x3 = Conv(data=data, num_filter=num_d7_3x3_red, name=('%s_tower_1' % name), suffix='_conv')
tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_1, kernel=(1, 7), pad=(0, 3), name=('%s_tower_1' % name), suffix='_conv_1')
tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_2, kernel=(7, 1), pad=(3, 0), name=('%s_tower_1' % name), suffix='_conv_2')
tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_3x3, kernel=(3, 3), stride=(2, 2), name=('%s_tower_1' % name), suffix='_conv_3')
pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
# concat
concat = mx.sym.Concat(*[tower_3x3, tower_d7_3x3, pooling], name='ch_concat_%s_chconcat' % name)
return concat
def Inception7E(data,
num_1x1,
num_d3_red, num_d3_1, num_d3_2,
num_3x3_d3_red, num_3x3, num_3x3_d3_1, num_3x3_d3_2,
pool, proj,
name):
tower_1x1 = Conv(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_conv' % name))
tower_d3 = Conv(data=data, num_filter=num_d3_red, name=('%s_tower' % name), suffix='_conv')
tower_d3_a = Conv(data=tower_d3, num_filter=num_d3_1, kernel=(1, 3), pad=(0, 1), name=('%s_tower' % name), suffix='_mixed_conv')
tower_d3_b = Conv(data=tower_d3, num_filter=num_d3_2, kernel=(3, 1), pad=(1, 0), name=('%s_tower' % name), suffix='_mixed_conv_1')
tower_3x3_d3 = Conv(data=data, num_filter=num_3x3_d3_red, name=('%s_tower_1' % name), suffix='_conv')
tower_3x3_d3 = Conv(data=tower_3x3_d3, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name), suffix='_conv_1')
tower_3x3_d3_a = Conv(data=tower_3x3_d3, num_filter=num_3x3_d3_1, kernel=(1, 3), pad=(0, 1), name=('%s_tower_1' % name), suffix='_mixed_conv')
tower_3x3_d3_b = Conv(data=tower_3x3_d3, num_filter=num_3x3_d3_2, kernel=(3, 1), pad=(1, 0), name=('%s_tower_1' % name), suffix='_mixed_conv_1')
pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_tower_2' % name), suffix='_conv')
# concat
concat = mx.sym.Concat(*[tower_1x1, tower_d3_a, tower_d3_b, tower_3x3_d3_a, tower_3x3_d3_b, cproj], name='ch_concat_%s_chconcat' % name)
return concat
# In[49]:
def get_symbol(num_classes=1000, dtype='float32', **kwargs):
data = mx.sym.Variable(name="data")
if dtype == 'float32':
data = mx.sym.identity(data=data, name='id')
else:
if dtype == 'float16':
data = mx.sym.Cast(data=data, dtype=np.float16)
# stage 1
conv = Conv(data, 32, kernel=(3, 3), stride=(2, 2), name="conv")
conv_1 = Conv(conv, 32, kernel=(3, 3), name="conv_1")
conv_2 = Conv(conv_1, 64, kernel=(3, 3), pad=(1, 1), name="conv_2")
pool = mx.sym.Pooling(data=conv_2, kernel=(3, 3), stride=(2, 2), pool_type="max", name="pool")
# stage 2
conv_3 = Conv(pool, 80, kernel=(1, 1), name="conv_3")
conv_4 = Conv(conv_3, 192, kernel=(3, 3), name="conv_4")
pool1 = mx.sym.Pooling(data=conv_4, kernel=(3, 3), stride=(2, 2), pool_type="max", name="pool1")
# stage 3
in3a = Inception7A(pool1, 64,
64, 96, 96,
48, 64,
"avg", 32, "mixed")
in3b = Inception7A(in3a, 64,
64, 96, 96,
48, 64,
"avg", 64, "mixed_1")
in3c = Inception7A(in3b, 64,
64, 96, 96,
48, 64,
"avg", 64, "mixed_2")
in3d = Inception7B(in3c, 384,
64, 96, 96,
"max", "mixed_3")
# stage 4
in4a = Inception7C(in3d, 192,
128, 128, 192,
128, 128, 128, 128, 192,
"avg", 192, "mixed_4")
in4b = Inception7C(in4a, 192,
160, 160, 192,
160, 160, 160, 160, 192,
"avg", 192, "mixed_5")
in4c = Inception7C(in4b, 192,
160, 160, 192,
160, 160, 160, 160, 192,
"avg", 192, "mixed_6")
in4d = Inception7C(in4c, 192,
192, 192, 192,
192, 192, 192, 192, 192,
"avg", 192, "mixed_7")
in4e = Inception7D(in4d, 192, 320,
192, 192, 192, 192,
"max", "mixed_8")
# stage 5
in5a = Inception7E(in4e, 320,
384, 384, 384,
448, 384, 384, 384,
"avg", 192, "mixed_9")
in5b = Inception7E(in5a, 320,
384, 384, 384,
448, 384, 384, 384,
"max", 192, "mixed_10")
# pool
pool = mx.sym.Pooling(data=in5b, kernel=(8, 8), stride=(1, 1), pool_type="avg", name="global_pool")
flatten = mx.sym.Flatten(data=pool, name="flatten")
fc1 = mx.sym.FullyConnected(data=flatten, num_hidden=num_classes, name='fc1')
if dtype == 'float16':
fc1 = mx.sym.Cast(data=fc1, dtype=np.float32)
softmax = mx.sym.SoftmaxOutput(data=fc1, name='softmax')
return softmax