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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/05 22:10:21 UTC
[GitHub] zhreshold commented on a change in pull request #8894: Mobilenet
zhreshold commented on a change in pull request #8894: Mobilenet
URL: https://github.com/apache/incubator-mxnet/pull/8894#discussion_r155091147
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File path: example/image-classification/symbols/mobilenet.py
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@@ -14,48 +14,129 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-
+# -*- coding:utf-8 -*-
import mxnet as mx
-def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):
- conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, 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))
+__author__ = 'qingzhouzhen'
+modified_date = '17/8/5'
+__modify__ = 'dwSun'
+modified_date = '17/11/30'
+
+'''
+mobilenet
+ Suittable for image with around resolution x resolution, resolution is multiple of 32.
+
+Reference:
+ MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
+ https://arxiv.org/abs/1704.04861
+'''
+
+alpha_values = [0.25, 0.50, 0.75, 1.0]
+
+
+def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name='', suffix=''):
+ conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, 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 get_symbol(num_classes, **kwargs):
- data = mx.symbol.Variable(name="data") # 224
- conv_1 = Conv(data, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 224/112
- conv_2_dw = Conv(conv_1, num_group=32, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112
- conv_2 = Conv(conv_2_dw, num_filter=64, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 112/112
- conv_3_dw = Conv(conv_2, num_group=64, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_3_dw") # 112/56
- conv_3 = Conv(conv_3_dw, num_filter=128, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_3") # 56/56
- conv_4_dw = Conv(conv_3, num_group=128, num_filter=128, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_4_dw") # 56/56
- conv_4 = Conv(conv_4_dw, num_filter=128, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_4") # 56/56
- conv_5_dw = Conv(conv_4, num_group=128, num_filter=128, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_5_dw") # 56/28
- conv_5 = Conv(conv_5_dw, num_filter=256, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_5") # 28/28
- conv_6_dw = Conv(conv_5, num_group=256, num_filter=256, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_6_dw") # 28/28
- conv_6 = Conv(conv_6_dw, num_filter=256, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_6") # 28/28
- conv_7_dw = Conv(conv_6, num_group=256, num_filter=256, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_7_dw") # 28/14
- conv_7 = Conv(conv_7_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_7") # 14/14
-
- conv_8_dw = Conv(conv_7, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_8_dw") # 14/14
- conv_8 = Conv(conv_8_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_8") # 14/14
- conv_9_dw = Conv(conv_8, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_9_dw") # 14/14
- conv_9 = Conv(conv_9_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_9") # 14/14
- conv_10_dw = Conv(conv_9, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_10_dw") # 14/14
- conv_10 = Conv(conv_10_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_10") # 14/14
- conv_11_dw = Conv(conv_10, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_11_dw") # 14/14
- conv_11 = Conv(conv_11_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_11") # 14/14
- conv_12_dw = Conv(conv_11, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_12_dw") # 14/14
- conv_12 = Conv(conv_12_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_12") # 14/14
-
- conv_13_dw = Conv(conv_12, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_13_dw") # 14/7
- conv_13 = Conv(conv_13_dw, num_filter=1024, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_13") # 7/7
- conv_14_dw = Conv(conv_13, num_group=1024, num_filter=1024, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_14_dw") # 7/7
- conv_14 = Conv(conv_14_dw, num_filter=1024, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_14") # 7/7
-
- pool = mx.sym.Pooling(data=conv_14, kernel=(7, 7), stride=(1, 1), pool_type="avg", name="global_pool")
+
+def Conv_DPW(data, depth=1, stride=(1, 1), name='', idx=0, suffix=''):
+ conv_dw = Conv(data, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=stride, name="conv_%d_dw" % (idx), suffix=suffix)
+ conv = Conv(conv_dw, num_filter=depth * stride[0], kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_%d" % (idx), suffix=suffix)
+ return conv
+
+
+def get_symbol_compact(num_classes, alpha=1, resolution=224, **kwargs):
Review comment:
This function name will not be recognized by train_imagenet.py
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