You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/16 07:58:39 UTC
[GitHub] szha closed pull request #9099: MobileNet
szha closed pull request #9099: MobileNet
URL: https://github.com/apache/incubator-mxnet/pull/9099
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/example/image-classification/symbols/mobilenet.py b/example/image-classification/symbols/mobilenet.py
index 42b9636261..bf3de4a2c6 100644
--- a/example/image-classification/symbols/mobilenet.py
+++ b/example/image-classification/symbols/mobilenet.py
@@ -14,48 +14,130 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
+# -*- coding:utf-8 -*-
+'''
+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
+'''
+
+__author__ = 'qingzhouzhen'
+__date__ = '17/8/5'
+__modify__ = 'dwSun'
+__modified_date__ = '17/11/30'
+
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))
+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):
+ assert alpha in alpha_values, 'Invalid alpha={0}, must be one of {1}'.format(alpha, alpha_values)
+ assert resolution % 32 == 0, 'resolution must be multiple of 32'
+
+ base = int(32 * alpha)
+
+ data = mx.symbol.Variable(name="data") # 224
+ conv_1 = Conv(data, num_filter=base, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 32*alpha, 224/112
+
+ conv_2_dw = Conv(conv_1, num_group=base, num_filter=base, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112
+ conv_2 = Conv(conv_2_dw, num_filter=base * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 32*alpha, 112/112
+
+ conv_3_dpw = Conv_DPW(conv_2, depth=base * 2, stride=(2, 2), idx=3) # 64*alpha, 112/56 => 56/56
+ conv_4_dpw = Conv_DPW(conv_3_dpw, depth=base * 4, stride=(1, 1), idx=4) # 128*alpha, 56/56 =>56/56
+ conv_5_dpw = Conv_DPW(conv_4_dpw, depth=base * 4, stride=(2, 2), idx=5) # 128*alpha, 56/28 => 28/28
+ conv_6_dpw = Conv_DPW(conv_5_dpw, depth=base * 8, stride=(1, 1), idx=6) # 256*alpha, 28/28 => 28/28
+ conv_7_dpw = Conv_DPW(conv_6_dpw, depth=base * 8, stride=(2, 2), idx=7) # 256*alpha, 28/14 => 14/14
+ conv_dpw = conv_7_dpw
+
+ for idx in range(8, 13):
+ conv_dpw = Conv_DPW(conv_dpw, depth=base * 16, stride=(1, 1), idx=idx) # 512*alpha, 14/14
+
+ conv_12_dpw = conv_dpw
+ conv_13_dpw = Conv_DPW(conv_12_dpw, depth=base * 16, stride=(2, 2), idx=13) # 512*alpha, 14/7 => 7/7
+ conv_14_dpw = Conv_DPW(conv_13_dpw, depth=base * 32, stride=(1, 1), idx=14) # 1024*alpha, 7/7 => 7/7
+
+ pool_size = int(resolution / 32)
+ pool = mx.sym.Pooling(data=conv_14_dpw, kernel=(pool_size, pool_size), stride=(1, 1), pool_type="avg", name="global_pool")
+ flatten = mx.sym.Flatten(data=pool, name="flatten")
+ fc = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fc')
+ softmax = mx.symbol.SoftmaxOutput(data=fc, name='softmax')
+ return softmax
+
+
+def get_symbol(num_classes, alpha=1, resolution=224, **kwargs):
+ assert alpha in alpha_values, 'Invalid alpha=[{0}], must be one of [{1}]'.format(alpha, alpha_values)
+ assert resolution % 32 == 0, 'resolution must be multpile of 32'
+
+ base = int(32 * alpha)
+
+ data = mx.symbol.Variable(name="data") # 224
+ depth = base # 32*alpha
+ conv_1 = Conv(data, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 224/112
+
+ depth = base # 32*alpha
+ conv_2_dw = Conv(conv_1, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112
+ conv_2 = Conv(conv_2_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 112/112
+
+ depth = base * 2 # 64*alpha
+ conv_3_dw = Conv(conv_2, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_3_dw") # 112/56
+ conv_3 = Conv(conv_3_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_3") # 56/56
+
+ depth = base * 4 # 128*alpha
+ conv_4_dw = Conv(conv_3, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_4_dw") # 56/56
+ conv_4 = Conv(conv_4_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_4") # 56/56
+
+ depth = base * 4 # 128*alpha
+ conv_5_dw = Conv(conv_4, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_5_dw") # 56/28
+ conv_5 = Conv(conv_5_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_5") # 28/28
+
+ depth = base * 8 # 256*alpha
+ conv_6_dw = Conv(conv_5, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_6_dw") # 28/28
+ conv_6 = Conv(conv_6_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_6") # 28/28
+
+ depth = base * 8 # 256*alpha
+ conv_7_dw = Conv(conv_6, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_7_dw") # 28/14
+ conv_7 = Conv(conv_7_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_7") # 14/14
+
+ depth = base * 16 # 512*alpha
+ conv_8_dw = Conv(conv_7, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_8_dw") # 14/14
+ conv_8 = Conv(conv_8_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_8") # 14/14
+ conv_9_dw = Conv(conv_8, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_9_dw") # 14/14
+ conv_9 = Conv(conv_9_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_9") # 14/14
+ conv_10_dw = Conv(conv_9, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_10_dw") # 14/14
+ conv_10 = Conv(conv_10_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_10") # 14/14
+ conv_11_dw = Conv(conv_10, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_11_dw") # 14/14
+ conv_11 = Conv(conv_11_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_11") # 14/14
+ conv_12_dw = Conv(conv_11, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_12_dw") # 14/14
+ conv_12 = Conv(conv_12_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_12") # 14/14
+
+ depth = base * 16 # 512*alpha
+ conv_13_dw = Conv(conv_12, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_13_dw") # 14/7
+ conv_13 = Conv(conv_13_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_13") # 7/7
+
+ depth = base * 32 # 1024*alpha
+ conv_14_dw = Conv(conv_13, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_14_dw") # 7/7
+ conv_14 = Conv(conv_14_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_14") # 7/7
+
+ pool_size = int(resolution / 32)
+ pool = mx.sym.Pooling(data=conv_14, kernel=(pool_size, pool_size), stride=(1, 1), pool_type="avg", name="global_pool")
flatten = mx.sym.Flatten(data=pool, name="flatten")
fc = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fc')
softmax = mx.symbol.SoftmaxOutput(data=fc, name='softmax')
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services