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new 8519eaf add mobilenet (#7121)
8519eaf is described below
commit 8519eafd1d355f66245d9ce074c4f451bceef311
Author: qingzhouzhen <[email protected]>
AuthorDate: Fri Aug 4 04:12:28 2017 +0800
add mobilenet (#7121)
---
example/image-classification/symbols/mobilenet.py | 45 +++++++++++++++++++++++
1 file changed, 45 insertions(+)
diff --git a/example/image-classification/symbols/mobilenet.py
b/example/image-classification/symbols/mobilenet.py
new file mode 100644
index 0000000..cf470ba
--- /dev/null
+++ b/example/image-classification/symbols/mobilenet.py
@@ -0,0 +1,45 @@
+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))
+ 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_3_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")
+ 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
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