[example] add a deep residual net example

example/image-classification/symbol_resnet-small.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python 
+'''
+MSRA Paper: http://arxiv.org/pdf/1512.03385v1.pdf
+'''
+
+import mxnet as mx
+def ConvFactory(data, num_filter, kernel, stride=(1, 1), pad=(0, 0), act_type = 'relu',last=False):
+    conv = mx.symbol.Convolution(data = data, num_filter = num_filter, kernel = kernel, stride = stride, pad = pad)
+    if last:
+        return conv
+    else:
+        bn = mx.symbol.BatchNorm(data=conv)
+        act = mx.symbol.Activation(data=bn, act_type=act_type)
+        return act
+
+def ResidualFactory(data, num_filter, diff_dim=False):
+    if diff_dim:
+        conv1 = ConvFactory(          data=data,  num_filter=num_filter[0], kernel=(3,3), stride=(2,2), pad=(1,1), last=False)
+        conv2 = ConvFactory(          data=conv1, num_filter=num_filter[1], kernel=(3,3), stride=(1,1), pad=(1,1), last=True)
+        _data = mx.symbol.Convolution(data=data,  num_filter=num_filter[1], kernel=(3,3), stride=(2,2), pad=(1,1))
+        data  = _data+conv2
+        bn    = mx.symbol.BatchNorm(data=data)
+        act   = mx.symbol.Activation(data=bn, act_type='relu')
+        return act
+    else:
+        _data=data
+        conv1 = ConvFactory(data=data,  num_filter=num_filter[0], kernel=(3,3), stride=(1,1), pad=(1,1), last=False)
+        conv2 = ConvFactory(data=conv1, num_filter=num_filter[1], kernel=(3,3), stride=(1,1), pad=(1,1), last=True)
+        data  = _data+conv2
+        bn    = mx.symbol.BatchNorm(data=data)
+        act   = mx.symbol.Activation(data=bn, act_type='relu')
+        return act
+
+def ResidualSymbol(data, n=9):
+    "stage 1"
+    for i in xrange(n):
+        data = ResidualFactory(data, (16, 16))
+    "stage 2"
+    for i in xrange(n):
+        if i == 0:
+            data = ResidualFactory(data, (32, 32), True)
+        else:
+            data = ResidualFactory(data, (32, 32))
+    "stage 3"
+    for i in xrange(n):
+        if i == 0:
+            data = ResidualFactory(data, (64, 64), True)
+        else:
+            data = ResidualFactory(data, (64, 64))
+    return data
+
+def get_symbol(num_classes=10):
+    data    = ConvFactory(data=mx.symbol.Variable(name='data'), num_filter=16, kernel=(3,3), stride=(1,1), pad=(1,1))
+    res     = ResidualSymbol(data)
+    pool    = mx.symbol.Pooling(data=res, kernel=(7,7), pool_type='avg')
+    flatten = mx.symbol.Flatten(data=pool, name='flatten')
+    fc      = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fc1')
+    softmax = mx.symbol.SoftmaxOutput(data=fc, name='softmax')
+    return softmax