Add more algorithms

others/paddlepaddle/lib/models/resnet.py

@@ -0,0 +1,65 @@
+import paddle
+import paddle.fluid as fluid
+
+
+def conv_bn_layer(input,
+          ch_out,
+          filter_size,
+          stride,
+          padding,
+          act='relu',
+          bias_attr=False):
+  tmp = fluid.layers.conv2d(
+    input=input,
+    filter_size=filter_size,
+    num_filters=ch_out,
+    stride=stride,
+    padding=padding,
+    act=None,
+    bias_attr=bias_attr)
+  return fluid.layers.batch_norm(input=tmp, act=act)
+
+
+def shortcut(input, ch_in, ch_out, stride):
+  if stride == 2:
+    temp = fluid.layers.pool2d(input, pool_size=2, pool_type='avg', pool_stride=2)
+    temp = fluid.layers.conv2d(temp , filter_size=1, num_filters=ch_out, stride=1, padding=0, act=None, bias_attr=None)
+    return temp
+  elif ch_in != ch_out:
+    return conv_bn_layer(input, ch_out, 1, stride, 0, None, None)
+  else:
+    return input
+
+
+def basicblock(input, ch_in, ch_out, stride):
+  tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
+  tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True)
+  short = shortcut(input, ch_in, ch_out, stride)
+  return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
+
+
+def layer_warp(block_func, input, ch_in, ch_out, count, stride):
+  tmp = block_func(input, ch_in, ch_out, stride)
+  for i in range(1, count):
+    tmp = block_func(tmp, ch_out, ch_out, 1)
+  return tmp
+
+
+def resnet_cifar(ipt, depth, class_num):
+  # depth should be one of 20, 32, 44, 56, 110, 1202
+  assert (depth - 2) % 6 == 0
+  n = (depth - 2) // 6
+  print('[resnet] depth : {:}, class_num : {:}'.format(depth, class_num))
+  conv1 = conv_bn_layer(ipt, ch_out=16, filter_size=3, stride=1, padding=1)
+  print('conv-1 : shape = {:}'.format(conv1.shape))
+  res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
+  print('res--1 : shape = {:}'.format(res1.shape))
+  res2 = layer_warp(basicblock, res1 , 16, 32, n, 2)
+  print('res--2 : shape = {:}'.format(res2.shape))
+  res3 = layer_warp(basicblock, res2 , 32, 64, n, 2)
+  print('res--3 : shape = {:}'.format(res3.shape))
+  pool = fluid.layers.pool2d(input=res3, pool_size=8, pool_type='avg', pool_stride=1)
+  print('pool   : shape = {:}'.format(pool.shape))
+  predict = fluid.layers.fc(input=pool, size=class_num, act='softmax')
+  print('predict: shape = {:}'.format(predict.shape))
+  return predict