Add inception_v4 config in fluid

fluid/image_classification/inception_v4.py

+import os
+import paddle.fluid as fluid
+
+
+def inception_v4(image, label):
+
+    tmp = stem(input=image)
+    for i in range(0, 4):
+        tmp = inception_A(input=tmp, depth=i)
+    tmp = reduction_A(input=tmp)
+
+    for i in range(0, 7):
+        tmp = inception_B(input=tmp, depth=i)
+    reduction_B(input=tmp)
+
+    for i in range(0, 3):
+        tmp = inception_C(input=tmp, depth=i)
+
+    pool = fluid.layers.pool2d(
+        pool_type='ave', input=tmp, pool_size=7, pool_stride=1)
+    dropout = fluid.layers.dropout(input=pool, drop_prob=0.2)
+    out = fluid.layers.softmax(input=dropout)
+    return out
+
+
+def conv_bn_layer(input,
+                  num_filters,
+                  filter_size,
+                  padding=1,
+                  stride=1,
+                  groups=1,
+                  act=None):
+    conv = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        stride=stride,
+        padding=padding,
+        groups=groups,
+        act=None,
+        bias_attr=False)
+    return fluid.layers.batch_norm(input=conv, act=act)
+
+
+def stem(input):
+    conv1 = conv_bn_layer(input=input, num_filters=32, filter_size=3, stride=2)
+    conv2 = conv_bn_layer(input=conv1, num_filters=32, filter_size=3)
+    conv3 = conv_bn_layer(input=conv2, num_filters=64, filter_size=3)
+
+    def block0(input):
+        pool0 = fluid.layers.pool2d(
+            input=input, pool_size=3, pool_stride=2, pool_type='max')
+        conv0 = conv_bn_layer(
+            input=input, num_filters=96, filter_size=3, stride=2)
+        return fluid.layers.concat(input=[pool0, conv0])
+
+    def block1(input):
+        l_conv0 = conv_bn_layer(
+            input=input, num_filters=64, filter_size=1, stride=1, padding=0)
+        l_conv1 = conv_bn_layer(
+            input=l_conv0, num_filters=96, filter_size=3, stride=1, padding=1)
+        r_conv0 = conv_bn_layer(
+            input=input, num_filters=64, filter_size=1, stride=1, padding=0)
+        r_conv1 = conv_bn_layer(
+            input=r_conv0,
+            num_filters=64,
+            filter_size=(7, 1),
+            stride=1,
+            padding=(3, 0))
+        r_conv2 = conv_bn_layer(
+            input=r_conv1,
+            num_filters=64,
+            filter_size=(1, 7),
+            stride=1,
+            padding=(0, 3))
+        r_conv3 = conv_bn_layer(
+            input=r_conv2, num_filters=96, filter_size=3, stride=1, padding=1)
+        return fluid.layers.concat(input=[l_conv1, r_conv3])
+
+    def block2(input):
+        conv0 = conv_bn_layer(
+            input=input, num_filters=192, filter_size=3, stride=2, padding=1)
+        pool0 = fluid.layers.pool2d(
+            input=input, pool_size=3, pool_stride=2, pool_type='max')
+        return fluid.layers.concat(input=[conv0, pool0])
+
+    conv3 = block0(conv2)
+    conv4 = block1(conv3)
+    conv5 = block2(conv4)
+    return conv5
+
+
+def inception_A(input, depth):
+    b0_pool0 = paddle.layer.pool2d(
+        name='inceptA{0}_branch0_pool0'.format(depth),
+        input=input,
+        pool_size=3,
+        stride=1,
+        padding=1,
+        pool_type='avg')
+    b0_conv0 = conv_bn_layer(
+        name='inceptA{0}_branch0_conv0'.format(depth),
+        input=b0_pool0,
+        num_filters=96,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b1_conv0 = conv_bn_layer(
+        name='inceptA{0}_branch1_conv0'.format(depth),
+        input=input,
+        num_filters=96,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv0 = conv_bn_layer(
+        name='inceptA{0}_branch2_conv0'.format(depth),
+        input=input,
+        num_filters=64,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = conv_bn_layer(
+        name='inceptA{0}_branch2_conv1'.format(depth),
+        input=b2_conv0,
+        num_channels=64,
+        num_filters=96,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    b3_conv0 = conv_bn_layer(
+        name='inceptA{0}_branch3_conv0'.format(depth),
+        input=input,
+        num_channels=384,
+        num_filters=64,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b3_conv1 = conv_bn_layer(
+        name='inceptA{0}_branch3_conv1'.format(depth),
+        input=b3_conv0,
+        num_filters=96,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    b3_conv2 = conv_bn_layer(
+        name='inceptA{0}_branch3_conv2'.format(depth),
+        input=b3_conv1,
+        num_filters=96,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    return paddle.layer.concat(input=[b0_conv0, b1_conv0, b2_conv1, b3_conv2])
+
+
+def reduction_A(input):
+    b0_pool0 = fluid.layers.pool2d(
+        name='ReductA_branch0_pool0',
+        input=input,
+        pool_size=3,
+        pool_stride=2,
+        pool_type='max')
+    b1_conv0 = conv_bn_layer(
+        name='ReductA_branch1_conv0',
+        input=input,
+        num_filters=384,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    b2_conv0 = conv_bn_layer(
+        name='ReductA_branch2_conv0',
+        input=input,
+        num_filters=192,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = conv_bn_layer(
+        name='ReductA_branch2_conv1',
+        input=b2_conv0,
+        num_filters=224,
+        filter_size=3,
+        stride=1,
+        padding=1)
+    b2_conv2 = conv_bn_layer(
+        name='ReductA_branch2_conv2',
+        input=b2_conv1,
+        num_filters=256,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    return fluid.layers.concat(input=[b0_pool0, b1_conv0, b2_conv2])
+
+
+def inception_B(input, depth):
+    b0_pool0 = fluid.layers.pool2d(
+        name='inceptB{0}_branch0_pool0'.format(depth),
+        input=input,
+        pool_size=3,
+        pool_stride=1,
+        pool_padding=1,
+        pool_type='avg')
+    b0_conv0 = conv_bn_layer(
+        name='inceptB{0}_branch0_conv0'.format(depth),
+        input=b0_pool0,
+        num_filters=128,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b1_conv0 = conv_bn_layer(
+        name='inceptB{0}_branch1_conv0'.format(depth),
+        input=input,
+        num_filters=384,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv0 = conv_bn_layer(
+        name='inceptB{0}_branch2_conv0'.format(depth),
+        input=input,
+        num_filters=192,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = conv_bn_layer(
+        name='inceptB{0}_branch2_conv1'.format(depth),
+        input=b2_conv0,
+        num_filters=224,
+        filter_size=(1, 7),
+        stride=1,
+        padding=(0, 3))
+    b2_conv2 = conv_bn_layer(
+        name='inceptB{0}_branch2_conv2'.format(depth),
+        input=b2_conv1,
+        num_filters=256,
+        filter_size=(7, 1),
+        stride=1,
+        padding=(3, 0))
+    b3_conv0 = conv_bn_layer(
+        name='inceptB{0}_branch3_conv0'.format(depth),
+        input=input,
+        num_filters=192,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b3_conv1 = conv_bn_layer(
+        name='inceptB{0}_branch3_conv1'.format(depth),
+        input=b3_conv0,
+        num_filters=192,
+        filter_size=(1, 7),
+        stride=1,
+        padding=(0, 3))
+    b3_conv2 = conv_bn_layer(
+        name='inceptB{0}_branch3_conv2'.format(depth),
+        input=b3_conv1,
+        num_filters=224,
+        filter_size=(7, 1),
+        stride=1,
+        padding=(3, 0))
+    b3_conv3 = conv_bn_layer(
+        name='inceptB{0}_branch3_conv3'.format(depth),
+        input=b3_conv2,
+        num_filters=224,
+        filter_size=(1, 7),
+        stride=1,
+        padding=(0, 3))
+    b3_conv4 = conv_bn_layer(
+        name='inceptB{0}_branch3_conv4'.format(depth),
+        input=b3_conv3,
+        num_filters=256,
+        filter_size=(7, 1),
+        stride=1,
+        padding=(3, 0))
+    return fluid.layers.concat(input=[b0_conv0, b1_conv0, b2_conv2, b3_conv4])
+
+
+def reduction_B(input):
+    b0_pool0 = fluid.layers.pool2d(
+        name='ReductB_branch0_pool0',
+        input=input,
+        pool_size=3,
+        pool_stride=2,
+        pool_type='max')
+    b1_conv0 = conv_bn_layer(
+        name='ReductB_branch1_conv0',
+        input=input,
+        num_filters=192,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b1_conv1 = conv_bn_layer(
+        name='ReductB_branch1_conv1',
+        input=b1_conv0,
+        num_filters=192,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    b2_conv0 = conv_bn_layer(
+        name='ReductB_branch2_conv0',
+        input=input,
+        num_filters=256,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = conv_bn_layer(
+        name='ReductB_branch2_conv1',
+        input=b2_conv0,
+        num_filters=256,
+        filter_size=(1, 7),
+        stride=1,
+        padding=(0, 3))
+    b2_conv2 = conv_bn_layer(
+        name='ReductB_branch2_conv2',
+        input=b2_conv1,
+        num_filters=320,
+        filter_size=(7, 1),
+        stride=1,
+        padding=(3, 0))
+    b2_conv3 = conv_bn_layer(
+        name='ReductB_branch2_conv3',
+        input=b2_conv2,
+        num_filters=320,
+        filter_size=3,
+        stride=2,
+        padding=1)
+    return fluid.layers.concat(input=[b0_pool0, b1_conv1, b2_conv3])
+
+
+def inception_C(input, depth):
+    b0_pool0 = fluid.layers.pool2d(
+        name='inceptC{0}_branch0_pool0'.format(depth),
+        input=input,
+        pool_size=3,
+        pool_stride=1,
+        pool_padding=1,
+        pool_type='avg')
+    b0_conv0 = conv_bn_layer(
+        name='inceptC{0}_branch0_conv0'.format(depth),
+        input=b0_pool0,
+        num_filters=256,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b1_conv0 = conv_bn_layer(
+        name='inceptC{0}_branch1_conv0'.format(depth),
+        input=input,
+        num_filters=256,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv0 = conv_bn_layer(
+        name='inceptC{0}_branch2_conv0'.format(depth),
+        input=input,
+        num_filters=384,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b2_conv1 = conv_bn_layer(
+        name='inceptC{0}_branch2_conv1'.format(depth),
+        input=b2_conv0,
+        num_filters=256,
+        filter_size=(1, 3),
+        stride=1,
+        padding=(0, 1))
+    b2_conv2 = conv_bn_layer(
+        name='inceptC{0}_branch2_conv2'.format(depth),
+        input=b2_conv0,
+        num_filters=256,
+        filter_size=(3, 1),
+        stride=1,
+        padding=(1, 0))
+    b3_conv0 = conv_bn_layer(
+        name='inceptC{0}_branch3_conv0'.format(depth),
+        input=input,
+        num_filters=384,
+        filter_size=1,
+        stride=1,
+        padding=0)
+    b3_conv1 = conv_bn_layer(
+        name='inceptC{0}_branch3_conv1'.format(depth),
+        input=b3_conv0,
+        num_filters=448,
+        filter_size=(1, 3),
+        stride=1,
+        padding=(0, 1))
+    b3_conv2 = conv_bn_layer(
+        name='inceptC{0}_branch3_conv2'.format(depth),
+        input=b3_conv1,
+        num_filters=512,
+        filter_size=(3, 1),
+        stride=1,
+        padding=(1, 0))
+    b3_conv3 = conv_bn_layer(
+        name='inceptC{0}_branch3_conv3'.format(depth),
+        input=b3_conv2,
+        num_filters=256,
+        filter_size=(3, 1),
+        stride=1,
+        padding=(1, 0))
+    b3_conv4 = conv_bn_layer(
+        name='inceptC{0}_branch3_conv4'.format(depth),
+        input=b3_conv2,
+        num_filters=256,
+        filter_size=(1, 3),
+        stride=1,
+        padding=(0, 1))
+    return fluid.layers.concat(
+        input=[b0_conv0, b1_conv0, b2_conv1, b2_conv2, b3_conv3, b3_conv4])

fluid/image_classification/train.py

@@ -6,6 +6,7 @@ import paddle
 import paddle.fluid as fluid
 from se_resnext import SE_ResNeXt
 from mobilenet import mobile_net
+from inception_v4 import inception_v4
 import reader
 import argparse
 import functools
@@ -70,8 +71,10 @@ def train_parallel_do(args,
             if args.model is 'se_resnext':
                 out = SE_ResNeXt(
                     input=image_, class_dim=class_dim, layers=layers)
-            else:
+            elif args.model is 'mobile_net':
                 out = mobile_net(img=image_, class_dim=class_dim)
+            else:
+                out = inception_v4(img=image_, class_dim=class_dim)
 
             cost = fluid.layers.cross_entropy(input=out, label=label_)
             avg_cost = fluid.layers.mean(x=cost)
@@ -88,8 +91,10 @@ def train_parallel_do(args,
     else:
         if args.model is 'se_resnext':
             out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers)
-        else:
+        elif args.model is 'mobile_net':
             out = mobile_net(img=image, class_dim=class_dim)
+        else:
+            out = inception_v4(img=image, class_dim=class_dim)
 
         cost = fluid.layers.cross_entropy(input=out, label=label)
         avg_cost = fluid.layers.mean(x=cost)
@@ -224,8 +229,10 @@ def train_parallel_exe(args,
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
     if args.model is 'se_resnext':
         out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers)
-    else:
+    elif args.model is 'mobile_net':
         out = mobile_net(img=image, class_dim=class_dim)
+    else:
+        out = inception_v4(img=image, class_dim=class_dim)
 
     cost = fluid.layers.cross_entropy(input=out, label=label)
     acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)