Add xception model for image classification

image_classification/infer.py

@@ -26,7 +26,10 @@ def main():
     parser.add_argument(
         'model',
         help='The model for image classification',
-        choices=['alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet'])
+        choices=[
+            'alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet',
+            'xception'
+        ])
     parser.add_argument(
         'params_path', help='The file which stores the parameters')
     args = parser.parse_args()
@@ -49,6 +52,8 @@ def main():
         out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM)
     elif args.model == 'googlenet':
         out, _, _ = googlenet.googlenet(image, class_dim=CLASS_DIM)
+    elif args.model == 'xception':
+        out = xception.xception(image, class_dim=CLASS_DIM)
 
     # load parameters
     with gzip.open(args.params_path, 'r') as f:

image_classification/train.py

@@ -19,7 +19,10 @@ def main():
     parser.add_argument(
         'model',
         help='The model for image classification',
-        choices=['alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet'])
+        choices=[
+            'alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet',
+            'xception'
+        ])
     args = parser.parse_args()
 
     # PaddlePaddle init
@@ -52,6 +55,8 @@ def main():
             input=out2, label=lbl, coeff=0.3)
         paddle.evaluator.classification_error(input=out2, label=lbl)
         extra_layers = [loss1, loss2]
+    elif args.model == 'xception':
+        out = xception.xception(image, class_dim=CLASS_DIM)
 
     cost = paddle.layer.classification_cost(input=out, label=lbl)
 

image_classification/xception.py

+import paddle.v2 as paddle
+
+__all__ = ['xception']
+
+
+def img_separable_conv_bn(name, input, num_channels, num_out_channels,
+                          filter_size, stride, padding, act):
+    conv = paddle.networks.img_separable_conv(
+        name=name,
+        input=input,
+        num_channels=num_channels,
+        num_out_channels=num_out_channels,
+        filter_size=filter_size,
+        stride=stride,
+        padding=padding,
+        act=paddle.activation.Linear())
+    norm = paddle.layer.batch_norm(name=name + '_norm', input=conv, act=act)
+    return norm
+
+
+def img_conv_bn(name, input, num_channels, num_filters, filter_size, stride,
+                padding, act):
+    conv = paddle.layer.img_conv(
+        name=name,
+        input=input,
+        num_channels=num_channels,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        stride=stride,
+        padding=padding,
+        act=paddle.activation.Linear())
+    norm = paddle.layer.batch_norm(name=name + '_norm', input=conv, act=act)
+    return norm
+
+
+def conv_block0(input,
+                group,
+                num_channels,
+                num_filters,
+                num_filters2=None,
+                filter_size=3,
+                pool_padding=0,
+                entry_relu=True):
+    if num_filters2 is None:
+        num_filters2 = num_filters
+
+    if entry_relu:
+        act_input = paddle.layer.mixed(
+            input=paddle.layer.identity_projection(input=input),
+            act=paddle.activation.Relu())
+    else:
+        act_input = input
+    conv0 = img_separable_conv_bn(
+        name='xception_block{0}_conv0'.format(group),
+        input=act_input,
+        num_channels=num_channels,
+        num_out_channels=num_filters,
+        filter_size=filter_size,
+        stride=1,
+        padding=(filter_size - 1) / 2,
+        act=paddle.activation.Relu())
+    conv1 = img_separable_conv_bn(
+        name='xception_block{0}_conv1'.format(group),
+        input=conv0,
+        num_channels=num_filters,
+        num_out_channels=num_filters2,
+        filter_size=filter_size,
+        stride=1,
+        padding=(filter_size - 1) / 2,
+        act=paddle.activation.Linear())
+    pool0 = paddle.layer.img_pool(
+        name='xception_block{0}_pool'.format(group),
+        input=conv1,
+        pool_size=3,
+        stride=2,
+        padding=pool_padding,
+        num_channels=num_filters2,
+        pool_type=paddle.pooling.CudnnMax())
+
+    shortcut = img_conv_bn(
+        name='xception_block{0}_shortcut'.format(group),
+        input=input,
+        num_channels=num_channels,
+        num_filters=num_filters2,
+        filter_size=1,
+        stride=2,
+        padding=0,
+        act=paddle.activation.Linear())
+
+    return paddle.layer.addto(
+        input=[pool0, shortcut], act=paddle.activation.Linear())
+
+
+def conv_block1(input, group, num_channels, num_filters, filter_size=3):
+    act_input = paddle.layer.mixed(
+        input=paddle.layer.identity_projection(input=input),
+        act=paddle.activation.Relu())
+    conv0 = img_separable_conv_bn(
+        name='xception_block{0}_conv0'.format(group),
+        input=act_input,
+        num_channels=num_channels,
+        num_out_channels=num_filters,
+        filter_size=filter_size,
+        stride=1,
+        padding=(filter_size - 1) / 2,
+        act=paddle.activation.Relu())
+    conv1 = img_separable_conv_bn(
+        name='xception_block{0}_conv1'.format(group),
+        input=conv0,
+        num_channels=num_filters,
+        num_out_channels=num_filters,
+        filter_size=filter_size,
+        stride=1,
+        padding=(filter_size - 1) / 2,
+        act=paddle.activation.Relu())
+    conv2 = img_separable_conv_bn(
+        name='xception_block{0}_conv2'.format(group),
+        input=conv1,
+        num_channels=num_filters,
+        num_out_channels=num_filters,
+        filter_size=filter_size,
+        stride=1,
+        padding=(filter_size - 1) / 2,
+        act=paddle.activation.Linear())
+
+    shortcut = input
+    return paddle.layer.addto(
+        input=[conv2, shortcut], act=paddle.activation.Linear())
+
+
+def xception(input, class_dim):
+    conv = img_conv_bn(
+        name='xception_conv0',
+        input=input,
+        num_channels=3,
+        num_filters=32,
+        filter_size=3,
+        stride=2,
+        padding=1,
+        act=paddle.activation.Relu())
+    conv = img_conv_bn(
+        name='xception_conv1',
+        input=conv,
+        num_channels=32,
+        num_filters=64,
+        filter_size=3,
+        stride=1,
+        padding=1,
+        act=paddle.activation.Relu())
+    conv = conv_block0(
+        input=conv, group=2, num_channels=64, num_filters=128, entry_relu=False)
+    conv = conv_block0(input=conv, group=3, num_channels=128, num_filters=256)
+    conv = conv_block0(input=conv, group=4, num_channels=256, num_filters=728)
+    for group in range(5, 13):
+        conv = conv_block1(
+            input=conv, group=group, num_channels=728, num_filters=728)
+    conv = conv_block0(
+        input=conv,
+        group=13,
+        num_channels=728,
+        num_filters=728,
+        num_filters2=1024)
+    conv = img_separable_conv_bn(
+        name='xception_conv14',
+        input=conv,
+        num_channels=1024,
+        num_out_channels=1536,
+        filter_size=3,
+        stride=1,
+        padding=1,
+        act=paddle.activation.Relu())
+    conv = img_separable_conv_bn(
+        name='xception_conv15',
+        input=conv,
+        num_channels=1536,
+        num_out_channels=2048,
+        filter_size=3,
+        stride=1,
+        padding=1,
+        act=paddle.activation.Relu())
+    pool = paddle.layer.img_pool(
+        name='xception_global_pool',
+        input=conv,
+        pool_size=7,
+        stride=1,
+        num_channels=2048,
+        pool_type=paddle.pooling.CudnnAvg())
+    out = paddle.layer.fc(
+        name='xception_fc',
+        input=pool,
+        size=class_dim,
+        act=paddle.activation.Softmax())
+    return out