add xmap for image list and modify the image reader of infer.py

image_classification/README.md

@@ -147,11 +147,11 @@ dataset_100/train_images/n02643566_75.jpeg   8
 ```python
 train_reader = paddle.batch(
     paddle.reader.shuffle(
-        reader.test_reader('train.list'),
+        reader.train_reader('train.list'),
         buf_size=1000),
     batch_size=BATCH_SIZE)
 test_reader = paddle.batch(
-    reader.train_reader('val.list'),
+    reader.test_reader('val.list'),
     batch_size=BATCH_SIZE)
 ```
 
@@ -209,24 +209,10 @@ trainer.train(
 with gzip.open('params_pass_10.tar.gz', 'r') as f:
     parameters = paddle.parameters.Parameters.from_tar(f)
 
-def load_image(file):
-    im = Image.open(file)
-    im = im.resize((224, 224), Image.ANTIALIAS)
-    im = np.array(im).astype(np.float32)
-    # The storage order of the loaded image is W(widht),
-    # H(height), C(channel). PaddlePaddle requires
-    # the CHW order, so transpose them.
-    im = im.transpose((2, 0, 1))  # CHW
-    # In the training phase, the channel order of CIFAR
-    # image is B(Blue), G(green), R(Red). But PIL open
-    # image in RGB mode. It must swap the channel order.
-    im = im[(2, 1, 0), :, :]  # BGR
-    im = im.flatten()
-    im = im / 255.0
-    return im
-
 file_list = [line.strip() for line in open(image_list_file)]
-test_data = [(load_image(image_file),) for image_file in file_list]
+test_data = [(paddle.image.load_and_transform(image_file, 256, 224, False)
+              .flatten().astype('float32'), )
+             for image_file in file_list]
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)
 lab = np.argsort(-probs)
@@ -234,4 +220,4 @@ for file_name, result in zip(file_list, lab):
     print "Label of %s is: %d" % (file_name, result[0])
 ```
 
-首先从文件中加载训练好的模型（代码里以第10轮迭代的结果为例），然后读取`image_list_file`中的图像。`image_list_file`是一个文本文件，每一行为一个图像路径。`load_image`是一个加载图像的函数。代码使用`paddle.infer`判断`image_list_file`中每个图像的类别，并进行输出。
+首先从文件中加载训练好的模型（代码里以第10轮迭代的结果为例），然后读取`image_list_file`中的图像。`image_list_file`是一个文本文件，每一行为一个图像路径。代码使用`paddle.infer`判断`image_list_file`中每个图像的类别，并进行输出。

image_classification/alexnet.py

@@ -3,7 +3,7 @@ import paddle.v2 as paddle
 __all__ = ['alexnet']
 
 
-def alexnet(input, class_dim=100):
+def alexnet(input, class_dim):
     conv1 = paddle.layer.img_conv(
         input=input,
         filter_size=11,

image_classification/googlenet.py

@@ -3,8 +3,8 @@ import paddle.v2 as paddle
 __all__ = ['googlenet']
 
 
-def inception2(name, input, channels, filter1, filter3R, filter3, filter5R,
-               filter5, proj):
+def inception(name, input, channels, filter1, filter3R, filter3, filter5R,
+              filter5, proj):
     cov1 = paddle.layer.img_conv(
         name=name + '_1',
         input=input,
@@ -65,7 +65,7 @@ def inception2(name, input, channels, filter1, filter3R, filter3, filter5R,
     return cat
 
 
-def googlenet(input, class_dim=100):
+def googlenet(input, class_dim):
     # stage 1
     conv1 = paddle.layer.img_conv(
         name="conv1",
@@ -97,23 +97,23 @@ def googlenet(input, class_dim=100):
         name="pool2", input=conv2_2, pool_size=3, num_channels=192, stride=2)
 
     # stage 3
-    ince3a = inception2("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32)
-    ince3b = inception2("ince3b", ince3a, 256, 128, 128, 192, 32, 96, 64)
+    ince3a = inception("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32)
+    ince3b = inception("ince3b", ince3a, 256, 128, 128, 192, 32, 96, 64)
     pool3 = paddle.layer.img_pool(
         name="pool3", input=ince3b, num_channels=480, pool_size=3, stride=2)
 
     # stage 4
-    ince4a = inception2("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64)
-    ince4b = inception2("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64)
-    ince4c = inception2("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64)
-    ince4d = inception2("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64)
-    ince4e = inception2("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128)
+    ince4a = inception("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64)
+    ince4b = inception("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64)
+    ince4c = inception("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64)
+    ince4d = inception("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64)
+    ince4e = inception("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128)
     pool4 = paddle.layer.img_pool(
         name="pool4", input=ince4e, num_channels=832, pool_size=3, stride=2)
 
     # stage 5
-    ince5a = inception2("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128)
-    ince5b = inception2("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128)
+    ince5a = inception("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128)
+    ince5b = inception("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128)
     pool5 = paddle.layer.img_pool(
         name="pool5",
         input=ince5b,

image_classification/infer.py

@@ -54,24 +54,9 @@ def main():
     with gzip.open(args.params_path, 'r') as f:
         parameters = paddle.parameters.Parameters.from_tar(f)
 
-    def load_image(file):
-        im = Image.open(file)
-        im = im.resize((WIDTH, HEIGHT), Image.ANTIALIAS)
-        im = np.array(im).astype(np.float32)
-        # The storage order of the loaded image is W(widht),
-        # H(height), C(channel). PaddlePaddle requires
-        # the CHW order, so transpose them.
-        im = im.transpose((2, 0, 1))  # CHW
-        # In the training phase, the channel order of CIFAR
-        # image is B(Blue), G(green), R(Red). But PIL open
-        # image in RGB mode. It must swap the channel order.
-        im = im[(2, 1, 0), :, :]  # BGR
-        im = im.flatten()
-        im = im / 255.0
-        return im
-
     file_list = [line.strip() for line in open(args.data_list)]
-    test_data = [(load_image(image_file), ) for image_file in file_list]
+    test_data = [(paddle.image.load_and_transform(image_file, 256, 224, False)
+                  .flatten().astype('float32'), ) for image_file in file_list]
     probs = paddle.infer(
         output_layer=out, parameters=parameters, input=test_data)
     lab = np.argsort(-probs)

image_classification/reader.py

-# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License
-
 import random
 from paddle.v2.image import load_and_transform
+import paddle.v2 as paddle
+from multiprocessing import cpu_count
+
+
+def train_mapper(sample):
+    '''
+    map image path to type needed by model input layer for the training set
+    '''
+    img, label = sample
+    img = paddle.image.load_image(img)
+    img = paddle.image.simple_transform(img, 256, 224, True)
+    return img.flatten().astype('float32'), label
+
+
+def test_mapper(sample):
+    '''
+    map image path to type needed by model input layer for the test set
+    '''
+    img, label = sample
+    img = paddle.image.load_image(img)
+    img = paddle.image.simple_transform(img, 256, 224, True)
+    return img.flatten().astype('float32'), label
 
 
-def train_reader(train_list):
+def train_reader(train_list, buffered_size=1024):
     def reader():
         with open(train_list, 'r') as f:
             lines = [line.strip() for line in f]
-            random.shuffle(lines)
             for line in lines:
                 img_path, lab = line.strip().split('\t')
-                im = load_and_transform(img_path, 256, 224, True)
-                yield im.flatten().astype('float32'), int(lab)
+                yield img_path, int(lab)
 
-    return reader
+    return paddle.reader.xmap_readers(train_mapper, reader,
+                                      cpu_count(), buffered_size)
 
 
-def test_reader(test_list):
+def test_reader(test_list, buffered_size=1024):
     def reader():
         with open(test_list, 'r') as f:
             lines = [line.strip() for line in f]
             for line in lines:
                 img_path, lab = line.strip().split('\t')
-                im = load_and_transform(img_path, 256, 224, False)
-                yield im.flatten().astype('float32'), int(lab)
+                yield img_path, int(lab)
 
-    return reader
+    return paddle.reader.xmap_readers(test_mapper, reader,
+                                      cpu_count(), buffered_size)
 
 
 if __name__ == '__main__':

image_classification/resnet.py

@@ -55,7 +55,7 @@ def layer_warp(block_func, input, ch_in, ch_out, count, stride):
     return conv
 
 
-def resnet_imagenet(input, depth=50, class_dim=100):
+def resnet_imagenet(input, class_dim, depth=50):
     cfg = {
         18: ([2, 2, 2, 1], basicblock),
         34: ([3, 4, 6, 3], basicblock),
@@ -78,7 +78,7 @@ def resnet_imagenet(input, depth=50, class_dim=100):
     return out
 
 
-def resnet_cifar10(input, depth=32, class_dim=10):
+def resnet_cifar10(input, class_dim, depth=32):
     # depth should be one of 20, 32, 44, 56, 110, 1202
     assert (depth - 2) % 6 == 0
     n = (depth - 2) / 6

image_classification/train.py

@@ -72,13 +72,13 @@ def main():
         paddle.reader.shuffle(
             flowers.train(),
             # To use other data, replace the above line with:
-            # reader.test_reader('train.list'),
+            # reader.train_reader('train.list'),
             buf_size=1000),
         batch_size=BATCH_SIZE)
     test_reader = paddle.batch(
         flowers.valid(),
         # To use other data, replace the above line with:
-        # reader.train_reader('val.list'),
+        # reader.test_reader('val.list'),
         batch_size=BATCH_SIZE)
 
     # End batch and end pass event handler

image_classification/vgg.py

@@ -17,7 +17,7 @@ import paddle.v2 as paddle
 __all__ = ['vgg13', 'vgg16', 'vgg19']
 
 
-def vgg(input, nums, class_dim=100):
+def vgg(input, nums, class_dim):
     def conv_block(input, num_filter, groups, num_channels=None):
         return paddle.networks.img_conv_group(
             input=input,
@@ -53,16 +53,16 @@ def vgg(input, nums, class_dim=100):
     return out
 
 
-def vgg13(input, class_dim=100):
+def vgg13(input, class_dim):
     nums = [2, 2, 2, 2, 2]
     return vgg(input, nums, class_dim)
 
 
-def vgg16(input, class_dim=100):
+def vgg16(input, class_dim):
     nums = [2, 2, 3, 3, 3]
     return vgg(input, nums, class_dim)
 
 
-def vgg19(input, class_dim=100):
+def vgg19(input, class_dim):
     nums = [2, 2, 4, 4, 4]
     return vgg(input, nums, class_dim)