Merge pull request #39 from qingqing01/develop

Add models for image classification

image_classification/README.md

+TBD

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
+
+
+def train_reader(train_list):
+    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)
+
+    return reader
+
+
+def test_reader(test_list):
+    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)
+
+    return reader
+
+
+if __name__ == '__main__':
+    for im in train_reader('train.list'):
+        print len(im[0])
+    for im in train_reader('test.list'):
+        print len(im[0])

image_classification/train.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 gzip
+
+import paddle.v2 as paddle
+import reader
+import vgg
+
+DATA_DIM = 3 * 224 * 224
+CLASS_DIM = 1000
+BATCH_SIZE = 128
+
+
+def main():
+
+    # PaddlePaddle init
+    paddle.init(use_gpu=True, trainer_count=4)
+
+    image = paddle.layer.data(
+        name="image", type=paddle.data_type.dense_vector(DATA_DIM))
+    lbl = paddle.layer.data(
+        name="label", type=paddle.data_type.integer_value(CLASS_DIM))
+    net = vgg.vgg13(image)
+    out = paddle.layer.fc(
+        input=net, size=CLASS_DIM, act=paddle.activation.Softmax())
+    cost = paddle.layer.classification_cost(input=out, label=lbl)
+
+    # Create parameters
+    parameters = paddle.parameters.create(cost)
+
+    # Create optimizer
+    optimizer = paddle.optimizer.Momentum(
+        momentum=0.9,
+        regularization=paddle.optimizer.L2Regularization(rate=0.0005 *
+                                                         BATCH_SIZE),
+        learning_rate=0.01 / BATCH_SIZE,
+        learning_rate_decay_a=0.1,
+        learning_rate_decay_b=128000 * 35,
+        learning_rate_schedule="discexp", )
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(reader.test_reader("train.list"), buf_size=1000),
+        batch_size=BATCH_SIZE)
+    test_reader = paddle.batch(
+        reader.train_reader("test.list"), batch_size=BATCH_SIZE)
+
+    # End batch and end pass event handler
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 1 == 0:
+                print "\nPass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+        if isinstance(event, paddle.event.EndPass):
+            with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
+                parameters.to_tar(f)
+
+            result = trainer.test(reader=test_reader)
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+
+    # Create trainer
+    trainer = paddle.trainer.SGD(
+        cost=cost, parameters=parameters, update_equation=optimizer)
+
+    trainer.train(
+        reader=train_reader, num_passes=200, event_handler=event_handler)
+
+
+if __name__ == '__main__':
+    main()

image_classification/vgg.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 paddle.v2 as paddle
+
+__all__ = ['vgg13', 'vgg16', 'vgg19']
+
+
+def vgg(input, nums):
+    def conv_block(input, num_filter, groups, num_channels=None):
+        return paddle.networks.img_conv_group(
+            input=input,
+            num_channels=num_channels,
+            pool_size=2,
+            pool_stride=2,
+            conv_num_filter=[num_filter] * groups,
+            conv_filter_size=3,
+            conv_act=paddle.activation.Relu(),
+            pool_type=paddle.pooling.Max())
+
+    assert len(nums) == 5
+    # the channel of input feature is 3
+    conv1 = conv_block(input, 64, nums[0], 3)
+    conv2 = conv_block(conv1, 128, nums[1])
+    conv3 = conv_block(conv2, 256, nums[2])
+    conv4 = conv_block(conv3, 512, nums[3])
+    conv5 = conv_block(conv4, 512, nums[4])
+
+    fc_dim = 4096
+    fc1 = paddle.layer.fc(
+        input=conv5,
+        size=fc_dim,
+        act=paddle.activation.Relu(),
+        layer_attr=paddle.attr.Extra(drop_rate=0.5))
+    fc2 = paddle.layer.fc(
+        input=fc1,
+        size=fc_dim,
+        act=paddle.activation.Relu(),
+        layer_attr=paddle.attr.Extra(drop_rate=0.5))
+    return fc2
+
+
+def vgg13(input):
+    nums = [2, 2, 2, 2, 2]
+    return vgg(input, nums)
+
+
+def vgg16(input):
+    nums = [2, 2, 3, 3, 3]
+    return vgg(input, nums)
+
+
+def vgg19(input):
+    nums = [2, 2, 4, 4, 4]
+    return vgg(input, nums)