add mobilenet fluid config

fluid/image_classification/mobilenet.py

+import os
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import time
+
+
+def conv_bn_layer(input,
+                  filter_size,
+                  num_filters,
+                  stride,
+                  padding,
+                  channels=None,
+                  num_groups=1,
+                  act='relu',
+                  use_cudnn=True):
+    conv = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        stride=stride,
+        padding=padding,
+        groups=num_groups,
+        act=None,
+        use_cudnn=use_cudnn,
+        bias_attr=False)
+    return fluid.layers.batch_norm(input=conv, act=act)
+
+
+def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride,
+                        scale):
+    """
+    """
+    tmp = conv_bn_layer(
+        input=input,
+        filter_size=3,
+        num_filters=int(num_filters1 * scale),
+        stride=stride,
+        padding=1,
+        num_groups=int(num_groups * scale),
+        use_cudnn=False)
+
+    tmp = conv_bn_layer(
+        input=tmp,
+        filter_size=1,
+        num_filters=int(num_filters2 * scale),
+        stride=1,
+        padding=0)
+    return tmp
+
+
+def mobile_net(img, class_dim, scale=1.0):
+
+    # conv1: 112x112
+    tmp = conv_bn_layer(
+        img,
+        filter_size=3,
+        channels=3,
+        num_filters=int(32 * scale),
+        stride=2,
+        padding=1)
+
+    # 56x56
+    tmp = depthwise_separable(
+        tmp,
+        num_filters1=32,
+        num_filters2=64,
+        num_groups=32,
+        stride=1,
+        scale=scale)
+    tmp = depthwise_separable(
+        tmp,
+        num_filters1=64,
+        num_filters2=128,
+        num_groups=64,
+        stride=2,
+        scale=scale)
+    # 28x28
+    tmp = depthwise_separable(
+        tmp,
+        num_filters1=128,
+        num_filters2=128,
+        num_groups=128,
+        stride=1,
+        scale=scale)
+    tmp = depthwise_separable(
+        tmp,
+        num_filters1=128,
+        num_filters2=256,
+        num_groups=128,
+        stride=2,
+        scale=scale)
+    # 14x14
+    tmp = depthwise_separable(
+        tmp,
+        num_filters1=256,
+        num_filters2=256,
+        num_groups=256,
+        stride=1,
+        scale=scale)
+    tmp = depthwise_separable(
+        tmp,
+        num_filters1=256,
+        num_filters2=512,
+        num_groups=256,
+        stride=2,
+        scale=scale)
+    # 14x14
+    for i in range(5):
+        tmp = depthwise_separable(
+            tmp,
+            num_filters1=512,
+            num_filters2=512,
+            num_groups=512,
+            stride=1,
+            scale=scale)
+    # 7x7
+    tmp = depthwise_separable(
+        tmp,
+        num_filters1=512,
+        num_filters2=1024,
+        num_groups=512,
+        stride=2,
+        scale=scale)
+    tmp = depthwise_separable(
+        tmp,
+        num_filters1=1024,
+        num_filters2=1024,
+        num_groups=1024,
+        stride=1,
+        scale=scale)
+
+    tmp = fluid.layers.pool2d(
+        input=tmp, pool_size=7, pool_stride=1, pool_type='avg')
+
+    tmp = fluid.layers.fc(input=tmp, size=class_dim, act='softmax')
+    return tmp
+
+
+def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
+    class_dim = 102
+    image_shape = [3, 224, 224]
+
+    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    out = mobile_net(image, class_dim=class_dim)
+
+    cost = fluid.layers.cross_entropy(input=out, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=learning_rate,
+        momentum=0.9,
+        regularization=fluid.regularizer.L2Decay(5 * 1e-5))
+    opts = optimizer.minimize(avg_cost)
+    accuracy = fluid.evaluator.Accuracy(input=out, label=label)
+
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        test_accuracy = fluid.evaluator.Accuracy(input=out, label=label)
+        test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
+        inference_program = fluid.io.get_inference_program(test_target)
+
+    place = fluid.CUDAPlace(0)
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    train_reader = paddle.batch(
+        paddle.dataset.flowers.train(), batch_size=batch_size)
+    test_reader = paddle.batch(
+        paddle.dataset.flowers.test(), batch_size=batch_size)
+    feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
+
+    for pass_id in range(num_passes):
+        accuracy.reset(exe)
+        for batch_id, data in enumerate(train_reader()):
+            start_time = time.time()
+            loss, acc = exe.run(fluid.default_main_program(),
+                                feed=feeder.feed(data),
+                                fetch_list=[avg_cost] + accuracy.metrics)
+            pass_elapsed = time.time() - start_time
+            print("Pass {0}, batch {1}, loss {2}, acc {3}".format(
+                pass_id, batch_id, loss[0], acc[0]))
+            print 'cost : %f s' % (pass_elapsed)
+        pass_acc = accuracy.eval(exe)
+
+        test_accuracy.reset(exe)
+        for data in test_reader():
+            loss, acc = exe.run(inference_program,
+                                feed=feeder.feed(data),
+                                fetch_list=[avg_cost] + test_accuracy.metrics)
+        test_pass_acc = test_accuracy.eval(exe)
+        print("End pass {0}, train_acc {1}, test_acc {2}".format(
+            pass_id, pass_acc, test_pass_acc))
+        if pass_id % 10 == 0:
+            print 'save models'
+            model_path = os.path.join(model_save_dir, str(pass_id))
+            fluid.io.save_inference_model(model_path, ['image'], [out], exe)
+
+
+if __name__ == '__main__':
+    train(learning_rate=0.005, batch_size=80, num_passes=400)