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120 lines
2.8 KiB
120 lines
2.8 KiB
#!/usr/bin/env python
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from paddle.trainer_config_helpers import *
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height = 224
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width = 224
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num_class = 1000
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batch_size = get_config_arg('batch_size', int, 64)
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layer_num = get_config_arg('layer_num', int, 19)
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is_infer = get_config_arg("is_infer", bool, False)
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num_samples = get_config_arg('num_samples', int, 2560)
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args = {
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'height': height,
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'width': width,
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'color': True,
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'num_class': num_class,
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'is_infer': is_infer,
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'num_samples': num_samples
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}
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define_py_data_sources2(
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"train.list" if not is_infer else None,
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"test.list" if is_infer else None,
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module="provider",
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obj="process",
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args=args)
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settings(
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batch_size=batch_size,
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learning_rate=0.001 / batch_size,
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learning_method=MomentumOptimizer(0.9),
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regularization=L2Regularization(0.0005 * batch_size))
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img = data_layer(name='image', size=height * width * 3)
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def vgg_network(vgg_num=3):
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tmp = img_conv_group(
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input=img,
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num_channels=3,
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conv_padding=1,
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conv_num_filter=[64, 64],
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_size=2,
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pool_stride=2,
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pool_type=MaxPooling())
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=[128, 128],
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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channels = []
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for i in range(vgg_num):
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channels.append(256)
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=channels,
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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channels = []
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for i in range(vgg_num):
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channels.append(512)
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=channels,
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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tmp = img_conv_group(
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input=tmp,
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conv_num_filter=channels,
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conv_padding=1,
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conv_filter_size=3,
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conv_act=ReluActivation(),
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pool_stride=2,
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pool_type=MaxPooling(),
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pool_size=2)
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tmp = fc_layer(
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input=tmp,
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size=4096,
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act=ReluActivation(),
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layer_attr=ExtraAttr(drop_rate=0.5))
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tmp = fc_layer(
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input=tmp,
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size=4096,
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act=ReluActivation(),
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layer_attr=ExtraAttr(drop_rate=0.5))
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return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
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if layer_num == 16:
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vgg = vgg_network(3)
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elif layer_num == 19:
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vgg = vgg_network(4)
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else:
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print("Wrong layer number.")
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if is_infer:
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outputs(vgg)
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else:
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lab = data_layer('label', num_class)
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loss = cross_entropy(input=vgg, label=lab)
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outputs(loss)
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