def __init__(self, train_conf, model_dir=None,
resize_dim=256, crop_dim=224,
mean_file=None,
output_layer=None,
oversample=False, is_color=True):
"""
train_conf: network configure.
model_dir: string, directory of model.
resize_dim: int, resized image size.
crop_dim: int, crop size.
mean_file: string, image mean file.
oversample: bool, oversample means multiple crops, namely five
patches (the four corner patches and the center
patch) as well as their horizontal reflections,
ten crops in all.
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.output_layer = output_layer
if self.output_layer:
assert isinstance(self.output_layer, basestring)
self.output_layer = self.output_layer.split(",")
self.transformer = image_util.ImageTransformer(is_color = is_color)
self.transformer.set_transpose((2,0,1))
self.transformer.set_channel_swap((2,1,0))
self.mean_file = mean_file
if self.mean_file is not None:
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
else:
# if you use three mean value, set like:
# this three mean value is calculated from ImageNet.
self.transformer.set_mean(np.array([103.939,116.779,123.68]))
conf_args = "is_test=1,use_gpu=1,is_predict=1"
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=1")
self.network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
slots = [DenseSlot(data_size)]
is_sequence = False
self.converter = util.DataProviderWrapperConverter(is_sequence, slots)
def __init__(self,
train_conf,
use_gpu=True,
model_dir=None,
resize_dim=None,
crop_dim=None,
mean_file=None,
oversample=False,
is_color=False):
"""
train_conf: 网络配置文件
model_dir: 模型路径
resize_dim: 设为原图大小
crop_dim: 图像裁剪大小，一般设为原图大小
oversample: bool, oversample表示多次裁剪,这里禁用
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.transformer = image_util.ImageTransformer(is_color = is_color)
self.transformer.set_transpose((2,0,1))
self.mean_file = mean_file
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(1, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
gpu = 1 if use_gpu else 0
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (gpu)
#使用 parse_config() 解析训练时的配置文件
conf = parse_config(train_conf, conf_args)
#PaddlePaddle目前使用Swig对其常用的预测接口进行了封装，使在Python环境下的预测接口更加简单
#使用 swig_paddle.initPaddle() 传入命令行参数初始化 PaddlePaddle
swig_paddle.initPaddle("--use_gpu=%d" % (int(use_gpu)))
#使用 swig_paddle.GradientMachine.createFromConfigproto() 根据上一步解析好的配置创建神经网络
self.network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
#从模型文件加载参数
self.network.loadParameters(self.model_dir)
data_size = 1 * self.crop_dims[0] * self.crop_dims[1]
slots = [dense_vector(data_size)]
'''
创建一个 DataProviderConverter 对象converter。
swig_paddle接受的原始数据是C++的Matrix，也就是直接写内存的float数组。 这个接口并不用户友好。所以，我们提供了一个工具类DataProviderConverter。 这个工具类接收和PyDataProvider2一样的输入数据
'''
self.converter = DataProviderConverter(slots)
def __init__(self,
train_conf,
use_gpu=True,
model_dir=None,
resize_dim=None,
crop_dim=None,
mean_file=None,
oversample=False,
is_color=True):
"""
train_conf: network configure.
model_dir: string, directory of model.
resize_dim: int, resized image size.
crop_dim: int, crop size.
mean_file: string, image mean file.
oversample: bool, oversample means multiple crops, namely five
patches (the four corner patches and the center
patch) as well as their horizontal reflections,
ten crops in all.
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.transformer = image_util.ImageTransformer(is_color=is_color)
self.transformer.set_transpose((2, 0, 1))
self.mean_file = mean_file
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
gpu = 1 if use_gpu else 0
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (gpu)
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=%d" % (gpu))
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
slots = [DenseSlot(data_size)]
self.converter = util.DataProviderWrapperConverter(False, slots)
