ISSUE=4596311 git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1431 1ad973e4-5ce8-4261-8a94-b56d1f490c56avx_docs
dangqingqing
9 years ago
parent
5db9e5900f
commit
13f4602999
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../model_zoo/resnet/classify.py
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import numpy as np
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from optparse import OptionParser
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from py_paddle import swig_paddle, util, DataProviderWrapperConverter
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from paddle.trainer.PyDataProviderWrapper import DenseSlot
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from paddle.trainer.config_parser import parse_config
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"""
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Will merge predictor from Qingqing.
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"""
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os,sys
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import numpy as np
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import logging
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from PIL import Image
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from optparse import OptionParser
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import paddle.utils.image_util as image_util
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from py_paddle import swig_paddle, util
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from py_paddle import DataProviderWrapperConverter
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from paddle.trainer.PyDataProviderWrapper import DenseSlot
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from paddle.trainer.config_parser import parse_config
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logging.basicConfig(format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s')
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logging.getLogger().setLevel(logging.INFO)
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class ImageClassifier():
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def __init__(self,
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train_conf,
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use_gpu=True,
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model_dir=None,
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resize_dim=None,
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crop_dim=None,
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mean_file=None,
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oversample=False,
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is_color=True):
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"""
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train_conf: network configure.
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model_dir: string, directory of model.
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resize_dim: int, resized image size.
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crop_dim: int, crop size.
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mean_file: string, image mean file.
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oversample: bool, oversample means multiple crops, namely five
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patches (the four corner patches and the center
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patch) as well as their horizontal reflections,
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ten crops in all.
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"""
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self.train_conf = train_conf
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self.model_dir = model_dir
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if model_dir is None:
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self.model_dir = os.path.dirname(train_conf)
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self.resize_dim = resize_dim
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self.crop_dims = [crop_dim, crop_dim]
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self.oversample = oversample
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self.is_color = is_color
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self.transformer = image_util.ImageTransformer(is_color = is_color)
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self.transformer.set_transpose((2,0,1))
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self.mean_file = mean_file
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mean = np.load(self.mean_file)['data_mean']
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mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
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self.transformer.set_mean(mean) # mean pixel
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gpu = 1 if use_gpu else 0
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conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (gpu)
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conf = parse_config(train_conf, conf_args)
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swig_paddle.initPaddle("--use_gpu=%d" % (gpu))
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self.network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
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assert isinstance(self.network, swig_paddle.GradientMachine)
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self.network.loadParameters(self.model_dir)
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data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
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slots = [DenseSlot(data_size)]
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self.converter = util.DataProviderWrapperConverter(False, slots)
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def get_data(self, img_path):
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"""
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1. load image from img_path.
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2. resize or oversampling.
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3. transformer data: transpose, sub mean.
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return K x H x W ndarray.
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img_path: image path.
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"""
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image = image_util.load_image(img_path, self.is_color)
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if self.oversample:
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# image_util.resize_image: short side is self.resize_dim
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image = image_util.resize_image(image, self.resize_dim)
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image = np.array(image)
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input = np.zeros((1, image.shape[0], image.shape[1], 3),
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dtype=np.float32)
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input[0] = image.astype(np.float32)
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input = image_util.oversample(input, self.crop_dims)
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else:
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image = image.resize(self.crop_dims, Image.ANTIALIAS)
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input = np.zeros((1, self.crop_dims[0], self.crop_dims[1], 3),
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dtype=np.float32)
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input[0] = np.array(image).astype(np.float32)
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data_in = []
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for img in input:
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img = self.transformer.transformer(img).flatten()
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data_in.append([img.tolist()])
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return data_in
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def forward(self, input_data):
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in_arg = self.converter(input_data)
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return self.network.forwardTest(in_arg)
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def forward(self, data, output_layer):
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"""
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input_data: py_paddle input data.
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output_layer: specify the name of probability, namely the layer with
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softmax activation.
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return: the predicting probability of each label.
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"""
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input = self.converter(data)
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self.network.forwardTest(input)
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output = self.network.getLayerOutputs(output_layer)
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# For oversampling, average predictions across crops.
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# If not, the shape of output[name]: (1, class_number),
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# the mean is also applicable.
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return output[output_layer].mean(0)
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def predict(self, image=None, output_layer=None):
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assert isinstance(image, basestring)
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assert isinstance(output_layer, basestring)
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data = self.get_data(image)
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prob = self.forward(data, output_layer)
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lab = np.argsort(-prob)
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logging.info("Label of %s is: %d", image, lab[0])
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if __name__ == '__main__':
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image_size=32
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crop_size=32
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multi_crop=True
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config="vgg_16_cifar.py"
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output_layer="__fc_layer_1__"
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mean_path="data/cifar-out/batches/batches.meta"
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model_path=sys.argv[1]
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image=sys.argv[2]
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use_gpu=bool(int(sys.argv[3]))
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obj = ImageClassifier(train_conf=config,
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model_dir=model_path,
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resize_dim=image_size,
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crop_dim=crop_size,
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mean_file=mean_path,
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use_gpu=use_gpu,
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oversample=multi_crop)
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obj.predict(image, output_layer)
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