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313 lines
11 KiB
313 lines
11 KiB
# Copyright (c) 2016 PaddlePaddle Authors. 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 sys
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import cPickle
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import logging
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from PIL import Image
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import numpy as np
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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, DataProviderConverter
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from paddle.trainer.PyDataProvider2 import dense_vector
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from paddle.trainer.config_parser import parse_config
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logging.basicConfig(
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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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model_dir=None,
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resize_dim=256,
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crop_dim=224,
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use_gpu=True,
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mean_file=None,
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output_layer=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.output_layer = output_layer
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if self.output_layer:
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assert isinstance(self.output_layer, basestring)
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self.output_layer = self.output_layer.split(",")
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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.transformer.set_channel_swap((2, 1, 0))
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self.mean_file = mean_file
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if self.mean_file is not None:
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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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else:
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# if you use three mean value, set like:
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# this three mean value is calculated from ImageNet.
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self.transformer.set_mean(np.array([103.939, 116.779, 123.68]))
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conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (int(use_gpu))
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conf = parse_config(train_conf, conf_args)
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swig_paddle.initPaddle("--use_gpu=%d" % (int(use_gpu)))
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self.network = swig_paddle.GradientMachine.createFromConfigProto(
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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 = [dense_vector(data_size)]
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self.converter = DataProviderConverter(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, channel swap, 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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# Another way to extract oversampled features is that
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# cropping and averaging from large feature map which is
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# calculated by large size of image.
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# This way reduces the computation.
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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(
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(1, image.shape[0], image.shape[1], 3), 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(
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(1, self.crop_dims[0], self.crop_dims[1], 3), 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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# paddle input: [[[]],[[]],...], [[]] is one sample.
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return data_in
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def forward(self, input_data):
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"""
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return output arguments which are the Outputs() in network configure.
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input_data: py_paddle input data.
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call forward.
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"""
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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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return output arguments which are the Outputs() in network configure.
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input_data: py_paddle input data.
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call forward.
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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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res = {}
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if isinstance(output_layer, basestring):
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output_layer = [output_layer]
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for name in 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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res[name] = output[name]['value'].mean(0)
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return res
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def predict(self, data_file):
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"""
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call forward and predicting.
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data_file: input image list.
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"""
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image_files = open(data_file, 'rb').readlines()
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results = {}
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if self.output_layer is None:
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self.output_layer = ["output"]
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for line in image_files:
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image = line.split()[0]
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data = self.get_data(image)
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prob = self.forward(data, self.output_layer)
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lab = np.argsort(-prob[self.output_layer[0]])
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results[image] = lab[0]
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logging.info("Label of %s is: %d", image, lab[0])
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return results
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def extract(self, data_file, output_dir, batch_size=10000):
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"""
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extract and save features of output layers, which are
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specify in Outputs() in network configure.
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data_file: file name of input data.
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output_dir: saved directory of extracted features.
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batch_size: sample number of one batch file.
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"""
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if not os.path.exists(output_dir):
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os.mkdir(output_dir)
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sample_num = 0
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batch_num = 0
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image_feature = {}
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image_files = open(data_file, 'rb').readlines()
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for idx, line in enumerate(image_files):
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image = line.split()[0]
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data = self.get_data(image)
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feature = self.forward(data, self.output_layer)
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# save extracted features
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file_name = image.split("/")[-1]
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image_feature[file_name] = feature
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sample_num += 1
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if sample_num == batch_size:
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batch_name = os.path.join(output_dir, 'batch_%d' % (batch_num))
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self.save_file(image_feature, batch_name)
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logging.info('Finish batch %d', batch_num)
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batch_num += 1
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sample_num = 0
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image_feature = {}
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if idx % 1000 == 0:
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logging.info('%d/%d, %s', idx, len(image_files), file_name)
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if sample_num > 0:
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batch_name = os.path.join(output_dir, 'batch_%d' % (batch_num))
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self.save_file(image_feature, batch_name)
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logging.info('Finish batch %d', batch_num)
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logging.info('Done: make image feature batch')
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def save_file(self, data, file):
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of = open(file, 'wb')
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cPickle.dump(data, of, protocol=cPickle.HIGHEST_PROTOCOL)
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def option_parser():
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"""
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Main entry for predciting
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"""
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usage = "%prog -c config -i data_list -w model_dir [options]"
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parser = OptionParser(usage="usage: %s" % usage)
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parser.add_option(
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"-j",
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"--job",
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action="store",
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dest="job_type",
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help="job type: predict, extract\
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predict: predicting,\
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extract: extract features")
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parser.add_option(
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"-c",
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"--conf",
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action="store",
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dest="train_conf",
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help="network config")
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parser.add_option(
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"-i", "--data", action="store", dest="data_file", help="image list")
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parser.add_option(
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"-w",
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"--model",
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action="store",
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dest="model_path",
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default=None,
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help="model path")
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parser.add_option(
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"-g",
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"--use_gpu",
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action="store",
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dest="use_gpu",
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default=True,
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help="Whether to use gpu mode.")
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parser.add_option(
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"-o",
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"--output_dir",
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action="store",
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dest="output_dir",
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default="output",
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help="output path")
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parser.add_option(
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"-m",
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"--mean",
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action="store",
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dest="mean",
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default=None,
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help="mean file.")
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parser.add_option(
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"-p",
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"--multi_crop",
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action="store_true",
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dest="multi_crop",
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default=False,
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help="Wether to use multiple crops on image.")
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parser.add_option("-l", "--output_layer", action="store",
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dest="output_layer", default=None,
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help="--job=extract, specify layers to extract "\
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"features, --job=predict, specify layer of "
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"classification probability, output in resnet.py.")
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return parser.parse_args()
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def main():
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"""
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1. parse input arguments.
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2. predicting or extract features according job type.
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"""
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options, args = option_parser()
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obj = ImageClassifier(
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options.train_conf,
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options.model_path,
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use_gpu=options.use_gpu,
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mean_file=options.mean,
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output_layer=options.output_layer,
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oversample=options.multi_crop)
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if options.job_type == "predict":
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obj.predict(options.data_file)
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elif options.job_type == "extract":
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obj.extract(options.data_file, options.output_dir)
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if __name__ == '__main__':
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main()
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