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Image Classification Tutorial
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*.pyc | ||
train.log | ||
output | ||
data/cifar-10-batches-py/ | ||
data/cifar-10-python.tar.gz | ||
data/*.txt | ||
data/*.list | ||
data/mean.meta |
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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import os, sys | ||
import cPickle | ||
import numpy as np | ||
from PIL import Image | ||
from optparse import OptionParser | ||
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import paddle.utils.image_util as image_util | ||
from py_paddle import swig_paddle, DataProviderConverter | ||
from paddle.trainer.PyDataProvider2 import dense_vector | ||
from paddle.trainer.config_parser import parse_config | ||
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import logging | ||
logging.basicConfig( | ||
format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s') | ||
logging.getLogger().setLevel(logging.INFO) | ||
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def vis_square(data, fname): | ||
import matplotlib | ||
matplotlib.use('Agg') | ||
import matplotlib.pyplot as plt | ||
"""Take an array of shape (n, height, width) or (n, height, width, 3) | ||
and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)""" | ||
# normalize data for display | ||
data = (data - data.min()) / (data.max() - data.min()) | ||
# force the number of filters to be square | ||
n = int(np.ceil(np.sqrt(data.shape[0]))) | ||
padding = ( | ||
((0, n**2 - data.shape[0]), (0, 1), | ||
(0, 1)) # add some space between filters | ||
+ ((0, 0), ) * | ||
(data.ndim - 3)) # don't pad the last dimension (if there is one) | ||
data = np.pad(data, padding, mode='constant', | ||
constant_values=1) # pad with ones (white) | ||
# tile the filters into an image | ||
data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple( | ||
range(4, data.ndim + 1))) | ||
data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:]) | ||
plt.imshow(data, cmap='gray') | ||
plt.savefig(fname) | ||
plt.axis('off') | ||
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class ImageClassifier(): | ||
def __init__(self, | ||
train_conf, | ||
resize_dim, | ||
crop_dim, | ||
model_dir=None, | ||
use_gpu=True, | ||
mean_file=None, | ||
oversample=False, | ||
is_color=True): | ||
self.train_conf = train_conf | ||
self.model_dir = model_dir | ||
if model_dir is None: | ||
self.model_dir = os.path.dirname(train_conf) | ||
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self.resize_dim = resize_dim | ||
self.crop_dims = [crop_dim, crop_dim] | ||
self.oversample = oversample | ||
self.is_color = is_color | ||
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self.transformer = image_util.ImageTransformer(is_color=is_color) | ||
self.transformer.set_transpose((2, 0, 1)) | ||
self.transformer.set_channel_swap((2, 1, 0)) | ||
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self.mean_file = mean_file | ||
if self.mean_file is not None: | ||
mean = np.load(self.mean_file)['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])) | ||
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conf_args = "use_gpu=%d,is_predict=1" % (int(use_gpu)) | ||
conf = parse_config(train_conf, conf_args) | ||
swig_paddle.initPaddle("--use_gpu=%d" % (int(use_gpu))) | ||
self.network = swig_paddle.GradientMachine.createFromConfigProto( | ||
conf.model_config) | ||
assert isinstance(self.network, swig_paddle.GradientMachine) | ||
self.network.loadParameters(self.model_dir) | ||
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dim = 3 * self.crop_dims[0] * self.crop_dims[1] | ||
slots = [dense_vector(dim)] | ||
self.converter = DataProviderConverter(slots) | ||
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def get_data(self, img_path): | ||
""" | ||
1. load image from img_path. | ||
2. resize or oversampling. | ||
3. transformer data: transpose, channel swap, sub mean. | ||
return K x H x W ndarray. | ||
img_path: image path. | ||
""" | ||
image = image_util.load_image(img_path, self.is_color) | ||
# Another way to extract oversampled features is that | ||
# cropping and averaging from large feature map which is | ||
# calculated by large size of image. | ||
# This way reduces the computation. | ||
if self.oversample: | ||
image = image_util.resize_image(image, self.resize_dim) | ||
image = np.array(image) | ||
input = np.zeros( | ||
(1, image.shape[0], image.shape[1], 3), dtype=np.float32) | ||
input[0] = image.astype(np.float32) | ||
input = image_util.oversample(input, self.crop_dims) | ||
else: | ||
image = image.resize(self.crop_dims, Image.ANTIALIAS) | ||
input = np.zeros( | ||
(1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32) | ||
input[0] = np.array(image).astype(np.float32) | ||
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data_in = [] | ||
for img in input: | ||
img = self.transformer.transformer(img).flatten() | ||
data_in.append([img.tolist()]) | ||
return data_in | ||
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def forward(self, input_data): | ||
in_arg = self.converter(input_data) | ||
return self.network.forwardTest(in_arg) | ||
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def forward(self, data, output_layer): | ||
input = self.converter(data) | ||
self.network.forwardTest(input) | ||
output = self.network.getLayerOutputs(output_layer) | ||
res = {} | ||
if isinstance(output_layer, basestring): | ||
output_layer = [output_layer] | ||
for name in output_layer: | ||
# For oversampling, average predictions across crops. | ||
# If not, the shape of output[name]: (1, class_number), | ||
# the mean is also applicable. | ||
res[name] = output[name].mean(0) | ||
return res | ||
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def option_parser(): | ||
usage = "%prog -c config -i data_list -w model_dir [options]" | ||
parser = OptionParser(usage="usage: %s" % usage) | ||
parser.add_option( | ||
"--job", | ||
action="store", | ||
dest="job_type", | ||
choices=[ | ||
'predict', | ||
'extract', | ||
], | ||
default='predict', | ||
help="The job type. \ | ||
predict: predicting,\ | ||
extract: extract features") | ||
parser.add_option( | ||
"--conf", | ||
action="store", | ||
dest="train_conf", | ||
default='models/vgg.py', | ||
help="network config") | ||
parser.add_option( | ||
"--data", | ||
action="store", | ||
dest="data_file", | ||
default='image/dog.png', | ||
help="image list") | ||
parser.add_option( | ||
"--model", | ||
action="store", | ||
dest="model_path", | ||
default=None, | ||
help="model path") | ||
parser.add_option( | ||
"-c", dest="cpu_gpu", action="store_false", help="Use cpu mode.") | ||
parser.add_option( | ||
"-g", | ||
dest="cpu_gpu", | ||
default=True, | ||
action="store_true", | ||
help="Use gpu mode.") | ||
parser.add_option( | ||
"--mean", | ||
action="store", | ||
dest="mean", | ||
default='data/mean.meta', | ||
help="The mean file.") | ||
parser.add_option( | ||
"--multi_crop", | ||
action="store_true", | ||
dest="multi_crop", | ||
default=False, | ||
help="Wether to use multiple crops on image.") | ||
return parser.parse_args() | ||
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def main(): | ||
options, args = option_parser() | ||
mean = 'data/mean.meta' if not options.mean else options.mean | ||
conf = 'models/vgg.py' if not options.train_conf else options.train_conf | ||
obj = ImageClassifier( | ||
conf, | ||
32, | ||
32, | ||
options.model_path, | ||
use_gpu=options.cpu_gpu, | ||
mean_file=mean, | ||
oversample=options.multi_crop) | ||
image_path = options.data_file | ||
if options.job_type == 'predict': | ||
output_layer = '__fc_layer_2__' | ||
data = obj.get_data(image_path) | ||
prob = obj.forward(data, output_layer) | ||
lab = np.argsort(-prob[output_layer]) | ||
logging.info("Label of %s is: %d", image_path, lab[0]) | ||
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elif options.job_type == "extract": | ||
output_layer = '__conv_0__' | ||
data = obj.get_data(options.data_file) | ||
features = obj.forward(data, output_layer) | ||
dshape = (64, 32, 32) | ||
fea = features[output_layer].reshape(dshape) | ||
vis_square(fea, 'fea_conv0.png') | ||
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if __name__ == '__main__': | ||
main() |
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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import os | ||
import numpy as np | ||
import cPickle | ||
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DATA = "cifar-10-batches-py" | ||
CHANNEL = 3 | ||
HEIGHT = 32 | ||
WIDTH = 32 | ||
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def create_mean(dataset): | ||
if not os.path.isfile("mean.meta"): | ||
mean = np.zeros(CHANNEL * HEIGHT * WIDTH) | ||
num = 0 | ||
for f in dataset: | ||
batch = np.load(f) | ||
mean += batch['data'].sum(0) | ||
num += len(batch['data']) | ||
mean /= num | ||
print mean.size | ||
data = {"mean": mean, "size": mean.size} | ||
cPickle.dump( | ||
data, open("mean.meta", 'w'), protocol=cPickle.HIGHEST_PROTOCOL) | ||
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def create_data(): | ||
train_set = [DATA + "/data_batch_%d" % (i + 1) for i in xrange(0, 5)] | ||
test_set = [DATA + "/test_batch"] | ||
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# create mean values | ||
create_mean(train_set) | ||
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# create dataset lists | ||
if not os.path.isfile("train.txt"): | ||
train = ["data/" + i for i in train_set] | ||
open("train.txt", "w").write("\n".join(train)) | ||
open("train.list", "w").write("\n".join(["data/train.txt"])) | ||
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if not os.path.isfile("text.txt"): | ||
test = ["data/" + i for i in test_set] | ||
open("test.txt", "w").write("\n".join(test)) | ||
open("test.list", "w").write("\n".join(["data/test.txt"])) | ||
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if __name__ == '__main__': | ||
create_data() |
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
set -e | ||
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wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz | ||
tar zxf cifar-10-python.tar.gz | ||
rm cifar-10-python.tar.gz | ||
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python cifar10.py |
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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import numpy as np | ||
import cPickle | ||
from paddle.trainer.PyDataProvider2 import * | ||
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def initializer(settings, mean_path, is_train, **kwargs): | ||
settings.is_train = is_train | ||
settings.input_size = 3 * 32 * 32 | ||
settings.mean = np.load(mean_path)['mean'] | ||
settings.input_types = { | ||
'image': dense_vector(settings.input_size), | ||
'label': integer_value(10) | ||
} | ||
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@provider(init_hook=initializer, cache=CacheType.CACHE_PASS_IN_MEM) | ||
def process(settings, file_list): | ||
with open(file_list, 'r') as fdata: | ||
for fname in fdata: | ||
fo = open(fname.strip(), 'rb') | ||
batch = cPickle.load(fo) | ||
fo.close() | ||
images = batch['data'] | ||
labels = batch['labels'] | ||
for im, lab in zip(images, labels): | ||
if settings.is_train and np.random.randint(2): | ||
im = im[:, :, ::-1] | ||
im = im - settings.mean | ||
yield {'image': im.astype('float32'), 'label': int(lab)} |
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#!/bin/bash | ||
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
set -e | ||
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python classify.py --job=extract --model=output/pass-00299 --data=image/dog.png # -c |
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