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# Copyright (c) 2018 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 executor | ||
from . import core | ||
from threading import Thread | ||
from Queue import Queue | ||
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def run_exe(q, idx, exe, program, feed, fetch_list, feed_var_name, | ||
fetch_var_name, cur_scope, return_numpy): | ||
q.put((idx, exe.run(program, feed, fetch_list, feed_var_name, | ||
fetch_var_name, cur_scope, return_numpy))) | ||
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class ParallelExecutor(object): | ||
def __init__(self, places): | ||
self.scopes = {} | ||
self.executors = [] | ||
for place in places: | ||
self.executors.append(executor.Executor(place)) | ||
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def run(self, | ||
program=None, | ||
feed=None, | ||
fetch_list=None, | ||
feed_var_name='feed', | ||
fetch_var_name='fetch', | ||
scope=None, | ||
return_numpy=True): | ||
# TODO(helin): split input | ||
q = Queue(maxsize=len(self.executors)) | ||
for idx, exe in enumerate(self.executors): | ||
if scope is None: | ||
if idx == 0: | ||
cur_scope = executor.global_scope() | ||
else: | ||
if idx in self.scopes: | ||
cur_scope = self.scopes[idx] | ||
else: | ||
cur_scope = core.Scope() | ||
self.scopes[idx] = cur_scope | ||
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t = Thread( | ||
target=run_exe, | ||
args=(q, idx, exe, program, feed, fetch_list, feed_var_name, | ||
fetch_var_name, cur_scope, return_numpy)) | ||
t.daemon = True | ||
t.start() | ||
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results = [] | ||
for _ in self.executors: | ||
results.append(q.get()) | ||
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results.sort(key=lambda x: x[0]) | ||
# TODO(helin): concat output | ||
return results[0][1] |
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180
python/paddle/fluid/tests/book/parallel_executor_example.py
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# Copyright (c) 2018 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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from __future__ import print_function | ||
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import paddle.v2 as paddle | ||
import paddle.v2.fluid as fluid | ||
import math | ||
import sys | ||
import numpy | ||
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def resnet_cifar10(input, depth=32): | ||
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'): | ||
tmp = fluid.layers.conv2d( | ||
input=input, | ||
filter_size=filter_size, | ||
num_filters=ch_out, | ||
stride=stride, | ||
padding=padding, | ||
act=None, | ||
bias_attr=False) | ||
return fluid.layers.batch_norm(input=tmp, act=act) | ||
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def shortcut(input, ch_in, ch_out, stride): | ||
if ch_in != ch_out: | ||
return conv_bn_layer(input, ch_out, 1, stride, 0, None) | ||
else: | ||
return input | ||
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def basicblock(input, ch_in, ch_out, stride): | ||
tmp = conv_bn_layer(input, ch_out, 3, stride, 1) | ||
tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None) | ||
short = shortcut(input, ch_in, ch_out, stride) | ||
return fluid.layers.elementwise_add(x=tmp, y=short, act='relu') | ||
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def layer_warp(block_func, input, ch_in, ch_out, count, stride): | ||
tmp = block_func(input, ch_in, ch_out, stride) | ||
for i in range(1, count): | ||
tmp = block_func(tmp, ch_out, ch_out, 1) | ||
return tmp | ||
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assert (depth - 2) % 6 == 0 | ||
n = (depth - 2) / 6 | ||
conv1 = conv_bn_layer( | ||
input=input, ch_out=16, filter_size=3, stride=1, padding=1) | ||
res1 = layer_warp(basicblock, conv1, 16, 16, n, 1) | ||
res2 = layer_warp(basicblock, res1, 16, 32, n, 2) | ||
res3 = layer_warp(basicblock, res2, 32, 64, n, 2) | ||
pool = fluid.layers.pool2d( | ||
input=res3, pool_size=8, pool_type='avg', pool_stride=1) | ||
return pool | ||
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def vgg16_bn_drop(input): | ||
def conv_block(input, num_filter, groups, dropouts): | ||
return fluid.nets.img_conv_group( | ||
input=input, | ||
pool_size=2, | ||
pool_stride=2, | ||
conv_num_filter=[num_filter] * groups, | ||
conv_filter_size=3, | ||
conv_act='relu', | ||
conv_with_batchnorm=True, | ||
conv_batchnorm_drop_rate=dropouts, | ||
pool_type='max') | ||
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conv1 = conv_block(input, 64, 2, [0.3, 0]) | ||
conv2 = conv_block(conv1, 128, 2, [0.4, 0]) | ||
conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0]) | ||
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0]) | ||
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0]) | ||
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drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5) | ||
fc1 = fluid.layers.fc(input=drop, size=512, act=None) | ||
bn = fluid.layers.batch_norm(input=fc1, act='relu') | ||
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5) | ||
fc2 = fluid.layers.fc(input=drop2, size=512, act=None) | ||
return fc2 | ||
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def train(net_type, use_cuda, save_dirname): | ||
classdim = 10 | ||
data_shape = [3, 32, 32] | ||
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r = paddle.dataset.cifar.train10() | ||
for data in r(): | ||
img, l = data[0], data[1] | ||
break | ||
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#images = fluid.layers.fill_constant(shape=data_shape, dtype='float32', value=3) | ||
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32') | ||
#label = fluid.layers.fill_constant(shape=[1], dtype='int64', value=2) | ||
label = fluid.layers.data(name='label', shape=[1], dtype='int64') | ||
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if net_type == "vgg": | ||
print("train vgg net") | ||
net = vgg16_bn_drop(images) | ||
elif net_type == "resnet": | ||
print("train resnet") | ||
net = resnet_cifar10(images, 32) | ||
else: | ||
raise ValueError("%s network is not supported" % net_type) | ||
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predict = fluid.layers.fc(input=net, size=classdim, act='softmax') | ||
cost = fluid.layers.cross_entropy(input=predict, label=label) | ||
avg_cost = fluid.layers.mean(x=cost) | ||
acc = fluid.layers.accuracy(input=predict, label=label) | ||
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# Test program | ||
test_program = fluid.default_main_program().clone() | ||
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optimizer = fluid.optimizer.Adam(learning_rate=0.001) | ||
optimizer.minimize(avg_cost) | ||
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BATCH_SIZE = 128 | ||
PASS_NUM = 1 | ||
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train_reader = paddle.batch( | ||
paddle.reader.shuffle( | ||
paddle.dataset.cifar.train10(), buf_size=128 * 10), | ||
batch_size=BATCH_SIZE) | ||
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test_reader = paddle.batch( | ||
paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE) | ||
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#place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() | ||
exe = fluid.ParallelExecutor([fluid.CUDAPlace(0), fluid.CUDAPlace(1)]) | ||
exe.run(fluid.default_startup_program()) | ||
feeder = fluid.DataFeeder(place=fluid.CPUPlace(), feed_list=[images, label]) | ||
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loss = 0.0 | ||
for pass_id in range(PASS_NUM): | ||
for batch_id, data in enumerate(train_reader()): | ||
exe.run(feed=feeder.feed(data)) | ||
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if (batch_id % 10) == 0: | ||
acc_list = [] | ||
avg_loss_list = [] | ||
for tid, test_data in enumerate(test_reader()): | ||
loss_t, acc_t = exe.run(program=test_program, | ||
feed=feeder.feed(test_data), | ||
fetch_list=[avg_cost, acc]) | ||
if math.isnan(float(loss_t)): | ||
sys.exit("got NaN loss, training failed.") | ||
acc_list.append(float(acc_t)) | ||
avg_loss_list.append(float(loss_t)) | ||
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acc_value = numpy.array(acc_list).mean() | ||
avg_loss_value = numpy.array(avg_loss_list).mean() | ||
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print( | ||
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'. | ||
format(pass_id, batch_id + 1, | ||
float(avg_loss_value), float(acc_value))) | ||
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def main(net_type, use_cuda): | ||
if use_cuda and not fluid.core.is_compiled_with_cuda(): | ||
return | ||
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# Directory for saving the trained model | ||
save_dirname = "image_classification_" + net_type + ".inference.model" | ||
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train(net_type, use_cuda, save_dirname) | ||
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if __name__ == '__main__': | ||
main('vgg', use_cuda=True) |