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January 25, 2018 09:56
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import os | |
import paddle.v2 as paddle | |
import paddle.v2.fluid as fluid | |
import time | |
def conv_bn_layer(input, filter_size, num_filters, stride, padding, channels = None, num_groups=1, | |
act='relu', use_cudnn=False): | |
conv = fluid.layers.conv2d( | |
input=input, | |
num_filters=num_filters, | |
filter_size=filter_size, | |
stride=stride, | |
padding=padding, | |
groups=num_groups, | |
act=None, | |
use_cudnn=use_cudnn, | |
bias_attr=False) | |
return fluid.layers.batch_norm(input=conv, act=act) | |
def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride, | |
scale): | |
""" | |
""" | |
tmp = conv_bn_layer( | |
input=input, | |
filter_size=3, | |
num_filters=int(num_filters1 * scale), | |
stride=stride, | |
padding=1, | |
num_groups=int(num_groups * scale), | |
use_cudnn=False) | |
tmp = conv_bn_layer( | |
input=tmp, | |
filter_size=1, | |
num_filters=int(num_filters2 * scale), | |
stride=1, | |
padding=0) | |
return tmp | |
def mobile_net(img, class_dim, scale=1.0): | |
# conv1: 112x112 | |
tmp = conv_bn_layer( | |
img, | |
filter_size=3, | |
channels=3, | |
num_filters=int(32 * scale), | |
stride=2, | |
padding=1) | |
# 56x56 | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=32, | |
num_filters2=64, | |
num_groups=32, | |
stride=1, | |
scale=scale) | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=64, | |
num_filters2=128, | |
num_groups=64, | |
stride=2, | |
scale=scale) | |
# 28x28 | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=128, | |
num_filters2=128, | |
num_groups=128, | |
stride=1, | |
scale=scale) | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=128, | |
num_filters2=256, | |
num_groups=128, | |
stride=2, | |
scale=scale) | |
# 14x14 | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=256, | |
num_filters2=256, | |
num_groups=256, | |
stride=1, | |
scale=scale) | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=256, | |
num_filters2=512, | |
num_groups=256, | |
stride=2, | |
scale=scale) | |
# 14x14 | |
for i in range(5): | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=512, | |
num_filters2=512, | |
num_groups=512, | |
stride=1, | |
scale=scale) | |
# 7x7 | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=512, | |
num_filters2=1024, | |
num_groups=512, | |
stride=2, | |
scale=scale) | |
tmp = depthwise_separable( | |
tmp, | |
num_filters1=1024, | |
num_filters2=1024, | |
num_groups=1024, | |
stride=1, | |
scale=scale) | |
tmp = fluid.layers.pool2d( | |
input=tmp, pool_size=7, pool_stride=1, pool_type='avg') | |
tmp = fluid.layers.fc(input=tmp, | |
size=class_dim, | |
act='softmax') | |
return tmp | |
def train(learning_rate, batch_size, num_passes, model_save_dir='model'): | |
class_dim = 102 | |
image_shape = [3, 224, 224] | |
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') | |
label = fluid.layers.data(name='label', shape=[1], dtype='int64') | |
out = mobile_net(image, class_dim=class_dim) | |
cost = fluid.layers.cross_entropy(input=out, label=label) | |
avg_cost = fluid.layers.mean(x=cost) | |
optimizer = fluid.optimizer.Momentum( | |
learning_rate=learning_rate, | |
momentum=0.9, | |
regularization=fluid.regularizer.L2Decay(5 * 1e-5)) | |
opts = optimizer.minimize(avg_cost) | |
accuracy = fluid.evaluator.Accuracy(input=out, label=label) | |
inference_program = fluid.default_main_program().clone() | |
with fluid.program_guard(inference_program): | |
test_accuracy = fluid.evaluator.Accuracy(input=out, label=label) | |
test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states | |
inference_program = fluid.io.get_inference_program(test_target) | |
place = fluid.CUDAPlace(1) | |
exe = fluid.Executor(place) | |
exe.run(fluid.default_startup_program()) | |
train_reader = paddle.batch(paddle.dataset.flowers.train(), batch_size=batch_size) | |
test_reader = paddle.batch(paddle.dataset.flowers.test(), batch_size=batch_size) | |
feeder = fluid.DataFeeder(place=place, feed_list=[image, label]) | |
for pass_id in range(num_passes): | |
accuracy.reset(exe) | |
for batch_id, data in enumerate(train_reader()): | |
start_time = time.time() | |
loss, acc = exe.run(fluid.default_main_program(), | |
feed=feeder.feed(data), | |
fetch_list=[avg_cost] + accuracy.metrics) | |
pass_elapsed = time.time() - start_time | |
print("Pass {0}, batch {1}, loss {2}, acc {3}".format( | |
pass_id, batch_id, loss[0], acc[0])) | |
#print 'cost : %f s' % (pass_elapsed) | |
pass_acc = accuracy.eval(exe) | |
test_accuracy.reset(exe) | |
for data in test_reader(): | |
loss, acc = exe.run(inference_program, | |
feed=feeder.feed(data), | |
fetch_list=[avg_cost] + test_accuracy.metrics) | |
test_pass_acc = test_accuracy.eval(exe) | |
print("End pass {0}, train_acc {1}, test_acc {2}".format( | |
pass_id, pass_acc, test_pass_acc)) | |
if pass_id % 10 == 0: | |
print 'save models' | |
model_path = os.path.join(model_save_dir, str(pass_id)) | |
fluid.io.save_inference_model(model_path, ['image'], [out], exe) | |
if __name__ == '__main__': | |
train(learning_rate=0.005, batch_size=80, num_passes=400) |
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