未验证 提交 620392ef 编写于 作者: W Wu Yi 提交者: GitHub

Merge pull request #13334 from typhoonzero/update_fluid_benchmark_resnet

sync resnet model for fluid_benchmark
......@@ -20,6 +20,7 @@ import functools
import numpy as np
import time
import os
import math
import cProfile, pstats, StringIO
......@@ -27,128 +28,120 @@ import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.profiler as profiler
# from recordio_converter import imagenet_train, imagenet_test
from imagenet_reader import train, val
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class ResNet():
def __init__(self, layers=50, is_train=True):
self.params = train_parameters
self.layers = layers
self.is_train = is_train
def net(self, input, class_dim=1000):
layers = self.layers
supported_layers = [50, 101, 152]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers)
if layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
num_filters = [64, 128, 256, 512]
conv = self.conv_bn_layer(
input=input, num_filters=64, filter_size=7, stride=2, act='relu')
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
for block in range(len(depth)):
for i in range(depth[block]):
conv = self.bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1)
pool = fluid.layers.pool2d(
input=conv, pool_size=7, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(input=pool,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv,
stdv)))
return out
def conv_bn_layer(input,
ch_out,
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride,
padding,
act='relu',
is_train=True):
conv1 = fluid.layers.conv2d(
stride=1,
groups=1,
act=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
bias_attr=False)
return fluid.layers.batch_norm(input=conv1, act=act, is_test=not is_train)
return fluid.layers.batch_norm(
input=conv, act=act, is_test=not self.is_train)
def shortcut(input, ch_out, stride, is_train=True):
ch_in = input.shape[1] # if args.data_format == 'NCHW' else input.shape[-1]
if ch_in != ch_out:
return conv_bn_layer(
input, ch_out, 1, stride, 0, None, is_train=is_train)
def shortcut(self, input, ch_out, stride):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
return self.conv_bn_layer(input, ch_out, 1, stride)
else:
return input
def bottleneck_block(self, input, num_filters, stride):
conv0 = self.conv_bn_layer(
input=input, num_filters=num_filters, filter_size=1, act='relu')
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu')
conv2 = self.conv_bn_layer(
input=conv1, num_filters=num_filters * 4, filter_size=1, act=None)
def basicblock(input, ch_out, stride, is_train=True):
short = shortcut(input, ch_out, stride, is_train=is_train)
conv1 = conv_bn_layer(input, ch_out, 3, stride, 1, is_train=is_train)
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None, is_train=is_train)
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
def bottleneck(input, ch_out, stride, is_train=True):
short = shortcut(input, ch_out * 4, stride, is_train=is_train)
conv1 = conv_bn_layer(input, ch_out, 1, stride, 0, is_train=is_train)
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, is_train=is_train)
conv3 = conv_bn_layer(
conv2, ch_out * 4, 1, 1, 0, act=None, is_train=is_train)
return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')
def layer_warp(block_func, input, ch_out, count, stride):
res_out = block_func(input, ch_out, stride)
for i in range(1, count):
res_out = block_func(res_out, ch_out, 1)
return res_out
def resnet_imagenet(input,
class_dim,
depth=50,
data_format='NCHW',
is_train=True):
cfg = {
18: ([2, 2, 2, 1], basicblock),
34: ([3, 4, 6, 3], basicblock),
50: ([3, 4, 6, 3], bottleneck),
101: ([3, 4, 23, 3], bottleneck),
152: ([3, 8, 36, 3], bottleneck)
}
stages, block_func = cfg[depth]
conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
pool1 = fluid.layers.pool2d(
input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
res2 = layer_warp(block_func, res1, 128, stages[1], 2)
res3 = layer_warp(block_func, res2, 256, stages[2], 2)
res4 = layer_warp(block_func, res3, 512, stages[3], 2)
pool2 = fluid.layers.pool2d(
input=res4,
pool_size=7,
pool_type='avg',
pool_stride=1,
global_pooling=True)
out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax')
return out
def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
assert (depth - 2) % 6 == 0
n = (depth - 2) // 6
short = self.shortcut(input, num_filters * 4, stride)
conv1 = conv_bn_layer(
input=input, ch_out=16, filter_size=3, stride=1, padding=1)
res1 = layer_warp(basicblock, conv1, 16, n, 1)
res2 = layer_warp(basicblock, res1, 32, n, 2)
res3 = layer_warp(basicblock, res2, 64, n, 2)
pool = fluid.layers.pool2d(
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
out = fluid.layers.fc(input=pool, size=class_dim, act='softmax')
return out
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
def _model_reader_dshape_classdim(args, is_train):
model = resnet_cifar10
model = None
reader = None
if args.data_set == "cifar10":
class_dim = 10
if args.data_format == 'NCHW':
dshape = [3, 32, 32]
else:
dshape = [32, 32, 3]
model = resnet_cifar10
if is_train:
reader = paddle.dataset.cifar.train10()
else:
reader = paddle.dataset.cifar.test10()
elif args.data_set == "flowers":
if args.data_set == "flowers":
class_dim = 102
if args.data_format == 'NCHW':
dshape = [3, 224, 224]
else:
dshape = [224, 224, 3]
model = resnet_imagenet
if is_train:
reader = paddle.dataset.flowers.train()
else:
......@@ -159,7 +152,6 @@ def _model_reader_dshape_classdim(args, is_train):
dshape = [3, 224, 224]
else:
dshape = [224, 224, 3]
model = resnet_imagenet
if not args.data_path:
raise Exception(
"Must specify --data_path when training with imagenet")
......@@ -173,12 +165,11 @@ def _model_reader_dshape_classdim(args, is_train):
reader = train(xmap=False)
else:
reader = val(xmap=False)
return model, reader, dshape, class_dim
return reader, dshape, class_dim
def get_model(args, is_train, main_prog, startup_prog):
model, reader, dshape, class_dim = _model_reader_dshape_classdim(args,
is_train)
reader, dshape, class_dim = _model_reader_dshape_classdim(args, is_train)
pyreader = None
trainer_count = int(os.getenv("PADDLE_TRAINERS"))
......@@ -198,7 +189,8 @@ def get_model(args, is_train, main_prog, startup_prog):
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
predict = model(input, class_dim, is_train=is_train)
model = ResNet(is_train=is_train)
predict = model.net(input, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
......@@ -216,15 +208,14 @@ def get_model(args, is_train, main_prog, startup_prog):
total_images = 1281167 / trainer_count
step = int(total_images / args.batch_size + 1)
epochs = [30, 60, 80, 90]
epochs = [30, 60, 90]
bd = [step * e for e in epochs]
base_lr = args.learning_rate
lr = []
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
optimizer = fluid.optimizer.Momentum(
learning_rate=base_lr,
#learning_rate=fluid.layers.piecewise_decay(
# boundaries=bd, values=lr),
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
optimizer.minimize(avg_cost)
......
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