Benchmark/Integrate benchmark scripts (#10707)
* wip integrate benchmark scripts * testing nlp models * k8s script to start dist benchmark job * update script * done support all models * add README.md * update by comment * clean up * follow commentsshanyi15-patch-3
Wu Yi
8 years ago
committed by
GitHub
parent
530556dd97
commit
55d3951bed
@ -0,0 +1,60 @@
|
||||
# Fluid Benchmark
|
||||
|
||||
This directory contains several models configurations and tools that used to run
|
||||
Fluid benchmarks for local and distributed training.
|
||||
|
||||
|
||||
## Run the Benchmark
|
||||
|
||||
To start, run the following command to get the full help message:
|
||||
|
||||
```bash
|
||||
python fluid_benchmark.py --help
|
||||
```
|
||||
|
||||
Currently supported `--model` argument include:
|
||||
|
||||
* mnist
|
||||
* resnet
|
||||
* you can chose to use different dataset using `--data_set cifar10` or
|
||||
`--data_set flowers`.
|
||||
* vgg
|
||||
* stacked_dynamic_lstm
|
||||
* machine_translation
|
||||
|
||||
* Run the following command to start a benchmark job locally:
|
||||
```bash
|
||||
python fluid_benchmark.py --model mnist --parallel 1 --device GPU --with_test
|
||||
```
|
||||
You can choose to use GPU/CPU training. With GPU training, you can specify
|
||||
`--parallel 1` to run multi GPU training.
|
||||
* Run distributed training with parameter servers:
|
||||
* start parameter servers:
|
||||
```bash
|
||||
PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method pserver
|
||||
```
|
||||
* start trainers:
|
||||
```bash
|
||||
PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method pserver
|
||||
```
|
||||
* Run distributed training using NCCL2
|
||||
```bash
|
||||
PADDLE_PSERVER_PORT=7164 PADDLE_TRAINER_IPS=192.168.0.2,192.168.0.3 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method nccl2
|
||||
```
|
||||
|
||||
## Run Distributed Benchmark on Kubernetes Cluster
|
||||
|
||||
We provide a script `kube_gen_job.py` to generate Kubernetes yaml files to submit
|
||||
distributed benchmark jobs to your cluster. To generate a job yaml, just run:
|
||||
|
||||
```bash
|
||||
python kube_gen_job.py --jobname myjob --pscpu 4 --cpu 8 --gpu 8 --psmemory 20 --memory 40 --pservers 4 --trainers 4 --entry "python fluid_benchmark.py --model mnist --parallel 1 --device GPU --update_method pserver --with_test" --disttype pserver
|
||||
```
|
||||
|
||||
Then the yaml files are generated under directory `myjob`, you can run:
|
||||
|
||||
```bash
|
||||
kubectl create -f myjob/
|
||||
```
|
||||
|
||||
The job shall start.
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,190 @@
|
||||
# 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.
|
||||
|
||||
import yaml
|
||||
import copy
|
||||
import argparse
|
||||
import random
|
||||
import os
|
||||
from kube_templates import pserver, trainer, envs
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description='Generate dist job yamls.')
|
||||
|
||||
parser.add_argument(
|
||||
'--jobname', default="paddlejob", help='unique job name')
|
||||
parser.add_argument(
|
||||
'--cpu', default=1, type=int, help='CPU cores per trainer node')
|
||||
parser.add_argument(
|
||||
'--pscpu', default=1, type=int, help='CPU cores per pserver node')
|
||||
parser.add_argument(
|
||||
'--gpu', default=0, type=int, help='num of GPUs per node')
|
||||
parser.add_argument(
|
||||
'--image',
|
||||
default="bootstrapper:5000/fluid_benchmark:gpu",
|
||||
help='num of GPUs per node')
|
||||
parser.add_argument(
|
||||
'--pservers', default=1, type=int, help='num of pservers')
|
||||
parser.add_argument(
|
||||
'--trainers', default=1, type=int, help='num of trainers')
|
||||
parser.add_argument('--memory', default=1, type=int, help='trainer memory')
|
||||
parser.add_argument(
|
||||
'--psmemory', default=1, type=int, help='pserver memory')
|
||||
parser.add_argument(
|
||||
'--port', default=30236, type=int, help='num of trainers')
|
||||
parser.add_argument(
|
||||
'--entry', default="python train.py", help='command to run')
|
||||
parser.add_argument(
|
||||
'--fluid', default=1, type=int, help='whether is fluid job')
|
||||
parser.add_argument(
|
||||
'--rdma', action='store_ture', help='whether mount rdma libs')
|
||||
parser.add_argument(
|
||||
'--disttype',
|
||||
default="pserver",
|
||||
type=str,
|
||||
choices=['pserver', 'nccl2', 'local'],
|
||||
help='pserver or nccl2 or local')
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def gen_job():
|
||||
ps = pserver
|
||||
tn = trainer
|
||||
args = parse_args()
|
||||
|
||||
ps_container = ps["spec"]["template"]["spec"]["containers"][0]
|
||||
tn_container = tn["spec"]["template"]["spec"]["containers"][0]
|
||||
|
||||
if args.fluid == 1:
|
||||
ps_container["command"] = \
|
||||
["paddle_k8s", "start_fluid"]
|
||||
tn_container["command"] = \
|
||||
["paddle_k8s", "start_fluid"]
|
||||
ps["metadata"]["name"] = args.jobname + "-pserver"
|
||||
ps["spec"]["template"]["metadata"]["labels"][
|
||||
"paddle-job-pserver"] = args.jobname
|
||||
tn["metadata"]["name"] = args.jobname + "-trainer"
|
||||
tn["spec"]["template"]["metadata"]["labels"]["paddle-job"] = args.jobname
|
||||
|
||||
ps_container["image"] = args.image
|
||||
tn_container["image"] = args.image
|
||||
|
||||
ps_container["resources"]["requests"]["cpu"] = str(args.pscpu)
|
||||
ps_container["resources"]["requests"]["memory"] = str(args.psmemory) + "Gi"
|
||||
ps_container["resources"]["limits"]["cpu"] = str(args.pscpu)
|
||||
ps_container["resources"]["limits"]["memory"] = str(args.psmemory) + "Gi"
|
||||
|
||||
tn_container["resources"]["requests"]["cpu"] = str(args.cpu)
|
||||
tn_container["resources"]["requests"]["memory"] = str(args.memory) + "Gi"
|
||||
tn_container["resources"]["limits"]["cpu"] = str(args.cpu)
|
||||
tn_container["resources"]["limits"]["memory"] = str(args.memory) + "Gi"
|
||||
if args.gpu > 0:
|
||||
tn_container["resources"]["requests"][
|
||||
"alpha.kubernetes.io/nvidia-gpu"] = str(args.gpu)
|
||||
tn_container["resources"]["limits"][
|
||||
"alpha.kubernetes.io/nvidia-gpu"] = str(args.gpu)
|
||||
|
||||
ps["spec"]["replicas"] = int(args.pservers)
|
||||
tn["spec"]["parallelism"] = int(args.trainers)
|
||||
tn["spec"]["completions"] = int(args.trainers)
|
||||
ps_container["ports"][0]["name"] = "jobport-" + str(args.port)
|
||||
ps_container["ports"][0]["containerPort"] = args.port
|
||||
spreadport = random.randint(40000, 60000)
|
||||
tn_container["ports"][0]["name"] = "spr-" + str(spreadport)
|
||||
tn_container["ports"][0]["containerPort"] = spreadport
|
||||
|
||||
envs.append({"name": "PADDLE_JOB_NAME", "value": args.jobname})
|
||||
envs.append({"name": "TRAINERS", "value": str(args.trainers)})
|
||||
envs.append({"name": "PSERVERS", "value": str(args.pservers)})
|
||||
envs.append({"name": "ENTRY", "value": args.entry})
|
||||
envs.append({"name": "PADDLE_INIT_PORT", "value": str(args.port)})
|
||||
# NOTE: these directories below are cluster specific, please modify
|
||||
# this settings before you run on your own cluster.
|
||||
envs.append({
|
||||
"name": "LD_LIBRARY_PATH",
|
||||
"value":
|
||||
"/usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind"
|
||||
})
|
||||
|
||||
volumes = [{
|
||||
"name": "nvidia-driver",
|
||||
"hostPath": {
|
||||
"path": "/usr/local/nvidia/lib64"
|
||||
}
|
||||
}]
|
||||
volumeMounts = [{
|
||||
"mountPath": "/usr/local/nvidia/lib64",
|
||||
"name": "nvidia-driver"
|
||||
}]
|
||||
|
||||
if args.rdma:
|
||||
volumes.extend([{
|
||||
"name": "ibetc",
|
||||
"hostPath": {
|
||||
"path": "/etc/libibverbs.d"
|
||||
}
|
||||
}, {
|
||||
"name": "iblibs",
|
||||
"hostPath": {
|
||||
"path": "/usr/local/rdma"
|
||||
}
|
||||
}, {
|
||||
"name": "valgrind",
|
||||
"hostPath": {
|
||||
"path": "/usr/lib64/mlnx_ofed/valgrind"
|
||||
}
|
||||
}])
|
||||
volumeMounts.extend([{
|
||||
"mountPath": "/etc/libibverbs.d",
|
||||
"name": "ibetc"
|
||||
}, {
|
||||
"mountPath": "/usr/local/rdma",
|
||||
"name": "iblibs"
|
||||
}, {
|
||||
"mountPath": "/usr/lib64/mlnx_ofed/valgrind",
|
||||
"name": "valgrind"
|
||||
}])
|
||||
# append shm for NCCL2
|
||||
volumes.append({"name": "dshm", "emptyDir": {"medium": "Memory"}})
|
||||
volumeMounts.append({"mountPath": "/dev/shm", "name": "dshm"})
|
||||
|
||||
tn["spec"]["template"]["spec"]["volumes"] = volumes
|
||||
tn_container["volumeMounts"] = volumeMounts
|
||||
|
||||
ps_container["env"] = envs
|
||||
ps_container["env"].append({"name": "TRAINING_ROLE", "value": "PSERVER"})
|
||||
tn_container["env"] = envs
|
||||
if args.disttype == "pserver":
|
||||
tn_container["env"].append({
|
||||
"name": "TRAINING_ROLE",
|
||||
"value": "TRAINER"
|
||||
})
|
||||
elif args.disttype == "nccl2" or args.disttype == "local":
|
||||
# NCCL2 have no training role, set to plain WORKER
|
||||
tn_container["env"].append({"name": "TRAINING_ROLE", "value": "WORKER"})
|
||||
|
||||
os.mkdir(args.jobname)
|
||||
if args.disttype == "pserver":
|
||||
with open("%s/pserver.yaml" % args.jobname, "w") as fn:
|
||||
yaml.dump(ps, fn)
|
||||
|
||||
with open("%s/trainer.yaml" % args.jobname, "w") as fn:
|
||||
yaml.dump(tn, fn)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
gen_job()
|
||||
@ -0,0 +1,58 @@
|
||||
# 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.
|
||||
|
||||
from pserver import pserver
|
||||
from trainer import trainer
|
||||
|
||||
__all__ = ["pserver", "trainer", "envs"]
|
||||
|
||||
envs = [
|
||||
# envs that don't need to change
|
||||
{
|
||||
"name": "GLOG_v",
|
||||
"value": "0"
|
||||
},
|
||||
{
|
||||
"name": "GLOG_logtostderr",
|
||||
"value": "1"
|
||||
},
|
||||
{
|
||||
"name": "TOPOLOGY",
|
||||
"value": ""
|
||||
},
|
||||
{
|
||||
"name": "TRAINER_PACKAGE",
|
||||
"value": "/workspace"
|
||||
},
|
||||
{
|
||||
"name": "PADDLE_INIT_NICS",
|
||||
"value": "eth2"
|
||||
},
|
||||
{
|
||||
"name": "NAMESPACE",
|
||||
"valueFrom": {
|
||||
"fieldRef": {
|
||||
"fieldPath": "metadata.namespace"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "POD_IP",
|
||||
"valueFrom": {
|
||||
"fieldRef": {
|
||||
"fieldPath": "status.podIP"
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
@ -0,0 +1,58 @@
|
||||
# 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.
|
||||
|
||||
pserver = {
|
||||
"apiVersion": "extensions/v1beta1",
|
||||
"kind": "ReplicaSet",
|
||||
"metadata": {
|
||||
"name": "jobname-pserver"
|
||||
},
|
||||
"spec": {
|
||||
"replicas": 1,
|
||||
"template": {
|
||||
"metadata": {
|
||||
"labels": {
|
||||
"paddle-job-pserver": "jobname"
|
||||
}
|
||||
},
|
||||
"spec": {
|
||||
"hostNetwork": True,
|
||||
"imagePullSecrets": [{
|
||||
"name": "job-registry-secret"
|
||||
}],
|
||||
"containers": [{
|
||||
"name": "pserver",
|
||||
"image": "",
|
||||
"imagePullPolicy": "Always",
|
||||
"ports": [{
|
||||
"name": "jobport-1",
|
||||
"containerPort": 1
|
||||
}],
|
||||
"env": [],
|
||||
"command": ["paddle_k8s", "start_pserver"],
|
||||
"resources": {
|
||||
"requests": {
|
||||
"memory": "10Gi",
|
||||
"cpu": "4"
|
||||
},
|
||||
"limits": {
|
||||
"memory": "10Gi",
|
||||
"cpu": "4"
|
||||
}
|
||||
}
|
||||
}]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,70 @@
|
||||
# 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.
|
||||
|
||||
trainer = {
|
||||
"apiVersion": "batch/v1",
|
||||
"kind": "Job",
|
||||
"metadata": {
|
||||
"name": "jobname-pserver"
|
||||
},
|
||||
"spec": {
|
||||
"parallelism": 4,
|
||||
"completions": 4,
|
||||
"template": {
|
||||
"metadata": {
|
||||
"labels": {
|
||||
"paddle-job": "jobname"
|
||||
}
|
||||
},
|
||||
"spec": {
|
||||
"hostNetwork": True,
|
||||
"imagePullSecrets": [{
|
||||
"name": "job-registry-secret"
|
||||
}],
|
||||
"restartPolicy": "Never",
|
||||
"containers": [{
|
||||
"name": "trainer",
|
||||
"image": "",
|
||||
"imagePullPolicy": "Always",
|
||||
# to let container set rlimit
|
||||
"securityContext": {
|
||||
"privileged": True
|
||||
# TODO(wuyi): use below specific cap instead of privileged,
|
||||
# using privileged will cause all GPU device are visible
|
||||
# in the container.
|
||||
# "capabilities": {
|
||||
# "add": ["SYS_RESOURCE"]
|
||||
# }
|
||||
},
|
||||
"ports": [{
|
||||
"name": "jobport-1",
|
||||
"containerPort": 1
|
||||
}],
|
||||
"env": [],
|
||||
"command": ["paddle_k8s", "start_trainer", "v2"],
|
||||
"resources": {
|
||||
"requests": {
|
||||
"memory": "10Gi",
|
||||
"cpu": "4",
|
||||
},
|
||||
"limits": {
|
||||
"memory": "10Gi",
|
||||
"cpu": "4",
|
||||
}
|
||||
}
|
||||
}]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,228 +0,0 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
import argparse
|
||||
import time
|
||||
|
||||
import paddle
|
||||
import paddle.fluid as fluid
|
||||
import paddle.fluid.profiler as profiler
|
||||
|
||||
SEED = 1
|
||||
DTYPE = "float32"
|
||||
|
||||
# random seed must set before configuring the network.
|
||||
# fluid.default_startup_program().random_seed = SEED
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser("mnist model benchmark.")
|
||||
parser.add_argument(
|
||||
'--batch_size', type=int, default=128, help='The minibatch size.')
|
||||
parser.add_argument(
|
||||
'--skip_batch_num',
|
||||
type=int,
|
||||
default=5,
|
||||
help='The first num of minibatch num to skip, for better performance test'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--iterations', type=int, default=35, help='The number of minibatches.')
|
||||
parser.add_argument(
|
||||
'--pass_num', type=int, default=5, help='The number of passes.')
|
||||
parser.add_argument(
|
||||
'--device',
|
||||
type=str,
|
||||
default='GPU',
|
||||
choices=['CPU', 'GPU'],
|
||||
help='The device type.')
|
||||
parser.add_argument(
|
||||
'--infer_only', action='store_true', help='If set, run forward only.')
|
||||
parser.add_argument(
|
||||
'--use_cprof', action='store_true', help='If set, use cProfile.')
|
||||
parser.add_argument(
|
||||
'--use_nvprof',
|
||||
action='store_true',
|
||||
help='If set, use nvprof for CUDA.')
|
||||
parser.add_argument(
|
||||
'--with_test',
|
||||
action='store_true',
|
||||
help='If set, test the testset during training.')
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
def cnn_model(data):
|
||||
conv_pool_1 = fluid.nets.simple_img_conv_pool(
|
||||
input=data,
|
||||
filter_size=5,
|
||||
num_filters=20,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act="relu")
|
||||
conv_pool_2 = fluid.nets.simple_img_conv_pool(
|
||||
input=conv_pool_1,
|
||||
filter_size=5,
|
||||
num_filters=50,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act="relu")
|
||||
|
||||
# TODO(dzhwinter) : refine the initializer and random seed settting
|
||||
SIZE = 10
|
||||
input_shape = conv_pool_2.shape
|
||||
param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
|
||||
scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
|
||||
|
||||
predict = fluid.layers.fc(
|
||||
input=conv_pool_2,
|
||||
size=SIZE,
|
||||
act="softmax",
|
||||
param_attr=fluid.param_attr.ParamAttr(
|
||||
initializer=fluid.initializer.NormalInitializer(
|
||||
loc=0.0, scale=scale)))
|
||||
return predict
|
||||
|
||||
|
||||
def eval_test(exe, batch_acc, batch_size_tensor, inference_program):
|
||||
test_reader = paddle.batch(
|
||||
paddle.dataset.mnist.test(), batch_size=args.batch_size)
|
||||
test_pass_acc = fluid.average.WeightedAverage()
|
||||
for batch_id, data in enumerate(test_reader()):
|
||||
img_data = np.array(map(lambda x: x[0].reshape([1, 28, 28]),
|
||||
data)).astype(DTYPE)
|
||||
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
|
||||
y_data = y_data.reshape([len(y_data), 1])
|
||||
|
||||
acc, weight = exe.run(inference_program,
|
||||
feed={"pixel": img_data,
|
||||
"label": y_data},
|
||||
fetch_list=[batch_acc, batch_size_tensor])
|
||||
test_pass_acc.add(value=acc, weight=weight)
|
||||
pass_acc = test_pass_acc.eval()
|
||||
return pass_acc
|
||||
|
||||
|
||||
def run_benchmark(model, args):
|
||||
if args.use_cprof:
|
||||
pr = cProfile.Profile()
|
||||
pr.enable()
|
||||
start_time = time.time()
|
||||
# Input data
|
||||
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
|
||||
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
||||
|
||||
# Train program
|
||||
predict = model(images)
|
||||
cost = fluid.layers.cross_entropy(input=predict, label=label)
|
||||
avg_cost = fluid.layers.mean(x=cost)
|
||||
|
||||
# Evaluator
|
||||
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
|
||||
batch_acc = fluid.layers.accuracy(
|
||||
input=predict, label=label, total=batch_size_tensor)
|
||||
|
||||
# inference program
|
||||
inference_program = fluid.default_main_program().clone()
|
||||
|
||||
# Optimization
|
||||
opt = fluid.optimizer.AdamOptimizer(
|
||||
learning_rate=0.001, beta1=0.9, beta2=0.999)
|
||||
opt.minimize(avg_cost)
|
||||
|
||||
fluid.memory_optimize(fluid.default_main_program())
|
||||
|
||||
# Initialize executor
|
||||
place = fluid.CPUPlace() if args.device == 'CPU' else fluid.CUDAPlace(0)
|
||||
exe = fluid.Executor(place)
|
||||
|
||||
# Parameter initialization
|
||||
exe.run(fluid.default_startup_program())
|
||||
|
||||
# Reader
|
||||
train_reader = paddle.batch(
|
||||
paddle.dataset.mnist.train(), batch_size=args.batch_size)
|
||||
|
||||
accuracy = fluid.metrics.Accuracy()
|
||||
train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
|
||||
iters, num_samples, start_time = 0, 0, time.time()
|
||||
for pass_id in range(args.pass_num):
|
||||
accuracy.reset()
|
||||
train_accs = []
|
||||
train_losses = []
|
||||
for batch_id, data in enumerate(train_reader()):
|
||||
if iters == args.skip_batch_num:
|
||||
start_time = time.time()
|
||||
num_samples = 0
|
||||
if iters == args.iterations:
|
||||
break
|
||||
img_data = np.array(
|
||||
map(lambda x: x[0].reshape([1, 28, 28]), data)).astype(DTYPE)
|
||||
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
|
||||
y_data = y_data.reshape([len(y_data), 1])
|
||||
|
||||
outs = train_exe.run(
|
||||
feed={"pixel": img_data,
|
||||
"label": y_data},
|
||||
fetch_list=[
|
||||
avg_cost.name, batch_acc.name, batch_size_tensor.name
|
||||
]
|
||||
) # The accuracy is the accumulation of batches, but not the current batch.
|
||||
accuracy.update(
|
||||
value=np.array(np.mean(outs[1])),
|
||||
weight=np.mean(np.array(outs[2])))
|
||||
iters += 1
|
||||
num_samples += len(y_data)
|
||||
loss = np.mean(np.array(outs[0]))
|
||||
acc = np.mean(np.array(outs[1]))
|
||||
train_losses.append(loss)
|
||||
train_accs.append(acc)
|
||||
print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %
|
||||
(pass_id, iters, loss, acc))
|
||||
|
||||
print("Pass: %d, Loss: %f, Train Accuray: %f\n" %
|
||||
(pass_id, np.mean(train_losses), np.mean(train_accs)))
|
||||
train_elapsed = time.time() - start_time
|
||||
examples_per_sec = num_samples / train_elapsed
|
||||
|
||||
print('\nTotal examples: %d, total time: %.5f, %.5f examples/sed\n' %
|
||||
(num_samples, train_elapsed, examples_per_sec))
|
||||
# evaluation
|
||||
if args.with_test:
|
||||
test_avg_acc = eval_test(exe, batch_acc, batch_size_tensor,
|
||||
inference_program)
|
||||
exit(0)
|
||||
|
||||
|
||||
def print_arguments(args):
|
||||
vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
|
||||
vars(args)['device'] == 'GPU')
|
||||
print('----------- mnist Configuration Arguments -----------')
|
||||
for arg, value in sorted(vars(args).iteritems()):
|
||||
print('%s: %s' % (arg, value))
|
||||
print('------------------------------------------------')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_args()
|
||||
print_arguments(args)
|
||||
if args.use_nvprof and args.device == 'GPU':
|
||||
with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
|
||||
run_benchmark(cnn_model, args)
|
||||
else:
|
||||
run_benchmark(cnn_model, args)
|
||||
@ -0,0 +1,17 @@
|
||||
# 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.
|
||||
|
||||
__all__ = [
|
||||
"machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
|
||||
]
|
||||
@ -0,0 +1,94 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
import argparse
|
||||
import time
|
||||
import cProfile
|
||||
|
||||
import paddle
|
||||
import paddle.fluid as fluid
|
||||
import paddle.fluid.profiler as profiler
|
||||
|
||||
SEED = 1
|
||||
DTYPE = "float32"
|
||||
|
||||
# random seed must set before configuring the network.
|
||||
# fluid.default_startup_program().random_seed = SEED
|
||||
|
||||
|
||||
def cnn_model(data):
|
||||
conv_pool_1 = fluid.nets.simple_img_conv_pool(
|
||||
input=data,
|
||||
filter_size=5,
|
||||
num_filters=20,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act="relu")
|
||||
conv_pool_2 = fluid.nets.simple_img_conv_pool(
|
||||
input=conv_pool_1,
|
||||
filter_size=5,
|
||||
num_filters=50,
|
||||
pool_size=2,
|
||||
pool_stride=2,
|
||||
act="relu")
|
||||
|
||||
# TODO(dzhwinter) : refine the initializer and random seed settting
|
||||
SIZE = 10
|
||||
input_shape = conv_pool_2.shape
|
||||
param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
|
||||
scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
|
||||
|
||||
predict = fluid.layers.fc(
|
||||
input=conv_pool_2,
|
||||
size=SIZE,
|
||||
act="softmax",
|
||||
param_attr=fluid.param_attr.ParamAttr(
|
||||
initializer=fluid.initializer.NormalInitializer(
|
||||
loc=0.0, scale=scale)))
|
||||
return predict
|
||||
|
||||
|
||||
def get_model(args):
|
||||
# Input data
|
||||
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
|
||||
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
||||
|
||||
# Train program
|
||||
predict = cnn_model(images)
|
||||
cost = fluid.layers.cross_entropy(input=predict, label=label)
|
||||
avg_cost = fluid.layers.mean(x=cost)
|
||||
|
||||
# Evaluator
|
||||
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
|
||||
batch_acc = fluid.layers.accuracy(
|
||||
input=predict, label=label, total=batch_size_tensor)
|
||||
|
||||
# inference program
|
||||
inference_program = fluid.default_main_program().clone()
|
||||
|
||||
# Optimization
|
||||
opt = fluid.optimizer.AdamOptimizer(
|
||||
learning_rate=0.001, beta1=0.9, beta2=0.999)
|
||||
|
||||
# Reader
|
||||
train_reader = paddle.batch(
|
||||
paddle.dataset.mnist.train(), batch_size=args.batch_size)
|
||||
test_reader = paddle.batch(
|
||||
paddle.dataset.mnist.test(), batch_size=args.batch_size)
|
||||
return avg_cost, inference_program, opt, train_reader, test_reader, batch_acc
|
||||
@ -0,0 +1,161 @@
|
||||
# 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.
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import functools
|
||||
import numpy as np
|
||||
import time
|
||||
|
||||
import cProfile, pstats, StringIO
|
||||
|
||||
import paddle
|
||||
import paddle.fluid as fluid
|
||||
import paddle.fluid.core as core
|
||||
import paddle.fluid.profiler as profiler
|
||||
|
||||
|
||||
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
|
||||
conv1 = 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=conv1, act=act)
|
||||
|
||||
|
||||
def shortcut(input, ch_out, stride):
|
||||
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)
|
||||
else:
|
||||
return input
|
||||
|
||||
|
||||
def basicblock(input, ch_out, stride):
|
||||
short = shortcut(input, ch_out, stride)
|
||||
conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
|
||||
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None)
|
||||
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
|
||||
|
||||
|
||||
def bottleneck(input, ch_out, stride):
|
||||
short = shortcut(input, ch_out * 4, stride)
|
||||
conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
|
||||
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
|
||||
conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0, act=None)
|
||||
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'):
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
|
||||
def get_model(args):
|
||||
model = resnet_cifar10
|
||||
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
|
||||
else:
|
||||
class_dim = 102
|
||||
if args.data_format == 'NCHW':
|
||||
dshape = [3, 224, 224]
|
||||
else:
|
||||
dshape = [224, 224, 3]
|
||||
model = resnet_imagenet
|
||||
|
||||
input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
|
||||
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
||||
predict = model(input, class_dim)
|
||||
cost = fluid.layers.cross_entropy(input=predict, label=label)
|
||||
avg_cost = fluid.layers.mean(x=cost)
|
||||
|
||||
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
|
||||
batch_acc = fluid.layers.accuracy(
|
||||
input=predict, label=label, total=batch_size_tensor)
|
||||
|
||||
inference_program = fluid.default_main_program().clone()
|
||||
with fluid.program_guard(inference_program):
|
||||
inference_program = fluid.io.get_inference_program(
|
||||
target_vars=[batch_acc, batch_size_tensor])
|
||||
|
||||
optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
|
||||
|
||||
train_reader = paddle.batch(
|
||||
paddle.reader.shuffle(
|
||||
paddle.dataset.cifar.train10()
|
||||
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
|
||||
buf_size=5120),
|
||||
batch_size=args.batch_size)
|
||||
test_reader = paddle.batch(
|
||||
paddle.dataset.cifar.test10()
|
||||
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
|
||||
batch_size=args.batch_size)
|
||||
|
||||
return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc
|
||||
@ -0,0 +1,104 @@
|
||||
# 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.
|
||||
"""VGG16 benchmark in Fluid"""
|
||||
from __future__ import print_function
|
||||
|
||||
import sys
|
||||
import time
|
||||
import numpy as np
|
||||
import paddle
|
||||
import paddle.fluid as fluid
|
||||
import paddle.fluid.core as core
|
||||
import argparse
|
||||
import functools
|
||||
|
||||
|
||||
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')
|
||||
|
||||
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])
|
||||
|
||||
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
|
||||
|
||||
|
||||
def get_model(args):
|
||||
if args.data_set == "cifar10":
|
||||
classdim = 10
|
||||
if args.data_format == 'NCHW':
|
||||
data_shape = [3, 32, 32]
|
||||
else:
|
||||
data_shape = [32, 32, 3]
|
||||
else:
|
||||
classdim = 102
|
||||
if args.data_format == 'NCHW':
|
||||
data_shape = [3, 224, 224]
|
||||
else:
|
||||
data_shape = [224, 224, 3]
|
||||
|
||||
# Input data
|
||||
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
|
||||
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
||||
|
||||
# Train program
|
||||
net = vgg16_bn_drop(images)
|
||||
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)
|
||||
|
||||
# Evaluator
|
||||
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
|
||||
batch_acc = fluid.layers.accuracy(
|
||||
input=predict, label=label, total=batch_size_tensor)
|
||||
|
||||
# inference program
|
||||
inference_program = fluid.default_main_program().clone()
|
||||
with fluid.program_guard(inference_program):
|
||||
inference_program = fluid.io.get_inference_program(
|
||||
target_vars=[batch_acc, batch_size_tensor])
|
||||
|
||||
# Optimization
|
||||
optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
|
||||
|
||||
# data reader
|
||||
train_reader = paddle.batch(
|
||||
paddle.reader.shuffle(
|
||||
paddle.dataset.cifar.train10()
|
||||
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
|
||||
buf_size=5120),
|
||||
batch_size=args.batch_size)
|
||||
test_reader = paddle.batch(
|
||||
paddle.dataset.cifar.test10()
|
||||
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
|
||||
batch_size=args.batch_size)
|
||||
|
||||
return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,228 +0,0 @@
|
||||
# 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.
|
||||
"""VGG16 benchmark in Fluid"""
|
||||
from __future__ import print_function
|
||||
|
||||
import sys
|
||||
import time
|
||||
import numpy as np
|
||||
import paddle
|
||||
import paddle.fluid as fluid
|
||||
import paddle.fluid.core as core
|
||||
import argparse
|
||||
import functools
|
||||
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument(
|
||||
'--batch_size', type=int, default=128, help="Batch size for training.")
|
||||
parser.add_argument(
|
||||
'--skip_batch_num',
|
||||
type=int,
|
||||
default=5,
|
||||
help='The first num of minibatch num to skip, for better performance test')
|
||||
parser.add_argument(
|
||||
'--iterations', type=int, default=80, help='The number of minibatches.')
|
||||
parser.add_argument(
|
||||
'--learning_rate',
|
||||
type=float,
|
||||
default=1e-3,
|
||||
help="Learning rate for training.")
|
||||
parser.add_argument('--pass_num', type=int, default=50, help="No. of passes.")
|
||||
parser.add_argument(
|
||||
'--device',
|
||||
type=str,
|
||||
default='GPU',
|
||||
choices=['CPU', 'GPU'],
|
||||
help="The device type.")
|
||||
parser.add_argument(
|
||||
'--data_format',
|
||||
type=str,
|
||||
default='NCHW',
|
||||
choices=['NCHW', 'NHWC'],
|
||||
help='The data order, now only support NCHW.')
|
||||
parser.add_argument(
|
||||
'--data_set',
|
||||
type=str,
|
||||
default='cifar10',
|
||||
choices=['cifar10', 'flowers'],
|
||||
help='Optional dataset for benchmark.')
|
||||
parser.add_argument(
|
||||
'--with_test',
|
||||
action='store_true',
|
||||
help='If set, test the testset during training.')
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
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')
|
||||
|
||||
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])
|
||||
|
||||
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
|
||||
|
||||
|
||||
def main():
|
||||
if args.data_set == "cifar10":
|
||||
classdim = 10
|
||||
if args.data_format == 'NCHW':
|
||||
data_shape = [3, 32, 32]
|
||||
else:
|
||||
data_shape = [32, 32, 3]
|
||||
else:
|
||||
classdim = 102
|
||||
if args.data_format == 'NCHW':
|
||||
data_shape = [3, 224, 224]
|
||||
else:
|
||||
data_shape = [224, 224, 3]
|
||||
|
||||
# Input data
|
||||
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
|
||||
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
||||
|
||||
# Train program
|
||||
net = vgg16_bn_drop(images)
|
||||
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)
|
||||
|
||||
# Evaluator
|
||||
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
|
||||
batch_acc = fluid.layers.accuracy(
|
||||
input=predict, label=label, total=batch_size_tensor)
|
||||
|
||||
# inference program
|
||||
inference_program = fluid.default_main_program().clone()
|
||||
with fluid.program_guard(inference_program):
|
||||
inference_program = fluid.io.get_inference_program(
|
||||
target_vars=[batch_acc, batch_size_tensor])
|
||||
|
||||
# Optimization
|
||||
optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
|
||||
opts = optimizer.minimize(avg_cost)
|
||||
|
||||
fluid.memory_optimize(fluid.default_main_program())
|
||||
|
||||
# Initialize executor
|
||||
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
|
||||
exe = fluid.Executor(place)
|
||||
|
||||
# Parameter initialization
|
||||
exe.run(fluid.default_startup_program())
|
||||
|
||||
# data reader
|
||||
train_reader = paddle.batch(
|
||||
paddle.reader.shuffle(
|
||||
paddle.dataset.cifar.train10()
|
||||
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
|
||||
buf_size=5120),
|
||||
batch_size=args.batch_size)
|
||||
test_reader = paddle.batch(
|
||||
paddle.dataset.cifar.test10()
|
||||
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
|
||||
batch_size=args.batch_size)
|
||||
|
||||
# test
|
||||
def test(exe):
|
||||
test_accuracy = fluid.average.WeightedAverage()
|
||||
for batch_id, data in enumerate(test_reader()):
|
||||
img_data = np.array(map(lambda x: x[0].reshape(data_shape),
|
||||
data)).astype("float32")
|
||||
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
|
||||
y_data = y_data.reshape([-1, 1])
|
||||
|
||||
acc, weight = exe.run(inference_program,
|
||||
feed={"pixel": img_data,
|
||||
"label": y_data},
|
||||
fetch_list=[batch_acc, batch_size_tensor])
|
||||
test_accuracy.add(value=acc, weight=weight)
|
||||
return test_accuracy.eval()
|
||||
|
||||
iters, num_samples, start_time = 0, 0, time.time()
|
||||
accuracy = fluid.average.WeightedAverage()
|
||||
train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
|
||||
for pass_id in range(args.pass_num):
|
||||
accuracy.reset()
|
||||
train_accs = []
|
||||
train_losses = []
|
||||
for batch_id, data in enumerate(train_reader()):
|
||||
if iters == args.skip_batch_num:
|
||||
start_time = time.time()
|
||||
num_samples = 0
|
||||
if iters == args.iterations:
|
||||
break
|
||||
img_data = np.array(map(lambda x: x[0].reshape(data_shape),
|
||||
data)).astype("float32")
|
||||
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
|
||||
y_data = y_data.reshape([-1, 1])
|
||||
|
||||
loss, acc, weight = train_exe.run(
|
||||
feed={"pixel": img_data,
|
||||
"label": y_data},
|
||||
fetch_list=[
|
||||
avg_cost.name, batch_acc.name, batch_size_tensor.name
|
||||
])
|
||||
accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
|
||||
iters += 1
|
||||
num_samples += len(y_data)
|
||||
loss = np.mean(np.array(loss))
|
||||
acc = np.mean(np.array(acc))
|
||||
print(
|
||||
"Pass = %d, Iter = %d, Loss = %f, Accuracy = %f" %
|
||||
(pass_id, iters, loss, acc)
|
||||
) # The accuracy is the accumulation of batches, but not the current batch.
|
||||
|
||||
# pass_train_acc = accuracy.eval()
|
||||
train_losses.append(loss)
|
||||
train_accs.append(acc)
|
||||
print("Pass: %d, Loss: %f, Train Accuray: %f\n" %
|
||||
(pass_id, np.mean(train_losses), np.mean(train_accs)))
|
||||
train_elapsed = time.time() - start_time
|
||||
examples_per_sec = num_samples / train_elapsed
|
||||
print('\nTotal examples: %d, total time: %.5f, %.5f examples/sed\n' %
|
||||
(num_samples, train_elapsed, examples_per_sec))
|
||||
# evaluation
|
||||
if args.with_test:
|
||||
pass_test_acc = test(exe)
|
||||
exit(0)
|
||||
|
||||
|
||||
def print_arguments():
|
||||
print('----------- vgg Configuration Arguments -----------')
|
||||
for arg, value in sorted(vars(args).iteritems()):
|
||||
print('%s: %s' % (arg, value))
|
||||
print('------------------------------------------------')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print_arguments()
|
||||
main()
|
||||
Loading…
Reference in new issue