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"""
Benchmark for common convnets.
Speed on Titan X, with 10 warmup steps and 10 main steps and with different
versions of cudnn, are as follows (time reported below is per-batch time,
forward / forward+backward):
CuDNN V3 CuDNN v4
AlexNet 32.5 / 108.0 27.4 / 90.1
OverFeat 113.0 / 342.3 91.7 / 276.5
Inception 134.5 / 485.8 125.7 / 450.6
VGG (batch 64) 200.8 / 650.0 164.1 / 551.7
Speed on Inception with varied batch sizes and CuDNN v4 is as follows:
Batch Size Speed per batch Speed per image
16 22.8 / 72.7 1.43 / 4.54
32 38.0 / 127.5 1.19 / 3.98
64 67.2 / 233.6 1.05 / 3.65
128 125.7 / 450.6 0.98 / 3.52
Speed on Tesla M40, which 10 warmup steps and 10 main steps and with cudnn
v4, is as follows:
AlexNet 68.4 / 218.1
OverFeat 210.5 / 630.3
Inception 300.2 / 1122.2
VGG (batch 64) 405.8 / 1327.7
(Note that these numbers involve a "full" backprop, i.e. the gradient
with respect to the input image is also computed.)
To get the numbers, simply run:
for MODEL in AlexNet OverFeat Inception; do
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size 128 --model $MODEL --forward_only True
done
for MODEL in AlexNet OverFeat Inception; do
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size 128 --model $MODEL
done
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size 64 --model VGGA --forward_only True
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size 64 --model VGGA
for BS in 16 32 64 128; do
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size $BS --model Inception --forward_only True
PYTHONPATH=../gen:$PYTHONPATH python convnet_benchmarks.py \
--batch_size $BS --model Inception
done
Note that VGG needs to be run at batch 64 due to memory limit on the backward
pass.
"""
import argparse
from caffe2.python import cnn, workspace
def MLP(order):
model = cnn.CNNModelHelper()
d = 256
depth = 20
width = 3
for i in range(depth):
for j in range(width):
current = "fc_{}_{}".format(i, j) if i > 0 else "data"
next_ = "fc_{}_{}".format(i + 1, j)
model.FC(
current, next_,
dim_in=d, dim_out=d,
weight_init=model.XavierInit,
bias_init=model.XavierInit)
model.Sum(["fc_{}_{}".format(depth, j) for j in range(width)], ["sum"])
model.FC("sum", "last",
dim_in=d, dim_out=1000,
weight_init=model.XavierInit,
bias_init=model.XavierInit)
xent = model.LabelCrossEntropy(["last", "label"], "xent")
model.AveragedLoss(xent, "loss")
return model, d
def AlexNet(order):
model = cnn.CNNModelHelper(
order, name="alexnet",
use_cudnn=True, cudnn_exhaustive_search=True)
conv1 = model.Conv(
"data",
"conv1",
3,
64,
11,
('XavierFill', {}),
('ConstantFill', {}),
stride=4,
pad=2
)
relu1 = model.Relu(conv1, "conv1")
pool1 = model.MaxPool(relu1, "pool1", kernel=3, stride=2)
conv2 = model.Conv(
pool1,
"conv2",
64,
192,
5,
('XavierFill', {}),
('ConstantFill', {}),
pad=2
)
relu2 = model.Relu(conv2, "conv2")
pool2 = model.MaxPool(relu2, "pool2", kernel=3, stride=2)
conv3 = model.Conv(
pool2,
"conv3",
192,
384,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu3 = model.Relu(conv3, "conv3")
conv4 = model.Conv(
relu3,
"conv4",
384,
256,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu4 = model.Relu(conv4, "conv4")
conv5 = model.Conv(
relu4,
"conv5",
256,
256,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu5 = model.Relu(conv5, "conv5")
pool5 = model.MaxPool(relu5, "pool5", kernel=3, stride=2)
fc6 = model.FC(
pool5, "fc6", 256 * 6 * 6, 4096, ('XavierFill', {}),
('ConstantFill', {})
)
relu6 = model.Relu(fc6, "fc6")
fc7 = model.FC(
relu6, "fc7", 4096, 4096, ('XavierFill', {}), ('ConstantFill', {})
)
relu7 = model.Relu(fc7, "fc7")
fc8 = model.FC(
relu7, "fc8", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {})
)
pred = model.Softmax(fc8, "pred")
xent = model.LabelCrossEntropy([pred, "label"], "xent")
loss = model.AveragedLoss(xent, "loss")
return model, 224
def OverFeat(order):
model = cnn.CNNModelHelper(
order, name="overfeat",
use_cudnn=True, cudnn_exhaustive_search=True)
conv1 = model.Conv(
"data",
"conv1",
3,
96,
11,
('XavierFill', {}),
('ConstantFill', {}),
stride=4
)
relu1 = model.Relu(conv1, "conv1")
pool1 = model.MaxPool(relu1, "pool1", kernel=2, stride=2)
conv2 = model.Conv(
pool1, "conv2", 96, 256, 5, ('XavierFill', {}), ('ConstantFill', {})
)
relu2 = model.Relu(conv2, "conv2")
pool2 = model.MaxPool(relu2, "pool2", kernel=2, stride=2)
conv3 = model.Conv(
pool2,
"conv3",
256,
512,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu3 = model.Relu(conv3, "conv3")
conv4 = model.Conv(
relu3,
"conv4",
512,
1024,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu4 = model.Relu(conv4, "conv4")
conv5 = model.Conv(
relu4,
"conv5",
1024,
1024,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu5 = model.Relu(conv5, "conv5")
pool5 = model.MaxPool(relu5, "pool5", kernel=2, stride=2)
fc6 = model.FC(
pool5, "fc6", 1024 * 6 * 6, 3072, ('XavierFill', {}),
('ConstantFill', {})
)
relu6 = model.Relu(fc6, "fc6")
fc7 = model.FC(
relu6, "fc7", 3072, 4096, ('XavierFill', {}), ('ConstantFill', {})
)
relu7 = model.Relu(fc7, "fc7")
fc8 = model.FC(
relu7, "fc8", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {})
)
pred = model.Softmax(fc8, "pred")
xent = model.LabelCrossEntropy([pred, "label"], "xent")
loss = model.AveragedLoss(xent, "loss")
return model, 231
def VGGA(order):
model = cnn.CNNModelHelper(
order, name='vgg-a',
use_cudnn=True, cudnn_exhaustive_search=True)
conv1 = model.Conv(
"data",
"conv1",
3,
64,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu1 = model.Relu(conv1, "conv1")
pool1 = model.MaxPool(relu1, "pool1", kernel=2, stride=2)
conv2 = model.Conv(
pool1,
"conv2",
64,
128,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu2 = model.Relu(conv2, "conv2")
pool2 = model.MaxPool(relu2, "pool2", kernel=2, stride=2)
conv3 = model.Conv(
pool2,
"conv3",
128,
256,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu3 = model.Relu(conv3, "conv3")
conv4 = model.Conv(
relu3,
"conv4",
256,
256,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu4 = model.Relu(conv4, "conv4")
pool4 = model.MaxPool(relu4, "pool4", kernel=2, stride=2)
conv5 = model.Conv(
pool4,
"conv5",
256,
512,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu5 = model.Relu(conv5, "conv5")
conv6 = model.Conv(
relu5,
"conv6",
512,
512,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu6 = model.Relu(conv6, "conv6")
pool6 = model.MaxPool(relu6, "pool6", kernel=2, stride=2)
conv7 = model.Conv(
pool6,
"conv7",
512,
512,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu7 = model.Relu(conv7, "conv7")
conv8 = model.Conv(
relu7,
"conv8",
512,
512,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu8 = model.Relu(conv8, "conv8")
pool8 = model.MaxPool(relu8, "pool8", kernel=2, stride=2)
fcix = model.FC(
pool8, "fcix", 512 * 7 * 7, 4096, ('XavierFill', {}),
('ConstantFill', {})
)
reluix = model.Relu(fcix, "fcix")
fcx = model.FC(
reluix, "fcx", 4096, 4096, ('XavierFill', {}), ('ConstantFill', {})
)
relux = model.Relu(fcx, "fcx")
fcxi = model.FC(
relux, "fcxi", 4096, 1000, ('XavierFill', {}), ('ConstantFill', {})
)
pred = model.Softmax(fcxi, "pred")
xent = model.LabelCrossEntropy([pred, "label"], "xent")
loss = model.AveragedLoss(xent, "loss")
return model, 231
def _InceptionModule(
model, input_blob, input_depth, output_name, conv1_depth, conv3_depths,
conv5_depths, pool_depth
):
# path 1: 1x1 conv
conv1 = model.Conv(
input_blob, output_name + ":conv1", input_depth, conv1_depth, 1,
('XavierFill', {}), ('ConstantFill', {})
)
conv1 = model.Relu(conv1, conv1)
# path 2: 1x1 conv + 3x3 conv
conv3_reduce = model.Conv(
input_blob, output_name + ":conv3_reduce", input_depth, conv3_depths[0],
1, ('XavierFill', {}), ('ConstantFill', {})
)
conv3_reduce = model.Relu(conv3_reduce, conv3_reduce)
conv3 = model.Conv(
conv3_reduce,
output_name + ":conv3",
conv3_depths[0],
conv3_depths[1],
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
conv3 = model.Relu(conv3, conv3)
# path 3: 1x1 conv + 5x5 conv
conv5_reduce = model.Conv(
input_blob, output_name + ":conv5_reduce", input_depth, conv5_depths[0],
1, ('XavierFill', {}), ('ConstantFill', {})
)
conv5_reduce = model.Relu(conv5_reduce, conv5_reduce)
conv5 = model.Conv(
conv5_reduce,
output_name + ":conv5",
conv5_depths[0],
conv5_depths[1],
5,
('XavierFill', {}),
('ConstantFill', {}),
pad=2
)
conv5 = model.Relu(conv5, conv5)
# path 4: pool + 1x1 conv
pool = model.MaxPool(
input_blob,
output_name + ":pool",
kernel=3,
stride=1,
pad=1
)
pool_proj = model.Conv(
pool, output_name + ":pool_proj", input_depth, pool_depth, 1,
('XavierFill', {}), ('ConstantFill', {})
)
pool_proj = model.Relu(pool_proj, pool_proj)
output = model.Concat([conv1, conv3, conv5, pool_proj], output_name)
return output
def Inception(order):
model = cnn.CNNModelHelper(
order, name="inception",
use_cudnn=True, cudnn_exhaustive_search=True)
conv1 = model.Conv(
"data",
"conv1",
3,
64,
7,
('XavierFill', {}),
('ConstantFill', {}),
stride=2,
pad=3
)
relu1 = model.Relu(conv1, "conv1")
pool1 = model.MaxPool(relu1, "pool1", kernel=3, stride=2, pad=1)
conv2a = model.Conv(
pool1, "conv2a", 64, 64, 1, ('XavierFill', {}), ('ConstantFill', {})
)
conv2a = model.Relu(conv2a, conv2a)
conv2 = model.Conv(
conv2a,
"conv2",
64,
192,
3,
('XavierFill', {}),
('ConstantFill', {}),
pad=1
)
relu2 = model.Relu(conv2, "conv2")
pool2 = model.MaxPool(relu2, "pool2", kernel=3, stride=2, pad=1)
# Inception modules
inc3 = _InceptionModule(
model, pool2, 192, "inc3", 64, [96, 128], [16, 32], 32
)
inc4 = _InceptionModule(
model, inc3, 256, "inc4", 128, [128, 192], [32, 96], 64
)
pool5 = model.MaxPool(inc4, "pool5", kernel=3, stride=2, pad=1)
inc5 = _InceptionModule(
model, pool5, 480, "inc5", 192, [96, 208], [16, 48], 64
)
inc6 = _InceptionModule(
model, inc5, 512, "inc6", 160, [112, 224], [24, 64], 64
)
inc7 = _InceptionModule(
model, inc6, 512, "inc7", 128, [128, 256], [24, 64], 64
)
inc8 = _InceptionModule(
model, inc7, 512, "inc8", 112, [144, 288], [32, 64], 64
)
inc9 = _InceptionModule(
model, inc8, 528, "inc9", 256, [160, 320], [32, 128], 128
)
pool9 = model.MaxPool(inc9, "pool9", kernel=3, stride=2, pad=1)
inc10 = _InceptionModule(
model, pool9, 832, "inc10", 256, [160, 320], [32, 128], 128
)
inc11 = _InceptionModule(
model, inc10, 832, "inc11", 384, [192, 384], [48, 128], 128
)
pool11 = model.AveragePool(inc11, "pool11", kernel=7, stride=1)
fc = model.FC(
pool11, "fc", 1024, 1000, ('XavierFill', {}), ('ConstantFill', {})
)
# It seems that Soumith's benchmark does not have softmax on top
# for Inception. We will add it anyway so we can have a proper
# backward pass.
pred = model.Softmax(fc, "pred")
xent = model.LabelCrossEntropy([pred, "label"], "xent")
loss = model.AveragedLoss(xent, "loss")
return model, 224
def AddParameterUpdate(model):
""" Simple plain SGD update -- not tuned to actually train the models """
ITER = model.Iter("iter")
LR = model.LearningRate(
ITER, "LR", base_lr=-1e-8, policy="step", stepsize=10000, gamma=0.999)
ONE = model.param_init_net.ConstantFill([], "ONE", shape=[1], value=1.0)
for param in model.params:
param_grad = model.param_to_grad[param]
model.WeightedSum([param, ONE, param_grad, LR], param)
def Benchmark(model_gen, arg):
model, input_size = model_gen(arg.order)
model.Proto().type = arg.net_type
model.Proto().num_workers = arg.num_workers
# In order to be able to run everything without feeding more stuff, let's
# add the data and label blobs to the parameter initialization net as well.
if arg.order == "NCHW":
input_shape = [arg.batch_size, 3, input_size, input_size]
else:
input_shape = [arg.batch_size, input_size, input_size, 3]
if arg.model == "MLP":
input_shape = [arg.batch_size, input_size]
model.param_init_net.GaussianFill(
[],
"data",
shape=input_shape,
mean=0.0,
std=1.0
)
model.param_init_net.UniformIntFill(
[],
"label",
shape=[arg.batch_size, ],
min=0,
max=999
)
if arg.forward_only:
print('{}: running forward only.'.format(arg.model))
else:
print('{}: running forward-backward.'.format(arg.model))
model.AddGradientOperators(["loss"])
AddParameterUpdate(model)
if arg.order == 'NHWC':
print(
'==WARNING==\n'
'NHWC order with CuDNN may not be supported yet, so I might\n'
'exit suddenly.'
)
if not arg.cpu:
model.param_init_net.RunAllOnGPU()
model.net.RunAllOnGPU()
if arg.engine:
for op in model.net.Proto().op:
op.engine = arg.engine
if arg.dump_model:
# Writes out the pbtxt for benchmarks on e.g. Android
with open(
"{0}_init_batch_{1}.pbtxt".format(arg.model, arg.batch_size), "w"
) as fid:
fid.write(str(model.param_init_net.Proto()))
with open("{0}.pbtxt".format(arg.model, arg.batch_size), "w") as fid:
fid.write(str(model.net.Proto()))
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
workspace.BenchmarkNet(
model.net.Proto().name, arg.warmup_iterations, arg.iterations,
arg.layer_wise_benchmark)
def GetArgumentParser():
parser = argparse.ArgumentParser(description="Caffe2 benchmark.")
parser.add_argument(
"--batch_size",
type=int,
default=128,
help="The batch size."
)
parser.add_argument("--model", type=str, help="The model to benchmark.")
parser.add_argument(
"--order",
type=str,
default="NCHW",
help="The order to evaluate."
)
parser.add_argument(
"--cudnn_ws",
type=int,
default=-1,
help="The cudnn workspace size."
)
parser.add_argument(
"--iterations",
type=int,
default=10,
help="Number of iterations to run the network."
)
parser.add_argument(
"--warmup_iterations",
type=int,
default=10,
help="Number of warm-up iterations before benchmarking."
)
parser.add_argument(
"--forward_only",
action='store_true',
help="If set, only run the forward pass."
)
parser.add_argument(
"--layer_wise_benchmark",
action='store_true',
help="If True, run the layer-wise benchmark as well."
)
parser.add_argument(
"--cpu",
action='store_true',
help="If True, run testing on CPU instead of GPU."
)
parser.add_argument(
"--engine",
type=str,
default="",
help="If set, blindly prefer the given engine(s) for every op.")
parser.add_argument(
"--dump_model",
action='store_true',
help="If True, dump the model prototxts to disk."
)
parser.add_argument("--net_type", type=str, default="dag")
parser.add_argument("--num_workers", type=int, default=2)
parser.add_argument("--use-nvtx", default=False, action='store_true')
parser.add_argument("--htrace_span_log_path", type=str)
return parser
if __name__ == '__main__':
args = GetArgumentParser().parse_args()
if (
not args.batch_size or not args.model or not args.order or
not args.cudnn_ws
):
GetArgumentParser().print_help()
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0'] +
(['--caffe2_use_nvtx'] if args.use_nvtx else []) +
(['--caffe2_htrace_span_log_path=' + args.htrace_span_log_path]
if args.htrace_span_log_path else []))
model_map = {
'AlexNet': AlexNet,
'OverFeat': OverFeat,
'VGGA': VGGA,
'Inception': Inception,
'MLP': MLP,
}
Benchmark(model_map[args.model], args)