def ReallyCreate(inputs, outputs, name='', device_option=None,
args=None, **kwargs):
operator = caffe2_pb2.OperatorDef()
operator.type = operator_type
operator.name = name
if type(inputs) is str or type(inputs) is BlobReference:
inputs = [inputs]
elif type(inputs) is not list:
raise ValueError("Unknown input format: %s." % str(inputs))
if type(outputs) is str or type(outputs) is BlobReference:
outputs = [outputs]
elif type(outputs) is not list:
raise ValueError("Unknown output format: %s of type %s."
% (str(outputs), type(outputs)))
operator.inputs.extend([str(i) for i in inputs])
operator.outputs.extend([str(o) for o in outputs])
if device_option:
operator.device_option.CopyFrom(device_option)
# random seed is defined in the device option, so we need to do special
# care.
if 'random_seed' in kwargs:
operator.device_option.random_seed = kwargs['random_seed']
del kwargs['random_seed']
# Add given arguments that do not need parsing
if args:
operator.args.extend(args)
# Add all other arguments
for key, value in kwargs.iteritems():
operator.args.add().CopyFrom(utils.MakeArgument(key, value))
return operator
def Benchmark(model_gen, arg):
model, input_size = model_gen(arg.order)
# 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]
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)
# Note: even when we are running things on CPU, adding a few engine related
# argument will not hurt since the CPU operator registy will simply ignore
# these options and go the default path.
for op in model.net.Proto().op:
if op.type == 'Conv' or op.type == 'ConvFp16':
op.engine = 'CUDNN'
#op.arg.add().CopyFrom(utils.MakeArgument('ws_nbytes_limit', arg.cudnn_limit))
op.arg.add().CopyFrom(utils.MakeArgument('exhaustive_search', 1))
op.arg.add().CopyFrom(
utils.MakeArgument('shared_ws_name', 'cudnn_workspace'))
elif op.type in [
'MaxPool', 'MaxPoolFp16', 'AveragePool', 'AveragePoolFp16',
'Relu', 'ReluFp16', 'Softmax', 'SoftmaxFp16'
]:
op.engine = 'CUDNN'
if arg.forward_only:
print arg.model, ': running forward only.'
else:
print arg.model, ': running forward-backward.'
model.AddGradientOperators()
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()
workspace.RunNetOnce(model.param_init_net)
workspace.CreateNet(model.net)
for i in range(arg.warmup_iterations):
workspace.RunNet(model.net.Proto().name)
start = time.time()
for i in range(arg.iterations):
workspace.RunNet(model.net.Proto().name)
print 'Spent: ', (time.time() - start) / arg.iterations
if arg.layer_wise_benchmark:
print 'Layer-wise benchmark.'
workspace.BenchmarkNet(model.net.Proto().name, 1, arg.iterations, True)
# 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()))
def AddArgument(op, key, value):
"""Makes an argument based on the value type."""
op.arg.extend([utils.MakeArgument(key, value)])
