| """Utility for benchmark""" |
| |
| import sys |
| import nnvm |
| |
| def get_network(name, batch_size, dtype='float32'): |
| """Get the symbol definition and random weight of a network |
| |
| Parameters |
| ---------- |
| name: str |
| The name of the network, can be 'resnet-18', 'resnet-50', 'vgg-16', 'inception_v3', 'mobilenet', ... |
| batch_size: int |
| batch size |
| dtype: str |
| Data type |
| |
| Returns |
| ------- |
| net: nnvm.symbol |
| The NNVM symbol of network definition |
| params: dict |
| The random parameters for benchmark |
| input_shape: tuple |
| The shape of input tensor |
| output_shape: tuple |
| The shape of output tensor |
| """ |
| input_shape = (batch_size, 3, 224, 224) |
| output_shape = (batch_size, 1000) |
| |
| if name == 'mobilenet': |
| net, params = nnvm.testing.mobilenet.get_workload(batch_size=batch_size, dtype=dtype) |
| elif name == 'mobilenet_v2': |
| net, params = nnvm.testing.mobilenet_v2.get_workload(batch_size=batch_size, dtype=dtype) |
| elif name == 'inception_v3': |
| input_shape = (batch_size, 3, 299, 299) |
| net, params = nnvm.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype) |
| elif "resnet" in name: |
| n_layer = int(name.split('-')[1]) |
| net, params = nnvm.testing.resnet.get_workload(num_layers=n_layer, batch_size=batch_size, dtype=dtype) |
| elif "vgg" in name: |
| n_layer = int(name.split('-')[1]) |
| net, params = nnvm.testing.vgg.get_workload(num_layers=n_layer, batch_size=batch_size, dtype=dtype) |
| elif "densenet" in name: |
| n_layer = int(name.split('-')[1]) |
| net, params = nnvm.testing.densenet.get_workload(num_layers=n_layer, batch_size=batch_size, dtype=dtype) |
| elif "squeezenet" in name: |
| version = name.split("_v")[1] |
| net, params = nnvm.testing.squeezenet.get_workload(batch_size=batch_size, version=version, dtype=dtype) |
| elif name == 'custom': |
| # an example for custom network |
| from nnvm.testing import utils |
| net = nnvm.sym.Variable('data') |
| net = nnvm.sym.conv2d(net, channels=4, kernel_size=(3,3), padding=(1,1)) |
| net = nnvm.sym.flatten(net) |
| net = nnvm.sym.dense(net, units=1000) |
| net, params = utils.create_workload(net, batch_size, (3, 224, 224), dtype=dtype) |
| elif name == 'mxnet': |
| # an example for mxnet model |
| from mxnet.gluon.model_zoo.vision import get_model |
| block = get_model('resnet18_v1', pretrained=True) |
| net, params = nnvm.frontend.from_mxnet(block) |
| net = nnvm.sym.softmax(net) |
| else: |
| raise ValueError("Unsupported network: " + name) |
| |
| return net, params, input_shape, output_shape |
| |
| def print_progress(msg): |
| """print progress message |
| |
| Parameters |
| ---------- |
| msg: str |
| The message to print |
| """ |
| sys.stdout.write(msg + "\r") |
| sys.stdout.flush() |