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build.py
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import tvm
import nnvm
import numpy as np
from nnvm import testing
from tvm import autotvm
from tvm.contrib.util import tempdir
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
from tool.load_deploy_model import load_model, load_mxnet_model
from gluoncv import model_zoo, data, utils
from utils import *
def get_network(name, batch_size, input_size):
"""Get the symbol definition and random weight of a network"""
input_shape = (batch_size, 3, 224, 224)
output_shape = (batch_size, 1000)
if "resnet" in name:
n_layer = int(name.split('-')[1])
net, params = testing.resnet.get_workload(num_layers=n_layer, batch_size=batch_size)
elif "vgg" in name:
n_layer = int(name.split('-')[1])
net, params = testing.vgg.get_workload(num_layers=n_layer, batch_size=batch_size)
elif name == 'mobilenet':
net, params = nnvm.testing.mobilenet.get_workload(batch_size=batch_size)
elif name == 'squeezenet_v1.1':
net, params = nnvm.testing.squeezenet.get_workload(batch_size=batch_size, version='1.1')
elif name == 'inception_v3':
#input_shape = (1, 3, 512, 512)
net, params = nnvm.testing.inception_v3.get_workload(batch_size=batch_size)
elif name == 'ssd-mobilenetv2':
net, params = load_mxnet_model('deploy_ssd_mobilenet_v2_680-det', 240, 'model')
net, params = nnvm.frontend.from_mxnet(net, params)
input_shape = (1, input_size, input_size, 3)
input_shape = (1, 680, 680, 3)
output_shape = None
elif name == 'yolo3_darknet53_coco_gluoncv_block':
block = model_zoo.yolo3_darknet53_coco(pretrained_base=True, pretrained=True)
input_shape = (1, 3, input_size, input_size)
mod, params = tvm.relay.frontend.from_mxnet(block, {"data": input_shape})
net = mod[mod.entry_func]
net = tvm.relay.Function(net.params, (net.body), None, net.type_params, net.attrs)
output_shape = None
elif name == 'yolo3_mobilenet1_0':
net, params = load_mxnet_model('yolo3_mobilenet1_0', 0, 'model')
print(net)
#net, params = nnvm.frontend.from_mxnet(net, params)
net, params = tvm.relay.frontend.from_mxnet(net)
input_shape = (1, input_size, input_size, 3)
input_shape = (1, 320, 320, 3)
output_shape = None
elif name == 'yolo3-mobilenet1_0_split':
net, params = load_mxnet_model('yolo3-mobilenet1_0_split', 0, 'model')
#net, params = nnvm.frontend.from_mxnet(net, params)
net, params = tvm.relay.frontend.from_mxnet(net)
raise
input_shape = (1, input_size, input_size, 3)
input_shape = (1, 320, 320, 3)
output_shape = None
elif name == 'yolo3-mobilenet1_0_no_arange':
net, params = load_mxnet_model('yolo3-mobilenet1_0_no_arange', 0, 'model')
net, params = nnvm.frontend.from_mxnet(net, params)
input_shape = (1, input_size, input_size, 3)
input_shape = (1, 320, 320, 3)
output_shape = None
elif name == 'ssd-inceptionv3-nms':
net, params = load_mxnet_model('deploy_ssd_inceptionv3_512-det', 215, 'model')
net, params = nnvm.frontend.from_mxnet(net, params)
input_shape = (1, 512, 512, 3)
output_shape = None
elif name == 'ssd-inceptionv3':
net, params = load_mxnet_model('deploy_ssd_inceptionv3_512-det', 215, 'model')
net, params = nnvm.frontend.from_mxnet(net, params)
input_shape = (1, input_size, input_size, 3)
input_shape = (1, 512, 512, 3)
output_shape = None
elif name == 'ssd-inceptionv3-fp16':
net, params = load_mxnet_model('deploy_ssd_inceptionv3_fp16_512-det', 215, 'model')
net, params = nnvm.frontend.from_mxnet(net, params)
input_shape = (1, 512, 512, 3)
output_shape = None
elif name == 'yolov3-darknet':
ins = 'yolov3.x86.gpu'
graph = load_tvm_graph('graph/{}'.format(ins))
params = load_tvm_params('params/{}'.format(ins))
net = graph.symbol
input_shape = (1, 3, 608, 608)
output_shape = None
elif name == 'yolov3-darknet-320':
ins = 'yolov3-darknet-320.x86.gpu'
graph = load_tvm_graph('graph/{}'.format(ins))
params = load_tvm_params('params/{}'.format(ins))
net = graph.symbol
input_shape = (1, 3, 320, 320)
output_shape = None
elif name == 'yolov3-darknet-tiny':
ins = 'yolov3-darknet-tiny.x86.gpu'
graph = load_tvm_graph('graph/{}'.format(ins))
params = load_tvm_params('params/{}'.format(ins))
net = graph.symbol
input_shape = (1, 3, 224, 224)
output_shape = None
elif name == 'yolov3-darnknet':
net, params = load_mxnet_model('yolo3_darknet53_voc', 0, 'model')
net, params = nnvm.frontend.from_mxnet(net, params)
input_shape = (1, 3, 416, 416)
output_shape = None
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))
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)
elif name == 'test_nms':
net, params, input_shape = load_model('test_nms')
net, params = nnvm.frontend.from_mxnet(net)
elif name == 'test':
net, params, input_shape = load_model('test')
net, params = nnvm.frontend.from_mxnet(net)
else:
raise ValueError("Unsupported network: " + name)
return net, params, input_shape, output_shape