r, g, b = 123, 117, 104
#params['mean'] = nd.array(np.array([r, g, b]).astype(np.uint8).reshape([1, 3, 1, 1]))
params['mean'] = nd.array(np.array([r, g, b]).reshape([1, 3, 1, 1]))
net, params = nnvm.frontend.from_mxnet(net, params)
inputs = np.ones(shapes)
print("[*] Compile...")
with autotvm.apply_history_best('log/ssd-inceptionv3.log'):
with compiler.build_config(opt_level=3):
graph, lib, params = compiler.build(net,
target, {
"data": shapes,
"mean": (1, 3, 1, 1)
},
params=params)
#graph, lib, params = compiler.build(net, target, {"data": shapes, "mean" : (1, 3, 1, 1)}, params = params, dtype = dtypes)
#graph, lib, params = compiler.build(net, target, {"data": shapes, "mean" : (1, 3, 1, 1)}, params = params, dtype = {'data' : 'uint8', 'mean' : 'uint8'})
#out = 'ssd-mobilenetv2-680-det'
#out = 'ssd-resnetx50-512-det'
#out = 'ssd-resnet50-512-det'
out = 'ssd-inceptionv3-512-det'
lib.export_library('so/{}.tvm.so'.format(out))
#lib.export_library('so/{}.tvm.so'.format('ssd-inceptionv3-512-det'))
print('[*] Model is Compiled')
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--frontend",
help="Frontend for compilation, nnvm or relay",
type=str,
default="nnvm")
args = parser.parse_args()
if args.frontend == "relay":
net, params = relay.frontend.from_mxnet(sym, {"data": dshape}, arg_params=arg_params, aux_params=aux_params)
with relay.build_config(opt_level=3):
graph, lib, params = relay.build(net, target, params=params)
elif args.frontend == "nnvm":
net, params = from_mxnet(sym, arg_params, aux_params)
with compiler.build_config(opt_level=3):
graph, lib, params = compiler.build(
net, target, {"data": dshape}, params=params)
else:
parser.print_help()
parser.exit()
######################################################################
# Create TVM runtime and do inference
# Preprocess image
image = cv2.imread(test_image_path)
img_data = cv2.resize(image, (dshape[2], dshape[3]))
img_data = img_data[:, :, (2, 1, 0)].astype(np.float32)
img_data -= np.array([123, 117, 104])
img_data = np.transpose(np.array(img_data), (2, 0, 1))
img_data = np.expand_dims(img_data, axis=0)
# Build TVM runtime
zip_ref = zipfile.ZipFile(model_file_path, 'r')
zip_ref.extractall(dir)
zip_ref.close()
zip_ref = zipfile.ZipFile(inference_symbol_path)
zip_ref.extractall(dir)
zip_ref.close()
######################################################################
# Convert and compile model with NNVM for CPU.
sym = mx.sym.load("%s/%s/ssd_resnet50_inference.json" % (dir, inference_symbol_folder))
_, arg_params, aux_params = load_checkpoint("%s/%s" % (dir, model_name), 0)
net, params = from_mxnet(sym, arg_params, aux_params)
with compiler.build_config(opt_level=3):
graph, lib, params = compiler.build(net, target, {"data": dshape}, params=params)
######################################################################
# Create TVM runtime and do inference
# Preprocess image
image = cv2.imread(test_image_path)
img_data = cv2.resize(image, (dshape[2], dshape[3]))
img_data = img_data[:, :, (2, 1, 0)].astype(np.float32)
img_data -= np.array([123, 117, 104])
img_data = np.transpose(np.array(img_data), (2, 0, 1))
img_data = np.expand_dims(img_data, axis=0)
# Build TVM runtime
m = graph_runtime.create(graph, lib, ctx)
m.set_input('data', tvm.nd.array(img_data.astype(dtype)))
m.set_input(**params)
'model')
r, g, b = 123, 117, 104
params['mean'] = nd.array(
np.array([r, g, b]).astype(np.float16).reshape([1, 3, 1, 1]))
net, params = nnvm.frontend.from_mxnet(net, params)
print("[*] Compile...")
#with autotvm.apply_history_best('log/ssd-inceptionv3.log'):
with compiler.build_config(opt_level=3):
graph, lib, params = compiler.build(net,
target, {
"data": shapes,
"mean": (1, 3, 1, 1)
},
dtype='float16')
out = 'ssd-inceptionv3-512-fp16-det'
lib.export_library('so/{}.tvm.so'.format(out))
print('[*] Model is Compiled')
m = graph_runtime.create(graph, lib, ctx)
#save_tvm_params('params/{}'.format(out), params)
#save_tvm_graph('graph/{}'.format(out), graph)
shapes = dict()
shapes['cls_prob'] = (1, 21, 5186)
shapes['loc_preds'] = (1, 20744)
shapes['anchor_boxes'] = (1, 5186, 4)
net = load_nms('model/deploy_ssd_inceptionv3_512-nms-symbol')
net, params = nnvm.frontend.from_mxnet(net)
print("[*] Compile...")
#with autotvm.apply_history_best('ssd-inceptionv3.log'):
with compiler.build_config(opt_level=3):
graph, lib, params = compiler.build(net, target, shapes)
lib.export_library('so/{}.tvm.so'.format('ssd-inceptionv3-512-nms'))
print('[*] Model is Compiled')
m = graph_runtime.create(graph, lib, ctx)
for s in shapes.keys():
m.set_input(s, tvm.nd.array(np.zeros(shapes[s]).astype(np.float32)))
start = time()
print(graph)
target = tvm.target.cuda()
shapes = (1, 3, 224, 224)
inputs = np.ones(shapes)
net, params, input_shape = load_model('test') # load mxnet model
net, params = nnvm.frontend.from_mxnet(net, params)
with autotvm.apply_history_best('test.log'):
print("[*] Compile...")
with compiler.build_config(opt_level=3):
graph, lib, params = compiler.build(net,
target, {"data": [1, 3, 224, 224]},
params=params)
m = graph_runtime.create(graph, lib, ctx)
m.set_input('data', tvm.nd.array(inputs.astype(np.float32)))
m.set_input(**params)
m.run()
tvm_output = m.get_output(0)
save_tvm_params('test', params)
save_tvm_graph('test', graph)
