def main(argv=sys.argv):
args = parse_args(argv)
seed_all(12345)
init_algorithms(deterministic=True)
# Load the model into system memory (CPU, not GPU)
model_state = torch.load(args.input, map_location='cpu')
model_desc = model_state['model_desc']
model = create_model(model_desc)
model.load_state_dict(model_state['state_dict'])
if args.format == 'pytorch':
new_model_state = {
'state_dict': model.state_dict(),
'model_desc': model_desc,
'train_datasets': model_state.get('train_datasets', []),
}
torch.save(new_model_state, args.output)
elif args.format == 'onnx':
image_height = model.data_specs.input_specs.height
image_width = model.data_specs.input_specs.width
dummy_input = torch.randn(1, 3, image_height, image_width)
onnx.export(model, (dummy_input, ), args.output, verbose=False)
else:
raise Exception('Unrecognised model format: {}'.format(args.format))
def convert_pytorch(nlp: Pipeline, opset: int, output: str,
use_external_format: bool):
if not is_torch_available():
raise Exception(
"Cannot convert because PyTorch is not installed. Please install torch first."
)
import torch
from torch.onnx import export
print("Using framework PyTorch: {}".format(torch.__version__))
with torch.no_grad():
input_names, output_names, dynamic_axes, tokens = infer_shapes(
nlp, "pt")
ordered_input_names, model_args = ensure_valid_input(
nlp.model, tokens, input_names)
export(
nlp.model,
model_args,
f=output,
input_names=ordered_input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
use_external_data_format=use_external_format,
enable_onnx_checker=True,
opset_version=opset,
)
def main():
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
output_path = './test.onnx'
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
load_checkpoint(model, args.checkpoint, map_location='cpu')
model.eval()
output_h, output_w = 512, 512
for k, v in cfg.items():
if k.startswith('test'):
for k_, v_ in v.items():
if k_.endswith('img_scale'):
output_w, output_h = v_
assert 'forward_export' in model.__dir__()
model.forward = model.forward_export
with torch.no_grad():
export(model,
torch.zeros((1, 3, output_h, output_w), dtype=torch.float32),
output_path,
opset_version=9,
do_constant_folding=True)
similarity_test(model, args.out, height_width=(output_h, output_w))
def save_onnx(path, model, *input_shapes, device="cpu"):
model = model.eval()
args = [
torch.ones([1, *shape], requires_grad=True, device=device)
for shape in input_shapes
]
outputs = model(*args)
if torch.is_tensor(outputs):
outputs = [outputs]
input_names = [f"input_{i}" for i, _ in enumerate(args)]
output_names = [f"output_{i}" for i, _ in enumerate(outputs)]
onnx.export(
model,
tuple(args),
path,
export_params=True,
verbose=True,
do_constant_folding=True,
input_names=input_names,
output_names=output_names,
dynamic_axes={
n: {
0: "batch_size"
}
for n in input_names + output_names
},
opset_version=10,
)
def toonnx(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if not args.full_model:
model = darknet.DarkNet().to(device)
model_state_dict = model.state_dict()
state_dict = torch.load(args.pytorch_model, map_location=device)
state_dict = {
k: v
for k, v in state_dict.items() if k in model_state_dict
}
state_dict = collections.OrderedDict(state_dict)
model_state_dict.update(state_dict)
model.load_state_dict(model_state_dict)
else:
print('Warning: this function has not been tested yet!')
model = torch.load(args.pytorch_model)
dummy_input = torch.rand(1,
3,
args.insize[0],
args.insize[1],
device=device)
onnx.export(model,
dummy_input,
args.onnx_model,
verbose=True,
input_names=['data'])
session = ort.InferenceSession(args.onnx_model)
outputs = session.run(None, {'data': dummy_input.cpu().numpy()})
for i, output in enumerate(outputs):
print('branch {} output size is {}'.format(i, output.shape))
def main():
x_data, char_matrix = prepare_moses(return_matrix=True, limit=DATA_LIMIT)
model = GRU(input_size=INPUT_SIZE, hidden_size=HIDDEN_SIZE, batch_size=BATCH_SIZE,
output_size=OUTPUT_SIZE, n_layers=N_LAYERS, bidirectional=BIDIRECTIONAL)
global MODEL_NAME
if MODEL_NAME is None:
MODEL_NAME = 'GRU-Model-{}-{}.onnx'.format(N_LAYERS, HIDDEN_SIZE)
criterion = nn.CrossEntropyLoss()
for epoch in range(1):
for iteration, element in enumerate(x_data):
element = torch.Tensor([element])
sequence, target = element[0][:-1], element[0][1:]
loss = train(model, sequence, target, criterion=criterion)
smile_str = evaluate(model, char_matrix, prime_str='CC(=O)')
print('{}\t Loss: {}\n'.format(smile_str, loss))
if iteration >= ITERATION_LIMIT:
break
onnx.export(model, sequence[0], MODEL_NAME)
print(evaluate(model, prime_str='CC(=O)', embedding_matrix=char_matrix))
def deploy_onnx_quantized(dataloader: DataLoader, model: nn.Module, fuse: bool,
name: str):
model = deepcopy(model)
path = f'./{name}'
model = model.eval()
if fuse:
model.fuse()
path += '_fused'
float_path = path + '_float.onnx'
quantized_path = path + '_quant.onnx'
example_input = torch.rand(1, 3, 224, 224)
onnx.export(model=model,
args=(example_input, ),
f=float_path,
input_names=['input_image'],
output_names=['logits'],
opset_version=12)
onnx_q_loader = ONNXQuantizationDataReader(quant_loader=dataloader,
input_name='input_image')
quantize_static(model_input=float_path,
model_output=quantized_path,
calibration_data_reader=onnx_q_loader)
avg_time = benchmark_onnx_model(rt.InferenceSession(float_path))
size = Path(float_path).stat().st_size / 1e6
print(
f'Benchmarking {float_path}: Avg. inference@CPU: {avg_time:3.2f} ms, Size: {size:2.2f} MB'
)
avg_time = benchmark_onnx_model(rt.InferenceSession(quantized_path))
size = Path(quantized_path).stat().st_size / 1e6
print(
f'Benchmarking {quantized_path}: Avg. inference@CPU: {avg_time:3.2f} ms, Size: {size:2.2f} MB'
)
def convert_pytorch(nlp: Pipeline, opset: int, output: Path,
use_external_format: bool):
"""
Export a PyTorch backed pipeline to ONNX Intermediate Representation (IR
Args:
nlp: The pipeline to be exported
opset: The actual version of the ONNX operator set to use
output: Path where will be stored the generated ONNX model
use_external_format: Split the model definition from its parameters to allow model bigger than 2GB
Returns:
"""
if not is_torch_available():
raise Exception(
"Cannot convert because PyTorch is not installed. Please install torch first."
)
import torch
from torch.onnx import export
from .pytorch_utils import is_torch_less_than_1_11
print(f"Using framework PyTorch: {torch.__version__}")
with torch.no_grad():
input_names, output_names, dynamic_axes, tokens = infer_shapes(
nlp, "pt")
ordered_input_names, model_args = ensure_valid_input(
nlp.model, tokens, input_names)
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
nlp.model,
model_args,
f=output.as_posix(),
input_names=ordered_input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
use_external_data_format=use_external_format,
enable_onnx_checker=True,
opset_version=opset,
)
else:
export(
nlp.model,
model_args,
f=output.as_posix(),
input_names=ordered_input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
opset_version=opset,
)
def to_onnx(self, outdir="model/openvino"):
os.makedirs(outdir, exist_ok=True)
mfcc = torch.rand(1, 1, self.n_mfcc, self.max_frame).to(self.device)
onnx.export(self.model,
mfcc,
f"{outdir}/diarization.onnx",
input_names=["input"],
output_names=["output"])
print(f"model is exported as {outdir}/diarization.onnx")
def export(model, device, path):
dummy_input = randn(1, 1, 28, 28, device=device)
onnx.export(
model,
dummy_input,
path,
input_names=["Input3"],
output_names=["Plus214_Output_0"],
)
def export(dir):
dummy_input = Variable(torch.randn(1, 3, 4, 4))
model = broadcast_add()
model.eval()
torch.save(model.state_dict(), os.path.join(dir, "broadcast_add.pth"))
onnx.export(model,
dummy_input,
os.path.join(dir, "broadcast_add.onnx"),
verbose=True)
def export(dir):
file_path = os.path.realpath(__file__)
file_dir = os.path.dirname(file_path)
dummy_input = Variable(torch.randn(1, 3, 32, 32))
model = ResNet34()
# model = load_network(model,os.path.join(file_dir,'..','model','pose_v02.pth'))
model.eval()
torch.save(model.state_dict(),os.path.join(dir,"resnet.pth"))
onnx.export(model, dummy_input,os.path.join(dir,"resnet.onnx"), verbose=True)
def export_roihead(self, input_shape, export_name):
inputs = torch.randn(*input_shape)
mask = self.forward('centerhead', inputs)
export(self, ('centerhead', inputs),
export_name,
input_names=['roipool'],
output_names=['mask'],
opset_version=11)
return mask
def export_fpn(self, input_shape, export_name):
inputs = torch.randn(*input_shape)
features = self.forward('fpn', inputs)
export(self, ('fpn', inputs),
export_name,
input_names=['images'],
output_names=list(features.keys()),
opset_version=11)
return features
def export(dir):
dummy_input = Variable(torch.randn(1, 3, 224, 224))
model = MobileNetV2()
model.eval()
torch.save(model.state_dict(), os.path.join(dir, "MobileNetV2.pth"))
onnx.export(model,
dummy_input,
os.path.join(dir, "MobileNetV2.onnx"),
verbose=True)
def to_onnx(self, fname="speaker_diarization.onnx", outdir="model"):
os.makedirs(outdir, exist_ok=True)
audio = np.ones(self.chunk_len)
audio = self._to_melspec(audio)
hidden = torch.rand(self.n_layer, 1, self.n_hidden)
export(self.model, (audio, hidden),
f"{outdir}/{fname}",
input_names=["input"],
output_names=["output"])
def save_model(model, device, path, name, no_cuda):
if not no_cuda:
x = torch.randint(255, (1, 28*28), dtype=torch.float).to(device) / 255
else:
x = torch.randint(255, (1, 28*28), dtype=torch.float) / 255
onnx.export(model, x, "./outputs/{}.onnx".format(name))
print('Saved onnx model to model.onnx')
torch.save(model.state_dict(), "./outputs/{}.pth".format(name))
print('Saved PyTorch Model to model.pth')
def test_onnx_export():
dummy_input = torch.randn(1, 3, 256, 256)
model = MargiPoseModel(CanonicalSkeletonDesc,
n_stages=1,
axis_permutation=True,
feature_extractor='inceptionv4',
pixelwise_loss='jsd')
model.eval()
with TemporaryDirectory() as d:
onnx_file = os.path.join(d, 'model.onnx')
onnx.export(model, (dummy_input, ), onnx_file, verbose=False)
def torch2onnx(self, model, weight_path=None):
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
if weight_path is not None:
self.model_process.loadLatestModelWeight(weight_path, model)
save_onnx_path = os.path.join(self.save_dir,
"%s.onnx" % model.get_name())
onnx.export(model,
self.input_x,
save_onnx_path,
export_params=True,
verbose=False)
return save_onnx_path
def export(dir):
file_path = os.path.realpath(__file__)
file_dir = os.path.dirname(file_path)
dummy_input = torch.randn(1, 3, 224, 224)
model = resnet50()
# model = load_network(model,os.path.join(file_dir,'..','model','pose_v02.pth'))
model.eval()
torch.save(model.state_dict(), os.path.join(dir, "resnet50.pth"))
onnx.export(model,
dummy_input,
os.path.join(dir, "resnet50.onnx"),
opset_version=9,
verbose=True)
def save_model(model, device, path, name):
base = Path(path)
onnx_file = base.joinpath('{}.onnx'.format(name)).resolve()
pth_file = base.joinpath('{}.pth'.format(name)).resolve()
# create dummy variable to traverse graph
x = torch.randint(255, (1, 28 * 28), dtype=torch.float).to(device) / 255
onnx.export(model, x, onnx_file)
print('Saved onnx model to {}'.format(onnx_file))
# saving PyTorch Model Dictionary
torch.save(model.state_dict(), pth_file)
print('Saved PyTorch Model to {}'.format(pth_file))
def export_fcoshead(self,
features,
export_name,
input_names=None,
output_names=None):
self.precal_locations = self.proposal_generator.compute_locations(
features)
proposals = self.forward('fcoshead', features)
export(self, ('fcoshead', features),
export_name,
input_names=input_names,
output_names=output_names,
opset_version=11)
return proposals
def test_onnx():
class SimpleModel(nn.Module):
def forward(self, x):
x = flat_softmax(x)
x = dsnt(x, normalized_coordinates=True)
return x
dummy_input = torch.randn((2, 4, 32, 32))
model = SimpleModel()
model.eval()
with TemporaryDirectory() as d:
onnx_file = os.path.join(d, 'model.onnx')
onnx.export(model, (dummy_input, ), onnx_file, verbose=False)
assert os.path.isfile(onnx_file)
def convert_pytorch(nlp: Pipeline, opset: int, output: Path,
use_external_format: bool):
"""
Export a PyTorch backed pipeline to ONNX Intermediate Representation (IR
Args:
nlp: The pipeline to be exported
opset: The actual version of the ONNX operator set to use
output: Path where will be stored the generated ONNX model
use_external_format: Split the model definition from its parameters to allow model bigger than 2GB
Returns:
"""
if not is_torch_available():
raise Exception(
"Cannot convert because PyTorch is not installed. Please install torch first."
)
import torch
from torch.onnx import export
print(f"Using framework PyTorch: {torch.__version__}")
with torch.no_grad():
input_names, output_names, dynamic_axes, tokens = infer_shapes(
nlp, "pt")
ordered_input_names, model_args = ensure_valid_input(
nlp.model, tokens, input_names)
print('Exporting from PyTorch to ONNX...')
print('input_names', input_names)
print('output_names', output_names)
print('dynamic_axes', dynamic_axes)
print('tokens', tokens)
print('model_args', model_args)
export(nlp.model,
model_args,
f=output.as_posix(),
input_names=ordered_input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
use_external_data_format=use_external_format,
enable_onnx_checker=True,
opset_version=opset,
verbose=True)
def ptconversion():
model = torch.load('entire_model.pt')
x = Variable(torch.randn(1, 10, 6))
dot = make_dot(model(x), params=dict(model.named_parameters()))
dot.format = 'png'
dot.render('torchviz-sample')
model.eval()
input_shape = (1, 10, 6)
model_onnx_path = "torch_model.onnx"
dummy_input = Variable(torch.randn(1, *input_shape).type(dtype),
requires_grad=True)
# plot graph of variable, not of a nn.Module
output = torch_onnx.export(net, dummy_input, model_onnx_path, verbose=True)
# plot graph of variable, not of a nn.Module
print("Export of torch_model.onnx complete!")
onnx_model = onnx.load("torch_model.onnx")
s = MessageToJson(onnx_model)
onnx_json = json.loads(s)
# Convert JSON to String
onnx_str = json.dumps(onnx_json)
with open("model2.json", "w") as json_file:
json_file.write(onnx_str)
resp = make_response(onnx_str)
resp.headers['Access-Control-Allow-Origin'] = '*'
return resp
def train(cfg, args):
#logging.basicConfig(filename='./output/LOG/'+__name__+'.log',format='[%(asctime)s-%(filename)s-%(levelname)s:%(message)s]', level = logging.INFO,filemode='a',datefmt='%Y-%m-%d%I:%M:%S %p')
logging.basicConfig(
filename='./output/LOG/' + __name__ + '.log',
format='[%(asctime)s-%(filename)s-%(levelname)s:%(message)s]',
level=logging.INFO)
logger = logging.getLogger('SSD.trainer')
# -----------------------------------------------------------------------------
# Model
# -----------------------------------------------------------------------------
ssd_model = build_ssd_model(cfg)
ssd_model.init_from_base_net(args.vgg)
ssd_model = torch.nn.DataParallel(ssd_model,
device_ids=range(
torch.cuda.device_count()))
device = torch.device(cfg.MODEL.DEVICE)
print(ssd_model)
logger.info(ssd_model)
model = torchvision.models.AlexNet(num_classes=10)
logger.info(model)
writer = tensorboardX.SummaryWriter(log_dir="./output/model_graph/",
comment="myresnet")
#dummy_input = torch.autograd.Variable(torch.rand(1, 3, 227, 227))
dummy_input = torch.autograd.Variable(torch.rand(1, 3, 300, 300))
#writer.add_graph(model=ssd_model, input_to_model=(dummy_input, ))
model_onnx_path = 'torch_model.onnx'
output = torch_onnx.export(ssd_model,
dummy_input,
model_onnx_path,
verbose=False)
#ssd_model.to(device)
print('----------------')
def train(epoch, dataloader, module):
for i in range(epoch):
for batch_idx, (data, target) in enumerate(dataloader):
data, target = Variable(data), Variable(target)
optimizer = optim.SGD(module.parameters(), lr=0.01, momentum=0.9)
optimizer.zero_grad()
output = module(data)
loss = F.kl_div(output, target)
loss.backward()
optimizer.step()
if batch_idx % 200 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
i, batch_idx * len(data), len(dataloader.dataset),
100. * batch_idx / len(dataloader), loss.data))
torch.save(module, "./module/my_model.pkl")
dummy_input = torch.randn(40)
torch_onnx.export(module, args=dummy_input, f="./module/my_model.onnx")
def export(self, output_dir):
assert self.model is not None, "Only a trained model can be exported."
self.model.eval().cpu()
dummy_input = torch.randn((1, 1, 1)).float()
output_filename = path.join(output_dir, f'{self.name()}.onnx')
onnx.export(self.model,
dummy_input,
output_filename,
input_names=['input'],
output_names=['output'],
do_constant_folding=True,
dynamic_axes={
'input': [0, 2],
'output': [0, 2]
})
def toonnx(args):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if not args.full_model:
if args.backbone == 'darknet':
model = darknet.DarkNet(np.random.randint(0, 100,
(12, 2))).to(device)
elif args.backbone == 'shufflenetv2':
model = shufflenetv2.ShuffleNetV2(np.random.randint(
0, 100, (12, 2)),
model_size=args.thin).to(device)
else:
print('unknown backbone architecture!')
sys.exit(0)
if args.pytorch_model:
model_state_dict = model.state_dict()
state_dict = torch.load(args.pytorch_model, map_location=device)
state_dict = {
k: v
for k, v in state_dict.items() if k in model_state_dict
}
state_dict = collections.OrderedDict(state_dict)
model_state_dict.update(state_dict)
model.load_state_dict(model_state_dict)
else:
print('Warning: this function has not been tested yet!')
model = torch.load(args.pytorch_model)
model.eval()
dummy_input = torch.rand(1,
3,
args.insize[0],
args.insize[1],
device=device)
onnx.export(model,
dummy_input,
args.onnx_model,
verbose=True,
input_names=['data'],
output_names=['out1', 'out2', 'out3'],
opset_version=11)
session = ort.InferenceSession(args.onnx_model)
outputs = session.run(None, {'data': dummy_input.cpu().numpy()})
for i, output in enumerate(outputs):
print('branch {} output size is {}'.format(i, output.shape))
def stylization(p_wct, content_image_path, style_image_path, content_seg_path, style_seg_path, output_image_path, cuda, args):
# Load image
cont_img = Image.open(content_image_path).convert('RGB')
styl_img = Image.open(style_image_path).convert('RGB')
try:
cont_seg = Image.open(content_seg_path)
styl_seg = Image.open(style_seg_path)
except:
cont_seg = []
styl_seg = []
cont_img = transforms.ToTensor()(cont_img).unsqueeze(0)
styl_img = transforms.ToTensor()(styl_img).unsqueeze(0)
if cuda:
cont_img = cont_img.cuda(0)
styl_img = styl_img.cuda(0)
p_wct.cuda(0)
cont_img = Variable(cont_img, requires_grad=False)
styl_img = Variable(styl_img, requires_grad=False)
cont_seg = torch.FloatTensor(np.asarray(cont_seg))
styl_seg = torch.FloatTensor(np.asarray(styl_seg))
if args.export_onnx:
assert args.export_onnx.endswith(".onnx"), "Export model file should end with .onnx"
export(p_wct, [cont_img, styl_img, cont_seg, styl_seg],
f=args.export_onnx, verbose=args.verbose)
with Timer("Elapsed time in stylization: %f"):
stylized_img = p_wct.transform(cont_img, styl_img, cont_seg, styl_seg)
utils.save_image(stylized_img.data.cpu().float(), output_image_path, nrow=1)
with Timer("Elapsed time in propagation: %f"):
out_img = p_pro.process(output_image_path, content_image_path)
out_img.save(output_image_path)
if not cuda:
print("NotImplemented: The CPU version of smooth filter has not been implemented currently.")
return
with Timer("Elapsed time in post processing: %f"):
out_img = smooth_filter(output_image_path, content_image_path, f_radius=15, f_edge=1e-1)
out_img.save(output_image_path)
