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| def test_model():
model = models.resnet101(pretrained=True).eval().to(device)
for inputs, labels in tqdm(dataloaders['valid']):
inputs = inputs.to(device)
outputs = model(inputs)
def test_autocast_model():
model = models.resnet101(pretrained=True).eval().to(device)
with torch.autocast(device_type="cuda"):
for inputs, labels in tqdm(dataloaders['valid']):
inputs = inputs.to(device)
outputs = model(inputs)
def test_jit_model():
model = models.resnet101(pretrained=True).eval().to(device)
model = torch.jit.script(model)
model = torch.jit.freeze(model)
for inputs, labels in tqdm(dataloaders['valid']):
inputs = inputs.to(device)
outputs = model(inputs)
def test_antocast_jit_model():
model = models.resnet101(pretrained=True).eval().to(device)
with torch.cuda.amp.autocast(cache_enabled=False):
model = torch.jit.script(model)
model = torch.jit.freeze(model)
for inputs, labels in tqdm(dataloaders['valid']):
inputs = inputs.to(device)
outputs = model(inputs)
def test_trt_model():
model = models.resnet101(pretrained=True).eval().to(device)
x = torch.ones((1, 3, 512, 512)).cuda()
model_trt = torch2trt(model, [x])
model = TRTModule()
model.load_state_dict(model_trt.state_dict())
for inputs, labels in tqdm(dataloaders['valid']):
inputs = inputs.to(device)
outputs = model(inputs)
def test_autocast_trt_model():
model = models.resnet101(pretrained=True).eval().to(device)
x = torch.ones((1, 3, 512, 512)).cuda()
model_trt = torch2trt(model, [x])
model = TRTModule()
model.load_state_dict(model_trt.state_dict())
with torch.autocast(device_type="cuda"):
for inputs, labels in tqdm(dataloaders['valid']):
inputs = inputs.to(device)
outputs = model(inputs)
|