dtype = torch_dtype_from_trt(self.engine.get_binding_dtype(idx)) shape = tuple(self.context.get_binding_shape(idx)) device = torch_device_from_trt(self.engine.get_location(idx)) output = torch.empty(size=shape, dtype=dtype, device=device) outputs[i] = output bindings[idx] = output.da...
fromtorch2trt.torch2trtimport*@tensorrt_converter('torch.zeros')defconvert_zeros(ctx):size=ctx.method_argsoutput=ctx.method_returnkwargs=ctx.method_kwargsdtype=kwargs.get('dtype')zeros_tensor=torch.zeros(*size,dtype=dtype)output._trt=add_trt_constant(ctx.network,zeros_tensor) 再运行convert.py...
# 需要导入模块: import torch [as 别名]# 或者: from torch importint8[as 别名]def_create_typed_const(data, dtype):"""create a (scalar) constant of given value and dtype. dtype should be a TVM dtype"""ifdtype =="float64": typed_data = _expr.const(np.float64(data), dtype=dtype)e...
确实很简单易用,但是坑也不少哈,如下代码, importtorchfromtorch2trtimporttorch2trtfromtorchvision.models.alexnetimportalexneta=torch.randn([4,4]).cuda()b=torch.tensor([3]).cuda()c=torch.tensor([1,3]).cuda()d=torch.randn([6])e=torch.tensor([True,True,True,True,False,True],dtype=torch....
returnagent_states# Instantiate the model and prepare inputmodel=SimpleModel().cuda()input_tensor=torch.randn(1,10,dtype=torch.float32).cuda()# Configure TensorRT optionsenabled_precisions={torch.float16,torch.float32}compilation_kwargs={"enabled_precisions":enabled_precisions,"debug":True,"dry...
from_pretrained( model_id, revision="fp16", torch_dtype=torch.float16 ) backend = "torch_tensorrt" model = pipe.unet model.half() model.to(device) inputs = torch.load("/opt/torch_tensorrt/refitting/sample_input.pt") exp_program = torch.export.export(model, tuple(inputs)) enabled_...
from torch2trt import torch2trt batch_size = 1 ## export resnet50 input: batchsize 3 224 224 net = torchvision.models.resnet50(pretrained=True).cuda() input_data = torch.rand((batch_size, 3, 224, 224), dtype=torch.float).cuda() ...
qscheme=torch.per_tensor_symmetric, dtype=torch.qint8 ), weight=ao.quantization.observer.default_per_channel_weight_observer ) 然后单独对模型中的某一类型算子操作torch.nn.ConvTranspose2d进行设置,这个qconfig会优先匹配,优先级比整体qconfig高,具体细节可以参考_propagate_qconfig_helper这个函数。
Source File: common.py From torch2trt with MIT License 5 votes def torch_to_np_dtype_map(): import torch type_map = { torch.float16: np.dtype(np.float16), torch.float32: np.dtype(np.float32), torch.float64: np.dtype(np.float64), torch.int32: np.dtype(np.int32), torch....
pagelocked_empty(trt.volume(engine.get_binding_shape(1)), dtype=trt.nptype(trt.float32)) d_input = cuda.mem_alloc(h_input.nbytes) d_output = cuda.mem_alloc(h_output.nbytes) stream = cuda.Stream() return h_input, d_input, h_output, d_output, stream def load_normalized_test_case...