rmccorm4

    # Query input names and shapes from parsed TensorRT network
    network_inputs = [network.get_input(i) for i in range(network.num_inputs)]
    input_names = [_input.name for _input in network_inputs]   # ex: ["actual_input1"]
    # Note the original model must have dynamic (-1) dimensions for variable min/opt/max values 
    # in your profile dimensions such as the batch dimension in this example
    input_shapes = [_input.shape for _input in network_inputs] # ex: [(-1, 3, 224, 224)]
    max_batch_size = 64
    
    # Create optimization profile for dynamic batch dimension
    profile0 = builder.create_optimization_profile()

rmccorm4

import tensorrt as trt

TRT_LOGGER = trt.Logger(trt.Logger.INFO)
EXPLICIT_BATCH = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)

with trt.Builder(TRT_LOGGER) as builder, \
     builder.create_network(EXPLICIT_BATCH) as network, \
     builder.create_builder_config() as config, \
     trt.OnnxParser(network, TRT_LOGGER) as parser:

rmccorm4

import tensorrt as trt

TRT_LOGGER = trt.Logger(trt.Logger.INFO)
EXPLICIT_BATCH = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)

with trt.Builder(TRT_LOGGER) as builder, \
     builder.create_network(EXPLICIT_BATCH) as network, \
     builder.create_builder_config() as config, \
     trt.OnnxParser(network, TRT_LOGGER) as parser:

rmccorm4

import argparse
import torch
import torchvision

parser = argparse.ArgumentParser()
parser.add_argument("--opset", type=int, default=11, help="ONNX opset version to generate models with.")
args = parser.parse_args()

dummy_input = torch.randn(10, 3, 224, 224, device='cuda')
model = torchvision.models.alexnet(pretrained=True).cuda()

rmccorm4