import onnx
import numpy as np
import tvm
from tvm import runtime
import tvm.relay as relay
import tvm.relax as relax
from tvm.contrib import graph_executor,debugger
from tvm.relay.op.contrib import tensorrt
import logging
from tvm.relay.op.contrib.tensorrt import
partition_for_tensorrt,get_tensorrt_version,is_tensorrt_runtime_enabled,get_tensorrt_target
######################################################################
# Load pretrained ONNX model
# ---------------------------------------------
print(is_tensorrt_runtime_enabled())
print(get_tensorrt_target())
print(get_tensorrt_version())
model_path = "models/yolov5s.v5.onnx"
print(tvm.__file__)
logging.basicConfig(level=logging.DEBUG)
onnx_model = onnx.load(model_path)
# Compile the model with relay
BATCH_SIZE = 1
input_shape = (BATCH_SIZE, 3, 640, 640)
input_name = "images"
dtype="float16"
shape_dict = {input_name: input_shape}
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict,dtype=dtype)
mod = relay.transform.InferType()(mod)
mod = tensorrt.partition_for_tensorrt(mod)
with tvm.transform.PassContext(opt_level=3):
lib = relay.build(mod,target="cuda",params=params)
dev = tvm.cuda(0)
module_exec = graph_executor.GraphModule(lib["default"](dev))
x_data = np.random.uniform(-1, 1, input_shape).astype(dtype)
module_exec.set_input(input_name, x_data)
print(module_exec.benchmark(dev,number=1,repeat=1))
---
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