tensorRT怎么量化yolov7

您可以使用TensorRT中的Python API来执行Yolo v7网络的量化。步骤如下：

1. 导入必要的TensorRT和NumPy库：

import tensorrt as trt
import numpy as np

2. 创建TensorRT中的IBuilder和INetworkDefinition对象：

builder = trt.Builder(trt.Logger(trt.Logger.WARNING))
network = builder.create_network()

3. 加载预训练的Yolo v7模型：

with open('yolov7.trt', 'rb') as f:
    engine_data = f.read()
engine = builder.load_engine(engine_data)

4. 使用预先定义的函数将权重从Engine中提取出来：

def getBinding(engine, name):
    idx = engine.get_binding_index(name)
    if idx == -1:
        raise IndexError("Invalid binding name")
    return idx

input_shape = (3, 416, 416) # 根据实际情况设置
output_shapes = [(1, 255, 13, 13), (1, 255, 26, 26), (1, 255, 52, 52)] # 根据实际情况设置
input_index = getBinding(engine, "input")
output_indices = [getBinding(engine, "output_1"), getBinding(engine, "output_2"), getBinding(engine, "output_3")]
w = engine.get_binding_shape(output_indices[0]) # 获取权重大小
weights = [np.empty(w, dtype=np.float32) for i in range(len(output_indices))]
for idx in output_indices:
    arr = engine.get_binding_shape(idx)
    size = np.multiply.reduce(arr)
    buf = engine.get_binding_shape(idx)
    np.copyto(weights[output_indices.index(idx)], buf)

5. 将权重传递给网络并运行推理：

h_input = trt.tensor(w.shape, trt.float32)
h_output = [trt.tensor(w.shape, trt.float32) for i in range(len(output_shapes))]
cuda_inputs = [cuda.mem_alloc(h_input.nbytes) for i in range(1)]
cuda_outputs = [cuda.mem_alloc(x.nbytes) for x in output_shapes]
stream = cuda.Stream()
with engine.create_execution_context() as context:
    cuda.memcpy_htod(cuda_inputs[0], inputs[0])
    context.execute_v2(bindings=[int(x) for x in cuda_inputs+cuda_outputs])
    for i in range(len(output_shapes)):
        cuda.memcpy_dtoh(h_output[i].host, cuda_outputs[i].device)

6. 进行量化以得到量化模型：

dynamic_ranges = [(np.min(x), np.max(x)) for x in weights] # 获取权重的min-max值
quant_mode = trt.quantize.QuantizationMode.CALIBRATION_2D
calibrator = trt.quantize.UffCalibrator("calib.uff", input_shape)
int8_converter = trt.IInt8LegacyCalibrator()
int8_converter.calibrate([network], 1, calibrator)
builder.int8_mode = True
builder.int8_calibrator = int8_converter
builder.fp16_mode = False
build_config = builder.create_builder_config()
build_config.max_workspace_size = 1 << 30  # 1GB
engine = builder.build_engine(network, build_config)

7. 保存量化模型：

with open("yolov7_int8.trt", "wb") as f:
    f.write(engine.serialize())

这样，您就可以通过TensorRT将预训练的Yolo v7模型量化为INT8精度的模型了。