SNPE Profile

snpe_profile.py

import torch
import torch.nn as nn
import torchvision

from torchvision import models


batch = 1 
dim = 3
res = 224
model = models.resnet101(pretrained=False).eval()
model_name = "resnet101_quant"

def remove_bn(module):
    module_output = module
    if isinstance(module, (nn.BatchNorm2d, nn.Dropout, nn.LayerNorm, nn.Dropout, nn.ReLU, nn.ReLU6)):
        module_output = nn.Identity()

    for name, child in module.named_children():
        module_output.add_module(name, remove_bn(child))
    del module
    return module_output

model = remove_bn(model)
dummy_input = torch.randn(batch, dim, res, res)
torch.onnx.export(model, dummy_input, f"onnx_zoos/{model_name}.onnx", )

import os
os.system(f"snpe-onnx-to-dlc -i onnx_zoos/{model_name}.onnx  -o dlc/{model_name}.dlc")
os.system(f"snpe-dlc-quantize --input_dlc dlc/{model_name}.dlc --output_dlc dlc/{model_name}_quant.dlc --input_list random_inputs/random_raw_list.txt" )

json_info = {
    "Name": f"{model_name}",
    "HostRootPath": f"{model_name}",
    "HostResultsDir": f"{model_name}/results",
    "DevicePath":"/data/local/tmp/snpebm",
    "Devices":["LMG820QM89db118f"],
    "HostName": "localhost",
    "Runs":5,

    "Model": {
        "Name": f"{model_name}",
        "Dlc": f"./dlc/{model_name}_quant.dlc",
        "RandomInput": 1
    },
    "Runtimes":["CPU", "GPU", "DSP"],
    "Measurements": ["timing"]
}

import json
with open("tmp.json", "w") as fp:
    json.dump(json_info, fp)

# /home/<user>/snpe-<version>/benchmarks
os.system("python snpe_bench.py -c tmp.json -json -l moderate")