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export.py
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export.py
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import torch
import numpy as np
import argparse
# from tqdm.autonotebook import tqdm
import os
import torch
from model.TwinLite import TwinLiteNet as Net
import onnx
import torch
import pandas as pd
from pathlib import Path
def select_device(device='', batch_size=0, newline=True):
# device = None or 'cpu' or 0 or '0' or '0,1,2,3'
s = f'TwinLiteNet 🚀 torch-{torch.__version__} '
device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'
cpu = device == 'cpu'
mps = device == 'mps' # Apple Metal Performance Shaders (MPS)
if cpu or mps:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False
elif device: # non-cpu device requested
os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()
assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7
n = len(devices) # device count
if n > 1 and batch_size > 0: # check batch_size is divisible by device_count
assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
space = ' ' * (len(s) + 1)
for i, d in enumerate(devices):
p = torch.cuda.get_device_properties(i)
s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
arg = 'cuda:0'
elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available
s += 'MPS\n'
arg = 'mps'
else: # revert to CPU
s += 'CPU\n'
arg = 'cpu'
if not newline:
s = s.rstrip()
# LOGGER.info(s)
return torch.device(arg)
def export_formats():
# YOLOv5 export formats
x = [
['PyTorch', '-', '.pt', True, True],
['TorchScript', 'torchscript', '.torchscript', True, True],
['ONNX', 'onnx', '.onnx', True, True],
['TensorRT', 'engine', '.engine', False, True],]
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
def export_torchscript(model, im, file):
f = file.with_suffix('.torchscript')
ts = torch.jit.trace(model, im)
ts.save(str(f))
def export_onnx(model, im, file, opset=12):
f = file.with_suffix('.onnx')
torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
do_constant_folding=True,
input_names=["images"],
output_names=["da", "ll"])
model_onnx = onnx.load(f) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
onnx.save(model_onnx, f)
def export_engine(model, im, file):
import tensorrt as trt
export_onnx(model, im, file, 12) # opset 12
onnx = file.with_suffix('.onnx')
f = file.with_suffix('.engine') # TensorRT engine file
logger = trt.Logger(trt.Logger.INFO)
# logger.min_severity = trt.Logger.Severity.VERBOSE
builder = trt.Builder(logger)
config = builder.create_builder_config()
config.max_workspace_size = 4 * 1 << 30
# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
network = builder.create_network(flag)
parser = trt.OnnxParser(network, logger)
if not parser.parse_from_file(str(onnx)):
raise RuntimeError(f'failed to load ONNX file: {onnx}')
inputs = [network.get_input(i) for i in range(network.num_inputs)]
outputs = [network.get_output(i) for i in range(network.num_outputs)]
for inp in inputs:
print(
f' input "{inp.name}" with shape{inp.shape} {inp.dtype}')
for out in outputs:
print(
f'output "{out.name}" with shape{out.shape} {out.dtype}')
print(
f'building FP {16} engine as {f}')
if builder.platform_has_fast_fp16:
config.set_flag(trt.BuilderFlag.FP16)
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
t.write(engine.serialize())
def run(
weights='',
imgsz=[],
batch_size=1,
device='',
include=[]
):
model = net.Net()
model = model.cuda()
model.load_state_dict(torch.load(weights))
device = select_device(device)
include = [x.lower() for x in include] # to lowercase
fmts = tuple(export_formats()['Argument'][1:]) # --include arguments
flags = [x in include for x in fmts]
assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
jit, onnx, engine = flags # export booleans
file = Path(weights) # PyTorch weights
# Input
imgsz = [x for x in imgsz] # verify img_size are gs-multiples
im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
# Exports
f = [''] * len(fmts) # exported filenames
# warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
if jit: # TorchScript
export_torchscript(model, im, file)
if engine: # TensorRT required before ONNX
export_engine(model, im, file)
if onnx: # OpenVINO requires ONNX
export_onnx(model, im, file)
def parse_opt(known=False):
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str,
default='pretrained/model_best.pth', help='model.pt path(s)')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[360, 640], help='image (h, w)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument(
'--include',
nargs='+',
default=['engine'],
help='torchscript, onnx, engine')
opt = parser.parse_known_args()[0] if known else parser.parse_args()
return opt
def main(opt):
run(**vars(opt))
if __name__ == '__main__':
opt = parse_opt()
main(opt)