mirror of https://github.com/WongKinYiu/yolov7.git
127 lines
5.2 KiB
Python
127 lines
5.2 KiB
Python
import argparse
|
|
import sys
|
|
import time
|
|
|
|
sys.path.append('./') # to run '$ python *.py' files in subdirectories
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
import models
|
|
from models.experimental import attempt_load
|
|
from utils.activations import Hardswish, SiLU
|
|
from utils.general import set_logging, check_img_size
|
|
from utils.torch_utils import select_device
|
|
from utils.add_nms import RegisterNMS
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--weights', type=str, default='./yolor-csp-c.pt', help='weights path')
|
|
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
|
|
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
|
|
parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes')
|
|
parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
|
|
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
|
parser.add_argument('--simplify', action='store_true', help='simplify onnx model')
|
|
parser.add_argument('--include-nms', action='store_true', help='export end2end onnx')
|
|
opt = parser.parse_args()
|
|
opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
|
|
print(opt)
|
|
set_logging()
|
|
t = time.time()
|
|
|
|
# Load PyTorch model
|
|
device = select_device(opt.device)
|
|
model = attempt_load(opt.weights, map_location=device) # load FP32 model
|
|
labels = model.names
|
|
|
|
# Checks
|
|
gs = int(max(model.stride)) # grid size (max stride)
|
|
opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
|
|
|
|
# Input
|
|
img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
|
|
|
|
# Update model
|
|
for k, m in model.named_modules():
|
|
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
|
|
if isinstance(m, models.common.Conv): # assign export-friendly activations
|
|
if isinstance(m.act, nn.Hardswish):
|
|
m.act = Hardswish()
|
|
elif isinstance(m.act, nn.SiLU):
|
|
m.act = SiLU()
|
|
# elif isinstance(m, models.yolo.Detect):
|
|
# m.forward = m.forward_export # assign forward (optional)
|
|
model.model[-1].export = not opt.grid # set Detect() layer grid export
|
|
y = model(img) # dry run
|
|
if opt.include_nms:
|
|
model.model[-1].include_nms = True
|
|
y = None
|
|
# TorchScript export
|
|
try:
|
|
print('\nStarting TorchScript export with torch %s...' % torch.__version__)
|
|
f = opt.weights.replace('.pt', '.torchscript.pt') # filename
|
|
ts = torch.jit.trace(model, img, strict=False)
|
|
ts.save(f)
|
|
print('TorchScript export success, saved as %s' % f)
|
|
except Exception as e:
|
|
print('TorchScript export failure: %s' % e)
|
|
|
|
# ONNX export
|
|
try:
|
|
import onnx
|
|
|
|
print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
|
|
f = opt.weights.replace('.pt', '.onnx') # filename
|
|
model.eval()
|
|
torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'],
|
|
output_names=['classes', 'boxes'] if y is None else ['output'],
|
|
dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640)
|
|
'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
|
|
|
|
if opt.include_nms:
|
|
print('Registering NMS plugin...')
|
|
mo = RegisterNMS(f)
|
|
mo.register_nms()
|
|
mo.save(f)
|
|
else:
|
|
# Checks
|
|
onnx_model = onnx.load(f) # load onnx model
|
|
onnx.checker.check_model(onnx_model) # check onnx model
|
|
# print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model
|
|
|
|
# # Metadata
|
|
# d = {'stride': int(max(model.stride))}
|
|
# for k, v in d.items():
|
|
# meta = onnx_model.metadata_props.add()
|
|
# meta.key, meta.value = k, str(v)
|
|
# onnx.save(onnx_model, f)
|
|
|
|
if opt.simplify:
|
|
try:
|
|
import onnxsim
|
|
|
|
print('\nStarting to simplify ONNX...')
|
|
onnx_model, check = onnxsim.simplify(onnx_model)
|
|
assert check, 'assert check failed'
|
|
except Exception as e:
|
|
print(f'Simplifier failure: {e}')
|
|
print('ONNX export success, saved as %s' % f)
|
|
except Exception as e:
|
|
print('ONNX export failure: %s' % e)
|
|
# CoreML export
|
|
try:
|
|
import coremltools as ct
|
|
|
|
print('\nStarting CoreML export with coremltools %s...' % ct.__version__)
|
|
# convert model from torchscript and apply pixel scaling as per detect.py
|
|
model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])])
|
|
f = opt.weights.replace('.pt', '.mlmodel') # filename
|
|
model.save(f)
|
|
print('CoreML export success, saved as %s' % f)
|
|
except Exception as e:
|
|
print('CoreML export failure: %s' % e)
|
|
|
|
# Finish
|
|
print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))
|