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GPU export options #2297

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merged 7 commits into from
Mar 6, 2021
Merged

GPU export options #2297

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49bd2fe
option for skip last layer and cuda export support
Feb 25, 2021
93b41d7
added parameter device
Mar 2, 2021
e3f5bdb
fix import
Mar 2, 2021
6373c3f
cleanup 1
glenn-jocher Mar 6, 2021
b482563
cleanup 2
glenn-jocher Mar 6, 2021
435cb83
opt-in grid
glenn-jocher Mar 6, 2021
2b2c800
default --device cpu
glenn-jocher Mar 6, 2021
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12 changes: 8 additions & 4 deletions models/export.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,29 +17,33 @@
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

if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes')
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')
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
model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 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) # image size(1,3,320,192) iDetection
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():
Expand All @@ -51,7 +55,7 @@
m.act = SiLU()
# elif isinstance(m, models.yolo.Detect):
# m.forward = m.forward_export # assign forward (optional)
model.model[-1].export = True # set Detect() layer export=True
model.model[-1].export = not opt.grid # set Detect() layer grid export
y = model(img) # dry run

# TorchScript export
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