Add support for FP16 (half) to export.py (#3010)
Browse files* Added support for fp16 (half) to export.py
* minimize code additions
Co-authored-by: Glenn Jocher <[email protected]>
- models/export.py +4 -0
models/export.py
CHANGED
@@ -28,6 +28,7 @@ if __name__ == '__main__':
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
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parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only
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parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only
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opt = parser.parse_args()
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@@ -44,11 +45,14 @@ if __name__ == '__main__':
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# Checks
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gs = int(max(model.stride)) # grid size (max stride)
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opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
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# Input
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img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
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# Update model
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for k, m in model.named_modules():
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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if isinstance(m, models.common.Conv): # assign export-friendly activations
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
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parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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+
parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
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parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only
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parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only
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opt = parser.parse_args()
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# Checks
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gs = int(max(model.stride)) # grid size (max stride)
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opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
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assert not (opt.device.lower() == "cpu" and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
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# Input
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img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
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# Update model
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if opt.half:
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img, model = img.half(), model.half() # to FP16
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for k, m in model.named_modules():
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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if isinstance(m, models.common.Conv): # assign export-friendly activations
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