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Add dynamic batch-size (ONNX) + Fixes

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This commit is contained in:
Marcos Luciano
2023-05-28 13:46:46 -03:00
parent 134960d389
commit 141c0f2fee
20 changed files with 272 additions and 33 deletions
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29
utils/export_damoyolo.py
View File

@@ -46,9 +46,21 @@ def damoyolo_export(weights, config_file, device):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening DAMO-YOLO model')
device = torch.device('cpu')
cfg, model = damoyolo_export(args.weights, args.config, device)
if len(cfg.dataset['class_names']) > 0:
print('Creating labels.txt file')
f = open('labels.txt', 'w')
for name in cfg.dataset['class_names']:
f.write(name + '\n')
f.close()
model = nn.Sequential(model, DeepStreamOutput())
img_size = args.size * 2 if len(args.size) == 1 else args.size
@@ -56,15 +68,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = cfg.miscs['exp_name'] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('Exporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream DAMO-YOLO conversion')
@@ -73,6 +99,7 @@ def parse_args():
parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
parser.add_argument('--opset', type=int, default=11, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if not os.path.isfile(args.weights):
raise SystemExit('Invalid weights file')

20
utils/export_ppyoloe.py
View File

@@ -8,6 +8,7 @@ from ppdet.utils.check import check_version, check_config
from ppdet.utils.cli import ArgsParser
from ppdet.engine import Trainer
from ppdet.slim import build_slim_model
from ppdet.data.source.category import get_categories
class DeepStreamOutput(nn.Layer):
@@ -39,13 +40,26 @@ def ppyoloe_export(FLAGS):
os.makedirs('.tmp')
static_model, _ = trainer._get_infer_cfg_and_input_spec('.tmp')
os.system('rm -r .tmp')
return cfg, static_model
return trainer.cfg, static_model
def main(FLAGS):
print('\nStarting: %s' % FLAGS.weights)
print('\nOpening PPYOLOE model\n')
paddle.set_device('cpu')
cfg, model = ppyoloe_export(FLAGS)
anno_file = cfg['TestDataset'].get_anno()
if os.path.isfile(anno_file):
_, catid2name = get_categories(cfg['metric'], anno_file, 'detection_arch')
print('\nCreating labels.txt file')
f = open('labels.txt', 'w')
for name in catid2name.values():
f.write(str(name) + '\n')
f.close()
model = nn.Sequential(model, DeepStreamOutput())
img_size = [cfg.eval_height, cfg.eval_width]
@@ -55,14 +69,18 @@ def main(FLAGS):
onnx_input_im['scale_factor'] = paddle.static.InputSpec(shape=[None, 2], dtype='float32', name='scale_factor')
onnx_output_file = cfg.filename + '.onnx'
print('\nExporting the model to ONNX\n')
paddle.onnx.export(model, cfg.filename, input_spec=[onnx_input_im], opset_version=FLAGS.opset)
if FLAGS.simplify:
print('\nSimplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('\nDone: %s\n' % onnx_output_file)
def parse_args():
parser = ArgsParser()

29
utils/export_yoloV5.py
View File

@@ -41,9 +41,21 @@ def yolov5_export(weights, device):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening YOLOv5 model\n')
device = select_device('cpu')
model = yolov5_export(args.weights, device)
if len(model.names.keys()) > 0:
print('\nCreating labels.txt file')
f = open('labels.txt', 'w')
for name in model.names.values():
f.write(name + '\n')
f.close()
model = nn.Sequential(model, DeepStreamOutput())
img_size = args.size * 2 if len(args.size) == 1 else args.size
@@ -54,15 +66,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('\nExporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream YOLOv5 conversion')
@@ -71,6 +97,7 @@ def parse_args():
parser.add_argument('--p6', action='store_true', help='P6 model')
parser.add_argument('--opset', type=int, default=17, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if not os.path.isfile(args.weights):
raise SystemExit('Invalid weights file')

22
utils/export_yoloV6.py
View File

@@ -51,6 +51,11 @@ def yolov6_export(weights, device):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening YOLOv6 model\n')
device = torch.device('cpu')
model = yolov6_export(args.weights, device)
@@ -64,15 +69,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('\nExporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream YOLOv6 conversion')
@@ -81,6 +100,7 @@ def parse_args():
parser.add_argument('--p6', action='store_true', help='P6 model')
parser.add_argument('--opset', type=int, default=13, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if not os.path.isfile(args.weights):
raise SystemExit('Invalid weights file')

29
utils/export_yoloV7.py
View File

@@ -45,9 +45,21 @@ def yolov7_export(weights, device):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening YOLOv7 model\n')
device = select_device('cpu')
model = yolov7_export(args.weights, device)
if len(model.names) > 0:
print('\nCreating labels.txt file')
f = open('labels.txt', 'w')
for name in model.names:
f.write(name + '\n')
f.close()
model = nn.Sequential(model, DeepStreamOutput())
img_size = args.size * 2 if len(args.size) == 1 else args.size
@@ -58,15 +70,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('\nExporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream YOLOv7 conversion')
@@ -75,6 +101,7 @@ def parse_args():
parser.add_argument('--p6', action='store_true', help='P6 model')
parser.add_argument('--opset', type=int, default=12, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if not os.path.isfile(args.weights):
raise SystemExit('Invalid weights file')

29
utils/export_yoloV7_u6.py
View File

@@ -40,9 +40,21 @@ def yolov7_u6_export(weights, device):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening YOLOv7_u6 model\n')
device = select_device('cpu')
model = yolov7_u6_export(args.weights, device)
if len(model.names.keys()) > 0:
print('\nCreating labels.txt file')
f = open('labels.txt', 'w')
for name in model.names.values():
f.write(name + '\n')
f.close()
model = nn.Sequential(model, DeepStreamOutput())
img_size = args.size * 2 if len(args.size) == 1 else args.size
@@ -50,15 +62,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('\nExporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream YOLOv7-u6 conversion')
@@ -66,6 +92,7 @@ def parse_args():
parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
parser.add_argument('--opset', type=int, default=12, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if not os.path.isfile(args.weights):
raise SystemExit('Invalid weights file')

29
utils/export_yoloV8.py
View File

@@ -48,9 +48,21 @@ def yolov8_export(weights, device):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening YOLOv8 model\n')
device = select_device('cpu')
model = yolov8_export(args.weights, device)
if len(model.names.keys()) > 0:
print('\nCreating labels.txt file')
f = open('labels.txt', 'w')
for name in model.names.values():
f.write(name + '\n')
f.close()
model = nn.Sequential(model, DeepStreamOutput())
img_size = args.size * 2 if len(args.size) == 1 else args.size
@@ -58,15 +70,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('\nExporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream YOLOv8 conversion')
@@ -74,6 +100,7 @@ def parse_args():
parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
parser.add_argument('--opset', type=int, default=16, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if not os.path.isfile(args.weights):
raise SystemExit('Invalid weights file')

22
utils/export_yolonas.py
View File

@@ -34,6 +34,11 @@ def yolonas_export(model_name, weights, num_classes, size):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening YOLO-NAS model\n')
device = torch.device('cpu')
model = yolonas_export(args.model, args.weights, args.classes, args.size)
@@ -44,15 +49,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('\nExporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream YOLO-NAS conversion')
@@ -62,6 +81,7 @@ def parse_args():
parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
parser.add_argument('--opset', type=int, default=14, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if args.model == '':
raise SystemExit('Invalid model name')

29
utils/export_yolor.py
View File

@@ -57,9 +57,21 @@ def yolor_export(weights, cfg, size, device):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening YOLOR model\n')
device = torch.device('cpu')
model = yolor_export(args.weights, args.cfg, args.size, device)
if hasattr(model, 'names') and len(model.names) > 0:
print('\nCreating labels.txt file')
f = open('labels.txt', 'w')
for name in model.names:
f.write(name + '\n')
f.close()
model = nn.Sequential(model, DeepStreamOutput())
img_size = args.size * 2 if len(args.size) == 1 else args.size
@@ -70,15 +82,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('\nExporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream YOLOR conversion')
@@ -88,6 +114,7 @@ def parse_args():
parser.add_argument('--p6', action='store_true', help='P6 model')
parser.add_argument('--opset', type=int, default=12, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if not os.path.isfile(args.weights):
raise SystemExit('Invalid weights file')

22
utils/export_yolox.py
View File

@@ -42,6 +42,11 @@ def yolox_export(weights, exp_file):
def main(args):
suppress_warnings()
print('\nStarting: %s' % args.weights)
print('Opening YOLOX model')
device = torch.device('cpu')
model, exp = yolox_export(args.weights, args.exp)
@@ -52,15 +57,29 @@ def main(args):
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
dynamic_axes = {
'input': {
0: 'batch'
},
'output': {
0: 'batch'
}
}
print('Exporting the model to ONNX')
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes=dynamic_axes if args.dynamic else None)
if args.simplify:
print('Simplifying the ONNX model')
import onnxsim
model_onnx = onnx.load(onnx_output_file)
model_onnx, _ = onnxsim.simplify(model_onnx)
onnx.save(model_onnx, onnx_output_file)
print('Done: %s\n' % onnx_output_file)
def parse_args():
parser = argparse.ArgumentParser(description='DeepStream YOLOX conversion')
@@ -68,6 +87,7 @@ def parse_args():
parser.add_argument('-c', '--exp', required=True, help='Input exp (.py) file path (required)')
parser.add_argument('--opset', type=int, default=11, help='ONNX opset version')
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
args = parser.parse_args()
if not os.path.isfile(args.weights):
raise SystemExit('Invalid weights file')