Update gen_wts
This commit is contained in:
Marcos Luciano
@@ -17,70 +17,62 @@ class YoloLayers():
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break
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return route
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def route(self, layers=""):
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return "\n[route]\n" + \
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"layers=%s\n" % layers
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def route(self, layers=''):
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return '\n[route]\n' + \
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'layers=%s\n' % layers
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def reorg(self):
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return "\n[reorg]\n"
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return '\n[reorg]\n'
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def shortcut(self, route=-1, activation="linear"):
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return "\n[shortcut]\n" + \
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"from=%d\n" % route + \
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"activation=%s\n" % activation
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def shortcut(self, route=-1, activation='linear'):
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return '\n[shortcut]\n' + \
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'from=%d\n' % route + \
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'activation=%s\n' % activation
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def maxpool(self, stride=1, size=1):
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return "\n[maxpool]\n" + \
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"stride=%d\n" % stride + \
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"size=%d\n" % size
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return '\n[maxpool]\n' + \
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'stride=%d\n' % stride + \
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'size=%d\n' % size
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def upsample(self, stride=1):
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return "\n[upsample]\n" + \
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"stride=%d\n" % stride
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return '\n[upsample]\n' + \
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'stride=%d\n' % stride
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def convolutional(self, bn=False, size=1, stride=1, pad=1, filters=1, groups=1, activation="linear"):
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b = "batch_normalize=1\n" if bn is True else ""
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g = "groups=%d\n" % groups if groups > 1 else ""
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return "\n[convolutional]\n" + \
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def convolutional(self, bn=False, size=1, stride=1, pad=1, filters=1, groups=1, activation='linear'):
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b = 'batch_normalize=1\n' if bn is True else ''
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g = 'groups=%d\n' % groups if groups > 1 else ''
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return '\n[convolutional]\n' + \
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b + \
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"filters=%d\n" % filters + \
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"size=%d\n" % size + \
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"stride=%d\n" % stride + \
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"pad=%d\n" % pad + \
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'filters=%d\n' % filters + \
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'size=%d\n' % size + \
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'stride=%d\n' % stride + \
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'pad=%d\n' % pad + \
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g + \
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"activation=%s\n" % activation
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'activation=%s\n' % activation
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def yolo(self, mask="", anchors="", classes=80, num=3):
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return "\n[yolo]\n" + \
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"mask=%s\n" % mask + \
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"anchors=%s\n" % anchors + \
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"classes=%d\n" % classes + \
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"num=%d\n" % num + \
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"scale_x_y=2.0\n" + \
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"beta_nms=0.6\n" + \
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"new_coords=1\n"
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def yolo(self, mask='', anchors='', classes=80, num=3):
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return '\n[yolo]\n' + \
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'mask=%s\n' % mask + \
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'anchors=%s\n' % anchors + \
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'classes=%d\n' % classes + \
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'num=%d\n' % num + \
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'scale_x_y=2.0\n' + \
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'beta_nms=0.6\n' + \
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'new_coords=1\n'
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def parse_args():
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parser = argparse.ArgumentParser(description="PyTorch YOLOv5 conversion")
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parser.add_argument("-w", "--weights", required=True, help="Input weights (.pt) file path (required)")
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parser.add_argument("-c", "--yaml", help="Input cfg (.yaml) file path")
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parser.add_argument("-mw", "--width", type=int, help="Model width (default = 640 / 1280 [P6])")
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parser.add_argument("-mh", "--height", type=int, help="Model height (default = 640 / 1280 [P6])")
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parser.add_argument("-mc", "--channels", type=int, help="Model channels (default = 3)")
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parser.add_argument("--p6", action="store_true", help="P6 model")
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parser = argparse.ArgumentParser(description='PyTorch YOLOv5 conversion')
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parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
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parser.add_argument('-c', '--yaml', help='Input cfg (.yaml) file path')
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parser.add_argument(
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'-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
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args = parser.parse_args()
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if not os.path.isfile(args.weights):
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raise SystemExit("Invalid weights file")
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raise SystemExit('Invalid weights file')
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if not args.yaml:
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args.yaml = ""
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if not args.width:
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args.width = 1280 if args.p6 else 640
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if not args.height:
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args.height = 1280 if args.p6 else 640
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if not args.channels:
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args.channels = 3
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return args.weights, args.yaml, args.width, args.height, args.channels, args.p6
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args.yaml = ''
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return args.weights, args.yaml, args.size
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def get_width(x, gw, divisor=8):
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@@ -96,40 +88,40 @@ def get_depth(x, gd):
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return max(r, 1)
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pt_file, yaml_file, model_width, model_height, model_channels, p6 = parse_args()
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pt_file, yaml_file, inference_size = parse_args()
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model_name = pt_file.split(".pt")[0]
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wts_file = model_name + ".wts" if "yolov5" in model_name else "yolov5_" + model_name + ".wts"
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cfg_file = model_name + ".cfg" if "yolov5" in model_name else "yolov5_" + model_name + ".cfg"
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model_name = os.path.basename(pt_file).split('.pt')[0]
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wts_file = model_name + '.wts' if 'yolov5' in model_name else 'yolov5_' + model_name + '.wts'
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cfg_file = model_name + '.cfg' if 'yolov5' in model_name else 'yolov5_' + model_name + '.cfg'
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if yaml_file == "":
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yaml_file = "models/" + model_name + ".yaml"
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if yaml_file == '':
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yaml_file = 'models/' + model_name + '.yaml'
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if not os.path.isfile(yaml_file):
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yaml_file = "models/hub/" + model_name + ".yaml"
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yaml_file = 'models/hub/' + model_name + '.yaml'
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if not os.path.isfile(yaml_file):
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raise SystemExit("YAML file not found")
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raise SystemExit('YAML file not found')
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elif not os.path.isfile(yaml_file):
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raise SystemExit("Invalid YAML file")
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raise SystemExit('Invalid YAML file')
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device = select_device("cpu")
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model = torch.load(pt_file, map_location=device)["model"].float()
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device = select_device('cpu')
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model = torch.load(pt_file, map_location=device)['model'].float()
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anchor_grid = model.model[-1].anchors * model.model[-1].stride[..., None, None]
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delattr(model.model[-1], "anchor_grid")
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model.model[-1].register_buffer("anchor_grid", anchor_grid)
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delattr(model.model[-1], 'anchor_grid')
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model.model[-1].register_buffer('anchor_grid', anchor_grid)
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model.to(device).eval()
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nc = 0
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anchors = ""
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anchors = ''
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masks = []
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yolo_idx = 0
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spp_idx = 0
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for k, v in model.state_dict().items():
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if "anchor_grid" in k:
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yolo_idx = int(k.split(".")[1])
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if 'anchor_grid' in k:
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yolo_idx = int(k.split('.')[1])
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vr = v.cpu().numpy().tolist()
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a = v.reshape(-1).cpu().numpy().astype(float).tolist()
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anchors = str(a)[1:-1]
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@@ -140,116 +132,116 @@ for k, v in model.state_dict().items():
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mask.append(num)
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num += 1
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masks.append(mask)
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elif ".%d.m.0.weight" % yolo_idx in k:
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elif '.%d.m.0.weight' % yolo_idx in k:
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vr = v.cpu().numpy().tolist()
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nc = int((len(vr) / len(masks[0])) - 5)
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with open(cfg_file, "w") as c:
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with open(yaml_file, "r", encoding="utf-8") as f:
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c.write("[net]\n")
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c.write("width=%d\n" % model_width)
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c.write("height=%d\n" % model_height)
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c.write("channels=%d\n" % model_channels)
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c.write("letter_box=1\n")
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with open(cfg_file, 'w') as c:
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with open(yaml_file, 'r', encoding='utf-8') as f:
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c.write('[net]\n')
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c.write('width=%d\n' % (inference_size[0] if len(inference_size) == 1 else inference_size[1]))
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c.write('height=%d\n' % inference_size[0])
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c.write('channels=3\n')
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c.write('letter_box=1\n')
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depth_multiple = 0
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width_multiple = 0
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layers = []
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yoloLayers = YoloLayers()
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f = yaml.load(f, Loader=yaml.FullLoader)
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for topic in f:
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if topic == "depth_multiple":
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if topic == 'depth_multiple':
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depth_multiple = f[topic]
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elif topic == "width_multiple":
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elif topic == 'width_multiple':
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width_multiple = f[topic]
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elif topic == "backbone" or topic == "head":
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elif topic == 'backbone' or topic == 'head':
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for v in f[topic]:
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if v[2] == "Focus":
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layer = "\n# Focus\n"
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if v[2] == 'Focus':
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layer = '\n# Focus\n'
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blocks = 0
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layer += yoloLayers.reorg()
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blocks += 1
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple), size=v[3][1],
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activation="silu")
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activation='silu')
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blocks += 1
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layers.append([layer, blocks])
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if v[2] == "Conv":
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layer = "\n# Conv\n"
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if v[2] == 'Conv':
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layer = '\n# Conv\n'
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blocks = 0
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple), size=v[3][1],
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stride=v[3][2], activation="silu")
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stride=v[3][2], activation='silu')
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "C3":
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layer = "\n# C3\n"
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elif v[2] == 'C3':
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layer = '\n# C3\n'
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blocks = 0
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# SPLIT
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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activation='silu')
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blocks += 1
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layer += yoloLayers.route(layers="-2")
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layer += yoloLayers.route(layers='-2')
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blocks += 1
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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activation='silu')
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blocks += 1
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# Residual Block
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if len(v[3]) == 1 or v[3][1] is True:
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for _ in range(get_depth(v[1], depth_multiple)):
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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activation='silu')
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blocks += 1
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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size=3, activation="silu")
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size=3, activation='silu')
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blocks += 1
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layer += yoloLayers.shortcut(route=-3)
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blocks += 1
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# Merge
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layer += yoloLayers.route(layers="-1, -%d" % (3 * get_depth(v[1], depth_multiple) + 3))
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layer += yoloLayers.route(layers='-1, -%d' % (3 * get_depth(v[1], depth_multiple) + 3))
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blocks += 1
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else:
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for _ in range(get_depth(v[1], depth_multiple)):
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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activation='silu')
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blocks += 1
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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size=3, activation="silu")
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size=3, activation='silu')
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blocks += 1
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# Merge
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layer += yoloLayers.route(layers="-1, -%d" % (2 * get_depth(v[1], depth_multiple) + 3))
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layer += yoloLayers.route(layers='-1, -%d' % (2 * get_depth(v[1], depth_multiple) + 3))
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blocks += 1
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# Transition
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple),
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activation="silu")
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activation='silu')
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "SPP":
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elif v[2] == 'SPP':
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spp_idx = len(layers)
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layer = "\n# SPP\n"
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layer = '\n# SPP\n'
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blocks = 0
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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activation='silu')
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blocks += 1
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layer += yoloLayers.maxpool(size=v[3][1][0])
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blocks += 1
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layer += yoloLayers.route(layers="-2")
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layer += yoloLayers.route(layers='-2')
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blocks += 1
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layer += yoloLayers.maxpool(size=v[3][1][1])
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blocks += 1
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layer += yoloLayers.route(layers="-4")
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layer += yoloLayers.route(layers='-4')
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blocks += 1
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layer += yoloLayers.maxpool(size=v[3][1][2])
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blocks += 1
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layer += yoloLayers.route(layers="-6, -5, -3, -1")
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layer += yoloLayers.route(layers='-6, -5, -3, -1')
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blocks += 1
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple),
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activation="silu")
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activation='silu')
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "SPPF":
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elif v[2] == 'SPPF':
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spp_idx = len(layers)
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layer = "\n# SPPF\n"
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layer = '\n# SPPF\n'
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blocks = 0
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple) / 2,
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activation="silu")
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activation='silu')
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blocks += 1
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layer += yoloLayers.maxpool(size=v[3][1])
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blocks += 1
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@@ -257,35 +249,35 @@ with open(cfg_file, "w") as c:
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blocks += 1
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layer += yoloLayers.maxpool(size=v[3][1])
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blocks += 1
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layer += yoloLayers.route(layers="-4, -3, -2, -1")
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layer += yoloLayers.route(layers='-4, -3, -2, -1')
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blocks += 1
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layer += yoloLayers.convolutional(bn=True, filters=get_width(v[3][0], width_multiple),
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activation="silu")
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activation='silu')
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "nn.Upsample":
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layer = "\n# nn.Upsample\n"
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elif v[2] == 'nn.Upsample':
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layer = '\n# nn.Upsample\n'
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blocks = 0
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layer += yoloLayers.upsample(stride=v[3][1])
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "Concat":
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elif v[2] == 'Concat':
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route = v[0][1]
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route = yoloLayers.get_route(route, layers) if route > 0 else \
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yoloLayers.get_route(len(layers) + route, layers)
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layer = "\n# Concat\n"
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layer = '\n# Concat\n'
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blocks = 0
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layer += yoloLayers.route(layers="-1, %d" % (route - 1))
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layer += yoloLayers.route(layers='-1, %d' % (route - 1))
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blocks += 1
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layers.append([layer, blocks])
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elif v[2] == "Detect":
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elif v[2] == 'Detect':
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for i, n in enumerate(v[0]):
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route = yoloLayers.get_route(n, layers)
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layer = "\n# Detect\n"
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layer = '\n# Detect\n'
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blocks = 0
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layer += yoloLayers.route(layers="%d" % (route - 1))
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layer += yoloLayers.route(layers='%d' % (route - 1))
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blocks += 1
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layer += yoloLayers.convolutional(filters=((nc + 5) * len(masks[i])), activation="logistic")
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layer += yoloLayers.convolutional(filters=((nc + 5) * len(masks[i])), activation='logistic')
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blocks += 1
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layer += yoloLayers.yolo(mask=str(masks[i])[1:-1], anchors=anchors, classes=nc, num=num)
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blocks += 1
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@@ -293,44 +285,44 @@ with open(cfg_file, "w") as c:
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for layer in layers:
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c.write(layer[0])
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with open(wts_file, "w") as f:
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wts_write = ""
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with open(wts_file, 'w') as f:
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wts_write = ''
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conv_count = 0
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cv1 = ""
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cv3 = ""
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cv1 = ''
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cv3 = ''
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cv3_idx = 0
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for k, v in model.state_dict().items():
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if "num_batches_tracked" not in k and "anchors" not in k and "anchor_grid" not in k:
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if 'num_batches_tracked' not in k and 'anchors' not in k and 'anchor_grid' not in k:
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vr = v.reshape(-1).cpu().numpy()
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idx = int(k.split(".")[1])
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if ".cv1." in k and ".m." not in k and idx != spp_idx:
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cv1 += "{} {} ".format(k, len(vr))
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idx = int(k.split('.')[1])
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if '.cv1.' in k and '.m.' not in k and idx != spp_idx:
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cv1 += '{} {} '.format(k, len(vr))
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for vv in vr:
|
||||
cv1 += " "
|
||||
cv1 += struct.pack(">f", float(vv)).hex()
|
||||
cv1 += "\n"
|
||||
cv1 += ' '
|
||||
cv1 += struct.pack('>f', float(vv)).hex()
|
||||
cv1 += '\n'
|
||||
conv_count += 1
|
||||
elif cv1 != "" and ".m." in k:
|
||||
elif cv1 != '' and '.m.' in k:
|
||||
wts_write += cv1
|
||||
cv1 = ""
|
||||
if ".cv3." in k:
|
||||
cv3 += "{} {} ".format(k, len(vr))
|
||||
cv1 = ''
|
||||
if '.cv3.' in k:
|
||||
cv3 += '{} {} '.format(k, len(vr))
|
||||
for vv in vr:
|
||||
cv3 += " "
|
||||
cv3 += struct.pack(">f", float(vv)).hex()
|
||||
cv3 += "\n"
|
||||
cv3 += ' '
|
||||
cv3 += struct.pack('>f', float(vv)).hex()
|
||||
cv3 += '\n'
|
||||
cv3_idx = idx
|
||||
conv_count += 1
|
||||
elif cv3 != "" and cv3_idx != idx:
|
||||
elif cv3 != '' and cv3_idx != idx:
|
||||
wts_write += cv3
|
||||
cv3 = ""
|
||||
cv3 = ''
|
||||
cv3_idx = 0
|
||||
if ".cv3." not in k and not (".cv1." in k and ".m." not in k and idx != spp_idx):
|
||||
wts_write += "{} {} ".format(k, len(vr))
|
||||
if '.cv3.' not in k and not ('.cv1.' in k and '.m.' not in k and idx != spp_idx):
|
||||
wts_write += '{} {} '.format(k, len(vr))
|
||||
for vv in vr:
|
||||
wts_write += " "
|
||||
wts_write += struct.pack(">f", float(vv)).hex()
|
||||
wts_write += "\n"
|
||||
wts_write += ' '
|
||||
wts_write += struct.pack('>f', float(vv)).hex()
|
||||
wts_write += '\n'
|
||||
conv_count += 1
|
||||
f.write("{}\n".format(conv_count))
|
||||
f.write('{}\n'.format(conv_count))
|
||||
f.write(wts_write)
|
||||
|
||||
@@ -7,39 +7,50 @@ from models.models import Darknet
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="PyTorch YOLOR conversion (main branch)")
|
||||
parser.add_argument("-w", "--weights", required=True, help="Input weights (.pt) file path (required)")
|
||||
parser.add_argument("-c", "--cfg", required=True, help="Input cfg (.cfg) file path (required)")
|
||||
parser = argparse.ArgumentParser(description='PyTorch YOLOR conversion (main branch)')
|
||||
parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
|
||||
parser.add_argument('-c', '--cfg', help='Input cfg (.cfg) file path')
|
||||
args = parser.parse_args()
|
||||
if not os.path.isfile(args.weights):
|
||||
raise SystemExit("Invalid weights file")
|
||||
raise SystemExit('Invalid weights file')
|
||||
if not os.path.isfile(args.cfg):
|
||||
raise SystemExit("Invalid cfg file")
|
||||
raise SystemExit('Invalid cfg file')
|
||||
return args.weights, args.cfg
|
||||
|
||||
|
||||
pt_file, cfg_file = parse_args()
|
||||
|
||||
wts_file = "%s.wts" % cfg_file.rsplit("/")[1].split(".cfg")[0]
|
||||
|
||||
device = select_device("cpu")
|
||||
model_name = os.path.basename(pt_file).split('.pt')[0]
|
||||
wts_file = model_name + '.wts' if 'yolor' in model_name else 'yolor_' + model_name + '.wts'
|
||||
new_cfg_file = model_name + '.cfg' if 'yolor' in model_name else 'yolor_' + model_name + '.cfg'
|
||||
|
||||
if cfg_file == '':
|
||||
cfg_file = 'cfg/' + model_name + '.cfg'
|
||||
if not os.path.isfile(cfg_file):
|
||||
raise SystemExit('CFG file not found')
|
||||
elif not os.path.isfile(cfg_file):
|
||||
raise SystemExit('Invalid CFG file')
|
||||
|
||||
device = select_device('cpu')
|
||||
model = Darknet(cfg_file).to(device)
|
||||
model.load_state_dict(torch.load(pt_file, map_location=device)["model"])
|
||||
model.load_state_dict(torch.load(pt_file, map_location=device)['model'])
|
||||
model.to(device).eval()
|
||||
|
||||
with open(wts_file, "w") as f:
|
||||
wts_write = ""
|
||||
with open(wts_file, 'w') as f:
|
||||
wts_write = ''
|
||||
conv_count = 0
|
||||
for k, v in model.state_dict().items():
|
||||
if "num_batches_tracked" not in k:
|
||||
if 'num_batches_tracked' not in k:
|
||||
vr = v.reshape(-1).cpu().numpy()
|
||||
wts_write += "{} {} ".format(k, len(vr))
|
||||
wts_write += '{} {} '.format(k, len(vr))
|
||||
for vv in vr:
|
||||
wts_write += " "
|
||||
wts_write += struct.pack(">f", float(vv)).hex()
|
||||
wts_write += "\n"
|
||||
wts_write += ' '
|
||||
wts_write += struct.pack('>f', float(vv)).hex()
|
||||
wts_write += '\n'
|
||||
conv_count += 1
|
||||
f.write("{}\n".format(conv_count))
|
||||
f.write('{}\n'.format(conv_count))
|
||||
f.write(wts_write)
|
||||
|
||||
os.system("cp %s ./" % cfg_file)
|
||||
if not os.path.isfile(new_cfg_file):
|
||||
os.system('cp %s %s' % (cfg_file, new_cfg_file))
|
||||
|
||||
Reference in New Issue
Block a user