329 lines
14 KiB
Python
329 lines
14 KiB
Python
import argparse
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import yaml
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import math
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import os
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import struct
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import torch
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from utils.torch_utils import select_device
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class YoloLayers():
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def get_route(self, n, layers):
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route = 0
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for i, layer in enumerate(layers):
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if i <= n:
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route += layer[1]
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else:
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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 reorg(self):
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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 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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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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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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g + \
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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 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(
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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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if not args.yaml:
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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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return int(math.ceil((x * gw) / divisor)) * divisor
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def get_depth(x, gd):
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if x == 1:
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return 1
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r = int(round(x * gd))
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if x * gd - int(x * gd) == 0.5 and int(x * gd) % 2 == 0:
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r -= 1
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return max(r, 1)
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pt_file, yaml_file, inference_size = parse_args()
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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 not os.path.isfile(yaml_file):
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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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elif not os.path.isfile(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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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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model.to(device).eval()
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nc = 0
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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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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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num = 0
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for m in vr:
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mask = []
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for _ in range(len(m)):
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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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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' % (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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depth_multiple = f[topic]
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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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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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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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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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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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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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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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blocks += 1
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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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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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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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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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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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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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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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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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blocks += 1
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layers.append([layer, blocks])
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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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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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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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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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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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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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blocks += 1
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layers.append([layer, blocks])
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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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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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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.maxpool(size=v[3][1])
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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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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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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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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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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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blocks = 0
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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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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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blocks = 0
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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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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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layers.append([layer, blocks])
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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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conv_count = 0
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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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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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for vv in vr:
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cv1 += ' '
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cv1 += struct.pack('>f', float(vv)).hex()
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cv1 += '\n'
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conv_count += 1
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elif cv1 != '' and '.m.' in k:
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wts_write += cv1
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cv1 = ''
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if '.cv3.' in k:
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cv3 += '{} {} '.format(k, len(vr))
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for vv in vr:
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cv3 += ' '
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cv3 += struct.pack('>f', float(vv)).hex()
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cv3 += '\n'
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cv3_idx = idx
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conv_count += 1
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elif cv3 != '' and cv3_idx != idx:
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wts_write += cv3
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cv3 = ''
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cv3_idx = 0
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if '.cv3.' not in k and not ('.cv1.' in k and '.m.' not in k and idx != spp_idx):
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wts_write += '{} {} '.format(k, len(vr))
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for vv in vr:
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wts_write += ' '
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wts_write += struct.pack('>f', float(vv)).hex()
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wts_write += '\n'
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conv_count += 1
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f.write('{}\n'.format(conv_count))
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f.write(wts_write)
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