updates
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10
models.py
10
models.py
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@ -4,6 +4,7 @@ from utils.parse_config import *
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from utils.utils import *
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from utils.utils import *
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ONNX_EXPORT = False
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ONNX_EXPORT = False
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arc = 'normal' # (normal, uCE, uBCE, uBCEs) detection architectures
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def create_modules(module_defs, img_size):
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def create_modules(module_defs, img_size):
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@ -77,8 +78,12 @@ def create_modules(module_defs, img_size):
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# Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
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# Initialize preceding Conv2d() bias (https://arxiv.org/pdf/1708.02002.pdf section 3.3)
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bias = module_list[-1][0].bias.view(len(mask), -1) # 255 to 3x85
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bias = module_list[-1][0].bias.view(len(mask), -1) # 255 to 3x85
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bias[:, 4] -= 5.0 # obj
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if arc == 'normal':
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bias[:, 5:] -= 4.0 # cls
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bias[:, 4] -= 5.0 # obj
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bias[:, 5:] -= 4.0 # cls
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elif arc == 'uCE':
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bias[:, 4] += 3.0 # obj
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bias[:, 5:] -= 4.0 # cls
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module_list[-1][0].bias = torch.nn.Parameter(bias.view(-1))
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module_list[-1][0].bias = torch.nn.Parameter(bias.view(-1))
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# for l in model.yolo_layers: # print pretrained biases
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# for l in model.yolo_layers: # print pretrained biases
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@ -168,7 +173,6 @@ class YOLOLayer(nn.Module):
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# io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method
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# io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method
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io[..., :4] *= self.stride
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io[..., :4] *= self.stride
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arc = 'normal' # (normal, uCE, uBCE, uBCEs) detection architectures
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if arc == 'normal':
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if arc == 'normal':
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torch.sigmoid_(io[..., 4:])
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torch.sigmoid_(io[..., 4:])
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elif arc == 'uCE': # unified CE (1 background + 80 classes)
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elif arc == 'uCE': # unified CE (1 background + 80 classes)
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