Cleanup TorchVision related functions (#18790)
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4 changed files with 8 additions and 13 deletions
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@ -1120,8 +1120,6 @@ class TorchVision(nn.Module):
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m (nn.Module): The loaded torchvision model, possibly truncated and unwrapped.
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Args:
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c1 (int): Input channels.
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c2 (): Output channels.
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model (str): Name of the torchvision model to load.
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weights (str, optional): Pre-trained weights to load. Default is "DEFAULT".
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unwrap (bool, optional): If True, unwraps the model to a sequential containing all but the last `truncate` layers. Default is True.
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@ -1129,7 +1127,7 @@ class TorchVision(nn.Module):
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split (bool, optional): Returns output from intermediate child modules as list. Default is False.
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"""
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def __init__(self, c1, c2, model, weights="DEFAULT", unwrap=True, truncate=2, split=False):
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def __init__(self, model, weights="DEFAULT", unwrap=True, truncate=2, split=False):
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"""Load the model and weights from torchvision."""
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import torchvision # scope for faster 'import ultralytics'
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@ -336,7 +336,7 @@ class Concat(nn.Module):
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class Index(nn.Module):
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"""Returns a particular index of the input."""
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def __init__(self, c1, c2, index=0):
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def __init__(self, index=0):
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"""Returns a particular index of the input."""
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super().__init__()
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self.index = index
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@ -1060,12 +1060,16 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
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m.legacy = legacy
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elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1
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args.insert(1, [ch[x] for x in f])
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elif m in frozenset({CBLinear, TorchVision, Index}):
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elif m is CBLinear:
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c2 = args[0]
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c1 = ch[f]
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args = [c1, c2, *args[1:]]
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elif m is CBFuse:
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c2 = ch[f[-1]]
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elif m in frozenset({TorchVision, Index}):
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c2 = args[0]
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c1 = ch[f]
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args = [*args[1:]]
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else:
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c2 = ch[f]
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