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https://github.com/huggingface/pytorch-image-models.git
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Remove eval to avoid safety concerns
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ea33d6a74d
commit
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@ -52,6 +52,16 @@ class BlockESE(nn.Module):
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return self.layers(x)
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return self.layers(x)
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def _get_block_type(block: str):
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block = block.lower().strip()
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if block == "block":
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return Block
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elif block == "blockese":
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return BlockESE
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else:
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assert False, f"Unknown block type ({block})."
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class DenseBlock(nn.Module):
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class DenseBlock(nn.Module):
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def __init__(
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def __init__(
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self,
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self,
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@ -80,7 +90,7 @@ class DenseBlock(nn.Module):
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self.drop_path = DropPath(drop_path_rate)
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self.drop_path = DropPath(drop_path_rate)
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self.layers = eval(block_type)(
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self.layers = _get_block_type(block_type)(
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in_chs=num_input_features,
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in_chs=num_input_features,
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inter_chs=inter_chs,
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inter_chs=inter_chs,
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out_chs=growth_rate,
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out_chs=growth_rate,
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@ -130,7 +140,7 @@ class RDNet(nn.Module):
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global_pool: str = 'avg', # timm option [--gp]
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global_pool: str = 'avg', # timm option [--gp]
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growth_rates: Union[List[int], Tuple[int]] = (64, 104, 128, 128, 128, 128, 224),
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growth_rates: Union[List[int], Tuple[int]] = (64, 104, 128, 128, 128, 128, 224),
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num_blocks_list: Union[List[int], Tuple[int]] = (3, 3, 3, 3, 3, 3, 3),
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num_blocks_list: Union[List[int], Tuple[int]] = (3, 3, 3, 3, 3, 3, 3),
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block_type: Union[List[int], Tuple[int]] = ("Block", "Block", "BlockESE", "BlockESE", "BlockESE", "BlockESE", "BlockESE"),
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block_type: Union[List[int], Tuple[int]] = ("Block",) * 2 + ("BlockESE",) * 5,
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is_downsample_block: Union[List[bool], Tuple[bool]] = (None, True, True, False, False, False, True),
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is_downsample_block: Union[List[bool], Tuple[bool]] = (None, True, True, False, False, False, True),
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bottleneck_width_ratio: float = 4.0,
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bottleneck_width_ratio: float = 4.0,
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transition_compression_ratio: float = 0.5,
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transition_compression_ratio: float = 0.5,
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