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Merge pull request #23 from ChanLumerico/autoprop-enum
AutoProp Enum
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""" | ||
`AutoProp` | ||
---------- | ||
Auto propagation system for complex neural networks of Luma Python library. | ||
""" | ||
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from .graph import LayerNode, LayerGraph |
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from enum import Enum | ||
import numpy as np | ||
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from luma.interface.typing import TensorLike | ||
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class MergeMode(Enum): | ||
CHCAT = "chcat" | ||
SUM = "sum" | ||
HADAMARD = "hadamard" | ||
AVERAGE = "average" | ||
MAX = "max" | ||
MIN = "min" | ||
DOT = "dot" | ||
SUBTRACT = "subtract" | ||
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def forward(self, f_queue: list[TensorLike]) -> TensorLike: | ||
match self: | ||
case MergeMode.CHCAT: | ||
return np.concatenate(f_queue, axis=1) | ||
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case MergeMode.SUM: | ||
return np.sum(f_queue, axis=0) | ||
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case MergeMode.HADAMARD: | ||
X = np.ones_like(f_queue[0]) | ||
for tensor in f_queue: | ||
X *= tensor | ||
return X | ||
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case MergeMode.AVERAGE: | ||
return np.mean(f_queue, axis=0) | ||
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case MergeMode.MAX: | ||
return np.maximum.reduce(f_queue) | ||
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case MergeMode.MIN: | ||
return np.minimum.reduce(f_queue) | ||
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case MergeMode.DOT: | ||
return np.dot(f_queue[0], f_queue[1]) | ||
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case MergeMode.SUBTRACT: | ||
result = f_queue[0] | ||
for tensor in f_queue[1:]: | ||
result -= tensor | ||
return result | ||
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def backward( | ||
self, f_queue: list[TensorLike], d_out: TensorLike, i: int | ||
) -> TensorLike: | ||
match self: | ||
case MergeMode.CHCAT: | ||
cum_ch = [0] | ||
for tensor in f_queue: | ||
cum_ch.append(cum_ch[-1] + tensor.shape[1]) | ||
return d_out[:, cum_ch[i] : cum_ch[i + 1], ...] | ||
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case MergeMode.SUM: | ||
return d_out | ||
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case MergeMode.HADAMARD: | ||
prod_except_current = np.ones_like(f_queue[0]) | ||
for j in range(len(f_queue)): | ||
if j != i: | ||
prod_except_current *= f_queue[j] | ||
return d_out * prod_except_current | ||
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case MergeMode.AVERAGE: | ||
return d_out / len(f_queue) | ||
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case MergeMode.MAX | MergeMode.MIN: | ||
return ( | ||
d_out * (f_queue[i] == getattr(np, self.value).reduce(f_queue)) | ||
).astype(d_out.dtype) | ||
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case MergeMode.DOT: | ||
if i == 0: | ||
return np.dot(d_out, f_queue[1].T) | ||
elif i == 1: | ||
return np.dot(f_queue[0].T, d_out) | ||
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case MergeMode.SUBTRACT: | ||
return d_out if i == 0 else -d_out |
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