Ilya Sherstyuk
28 lines
824 B
Python
28 lines
824 B
Python
import numpy as np
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def ref(inputs, attributes, version = "1"):
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assert version == "1"
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num_groups = attributes["num_groups"][0]
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epsilon = attributes["eps"][0]
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input = inputs["input"]
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bias = inputs["bias"]
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scale = inputs["scale"]
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output = input.copy()
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assert len(input.shape) == 4
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B, C, H, W = input.shape
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# Groups are a subdivision of the channel dimension.
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assert C % num_groups == 0
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# Normalize every group.
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output = output.reshape((B * num_groups, -1))
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output -= np.mean(output, axis=-1, keepdims=True)
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output /= np.sqrt(epsilon + np.var(output, axis=-1, keepdims=True))
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# Apply per-channel scale and bias.
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output = output.reshape(input.shape)
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output = bias.reshape(1, C, 1, 1) + scale.reshape(1, C, 1, 1) * output
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return {"output": output}
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