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https://github.com/zebrajr/pytorch.git
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9dad13e1f0
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/23449 Test Plan: Imported from OSS Differential Revision: D16524768 Pulled By: ezyang fbshipit-source-id: 9eb01b021011d1172317b5adb774c10c42ac2b86
37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
r"""Importing this file includes common utility methods for checking quantized
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tensors and modules.
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"""
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import numpy as np
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"""Computes the output shape given convolution parameters."""
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def _conv_output_shape(input_size, kernel_size, padding, stride, dilation,
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output_padding=0):
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return np.floor((input_size + 2 * padding - kernel_size - (kernel_size - 1)
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* (dilation - 1)) / stride) + 2 * output_padding + 1
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# Quantization references
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def _quantize(x, scale, zero_point, qmin=None, qmax=None, dtype=np.uint8):
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"""Quantizes a numpy array."""
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if qmin is None:
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qmin = np.iinfo(dtype).min
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if qmax is None:
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qmax = np.iinfo(dtype).max
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qx = np.round(x / scale + zero_point).astype(np.int64)
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qx = np.clip(qx, qmin, qmax)
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qx = qx.astype(dtype)
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return qx
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def _dequantize(qx, scale, zero_point):
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"""Dequantizes a numpy array."""
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x = (qx.astype(np.float) - zero_point) * scale
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return x
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def _requantize(x, multiplier, zero_point, qmin=0, qmax=255, qtype=np.uint8):
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"""Requantizes a numpy array, i.e., intermediate int32 or int16 values are
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converted back to given type"""
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qx = (x * multiplier).round() + zero_point
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qx = np.clip(qx, qmin, qmax).astype(qtype)
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return qx
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