From b634f4e964a7a770140c4d8cd9a1e13fd26b8043 Mon Sep 17 00:00:00 2001 From: souptc Date: Wed, 28 Nov 2018 15:28:02 -0800 Subject: [PATCH] remove the non ascii char --- onnxruntime/contrib_ops/contrib_ops.cc | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/onnxruntime/contrib_ops/contrib_ops.cc b/onnxruntime/contrib_ops/contrib_ops.cc index f6151690e7d75..3b3d948ee9bd0 100644 --- a/onnxruntime/contrib_ops/contrib_ops.cc +++ b/onnxruntime/contrib_ops/contrib_ops.cc @@ -147,7 +147,7 @@ The dequantization formula is y = (x - x_zero_point) * x_scale. .SinceVersion(1) .SetDoc(R"DOC( The convolution operator consumes a quantized input tensor, its scale and zero point, -a quantized filter, its scale and zero point, and output’s scale and zero point, +a quantized filter, its scale and zero point, and output's scale and zero point, and computes the quantized output. Each scale and zero point pair must have same shape. It means they must be either scalars (per tensor) or 1-D tensors (per channel).)DOC") .Input( @@ -163,8 +163,8 @@ It means they must be either scalars (per tensor) or 1-D tensors (per channel).) "to arrive with the dimension denotation of [DATA_BATCH, " "DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].", "T1") - .Input(1, "x_scale", "Scale tensor for input ‘x’. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it’s a 1-D tensor, its number of elements should be equal to the number of channels of input ‘x’.", "T3") - .Input(2, "x_zero_point", "Zero point tensor for input ‘x’. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it’s a 1-D tensor, its number of elements should be equal to the number of channels of input ‘x’.", "T1") + .Input(1, "x_scale", "Scale tensor for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of channels of input 'x'.", "T3") + .Input(2, "x_zero_point", "Zero point tensor for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of channels of input 'x'.", "T1") .Input( 3, "w", @@ -183,10 +183,10 @@ It means they must be either scalars (per tensor) or 1-D tensors (per channel).) "(assuming zero based indices for the shape array). " "Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. ", "T1") - .Input(4, "w_scale", "Scale tensor for input ‘w’. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it’s a 1-D tensor, its number of elements should be equal to the number of channels of input ‘w’.", "T3") - .Input(5, "w_zero_point", "Scale tensor for input ‘w’. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it’s a 1-D tensor, its number of elements should be equal to the number of channels of input ‘w’.", "T1") - .Input(6, "y_scale", "Scale tensor for output ‘y’. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it’s a 1-D tensor, its number of elements should be equal to the number of channels of input ‘y’.", "T3") - .Input(7, "y_zero_point", "Scale tensor for output ‘y’. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it’s a 1-D tensor, its number of elements should be equal to the number of channels of input ‘y’.", "T1") + .Input(4, "w_scale", "Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of channels of input 'w'.", "T3") + .Input(5, "w_zero_point", "Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of channels of input 'w'.", "T1") + .Input(6, "y_scale", "Scale tensor for output 'y'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of channels of input 'y'.", "T3") + .Input(7, "y_zero_point", "Scale tensor for output 'y'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of channels of input 'y'.", "T1") .Input(8, "B", "Optional 1D bias to be added to the convolution, has size of M.", "T2", OpSchema::Optional) .Output( 0,