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| # Copyright (c) ONNX Project Contributors | |
| # SPDX-License-Identifier: Apache-2.0 | |
| import numpy as np | |
| from onnx.reference.op_run import OpRun | |
| from onnx.reference.ops.op_conv import _conv_implementation | |
| class ConvInteger(OpRun): | |
| def _run( # type: ignore | |
| self, | |
| X, | |
| W, | |
| x_zero_point=None, | |
| w_zero_point=None, | |
| auto_pad=None, | |
| dilations=None, | |
| group=None, | |
| kernel_shape=None, | |
| pads=None, | |
| strides=None, | |
| ): | |
| if len(X.shape) < 3: | |
| raise ValueError( | |
| f"X must have at least 3 dimensions but its shape is {X.shape}." | |
| ) | |
| auto_pad = auto_pad or self.auto_pad # type: ignore | |
| dilations = dilations or self.dilations # type: ignore | |
| group = group or self.group # type: ignore | |
| kernel_shape = kernel_shape or self.kernel_shape # type: ignore | |
| pads = pads or self.pads # type: ignore | |
| strides = strides or self.strides # type: ignore | |
| X = X.astype(np.int32) | |
| if x_zero_point: | |
| X -= x_zero_point | |
| W = W.astype(np.int32) | |
| if w_zero_point: | |
| W -= w_zero_point | |
| return ( | |
| _conv_implementation( | |
| X, W, None, auto_pad, dilations, group, kernel_shape, pads, strides | |
| ).astype(np.int32), | |
| ) | |