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414 lines
14 KiB
Python
414 lines
14 KiB
Python
# Copyright (c) ONNX Project Contributors
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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from onnx.reference.ops._op_common_pool import CommonPool
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class MaxPool(CommonPool):
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def _run(
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self,
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x,
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auto_pad=None,
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ceil_mode=None,
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dilations=None,
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kernel_shape=None,
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pads=None,
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storage_order=None,
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strides=None,
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):
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if (
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dilations is not None
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and (min(dilations) != max(dilations) or min(dilations) != 1)
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) or (
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strides is not None and (min(strides) != max(strides) or min(strides) != 1)
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):
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return self._max_pool(
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x,
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auto_pad=auto_pad,
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ceil_mode=ceil_mode,
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dilations=dilations,
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kernel_shape=kernel_shape,
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pads=pads,
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storage_order=storage_order,
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strides=strides,
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)
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return CommonPool._run(
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self,
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"MAX",
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0,
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x,
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auto_pad=auto_pad,
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ceil_mode=ceil_mode,
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dilations=dilations,
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kernel_shape=kernel_shape,
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pads=pads,
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storage_order=storage_order,
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strides=strides,
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)
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def _max_pool(
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self,
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x,
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auto_pad,
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ceil_mode,
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dilations,
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kernel_shape,
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pads,
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storage_order,
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strides,
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):
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if pads is None:
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pads = [0 for i in range(len(kernel_shape) * 2)]
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if strides is None:
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strides = [1 for i in range(len(kernel_shape))]
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if dilations is None:
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dilations = [1 for i in range(len(kernel_shape))]
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n_dims = len(kernel_shape)
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new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
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input_spatial_shape = x.shape[2:]
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output_spatial_shape = [0 for s in input_spatial_shape]
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if ceil_mode:
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for i in range(len(input_spatial_shape)):
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output_spatial_shape[i] = int(
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np.ceil(
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(
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input_spatial_shape[i]
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+ new_pads[i].sum()
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- ((kernel_shape[i] - 1) * dilations[i] + 1)
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)
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/ strides[i]
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+ 1
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)
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)
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need_to_reduce_out_size_in_ceil_mode = (
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output_spatial_shape[i] - 1
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) * strides[i] >= input_spatial_shape[i] + new_pads[i][0]
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if need_to_reduce_out_size_in_ceil_mode:
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output_spatial_shape[i] -= 1
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else:
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for i in range(len(input_spatial_shape)):
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output_spatial_shape[i] = int(
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np.floor(
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(
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input_spatial_shape[i]
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+ new_pads[i].sum()
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- ((kernel_shape[i] - 1) * dilations[i] + 1)
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)
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/ strides[i]
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+ 1
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)
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)
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if auto_pad and auto_pad != "NOTSET":
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# Deprecated attribute
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if auto_pad in ("SAME_UPPER", "SAME_LOWER"):
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for i in range(len(input_spatial_shape)):
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if auto_pad == "SAME_UPPER":
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output_spatial_shape[i] = int(
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np.ceil(input_spatial_shape[i] / strides[i])
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)
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else:
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output_spatial_shape[i] = int(
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np.floor(input_spatial_shape[i] / strides[i])
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)
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pad_i = (
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(output_spatial_shape[i] - 1) * strides[i]
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+ ((kernel_shape[i] - 1) * dilations[i] + 1)
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- input_spatial_shape[i]
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)
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new_pads[i, 0] = pad_i // 2
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new_pads[i, 1] = pad_i - new_pads[i, 0]
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else:
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for i in range(len(input_spatial_shape)):
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output_spatial_shape[i] = int(
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np.ceil(
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(
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input_spatial_shape[i]
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- ((kernel_shape[i] - 1) * dilations[i] + 1)
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+ 1
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)
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/ strides[i]
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)
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)
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if len(input_spatial_shape) == 1:
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return self._max_pool_1d(
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x,
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auto_pad,
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ceil_mode,
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dilations,
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kernel_shape,
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new_pads,
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storage_order,
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strides,
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output_spatial_shape,
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)
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if len(input_spatial_shape) == 2:
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return self._max_pool_2d(
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x,
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auto_pad,
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ceil_mode,
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dilations,
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kernel_shape,
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new_pads,
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storage_order,
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strides,
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output_spatial_shape,
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)
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if len(input_spatial_shape) == 3:
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return self._max_pool_3d(
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x,
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auto_pad,
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ceil_mode,
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dilations,
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kernel_shape,
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new_pads,
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storage_order,
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strides,
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output_spatial_shape,
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)
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raise RuntimeError(f"Not implemented yet for shape {x.shape}.")
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def _max_pool_1d(
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self,
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x,
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auto_pad, # noqa: ARG002
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ceil_mode, # noqa: ARG002
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dilations,
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kernel_shape,
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new_pads,
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storage_order, # noqa: ARG002
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strides,
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output_spatial_shape,
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):
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global_pooling = False
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y_dims = x.shape[:2] + tuple(output_spatial_shape)
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y = np.zeros(y_dims, dtype=x.dtype)
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indices = np.full(y_dims, dtype=np.int64, fill_value=-1)
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x_dims = x.shape
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channels = x_dims[1]
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height = x_dims[2]
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pooled_height = y_dims[2]
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total_channels = x_dims[0] * channels
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stride_h = 1 if global_pooling else strides[0]
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x_step = height
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y_step = pooled_height
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dilation_h = dilations[0]
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X_data = x.ravel()
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Y_data = y.ravel()
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I_data = indices.ravel()
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def iteration(c):
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x_d = c * x_step
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y_d = c * y_step
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i_d = c * y_step
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for ph in range(pooled_height):
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hstart = ph * stride_h - new_pads[0, 0]
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hend = hstart + kernel_shape[0] * dilation_h
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Yh = None
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h_index = -1
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for h in range(hstart, hend, dilation_h):
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if h < 0 or h >= height:
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continue
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if Yh is None or X_data[x_d + h] > Yh:
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Yh = X_data[x_d + h]
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h_index = h
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Y_data[y_d + ph] = Yh
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I_data[i_d + ph] = c * x_step + h_index
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for c in range(total_channels):
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iteration(c)
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if len(self.output) == 1:
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return (Y_data.reshape(y_dims),)
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return (Y_data.reshape(y_dims), I_data.reshape(y_dims))
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def _max_pool_2d(
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self,
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x,
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auto_pad, # noqa: ARG002
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ceil_mode, # noqa: ARG002
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dilations,
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kernel_shape,
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new_pads,
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storage_order,
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strides,
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output_spatial_shape,
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):
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global_pooling = False
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y_dims = x.shape[:2] + tuple(output_spatial_shape)
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y = np.zeros(y_dims, dtype=x.dtype)
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indices = np.full(y_dims, dtype=np.int64, fill_value=-1)
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x_dims = x.shape
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channels = x_dims[1]
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height = x_dims[2]
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width = x_dims[3] if len(kernel_shape) > 1 else 1
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pooled_height = y_dims[2]
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pooled_width = y_dims[3] if len(kernel_shape) > 1 else 1
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total_channels = x_dims[0] * channels
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stride_h = 1 if global_pooling else strides[0]
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stride_w = 1 if global_pooling else strides[1]
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x_step = height * width
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y_step = pooled_height * pooled_width
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dilation_h = dilations[0]
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dilation_w = dilations[1]
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X_data = x.ravel()
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Y_data = y.ravel()
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I_data = indices.ravel()
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def iteration(c):
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x_d = c * x_step # X_data
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y_d = c * y_step # Y_data
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for ph in range(pooled_height):
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hstart = ph * stride_h - new_pads[0, 0]
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hend = hstart + kernel_shape[0] * dilation_h
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for pw in range(pooled_width):
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wstart = pw * stride_w - new_pads[1, 0]
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wend = wstart + kernel_shape[1] * dilation_w
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pool_index = ph * pooled_width + pw
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Yh = None
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h_index = -1
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w_index = -1
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for h in range(hstart, hend, dilation_h):
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if h < 0 or h >= height:
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continue
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for w in range(wstart, wend, dilation_w):
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if w < 0 or w >= width:
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continue
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input_index = h * width + w
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if input_index < 0 or input_index > X_data.shape[0]:
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continue
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if Yh is None or X_data[x_d + input_index] > Yh:
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Yh = X_data[x_d + input_index]
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h_index = h
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w_index = w
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if Yh is None:
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continue
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Y_data[y_d + pool_index] = Yh
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I_data[y_d + pool_index] = (
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c * x_step + h_index * width + w_index
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if storage_order == 0
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else c * x_step + h_index + w_index * height
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)
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for c in range(total_channels):
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iteration(c)
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if len(self.output) == 1:
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return (Y_data.reshape(y_dims),)
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return (Y_data.reshape(y_dims), I_data.reshape(y_dims))
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def _max_pool_3d(
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self,
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x,
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auto_pad, # noqa: ARG002
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ceil_mode, # noqa: ARG002
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dilations,
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kernel_shape,
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new_pads,
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storage_order,
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strides,
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output_spatial_shape,
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):
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global_pooling = False
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y_dims = x.shape[:2] + tuple(output_spatial_shape)
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y = np.zeros(y_dims, dtype=x.dtype)
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indices = np.full(y_dims, dtype=np.int64, fill_value=-1)
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x_dims = x.shape
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channels = x_dims[1]
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height = x_dims[2]
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width = x_dims[3] if len(kernel_shape) > 1 else 1
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depth = x_dims[4] if len(kernel_shape) > 2 else 1
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pooled_height = y_dims[2]
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pooled_width = y_dims[3] if len(kernel_shape) > 1 else 1
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pooled_depth = y_dims[4] if len(kernel_shape) > 2 else 1
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total_channels = x_dims[0] * channels
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stride_h = 1 if global_pooling else strides[0]
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stride_w = 1 if global_pooling else strides[1]
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stride_d = 1 if global_pooling else strides[2]
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x_step = height * width * depth
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y_step = pooled_height * pooled_width * pooled_depth
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dilation_h = dilations[0]
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dilation_w = dilations[1]
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dilation_d = dilations[2]
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X_data = x.ravel()
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Y_data = y.ravel()
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I_data = indices.ravel()
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def iteration(c):
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x_d = c * x_step
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y_d = c * y_step
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i_d = c * y_step
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for ph in range(pooled_height):
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hstart = ph * stride_h - new_pads[0, 0]
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hend = hstart + kernel_shape[0] * dilation_h
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for pw in range(pooled_width):
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wstart = pw * stride_w - new_pads[1, 0]
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wend = wstart + kernel_shape[1] * dilation_w
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for pd in range(pooled_depth):
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dstart = pd * stride_d - new_pads[2, 0]
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dend = dstart + kernel_shape[2] * dilation_d
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pool_index = (
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ph * pooled_width * pooled_depth + pw * pooled_depth + pd
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)
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Yh = None
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h_index = -1
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w_index = -1
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d_index = -1
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for h in range(hstart, hend, dilation_h):
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if h < 0 or h >= height:
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continue
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for w in range(wstart, wend, dilation_w):
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if w < 0 or w >= width:
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continue
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for d in range(dstart, dend, dilation_d):
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if d < 0 or d >= depth:
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continue
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input_index = h * width * depth + w * depth + d
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if Yh is None or X_data[x_d + input_index] > Yh:
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Yh = X_data[x_d + input_index]
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h_index = h
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w_index = w
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d_index = d
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Y_data[y_d + pool_index] = Yh
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I_data[i_d + pool_index] = (
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(
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c * x_step
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+ h_index * width * depth
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+ w_index * depth
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+ d_index
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)
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if storage_order == 0
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else (
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c * x_step
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+ h_index
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+ w_index * height
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+ d_index * height * width
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)
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)
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for c in range(total_channels):
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iteration(c)
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if len(self.output) == 1:
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return (Y_data.reshape(y_dims),)
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return (Y_data.reshape(y_dims), I_data.reshape(y_dims))
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