889 lines
26 KiB
Python
889 lines
26 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""VM implementations based on numpy."""
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import numpy as np
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from mindspore import _checkparam as validator
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def avg_pooling(x, pool_h, pool_w, stride):
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"""
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Applies average pooling over an input array.
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Args:
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x (numpy.ndarray): The input array to be average pooled.
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pool_h (int): Height of the pooling window.
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pool_w (int): Width of the pooling window.
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stride (int): The stride of the sliding window.
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Returns:
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numpy.ndarray, an output array after applying average pooling on input array.
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"""
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validator.check_positive_int(stride, "stride")
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num, channel, height, width = x.shape
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out_h = (height - pool_h) // stride + 1
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out_w = (width - pool_w) // stride + 1
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col = im2col(x, pool_h, pool_w, stride)
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col = col.reshape(-1, pool_h * pool_w)
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out = np.mean(col, axis=1)
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out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
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return out
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def avg_pool_grad(dout, origin_shape, pool_h, pool_w, stride):
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"""
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Gets grad of average pooling.
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Args:
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x (numpy.ndarray): The input array to be average pooled.
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dout (numpy.ndarray): The grad of pre-layer.
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pool_h (int): Height of the pooling window.
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pool_w (int): Width of the pooling window.
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stride (int): The stride of the sliding window.
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Returns:
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numpy.ndarray, grad of average pooling.
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"""
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# pylint: disable=unused-argument
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_, _, height, width = dout.shape
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dx = np.zeros(origin_shape)
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for i in range(height):
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for j in range(width):
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dx[:, :, i:(i + pool_h), j:(j + pool_w)] += np.ones((pool_h, pool_w))
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return dx
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def _batch_norm(x, scale, shift, running_mean=None, running_var=None,
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eps=1e-05, momentum=0.1, is_training=True):
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"""Batch Normalization over an array."""
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_, c_h_w = x.shape
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# Handle running_mean and running_var are not None
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# if running_mean is None:
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# running_mean = np.zeros(c_h_w)
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# running_var = np.zeros(c_h_w)
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running_mean = np.zeros(c_h_w)
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running_var = np.zeros(c_h_w)
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if np.ndim(scale) > 0:
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scale = scale.mean()
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if np.ndim(shift) > 0:
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shift = shift.mean()
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if is_training:
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x_mean = np.mean(x, axis=0)
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x_var = np.var(x, axis=0)
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# Normalization followed by Affine transformation
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x_norm = (x - x_mean) / np.sqrt(x_var + eps)
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# Estimate running average of mean and variance to use at test time
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running_mean = momentum * running_mean + (1 - momentum) * x_mean
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running_var = momentum * running_var + (1 - momentum) * x_var
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else:
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# normalize using running average
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x_norm = (x - running_mean) / np.sqrt(running_var + eps)
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x_mean = running_mean
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x_var = running_var
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out = scale * x_norm + shift
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return out, x_mean, x_var, running_mean, running_var
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def batch_norm(x, scale=1, shift=0, mean=None, variance=None,
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eps=1e-05, momentum=0.1, is_training=True):
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"""Batch Normalization over an array."""
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input_shape = x.shape
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if x.ndim != 2:
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batch_num = x.shape[0]
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x = x.reshape(batch_num, -1)
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out, _, _, running_mean, running_var = _batch_norm(x, scale, shift, mean, variance, \
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eps, momentum, is_training)
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return out.reshape(*input_shape), np.array(scale), np.array(shift), running_mean, running_var
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def _batch_norm_grad(dout, x, scale, save_mean, save_inv_variance, \
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eps=1e-05, momentum=0.1, is_training=True):
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"""Batch Normalization over an array."""
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if x.ndim != 2:
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batch_num = x.shape[0]
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x = x.reshape(batch_num, -1)
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if np.ndim(scale) > 0:
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scale = scale.mean()
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x_norm, x_mean, x_var, _, _ = _batch_norm(x, scale, shift=0, running_mean=save_mean, \
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running_var=save_inv_variance, \
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eps=eps, momentum=momentum, is_training=is_training)
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batch_size = x.shape[0]
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dx_norm = scale * dout
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dvar = np.sum(dx_norm * (x - x_mean) * ((x_var + eps) ** (-3.0 / 2)) * (-1.0 / 2), axis=0)
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dmean = np.sum(dx_norm * (-1.0 / np.sqrt(x_var + eps)), axis=0) \
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+ dvar * (np.sum(-2 * (x - x_mean), axis=0) * (1.0 / batch_size))
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dx = dx_norm * (1.0 / np.sqrt(x_var + eps)) + dvar * (2.0 * (x - x_mean) / batch_size) + dmean * (1.0 / batch_size)
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dgamma = np.sum(dout * x_norm, axis=0)
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dbeta = np.sum(dout, axis=0)
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return dx, dgamma, dbeta
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def batch_norm_grad(dy, x, scale, save_mean, save_inv_variance):
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"""Batch Normalization over an array."""
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if dy.ndim != 2:
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batch_size = dy.shape[0]
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dy = dy.reshape(batch_size, -1)
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dx, dgamma, dbeta = _batch_norm_grad(dy, x, scale, save_mean, save_inv_variance)
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input_shape = x.shape
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dx = dx.reshape(*input_shape)
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return dx, dgamma, dbeta
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def col2im(col, input_shape, filter_h, filter_w, stride=1, pad=0):
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"""Rearranges a row vector to an image."""
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if isinstance(stride, int):
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stride_h = stride
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stride_w = stride
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elif isinstance(stride, tuple) and len(stride) == 2:
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stride_h = stride[0]
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stride_w = stride[1]
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elif isinstance(stride, tuple) and len(stride) == 4:
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stride_h = stride[2]
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stride_w = stride[3]
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else:
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raise ValueError(f"The \'stride\' should be an int number or "
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f"a tuple of two or four int numbers, but got {stride}")
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if isinstance(pad, int):
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pad_top = pad
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pad_bottom = pad
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pad_left = pad
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pad_right = pad
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elif isinstance(pad, tuple) and len(pad) == 2:
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pad_top = pad[0]
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pad_bottom = pad[0]
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pad_left = pad[1]
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pad_right = pad[1]
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elif isinstance(pad, tuple) and len(pad) == 4:
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pad_top, pad_bottom, pad_left, pad_right = pad
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else:
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raise ValueError(f"The \'pad\' should be an int number or "
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f"a tuple of two or four int numbers, but got {pad}")
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batch_num, channel, height, width = input_shape
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out_h = (height + pad_top + pad_bottom - filter_h) // stride_h + 1
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out_w = (width + pad_left + pad_right - filter_w) // stride_w + 1
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col = col.reshape(batch_num, out_h, out_w, channel, filter_h, filter_w) \
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.transpose(0, 3, 4, 5, 1, 2)
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img = np.zeros((batch_num,
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channel,
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height + pad_top + pad_bottom + stride_h - 1,
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width + pad_left + pad_right + stride_w - 1)) \
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.astype(col.dtype)
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for y in range(filter_h):
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y_max = y + stride_h * out_h
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for x in range(filter_w):
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x_max = x + stride_h * out_w
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img[:, :, y:y_max:stride_h, x:x_max:stride_h] += col[:, :, y, x, :, :]
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return img[:, :, pad_top:height + pad_bottom, pad_left:width + pad_right]
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def convolve(x, w, b=None, pad_mode="valid"):
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"""
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Gets the discrete, linear convolution of two one-dimensional sequences.
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Args:
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x (numpy.ndarray): One-dimensional input array.
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w (numpy.ndarray): One-dimensional input array.
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b (numpy.ndarray): One-dimensional input array. Default: None.
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pad_mode (str): Padding mode which can be: "full" means returns the
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convolution at each point of overlap, with an output shape
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of (N+M-1,); "same" means returns output of length max(M, N);
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Amd "valid" means returns output of length max(M, N) - min(M, N)
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+ 1. Default: "valid".
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Returns:
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numpy.ndarray, discrete, linear convolution of x and w, then plus b.
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"""
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if pad_mode not in {"same", "valid"}:
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pad_mode = "full"
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y = np.convolve(x, w, pad_mode)
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if b:
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y += b
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return y
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def conv2d(x, weight, bias=None, stride=1, pad=0,
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dilation=1, groups=1, padding_mode='zeros'):
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"""Convolution 2D."""
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# pylint: disable=unused-argument
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validator.check_value_type('stride', stride, (int, tuple))
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if isinstance(stride, int):
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stride = (stride, stride)
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elif len(stride) == 4:
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stride = (stride[2], stride[3])
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if len(stride) != 2 or (not isinstance(stride[0], int)) or \
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(not isinstance(stride[1], int)) or \
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stride[0] < 1 or stride[1] < 1:
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raise ValueError(f"The \'stride\' of \'conv2d\' should be an positive int number or "
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f"a tuple of two positive int numbers, but got {stride}")
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stride_h = stride[0]
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stride_w = stride[1]
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validator.check_value_type('dilation', dilation, (int, tuple))
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if isinstance(dilation, int):
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dilation = (dilation, dilation)
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elif len(dilation) == 4:
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dilation = (dilation[2], dilation[3])
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if len(dilation) != 2 or (not isinstance(dilation[0], int)) or \
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(not isinstance(dilation[1], int)) or \
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dilation[0] < 1 or dilation[1] < 1:
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raise ValueError(f"The \'dilation\' of \'conv2d\' should be an positive int number or "
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f"a tuple of two positive int numbers, but got {dilation}")
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dilation_h = dilation[0]
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dilation_w = dilation[1]
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if isinstance(pad, int):
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pad_top = pad
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pad_bottom = pad
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pad_left = pad
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pad_right = pad
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elif isinstance(pad, tuple) and len(pad) == 4:
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pad_top, pad_bottom, pad_left, pad_right = pad
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else:
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raise ValueError(f"The \'pad\' should be an int number or "
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f"a tuple of two or four int numbers, but got {pad}")
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batch_num, _, x_h, x_w = x.shape
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filter_num, _, filter_h, filter_w = weight.shape
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out_h = 1 + int((x_h + pad_top + pad_bottom - filter_h - (filter_h - 1) * (dilation_h - 1)) / stride_h)
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out_w = 1 + int((x_w + pad_left + pad_right - filter_w - (filter_w - 1) * (dilation_w - 1)) / stride_w)
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col = im2col(x, filter_h, filter_w, stride, pad, dilation)
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col_w = np.reshape(weight, (filter_num, -1)).T
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out = np.dot(col, col_w)
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out = out.reshape((batch_num, out_h, out_w, -1)).transpose(0, 3, 1, 2)
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if bias is not None:
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out += bias
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return out
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def conv2d_backprop_filter(dout, x, w_size, stride=1, pad=0):
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"""Backpropagation filter for conv2d."""
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filter_num, channel, filter_height, filter_width = w_size
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dout = dout.transpose(0, 2, 3, 1).reshape(-1, filter_num)
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col = im2col(x, filter_height, filter_width, stride, pad)
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dw = np.dot(col.T, dout)
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dw = dw.transpose(1, 0).reshape((filter_num, channel, filter_height, filter_width))
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return dw
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def conv2d_backprop_input(dout, x_size, weight, stride=1, pad=0):
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"""Backpropagation input for conv2d."""
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filter_num, _, filter_h, filter_w = weight.shape
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dout = dout.transpose(0, 2, 3, 1).reshape(-1, filter_num)
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col_w = weight.reshape(filter_num, -1).T
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dcol = np.dot(dout, col_w.T)
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dx = col2im(dcol, x_size, filter_h, filter_w, stride, pad)
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return dx
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def flatten(x):
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"""
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Flattens an array to one dimension.
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Args:
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x (numpy.ndarray): An array to be flattened.
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Returns:
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numpy.ndarray, a flattened array in one dimension.
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"""
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return x.flatten()
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def flatten2(x):
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"""
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Flattens an array to one dimension by reshape.
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Args:
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x (numpy.ndarray): An array to be flattened.
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Returns:
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numpy.ndarray, a flattened array in one dimension.
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"""
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return x.reshape(1, -1)
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def flatten_batch(x):
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"""
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Flattens a batch of arrays to one dimension.
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Args:
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x (numpy.ndarray): A batch of arrays to be flattened.
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Returns:
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numpy.ndarray, a flattened one dimension array.
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"""
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return x.reshape(x.shape[0], -1)
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def flatten_grad(dout, x):
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"""Grad of flatten."""
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dout = np.reshape(dout, x)
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return dout
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def im2col(img, filter_h, filter_w, stride=1, pad=0, dilation=1):
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"""Rearranges an image to row vector."""
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if isinstance(stride, int):
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stride_h = stride
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stride_w = stride
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elif isinstance(stride, tuple) and len(stride) == 2:
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stride_h = stride[0]
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stride_w = stride[1]
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elif isinstance(stride, tuple) and len(stride) == 4:
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stride_h = stride[2]
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stride_w = stride[3]
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else:
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raise ValueError(f"The \'stride\' should be an int number or "
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f"a tuple of two or four int numbers, but got {stride}")
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if isinstance(dilation, int):
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dilation_h = dilation
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dilation_w = dilation
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elif isinstance(dilation, tuple) and len(dilation) == 2:
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dilation_h = dilation[0]
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dilation_w = dilation[1]
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elif isinstance(dilation, tuple) and len(dilation) == 4:
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dilation_h = dilation[2]
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dilation_w = dilation[3]
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else:
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raise ValueError(f"The \'dilation\' should be an int number or "
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f"a tuple of two or four int numbers, but got {dilation}")
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if isinstance(pad, int):
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pad_top = pad
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pad_bottom = pad
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pad_left = pad
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pad_right = pad
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elif isinstance(pad, tuple) and len(pad) == 4:
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pad_top, pad_bottom, pad_left, pad_right = pad
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else:
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raise ValueError(f"The \'pad\' should be an int number or "
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f"a tuple of two or four int numbers, but got {pad}")
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batch_num, channel, height, width = img.shape
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out_h = (height + pad_top + pad_bottom - filter_h - (filter_h - 1) * (dilation_h - 1)) // stride_h + 1
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out_w = (width + pad_left + pad_right - filter_w - (filter_w - 1) * (dilation_w - 1)) // stride_w + 1
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img = np.pad(img, [(0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)], 'constant')
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col = np.zeros((batch_num, channel, filter_h, filter_w, out_h, out_w)).astype(img.dtype)
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for y in range(filter_h):
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y_max = y + stride_h * out_h
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for x in range(filter_w):
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x_max = x + stride_h * out_w
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col[:, :, y, x, :, :] = img[:, :, y:y_max:stride_h, x:x_max:stride_h]
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col = col.transpose(0, 4, 5, 1, 2, 3).reshape(batch_num * out_h * out_w, -1)
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return col
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def matmul(x, w, b=None):
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"""
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Dot product of array x and w, then plus array b if b is not None.
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Args:
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x (numpy.ndarray): Represents the input array.
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w (numpy.ndarray): Represents weights array.
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b (numpy.ndarray): Represents bias array which has the same shape as x. Default: None.
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Returns:
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numpy.ndarray, the result of (x*w + b).
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"""
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y = np.dot(x, w)
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if b:
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y += b
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return y
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def max_pooling(x, pool_h, pool_w, stride):
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"""Max pooling."""
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validator.check_positive_int(stride, "stride")
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num, channel, height, width = x.shape
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out_h = (height - pool_h) // stride + 1
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out_w = (width - pool_w) // stride + 1
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col = im2col(x, pool_h, pool_w, stride)
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col = col.reshape(-1, pool_h * pool_w)
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out = np.max(col, axis=1)
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out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
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return out
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def max_pool_grad(x, dout, pool_h, pool_w, stride):
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"""Grad of max pooling."""
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dout = dout.transpose(0, 2, 3, 1)
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pool_size = pool_h * pool_w
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dmax = np.zeros((dout.size, pool_size), dout.dtype)
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col = im2col(x, pool_h, pool_w, stride)
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col = col.reshape(-1, pool_h * pool_w)
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arg_max = np.argmax(col, axis=1)
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dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten()
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dmax = dmax.reshape(dout.shape + (pool_size,))
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dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1)
|
|
dx = col2im(dcol, x.shape, pool_h, pool_w, stride)
|
|
return dx
|
|
|
|
|
|
def max_pool_grad_with_argmax(x, dout, arg_max, pool_h, pool_w, stride):
|
|
"""Grad of max pooling with argmax."""
|
|
dout = dout.transpose(0, 2, 3, 1)
|
|
pool_size = pool_h * pool_w
|
|
dmax = np.zeros((dout.size, pool_size), dout.dtype)
|
|
dmax[np.arange(arg_max.size), arg_max.flatten()] = dout.flatten()
|
|
dmax = dmax.reshape(dout.shape + (pool_size,))
|
|
dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1)
|
|
dx = col2im(dcol, x.shape, pool_h, pool_w, stride)
|
|
return dx
|
|
|
|
|
|
def max_pool_with_argmax(x, pool_h, pool_w, stride):
|
|
"""Max pooling with argmax."""
|
|
validator.check_positive_int(stride, "stride")
|
|
num, channel, height, width = x.shape
|
|
out_h = (height - pool_h) // stride + 1
|
|
out_w = (width - pool_w) // stride + 1
|
|
col = im2col(x, pool_h, pool_w, stride)
|
|
col = col.reshape(-1, pool_h * pool_w)
|
|
out = np.max(col, axis=1)
|
|
out_argmax = np.argmax(col, axis=1)
|
|
out = out.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
|
|
out_argmax = out_argmax.reshape((num, out_h, out_w, channel)).transpose(0, 3, 1, 2)
|
|
return out, out_argmax
|
|
|
|
|
|
def relu(x):
|
|
"""
|
|
Rectified linear unit.
|
|
|
|
Args:
|
|
x (numpy.ndarray): The input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, the array applied relu.
|
|
"""
|
|
return x * (x > 0)
|
|
|
|
|
|
def relu_grad(y):
|
|
"""
|
|
Grad of relu.
|
|
|
|
Args:
|
|
y (numpy.ndarray): The input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, the array applied grad of relu.
|
|
"""
|
|
y[y <= 0] = 0
|
|
y[y > 0] = 1
|
|
return y
|
|
|
|
|
|
def sigmoid(x):
|
|
"""
|
|
Sigmoid activation function.
|
|
|
|
Args:
|
|
x (numpy.ndarray): The input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, the array applied sigmoid.
|
|
"""
|
|
return 1 / (1 + np.exp(x * -1))
|
|
|
|
|
|
def tanh(x):
|
|
"""
|
|
Computes hyperbolic tangent element-wise.
|
|
|
|
Args:
|
|
x (numpy.ndarray): The input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, the array applied tanh.
|
|
"""
|
|
a = np.exp(x) - np.exp(x * -1)
|
|
b = np.exp(x) + np.exp(x * -1)
|
|
return a / b
|
|
|
|
|
|
def softmax(x, axis=None):
|
|
"""
|
|
Softmax function which is `softmax(x) = np.exp(x)/sum(np.exp(x))`.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
axis (Union[int, tuple[int]]): Axis to compute values along. Default: None.
|
|
|
|
Returns:
|
|
numpy.ndarray, has the same shape as x.
|
|
"""
|
|
from scipy.special import softmax as scipy_softmax
|
|
return scipy_softmax(x, axis)
|
|
|
|
|
|
def softmax_cross_entropy_with_logits(logits, labels):
|
|
sample_num = labels.shape[0]
|
|
prob = softmax(logits)
|
|
log_likelihood = -np.log(prob[range(sample_num)]) * labels
|
|
loss = np.sum(log_likelihood)
|
|
dx = prob.copy()
|
|
dx[range(sample_num)] -= labels
|
|
return loss, dx
|
|
|
|
|
|
def shape(x):
|
|
"""
|
|
Gets the array's dimensions.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
tuple, the shape/dimensions of the input array.
|
|
"""
|
|
return np.array(np.shape(x))
|
|
|
|
|
|
def expand_dims(x, axis):
|
|
"""
|
|
Expands the shape of an array.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
axis (int): Position in the expanded axes where the new axis is placed.
|
|
|
|
Returns:
|
|
numpy.ndarray, view of input array with the number of dimensions increased by one.
|
|
"""
|
|
return np.expand_dims(x, axis)
|
|
|
|
|
|
def squeeze(x, axis):
|
|
"""
|
|
Removes single-dimensional entries from the shape of an array.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
axis (Union[int, tuple[int]]): Selected subset of the single-dimensional entries in the shape.
|
|
|
|
Returns:
|
|
numpy.ndarray, the input numpy.ndarray, but with all or a subset of the dimensions of length
|
|
1 removed.
|
|
"""
|
|
return np.squeeze(x, tuple(axis))
|
|
|
|
|
|
def reshape(x, shp):
|
|
"""
|
|
Applies a new shape to an array without changing its data.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
shp (tuple[int]): New shape to apply to x.
|
|
|
|
Returns:
|
|
numpy.ndarray, a new view object or a copy of input array.
|
|
"""
|
|
return np.reshape(x, tuple(shp))
|
|
|
|
|
|
def rank(x):
|
|
"""
|
|
Gets number of array dimensions.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
int, number of input array dimensions.
|
|
"""
|
|
return np.array(np.ndim(x))
|
|
|
|
|
|
def logsoftmax(x):
|
|
"""
|
|
Log softmax function.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, the result of applying log softmax on the input array.
|
|
"""
|
|
return np.array(np.log(softmax(x)))
|
|
|
|
|
|
def transpose(x, axes=None):
|
|
"""
|
|
Transposes an input array according to axes.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
axes (list): The axes to be transposed. Default: None.
|
|
|
|
Returns:
|
|
numpy.ndarray, transposed array.
|
|
"""
|
|
return np.transpose(x, axes)
|
|
|
|
|
|
def invert_permutation(x):
|
|
"""
|
|
Gets the inverse permutation of an array.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
tuple, the inverse permutation of the input array.
|
|
"""
|
|
x = np.array(x)
|
|
y = np.argsort(x)
|
|
return tuple(y)
|
|
|
|
|
|
def select(cond, x, y):
|
|
"""
|
|
Gets elements from x or y depending on cond.
|
|
|
|
Args:
|
|
cond (bool): Where True, yield x, otherwise yield y.
|
|
x (numpy.ndarray): Values from which to choose.
|
|
y (numpy.ndarray): Values from which to choose.
|
|
|
|
Returns:
|
|
numpy.ndarray, elements from x where condition is True, and elements from y elsewhere.
|
|
"""
|
|
return np.where(cond, x, y)
|
|
|
|
|
|
def sum_by_axis(x, axis):
|
|
"""
|
|
Sum of array elements over a given axis.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
axis (Union[int, tuple[int]]): Axis or axes along which a sum is performed.
|
|
|
|
Returns:
|
|
numpy.ndarray, has the same shape as input array with the specified axis removed.
|
|
"""
|
|
return np.sum(x, axis)
|
|
|
|
|
|
def equal(x, y):
|
|
"""
|
|
Gets (x == y) element-wise.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
y (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, element-wise comparison of x and y.
|
|
"""
|
|
return np.equal(x, y)
|
|
|
|
|
|
def not_equal(x, y):
|
|
"""
|
|
Gets (x != y) element-wise.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
y (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, element-wise comparison of x and y.
|
|
"""
|
|
return np.not_equal(x, y)
|
|
|
|
|
|
def greater(x, y):
|
|
"""
|
|
Get the truth value of (x > y) element-wise.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
y (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, element-wise comparison of x and y.
|
|
"""
|
|
return np.greater(x, y)
|
|
|
|
|
|
def less(x, y):
|
|
"""
|
|
Get the truth value of (x < y) element-wise.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
y (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
Array, element-wise comparison of x and y.
|
|
"""
|
|
return np.less(x, y)
|
|
|
|
|
|
def logical_not(x):
|
|
"""
|
|
Gets the truth value of NOT x element-wise.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
bool, have the same shape as x of the NOT operation on elements of x.
|
|
"""
|
|
return np.logical_not(x)
|
|
|
|
|
|
def sqrt(x):
|
|
"""
|
|
Gets the non-negative square-root of an numpy.ndarray, element-wise.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, has the same shape as x, containing the positive square-root of each
|
|
element in x.
|
|
"""
|
|
return np.sqrt(x)
|
|
|
|
|
|
def power(x, y):
|
|
"""
|
|
First array elements raised to powers from second numpy.ndarray, element-wise.
|
|
|
|
Args:
|
|
x (numpy.ndarray): The bases array.
|
|
y (numpy.ndarray): The exponents array.
|
|
|
|
Returns:
|
|
numpy.ndarray, the bases in x raised to the exponents in y.
|
|
"""
|
|
return np.power(x, y)
|
|
|
|
|
|
def exp(x):
|
|
"""
|
|
Gets the exponential of all elements in the input array.
|
|
|
|
Args:
|
|
x (numpy.ndarray): Input array.
|
|
|
|
Returns:
|
|
numpy.ndarray, element-wise exponential of x.
|
|
"""
|
|
return np.exp(x)
|
|
|
|
|
|
def maximum(x, y):
|
|
"""
|
|
Gets the max of x and y element-wise.
|
|
|
|
If x > y, return x. Otherwise, return y.
|
|
|
|
Args:
|
|
x (numpy.ndarray): First input array.
|
|
y (numpy.ndarray): Second input array ave the same type as x.
|
|
|
|
Returns:
|
|
numpy.ndarray, has the same type as x.
|
|
"""
|
|
return np.maximum(x, y)
|
|
|
|
|
|
def minimum(x, y):
|
|
"""
|
|
Gets the min of x and y element-wise.
|
|
|
|
If x < y, return x. Otherwise, return y.
|
|
|
|
Args:
|
|
x (numpy.ndarray): First input array.
|
|
y (numpy.ndarray): Second input array have the same type as x.
|
|
|
|
Returns:
|
|
numpy.ndarray, has the same type as x.
|
|
"""
|
|
return np.minimum(x, y)
|
|
|
|
|
|
def all_(x, axis=(), keep_dims=False):
|
|
"""
|
|
Check all array elements along a given axis evaluate to True.
|
|
|
|
Args:
|
|
x (numpy.ndarray): An array to be reduced.
|
|
axis (Union[None, int, tuple(int)): Dimensions of reduction.
|
|
keep_dims (bool): Whether to keep the reduced dimensions.
|
|
|
|
Returns:
|
|
numpy.ndarray, has the same type as x.
|
|
"""
|
|
axis = None if axis == () else axis
|
|
return np.all(x, axis, keepdims=keep_dims)
|
|
|
|
|
|
def any_(x, axis=(), keep_dims=False):
|
|
"""
|
|
Check any array element along a given axis evaluate to True.
|
|
|
|
Args:
|
|
x (numpy.ndarray): An array to be reduced.
|
|
axis (Union[None, int, tuple(int)): Dimensions of reduction.
|
|
keep_dims (bool): Whether to keep the reduced dimensions.
|
|
|
|
Returns:
|
|
numpy.ndarray, has the same type as x.
|
|
"""
|
|
axis = None if axis == () else axis
|
|
return np.any(x, axis, keepdims=keep_dims)
|
|
|
|
|
|
def mean_(x, axis=(), keep_dims=False):
|
|
"""
|
|
Check mean array element along a given axis evaluate to True.
|
|
|
|
Args:
|
|
x (numpy.ndarray): An array to be reduced.
|
|
axis (Union[None, int, tuple(int)): Dimensions of reduction.
|
|
keep_dims (bool): Whether to keep the reduced dimensions.
|
|
|
|
Returns:
|
|
numpy.ndarray, has the same type as x.
|
|
"""
|
|
axis = None if axis == () else axis
|
|
return np.mean(x, axis, keepdims=keep_dims)
|