wehub-resource-sync
225 lines
4.4 KiB
Plaintext
225 lines
4.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 5.3 多输入通道和多输出通道\n",
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"## 5.3.1 多输入通道"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.4.1\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"import sys\n",
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"sys.path.append(\"..\") \n",
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"import d2lzh_pytorch as d2l\n",
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"\n",
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"print(torch.__version__)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def corr2d_multi_in(X, K):\n",
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" # 沿着X和K的第0维（通道维）分别计算再相加\n",
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" res = d2l.corr2d(X[0, :, :], K[0, :, :])\n",
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" for i in range(1, X.shape[0]):\n",
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" res += d2l.corr2d(X[i, :, :], K[i, :, :])\n",
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" return res"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 56., 72.],\n",
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" [104., 120.]])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X = torch.tensor([[[0, 1, 2], [3, 4, 5], [6, 7, 8]],\n",
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" [[1, 2, 3], [4, 5, 6], [7, 8, 9]]])\n",
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"K = torch.tensor([[[0, 1], [2, 3]], [[1, 2], [3, 4]]])\n",
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"\n",
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"corr2d_multi_in(X, K)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.3.2 多输出通道"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def corr2d_multi_in_out(X, K):\n",
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" # 对K的第0维遍历，每次同输入X做互相关计算。所有结果使用stack函数合并在一起\n",
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" return torch.stack([corr2d_multi_in(X, k) for k in K])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([3, 2, 2, 2])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"K = torch.stack([K, K + 1, K + 2])\n",
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"K.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[[ 56., 72.],\n",
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" [104., 120.]],\n",
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"\n",
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" [[ 76., 100.],\n",
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" [148., 172.]],\n",
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"\n",
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" [[ 96., 128.],\n",
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" [192., 224.]]])"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"corr2d_multi_in_out(X, K)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.3.3 $1\\times 1$卷积层"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def corr2d_multi_in_out_1x1(X, K):\n",
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" c_i, h, w = X.shape\n",
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" c_o = K.shape[0]\n",
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" X = X.view(c_i, h * w)\n",
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" K = K.view(c_o, c_i)\n",
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" Y = torch.mm(K, X) # 全连接层的矩阵乘法\n",
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" return Y.view(c_o, h, w)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X = torch.rand(3, 3, 3)\n",
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"K = torch.rand(2, 3, 1, 1)\n",
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"\n",
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"Y1 = corr2d_multi_in_out_1x1(X, K)\n",
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"Y2 = corr2d_multi_in_out(X, K)\n",
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"\n",
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"(Y1 - Y2).norm().item() < 1e-6"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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