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Eaven21 Nov 4, 2022
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Eaven21 Nov 13, 2022
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182 changes: 182 additions & 0 deletions models/ecbsr1d/demo.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 一、F.conv2d() 与 forward的等价性\n",
"\n",
"常用的卷积参数为 inp,oup,kernel_size,stride,padding\n",
"F.conv2d(inp, weight, bias, stride)\n",
"padding部分需要自己手动填充\n",
"2"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([64, 64, 3, 1])\n",
"torch.Size([64, 28, 28])\n"
]
}
],
"source": [
"conv0 = torch.nn.Conv2d(64, 64, kernel_size=(3,1), stride=(1,0), padding=(1,0))\n",
"k0 = conv0.weight\n",
"b0 = conv.bias\n",
"inp = torch.randn(64, 28, 28)\n",
"out1 = conv1(inp)\n",
"# out2 = F.conv2d(input=inp, weight=k0, bias=b0, stride=1)\n",
"print(k0.shape)\n",
"print(out1.shape)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'y0' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/user3/code/SimpleIR/models/ecbsr copy/demo.ipynb Cell 4'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bred_t4/home/user3/code/SimpleIR/models/ecbsr%20copy/demo.ipynb#ch0000008vscode-remote?line=0'>1</a>\u001b[0m y0 \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mpad(y0, (\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m), \u001b[39m'\u001b[39m\u001b[39mconstant\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m0\u001b[39m)\n",
"\u001b[0;31mNameError\u001b[0m: name 'y0' is not defined"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([64, 28, 28])\n",
"torch.Size([64, 64, 3, 3])\n",
"torch.Size([64, 28, 30])\n"
]
}
],
"source": [
"conv0= torch.nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)\n",
"k0 = conv0.weight\n",
"b0 = conv0.bias\n",
"inp = torch.randn(64, 28, 28)\n",
"out = conv0(inp)\n",
"# out2 = F.conv2d(input=inp, weight=k0, bias=b0, stride=1)\n",
"print(out.shape)\n",
"print(k0.shape)\n",
"out = F.pad(out, (1, 1, 0, 0), 'constant', 0)\n",
"print(out.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"conv0 = torch.nn.Conv2d(self.inp_planes, self.out_planes, kernel_size=1, padding=0)\n",
"self.k0 = conv0.weight\n",
"self.b0 = conv0.bias\n",
"\n",
"# init scale & bias\n",
"scale = torch.randn(size=(self.out_planes, 1, 1, 1)) * 1e-3\n",
"self.scale = nn.Parameter(scale)\n",
"# bias = 0.0\n",
"# bias = [bias for c in range(self.out_planes)]\n",
"# bias = torch.FloatTensor(bias)\n",
"bias = torch.randn(self.out_planes) * 1e-3\n",
"bias = torch.reshape(bias, (self.out_planes,))\n",
"self.bias = nn.Parameter(bias)\n",
"# init mask\n",
"self.mask = torch.zeros((self.out_planes, 1, 3, 3), dtype=torch.float32)\n",
"for i in range(self.out_planes):\n",
" self.mask[i, 0, 0, 0] = 1.0\n",
" self.mask[i, 0, 1, 0] = 2.0\n",
" self.mask[i, 0, 2, 0] = 1.0\n",
" self.mask[i, 0, 0, 2] = -1.0\n",
" self.mask[i, 0, 1, 2] = -2.0\n",
" self.mask[i, 0, 2, 2] = -1.0\n",
"self.mask = nn.Parameter(data=self.mask, requires_grad=False)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y0 = F.conv2d(input=x, weight=k0, bias=b0, stride=1)\n",
"# explicitly padding with bias\n",
"y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)\n",
"b0_pad = self.b0.view(1, -1, 1, 1)\n",
"y0[:, :, 0:1, :] = b0_pad\n",
"y0[:, :, -1:, :] = b0_pad\n",
"y0[:, :, :, 0:1] = b0_pad\n",
"y0[:, :, :, -1:] = b0_pad\n",
"# conv-3x3\n",
"y1 = F.conv2d(input=y0, weight=self.scale * self.mask, bias=self.bias, stride=1, groups=self.out_planes)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.13 ('py38': conda)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "798585ee0e69c52f6919ecef47b9e35918308944029e7c22636f87b86ab713f5"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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