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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": {
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+   "display_name": "Python 3.8.13 ('py38': conda)",
+   "language": "python",
+   "name": "python3"
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+  "language_info": {
+   "codemirror_mode": {
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+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+   "interpreter": {
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