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Commit 1bc644a1 authored by Nianchen Deng's avatar Nianchen Deng
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sync

parent 6294701e
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notebook/gen_user_study_images.ipynb → notebook/gen/gen_user_study_images.ipynb
View file @ 1bc644a1
......@@ -140,7 +140,7 @@
"\n",
"# Load Dataset\n",
"views = load_views('views.json')\n",
"#ref_dataset = SphericalViewSynDataset('ref.json', load_images=False, calculate_rays=False)\n",
"#ref_dataset = SphericalViewSynDataset('ref.json', load_colors=False, calculate_rays=False)\n",
"print('Dataset loaded.')\n",
"\n",
"print('views:', views.size())\n",
......@@ -226,4 +226,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
}
notebook/gen_video.ipynb → notebook/gen/gen_video.ipynb
View file @ 1bc644a1
......@@ -44,7 +44,7 @@
" return None\n",
"\n",
"\n",
"def load_views(data_desc_file) -> Tuple[view.Trans, torch.Tensor]:\n",
"def load_views(data_desc_file) -> tuple[view.Trans, torch.Tensor]:\n",
" with open(data_desc_file, 'r', encoding='utf-8') as file:\n",
" lines = file.readlines()\n",
" n = len(lines) // 7\n",
......
notebook/gen_crop.ipynb deleted 100644 → 0
View file @ 6294701e
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"import torch.nn.functional as nn_f\n",
"import matplotlib.pyplot as plt\n",
"\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(0)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
"from utils import img\n",
"from utils.view import *\n",
"\n",
"datadir = f\"{rootdir}/data/__new/__demo/for_crop\"\n",
"figs = ['our', 'gt', 'nerf', 'fgt']\n",
"crops = {\n",
" 'classroom_0': [[720, 790, 100], [370, 1160, 200]],\n",
" 'lobby_1': [[570, 1000, 100], [1300, 1000, 200]],\n",
" 'stones_2': [[720, 800, 100], [680, 1317, 200]],\n",
" 'barbershop_3': [[745, 810, 100], [950, 900, 200]]\n",
"}\n",
"colors = torch.tensor([[0, 1, 0, 1], [1, 1, 0, 1]], dtype=torch.float)\n",
"border = 10\n",
"\n",
"for scene in crops:\n",
" images = img.load([f\"{datadir}/origin/{scene}_{fig}.png\" for fig in figs])\n",
" halfw = images.size(-1) // 2\n",
" halfh = images.size(-2) // 2\n",
" crop = crops[scene]\n",
" fovea_patches = images[...,\n",
" crop[0][1] - crop[0][2] // 2: crop[0][1] + crop[0][2] // 2,\n",
" crop[0][0] - crop[0][2] // 2: crop[0][0] + crop[0][2] // 2]\n",
" periph_patches = images[...,\n",
" crop[1][1] - crop[1][2] // 2: crop[1][1] + crop[1][2] // 2,\n",
" crop[1][0] - crop[1][2] // 2: crop[1][0] + crop[1][2] // 2]\n",
" fovea_patches = nn_f.interpolate(fovea_patches, (128, 128))\n",
" periph_patches = nn_f.interpolate(periph_patches, (128, 128))\n",
" overlay = torch.zeros(1, 4, 1600, 1440)\n",
" mask = torch.zeros(2, 1600, 1440, dtype=torch.bool)\n",
" for i in range(2):\n",
" mask[i,\n",
" crop[i][1] - crop[i][2] // 2 - border: crop[i][1] + crop[i][2] // 2 + border,\n",
" crop[i][0] - crop[i][2] // 2 - border: crop[i][0] + crop[i][2] // 2 + border] = True\n",
" mask[i,\n",
" crop[i][1] - crop[i][2] // 2: crop[i][1] + crop[i][2] // 2,\n",
" crop[i][0] - crop[i][2] // 2: crop[i][0] + crop[i][2] // 2] = False\n",
" overlay[:, :, mask[0]] = colors[0][..., None]\n",
" overlay[:, :, mask[1]] = colors[1][..., None]\n",
" plt.figure(figsize=(12, 6))\n",
" plt.subplot(1, 2, 1)\n",
" img.plot(images[0])\n",
" plt.subplot(1, 2, 2)\n",
" img.plot(overlay)\n",
" plt.figure(figsize=(12, 6))\n",
" for i in range(4):\n",
" plt.subplot(2, 4, i + 1)\n",
" img.plot(fovea_patches[i])\n",
" for i in range(4):\n",
" plt.subplot(2, 4, i + 5)\n",
" img.plot(periph_patches[i])\n",
" img.save(fovea_patches, [f\"{datadir}/fovea/{scene}_{fig}.png\" for fig in figs])\n",
" img.save(periph_patches, [f\"{datadir}/periph/{scene}_{fig}.png\" for fig in figs])\n",
" img.save(torch.cat([fovea_patches, periph_patches], dim=-1),\n",
" [f\"{datadir}/patch/{scene}_{fig}.png\" for fig in figs])\n",
" img.save(overlay, f\"{datadir}/overlay/{scene}.png\")\n"
],
"outputs": [],
"metadata": {}
}
],
"metadata": {
"interpreter": {
"hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.8.5 64-bit ('base': conda)"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
notebook/test/__general.ipynb 0 → 100644
View file @ 1bc644a1
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performance of Randperm on CPU/GPU"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Node Random perm on host: host duration 6384.1ms, device duration 6384.4ms\n",
"Node Random perm on device: host duration 2525.0ms, device duration 2525.0ms\n"
]
}
],
"source": [
"from common import *\n",
"from utils.profile import debug_profile\n",
"from utils.mem_profiler import MemProfiler\n",
"\n",
"with debug_profile(\"Random perm on host\"):\n",
" torch.randperm(1024 * 1024 * 100)\n",
"\n",
"with debug_profile(\"Random perm on device\"),\\\n",
" MemProfiler(\"Random perm on host\", device=\"cuda:3\"):\n",
" torch.randperm(1024 * 1024 * 100, device=\"cuda:3\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \\_\\_getattribute\\_\\_ Method"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a.a: 1\n",
"a.b: 2\n"
]
},
{
"ename": "AttributeError",
"evalue": "'A' object has no attribute 'c'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-bdb259af4410>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a.a:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a.b:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a.c:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-3-bdb259af4410>\u001b[0m in \u001b[0;36m__getattribute__\u001b[0;34m(self, _A__name)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-3-bdb259af4410>\u001b[0m in \u001b[0;36m__getattribute__\u001b[0;34m(self, _A__name)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m__name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'A' object has no attribute 'c'"
]
}
],
"source": [
"class A(object):\n",
"\n",
"\n",
" def __init__(self, a, **extra) -> None:\n",
" super().__init__()\n",
" self.a = a\n",
" self.extra = extra\n",
"\n",
" def __getattribute__(self, __name: str):\n",
" try:\n",
" return super().__getattribute__(__name)\n",
" except AttributeError as e:\n",
" try:\n",
" return self.extra[__name]\n",
" except KeyError:\n",
" pass\n",
" err = e\n",
" raise err\n",
"\n",
"\n",
"a = A(a=1, b=2)\n",
"\n",
"\n",
"print(\"a.a:\", a.a)\n",
"print(\"a.b:\", a.b)\n",
"print(\"a.c:\", a.c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performance of Various Select/Scatter Methods"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.010159730911254883 tensor(100., device='cuda:0')\n",
"0.011237859725952148 tensor(1400., device='cuda:0')\n",
"0.032263755798339844 tensor(2700., device='cuda:0')\n",
"0.02148723602294922 tensor(4.1723e-07, device='cuda:0')\n",
"0.009927511215209961 tensor(4.1723e-07, device='cuda:0')\n",
"Mask set 0.02173590660095215\n",
"Inplace mask scatter 0.0041882991790771484\n",
"Mask scatter 0.00580906867980957\n",
"Index set 0.03358888626098633\n",
"Index put 0.01044917106628418\n"
]
}
],
"source": [
"from common import *\n",
"from time import time\n",
"\n",
"a = torch.zeros(200000, device=\"cuda\")\n",
"i = torch.randint(0, a.shape[0], [500000], device=\"cuda\")\n",
"start = time()\n",
"for _ in range(100):\n",
" a[i] += 1\n",
"end = time()\n",
"print(end - start, a.max())\n",
"start = time()\n",
"for _ in range(100):\n",
" a.index_add_(0, i, torch.ones_like(i, dtype=torch.float))\n",
"end = time()\n",
"print(end - start, a.max())\n",
"\n",
"start = time()\n",
"for _ in range(100):\n",
" ui, n = i.unique(return_counts=True)\n",
" a[ui] += n\n",
"end = time()\n",
"print(end - start, a.max())\n",
"\n",
"\n",
"a = torch.rand(2000, 2000, device=\"cuda\") - .5\n",
"m = a > 0\n",
"\n",
"start = time()\n",
"for _ in range(100):\n",
" b = a[m]\n",
"end = time()\n",
"print(end - start, b.min())\n",
"\n",
"start = time()\n",
"for _1 in range(20):\n",
" m1 = m.nonzero(as_tuple=True)\n",
" for _ in range(5):\n",
" b = a[m1]\n",
"end = time()\n",
"print(end - start, b.min())\n",
"\n",
"\n",
"c = torch.rand_like(b)\n",
"\n",
"start = time()\n",
"for _ in range(100):\n",
" a[m] = c\n",
"end = time()\n",
"print(\"Mask set\", end - start)\n",
"\n",
"start = time()\n",
"for _ in range(100):\n",
" a.masked_scatter_(m, c)\n",
"end = time()\n",
"print(\"Inplace mask scatter\", end - start)\n",
"\n",
"\n",
"start = time()\n",
"for _ in range(100):\n",
" a = a.masked_scatter(m, c)\n",
"end = time()\n",
"print(\"Mask scatter\", end - start)\n",
"\n",
"start = time()\n",
"for _1 in range(20):\n",
" m1 = m.nonzero(as_tuple=True)\n",
" for _ in range(5):\n",
" a[m1] = b\n",
"end = time()\n",
"print(\"Index set\", end - start)\n",
"\n",
"\n",
"start = time()\n",
"for _1 in range(20):\n",
" m1 = m.nonzero(as_tuple=True)\n",
" for _ in range(5):\n",
" a.index_put_(m1, b)\n",
"end = time()\n",
"print(\"Index put\", end - start)"
]
}
],
"metadata": {
"interpreter": {
"hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
},
"kernelspec": {
"display_name": "Python 3.8.12 ('base')",
"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": "3.8.12"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
notebook/test/common.py 0 → 100644
View file @ 1bc644a1
import sys
import torch
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
rootdir = Path(sys.path[0]).absolute().parents[1]
sys.path.append(str(rootdir))
torch.cuda.set_device(0)
\ No newline at end of file
notebook/test_constrast.ipynb → notebook/test/constrast.ipynb
View file @ 1bc644a1
......@@ -6,21 +6,11 @@
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"import torchvision.transforms.functional as trans_f\n",
"\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(2)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n",
"from common import *\n",
"from components import refine\n",
"from utils import img\n",
"\n",
"img = img.load(os.path.join(rootdir, \"data/gas_2021.01.04_all_in_one/output/mid_0536.png\"))\n",
"img = img.load(rootdir / \"data/gas_2021.01.04_all_in_one/output/mid_0536.png\")\n",
"\n",
"fe = 0.2\n",
"leng_sigma = [0,3,5]\n",
......@@ -37,8 +27,7 @@
"img.plot(enhanced)\n",
"plt.title('Enhanced')\n",
"plt.axis('off')\n",
"plt.show()\n",
"\n"
"plt.show()"
]
}
],
......@@ -63,4 +52,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
}
notebook/test/fisheye_undistort.ipynb 0 → 100644
View file @ 1bc644a1
This source diff could not be displayed because it is too large. You can view the blob instead.
notebook/test_image_filter.ipynb → notebook/test/image_filter.ipynb
View file @ 1bc644a1
......@@ -6,28 +6,17 @@
"metadata": {},
"outputs": [],
"source": [
"# 图像去噪平滑滤波\n",
"# 使用opencv的自带函数实现，与自编写作比较\n",
"# 产生椒盐噪声，高斯噪声等\n",
"# 使用中值滤波，平均滤波，高斯滤波，方框滤波\n",
"import sys\n",
"import os\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"\n",
"import numpy as np\n",
"import math\n",
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"import torch.nn.functional as nn_f\n",
"import torch\n",
"from loss.perc_loss import *\n",
"\n",
"from common import *\n",
"from utils import math\n",
"from loss import *\n",
"\n",
"loss = VGGPerceptualLoss().to('cuda')\n",
"\n",
"def psnr(input, gt):\n",
" input, gt = torch.from_numpy(input / 255).permute(2, 0, 1)[None, :].to('cuda', torch.float32), torch.from_numpy(gt / 255).permute(2, 0, 1)[None, :].to('cuda', torch.float32)\n",
" rmse = math.sqrt(nn_f.mse_loss(input, gt))\n",
" rmse = math.sqrt(mse_loss(input, gt))\n",
" #diff = target / 255 - ref / 255\n",
" #rmse = math.sqrt(np.mean(diff ** 2.))\n",
" #return rmse\n",
......@@ -35,8 +24,8 @@
"\n",
"\n",
"for i in range(3):\n",
" image_gt = cv2.imread(os.path.join (rootdir, 'data/gas_fovea_2020.12.31/train/view_%04d.png' % i))\n",
" image = cv2.imread(os.path.join (rootdir, 'data/gas_fovea_2020.12.31/new_fovea_rgb@nmsl-rgb_e10_fc128x4_d1-50_s32/output/model-epoch_300/train/out_view_%04d.png' % i))\n",
" image_gt = cv2.imread(rootdir / 'data/gas_fovea_2020.12.31/train/view_%04d.png' % i)\n",
" image = cv2.imread(rootdir / 'data/gas_fovea_2020.12.31/new_fovea_rgb@nmsl-rgb_e10_fc128x4_d1-50_s32/output/model-epoch_300/train/out_view_%04d.png' % i)\n",
" plt.figure(facecolor='white', figsize=(10,4))\n",
" plt.subplot(2, 3, 1)\n",
" plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
......@@ -112,4 +101,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
}
notebook/test_data_loader.ipynb → notebook/test/load_dataset.ipynb
View file @ 1bc644a1
......@@ -6,29 +6,18 @@
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"%matplotlib inline\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"rootdir = os.path.abspath('../')\n",
"sys.path.append(rootdir)\n",
"\n",
"from common import *\n",
"from utils import img\n",
"from utils import sphere\n",
"from utils import device\n",
"from utils import misc\n",
"from utils.mem_profiler import *\n",
"from data.dataset_factory import DatasetFactory\n",
"from data.loader import DataLoader\n",
"\n",
"# Select device\n",
"torch.cuda.set_device(0)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"from data import Dataset, RaysLoader\n",
"\n",
"MemProfiler.enable = True\n"
"MemProfiler.enable = False"
]
},
{
......@@ -57,27 +46,28 @@
"#DATA_DESC_FILE = f'{rootdir}/data/__nerf/horns/images_4.json'\n",
"#DATA_DESC_FILE = f'{rootdir}/data/__new/city_fovea_r360x80_t5.0/train1.json'\n",
"#DATA_DESC_FILE = f'{rootdir}/data/__captured/room/train.json'\n",
"DATA_DESC_FILE = f'{rootdir}/data/__pano/stones_fovea_t1.0/train.json'\n",
"DATA_DESC_FILE = f'{rootdir}/data/__captured/bedroom/images4_train.json'\n",
"\n",
"MemProfiler.print_memory_stats('Start')\n",
"\n",
"dataset = DatasetFactory.load(DATA_DESC_FILE)\n",
"dataset = Dataset(DATA_DESC_FILE)\n",
"res = dataset.res\n",
"data_loader = DataLoader(dataset, res[0] * res[1], chunk_max_items=6e8)\n",
"data_loader = RaysLoader(dataset, res[0] * res[1], device=torch.device(\"cuda\"))\n",
"\n",
"MemProfiler.print_memory_stats('After dataset loaded')\n",
"\n",
"fig = plt.figure(figsize=(12, 6))\n",
"i = 0\n",
"for indices, rays_o, rays_d, extras in data_loader:\n",
"for data in data_loader:\n",
" if i >= 4:\n",
" break\n",
" plt.subplot(2, 2, i + 1)\n",
" img.plot(extras['colors'].view(1, res[0], res[1], 3))\n",
" img.plot(data['color'].view(1, res[0], res[1], 3))\n",
" MemProfiler.print_memory_stats(f'After view {i} is plotted')\n",
" i += 1\n",
" time.sleep(1)\n",
"\n",
"#plt.show()\n",
"MemProfiler.print_memory_stats(f'After all views are plotted')"
]
},
......@@ -88,23 +78,23 @@
"outputs": [],
"source": [
"selector = torch.arange(res[0] * res[1]).reshape(res[0], res[1])\n",
"selector = selector[1024-512:1024+512:10, 2048-512:2048+512:5].flatten()\n",
"selector = selector[::3, ::3].flatten()\n",
"idx_range = [0, 4, 20, 24, 62, 100, 104, 120, 124]\n",
"for r in torch.arange(3, 3.5, 0.1):\n",
"for r in torch.arange(11, 50, 5):\n",
" p = None\n",
" centers = None\n",
" pixels = None\n",
" idx = 0\n",
" MemProfiler.print_memory_stats(f'Before iter')\n",
" for indices, rays_o, rays_d, extras in data_loader:\n",
" for data in data_loader:\n",
" if idx > max(idx_range):\n",
" break\n",
" if idx not in idx_range:\n",
" idx += 1\n",
" continue\n",
" colors = extras['colors'][selector]\n",
" rays_o = rays_o[selector]\n",
" rays_d = rays_d[selector]\n",
" colors = data['color'][selector]\n",
" rays_o = data['rays_o'][selector]\n",
" rays_d = data['rays_d'][selector]\n",
" r = torch.tensor([[r]], device=device.default())\n",
" p_ = misc.torch2np(sphere.ray_sphere_intersect(rays_o, rays_d, r)[0].view(-1, 3))\n",
" p = p_ if p is None else np.concatenate((p, p_), axis=0)\n",
......@@ -118,7 +108,6 @@
" plt.ylabel('z')\n",
" plt.title('r = %f' % r)\n",
" ax.scatter([0], [0], [0], color=\"k\", s=10)\n",
" print(p.shape, pixels.shape)\n",
" ax.scatter(p[:, 0], p[:, 2], p[:, 1], color=pixels, s=0.5)\n",
" ax.view_init(elev=0, azim=-90)\n"
]
......@@ -126,10 +115,10 @@
],
"metadata": {
"interpreter": {
"hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
"hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3.8.12 ('base')",
"language": "python",
"name": "python3"
},
......@@ -143,7 +132,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
"version": "3.8.12"
}
},
"nbformat": 4,
......
notebook/test/sphere.ipynb 0 → 100644
View file @ 1bc644a1
This source diff could not be displayed because it is too large. You can view the blob instead.
notebook/test_eccv.ipynb → notebook/test/test_eccv.ipynb
View file @ 1bc644a1
......@@ -80,7 +80,7 @@
"\n",
"images = img.load_seq(f'{datadir}/img%02d.png', 16, permute=False)\n",
"res=(int(p[0, 0]), int(p[0, 1]))\n",
"cam = CameraParam({\"fy\":-p[0, 2], \"fx\": p[0, 2], \"cx\":res[1]//2, \"cy\":res[0]//2}, res)\n",
"cam = Camera({\"fy\":-p[0, 2], \"fx\": p[0, 2], \"cx\":res[1]//2, \"cy\":res[0]//2}, res)\n",
"views = Trans(torch.tensor(t, dtype=torch.float), torch.tensor(r, dtype=torch.float))\n",
"_rays_o, _rays_d = cam.get_global_rays(views, flatten=True)\n",
"_patches = images.flatten(1, 2)\n",
......
notebook/test_foveation.ipynb → notebook/test/test_foveation.ipynb
View file @ 1bc644a1
......@@ -6,29 +6,28 @@
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import sys\n",
"import os\n",
"import struct\n",
"import torch\n",
"import torch.nn as nn\n",
"import matplotlib.pyplot as plt\n",
"\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(0)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n",
"torch.set_grad_enabled(False)\n",
"\n",
"from utils import img\n",
"from utils import device\n",
"from utils.view import *\n",
"from components.foveation import Foveation\n",
"\n",
"\n",
"def gen_mask_file(layer_mask, filename):\n",
" indices = torch.arange(layer_mask.size(0) * layer_mask.size(1),\n",
" device=layer_mask.device).view_as(layer_mask)\n",
" indices = indices[layer_mask>=0]\n",
" inverseIndices = torch.ones(layer_mask.size(0) * layer_mask.size(1), device=layer_mask.device, dtype=torch.long) * -1\n",
" indices = indices[layer_mask >= 0]\n",
" inverseIndices = torch.ones(layer_mask.size(0) * layer_mask.size(1),\n",
" device=layer_mask.device, dtype=torch.long) * -1\n",
" inverseIndices[indices] = torch.arange(indices.size(0), device=layer_mask.device)\n",
" with open(filename, 'wb') as fp:\n",
" fp.write(indices.size(0).to_bytes(4, 'little'))\n",
......@@ -36,32 +35,46 @@
" fp.write(inverseIndices.size(0).to_bytes(4, 'little'))\n",
" fp.write(struct.pack(f\"<{inverseIndices.size(0)}i\", *inverseIndices))\n",
"\n",
"\n",
"foveation = Foveation([20, 45, 110], [(256, 256), (256, 256), (256, 230)], (1600, 1440))\n",
"layers_mask = foveation.get_layers_mask()\n",
"layers_mask = foveation.get_layers_mask(gaze=(0, 0))\n",
"\n",
"plt.figure(figsize=(12, 4))\n",
"for i, mask in enumerate(layers_mask):\n",
" colored_mask = torch.zeros(mask.size(0), mask.size(1), 3, device=mask.device)\n",
" c = torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1]], device=mask.device)\n",
" c = torch.tensor([\n",
" [1, 1, 1, 1, 1, 1],\n",
" [1, 1, 1, 1, 1, 1],\n",
" [1, 1, 1, 0, 0, 0]\n",
" ], device=mask.device)\n",
" for bi in range(3):\n",
" region = torch.logical_and(mask >= bi, mask < bi + 1)\n",
" colored_mask[region] = c[bi] + (c[-1] - c[bi]) * (mask[region][..., None] - bi)\n",
" colored_mask[region] = c[bi, :3] + (c[bi, 3:] - c[bi, :3]) * (mask[region][..., None] - bi)\n",
" plt.subplot(1, len(layers_mask), i + 1)\n",
" img.plot(colored_mask)\n",
" img.save(colored_mask, f\"blend_{i}.png\")\n",
" n_skipped = torch.sum(mask < 0)\n",
" n_tot = len(mask.flatten())\n",
" print (f\"Layer {i}: {n_skipped}({n_skipped / n_tot * 100:.2f}%) pixels are masked as skipped, {n_tot - n_skipped} pixels need to be inferred\")\n",
" print(f\"Layer {i}: {n_skipped}({n_skipped / n_tot * 100:.2f}%) pixels are masked as skipped, \"\n",
" f\"{n_tot - n_skipped}({(n_tot - n_skipped) / n_tot * 100:.2f}%) pixels need to be inferred\")\n",
"\n",
"gen_mask_file(layers_mask[0], 'fovea.mask')\n",
"gen_mask_file(layers_mask[1], 'mid.mask')"
"plt.figure(figsize=(12, 4))\n",
"for i, mask in enumerate(layers_mask):\n",
" binary_mask = (mask >= 0).float().expand(3, -1, -1)\n",
" plt.subplot(1, len(layers_mask), i + 1)\n",
" img.plot(binary_mask)\n",
" img.save(binary_mask, f\"mask_{i}.png\")\n",
"#gen_mask_file(layers_mask[0], 'fovea.mask')\n",
"#gen_mask_file(layers_mask[1], 'mid.mask')\n"
]
}
],
"metadata": {
"interpreter": {
"hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
"hash": "65406b00395a48e1d89cf658ae895e7869e05878f5469716b06a752a3915211c"
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3.8.12 ('base')",
"language": "python",
"name": "python3"
},
......@@ -75,7 +88,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
"version": "3.8.12"
},
"metadata": {
"interpreter": {
......
notebook/test_mono_gen.ipynb → notebook/test/test_mono_gen.ipynb
View file @ 1bc644a1
......@@ -25,18 +25,11 @@
}
],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"from common import *\n",
"import torch.nn as nn\n",
"\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(0)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
"from configs.spherical_view_syn import SphericalViewSynConfig\n",
"from utils import netio\n",
"from utils import img\n",
"from utils import device\n",
......
notebook/test_mono_view.ipynb → notebook/test/test_mono_view.ipynb
View file @ 1bc644a1
......@@ -137,7 +137,7 @@
"test_view = views.get(*view_coord)\n",
"\n",
"cams = [\n",
" view.CameraParam({\n",
" view.Camera({\n",
" \"fov\": fov_list[i],\n",
" \"cx\": 0.5,\n",
" \"cy\": 0.5,\n",
......@@ -147,7 +147,7 @@
"]\n",
"fovea_cam, mid_cam, periph_cam = cams[0], cams[1], cams[2]\n",
"#guide_cam = ref_dataset.cam_params\n",
"vr_cam = view.CameraParam({\n",
"vr_cam = view.Camera({\n",
" 'fov': fov_list[-1],\n",
" 'cx': 0.5,\n",
" 'cy': 0.5,\n",
......
notebook/test_refinement.ipynb → notebook/test/test_refinement.ipynb
View file @ 1bc644a1
......@@ -63,7 +63,7 @@
" view_dataset.samples)\n",
"ref_indices = torch.arange(ref_dataset.n_views, device=device.default()).view(\n",
" ref_dataset.samples)\n",
"cam_params = view.CameraParam({\n",
"cam_params = view.Camera({\n",
" \"fov\": 20,\n",
" \"cx\": 0.5,\n",
" \"cy\": 0.5,\n",
......@@ -76,7 +76,7 @@
"netio.load(model_path, net)\n",
"print('Net loaded.')\n",
"\n",
"vr_cam = view.CameraParam({\n",
"vr_cam = view.Camera({\n",
" 'fov': 110,\n",
" 'cx': 0.5,\n",
" 'cy': 0.5,\n",
......
notebook/test_voxel.ipynb → notebook/test/voxel.ipynb
View file @ 1bc644a1
......@@ -6,13 +6,7 @@
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import sys\n",
"import os\n",
"\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"\n",
"from common import *\n",
"from utils.voxels import *\n",
"\n",
"bbox, steps = torch.tensor([[-2, -3.14159, 1], [2, 3.14159, 0]]), torch.tensor([2, 3, 3])\n",
......@@ -94,7 +88,7 @@
"voxel_indices_of_new_corner = voxel_indices_in_grid[to_flat_indices(to_grid_coords(new_corners, bbox, steps).min(steps - 1), steps) + 1]\n",
"print(voxel_indices_of_new_corner)\n",
"p_of_new_corners = (new_corners - voxels[voxel_indices_of_new_corner]) / voxel_size + .5\n",
"print(((new_corners - trilinear_interp(p_of_new_corners, emb(corner_indices[voxel_indices_of_new_corner]))) > 1e-6).sum())"
"print(((new_corners - linear_interp(p_of_new_corners, emb(corner_indices[voxel_indices_of_new_corner]))) > 1e-6).sum())"
]
}
],
......
notebook/test_lf_syn.ipynb deleted 100644 → 0
View file @ 6294701e
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import matplotlib.pyplot as plt\n",
"from data.lf_syn import LightFieldSynDataset\n",
"from utils import img\n",
"from utils import math\n",
"from nets.trans_unet import LatentSpaceTransformer\n",
"\n",
"device = torch.device(\"cuda:2\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test data loader"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DATA_DIR = '../data/lf_syn_2020.12.23'\n",
"TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'\n",
"\n",
"train_dataset = LightFieldSynDataset(TRAIN_DATA_DESC_FILE)\n",
"train_data_loader = torch.utils.data.DataLoader(\n",
" dataset=train_dataset,\n",
" batch_size=3,\n",
" num_workers=8,\n",
" pin_memory=True,\n",
" shuffle=True,\n",
" drop_last=False)\n",
"print(len(train_data_loader))\n",
"\n",
"print(train_dataset.cam_params)\n",
"print(train_dataset.sparse_view_positions)\n",
"print(train_dataset.diopter_of_layers)\n",
"plt.figure()\n",
"img.plot(train_dataset.sparse_view_images[0])\n",
"plt.figure()\n",
"img.plot(train_dataset.sparse_view_depths[0] / 255 * 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test disparity wrapper"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"\n",
"transformer = LatentSpaceTransformer(train_dataset.sparse_view_images.size()[2],\n",
" train_dataset.cam_params,\n",
" train_dataset.diopter_of_layers,\n",
" train_dataset.sparse_view_positions)\n",
"novel_views = torch.stack([\n",
" train_dataset.view_positions[13],\n",
" train_dataset.view_positions[30],\n",
" train_dataset.view_positions[57],\n",
"], dim=0)\n",
"trans_images = transformer(train_dataset.sparse_view_images.to(device),\n",
" train_dataset.sparse_view_depths.to(device),\n",
" novel_views)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"mask = (torch.sum(trans_images[0], 1) > math.tiny).to(dtype=torch.float)\n",
"blended = torch.sum(trans_images[0], 0)\n",
"weight = torch.sum(mask, 0)\n",
"blended = blended / weight.unsqueeze(0)\n",
"\n",
"plt.figure(figsize=(6, 6))\n",
"img.plot(train_dataset.view_images[13])\n",
"plt.figure(figsize=(6, 6))\n",
"img.plot(blended)\n",
"plt.figure(figsize=(12, 6))\n",
"plt.subplot(2, 4, 1)\n",
"img.plot(train_dataset.sparse_view_images[0])\n",
"plt.subplot(2, 4, 2)\n",
"img.plot(train_dataset.sparse_view_images[1])\n",
"plt.subplot(2, 4, 3)\n",
"img.plot(train_dataset.sparse_view_images[2])\n",
"plt.subplot(2, 4, 4)\n",
"img.plot(train_dataset.sparse_view_images[3])\n",
"\n",
"plt.subplot(2, 4, 5)\n",
"img.plot(trans_images[0, 0])\n",
"plt.subplot(2, 4, 6)\n",
"img.plot(trans_images[0, 1])\n",
"plt.subplot(2, 4, 7)\n",
"img.plot(trans_images[0, 2])\n",
"plt.subplot(2, 4, 8)\n",
"img.plot(trans_images[0, 3])\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7.9 64-bit ('pytorch': conda)",
"name": "python379jvsc74a57bd0660ca2a75467d3af74a68fcc6f40bc78ab96b99ff17d2f100b5ca821fbb183f2"
},
"language_info": {
"name": "python",
"version": ""
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
notebook/test_sphere.ipynb deleted 100644 → 0
View file @ 6294701e
This source diff could not be displayed because it is too large. You can view the blob instead.
notebook/test_ycbcr.ipynb deleted 100644 → 0
View file @ 6294701e
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"\n",
"os.chdir('../')\n",
"sys.path.append(os.getcwd())\n",
"\n",
"from utils import img\n",
"from utils import color\n",
"\n",
"input_img = img.load('data/gas_fovea_2020.12.31/upsampling_test/input/out_view_0000.png')\n",
"ycbcr = color.rgb2ycbcr(input_img)\n",
"rgb = color.ycbcr2rgb(ycbcr)\n",
"\n",
"plt.figure()\n",
"img.plot(input_img)\n",
"plt.figure()\n",
"plt.subplot(1, 4, 1)\n",
"img.plot(ycbcr)\n",
"plt.subplot(1, 4, 2)\n",
"img.plot(ycbcr[:, 0])\n",
"plt.subplot(1, 4, 3)\n",
"img.plot(ycbcr[:, 1])\n",
"plt.subplot(1, 4, 4)\n",
"img.plot(ycbcr[:, 2])\n",
"plt.figure()\n",
"img.plot(rgb)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7.9 64-bit ('pytorch': conda)",
"name": "python379jvsc74a57bd0660ca2a75467d3af74a68fcc6f40bc78ab96b99ff17d2f100b5ca821fbb183f2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
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