Skip to content

GitLab

Commit 2a1d7973 authored by Nianchen Deng's avatar Nianchen Deng
Browse files

for batch infer evaluation on multiple hosts

parent 7e0ade21
Expand all Show whitespace changes
Inline Side-by-side
notebook/gen_demo_views.ipynb deleted 100644 → 0
View file @ 7e0ade21
This diff is collapsed.
notebook/gen_for_eval.ipynb
View file @ 2a1d7973
...@@ -2,23 +2,16 @@ ...@@ -2,23 +2,16 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"source": [ "source": [
"import sys\n", "import sys\n",
"import os\n", "import os\n",
"import torch\n", "import torch\n",
"\n", "\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../'))\n", "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(2)\n", "torch.cuda.set_device(2)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n", "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n", "torch.autograd.set_grad_enabled(False)\n",
...@@ -74,24 +67,12 @@ ...@@ -74,24 +67,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Change working directory to /home/dengnc/deep_view_syn/data/__0_user_study/us_mc_all_in_one\n",
"Load net from fovea@nmsl-rgb_e10_fc128x4_d1-50_s32.pth ...\n",
"Load net from periph@nnmsl-rgb_e10_fc64x4_d1-50_s16.pth ...\n",
"Dataset loaded.\n",
"views: [110]\n"
]
}
],
"source": [ "source": [
"scene = 'mc'\n", "scene = 'mc'\n",
"os.chdir(sys.path[0] + '/../data/' + scenes[scene])\n", "os.chdir(os.path.join(rootdir, 'data/' + scenes[scene]))\n",
"print('Change working directory to ', os.getcwd())\n", "print('Change working directory to ', os.getcwd())\n",
"\n", "\n",
"fovea_net = load_net(find_file('fovea'))\n", "fovea_net = load_net(find_file('fovea'))\n",
...@@ -128,7 +109,7 @@ ...@@ -128,7 +109,7 @@
" # ), test_view.r), mono_trans=test_view, ret_raw=True)\n", " # ), test_view.r), mono_trans=test_view, ret_raw=True)\n",
" #plot_figures(images, center)\n", " #plot_figures(images, center)\n",
"\n", "\n",
" outputdir = '/home/dengnc/deep_view_syn/data/__1_eval/output_mono_periph/ref_as_right_eye/%s/' % scene\n", " outputdir = '../__1_eval/output_mono_periph/ref_as_right_eye/%s/' % scene\n",
" misc.create_dir(outputdir)\n", " misc.create_dir(outputdir)\n",
" #for key in images:\n", " #for key in images:\n",
" key = 'blended'\n", " key = 'blended'\n",
...@@ -137,21 +118,9 @@ ...@@ -137,21 +118,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"ename": "TypeError",
"evalue": "gen() takes 3 positional arguments but 4 were given",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-245bea67ea44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mcenter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\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 6\u001b[0m \u001b[0mtest_view\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mviews\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_view\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\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 8\u001b[0m \u001b[0;31m#plot_figures(images, center)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: gen() takes 3 positional arguments but 4 were given"
]
}
],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
"gaze_idx = 0\n", "gaze_idx = 0\n",
......
notebook/gen_teaser.ipynb
View file @ 2a1d7973
...@@ -2,24 +2,17 @@ ...@@ -2,24 +2,17 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"source": [ "source": [
"import sys\n", "import sys\n",
"import os\n", "import os\n",
"import torch\n", "import torch\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../'))\n", "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(2)\n", "torch.cuda.set_device(2)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n", "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n", "\n",
...@@ -97,43 +90,13 @@ ...@@ -97,43 +90,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Change working directory to /home/dengnc/deep_view_syn/data/lobby_all_in_one\n",
"==== Config fovea ====\n",
"Net type: nmsl\n",
"Encode dim: 10\n",
"Optimizer decay: 0\n",
"Normalize: False\n",
"Direction as input: False\n",
"Full-connected network parameters: {'nf': 128, 'n_layers': 4, 'skips': []}\n",
"Sample parameters {'spherical': True, 'depth_range': (1.0, 50.0), 'n_samples': 32, 'perturb_sample': False, 'lindisp': True, 'inverse_r': True}\n",
"==========================\n",
"Load net from fovea@nmsl-rgb_e10_fc128x4_d1.00-50.00_s32.pth ...\n",
"==== Config periph ====\n",
"Net type: nnmsl\n",
"Encode dim: 10\n",
"Optimizer decay: 0\n",
"Normalize: False\n",
"Direction as input: False\n",
"Full-connected network parameters: {'nf': 64, 'n_layers': 4, 'skips': []}\n",
"Sample parameters {'spherical': True, 'depth_range': (1.0, 50.0), 'n_samples': 16, 'perturb_sample': False, 'lindisp': True, 'inverse_r': True}\n",
"==========================\n",
"Load net from periph@nnmsl-rgb_e10_fc64x4_d1.00-50.00_s16.pth ...\n",
"Dataset loaded.\n",
"views: [13]\n"
]
}
],
"source": [ "source": [
"#os.chdir(sys.path[0] + '/../data/__0_user_study/us_gas_all_in_one')\n", "#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_gas_all_in_one'))\n",
"#os.chdir(sys.path[0] + '/../data/__0_user_study/us_mc_all_in_one')\n", "#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_mc_all_in_one'))\n",
"os.chdir(sys.path[0] + '/../data/lobby_all_in_one')\n", "os.chdir(os.path.join(rootdir, 'data/lobby_all_in_one'))\n",
"print('Change working directory to ', os.getcwd())\n", "print('Change working directory to ', os.getcwd())\n",
"torch.autograd.set_grad_enabled(False)\n", "torch.autograd.set_grad_enabled(False)\n",
"\n", "\n",
......
notebook/gen_test.ipynb
View file @ 2a1d7973
...@@ -19,7 +19,8 @@ ...@@ -19,7 +19,8 @@
"import torch\n", "import torch\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../'))\n", "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(2)\n", "torch.cuda.set_device(2)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n", "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n", "\n",
...@@ -97,43 +98,13 @@ ...@@ -97,43 +98,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Change working directory to /home/dengnc/deep_view_syn/data/__0_user_study/us_gas_all_in_one\n",
"==== Config fovea ====\n",
"Net type: nmsl\n",
"Encode dim: 10\n",
"Optimizer decay: 0\n",
"Normalize: False\n",
"Direction as input: False\n",
"Full-connected network parameters: {'nf': 128, 'n_layers': 4, 'skips': []}\n",
"Sample parameters {'spherical': True, 'depth_range': (1.0, 50.0), 'n_samples': 32, 'perturb_sample': False, 'lindisp': True, 'inverse_r': True}\n",
"==========================\n",
"Load net from fovea@nmsl-rgb_e10_fc128x4_d1-50_s32.pth ...\n",
"==== Config periph ====\n",
"Net type: nnmsl\n",
"Encode dim: 10\n",
"Optimizer decay: 0\n",
"Normalize: False\n",
"Direction as input: False\n",
"Full-connected network parameters: {'nf': 64, 'n_layers': 4, 'skips': []}\n",
"Sample parameters {'spherical': True, 'depth_range': (1.0, 50.0), 'n_samples': 16, 'perturb_sample': False, 'lindisp': True, 'inverse_r': True}\n",
"==========================\n",
"Load net from periph@nnmsl-rgb_e10_fc64x4_d1-50_s16.pth ...\n",
"Dataset loaded.\n",
"views: [110]\n"
]
}
],
"source": [ "source": [
"os.chdir(sys.path[0] + '/../data/__0_user_study/us_gas_all_in_one')\n", "os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_gas_all_in_one'))\n",
"#os.chdir(sys.path[0] + '/../data/__0_user_study/us_mc_all_in_one')\n", "#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_mc_all_in_one'))\n",
"#os.chdir(sys.path[0] + '/../data/lobby_all_in_one')\n", "#os.chdir(os.path.join(rootdir, 'data/__0_user_study/lobby_all_in_one'))\n",
"print('Change working directory to ', os.getcwd())\n", "print('Change working directory to ', os.getcwd())\n",
"torch.autograd.set_grad_enabled(False)\n", "torch.autograd.set_grad_enabled(False)\n",
"\n", "\n",
...@@ -156,133 +127,9 @@ ...@@ -156,133 +127,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"GetRays: 580.4ms\n",
"Sample: 2.1ms\n",
"Encode: 2.2ms\n",
"Rays: tensor([[-0.1711, 0.1711, 0.9702],\n",
" [-0.1685, 0.1711, 0.9707],\n",
" [-0.1659, 0.1713, 0.9712],\n",
" ...,\n",
" [ 0.1633, -0.1687, 0.9722],\n",
" [ 0.1660, -0.1687, 0.9717],\n",
" [ 0.1686, -0.1686, 0.9712]], device='cuda:2')\n",
"Spherical coords: tensor([[[1.0000, 1.7454, 1.3988],\n",
" [0.9684, 1.7454, 1.3988],\n",
" [0.9368, 1.7454, 1.3988],\n",
" ...,\n",
" [0.0832, 1.7454, 1.3988],\n",
" [0.0516, 1.7454, 1.3988],\n",
" [0.0200, 1.7454, 1.3988]],\n",
"\n",
" [[1.0000, 1.7426, 1.3988],\n",
" [0.9684, 1.7426, 1.3988],\n",
" [0.9368, 1.7426, 1.3988],\n",
" ...,\n",
" [0.0832, 1.7426, 1.3988],\n",
" [0.0516, 1.7426, 1.3988],\n",
" [0.0200, 1.7426, 1.3988]],\n",
"\n",
" [[1.0000, 1.7400, 1.3987],\n",
" [0.9684, 1.7400, 1.3987],\n",
" [0.9368, 1.7400, 1.3987],\n",
" ...,\n",
" [0.0832, 1.7400, 1.3987],\n",
" [0.0516, 1.7400, 1.3987],\n",
" [0.0200, 1.7400, 1.3987]],\n",
"\n",
" ...,\n",
"\n",
" [[1.0000, 1.4043, 1.7403],\n",
" [0.9684, 1.4043, 1.7403],\n",
" [0.9368, 1.4043, 1.7403],\n",
" ...,\n",
" [0.0832, 1.4043, 1.7403],\n",
" [0.0516, 1.4043, 1.7403],\n",
" [0.0200, 1.4043, 1.7403]],\n",
"\n",
" [[1.0000, 1.4016, 1.7403],\n",
" [0.9684, 1.4016, 1.7403],\n",
" [0.9368, 1.4016, 1.7403],\n",
" ...,\n",
" [0.0832, 1.4016, 1.7403],\n",
" [0.0516, 1.4016, 1.7403],\n",
" [0.0200, 1.4016, 1.7403]],\n",
"\n",
" [[1.0000, 1.3989, 1.7402],\n",
" [0.9684, 1.3989, 1.7402],\n",
" [0.9368, 1.3989, 1.7402],\n",
" ...,\n",
" [0.0832, 1.3989, 1.7402],\n",
" [0.0516, 1.3989, 1.7402],\n",
" [0.0200, 1.3989, 1.7402]]], device='cuda:2')\n",
"Depths: tensor([[ 1.0001, 1.0327, 1.0675, ..., 12.0161, 19.3760, 50.0026],\n",
" [ 1.0000, 1.0327, 1.0675, ..., 12.0159, 19.3757, 50.0017],\n",
" [ 1.0000, 1.0326, 1.0675, ..., 12.0151, 19.3744, 49.9984],\n",
" ...,\n",
" [ 0.9999, 1.0325, 1.0674, ..., 12.0140, 19.3726, 49.9938],\n",
" [ 0.9999, 1.0326, 1.0674, ..., 12.0144, 19.3732, 49.9954],\n",
" [ 1.0000, 1.0326, 1.0675, ..., 12.0152, 19.3745, 49.9987]],\n",
" device='cuda:2')\n",
"Encoded: tensor([[[ 1.0000, 1.7454, 1.3988, ..., -0.9968, 0.1395, 0.9952],\n",
" [ 0.9684, 1.7454, 1.3988, ..., 0.8486, 0.1395, 0.9952],\n",
" [ 0.9368, 1.7454, 1.3988, ..., -0.5103, 0.1395, 0.9952],\n",
" ...,\n",
" [ 0.0832, 1.7454, 1.3988, ..., 0.1988, 0.1395, 0.9952],\n",
" [ 0.0516, 1.7454, 1.3988, ..., 0.2742, 0.1395, 0.9952],\n",
" [ 0.0200, 1.7454, 1.3988, ..., -0.6857, 0.1395, 0.9952]],\n",
"\n",
" [[ 1.0000, 1.7426, 1.3988, ..., -0.9968, 0.9999, 0.9953],\n",
" [ 0.9684, 1.7426, 1.3988, ..., 0.8486, 0.9999, 0.9953],\n",
" [ 0.9368, 1.7426, 1.3988, ..., -0.5103, 0.9999, 0.9953],\n",
" ...,\n",
" [ 0.0832, 1.7426, 1.3988, ..., 0.1988, 0.9999, 0.9953],\n",
" [ 0.0516, 1.7426, 1.3988, ..., 0.2742, 0.9999, 0.9953],\n",
" [ 0.0200, 1.7426, 1.3988, ..., -0.6857, 0.9999, 0.9953]],\n",
"\n",
" [[ 1.0000, 1.7400, 1.3987, ..., -0.9968, 0.2253, 0.9881],\n",
" [ 0.9684, 1.7400, 1.3987, ..., 0.8486, 0.2253, 0.9881],\n",
" [ 0.9368, 1.7400, 1.3987, ..., -0.5103, 0.2253, 0.9881],\n",
" ...,\n",
" [ 0.0832, 1.7400, 1.3987, ..., 0.1988, 0.2253, 0.9881],\n",
" [ 0.0516, 1.7400, 1.3987, ..., 0.2742, 0.2253, 0.9881],\n",
" [ 0.0200, 1.7400, 1.3987, ..., -0.6857, 0.2253, 0.9881]],\n",
"\n",
" ...,\n",
"\n",
" [[ 1.0000, 1.4043, 1.7403, ..., -0.9968, -0.9210, 0.3760],\n",
" [ 0.9684, 1.4043, 1.7403, ..., 0.8486, -0.9210, 0.3760],\n",
" [ 0.9368, 1.4043, 1.7403, ..., -0.5103, -0.9210, 0.3760],\n",
" ...,\n",
" [ 0.0832, 1.4043, 1.7403, ..., 0.1988, -0.9210, 0.3760],\n",
" [ 0.0516, 1.4043, 1.7403, ..., 0.2742, -0.9210, 0.3760],\n",
" [ 0.0200, 1.4043, 1.7403, ..., -0.6857, -0.9210, 0.3760]],\n",
"\n",
" [[ 1.0000, 1.4016, 1.7403, ..., -0.9968, 0.2445, 0.3786],\n",
" [ 0.9684, 1.4016, 1.7403, ..., 0.8486, 0.2445, 0.3786],\n",
" [ 0.9368, 1.4016, 1.7403, ..., -0.5103, 0.2445, 0.3786],\n",
" ...,\n",
" [ 0.0832, 1.4016, 1.7403, ..., 0.1988, 0.2445, 0.3786],\n",
" [ 0.0516, 1.4016, 1.7403, ..., 0.2742, 0.2445, 0.3786],\n",
" [ 0.0200, 1.4016, 1.7403, ..., -0.6857, 0.2445, 0.3786]],\n",
"\n",
" [[ 1.0000, 1.3989, 1.7402, ..., -0.9968, 0.9995, 0.3247],\n",
" [ 0.9684, 1.3989, 1.7402, ..., 0.8486, 0.9995, 0.3247],\n",
" [ 0.9368, 1.3989, 1.7402, ..., -0.5103, 0.9995, 0.3247],\n",
" ...,\n",
" [ 0.0832, 1.3989, 1.7402, ..., 0.1988, 0.9995, 0.3247],\n",
" [ 0.0516, 1.3989, 1.7402, ..., 0.2742, 0.9995, 0.3247],\n",
" [ 0.0200, 1.3989, 1.7402, ..., -0.6857, 0.9995, 0.3247]]],\n",
" device='cuda:2')\n"
]
}
],
"source": [ "source": [
"test_view = view.Trans(\n", "test_view = view.Trans(\n",
" torch.tensor([[0.0, 0.0, 0.0]], device=device.default()),\n", " torch.tensor([[0.0, 0.0, 0.0]], device=device.default()),\n",
......
notebook/gen_user_study_images.ipynb
View file @ 2a1d7973
...@@ -2,24 +2,17 @@ ...@@ -2,24 +2,17 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"source": [ "source": [
"import sys\n", "import sys\n",
"import os\n", "import os\n",
"import torch\n", "import torch\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../'))\n", "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(2)\n", "torch.cuda.set_device(2)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n", "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n", "\n",
...@@ -132,43 +125,13 @@ ...@@ -132,43 +125,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Change working directory to /home/dengnc/deep_view_syn/data/__0_user_study/us_mc_all_in_one\n",
"==== Config fovea ====\n",
"Net type: nmsl\n",
"Encode dim: 10\n",
"Optimizer decay: 0\n",
"Normalize: False\n",
"Direction as input: False\n",
"Full-connected network parameters: {'nf': 128, 'n_layers': 4, 'skips': []}\n",
"Sample parameters {'spherical': True, 'depth_range': (1.0, 50.0), 'n_samples': 32, 'perturb_sample': False, 'lindisp': True, 'inverse_r': True}\n",
"==========================\n",
"Load net from fovea@nmsl-rgb_e10_fc128x4_d1-50_s32.pth ...\n",
"==== Config periph ====\n",
"Net type: nnmsl\n",
"Encode dim: 10\n",
"Optimizer decay: 0\n",
"Normalize: False\n",
"Direction as input: False\n",
"Full-connected network parameters: {'nf': 64, 'n_layers': 4, 'skips': []}\n",
"Sample parameters {'spherical': True, 'depth_range': (1.0, 50.0), 'n_samples': 16, 'perturb_sample': False, 'lindisp': True, 'inverse_r': True}\n",
"==========================\n",
"Load net from periph@nnmsl-rgb_e10_fc64x4_d1-50_s16.pth ...\n",
"Dataset loaded.\n",
"views: [5, 5, 5, 5, 5]\n"
]
}
],
"source": [ "source": [
"#os.chdir(sys.path[0] + '/../data/__0_user_study/us_gas_all_in_one')\n", "#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_gas_all_in_one'))\n",
"os.chdir(sys.path[0] + '/../data/__0_user_study/us_mc_all_in_one')\n", "os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_mc_all_in_one'))\n",
"#os.chdir(sys.path[0] + '/../data/bedroom_all_in_one')\n", "#os.chdir(os.path.join(rootdir, 'data/__0_user_study/bedroom_all_in_one'))\n",
"print('Change working directory to ', os.getcwd())\n", "print('Change working directory to ', os.getcwd())\n",
"torch.autograd.set_grad_enabled(False)\n", "torch.autograd.set_grad_enabled(False)\n",
"\n", "\n",
...@@ -192,19 +155,9 @@ ...@@ -192,19 +155,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"view_coord: [2, 2, 2, 2, 2]\n",
"shift: 3\n",
"shift: -3\n"
]
}
],
"source": [ "source": [
"centers = [\n", "centers = [\n",
" # ==gas==\n", " # ==gas==\n",
......
notebook/gen_video.ipynb
View file @ 2a1d7973
This diff is collapsed.
notebook/test_constrast.ipynb
View file @ 2a1d7973
This diff is collapsed.
notebook/test_foveation.ipynb 0 → 100644
View file @ 2a1d7973
This diff is collapsed.
notebook/test_image_filter.ipynb
View file @ 2a1d7973
This diff is collapsed.
notebook/test_mono_view.ipynb
View file @ 2a1d7973
...@@ -2,17 +2,9 @@ ...@@ -2,17 +2,9 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"source": [ "source": [
"import sys\n", "import sys\n",
"import os\n", "import os\n",
...@@ -20,7 +12,8 @@ ...@@ -20,7 +12,8 @@
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import torchvision.transforms.functional as trans_f\n", "import torchvision.transforms.functional as trans_f\n",
"\n", "\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../'))\n", "rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(2)\n", "torch.cuda.set_device(2)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n", "print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n", "\n",
...@@ -82,43 +75,13 @@ ...@@ -82,43 +75,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"Change working directory to /home/dengnc/deep_view_syn/data/__0_user_study/us_gas_all_in_one\n",
"==== Config fovea ====\n",
"Net type: nmsl\n",
"Encode dim: 10\n",
"Optimizer decay: 0\n",
"Normalize: False\n",
"Direction as input: False\n",
"Full-connected network parameters: {'nf': 128, 'n_layers': 4, 'skips': []}\n",
"Sample parameters {'spherical': True, 'depth_range': (1.0, 50.0), 'n_samples': 32, 'perturb_sample': False, 'lindisp': True, 'inverse_r': True}\n",
"==========================\n",
"Load net from fovea@nmsl-rgb_e10_fc128x4_d1-50_s32.pth ...\n",
"==== Config periph ====\n",
"Net type: nnmsl\n",
"Encode dim: 10\n",
"Optimizer decay: 0\n",
"Normalize: False\n",
"Direction as input: False\n",
"Full-connected network parameters: {'nf': 64, 'n_layers': 4, 'skips': []}\n",
"Sample parameters {'spherical': True, 'depth_range': (1.0, 50.0), 'n_samples': 16, 'perturb_sample': False, 'lindisp': True, 'inverse_r': True}\n",
"==========================\n",
"Load net from periph@nnmsl-rgb_e10_fc64x4_d1-50_s16.pth ...\n",
"Dataset loaded.\n",
"views: [5, 5, 5, 5, 5]\n"
]
}
],
"source": [ "source": [
"os.chdir(sys.path[0] + '/../data/__0_user_study/us_gas_all_in_one')\n", "os.chdir(os.path.join('data/__0_user_study/us_gas_all_in_one'))\n",
"#os.chdir(sys.path[0] + '/../data/__0_user_study/us_mc_all_in_one')\n", "#os.chdir(os.path.join('data/__0_user_study/us_mc_all_in_one'))\n",
"#os.chdir(sys.path[0] + '/../data/bedroom_all_in_one')\n", "#os.chdir(os.path.join('data/bedroom_all_in_one'))\n",
"print('Change working directory to ', os.getcwd())\n", "print('Change working directory to ', os.getcwd())\n",
"torch.autograd.set_grad_enabled(False)\n", "torch.autograd.set_grad_enabled(False)\n",
"\n", "\n",
...@@ -294,13 +257,6 @@ ...@@ -294,13 +257,6 @@
" img.save(\n", " img.save(\n",
" right_images[key], 'output/mono_test/set%d_%s_r.png' % (set_id, key))\n" " right_images[key], 'output/mono_test/set%d_%s_r.png' % (set_id, key))\n"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
......
notebook/test_refinement.ipynb
View file @ 2a1d7973
This diff is collapsed.
notebook/test_spherical_view_syn.ipynb
View file @ 2a1d7973
This source diff could not be displayed because it is too large. You can view the blob instead.
notebook/test_ycbcr.ipynb
View file @ 2a1d7973
This diff is collapsed.
run_lf_syn.py
View file @ 2a1d7973
import sys import sys
sys.path.append('/e/dengnc')
__package__ = "deep_view_syn"
import os import os
import torch import torch
import torch.optim import torch.optim
......
tools/convert_data_desc.py 0 → 100644
View file @ 2a1d7973
import sys
import os
import json
import argparse
from typing import Mapping
sys.path.append(os.path.abspath(sys.path[0] + '/../'))
from utils import view
parser = argparse.ArgumentParser()
parser.add_argument('-o', '--output', type=str, default='')
parser.add_argument('dataset', type=str)
args = parser.parse_args()
data_desc_path = args.dataset
data_desc_name = os.path.splitext(os.path.basename(data_desc_path))[0]
data_dir = os.path.dirname(data_desc_path) + '/'
with open(data_desc_path, 'r') as fp:
dataset_desc: Mapping = json.load(fp)
dataset_desc['cam_params'] = view.CameraParam.convert_camera_params(
dataset_desc['cam_params'],
(dataset_desc['view_res']['x'], dataset_desc['view_res']['x']))
dataset_desc['view_rots'] = [
view.euler_to_matrix([rot[1], rot[0], 0])
for rot in dataset_desc['view_rots']
] if len(dataset_desc['view_rots'][0]) == 2 else dataset_desc['view_rots']
if dataset_desc.get('gl_coord'):
del dataset_desc['gl_coord']
for i in range(len(dataset_desc['view_centers'])):
dataset_desc['view_centers'][i][2] *= -1
dataset_desc['view_rots'][i][2] *= -1
dataset_desc['view_rots'][i][5] *= -1
dataset_desc['view_rots'][i][6] *= -1
dataset_desc['view_rots'][i][7] *= -1
output_name = args.output if args.output else data_desc_name + '_cvt.json'
with open(os.path.join(data_dir, output_name), 'w') as fp:
json.dump(dataset_desc, fp, indent=4)
tools/depth_downsample.py
View file @ 2a1d7973
...@@ -2,12 +2,13 @@ import sys ...@@ -2,12 +2,13 @@ import sys
import os import os
import torch import torch
sys.path.append(os.path.abspath(sys.path[0] + '/../')) os.chdir('../')
sys.path.append(os.getcwd())
from utils import img from utils import img
from utils import misc from utils import misc
data_dir = '/home/dengnc/deep_view_syn/data/__7_challenge/classroom_r360x80_t0.3' data_dir = 'data/__7_challenge/classroom_r360x80_t0.3'
in_set = f'{data_dir}/train_depth' in_set = f'{data_dir}/train_depth'
out_set = f'{data_dir}/train_depth_low' out_set = f'{data_dir}/train_depth_low'
......
upsampling/run_upsampling.py
View file @ 2a1d7973
...@@ -8,7 +8,6 @@ import torch.nn.functional as nn_f ...@@ -8,7 +8,6 @@ import torch.nn.functional as nn_f
from tensorboardX.writer import SummaryWriter from tensorboardX.writer import SummaryWriter
sys.path.append(os.path.abspath(sys.path[0] + '/../')) sys.path.append(os.path.abspath(sys.path[0] + '/../'))
__package__ = "deep_view_syn"
# =========================================================== # ===========================================================
# Training settings # Training settings
......
utils/img.py
View file @ 2a1d7973
...@@ -45,7 +45,7 @@ def torch2np(input: torch.Tensor) -> np.ndarray: ...@@ -45,7 +45,7 @@ def torch2np(input: torch.Tensor) -> np.ndarray:
:param input `Tensor(HW|[B]CHW|[B]HWC)`: 2D, 3D or 4D torch-image(s) :param input `Tensor(HW|[B]CHW|[B]HWC)`: 2D, 3D or 4D torch-image(s)
:return `ndarray ([B]HWC)`: numpy-image(s) with channels transposed to the last dim :return `ndarray ([B]HWC)`: numpy-image(s) with channels transposed to the last dim
""" """
img = misc.torch2np(input.squeeze()) img = misc.torch2np(input)
if len(img.shape) == 2: # 2D(HW): Single channel image if len(img.shape) == 2: # 2D(HW): Single channel image
return img return img
batch_input = len(img.shape) == 4 batch_input = len(img.shape) == 4
...@@ -88,7 +88,9 @@ def save(input: torch.Tensor, *paths: str): ...@@ -88,7 +88,9 @@ def save(input: torch.Tensor, *paths: str):
new_paths = [] new_paths = []
for path in paths: for path in paths:
new_paths += [path] if isinstance(path, str) else list(path) new_paths += [path] if isinstance(path, str) else list(path)
if (len(input.size()) != 4 and len(new_paths) != 1) or input.size(0) != len(new_paths): if len(input.size()) < 4:
input = input[None]
if input.size(0) != len(new_paths):
raise ValueError raise ValueError
np_img = torch2np(input) np_img = torch2np(input)
if np_img.dtype.kind == 'f': if np_img.dtype.kind == 'f':
...@@ -111,7 +113,10 @@ def plot(input: torch.Tensor, *, ax: plt.Axes = None): ...@@ -111,7 +113,10 @@ def plot(input: torch.Tensor, *, ax: plt.Axes = None):
:param input `Tensor(HW|[B]CHW|[B]HWC)`: 2D, 3D or 4D torch-image(s) :param input `Tensor(HW|[B]CHW|[B]HWC)`: 2D, 3D or 4D torch-image(s)
:param ax `plt.Axes`: (optional) specify the axes to plot image :param ax `plt.Axes`: (optional) specify the axes to plot image
""" """
return plt.imshow(torch2np(input)) if ax is None else ax.imshow(torch2np(input)) im = torch2np(input)
if len(im.shape) == 4:
im = im[0]
return plt.imshow(im) if ax is None else ax.imshow(im)
def save_video(frames: torch.Tensor, path: str, fps: int, def save_video(frames: torch.Tensor, path: str, fps: int,
...@@ -138,7 +143,7 @@ def save_video(frames: torch.Tensor, path: str, fps: int, ...@@ -138,7 +143,7 @@ def save_video(frames: torch.Tensor, path: str, fps: int,
misc.create_dir(temp_out_dir) misc.create_dir(temp_out_dir)
os.chdir(temp_out_dir) os.chdir(temp_out_dir)
save_seq(frames, 'out_%04d.png') save_seq(frames, 'out_%04d.png')
os.system(f'ffmpeg -y -r {fps:d} -i out_%04d.png -c:v libx264 -vf fps={fps:d} -pix_fmt yuv420p ../{file_name}') os.system(f'ffmpeg -y -r {fps:d} -i out_%04d.png -c:v h264 -vf fps={fps:d} -pix_fmt yuv420p ../{file_name}')
os.chdir(cwd) os.chdir(cwd)
shutil.rmtree(os.path.join(dir, temp_out_dir)) shutil.rmtree(os.path.join(dir, temp_out_dir))
......
utils/view.py
View file @ 2a1d7973
...@@ -2,6 +2,7 @@ ...@@ -2,6 +2,7 @@
from typing import List, Mapping, Tuple, Union from typing import List, Mapping, Tuple, Union
import torch import torch
import math import math
import glm
from . import misc from . import misc
...@@ -14,7 +15,7 @@ class CameraParam(object): ...@@ -14,7 +15,7 @@ class CameraParam(object):
def __init__(self, params: Mapping[str, Union[float, bool]], def __init__(self, params: Mapping[str, Union[float, bool]],
res: Tuple[int, int], *, device=None) -> None: res: Tuple[int, int], *, device=None) -> None:
super().__init__() super().__init__()
params = self._convert_camera_params(params, res) params = CameraParam.convert_camera_params(params, res)
self.res = res self.res = res
self.f = torch.tensor([params['fx'], params['fy'], 1], device=device) self.f = torch.tensor([params['fx'], params['fy'], 1], device=device)
self.c = torch.tensor([params['cx'], params['cy']], device=device) self.c = torch.tensor([params['cx'], params['cy']], device=device)
...@@ -101,7 +102,8 @@ class CameraParam(object): ...@@ -101,7 +102,8 @@ class CameraParam(object):
rays_d = trans.trans_vector(rays) rays_d = trans.trans_vector(rays)
return rays_o, rays_d return rays_o, rays_d
def _convert_camera_params(self, input_camera_params: Mapping[str, Union[float, bool]], @staticmethod
def convert_camera_params(input_camera_params: Mapping[str, Union[float, bool]],
view_res: Tuple[int, int]) -> Mapping[str, Union[float, bool]]: view_res: Tuple[int, int]) -> Mapping[str, Union[float, bool]]:
""" """
Check and convert camera parameters in config file to pixel-space Check and convert camera parameters in config file to pixel-space
...@@ -135,7 +137,7 @@ class CameraParam(object): ...@@ -135,7 +137,7 @@ class CameraParam(object):
class Trans(object): class Trans(object):
def __init__(self, t: torch.Tensor, r: torch.Tensor) -> None: def __init__(self, t: torch.Tensor, r: torch.Tensor):
self.t = t self.t = t
self.r = r self.r = r
if len(self.t.size()) == 1: if len(self.t.size()) == 1:
...@@ -230,3 +232,8 @@ def trans_vector(v: torch.Tensor, r: torch.Tensor, inverse=False) -> torch.Tenso ...@@ -230,3 +232,8 @@ def trans_vector(v: torch.Tensor, r: torch.Tensor, inverse=False) -> torch.Tenso
out = torch.matmul(v.flatten(0, -2), r).view(out_size) out = torch.matmul(v.flatten(0, -2), r).view(out_size)
return out return out
def euler_to_matrix(euler: Union[Tuple[float, float, float], List[float]]) -> List[float]:
q = glm.quat(glm.radians(glm.vec3(euler[0], euler[1], euler[2])))
vec_list = glm.transpose(glm.mat3_cast(q)).to_list()
return vec_list[0] + vec_list[1] + vec_list[2]
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment