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Commit 2a1d7973 authored by Nianchen Deng's avatar Nianchen Deng
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for batch infer evaluation on multiple hosts

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notebook/gen_demo_views.ipynb deleted 100644 → 0
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notebook/gen_for_eval.ipynb
View file @ 2a1d7973
......@@ -2,23 +2,16 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import torch\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",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n",
......@@ -74,24 +67,12 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"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"
]
}
],
"outputs": [],
"source": [
"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",
"\n",
"fovea_net = load_net(find_file('fovea'))\n",
......@@ -128,7 +109,7 @@
" # ), test_view.r), mono_trans=test_view, ret_raw=True)\n",
" #plot_figures(images, center)\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",
" #for key in images:\n",
" key = 'blended'\n",
......@@ -137,21 +118,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"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"
]
}
],
"outputs": [],
"source": [
"import numpy as np\n",
"gaze_idx = 0\n",
......
notebook/gen_teaser.ipynb
View file @ 2a1d7973
......@@ -2,24 +2,17 @@
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt\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",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n",
......@@ -97,43 +90,13 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"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"
]
}
],
"outputs": [],
"source": [
"#os.chdir(sys.path[0] + '/../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(sys.path[0] + '/../data/lobby_all_in_one')\n",
"#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_gas_all_in_one'))\n",
"#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_mc_all_in_one'))\n",
"os.chdir(os.path.join(rootdir, 'data/lobby_all_in_one'))\n",
"print('Change working directory to ', os.getcwd())\n",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
......
notebook/gen_test.ipynb
View file @ 2a1d7973
......@@ -19,7 +19,8 @@
"import torch\n",
"import matplotlib.pyplot as plt\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",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n",
......@@ -97,43 +98,13 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"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"
]
}
],
"outputs": [],
"source": [
"os.chdir(sys.path[0] + '/../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(sys.path[0] + '/../data/lobby_all_in_one')\n",
"os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_gas_all_in_one'))\n",
"#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_mc_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",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
......@@ -156,133 +127,9 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"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"
]
}
],
"outputs": [],
"source": [
"test_view = view.Trans(\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 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt\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",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n",
......@@ -132,43 +125,13 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"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"
]
}
],
"outputs": [],
"source": [
"#os.chdir(sys.path[0] + '/../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(sys.path[0] + '/../data/bedroom_all_in_one')\n",
"#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_gas_all_in_one'))\n",
"os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_mc_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",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
......@@ -192,19 +155,9 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"view_coord: [2, 2, 2, 2, 2]\n",
"shift: 3\n",
"shift: -3\n"
]
}
],
"outputs": [],
"source": [
"centers = [\n",
" # ==gas==\n",
......
notebook/gen_video.ipynb
View file @ 2a1d7973
......@@ -2,24 +2,17 @@
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt\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",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"torch.autograd.set_grad_enabled(False)\n",
......@@ -83,27 +76,15 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"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",
"Load net from fovea@nmsl-rgb_e10_fc128x4_d1-50_s32.pth ...\n",
"Load net from periph_rgb@msl-rgb_e10_fc96x4_d1.00-50.00_s16.pth ...\n",
"Dataset loaded.\n",
"views: [1456]\n"
]
}
],
"outputs": [],
"source": [
"os.chdir(sys.path[0] + '/../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(sys.path[0] + '/../data/bedroom_all_in_one')\n",
"#os.chdir(sys.path[0] + '/../data/lobby_all_in_one')\n",
"#os.chdir(sys.path[0] + '/../data/gallery_all_in_one')\n",
"os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_gas_all_in_one'))\n",
"#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_mc_all_in_one'))\n",
"#os.chdir(os.path.join(rootdir, 'data/bedroom_all_in_one'))\n",
"#os.chdir(os.path.join(rootdir, 'data/lobby_all_in_one'))\n",
"#os.chdir(os.path.join(rootdir, 'data/gallery_all_in_one'))\n",
"print('Change working directory to ', os.getcwd())\n",
"\n",
"fovea_net = load_net(find_file('fovea'))\n",
......@@ -121,952 +102,9 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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},
{
"ename": "IndexError",
"evalue": "index 1850 is out of bounds for dimension 0 with size 1850",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-743f8021cdc9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mview_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgaze_centers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m left_center = (gaze_centers[view_idx * 2][0].item(),\n\u001b[0m\u001b[1;32m 3\u001b[0m gaze_centers[view_idx * 2][1].item())\n\u001b[1;32m 4\u001b[0m right_center = (gaze_centers[view_idx * 2 + 1][0].item(),\n\u001b[1;32m 5\u001b[0m gaze_centers[view_idx * 2 + 1][1].item())\n",
"\u001b[0;31mIndexError\u001b[0m: index 1850 is out of bounds for dimension 0 with size 1850"
]
}
],
"outputs": [],
"source": [
"for view_idx in range(gaze_centers.size(0) / 2):\n",
" left_center = (gaze_centers[view_idx * 2][0].item(),\n",
......
notebook/test_constrast.ipynb
View file @ 2a1d7973
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notebook/test_foveation.ipynb 0 → 100644
View file @ 2a1d7973
{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:0 as current device.\n",
"14496(22.12%) pixels in layer 0 are masked as skipped\n",
"15980(24.38%) pixels in layer 1 are masked as skipped\n"
]
},
{
"data": {
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\n",
"text/plain": "<Figure size 432x288 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import sys\n",
"import os\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",
"\n",
"from utils import img\n",
"from utils import device\n",
"from utils.view import *\n",
"from components.foveation import Foveation\n",
"\n",
"\n",
"foveation = Foveation([20, 45, 110], [(256, 256), (256, 256), (256, 230)], (1600, 1440))\n",
"layers_mask = foveation.get_layers_mask()\n",
"plt.figure()\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]], 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] * (mask[region][..., None] - bi)\n",
" plt.subplot(1, len(layers_mask), i + 1)\n",
" img.plot(colored_mask)\n",
" n_skipped = torch.sum(mask < 0)\n",
" n_tot = len(mask.flatten())\n",
" print (f\"{n_skipped}({n_skipped / n_tot * 100:.2f}%) pixels in layer {i} are masked as skipped\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.5 64-bit ('base': conda)",
"name": "python385jvsc74a57bd082066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
},
"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"
},
"metadata": {
"interpreter": {
"hash": "82066b63b621a9e3d15e3b7c11ca76da6238eff3834294910d715044bd0561e5"
}
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
notebook/test_image_filter.ipynb
View file @ 2a1d7973
This source diff could not be displayed because it is too large. You can view the blob instead.
notebook/test_mono_view.ipynb
View file @ 2a1d7973
......@@ -2,17 +2,9 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n"
]
}
],
"outputs": [],
"source": [
"import sys\n",
"import os\n",
......@@ -20,7 +12,8 @@
"import matplotlib.pyplot as plt\n",
"import torchvision.transforms.functional as trans_f\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",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n",
......@@ -82,43 +75,13 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"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"
]
}
],
"outputs": [],
"source": [
"os.chdir(sys.path[0] + '/../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(sys.path[0] + '/../data/bedroom_all_in_one')\n",
"os.chdir(os.path.join('data/__0_user_study/us_gas_all_in_one'))\n",
"#os.chdir(os.path.join('data/__0_user_study/us_mc_all_in_one'))\n",
"#os.chdir(os.path.join('data/bedroom_all_in_one'))\n",
"print('Change working directory to ', os.getcwd())\n",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
......@@ -294,13 +257,6 @@
" img.save(\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": {
......
notebook/test_refinement.ipynb
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notebook/test_spherical_view_syn.ipynb
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notebook/test_ycbcr.ipynb
View file @ 2a1d7973
......@@ -2,65 +2,21 @@
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "<matplotlib.image.AxesImage at 0x7f0214970810>"
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dunKvkoeRt4RXIWmqBbBqlG2k3ycNKqUkcmQ/jnXNtSgRTVJLpC9H+73ca+l9no9CEvomSVhpiQbI1M56+JiySNEWOtzuy5gnlq55iG0vsezSlcrL0nmWZi9poBhMgzlaGooMazAw3Ue5LXjH30rnmcGtlwPaMtM+9mLDcx/6PHxNjqzPYFA5BsgPt9sd6lJsPzQjOUFZ7kuhDtoz5XJ7IibZ9a54tdJAnoJ/EngnN7f0OfU/kaB/A4oZBosRAToHqEU44A4G8QTey6UA8IHOfRy7Lt+Rtw+Aawwvjz7X8a1K3iQ9iIe9JYXUDdhZZObxODSEoCfGXfZulQT2Eb59/3saM8J1PComBtQQpBmVJVgtGzSdIUGwofcuSdCsvSQSL6v5Yxnx2Z/6fmBcRWJUWjxl8lM1R4NXGRFpIrUsIzPzAJUeSw50SGy9vyGVvlOL6uB6OSoy9d8UKLRk6BxEww3Gn2M6cmLuMAz+KXTEZAMQr5iSuZ2gCKTYrUEdjwLFEuhzlUoAZ+nz3ZTj9k/njc/pttq/9vsc91sqR4bxM9yn7Nf3VRLbrUzsH8U2Ixm0s1QRQTo/fyUO0poBWiuTzxcOkUPPDAgugkJQSpeIgHp6PT3VRUCNFgMKQXpJ91H6hJshdmXw9sewiaP097eXEVMQrSLgGqQIlEk9QxCMH0F0I/SJkavJDy+02igzQqErQBeSBGPztMmAVqgkLTvl4OjpUjjSUbsbqNR4MXSSHkAFeFSyw2kx1YIQDZNuwYQpu4DGSFM0AXq/Ww6zLyyNcj+vggSGkFwJQ8AMg7dLTdDv0mfTBmfN0HH7l6EcB4ORKF7CB4D2VugmMji/Sf7dJaBl4A/nbzYspgPbeKoDB63vXTtjMIYKYTqdGqsk4ElvlNyk/X7xBU2AH7xuwg4pRcD9lkOTqOXfIDsbHSmJ3OZdPMSKXd+Gh2ZQRwwIuRsmgik9YzyUP+WoOulw0934gA/qf/e7Hj7WCH5kewD3ReOVz3dao50NzDj+lu/RgK8MOh0H2QSQpkOM6SXCBNbxfL4y6MggbYgMpov9Mb0LHhDFtI611zu6SZF7xxCCjzqPeJfPSZRugGFirRiarxZwrs93sszAVYPWH5l8zuVyTHngAI4GgAuzmjxvzvANgkjf80Mn6oAD0I7QVRKwBmBG11FdA8agFZ5NYIyPBsiQmEpRHEIH0mDYA20QaQiGS0/wKV9BqSPbaMNgo8mQmqhkqYf7Iesp3kQ95DitcYqd3sz0jzuOtHcqvf7bg19yxyzzhKSpZ9bCpQg3H5yvnx0kwEgAbjrFdzFdgAvfS5BxI5PzRmRgjcebTnOiLGwxZKUBQJaQw0S0joNA0r6l81BFQ5YJOnjJ1gOrWqbF91mWWfTyeXXpMssTtmOOgSEgH8q3ckT8mMKR7H9wjegxkCNvOWL/Eri1v7ZIzzJva1N+M9oV6XVfkR5wCzc8NQFwRQizNyOYxjPaMdgm9j8rAbxtiOycn5+w2LJ006BfL84Ibqr4ijAji1KutOG7ncN4W7ELpVoodu5zfMFiw+JG4YWyjeKtQVJ9KhOjPws93C3Np8v32zkGEkmSVAZtkFxt30YMPEVRKhK1MxvrVwKdib3ToOpCFdUBu0hOgjUB2jj8TkCfRLkMbIFfwfM0KpuomQANXhqcHER6NEdkj4pXMNxEdAeRObAIIx4bOLGoOLzcwnBQuFPFN0k84CIjd1k7Di+V4nMUS7xvIVPGQPpjEi0MIga9F6pzu6Jg4mWTJt2DtwzqOAh9N8N9l8HCuB6gTaPRcBTlr06xszYAsAG1hm5thBubmK9EM0syrWIWPoB46/EjS5JuJKdXIAzwcRrtRwbpYsY4kfBiuGQQjtuqyD5NGFB8lV4C+pDn+xwgU7vkwfouHlnuAkv7D8hleZ7CbTi1WS9p5Sr0s/Llc5t+W0koBgRjyUOlSM/Tz2jSzClJhF7yb2kwzudLhtL7ad50zfieaG1QEWyhJ+f4gSip+MpgWmVy04Uo4kpoVyvMrEZrS7tWceObavaecJhzC1ZX5/zA07/Oh1Ze4BG7zRtujatugxcX51loxeuLDV7aP8PnX3gYe23E+KahmlnsPBCOxdkYoOZheg3c1JDgUysBH94LaT2m8yFToTO53w0MnjAcFQuJMCfSquwdRs5Qvg5+4D5LHqIdSSAMAT7xZnUUOK50OP1eVB7Fy2XUnAmMGxsZW3jynUn+2sloEb8rwBRRsMkBN0oWyjfjtAPtYqO68E8d3jR4HM6DcA1lgVOozAFIh+AR3QV9GeEFvOwFZgpAh6FDs+thAneFUehculJDnebBHh1DSKJV31/TxpfAV2UCqp6p+QSkTgKY5n9+yKLT6Zb9wdO2shhw01jv+JtxHpmVMwHJdbELh7QOu98EjwOnyMEcvx4z0okEBhWn02bhwgwhvtQGhgOeSfek0IVEWabp2Y04zcmw7rtI34VKQXWgf7s4UBrtgS4DYji21ONLmWMwKdX+b7LBJXkkc4jynhKDT8dHOSVdM9v6VYZGtHTuZNvQo43yburRkWI3GkbjNg8i69M5VpQ3pu0RR919kc5jfIh8pIlpWr0GaSVqw92KDe3Xep7/QzVnnrrJ91x+nt+89hXW7YxVaWjUYsVjUbbMjLE4RuIxwKoR6tgoT1TbOG7RTl/MzeWB3acsu37Evo5otWLfj9n3I+Y64lq7zkIrnBo+fv0JthcTAG7tTek6izpBb41Ze8Fy7rMto1uLGGMBNCGCFOfC3zL6cjKCusJPRoc8W3RU4afH48IDBXDFgD8DrOD1KdDgsqeYAGAS9R6xsU95lGdRNsM/ndL7feeTHppDDglkoi+932jYqYp7xoitBLbigQ5Rj1FBZA2lCzN1n0L6HcickDXzG4EZ+JBExzobmXnQ2sXvgNxCRy/Qnjf4DYNbt4iJybLUI+LifVlOWjKbi1O8XuHp2XYZmGFa7ZNLuaUEU+V5D/mpxr9HacoDz5A4UCbZI106Tjn9tA6Mw3vETtFxfazuKhomT34cIvKy1JAMp51S7ywwsxY5mGcXssEU9H4ZeAZNSeNuql1MAUA0bh1xA0PSm7cN/t6plCy57OAF+y7PtzyZwhd1GFB4ennMlUcG5q0CbqxMH9nj0a1t3rP1OuM4AnkVTLzf5+vmLm/kcFEjzC5PWaxbbr4H/FjxtYaAntqDVTCKqWKIvBd+67s/xwfWX+KDk6/xdL3DRIRxlF597G+1JI8yg0OxS0+hBiZLD2bTKK3OaXUGQAu0CnO17E8q5lpj8XzD5DUO/BiAK+0mC19x4Ee8Pt/g+afP8eI3r6P7kzCgK2BBWglt7IXxTWFyXTn3mVnOrulGJrtESszaqTbYB44rD5iBW5RHQS/i9HtQXQedoIxQqYLPpZgjejt9xxXI7mzHOMMHl8Dhob1/l/YbxYR/FNuJuhiJUa8XJ0qZD9NKPSBqQFvwXczTMMWoJ4XTi38FNS/hjcdvetqLnua8x9CBOlQdpmmja+V9gEwCGIFq7rPOnPVmr33WtmFjDW6/XEAhMM5hnaQMO4Y47hVAuXz6rPPRh0XXgqvN4cEgnksKPbxsEj8yvR869Ok9vcMcNMjNHdzVa8h4HJ6Vc5jpBKoq5KK4z5KlBJYYcGK4SfcvNqd984wo/Ys7HPIQWn5ES/vD0hhRyiXFObJrZTy+zBOfB8oM3AFYbMNggDAxxfDiDDx17ga/5+Jn+P1rnwfCBLhFuOqmzLXmp83s2Ha7U/Ej4dYzFfuPeH7g+z7K+6Yvc7m+yTkzY904JhKYsyn6mB+88wYjgkVwKEYEr4q7i9HRFEzExw5Zi6FeatNw7o7kiP6+Ub/okFelRdn3ylwN9tFgRdv2NTs6Zu5rnqxvMVdLq4ZWLT9589v5V197NzfcZnZzDc8gSpSdYhY6fP5HlAfLwOVh5tWfQ7GgVZj2egi0sc9tMXC9WRZk8/bjLhL+lyIOg+tRTBpbHDYIdAmVO3z+CORlFULHNwVAOkyjxGTaiG/DNCinTb2A6DnkxnsZv+5oHrnKznf+Os0jb6DjGZgZ2OCDej9eKKZTVl9f9JpvDPcFBux6GaAPNV8JxEeRSZE+q4EmQJchI18GpUSE6P9q1K7TZ4l+vyom111tyFse9iF7APQG0nCsnrUcPHSWyY1NVj9tcVeuom1T1LdD6vvs6pGJpnYxHZldaVWsflOC83L7LUsey6WQRpIXSxlVmgHYDI/JVSwHFI3y9FIOhGS0CwblYLhLLnjiiP2anDsehdXX4PWvPsnfevZJXvvdW/zg2V/icjWmwrIiB3hgLCf303zs8hv8gz/311mVjpWi+1kR6gjOQAbo40r5m1kiFL5wWPeqGfD9UmddZulHndsv4U/6LbD5cMa5GhzCRFomtuWGm9Bg8Wpo1PJ9m5/ju977HLfevcKBH/NzN57lkx97F6uvGOo9pd5XrNXDA/xSefDJrCQCXbI2m+VeHZGhmAoqR4MNyyC0FFVQssfll2mYsCdMtwcBAQm8Y56Q0MFTghwwXQRpF9yM8jFe+yuXwEmNPTCMX9li86PfxP6HKtqL1+nOOTAO9W6YCOdeS7R856AYpXfHK8B74F3SN9ShsiydHEr1WpZlOaVkocUzGqwAZIpQe1MMqEpOnp/cHrMhdol9l2NuNxFm5yv0g4+w+pWVwMZv3greKe5uuNjtS3ruqYuG8Vr7LguH+ujAfU+Xfl9uMx1uy5yhYM3LRuZsx41APfzeDwYmMuxUB9OCdGHQt025X99fjWNAlCYLRb5k+An/7/H8bz3PHzj/SX7P6qtMRJjfhbHtdsWirEpHLVCXLJsIjhoAOT3FEkCXgbo/Z9iejjlqv6NkleXfSyafsNRpb+FqFRo1eEI0yEItDQavhrnWOASnBk8AdIA2LkBjxbNlD6il4/0br3Lzgyu8UF1mciUQxKBU3R7EH3g6WTggQGMVXmJ6VksMjU/LpUGBLaXeJ/l/vYVeOWIan3q/HjouAW5ooB6oMwgn0E7yg4+W5cj6TOfjPsQcDfES2Y1vSVcWkNZj52NG1x+F9VvMnWO2tou3DXmppfsoyXNDFcyy7pyCW2wE0mKsHKTtLF/GJHssbS8TXx2VGGiQTdDIYcBO223/11eS2z77nkvv154liHRPZvjdV+AmQrNZIbrF5NURZtGgXXd84MRJSwLwVIXi9DLoo8M6Duqf7icx9WP2W9a087Z4nLjojx+BO0edJhbtAljbJj4bC6ZJLDukSkgZI7PUwxDMETALZbTrWH8Zfunss8y/qeIDj/80E1FaJIPTSUqjlq+0ZwKgmRm2GG5r8UzEsVKA++1Ad/n3xNpLQC/B/KihPQ0QLToA6yQYBFAWWgxztcy1jtfytFpl0J5rjY8vkCumTW20XDtMPuaheps/+PCn+Ns3Nll0q4y2DdVcOc6sksoDBvB9nPl3OJWYthTC2zBFdBVhFeE8RlcQKoxYyIsCM3wplqaVuZQvE1oAdQ/MaNCZsiNBucSX95ltm+Tgr9oHDxAzkXUpzDbm700vZI4CDDmO8wDgHOLD2pgiysVfeJj9q8r16S0Uh69a/Oj+wgXNvA2GvHlhUErBANE1yU/rmOPYDGWkYiaUAfg42ViWmTXZdxcIxsX4zLSKQB2DaTQmzHJ1D9KlJCBe8FXPCge/aQ98biz5WF8RwgEiK3/1eypWX97kIQPSOmTRIU0Leydp1eKeU1CS7Wd6WrJfMicg6c4ZbBnOGPqd6VWOpXEmL4gNpBi0ANrk65uGLHsE1haiTu1Ceykk2xTCgGlazc/CdP3qPSV4Vwc+G7dN40Mf9yFf9bv+Z8MXv+tZ/vYf/m0AbNUH7Pr//cRN+7X9s/zQx/84YhRjNJtUNldnXFrd5fHVG3zf5ud5rLrBlmlYEc1g7iLYJoAumXpZWu0bt7dghdJo/70tDp2rxSG0UQ5p1dJqMGQ2agOrLjqoFU8TwXkkLn8O9QtRKF4NO35Kqzay9IqFr2nV4tTwLY+8zK/pYzTX1zCdYBeCbQ/fTyoPFsB1SjV/PzYxQE093cTsYhaVCtE5SIc3C7xsk4JlPOsY1jC6jtFR8dL0oayJ9YbFRgvgVRDn4ks1TKwkne9ZfHI/0z5sP5QAzIkuCRrcfVL0VpJh4jUlnjdfI8k7kbJVrmXt1RWqn/8Gds9uonWH2Ab4hydrWhG69TFmUgMrpZdbP7tIbloQMqztd/lYaR06tvhRhY57162SVaeXnvS5DH+velDP8oZIdl3MTZjAYsn3fOCBEhkulOciBxxpjN9Kx6WcLgFcwU2UvUeh+/AmW8+5PrnX8ydq2theUB2koCQZdou0AnxxL9ltL+2Tfk7SRgrCTfet0i93pwUoJ0mjlDq61Mfj9kxQyH1/kP9FGC7MoYp0IE35HhAJS3QDbXzxDukgpF1az9Zzjn/z09/K/GGH1p439n/lxG17YWWPP/vNP5eZt0OwKGPTMpKOFbNgRRa87jb4cjPl9W6TT+8+ysJXGPE8s3KN90xf4b2j17lge2guVap2iUm3BUufa4XXMItIIBu217k+0LPo5dlG+m7p5ZFWq0BUI8i3anGEvwtf5/O10e0RYOEr9roxm2szbr7fsHV2h6ar2FnUx8LCgwVwX2PbSwEwikVtAzC44DYYqYSKA23AHBCyEEqoro4Q3yG+6jss9NNR5weadcnATZI6SqD3DLTtIrK9B2UlGibTdQIjSew6DxiqmJi0PV8/FsEHZioeIx1Cy2jXUn91i0X3FNQeI4uTt60BN7H4kQmyQ+HZYVLwS2RiaU3JPFgR7k+xMUhCsuSRPGOyEbGUPkrNNoUYR1Du2XUB8ssgvQzmcZ8chJQT6xdMOyptfnl2kK5bgdZKN/HsbcL4ukU8+NHJmxYiWC7IzLocMFJ7l/ejVg/NYLK00Q3TEKRiunIf+gwNGgKoeq2a3HcTO9PE+KW4Vuq7kZlrDO4zbTRYRvKR3kVpfT8bbd2gbjquwgAdI2OnV1vqA8+1tsaPLLI4uYRyxhzwkbXPUEtgwCU73teKNjLV626NfT/mWrvOKwebLFyAr6ltOV/vslvdYL3whimdm1ols+nEqPtrjDJoJ/kDoD1mCppAugRmi6eBzKQdJg4KJjPt1ld4hLmvi3PZKKNY9t2Y3WbMyDoePrfNn3z8Yyx8zZV2g796TNs94EjMyFglrPgsXeiRvq4wrZB8odWOIzico5bHwsHRvazXqZs8XzWdj6G4gR2k6V4ZVt7rhkusuvzmCcelujofr+UD+EM+r3gfFzfVXkM2PZCHVKwdYgONUjSE7VuHrVvEzzDeYReKsQ1qlRDSf7KSAnhEYyBPBFTbKC5qyjnhT/IL3xjlYzVpzYllF9vTQOttAm8Oad8l+y6BaWCEKyMFC4btRjLwusjeF2YI+qXh0o/7z+HYEPHpRhE8xw47CtGeposgez/FBze7kEMm/EPBTYKbct4tSTlL6Xylo9eou367aTTkp5Eoe3SF7FH03Rzcm6l8+JxysSdNO223i36wruZ9YEwGaRdtOp4cgh4Yd+zXiya4XqYVkaY1OjK4sc3SSrXvUFvjq6JOJ2paoSW4/iVXO4ewIh01nlo8K9JxyR7A6A2+d+U5/syZX8p6dAnIN1zPmudaUcdRsI1adTIopmLw2cC4LIekUh6TtO10zv3oC+4zmAutr4JEQjBsWvEZ2L0Ke27Mwlf4+H27nXDQjThoR9w8mNI5g/eGa+06rVp2usmxbXdHABeRx4D/L3CJ8Lh/XFX/loicBf4x8CTwAvDHVPXm7U8W/2pkf5E6S2SsuWgURYSCQQegLg2MOZueS6Kg9p4Y+VzF37hLDzCRgaSBwevgs2lc3lkAcW7wAokW9THxBRGP2AZb7bNy7jNsn3mR2WSbplrENAdBmFzfe4j9qyP+7i/8LNd+uQFj+M73/97U5vfctj1QSr7PHhQlg+aA+RXNlBZ0SEw2R29W/Y66vHp8atpkcE7H0rNRX7LSgpVnEhLr4JZAIAN5uc2CrxU/Bn95zvgLU1auKJObPqwaHvfdfqpi/pBA/RrP/fRP0+3vEldPuXjS9s3tUmjzid2WUgcFC84GSnpNOhkX073ZBpgryQtkMCjFv6nPqRBSJeQ+nbxIYlBWF72inGJnQ3uKLNyQaKQZWZx5pcyZOqpw44rF2U32Hra0a4IfDWcXo22QV27w4r/9n5h9ag+M4Jo5J25bwOnQEGoJidbaAlR98Xu574GvI5AHODMRfAdMFzswjvbn7IG5BPbWV1lOSdJHYtwpgCfVY1Fcx0WPlHRtHx/ozI3Y6ca8tHeGV37tMptfhJVrDjtLgyvUrWfy5ISDh4XZE44XnzjL2HTM3PHTx7th4B3wF1X1EyKyDvyaiPwM8B8B/1pVf1REfhj4YeC/vPPpEiBLz9YSLUtgGV3xSmkiaH+uZ9YQpnMi0XtDs94bnnn/FvUvgRb+2QVbTpGIS+xaEusWQq4O50g6ttgOoUGqOW50E2c71DhEHMa2aDWjW32Bbus13GQfZ0JkJ7So2WduGvRgiz/1gd/G2rs+wq4qP/aTfxZCopcfPlnb9s04SAdLbtoe4BNbpmfvJXNMngnpe3qBjxoAeje/AnQLKaX3POmZdWJtGeiLNLhhfUPwVqkOwio+pgvH+pHgOpjvV1Sz4MecwZEgD0xuCN2qwZ8zXPzwR9icPgJ7cz75D/9vF0Xkmzhh3x2AaXLVLGYYqf694VHzPnaRwD5KHjbNivr+aRyDvOr5XPGaAZzJfVdclEM6H2SymDNGOo9Z9ACuxgS2bYDahnQONTCpcNMKX5uYPya4bbqRYX5GmF0UulUdzKrUBkOm2a944oO/H33fEzQy50v//V/mftq2LLX4rEebPDISATU0TlOAbWlUBKjpsOhAAmm1ylOfZFCEXtf2mGx0TL7aYVuVpZBgxBxq2AmsU1TqfjfmRrPC9fkqL14/Q9dZfGfQzkBjsPuGM1+ErefnVDdnQYUQgaaFazc5M3sE267RbBquzte4PN1hqz44tq3uCOCq+hrwWvy8KyKfBx4BPgJ8b9zt7wE/y12+BErKWx30NOMUX5nCSOIyKw86XcGOo/as1vRgZAIKaDY8AlE1XzYkZnAuZBbxCq7PPGaSdm4EISybZLUN0kKlaO2p6m1sfR0zeo1bl/8t8/EOTTVHpUXEYhhxyz2CdRcx7mlW51uhVmYbV32Z2fQmqxdHXJYxu7M9VsaXuLD1LNduvTw6SduWbnZJOyUCs2n7dvF1v3TaIDDG9gbCxCqDfNTPQLIMUYBzBuFS/ijOlcE5DixJgskr2VcxQCaBoQpuoriJoiPP9PMVK1c8k+ttLw9VQrtq6caKG8HO41VmsHYOa6853Khi95GzjN41wV+BtfkIYHY/fTeBmK9iP9YgpfhRPxjaBeBKF70ob8TUBcYFDxwvKQtkH76VDd3xWrbpwb1k2Sl/TXoXpHUZCCTGJGRJRBXRLqz2PqpwKxXdxOImhm5quPUuQ7ei+FERjBT7UBgUoFpIfnZuDO26Z3R5g9qvo3uKrEyQqkJdd6K2VchyQx1HYhPd9Go8o7htP7noIcFFL0onc62j0dBQS4fBY8VRx6TzQSIpvVCEeTSKJObuNPxNnialHNL7eKdrOBa+YuErdropRjxeDQtveX77PK9d3aT62oSn//FN5MoVdHsHP58jVYVZW6V771PsPjFh/v4p83OAwPg6XPzEBvUrN1idVBxcXOHVvU2eWbvGhzc+x18/pu3uSQMXkSeBDwEfAx6K4I6qviYiF+90vKbgHYVB0v2c5D8x4t4IWEoaUCb+jx4mGoNqotxiXNT1lMBIWlcYTJemUBLOExhrdPFTT2UXGHHoRKnqG3TTV9k796u0o5uocZGRg4hFqLDyKHU3pu5qhJhDhbCGZ8j3YkHniAqqFUafoLNzFqOWq+evsfn6Na4cLHj12hcgOLs9fa9tG4yxPQBkeanT6LMcGVYJ3pkxC9k7IjFMm/yz+2ukmWLy586ue6W5v59I9WBUMPNuRdHoJugmip0Lay/CeCfJV+HcvgJfWdxIaTaEZmMUt0Udfsm+FOSNkDZ0tC+sXPWYj0/YftZTHRgWX74OsMIJ+y5CTp+jhpAOR8n3kgJlUoCM6ZJRUvOAqiZkwE/Si/UBjNO7YJNxMW63B11M1zv0LxwkzwroF3/QvJCBWxvRTSsWm5a9R020DfQzoDDgh3ci1DWMxH0wWyELFX3DRvLVrSh7jwibX/XM92/g25aTtq0tgLtkx3W8sdKdr1VLg+0ljciyTUz24gnbWrUZpAH2/Sgz9Ca67iXXQCAbMRMYZ0NmZt0VN5pVZq6mU8OXr19g+8VNHvlZmL4+z9k514BnKo8fL9h+zxbdt5zBjciun+HdCzEL3kI1izPgEVx/75SLe6uYxrH6muPVK5tcObPOWXu8/+tdA7iIrAE/Bfynqrojpc58++N+EPhBgM31y/1UM/aOclUViZ0me5EU7m+lD3foXJ60SJq49IZoz54TgLvE4H1v1BQNEaCmQ6oOsXs4u4c3M7w5QKVDTMi+5qp9/PgmurYTQrKTsCzxSTBCug2MThCt4yAQ2VQURCP3DfcNSNzXG6GpPW5+g//h5/8mv/87f4R/8DN/7q6jTsq2HU+3YhtwSDbJbLdw/yvBOwNwyawja84AHtl8HiCWjY0liCeQi+eyTS8hmK43qHbTJI/0fWHgK61h0FAbl7SqeiOhlCDjyaCkPgxStlEmNzy31jpao3zhV/4+wEsn7bvVxpl+lpNmHAF3A4CT2LZmQO9z0VB4exCM4mmAbaP8lxh26XvdukxicjCSCNQWX9tsVPRphfY6DdLQTQJodytCs6lh8F1qWwDTDD1iSgDvG6L4mPY14McK+3Ne+Pm/R722RbN97URte/mRfjQe6OCiuKJDJPadDYKFH3bprjePWT2zG2A0PPolUE77eyQbGB2G/W7MTjfmlf0truysM9sf4ffrmIwqkJ16x7BxDSbXZ8FWpgpx8QgdGdzEMj8jdCvSe0CV/TX1I983b7ci+HGFWhP6yW7FjcVqrvdR5a4AXERqAnj/Q1X9J3HzFRF5OI6yDwNvHHWsqv448OMAj158v0pi0dFbP0cACqjvwU/U5+jEFGCToh+THp6niMmQmDxHkg7sYi5qUcR4jI/pa/EgLWZ0gJnuYFZfYLb6As30ddrJG4TsiAF4rV/B6hq1u0jVnkV0FIG6WN0nPZ2InhKdfFULOhrfcI03G1LmCgtv+G8/+vf4zse+iw889n38gxO27frWo/mV61c1SQ8Q0BQ4I5kN94BOXv4TyInrB4bHgoFmg16SFOzweIjGxlH4bXpVqHeUyfYwyZabhJV9mnWhXTU945ei3vmm+u/LoeL5eReufXRQzZSV9T1e/Kmf4PxT38L+r79yixO27/TSY1re9+Cd8mHgML6QTFwP3saFRQKSFGIXPj+fHEfgY0qGNDNtHVqHACwVReaxn9nwDN1KRbNRMT9jWJwR2jXopomgxHoWbW2jDFIGAS23ay5HYHA5IKfIUe8dX/rFv8eFxz7IG69+Mu16z237Td880lYNtXjSXxs7l43W3DKQJjHvBOJzX+MwOBXmOspBMr0RcmhobNViopF07mv2uzGtGjpv2e3GvLq3ydXtNfjSKme+qFx+fkb15a8iccFkAN1coz27ws6TEw4emuLGoDWDfDJl0rABqUqwoAyJiIBbqaORX6i3hWsHq+zrfRgxJQyp/yPweVUtpZh/Dvxp4Efj35++07kCaARnVGlckBSEkC4x6dfRdS9ePGjWnQseKM7l/Uzjej/syIiCtOGCHGJbZLSHGb0Bkxv49dfZXf8qzs7xtgVcThtiqDCsInqWcfs04mtEDcYn9iyYtGhe8CpFpI/3Co0ffNc1cPD4oPrwuXSexMiNn+JlwU/9b7/AI1uX+APf8B3sH3zlxG1bjB8RCIMGahZa+G73029R8NGY2EshAbjdqH9hcym07BQ84yZhwDWtsPEVpZ75yCijV0sluLHgqzCQzs71PupuRPZbXnZDKzt+isrMrFYO7yPFGCo+uiV6MAvPK//d/0r1+AXGv/V74df/15O3bwHedhZBXMBN++uaIhAnRTzaJqzsYtre59rOusiuNUpVJhvkVQBrcNOadq2iXbfMzgqLM9EbxPaRk32DRbBrQLyELlfO40o2vVyKATnfagE4h6JHCc/Bi/LGP/vHrJ25xMPf+Fu5+tInT9y2BpiIi3q2Za7BY2TFdDlkvfRGseKZ+5q51vHvKLPnPTfJxsVUnJqsWd9qV3h5f4vnXrsAV8esvmQ499mGeq/DLMJzWQfWadHRHs2ZMQeXJxx887vCwhG2b5Nkw0kGaNVhu6eEYVk+Se+e6335w4b+t3a9ojpwrFxp2H5qwpXXt/jvVn878MUj2+5uGPh3A38K+LSIfDJu+xHCA/oJEfkzwNeAf//Op5IIyjG1qesddwYjEaCJyeagHIdpAoMOskk04qiHSYefXsOPtvGja0ECEY+YBlPtQj1DpzvoZBEXcJlEjTpQCsMI0SlGp4hfQTQscBwGf+2ZH2kNT08OvlVC8FEM0NV+5+i21QcJpR/CvdW8+PIVfv2zr3Jta86P/MxfQcPj2DxZ2/ag4nMmwjBHKz1P0n4+AbQEycHXZFnA10MAN00w1tWLHgk0rosJ5Mx1vhLcqDCSmkL2sBL/koNyTFK+lsGj6C5lRy8DVZAe2Jen/0kG2rnxArs//ynqxy7x4uf/GsA3icjvPUn75joqWfYJs0XJ28uQ9VDfaLhsPdWsSHgWF7UQVdx0Qrs5olsxtNM08wmSka/DLKVdhW6tMDIW78kgmVXRHglIymjQI8sR9os8y1geKIrP8xe/yt6v/xrdmYf5jZ/5G8x3rnLStjWE1M0OsvEw3Rv00gkEjdpFD5EQIDMKoB3Z9jyGpbdq2e/G/Pr1R7hyYwNemyBdSpsrrN2Cei+s6FPFiORubZTbX63gxoZm3dCuCu16D97L6TtywJXpP2vsu1IAeG4/D8uLpUi8rhsLphHq3Y7RDjRXaz47efjYtrsbL5Rf5PjH/+E7HT8oiZhqAGDTxAx8zpNXY4m6rCRH2KSBdz7kFomBOIHNOEQ6GO/gtr5At/ZVms3P4k2gaqIjjNaIViBjjN/EuhUqt46Nzq1KYj5BDhHo9evszpHqXj45n39IS6uFN0fol08L098km/QpoAxQ8dRjF/ivf+T7uHDzEpP9c4xn5/gjP/lfbavq9Xtu29RWIhmQg9Ztiqla9J8vXAN7owo9E4gMW+Okw86hOlCmN/qVe9QGsE7n8LXQrAcdz00YdM4EwiV7gaHr4MBgVnaZjn5DmmWE5uynq9pfrMx4uHHhKZ76sb+Gv9iwtbXPp37/X/mcqv6LuOu9tS/kQcO2wdsleWlk42DRfqnNxSm29dj9ptexPdElVejWa/YvVczPCvMLRUh+BmnNx4QsgkXIPcX1pN81gXAuyxJq8WwOMWwpdkl94Yi3f/rU0zz9//gxpleE0bbypZ/6G+zdeOlEbSuE5UtbhYbgZ+0QWmmoJbgEGvHRwNm7/jXRpW/bTQca9sJX7Hdjnt89x9VPPsTZz8OFn38V2i68hysTdFKjo4putWZ+ccxiw7DYFNx0adArBsNeHuvbbmBEliEDzxGv0QZV2g/K9k/ERI3iaqGyIdfS5IbHjwwznR7bdg9+SbWAkGhtQl7e6BKFCcDiRxXSRTfCxLaJson2608a47Ar19GVV7n5zE8inMfIKiP3DLZdRXSMYYz4kN1QI6CGcyZWHCSOHMQAEdBDS2tpYcCgGsLpVNLCDuVtRekgTA/CzCA+KYEwtSWFytfhJUPwBm6u73JxPmJz7/iIq7ts1tA/ugTSoUMkn2Hb9kmkBr7ZdghCvg7ueQKc+5RGOQTmZwzdRDKrLj1UcnDKEf7RqfOmdSuJ4Ls8tZdizDzkcy39iwMFYFbgl9IKu8hIbaOc+axwbcPyR5/8JJ86cesyAGc3lkA8GqGaa86m2E2gKv29C5acIxwhrNl4ZsribM0b31Llm5UObFrBRelf+IFcUtx/Ubej/pZyWf6+zKrLUx/BLnM9BrOkOBc1sDir1HsgRy0Wcg/FKbRqmEiLl+CHnfy7R+JYkcWhJFIpgjL5YXsVZn7Ep65f5tUXz/Gev7XNI493LDYtt779YRYbEu0uvUdVtmcUfTalNCj5W5zMDmd9sW8GN90j2qokHL4/r7fDZ1LNwxc3jsQq5p2Z3OjoJjXd9Dj+/MCXVFOccWAFqUzMhOZiKLwAJroQ9tkBpYuhv13QDCWCqqVB7BxGC6w8hPFnMH6KoBitEGxg3nEYlexbGC1OQpRBfBzXYahVRwDPWwrnaAJg99Nqzew6AHnKd5ZX+Yz7prSz8dwK+IrOONR0mPtIio/XENXlDaYW1AnOhI5qXJgV9NKG9MBdDaWNrIFXCp3QRit68L/u5ZXsrULxEkDPPAu2MXBNS02dWEd47AMWMwD29MhkCDBpG1Kcv9g/6LjCaD/cx7qdn7xt06mrOGEcxVD6NrBxH205Se9MBkzbBIOlnXd9bpEo4vtR8MNWo+H5JE+W4j7KezoOpHPdjmHSh9j40jHLgJ23HYcZ6bf0XI5w6bzX0iIcRMPiRBytdDiEAz+mlg4U6iitpICbpH8fuDGdN9lweauZsjObIAvD/jNb7F8M0aTiY99Nkp4s9culvlkaI3PJ962D7bmv67A9B7YZjbKbz+Euuf8nL6w0YwPwtQ352lsNLp7HlAfMwB1q5ngmGBs6LxIAO8aig4sL3RbRkCYbMX2IiDTB2xPboBXU/mmMGyNqETrCvFvzNXurTmTWUZfWPP8ZepMkL5SIsAGU1ZNWlE3w3uuELjLqdL3+yasK/VHJQ6bLQ4NohRPFl3Ozk5SYZ8Ya8K3BW0USYMf5sKoOwDuxaM0AHliVT4E1Au3GEsAXZRk4w1X6pk5sZaBxewbsGpPagf4Exc9HvRh5m+k/D+rQ9S+PXXiku7981fmySbuvkj+95nSsweQgOVjHdMEV0Cwcsmjz9D2tNu5rg6slJLYqgKKUYPoGPaIux93O0vT/yNtO2yXQjqNAHA4/k8Fl4quTEpvdT/Me+BGvuzUu2b2Y+8TRatWnYxVysE6TUrpGI+YiBt10amm9Za8bs1gEWLv2vgo3CWTENJLz10DfN1OEb2bWkO0KCXDzrMcOj102Fg/eB4a5aVKfKIPslqObJfqSiyp+ZHo31NvAwgMFcCPb1P4z+PYp0LPRd9ViKhuBWtEUYRaT7SQW3q8OEqeblUfdOswMptsLDFtbRNowvZPUlj73ttAhY8ZDogwinpAtQItpjUEplniDwQCQq0F0d0yOwEQgTilYUUQ8vnhD4uwen2QcLMaDk4qD+uRUxo8s7eYIuwhP2zhFY4CGr4AUBJPAu1j1Ro8CjNgZc3ImYbAYwDKoJr281Avzy1J21vji26YMyipKBpf4tQCTMLAX+9H/FhohyERpP5XgkWJnhp+//ixw8pzVSfaxi3DtEEEK3dhQLTy2CVKTbUK0ZDUL0ZF24XpukKo5rTGNZ7TnMW3K6tVfB8g66ZEySLnf8owlbjtkgCx/ziAmeWZz6CKihwaJo85ZBqictLy2fYa//OWP8Mcf/zjfs/JlRngwCyZ+0nuaSHAPbGIukjYF48QLuyihdN5wbnOfm0bpttfCakSLAN4Dw2/Bmk3HgHAkhp0Av3SXHaQJht5ltBiAS1fSsJHBAhnLElsmgsTtxJgLJAeuHVce+KLGyB7GvAysgbEhoKZIzyqi5GXMSC98DMiJerWoD8ajKsghSpfnOx7fa9SDqAQfQT2hTBBhAweJgCvE/dLW/liJx4e3NwBzHgTivyCMDC1yWvwG4YZ8IdUogpqaduSYrZ18YVjRwJ67yfBplz7eyYi4bMQ8pIXb4CeTter4HDROM5fe89DJux6c+zox1BcjE+mZS4885QtQJs3K2OJ18BIOgL2cqvoyX0h4Oes94YtX7xxsecdSeM/0KQE0G6tMkzym+ilxWOi6kMacx8w7TJwi53s5BrhTORKQl8F7adAb7luSCBmARvr9EJAfOsewDqlO3QqDBT3uuVjlxu4q/+y1D/LeJ19hIm2WS1JY+/ICCil7YGLfnbcxw5/QOYt3BttKr2cnxlzO6AoGnb4PWPfSrM/EyTUUQF+8H2lbgpg+cyo5L06/b8K5/jo5x5OAiuAmJiyusfc2kVBUDUKDmGuo7oOZgATAyfqPT8EMxH9FNGZkvAYfFlNwPuyP60FcE0iXaNIbJHvATVa0ZXY8BOUsu2QIDoOMammFC9cLhlI/3B61bpWlbfFtEDF4MbQjx8HKfei0kbn6kXBIShf6HOFxv5LJBdCM8kliGUsdOzML7c+RT59ehthZB8BQdtoUWHIUi06uVgYGS8sNGE1xPunP0b+ES1NeCderd2H39bW7acXjS6pSbJtsCI6/hdwkmtulTJRGESkMIE2HuPow606XOgqob/c9brsdAB/L5OPGIweIIwaH5WPDLKf3KDpJEas0i4oX3zjLFx5+mMdG11mVsKqUU8kJpVJxpOjLwLpbH1LQNt6iKjgveG+oUzqD0u6ixd+IOWlR7SSpDGaX8W9m05RMnf6ZFkDdn18zjJiYerrfV/t94nnEKSkPP1VYMUsc1AfHDKh8HbxQRCuMeuBTqDyBVufpVieIW2BbRzVrY6Iqwg1G1hskch/8u8VhvMfF3NbGK952aAziSa0vpPb1iHRokSxIk7adwFk0b0vcm/w5GFvT0/BovwRZbtvD4K3ZtRCSNS/5kQvR19yDs8JB7Visn5yBQx88o3UPeKN9ZWEEKuimsYOmGqaXVuh18EoDA+/CIOrG0Q/cRzkltWvBvnVJAi1BIjNuLdwXM8sML06uU2Lyg5NRDOb0AFm8JHlV+3Sa5GtuhWoBa684pteEF++rdRmYStSCH8Fiy2BbpZop9c1ZSBq1DITGBBfCkEAHOodpfWZ7ScpaZsVSdrHjSFiWS7R/KEsV0MFyecChvjs8X/5t+TmU9Sqeh5toXiHppMVaj1fhr//Gh/ltT3+Z37L5JR6rrwfJpBtzo1ujNt1g1Z7kOrjwlrmraVyVQTzdg3TR/XLJ3RQoZBIdAHZm1yXY+6V94jbT9iA92CddI2ek7P9K6wtCIFlxCIvDSNgWo8hNF1aoP648WAAXoOpQdag6jHsJ6bbx7v19B4moO+g7olE+cRjtMBrm8TYuaTaTpGu7oEHH1vEA4ov+nH5zmW2n1j/MusnbFUKnKHyFfKKaSVZJ5xlYHJLOna6pcRYghC4YQByCv7aT+3scpfaWAmpCZKRkICy9RSCx7wRIitYaXDrHHmcEuWZzJ07shLDLoHMfWtw4jlvllHUZSEzSAYt9BuzniPvL56ffV1SHx6bBIqddOPzy3nvjhvYJ9ytoG0G3k2JgilHGlAs5S8pFTlqhSW1I72oXDpU+xPuQbHKUrnw3MslSAw4w/SimvXTu5c9Hzgi0Z7aHonZPUJwLKVe9MfzC157hua0L/NVnfmqwj1cT0t6Ij59DeH3jK+auZtFVgYGr4ONizyarq8d4jtC/MyVDT/2odA/M59C+P2X2rgX7LtsoBSK2vri2FvKJQtk3veSo3NS2vn6bSCiCgglRjKoKugM2uBXmCksPAMmXOa1FGVwEFaMeY3z2VEmeH5opUg+bvcGSwM7xJI0hSy06OCI/3cy+c6/32XMlSDJR6ikBnDLZRDE4SALz2BIJ+CXVxKDmPh5HBOLECtwogkYpe9C/jLmGIZdAkFBs+EcVPH3UGiSkMhx0+PTylmsv+rQsjZTsuGga4vd04SKwYdndanCt8haXNMN++/CFC3ZkzX1q+eU9acnGJB/y3HhluEKRITg0G0WMQcu89Y7+ZTUh7Wt+qY8BzqO+L0sfeeOST+CRPt3HldsNCrerU/Es76eogrrg+orC7MaUlzqDfUYz47biByvmODW0Punfhs4HCaX1YTWb3EcLt86y35QOX8aljIxDYpA+Q9LGlWKCH3/QDO4mJiJLwWR5fx9iXYbGz9AXxBUAnjzfbADx4KYp+V0+qjx4CYXe+xpxGOnCA7QSFtwVA871eqaA8R6jMUoyuu9Xfo5pXZhmaEdHh0oH6vpMhyWQZsNlCMxVyAij2U2CaIQs9PH0sMQVLB2C8TR+HLDwZCxNPuAJpUIUi8ZtGkPcw8IQHSoreFk9cbum3NIp73Q11z5YwSV2EFPASn9baZWbwMIVRp7RakNdO9rW4uuaKk5B8yCRGMjSoJCMLykj4wCcY1OUGnpveR++MKWWPfB0kcLIVBwPw0Gq1CE1zkLuN9BkuahRJDJvXwl+ZMAYpG2hBWxYOpDOM1iERINrrNJg96vEH3r7RL4hBu07ZNGat4dd+x9LLVvLcw0qzyEmPrjG7Vh6OlzoF5aG48H/rkoagAgM1AmuC37fVjwT07LrQ/4Tj2BUM/tuo/juvKFxlp3ZhK4LQB+WvgvGb9sW/bVYsDutF2o6ch6f3Ldjf0aIaX0L5hxv3DgfI8finbhAMnNJ+3c+uL0SGy+5Snc+z15DAr/Qb3ThkDNjfAXNxtsGwF1I2Yqg3qHGo36EaZvw8IwgLkYxJpAwqUEV8Q6TZBRxgSErqCZwDiHtQaXWohMGYPURmDUiQKl0J609+40PaGNCop7Nl8cOKW44txAf/sDPvKCaqvGaEuqtgmg/nT5JCcmTkv98yC/RTU0E3JItxgOk/66VQq2YkaOqXJjhiDnMXIoV0QcAnBM56YD1pxS+fSdN4CNZ606sJb04Jnm0pBKZdbnIcUpstQwcieWH88XDIy+4n6LxPCgZbFXAzjVLUClBFQCdhvVEYypjvKecKqdUyYdmGyUbLgDbL9e/fIaaanj8PoN7OQYP7haEjyTk9zM+dgL7VWCzXUjZ6ieGGy4Yng2eRQTv4HkSahCCdyTIJ65i3lZBitF+9mm64LKa1hmV1FaaZnsa4wYCGw5rE0CS4UzEHtNqfmZAAGAFfJGvvZRDIkgDRR9IsogJOOcUuhihkI5NRlERbOMBy21WVHvQAO5RFqimNfg6oEXU51kg0IM3ILHjpxSzIduBC8ZM4hJmncVVQb4QiavbD+b7GhmvL+BXit/SmxkHgoFeWtLGAsBzXSPrJjV8D+y91t0fSz5DXwvRNp6vX2vvnosWnTC+/Dm0nXzpIUNLLoaGuJ60YitHZT2VdThrgtucieH5yJAZS3oJegA/JFfk/jx8w8tIVtHIIiHrkYfurQSpYmCgyH2SrfuE/qPI4XVA76ccAbTJFNJ7pMSXUCRmavDDF5tQZ436uGnAFeuDHuVJcqwL4VGfy7oeJ40U7H5wrA6/H3G7hy+hx/x4D0U8mFlYaQkPphX8wuZAnpRatsx3Eny+Qy7vzhtab2idpeuChKKdyc++DKLJuJL8C3KO9oJdp+UEbZrNxRlcaexMA7DTMMuiPzfE/hgXl5GiT4Qb0hCZ6yLjLmZnuZ+oYucOlRHuNrDwwAEcPwMqRC2GGQYTX26JHdv0ub6FkPRKHcYnz1CHlZZKGsR2OFlQH0xw9R6ucijusCFRUmBP2K6awCP5gHf0areLHTJNSTXuV4J//3v4U3ioSD4y7BcHDcHl6XLYbmLdOio/QxCcuR9Xt57N+ZHBj6JHSgmSiSgkgI8+3z7+NbVjPO5YnyyojKe2nuubil0EMDLR5Smza0ksZyh5SLGwdE7iw/Day9sG7L0cAApwCFGPMvgsRA+IZEwa5B9TmomJEtL9ieBCj6kDV0XT/9XKIAsNL6Y10HbR1dX3rpEmpo+tK7DCaEdDfo7EspbkkSRnDLx+Cja+LDGVs47bAutRv5VJlpb2GYw/5e9vgm0BD9W+CVJeBdW+oCZEXFrx2BideaCjDN4pPezMhWCeeVvRNBVdU6GtQeaWbhoWYDat5sRjyWvJdD1Y9rNKjauCxc9xwejAwJc07NhfkxSSGygZsrUA6QTe2eVQ+37RuWFbGANdhzYNdncVmNJuHs8+HnAuFPAsQFtU6sDGBfAvInqRsFCCx3RdZlhBKnEYcVS0mKhfe7FR+6uoGotqG+WZHl4lGzaJXiRdllYKTyOCJ0h4K5O4QtovanPJ/Q80n6M/vnQXjIZM0SzLpBCfcoaRe790ONsGdqsrJ25b48A0PrRZ61Fr86o6OeLSkHNKAzmM3o8UmThs5VEVGmdDHmZn8BNFVLCLwsgjwUXvSCNlHih6Rj2QWpYYX+n9Vs680nkyWOWBKLxgvupnA6bJFD0cGgOBREOO8pDR8D5pYlHnLKUIMZUuva/xvAkvpTX9NUXC91RM/OyC+6F4yc/kjmVJFlk+6igf/Nuy96NYdzlLKwhA2iFfM5KA4nU4URENGS9NE/Lu2EU4+d9/7Tv5Lee+zNlqj1Yte25MF1n5LMomB92I3cWYztmQ37/ywbhcC2ottlHq/R5gRcHECO9wfxJX1DmqYj0zTzEF/W9JpvFLIF4Actv1xySwLmICVDV8X5qd4TzaNMisCcvXrR8fS/+AGbii0kV9OERQqhiQGaId4qsolQS5JEzRXQbxBKDh+LigmjdU8xHiTOTIyQskNab2nXI5FD6xxMTEs/xRukWkcyXPkzRy98iVPF0kX6MXjpOnSzaAxutKriOoOMQL4m8jdt2xaaMF3MRl5mJEYhdX3enXwtTsTRES+yhYjX6BIUjJecGIYI3iVxwqJuf4SAzaFO04MCCWfTHdbvru4RB4FMcs50jOx6TdzdJ2LY4ZMMT+S0pH6yZvAoAvg2e0H/QReMU/6JlXwb5yiTnuR3secabvsuUgv1TlO3qW3Cvjjtv1iH0OBfoUM87S+UVSXe+naPTHjue2TWjba7NVDvyITQ0RmJ1aFi5A1sJVzF1F44IOngixAGI0pGadxPchSSBE9ps8Sgh9RbIEsnTfRnovkTz71AGjlq6YYSW5N4G2c/3Mq+vQzhGiwDT6fHt0aWYo1oRtqkjbhfqMjp/mfB3SyTaoeEQrVJog6JsW8UUK2RisI0KQT3CYPP+P/4uoLM4w2p8iLgZQqMudUqP00QNwCdghuRMQJZzEkguqUQJ++b2QTYKWG49fklPILofpmpD+pzGLU6hRF1iuP7kGnqUF5/E2AG5g35J17uzvnda7rDUE7lTay8miwQ3LemrrGG/NUVvnYJuseSYmOqhE/Jt+j+w/6+Q+3/DwpT8GXNSSo0rDi5P1KQa5JHwaaONxaaCQcJwbxcUS7rMoDKNZbch/krVQT8+2C9AOhrECxFO/6zzjGw2mOeI1XKpuAhe9XfstM+ljTnnot4JxH7roEZ/LKA2VGBd0nyBuF6GP+BpsXEXqoKk5cCPmVZ09TlqNCzm44PudvFBUpQ/gATBhEYzkQ50WHc46dtKwu2JaqL3vWJiwR3adjZAUn7V4jgWLbuOyjd6jeT8fADzJJqpoAnf1DCzsddEX2g4E7PT4LKUPGMA7jN4kAFiFqg1plae/wXh/E8skpEuJWrdRj9Eus+MAe0EDF42LFRuHsQcgDWlJ1DINbI6GRArpowTrNKL6KHX0DsrhcZYh977/XLoaDihgSRn7zp49TrJFyuadwmxkhPeb99W6KQEOENyZvDK90bF/sUJjqvEcbVmBH3uSuCuiVJVjddxgRamtQ1Woa5df7jxd7semXlsvpqjZWKSEHBmRKQ/06QLMh0nD+qZMemXa5yiGnmY0A804M0TJht1qwX0VLT6Uk7l2HfzN+FsVX0QfG8frUDqBHthVwQX3w+WVXkrZ4kjPkGM8ao5i0vf0+yG55OgdB/V8E6yYtoWVq0Fi9FVcHUqV+cfO8YWth3h4tA1ApyZHXO53o5wD3Hmh6yxdY1EvqBfohGo35GtP4C2u924bJMgrZ0fJvS8PysVzTEBtoqtzAuSkczuHtu1hdu19lk560ihgw6AjIuGc1iJV8s0Mx3QT4cKZXb56TNs9cAkF8YXbkw+uhPUOVmdY3/RTDqXv6BLynwg+r3mZ3QjjX0OSVgqQzcwbeh07seUClEuJI7sLEkfjklH351aGAF5wkv52JTCvNJSgSTqJ1JTIKHWCM1t09fFLJ92xlBjo+03igu6dVtxJP4RUvgT6ZBVjlcp6xtYxth3WhNDm2joWKXFTCcAFme7zOhTfYz00u4EysNIP6lsCcwHA+bej8KHEjfJznHWU57VzT3WbcOQTFSEktxoRdVXN3gjJuJX6b65+klPSgFXkUundH4tbHMgYw8sf2Vx3KaHcyV3wuN+Xg1vCteVoL5l7KUrM9d8TAnHC+Ba8cbDOtc21uIp8zDiYElrFAJ7OWZwT1JkQDOTDP/H9rKcE75DUXTNByLJIkkkSKBeSiDrpDeHie6btXGDaUcvWpu09j3yyv+nAiB3+JjuIC59TxG5V5X10bYXFOfg9l77MrxzTdA8YwPO6NZENO0QMWoPVA2w3B1f1PTLhq2jQwq3D4DHiMXRB+hAXVG8NRjVn0nKm4YGUkogOADjJIT1ghyouv+h91sLDAN7fkS4fKpLIYdSCI3gnEI8hkCH3+FmcuURXPX5/rRvHE+OKdTBV6SZCNz3i1sINILWnqh2jylFbx2odvFC8Ctv1hFkdmNFo3w9lkwzYQ3AehCkn8B54naS6FeCt5bHpSz+dva2Gm8bE5FJo+scqXqn2O6rd+6Tg8VqksSZOotw4eM6YxgW3sVTiiy2JcVeW5D6Y7y0tdxfbS/tJ2fCa6T6PqNIAvG8DopktH6LZcCRiL+0bwPBw/QTCjO5+ADxhmgtgZzrQNmy7dTDlarPO1DSHDvPJ4B6jLxPzRiXk8ilYRqlzB9e99F2HRsjIspe9h9L+uU8WTJuuC4ZO59D4OWeDG9yniafoG0vigC7WBs29smAtVJbmkS1mj7b8mbP/jv/6mKZ78G6ENNGlr4oYGxJNddZjradKWrgP7oNifQZJq20Ebhf+ooCjUo+NOcTVtmQjZJRLQlH6hR7KfwUzl3C+tF1KoC+AOhkyl2ftmmSSuG6YlC4UeWWF0MG8GlRWUblAU/1F4BKG49e+u9tinA8GG0ltBtVMcVOhW9HeI6XSwL5jfpLJqGVat4xtx1q9oI6IezAZcWOsYXV4N5RBBoChR3/O+y+x6oFskqobp60aUysMVvkpvFkOXbs8lSGmdw3BF6PrB+Fc95FrfVBK1q/heq4GrUN+EzoX9E7VMB1O9+nLKUrPwlObmk5iLEO4RnmZQ9c95v6XjZzLhkggPO/BszoC0IvtiXz0A3K87zhg+FFBx09YxGleVizn/VANSwA6kw2XnQ9pY1sX/rlYx6ax+M6ExWDaOCh2Eh0DYlDbojuWdcui7RlzoW1nVq0KTZtBWV1k3N5nSSQBvZaDeHmPRhAbZBKMibKJhO+pmADe/uw6B49v8H1/5Rf5sc1P8Gh1vG3srgFcRCzwq8Arqvr9InIW+MfAk8ALwB9T1Zt3Ok92sdPwLZy7RWQP4SAuixZX3tFSOokvQPyXDG4qGo0X0Yd7EEmZaR5D75CS8mmxPxEpSqpRgnvyHSnBe7nnDjUADREywAjlLJ6zdPZRhIcQVlFZQ/0F/oe/8xE21h5KbX3PbStKiAjzoJUUbLRfuMHX9PlODCGgwAb9W0Qx8Z+NfyFMU/NtZaAYShS3rVfpepXHrwQacWYiKVdNNLgm8JalQzVdP24sSY5C1Tiq3aYHaw0+7r/8+R9nPNk8cdsObyhcS4rvKSgna6TLXifpZV2STzR5MkQD7TFj4OHrl38ZAnUJ4ofa69hzHgbvY+yXwWspR02Bes9rf+1v0l6/EW/1ZO0bsvrFETEWu1DmO2NeOdjk4ZUdGm/p1NBpCNxx3uC8oN5E2SRVUvIgE+QYP/DJDrwugnfn+ueWDI8QALnrAmj7wuUPeuA+jmlDTrMQv4Rm1eB5JNbCqEbqGqYTmsfOsH9pzK13GWaPdJj1lo2NHf7Q5ic4azquuDfHiPnngc8DG/H7DwP/WlV/VER+OH7/L293AiW4EYLv3dDQ6HGyh8g+xo8GoB3+elJ8dsClHlhDOH18YXTobggUWnfvEy55W6zVEk3sz38YjtO2IXSXy6al3QRlgrIOjEGmoE/g5DEa+0EqfRqrNdZ7Pvax/xcPnX2cZrGbTnjPbZt11xTlF/MKq5X4j7w0GpYQthzdB0sjeHoutXG4Qm/UQqJYao5h4M1yKX87CkhKiUDihZblgLJZi2unHMsafdLt3FFd28VvrOCmNW6l4mvXfpnJ2Uu0Pkso9962x9W/BFMhgAEEhlXKQCbIJZq8U9I/0zPao057W4+T5X1uh9bH+R7mYIijEX45cCXM3CAHXDlh9+d+kdGFh+iu3Ui7nqDvkvOp56r6kHvE7Ix4Y2+NrfEsMvAE3CZLJxDHyzC17w3raabc+RCyXnqLqAZAL937IGwDUB/0bBgCdvwtV905yhdIjDB8ofpslBoNoLKxjj+/Sbs2ol2vufnumt1nHB/+9k/x5x7612yZjlbhlh/xqeY8r7dbwOtHNt1dAbiIPAr8PuCvAP9Z3PwR4Hvj578H/Cx3eFAhuHxBiHwMq3GHvNoeqbcRuwt6hmykFIeYHriTt4GKxPWow8hofcxSiBIyCUXwHgTe+OJ78jNLf4ZUzks0qAym7dEVLJee5UEF8foSv6uss6i+B29+D8qTiNpgydbg1jla7LC1/znqV36GVz79k/yJb/pG/ukXnksnv+e2DTKBKUA1gnnKf5IiBmO+b2ofwbt/ub2GpL3r1ZxaHA7Der0ICftTuHdmd4F992HIS9U5AjOUKBlIWIG79FbJoD1gk+UzImdlM50yeWOBmXeI94FxuzCrcOfWmV+YcP19Nfrur7L733+Sf+8vfANf/J8/zfarJ2zbI0qvu4ec6b422WhFMkZZ0wN3NFSVqUIlLkqSGfNSW+V2McU1l0vpXxhnpyEobLmy5YmHh5eT0cg9Qmphs7RdCcvKzYXRLaF+7gZXf/VzPPK+D/Oc/3w65b3jgoI96DBNhx9VAw+Q8fUJ2+dX8GeFxgc/8IWztEXOE4iTHlGkM9GmkCSUyMBni8y6ca4H5K4L353rDYqpqdquNzo6l7NLipH+c1XkLzLSe5EAMhoNGLwZ1XDxHF/9wxf4PX/4l/mz53+eFYG6kBM/1Wzwl1/87Xzh555m60sh0CsE0P3ikW13twz8bwL/BbBebHtIVV8DUNXXROQu1qxS0vqT0cIE0Z2vne5gVnaoo3QSXAl97y4oijFhW1gQIvqL46k6ExcOUHzUuZOuLYfmgkNNO5TeRzx5iaiCF9Mz8EPuAenNMuA3cfIMTp4C+whChTBC5RLGP4TtWtb2X+Js+3lqv4Nqg/UN42abH/vVf8Z//M3vZt425bT7BG1LZB8ap48hzDwsKaa4sdCuEbxPonRiqmDwNdYzqhzjqmNiW4woY9NhRHl05RafrkI0pmmVQx4jFJ91iCe5ycONIAlYVDHN0vG5aaU/V2TX1YHDzoPfrFYmMFfn8ZOKbrVi++kRN7/Zw1qHOsFOWy6cucH1/+af8gf+83dhZns8148wJ2vb49g30KwLiy3LiomuZt4HIIjuhOI9WBuAPDdMAHMz7/KCA4cA+iiZ5FjfQo7fvnza8pWIJCUM8uUPoU6mFUbbIWdLWhHJdCGJ1wsf/2me+MD345rBSlL33r5OMU3XD2itz0bF8S2Y3woBbol1d87ivcluhN6nVLQSoSWQA281tKEHbZpe8nC+Z9HR+JhBvQBtdS6QNokufmlRY2sLCa1oc2N6nbuq0EvnuP6hLW6+B+SJferasTpp+J2XfoXfv/VJrvsx/+/tb+Effe5b8TfHyEKwc2F8Uzjzime8nXKpHN90dwRwEfl+4A1V/TUR+d47PozDx/8g8IMAmxtjAoAnFpJkDcWN93DjPQL6pBwl0R8zAbmkv4X8glJ5wbYVprPRp7r016bA6iFoHzUf1YIKDs2USaOsQMcgGyg1UCOcw/MhnLwfb95F7Tqsbxl3M8TvUbd7nNl7nofmn2DsruNkAXh++eXXOTsR3nNmlU9emS/V797adjzeGrrppX28Us+Ubi7RaElm3sZ4jNFgPDaese2oYu4JI8rEtJwb7eGnHjeq+sQ+CUOS//WdcGPZ39YHFn5oNfMI2tmgGYm9WTjs7hzpPN3mlG61YnZhBV8Jiw3h1jcpv/c7Psl7Vl/lZhdS8n7l517jKxeUd79/zFd+ZT8vw3XS9rVnzvT6cqk8iNJNhXYlbow6qoQT9KlGjQlG9tILhbSwg2IbobldFY8SpZcA+3bYng8r6r/8Y8ioGGdGbQD3wLZhtBvyiaTz33rpc9STNda2HmX7ynNHnPD2pWzbSb2BxMW4RWJG0uinPdpW6h2T3QeDK2FcxNibAN7RbTD7LsTXPqxhGmemkWlnPTvNlmLuksTIB6A9rG/v7pcMj4VcIuMRurZCd3EDN6lwY8P+wxXXvrvlj37o1/jPL/wiu1655Ue80J7nC4uH+cLsYf5/z30TG78wZfWKp9rvBjKSCuQl1o4pd8PAvxv4AyLye4EJsCEi/wC4IiIPx1H2YeCNow5W1R8HfhzgkctrKlni6OeGKh1qb4LZwLoWqx2i0b9busy0RcPnFGqXohmtdKzvbuGtY3vrCn30JeTgHREka1d9Tw68MK6Mo/GcJAua5jdWMKjUeP8Qzr2bxeiPIHIRK+vB+AaAp+4O2Lr+G2ztf5HH/S/iNGj0VivM7AxwBq06OrPgU9ee46OvvMEvv/pvaL3jICx++9RJ2nZj41H1tgjJjiUvsuvBT4J0IrVHjFLXfebBcdUxMh2VcbRqw2o5wPl6l0tPXOdKc55zn/O5U+Vzp7okzd0UzZYMekWkG/TH5xLyemEbjz3oqF+/hVYWXRmHBFFR23cbE659YIVb71F+6MP/imtt8A8+Ux1wvt6lVcvc17wy3+LjH3uJF//1TT71cx/FN45mv72ntl1u3/Fjj+mRTEjIy9HR9nqqupgmNEknyR85TwQiaFjD9FqHr2pmF5cuUBgLjxzbI2IPZJD+9FniGezrwI97b6TynkwjrLwmjG/qID2AKIx2AplyY4O3sPfGC9x8+XN84tUvoK7DtQtOigubk4dV5ovoRdbLTdK0rF5pmZ8dsdtMmHcVbWTfbVvhveDjSj646PctaSYR1oY9uGgQt8LmlzVIIplE9D77g6hI9UBw6TO27gNsErNe7stVhUwnzJ++wJVvH/On/uTP8LvWPsN7RoYKGxNweJ5rDX/x+T/Klz79GE/8iy4Ptg9Xwv5DcHDBYLYM1UypFoqd+bgQheZ38ahyRwBX1b8E/CWAyMD/r6r6J0XkvwH+NPCj8e9P3+lcqEcI061ggbeoBOXaVTO68QwdGZhH1m08VoLbYJZDYvsZbYPEIp5KGqxXrAOznA42GxCgp04GqMP39HIUskjQuivUbNDx3SiPgr0Yt03ArGHlIYyHurvOxeufYEVeY2yuh8UUZnPMwnOw9172uEQj63g7Cswdg7Yha+Lve/K38wcf32Nz9AU+s/15/slzn+FXXr36VeDf3mvbKoR0nLCU98FT73uqNRN8ZIXgcWI8k1FLbT3WBMbtY5jyRjVnxTSs2AWXqm02x3NeH3vcyAxZXiGPlOUoV7bkQORtqINpSsbRu3aJU3Rlgp9UtJsTXv0tY+aXOkZn5ozHLRuTK1yoG37++rN85uXLuJ0aaXukiqEB+N/0TZz/bY4PPfsi+7/xAl/4nz7Nyx99+URte6itl8YfN1baFUFXJtC24E3vz5u9TkwfYOR8P7DFzHghI96RmlLf0Ef8PjCCRrkgXK84RRXJTDRYp6XgbAurr/ZeSqaB0Z5SH3gm19swGTaCr+Mg4RU7Dxd498O/HXnow3TTiuv7X+Xzv/T/wTWzk+GCc7C7H8YcEajr4DcPVLst9V6dA8ucD+lju9YW7Ls/lWmS7h1mDzkDR9cN+2nSupM9whowVZBHkq0CAsM2EoBaBCZj3IVNvvqRNdxTc565dJVpteDy5Av8zpU3+P713wDgF+cr/Gef/mPsvrTB5Eq8lxmsd3Dl20fMHm+pNxZMJi0bkwVGlNYbms5yaz5mvjNm85MjVq94pm8c9oFP5X78wH8U+AkR+TPA14B//86HJFc/ItNNHdLibYe3Ldiob5PymCSPlCQG9fq4iavrCB6rio0acP+YpGAuUswxE/sPhklRwesKygpqz2G8BWq8bOH121B5AuUi42Yfq3MsBwivYboFVbvH5u4Xmeh1RuyBn+D9GNeust88yb4+SmPWcXYUNdDwAhvfUft9Kr2OsIZ0U/pIjhO0bZIkCABdGqZM47EpY5+El9gYpbaekXVY47HiqYyjNo4V07Bu56zbGet2xlq9CJZXY6K1Pz7NApyLR1zUKQ4YcTUTcQqjsGC16TQEv7SB2S0urtCtGLqJwdtV3EjoVoX2Gw544vwtHl27BcBeO2a3HfO5Vy8hL01Y2Q6ZEvOKQTYYXNt1pZl6JrZjPljO7iT99vYlXVNHNUZiyobCGHZoxkGSj4qZzBFG4H6glMH2QfH9W+RNQRCN9gwesDPJ5xQN2rZZwOS6z0nGUmpgO/dUe22+tB/ZbCA3bXDJkxjNiA/9q7jDE+ECXVzmMEUkAlQW03TYJujfWsomKnEZNpPdBonAHZZRS/nY4yWc6xunLMYEw2PSr6sqg7YYg26t022tcPDIBG8FNxZm54VnvvsF/sTlj/H9qy/zXGt5pdviucUl/pftb+Vqs87XDs4w/8wWm68L41uedlWC1LYOi3fP+NDjL/Ps+lXOVvuMTWjrha858CO+sn+Bz40fYr55jmom2MXxMH1PAK6qP0uwKqOq14EP38vxgd1WhORNNnammKjVKGod1jbB8yRFRwmIUaxpo194h6XFmLgqj/ggo3jF+iSdWPq0NKFIWvwxyx3pd0HcGt69B8d7cKM/gNgpgu33UI/4jouv/Crr3RdZrz7FRBvwFu1q5u1Z9mffwI35uzgYX+wHpzpNQ4kdKbBN6zrqZp8NeZ3z1ZexjefbJ+/jW977zfyO1370hG1LzKkR2GjKcaxG+kyCY5/9vq31OG8wVceZ8QGV8WzWc7bqAzarA9bNjFXTYFHOjfcZrbTAuI+ULPTvPnSdnuXE330tjGbBP9scNGHV9qpnP1qHGdgr31PTPTXn/Y+9yredeRGDsufGfGbnMp975RIvf+phplckG9KmiXSnhxxZpBtBs6nYp/f48+/9Bc7aPba/d5Vv+e5v569+4OUTt22humXAUwG8ZPc6HUfW7XxgfBB8fY0lud4BaGXj1NyQQrUTP1l23cvXTsC89HtadQag29QlgFeS58jml8PKNDm5U+exDdiFZ3yrxe40uLVRZu5uUvXh5y4SJw0Rp2Z/gRqDji121vKQeYQXRmfDFU/SvsYidY0uGmRlQl7BSKo8+O80Y1oXNHDnovTqBRoTgLs1mEbCak6d9Ol9U7t7D3Xds2uvmVmTWHfSuQFE8Oc2uPKdW9z8UMe/+F1/g8cqw1T6jKEeZdt7/s+f+w+59anzXP5oh517fCW4iWH9Itz4oOPZd7/C91x4jrFp8xqfjhDMlxZobtWy6yZ8bXaGX3/tEdxnNrEODh4S9i9X8M+ObroHmw9cLK3ZQmgRPwt6k3iMGjyP43kST40RRSQAtDUNQshTbaXD0mEkMHJjOoyNq/PENQiNhqWX+tKHsAfb8QTHOjP5XYh5FCMPgbGorqBMMTqlbhaMZzucff3LbI4+S223UelY6fZCwMHeRd7oPkDDBp1M8b7GuwneTsCZPrXoYO09xzn3Fca6i6WhMg2qhoPmHNv1I3i1y0rEPRVxSr3dghVcHTu4EXQk2HkwlElr0JFHamVcd2xM5tTG0anlXH3ARjVjxTasmgVb9oAte8CqNExtGwB/bLALT1oaKtl71UpYus0rdqGsfG0XrQx+FPI6SBveJr8yot0cMztfc+0DQv2NO5xZneFU+B3nv8RWfQDA3/3kd8GtmmrfIB2MFvHF9PQpXNM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"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light",
"transient": {}
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../'))\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('/home/dengnc/deep_view_syn/data/gas_fovea_2020.12.31/upsampling_test/input/out_view_0000.png')\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",
......
run_lf_syn.py
View file @ 2a1d7973
import sys
sys.path.append('/e/dengnc')
__package__ = "deep_view_syn"
import os
import torch
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
import os
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 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'
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
from tensorboardX.writer import SummaryWriter
sys.path.append(os.path.abspath(sys.path[0] + '/../'))
__package__ = "deep_view_syn"
# ===========================================================
# Training settings
......
utils/img.py
View file @ 2a1d7973
......@@ -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)
: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
return img
batch_input = len(img.shape) == 4
......@@ -88,7 +88,9 @@ def save(input: torch.Tensor, *paths: str):
new_paths = []
for path in paths:
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
np_img = torch2np(input)
if np_img.dtype.kind == 'f':
......@@ -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 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,
......@@ -138,7 +143,7 @@ def save_video(frames: torch.Tensor, path: str, fps: int,
misc.create_dir(temp_out_dir)
os.chdir(temp_out_dir)
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)
shutil.rmtree(os.path.join(dir, temp_out_dir))
......
utils/view.py
View file @ 2a1d7973
......@@ -2,6 +2,7 @@
from typing import List, Mapping, Tuple, Union
import torch
import math
import glm
from . import misc
......@@ -14,7 +15,7 @@ class CameraParam(object):
def __init__(self, params: Mapping[str, Union[float, bool]],
res: Tuple[int, int], *, device=None) -> None:
super().__init__()
params = self._convert_camera_params(params, res)
params = CameraParam.convert_camera_params(params, res)
self.res = res
self.f = torch.tensor([params['fx'], params['fy'], 1], device=device)
self.c = torch.tensor([params['cx'], params['cy']], device=device)
......@@ -32,7 +33,7 @@ class CameraParam(object):
self.c[0] = self.c[0] / self.res[1] * res[1]
self.c[1] = self.c[1] / self.res[0] * res[0]
self.res = res
def proj(self, p: torch.Tensor, normalize=False, center_as_origin=False) -> torch.Tensor:
"""
Project positions in local space to image plane
......@@ -101,8 +102,9 @@ class CameraParam(object):
rays_d = trans.trans_vector(rays)
return rays_o, rays_d
def _convert_camera_params(self, input_camera_params: Mapping[str, Union[float, bool]],
view_res: Tuple[int, int]) -> 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]]:
"""
Check and convert camera parameters in config file to pixel-space
......@@ -135,7 +137,7 @@ class CameraParam(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.r = r
if len(self.t.size()) == 1:
......@@ -156,13 +158,13 @@ class Trans(object):
size_M = list(self.r.size())[:-2]
out_size = size_M + size_N + [3]
t_size = size_M + [1 for _ in range(len(size_N))] + [3]
t = self.t.view(t_size) # (M.., 1.., 3)
t = self.t.view(t_size) # (M.., 1.., 3)
if inverse:
p = (p - t).view(size_M + [-1, 3])
r = self.r
else:
p = p.view(-1, 3)
r = self.r.movedim(-1, -2) # Transpose rotation matrices
r = self.r.movedim(-1, -2) # Transpose rotation matrices
out = torch.matmul(p, r).view(out_size)
if not inverse:
out = out + t
......@@ -178,13 +180,13 @@ class Trans(object):
:return `Tensor(M.., N.., 3)`: transformed vectors
"""
out_size = list(self.r.size())[:-2] + list(v.size())[:-1] + [3]
r = self.r if inverse else self.r.movedim(-1, -2) # Transpose rotation matrices
r = self.r if inverse else self.r.movedim(-1, -2) # Transpose rotation matrices
out = torch.matmul(v.view(-1, 3), r).view(out_size)
return out
def size(self) -> List[int]:
return list(self.t.size()[:-1])
def get(self, *index):
return Trans(self.t[index], self.r[index])
......@@ -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)
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]
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