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Commit 3554ba52 authored by Nianchen Deng's avatar Nianchen Deng
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sync

parent f7038e26
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image_scale.py
View file @ 3554ba52
import sys
import os
sys.path.append(os.path.abspath(sys.path[0] + '/../'))
__package__ = "deeplightfield"
__package__ = "deep_view_syn"
import argparse
from PIL import Image
......
msl_net.py
View file @ 3554ba52
......@@ -4,6 +4,8 @@ import torch.nn as nn
from .my import net_modules
from .my import util
from .my import device
from .my import color_mode
rand_gen = torch.Generator(device=device.GetDevice())
rand_gen.manual_seed(torch.seed())
......@@ -187,7 +189,7 @@ class Sampler(nn.Module):
class MslNet(nn.Module):
def __init__(self, fc_params, sampler_params,
gray=False,
color: int = color_mode.RGB,
encode_to_dim: int = 0,
export_mode: bool = False):
"""
......@@ -203,11 +205,24 @@ class MslNet(nn.Module):
self.input_encoder = net_modules.InputEncoder.Get(
encode_to_dim, self.in_chns)
fc_params['in_chns'] = self.input_encoder.out_dim
fc_params['out_chns'] = 2 if gray else 4
fc_params['out_chns'] = 2 if color == color_mode.GRAY else 4
self.sampler = Sampler(**sampler_params)
self.net = net_modules.FcNet(**fc_params)
self.rendering = Rendering()
self.export_mode = export_mode
if color == color_mode.YCbCr:
self.net1 = net_modules.FcNet(
in_chns=fc_params['in_chns'],
out_chns=fc_params['nf'] + 2,
nf=fc_params['nf'],
n_layers=fc_params['n_layers'] - 2)
self.net2 = net_modules.FcNet(
in_chns=fc_params['nf'],
out_chns=2,
nf=fc_params['nf'],
n_layers=1)
self.net = None
else:
self.net = net_modules.FcNet(**fc_params)
def forward(self, rays_o: torch.Tensor, rays_d: torch.Tensor,
ret_depth: bool = False) -> torch.Tensor:
......@@ -221,13 +236,23 @@ class MslNet(nn.Module):
coords, depths = self.sampler(rays_o, rays_d)
encoded = self.input_encoder(coords)
if not self.net:
mid_output = self.net1(encoded)
net2_output = self.net2(mid_output[..., :-2])
raw = torch.cat([
mid_output[..., -2:],
net2_output
], -1)
else:
raw = self.net(encoded)
if self.export_mode:
colors, alphas = self.rendering.raw2color(self.net(encoded), depths)
colors, alphas = self.rendering.raw2color(raw, depths)
return torch.cat([colors, alphas[..., None]], -1)
if ret_depth:
color_map, _, _, _, depth_map = self.rendering(
self.net(encoded), depths, ret_extra=True)
raw, depths, ret_extra=True)
return color_map, depth_map
return self.rendering(self.net(encoded), depths)
return self.rendering(raw, depths)
msl_net_1.py
View file @ 3554ba52
......@@ -209,7 +209,8 @@ class MslNet(nn.Module):
self.rendering = Rendering()
self.export_mode = export_mode
def forward(self, fc_input: torch.Tensor,
def forward(self, view_centers: torch.Tensor, view_rots: torch.Tensor, local_rays: torch.Tensor,
ret_depth: bool = False) -> torch.Tensor:
"""
rays -> colors
......@@ -218,10 +219,13 @@ class MslNet(nn.Module):
:param rays_d ```Tensor(B, 3)```: rays' direction
:return: ```Tensor(B, C)``, inferred images/pixels
"""
depths = torch.ones(4096, 16, device="cuda")
rays_o = local_rays * 0 + view_centers
rays_d = torch.matmul(local_rays.flatten(0, -2), r).view(out_size)
coords, depths = self.sampler(rays_o, rays_d)
encoded = self.input_encoder(coords)
if self.export_mode:
colors, alphas = self.rendering.raw2color(self.net(fc_input), depths)
colors, alphas = self.rendering.raw2color(self.net(encoded), depths)
return torch.cat([colors, alphas[..., None]], -1)
if ret_depth:
......
my/color_mode.py 0 → 100644
View file @ 3554ba52
RGB = 0
GRAY = 1
YCbCr = 2
def to_str(color_mode):
return "gray" if color_mode == GRAY \
else ("ybr" if color_mode == YCbCr
else "rgb")
def from_str(color_str):
return GRAY if color_str == 'gray' \
else (YCbCr if color_str == 'ybr'
else RGB)
my/fovea_refine.py 0 → 100644
View file @ 3554ba52
import torch
import torch.nn.functional as nn_f
from . import view
def get_warp(rays_o, rays_d, depthmap, tgt_o, tgt_r, tgt_cam):
print(rays_o.size(), rays_d.size(), depthmap.size())
pcloud = rays_o + rays_d * depthmap[..., None]
print(rays_o.size(), rays_d.size(), depthmap.size(), pcloud.size())
pcloud_in_tgt = view.trans_point(
pcloud, tgt_o, tgt_r, inverse=True)
print(pcloud_in_tgt.size())
pixel_positions = tgt_cam.proj(pcloud_in_tgt)
pixel_positions[..., 0] /= tgt_cam.res[1] * 0.5
pixel_positions[..., 1] /= tgt_cam.res[0] * 0.5
pixel_positions -= 1
return pixel_positions
def refine(image, depthmap, rays_o, rays_d, bounds_img, bounds_o,
bounds_r, ref_cam: view.CameraParam, net, is_lr):
if is_lr:
image = nn_f.upsample(
image[None, ...], scale_factor=2, mode='bicubic')[0]
depthmap = nn_f.upsample(
depthmap[None, None, ...], scale_factor=2, mode='bicubic')[0, 0]
bounds_rays_o, bounds_rays_d = ref_cam.get_global_rays(
bounds_o, bounds_r, flatten=True)
bounds_inferred = torch.stack([
net(bounds_rays_o[i], bounds_rays_d[i]).view(
ref_cam.res[0], ref_cam.res[1], -1).permute(2, 0, 1)
for i in range(bounds_img.size(0))
], 0)
bounds_diff = (bounds_img - bounds_inferred) / (bounds_inferred + 1e-5)
bounds_warp = get_warp(rays_o, rays_d, depthmap,
bounds_o, bounds_r, ref_cam)
warped_diff = nn_f.grid_sample(bounds_diff, bounds_warp)
print(bounds_warp.size(), warped_diff.size())
avg_diff = torch.mean(warped_diff, 0)
return image * (1 + avg_diff)
my/foveation.py
View file @ 3554ba52
......@@ -17,12 +17,16 @@ class Foveation(object):
self._gen_layer_blendmap(i)
for i in range(self.n_layers - 1)
] # blend maps of fovea layers
self.coords = util.MeshGrid(out_res).to(device=device)
def to(self, device):
self.eye_fovea_blend = [x.to(device=device) for x in self.eye_fovea_blend]
self.eye_fovea_blend = [x.to(device=device)
for x in self.eye_fovea_blend]
self.coords = self.coords.to(device=device)
return self
def synthesis(self, layers: List[torch.Tensor]) -> torch.Tensor:
def synthesis(self, layers: List[torch.Tensor],
normalized_fovea_center: Tuple[float, float]) -> torch.Tensor:
"""
Generate foveated retinal image by blending fovea layers
**Note: current implementation only support two fovea layers**
......@@ -32,12 +36,17 @@ class Foveation(object):
"""
output: torch.Tensor = nn_f.interpolate(layers[-1], self.out_res,
mode='bilinear', align_corners=False)
c = torch.tensor([
normalized_fovea_center[0] * self.out_res[1],
normalized_fovea_center[1] * self.out_res[0]
], device=self.coords.device)
for i in range(self.n_layers - 2, -1, -1):
output_roi = output[self.get_layer_region_in_final_image(i)]
image = nn_f.interpolate(layers[i], output_roi.size()[-2:],
mode='bilinear', align_corners=False)
blend = self.eye_fovea_blend[i]
output_roi.mul_(1 - blend).add_(image * blend)
if layers[i] == None:
continue
R = self.get_layer_size_in_final_image(i) / 2
grid = ((self.coords - c) / R)[None, ...]
blend = nn_f.grid_sample(self.eye_fovea_blend[i][None, None, ...], grid) # (1, 1, H:out, W:out)
output.mul_(1 - blend).add_(nn_f.grid_sample(layers[i], grid) * blend)
return output
def get_layer_size_in_final_image(self, i: int) -> int:
......@@ -52,7 +61,8 @@ class Foveation(object):
k = length_i / length
return int(math.ceil(self.out_res[0] * k))
def get_layer_region_in_final_image(self, i: int) -> Tuple[slice, slice]:
def get_layer_region_in_final_image(self, i: int,
normalized_fovea_center: Tuple[float, float]) -> Tuple[slice, slice]:
"""
Get region of fovea layer i in final image
......@@ -60,8 +70,10 @@ class Foveation(object):
:return: tuple of slice objects stores the start and end of region in horizontal and vertical
"""
roi_size = self.get_layer_size_in_final_image(i)
roi_offset_y = (self.out_res[0] - roi_size) // 2
roi_offset_x = (self.out_res[1] - roi_size) // 2
roi_center = (int(self.out_res[1] * normalized_fovea_center[0]),
int(self.out_res[0] * normalized_fovea_center[1]))
roi_offset_y = roi_center[1] - roi_size // 2
roi_offset_x = roi_center[0] - roi_size // 2
return (...,
slice(roi_offset_y, roi_offset_y + roi_size),
slice(roi_offset_x, roi_offset_x + roi_size)
......@@ -76,6 +88,7 @@ class Foveation(object):
"""
size = self.get_layer_size_in_final_image(i)
R = size / 2
p = util.MeshGrid((size, size)).to(device=self.device) # (size, size, 2)
p = util.MeshGrid((size, size)).to(
device=self.device) # (size, size, 2)
r = torch.norm(p - R, dim=2) # (size, size, 2)
return util.SmoothStep(R, R * 0.6, r)
my/simple_perf.py
View file @ 3554ba52
......@@ -24,6 +24,8 @@ class SimplePerf(object):
return
self.end_event.record()
torch.cuda.synchronize()
print('%s: %.1fms' % (name, self.start_event.elapsed_time(self.end_event)))
duration = self.start_event.elapsed_time(self.end_event)
print('%s: %.1fms' % (name, duration))
if not end:
self.start_event.record()
return duration
\ No newline at end of file
my/util.py
View file @ 3554ba52
......@@ -308,3 +308,74 @@ def view_like(input: torch.Tensor, ref: torch.Tensor) -> torch.Tensor:
out_shape = list(ref.size())
out_shape[-1] = -1
return input.view(out_shape)
def rgb2ycbcr(input: torch.Tensor) -> torch.Tensor:
"""
Convert input tensor from RGB to YCbCr
:param input ```Tensor(..., 3) | Tensor(..., 3, H, W)```:
:return ```Tensor(..., 3) | Tensor(..., 3, H, W)```:
"""
if input.size(-1) == 3:
r = input[..., 0:1]
g = input[..., 1:2]
b = input[..., 2:3]
dim_c = -1
else:
r = input[..., 0:1, :, :]
g = input[..., 1:2, :, :]
b = input[..., 2:3, :, :]
dim_c = -3
y = r * 0.25678824 + g * 0.50412941 + b * 0.09790588 + 0.0625
cb = r * -0.14822353 + g * -0.29099216 + b * 0.43921569 + 0.5
cr = r * 0.43921569 + g * -0.36778824 + b * -0.07142745 + 0.5
return torch.cat([y, cb, cr], dim_c)
def rgb2ycbcr(input: torch.Tensor) -> torch.Tensor:
"""
Convert input tensor from RGB to YCbCr
:param input ```Tensor(..., 3) | Tensor(..., 3, H, W)```:
:return ```Tensor(..., 3) | Tensor(..., 3, H, W)```:
"""
if input.size(-1) == 3:
r = input[..., 0:1]
g = input[..., 1:2]
b = input[..., 2:3]
dim_c = -1
else:
r = input[..., 0:1, :, :]
g = input[..., 1:2, :, :]
b = input[..., 2:3, :, :]
dim_c = -3
y = r * 0.257 + g * 0.504 + b * 0.098 + 0.0625
cb = r * -0.148 + g * -0.291 + b * 0.439 + 0.5
cr = r * 0.439 + g * -0.368 + b * -0.071 + 0.5
return torch.cat([cb, cr, y], dim_c)
def ycbcr2rgb(input: torch.Tensor) -> torch.Tensor:
"""
Convert input tensor from YCbCr to RGB
:param input ```Tensor(..., 3) | Tensor(..., 3, H, W)```:
:return ```Tensor(..., 3) | Tensor(..., 3, H, W)```:
"""
if input.size(-1) == 3:
cb = input[..., 0:1]
cr = input[..., 1:2]
y = input[..., 2:3]
dim_c = -1
else:
cb = input[..., 0:1, :, :]
cr = input[..., 1:2, :, :]
y = input[..., 2:3, :, :]
dim_c = -3
y = y - 0.0625
cb = cb - 0.5
cr = cr - 0.5
r = y * 1.164 + cr * 1.596
g = y * 1.164 + cb * -0.392 + cr * -0.813
b = y * 1.164 + cb * 2.017
return torch.cat([r, g, b], dim_c)
\ No newline at end of file
my/view.py
View file @ 3554ba52
......@@ -18,6 +18,13 @@ class CameraParam(object):
self.c = self.c.to(device)
return self
def resize(self, res: Tuple[int, int]):
self.f[0] = self.f[0] / self.res[1] * res[1]
self.f[1] = self.f[1] / self.res[0] * res[0]
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) -> torch.Tensor:
"""
Project positions in local space to image plane
......@@ -70,8 +77,8 @@ class CameraParam(object):
:return: [description]
"""
rays = self.get_local_rays(flatten, norm) # (M.., 3)
rays_o, _ = torch.broadcast_tensors(
t[..., None, None, :], rays) # (N.., M.., 3)
rays_o, _ = torch.broadcast_tensors(t[..., None, :], rays) if flatten \
else torch.broadcast_tensors(t[..., None, None, :], rays) # (N.., M.., 3)
rays_d = trans_vector(rays, r)
return rays_o, rays_d
......@@ -87,8 +94,11 @@ class CameraParam(object):
input_is_normalized = bool(input_camera_params.get('normalized'))
camera_params = {}
if 'fov' in input_camera_params:
camera_params['fx'] = camera_params['fy'] = \
(1 if input_is_normalized else view_res[0]) / \
if input_is_normalized:
camera_params['fy'] = 1 / util.Fov2Length(input_camera_params['fov'])
camera_params['fx'] = camera_params['fy'] / view_res[1] * view_res[0]
else:
camera_params['fx'] = camera_params['fy'] = view_res[0] / \
util.Fov2Length(input_camera_params['fov'])
camera_params['fy'] *= -1
else:
......@@ -114,15 +124,17 @@ def trans_point(p: torch.Tensor, t: torch.Tensor, r: torch.Tensor, inverse=False
:param inverse: whether perform inverse transform
:return ```Tensor(M.., N.., 3)```: transformed points
"""
out_size = list(r.size())[0:-2] + list(p.size())[0:-1] + [3]
t_size = list(t.size()[0:-1]) + \
[1 for _ in range(len(p.size()[0:-1]))] + [3]
size_N = list(p.size())[0:-1]
size_M = list(r.size())[0:-2]
out_size = size_M + size_N + [3]
t_size = size_M + [1 for _ in range(len(size_N))] + [3]
t = t.view(t_size)
if not inverse:
r = r.movedim(-1, -2) # Transpose rotation matrices
else:
p = p - t
out = torch.matmul(p.flatten(0, -2), r).view(out_size)
out = torch.matmul(p.view(size_M + [-1, 3]), r)
out = out.view(out_size)
if not inverse:
out = out + t
return out
......
notebook/compare.ipynb 0 → 100644
View file @ 3554ba52
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"import torchvision.transforms.functional as trans_f\n",
"\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../../'))\n",
"__package__ = \"deep_view_syn.notebook\"\n",
"\n",
"from ..my import util\n",
"\n",
"path_patts = [\n",
" '/home/dengnc/deep_view_syn/data/gas_fovea_2020.12.31/upsampling_test/gt/view_%04d.png',\n",
" '/home/dengnc/deep_view_syn/data/gas_fovea_2020.12.31/fovea_rgb@msl-rgb_e10_fc256x4_d1-50_s16/output/model-epoch_500/train/out_view_%04d.png',\n",
" '/home/dengnc/deep_view_syn/data/gas_fovea_2020.12.31/upsampling_test/input/out_view_%04d.png',\n",
" '/home/dengnc/deep_view_syn/data/gas_fovea_2020.12.31/upsampling_test/output/view_%04d.png'\n",
"]\n",
"titles = [ 'Ground truth', 'Normal', 'Low Res', 'Upsampling']\n",
"show_range = range(5)\n",
"\n",
"#os.chdir('/home/dengnc/deep_view_syn/data/')\n",
"image_seqs = [\n",
" util.ReadImageTensor([path_patt % i for i in show_range])\n",
" for path_patt in path_patts\n",
"]\n",
"\n",
"for i in show_range:\n",
" plt.figure(facecolor='white', figsize=(12, 4))\n",
" plt.suptitle('View %d' % i)\n",
" for j in range(len(image_seqs)):\n",
" plt.subplot(1, len(image_seqs), j + 1)\n",
" plt.title(titles[j])\n",
" util.PlotImageTensor(image_seqs[j][i])\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python",
"nbconvert_exporter": "python",
"version": "3.7.9-final"
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
notebook/test_foveation.ipynb
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notebook/test_image_filter.ipynb 0 → 100644
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notebook/test_lf_syn.ipynb
View file @ 3554ba52
......@@ -13,9 +13,9 @@
"import torch\n",
"from torch import nn\n",
"import matplotlib.pyplot as plt\n",
"from deeplightfield.data.lf_syn import LightFieldSynDataset\n",
"from deeplightfield.my import util\n",
"from deeplightfield.trans_unet import LatentSpaceTransformer\n",
"from deep_view_syn.data.lf_syn import LightFieldSynDataset\n",
"from deep_view_syn.my import util\n",
"from deep_view_syn.trans_unet import LatentSpaceTransformer\n",
"\n",
"device = torch.device(\"cuda:2\")\n"
]
......
notebook/test_refinement.ipynb
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notebook/test_refinement_new.ipynb
View file @ 3554ba52
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [
{
......@@ -10,16 +10,14 @@
"output_type": "stream",
"text": [
"Set CUDA:2 as current device.\n",
"Change working directory to /e/dengnc/deeplightfield/data/sp_view_syn_2020.12.31_fovea\n"
"Change working directory to /home/dengnc/deep_view_syn/data/sp_view_syn_2020.12.31_fovea\n"
]
},
{
"data": {
"text/plain": [
"<torch.autograd.grad_mode.set_grad_enabled at 0x7fea6b9c2d50>"
]
"text/plain": "<torch.autograd.grad_mode.set_grad_enabled at 0x7f6824144910>"
},
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
......@@ -34,16 +32,17 @@
"\n",
"\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../../'))\n",
"__package__ = \"deep_view_syn.notebook\"\n",
"torch.cuda.set_device(2)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n",
"from deeplightfield.data.spherical_view_syn import *\n",
"from deeplightfield.msl_net import MslNet\n",
"from deeplightfield.configs.spherical_view_syn import SphericalViewSynConfig\n",
"from deeplightfield.my import netio\n",
"from deeplightfield.my import util\n",
"from deeplightfield.my import device\n",
"from deeplightfield.my import view\n",
"from ..data.spherical_view_syn import *\n",
"from ..msl_net import MslNet\n",
"from ..configs.spherical_view_syn import SphericalViewSynConfig\n",
"from ..my import netio\n",
"from ..my import util\n",
"from ..my import device\n",
"from ..my import view\n",
"\n",
"\n",
"os.chdir(sys.path[0] + '/../data/sp_view_syn_2020.12.31_fovea')\n",
......
notebook/test_spherical_view_syn.ipynb
View file @ 3554ba52
......@@ -14,8 +14,8 @@
"import math\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from deeplightfield.my import util\n",
"from deeplightfield.msl_net import *\n",
"from deep_view_syn.my import util\n",
"from deep_view_syn.msl_net import *\n",
"\n",
"# Select device\n",
"torch.cuda.set_device(2)\n",
......@@ -121,8 +121,8 @@
"metadata": {},
"outputs": [],
"source": [
"from deeplightfield.data.spherical_view_syn import FastSphericalViewSynDataset\n",
"from deeplightfield.data.spherical_view_syn import FastDataLoader\n",
"from deep_view_syn.data.spherical_view_syn import FastSphericalViewSynDataset\n",
"from deep_view_syn.data.spherical_view_syn import FastDataLoader\n",
"\n",
"DATA_DIR = '../data/sp_view_syn_2020.12.28'\n",
"TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'\n",
......@@ -149,7 +149,7 @@
"metadata": {},
"outputs": [],
"source": [
"from deeplightfield.data.spherical_view_syn import SphericalViewSynDataset\n",
"from deep_view_syn.data.spherical_view_syn import SphericalViewSynDataset\n",
"\n",
"DATA_DIR = '../data/sp_view_syn_2020.12.26'\n",
"TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'\n",
......@@ -241,7 +241,7 @@
"metadata": {},
"outputs": [],
"source": [
"from deeplightfield.data.spherical_view_syn import SphericalViewSynDataset\n",
"from deep_view_syn.data.spherical_view_syn import SphericalViewSynDataset\n",
"\n",
"DATA_DIR = '../data/sp_view_syn_2020.12.29_finetrans'\n",
"TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'\n",
......@@ -304,7 +304,7 @@
"metadata": {},
"outputs": [],
"source": [
"from deeplightfield.data.spherical_view_syn import SphericalViewSynDataset\n",
"from deep_view_syn.data.spherical_view_syn import SphericalViewSynDataset\n",
"\n",
"DATA_DIR = '../data/sp_view_syn_2020.12.26_rotonly'\n",
"TRAIN_DATA_DESC_FILE = DATA_DIR + '/train.json'\n",
......@@ -381,9 +381,9 @@
"source": [
"import ipywidgets as widgets # 控件库\n",
"from IPython.display import display # 显示控件的方法\n",
"from deeplightfield.data.spherical_view_syn import SphericalViewSynDataset\n",
"from deeplightfield.spher_net import SpherNet\n",
"from deeplightfield.my import netio\n",
"from deep_view_syn.data.spherical_view_syn import SphericalViewSynDataset\n",
"from deep_view_syn.spher_net import SpherNet\n",
"from deep_view_syn.my import netio\n",
"\n",
"DATA_DIR = '../data/sp_view_syn_2020.12.28_small'\n",
"DATA_DESC_FILE = DATA_DIR + '/train.json'\n",
......
notebook/test_ycbcr.ipynb 0 → 100644
View file @ 3554ba52
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"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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"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {
"needs_background": "light",
"transient": {}
},
"output_type": "display_data"
}
],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"import torchvision.transforms.functional as trans_f\n",
"\n",
"sys.path.append(os.path.abspath(sys.path[0] + '/../../'))\n",
"__package__ = \"deep_view_syn.notebook\"\n",
"\n",
"from ..my import util\n",
"\n",
"input_img = util.ReadImageTensor('/home/dengnc/deep_view_syn/data/gas_fovea_2020.12.31/upsampling_test/input/out_view_0000.png')\n",
"ycbcr = util.rgb2ycbcr(input_img)\n",
"rgb = util.ycbcr2rgb(ycbcr)\n",
"\n",
"plt.figure()\n",
"util.PlotImageTensor(input_img)\n",
"plt.figure()\n",
"plt.subplot(1, 4, 1)\n",
"util.PlotImageTensor(ycbcr)\n",
"plt.subplot(1, 4, 2)\n",
"util.PlotImageTensor(ycbcr[:, 0])\n",
"plt.subplot(1, 4, 3)\n",
"util.PlotImageTensor(ycbcr[:, 1])\n",
"plt.subplot(1, 4, 4)\n",
"util.PlotImageTensor(ycbcr[:, 2])\n",
"plt.figure()\n",
"util.PlotImageTensor(rgb)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
},
"orig_nbformat": 2
},
"nbformat": 4,
"nbformat_minor": 2
}
\ No newline at end of file
refine_net.py 0 → 100644
View file @ 3554ba52
from __future__ import print_function
import sys
import torch
import torchvision
import torch.backends.cudnn as cudnn
import torch.nn as nn
from math import log10
from my.progress_bar import progress_bar
from my import color_mode
class Net(torch.nn.Module):
def __init__(self, color, base_filter):
super(Net, self).__init__()
self.color = color
if color == color_mode.GRAY:
self.layers = torch.nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=base_filter, kernel_size=9, stride=1, padding=4, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=base_filter, out_channels=base_filter // 2, kernel_size=1, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=base_filter // 2, out_channels=1, kernel_size=5, stride=1, padding=2, bias=True),
#nn.PixelShuffle(upscale_factor)
)
else:
self.net_1 = torch.nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=base_filter, kernel_size=9, stride=1, padding=4, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=base_filter, out_channels=base_filter // 2, kernel_size=1, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=base_filter // 2, out_channels=1, kernel_size=5, stride=1, padding=2, bias=True),
#nn.PixelShuffle(upscale_factor)
)
self.net_2 = torch.nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=base_filter, kernel_size=9, stride=1, padding=4, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=base_filter, out_channels=base_filter // 2, kernel_size=1, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=base_filter // 2, out_channels=1, kernel_size=5, stride=1, padding=2, bias=True),
#nn.PixelShuffle(upscale_factor)
)
self.net_3 = torch.nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=base_filter, kernel_size=9, stride=1, padding=4, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=base_filter, out_channels=base_filter // 2, kernel_size=1, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=base_filter // 2, out_channels=1, kernel_size=5, stride=1, padding=2, bias=True),
#nn.PixelShuffle(upscale_factor)
)
def forward(self, x):
if self.color == color_mode.GRAY:
out = self.layers(x)
else:
out = torch.cat([
self.net_1(x[:, 0:1]),
self.net_2(x[:, 1:2]),
self.net_3(x[:, 2:3])
], dim=1)
return out
def weight_init(self, mean, std):
for m in self._modules:
normal_init(self._modules[m], mean, std)
def normal_init(m, mean, std):
if isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Conv2d):
m.weight.data.normal_(mean, std)
m.bias.data.zero_()
class Solver(object):
def __init__(self, config, training_loader, testing_loader, writer=None):
super(Solver, self).__init__()
self.CUDA = torch.cuda.is_available()
self.device = torch.device('cuda' if self.CUDA else 'cpu')
self.model = None
self.lr = config.lr
self.nEpochs = config.nEpochs
self.criterion = None
self.optimizer = None
self.scheduler = None
self.seed = config.seed
self.upscale_factor = config.upscale_factor
self.training_loader = training_loader
self.testing_loader = testing_loader
self.writer = writer
self.color = config.color
def build_model(self):
self.model = Net(color=self.color, base_filter=64).to(self.device)
self.model.weight_init(mean=0.0, std=0.01)
self.criterion = torch.nn.MSELoss()
torch.manual_seed(self.seed)
if self.CUDA:
torch.cuda.manual_seed(self.seed)
cudnn.benchmark = True
self.criterion.cuda()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)
self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[50, 75, 100], gamma=0.5)
def save_model(self):
model_out_path = "model_path.pth"
torch.save(self.model, model_out_path)
print("Checkpoint saved to {}".format(model_out_path))
def train(self, epoch, iters, channels = None):
self.model.train()
train_loss = 0
for batch_num, (_, data, target) in enumerate(self.training_loader):
if channels:
data = data[..., channels, :, :]
target = target[..., channels, :, :]
data =data.to(self.device)
target = target.to(self.device)
self.optimizer.zero_grad()
out = self.model(data)
loss = self.criterion(out, target)
train_loss += loss.item()
loss.backward()
self.optimizer.step()
sys.stdout.write('Epoch %d: ' % epoch)
progress_bar(batch_num, len(self.training_loader), 'Loss: %.4f' % (train_loss / (batch_num + 1)))
if self.writer:
self.writer.add_scalar("loss", loss, iters)
if iters % 100 == 0:
output_vs_gt = torch.stack([out, target], 1) \
.flatten(0, 1).detach()
self.writer.add_image(
"Output_vs_gt",
torchvision.utils.make_grid(output_vs_gt, nrow=2).cpu().numpy(),
iters)
iters += 1
print(" Average Loss: {:.4f}".format(train_loss / len(self.training_loader)))
return iters
\ No newline at end of file
run_lf_syn.py
View file @ 3554ba52
import sys
sys.path.append('/e/dengnc')
__package__ = "deeplightfield"
__package__ = "deep_view_syn"
import os
import torch
......
run_refine.py 0 → 100644
View file @ 3554ba52
from __future__ import print_function
import argparse
import os
import sys
import torch
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
# ===========================================================
parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
# hyper-parameters
parser.add_argument('--device', type=int, default=3,
help='Which CUDA device to use.')
parser.add_argument('--batchSize', type=int, default=1,
help='training batch size')
parser.add_argument('--testBatchSize', type=int,
default=1, help='testing batch size')
parser.add_argument('--nEpochs', type=int, default=20,
help='number of epochs to train for')
parser.add_argument('--lr', type=float, default=0.01,
help='Learning Rate. Default=0.01')
parser.add_argument('--seed', type=int, default=123,
help='random seed to use. Default=123')
parser.add_argument('--dataset', type=str, required=True,
help='dataset directory')
parser.add_argument('--test', type=str, help='path of model to test')
parser.add_argument('--testOutPatt', type=str, help='test output path pattern')
parser.add_argument('--color', type=str, default='rgb',
help='color')
# model configuration
parser.add_argument('--upscale_factor', '-uf', type=int,
default=2, help="super resolution upscale factor")
#parser.add_argument('--model', '-m', type=str, default='srgan', help='choose which model is going to use')
args = parser.parse_args()
# Select device
torch.cuda.set_device(args.device)
print("Set CUDA:%d as current device." % torch.cuda.current_device())
from .my import util
from .my import netio
from .my import device
from .my import color_mode
from .refine_net import *
from .data.upsampling import UpsamplingDataset
from .data.loader import FastDataLoader
os.chdir(args.dataset)
print('Change working directory to ' + os.getcwd())
run_dir = 'run/'
args.color = color_mode.from_str(args.color)
def train():
util.CreateDirIfNeed(run_dir)
train_set = UpsamplingDataset('.', 'input/out_view_%04d.png',
'gt/view_%04d.png', color=args.color)
training_data_loader = FastDataLoader(dataset=train_set,
batch_size=args.batchSize,
shuffle=True,
drop_last=False)
trainer = Solver(args, training_data_loader, training_data_loader,
SummaryWriter(run_dir))
trainer.build_model()
iters = 0
for epoch in range(1, args.nEpochs + 1):
print("\n===> Epoch {} starts:".format(epoch))
iters = trainer.train(epoch, iters,
channels=slice(2, 3) if args.color == color_mode.YCbCr
else None)
netio.SaveNet(run_dir + 'model-epoch_%d.pth' % args.nEpochs, trainer.model)
def test():
util.CreateDirIfNeed(os.path.dirname(args.testOutPatt))
train_set = UpsamplingDataset(
'.', 'input/out_view_%04d.png', None, color=args.color)
training_data_loader = FastDataLoader(dataset=train_set,
batch_size=args.testBatchSize,
shuffle=False,
drop_last=False)
trainer = Solver(args, training_data_loader, training_data_loader,
SummaryWriter(run_dir))
trainer.build_model()
netio.LoadNet(args.test, trainer.model)
for idx, input, _ in training_data_loader:
if args.color == color_mode.YCbCr:
output_y = trainer.model(input[:, -1:])
output_cbcr = input[:, 0:2]
output = util.ycbcr2rgb(torch.cat([output_cbcr, output_y], -3))
else:
output = trainer.model(input)
util.WriteImageTensor(output, args.testOutPatt % idx)
def main():
if (args.test):
test()
else:
train()
if __name__ == '__main__':
main()
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