import numpy as np
import os, sys, time
import torch
import importlib
import options
from util import log
log.process(os.getpid())
log.title("[{}] (evaluate SDF-SRN)".format(sys.argv[0]))
opt_cmd = options.parse_arguments(sys.argv[1:])
opt = options.set(opt_cmd=opt_cmd)
with torch.cuda.device(opt.device):
model = importlib.import_module("model.{}".format(opt.model))
m = model.Model(opt)
m.load_dataset(opt,eval_split="test" if opt.data.dataset=="shapenet" else \
"val" if opt.data.dataset=="pascal3d" else None)
m.build_networks(opt)
m.restore_checkpoint(opt)
m.setup_visualizer(opt)
m.evaluate(opt)
from OpenGL.GL import *
import pygame
import texture
import options
fonts = {}
options.set("text_fontname", "monospace", False)
options.set("text_fontsize", 64, False)
def load_font(bold=False, italic=False):
id_ = options.get("text_fontname", str) + "-%s-%s" % (bold, italic)
if id_ not in fonts:
fonts[id_] = pygame.font.SysFont(options.get("text_fontname", str),
options.get("text_fontsize", int),
bold=bold,
italic=italic)
def draw_text(text, color, pos, offset, size, bold=False, italic=False):
load_font(bold=bold, italic=italic)
id_ = "text-%s-%s-%s-%s" % (text, color, bold, italic)
fontid = options.get("text_fontname", str) + "-%s-%s" % (bold, italic)
def render():
surf = fonts[fontid].render(text, True, color)
return surf
import numpy as np
import os, sys, time
import torch
import options, data, util
import model
print(util.yellow("======================================================="))
print(util.yellow("main.py (photometric mesh optimization)"))
print(util.yellow("======================================================="))
print(util.magenta("setting configurations..."))
opt = options.set()
print(util.magenta("reading list of sequences..."))
seq_list = data.load_sequence_list(opt, subset=1)
seq_list = [("02958343", "eebbce8b77bdb53c82382fde2cafeb9")]
with torch.cuda.device(opt.gpu):
pmo = model.Model(opt)
pmo.build_network(opt)
pmo.restore_checkpoint(opt)
print(util.yellow("======= OPTIMIZATION START ======="))
for c, m in seq_list:
pmo.load_sequence(opt, c, m)
pmo.setup_visualizer(opt)
pmo.setup_variables(opt)
pmo.setup_optimizer(opt)
pmo.time_start(opt)
pmo.optimize(opt)
import numpy as np
import time,os,sys
import util
print(util.toYellow("======================================================="))
print(util.toYellow("train_Donly.py (ST-GAN discriminator only)"))
print(util.toYellow("======================================================="))
import tensorflow as tf
import data
import graph,warp
import options
opt = options.set(training=True)
assert(opt.warpN==0)
# create directories for model output
main_folder = "/content/gdrive/My Drive/Colab Notebooks/spatial-transformer-GAN/glasses/"
os.makedirs(main_folder + "models_{0}".format(opt.group), exist_ok=True)
print(util.toMagenta("building graph..."))
tf.reset_default_graph()
# build graph
with tf.device(opt.GPUdevice):
# ------ define input data ------
imageRealData = tf.placeholder(tf.float32,shape=[opt.batchSize,opt.dataH,opt.dataW,3])
imageBGfakeData = tf.placeholder(tf.float32,shape=[opt.batchSize,opt.dataH,opt.dataW,3])
imageFGfake = tf.placeholder(tf.float32,shape=[opt.batchSize,opt.H,opt.W,4])
PH = [imageBGfakeData,imageRealData,imageFGfake]
# ------ generate perturbation ------
imageReal = data.perturbBG(opt,imageRealData)
import numpy as np
import time, os, sys
import util
print(util.toYellow("======================================================="))
print(util.toYellow("eval_STGAN.py (ST-GAN with homography)"))
print(util.toYellow("======================================================="))
import tensorflow as tf
import data
import graph, warp
import options
opt = options.set(training=False)
print(util.toMagenta("building graph..."))
tf.reset_default_graph()
# build graph
with tf.device(opt.GPUdevice):
# ------ define input data ------
imageBG = tf.placeholder(tf.float32,
shape=[opt.batchSize, opt.H, opt.W, 3])
imageFG = tf.placeholder(tf.float32,
shape=[opt.batchSize, opt.H, opt.W, 4])
PH = [imageBG, imageFG]
pPertFG = opt.pertFG * tf.random_normal([opt.batchSize, opt.warpDim])
# ------ define GP and D ------
geometric = graph.geometric_multires
# ------ geometric predictor ------
imageFGwarpAll, _, _ = geometric(opt, imageBG, imageFG, pPertFG)
# ------ composite image ------
from OpenGL.GL import *
import pygame
import texture
import options
fonts={}
options.set("text_fontname", "monospace", False)
options.set("text_fontsize", 64, False)
def load_font(bold=False, italic=False):
id_=options.get("text_fontname", str)+"-%s-%s" % (bold, italic)
if id_ not in fonts:
fonts[id_]=pygame.font.SysFont(options.get("text_fontname", str), options.get("text_fontsize", int), bold=bold, italic=italic)
def draw_text(text, color, pos, offset, size, bold=False, italic=False):
load_font(bold=bold, italic=italic)
id_="text-%s-%s-%s-%s" % (text, color, bold, italic)
fontid=options.get("text_fontname", str)+"-%s-%s" % (bold, italic)
def render():
surf=fonts[fontid].render(text, True, color)
return surf
texture.bind_with_func(render, id_)
texwidth, texheight=texture.surfaces[id_][0].get_size()
texwidth/=float(options.get("text_fontsize", int))
import time, os, sys
import util
print(util.toYellow(
"=======================================================")) # 컬러출력
print(util.toYellow("train_Donly.py (ST-GAN discriminator only)"))
print(util.toYellow("======================================================="))
import tensorflow.compat.v1 as tf
import data
import graph, warp
import options
tf.disable_v2_behavior() #버전 문제로 인해 추가한 코드
opt = options.set(training=True) # Training 시키는 것에 있어서 true
assert (opt.warpN == 0) # 뒤가 0이 아니면 멈춤
# create directories for model output
os.makedirs("models_{0}".format(opt.group),
exist_ok=True) #group은 따로 지정하지 않으면 기본값인 0
print(util.toMagenta("building graph...")) # 컬러로 출력
tf.reset_default_graph() # 지금까지 생성된 모든 텐서를 그래프에서 제거
# build graph
with tf.device(opt.GPUdevice):
# ------ define input data ------ 데이터 들어올 공간 마련
imageRealData = tf.placeholder(
tf.float32, shape=[opt.batchSize, opt.dataH, opt.dataW, 3]) #218x178
imageBGfakeData = tf.placeholder(
tf.float32, shape=[opt.batchSize, opt.dataH, opt.dataW, 3])
import numpy as np
import time,os,sys
import argparse
import options
print("setting configurations...")
opt = options.set()
import tensorflow as tf
import data,graph,warp,util
print("=======================================================")
print("train.py (training on MNIST)")
print("=======================================================")
# load data
print("loading MNIST dataset...")
trainData,validData,testData = data.loadMNIST("data/MNIST.npz")
# create directories for model output
util.mkdir("models_{0}".format(opt.group))
util.mkdir("models_{0}/interm".format(opt.group))
util.mkdir("models_{0}/final".format(opt.group))
print("training model {0}...".format(opt.model))
print("------------------------------------------")
print("warpScale: (pert) {0} (trans) {1}".format(opt.warpScale["pert"],opt.warpScale["trans"]))
print("warpType: {0}".format(opt.warpType))
print("batchSize: {0}".format(opt.batchSize))
print("GPU device: {0}".format(opt.gpu))
print("------------------------------------------")
options.add_bind("A", "strafe_left")
options.add_bind("D", "strafe_right")
options.add_bind("Space_Tap", "jump")
options.add_bind("Mouse1", "shoot")
options.add_bind("Left_Control", "shoot")
options.add_bind("Mouse4_Tap", "weapon_next")
options.add_bind("Mouse5_Tap", "weapon_prev")
options.add_bind("Mouse3", "scope")
options.add_bind("Escape", "quit")
options.set("gravity", 16, False)
options.set("player_superjump", True, False)
options.set("player_jump_force", 7, False)
options.set("player_step_height", 0.5, False)
options.set("player_damping_air", 1.0, False)
options.set("player_damping_ground", 0.15, False)
options.set("player_accel_air", 6, False)
options.set("player_accel_ground", 50, False)
options.set("view_gun_stiffness", 10, False)
options.set("dpy_width", 1024, False)
options.set("dpy_height", 600, False)
options.set("dpy_fov", 90, False)
options.set("dpy_fov_scope", 35, False)
