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)