Jerrypiglet

import mitsuba as mi
mi.set_variant('llvm_ad_rgb')
import drjit as dr

radiance = 10.
spp = 1
sensor_dict = {
        'type': 'radiancemeter',
        'film': {
            'type': 'hdrfilm',

Jerrypiglet

import numpy as np
from PIL import Image

im_width = 320
im_height = 240
bn = 1

import sys
sys.path.insert(0, '/home/ruizhu/Documents/Projects/semanticInverse/train')
import torch

Jerrypiglet

'''
The script loads OpenRooms HDR images (.hdr or .rgbe), scales and applies gamma correction to SRGB space.
'''
import numpy as np
import os.path as osp
import cv2

class openrooms(data.Dataset):
    def __init__(self, split, ...):
      ...

Jerrypiglet

a = torch.zeros(2, 2).float()
# aa = torch.zeros(2, 2).float()

b = torch.tensor([[1, 2], [3, 4]]).float()
# c = torch.tensor([[15, 21], [33, 44]]).float()

uub = torch.tensor([[1, 0], [1, 1]])
vvb = torch.tensor([[1, 1], [0, 1]])
uua = torch.tensor([[0, 1], [0, 1]])
vva = torch.tensor([[0, 0], [0, 1]])

Jerrypiglet

Jerrypiglet

def accu_model(input_dict, if_debug=False):
    negative_z = False
    yc_est, vb, y_person, v0, vc, f_pixels_yannick = input_dict['yc_est'], input_dict['vb'], input_dict['y_person'], input_dict['v0'], input_dict['vc'], input_dict['f_pixels_yannick']
    theta_yannick = input_dict['pitch_est']

    f = f_pixels_yannick
    uc = 0
    device = f.device
    cos = torch.cos(-theta_yannick) # looking down: theta < 0
    sin = torch.sin(-theta_yannick)

Jerrypiglet

import torch
from torch.autograd import Variable
import torch.functional as F
import dataLoader
import argparse
import torch.optim as optim
import torchvision.utils as vutils
from torch.utils.data import DataLoader
import faceNet
import torch.nn as nn

Jerrypiglet

import os, pickle, glob

results_path = '/home/ruizhu/Documents/Projects/adobe_rui_camera-calibration-redux/data/cocoapi/PythonAPI/results_tmp'
split_name = 'test_20200102-v4_alsoDetOnGT_merge_write_RE'

pickle_path = os.path.join(results_path, split_name, 'pickle')
pickle_files = sorted(glob.glob(pickle_path + '/*_person.data'))
print(len(pickle_files), pickle_files[:2])

for pickle_file in pickle_files[:2]: