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demo_MiddleBury.py
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demo_MiddleBury.py
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import time
import os
from torch.autograd import Variable
import math
import torch
import random
import numpy as np
import numpy
import networks
from my_args import args
from scipy.misc import imread, imsave
from AverageMeter import *
torch.backends.cudnn.benchmark = True # to speed up the
DO_MiddleBurryOther = True
MB_Other_DATA = "./MiddleBurySet/other-data/"
MB_Other_RESULT = "./MiddleBurySet/other-result-author/"
MB_Other_GT = "./MiddleBurySet/other-gt-interp/"
if not os.path.exists(MB_Other_RESULT):
os.mkdir(MB_Other_RESULT)
model = networks.__dict__[args.netName](channel=args.channels,
filter_size = args.filter_size ,
timestep=args.time_step,
training=False)
if args.use_cuda:
model = model.cuda()
args.SAVED_MODEL = './model_weights/best.pth'
if os.path.exists(args.SAVED_MODEL):
print("The testing model weight is: " + args.SAVED_MODEL)
if not args.use_cuda:
pretrained_dict = torch.load(args.SAVED_MODEL, map_location=lambda storage, loc: storage)
# model.load_state_dict(torch.load(args.SAVED_MODEL, map_location=lambda storage, loc: storage))
else:
pretrained_dict = torch.load(args.SAVED_MODEL)
# model.load_state_dict(torch.load(args.SAVED_MODEL))
model_dict = model.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(model_dict)
# 4. release the pretrained dict for saving memory
pretrained_dict = []
else:
print("*****************************************************************")
print("**** We don't load any trained weights **************************")
print("*****************************************************************")
model = model.eval() # deploy mode
use_cuda=args.use_cuda
save_which=args.save_which
dtype = args.dtype
unique_id =str(random.randint(0, 100000))
print("The unique id for current testing is: " + str(unique_id))
interp_error = AverageMeter()
if DO_MiddleBurryOther:
subdir = os.listdir(MB_Other_DATA)
gen_dir = os.path.join(MB_Other_RESULT, unique_id)
os.mkdir(gen_dir)
tot_timer = AverageMeter()
proc_timer = AverageMeter()
end = time.time()
for dir in subdir:
print(dir)
os.mkdir(os.path.join(gen_dir, dir))
arguments_strFirst = os.path.join(MB_Other_DATA, dir, "frame10.png")
arguments_strSecond = os.path.join(MB_Other_DATA, dir, "frame11.png")
arguments_strOut = os.path.join(gen_dir, dir, "frame10i11.png")
gt_path = os.path.join(MB_Other_GT, dir, "frame10i11.png")
X0 = torch.from_numpy( np.transpose(imread(arguments_strFirst) , (2,0,1)).astype("float32")/ 255.0).type(dtype)
X1 = torch.from_numpy( np.transpose(imread(arguments_strSecond) , (2,0,1)).astype("float32")/ 255.0).type(dtype)
y_ = torch.FloatTensor()
assert (X0.size(1) == X1.size(1))
assert (X0.size(2) == X1.size(2))
intWidth = X0.size(2)
intHeight = X0.size(1)
channel = X0.size(0)
if not channel == 3:
continue
if intWidth != ((intWidth >> 7) << 7):
intWidth_pad = (((intWidth >> 7) + 1) << 7) # more than necessary
intPaddingLeft =int(( intWidth_pad - intWidth)/2)
intPaddingRight = intWidth_pad - intWidth - intPaddingLeft
else:
intWidth_pad = intWidth
intPaddingLeft = 32
intPaddingRight= 32
if intHeight != ((intHeight >> 7) << 7):
intHeight_pad = (((intHeight >> 7) + 1) << 7) # more than necessary
intPaddingTop = int((intHeight_pad - intHeight) / 2)
intPaddingBottom = intHeight_pad - intHeight - intPaddingTop
else:
intHeight_pad = intHeight
intPaddingTop = 32
intPaddingBottom = 32
pader = torch.nn.ReplicationPad2d([intPaddingLeft, intPaddingRight , intPaddingTop, intPaddingBottom])
torch.set_grad_enabled(False)
X0 = Variable(torch.unsqueeze(X0,0))
X1 = Variable(torch.unsqueeze(X1,0))
X0 = pader(X0)
X1 = pader(X1)
if use_cuda:
X0 = X0.cuda()
X1 = X1.cuda()
proc_end = time.time()
y_s,offset,filter = model(torch.stack((X0, X1),dim = 0))
y_ = y_s[save_which]
proc_timer.update(time.time() -proc_end)
tot_timer.update(time.time() - end)
end = time.time()
print("*****************current image process time \t " + str(time.time()-proc_end )+"s ******************" )
if use_cuda:
X0 = X0.data.cpu().numpy()
y_ = y_.data.cpu().numpy()
offset = [offset_i.data.cpu().numpy() for offset_i in offset]
filter = [filter_i.data.cpu().numpy() for filter_i in filter] if filter[0] is not None else None
X1 = X1.data.cpu().numpy()
else:
X0 = X0.data.numpy()
y_ = y_.data.numpy()
offset = [offset_i.data.numpy() for offset_i in offset]
filter = [filter_i.data.numpy() for filter_i in filter]
X1 = X1.data.numpy()
X0 = np.transpose(255.0 * X0.clip(0,1.0)[0, :, intPaddingTop:intPaddingTop+intHeight, intPaddingLeft: intPaddingLeft+intWidth], (1, 2, 0))
y_ = np.transpose(255.0 * y_.clip(0,1.0)[0, :, intPaddingTop:intPaddingTop+intHeight, intPaddingLeft: intPaddingLeft+intWidth], (1, 2, 0))
offset = [np.transpose(offset_i[0, :, intPaddingTop:intPaddingTop+intHeight, intPaddingLeft: intPaddingLeft+intWidth], (1, 2, 0)) for offset_i in offset]
filter = [np.transpose(
filter_i[0, :, intPaddingTop:intPaddingTop + intHeight, intPaddingLeft: intPaddingLeft + intWidth],
(1, 2, 0)) for filter_i in filter] if filter is not None else None
X1 = np.transpose(255.0 * X1.clip(0,1.0)[0, :, intPaddingTop:intPaddingTop+intHeight, intPaddingLeft: intPaddingLeft+intWidth], (1, 2, 0))
imsave(arguments_strOut, np.round(y_).astype(numpy.uint8))
rec_rgb = imread(arguments_strOut)
gt_rgb = imread(gt_path)
diff_rgb = 128.0 + rec_rgb - gt_rgb
avg_interp_error_abs = np.mean(np.abs(diff_rgb - 128.0))
interp_error.update(avg_interp_error_abs, 1)
mse = numpy.mean((diff_rgb - 128.0) ** 2)
PIXEL_MAX = 255.0
psnr = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
print("interpolation error / PSNR : " + str(round(avg_interp_error_abs,4)) + " / " + str(round(psnr,4)))
metrics = "The average interpolation error / PSNR for all images are : " + str(round(interp_error.avg, 4))
print(metrics)