# python test_model.py model=iphone_orig dped_dir=dped/ test_subset=full iteration=all resolution=orig use_gpu=true
from scipy import misc
import numpy as np
import tensorflow as tf
from models import resnet
import utils
import os
import sys
# process command arguments
phone, dped_dir, test_subset, iteration, resolution, use_gpu = utils.process_test_model_args(
sys.argv)
# get all available image resolutions
res_sizes = utils.get_resolutions()
# get the specified image resolution
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE = utils.get_specified_res(
res_sizes, phone, resolution)
# disable gpu if specified
config = tf.ConfigProto(
device_count={'GPU': 0}) if use_gpu == "false" else None
# create placeholders for input images
x_ = tf.placeholder(tf.float32, [None, IMAGE_SIZE])
x_image = tf.reshape(x_, [-1, IMAGE_HEIGHT, IMAGE_WIDTH, 3])
# generate enhanced image
enhanced = resnet(x_image)
import numpy as np
import sys
import os
import time
from torch.utils.data import DataLoader
from torchvision import transforms
import torch
from load_data import LoadVisualData
from model import PyNET
import utils
to_image = transforms.Compose([transforms.ToPILImage()])
level, restore_epoch, dataset_dir, use_gpu, orig_model = utils.process_test_model_args(sys.argv)
dslr_scale = float(1) / (2 ** (level - 1))
def test_model():
if use_gpu == "true":
torch.backends.cudnn.deterministic = True
device = torch.device("cuda")
else:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
device = torch.device("cpu")
# Creating dataset loaders
## visual_dataset = LoadVisualData(dataset_dir, 10, dslr_scale, level, full_resolution=True)
# python test_model.py model=iphone_orig dped_dir=dped/ test_subset=full iteration=all resolution=orig use_gpu=true
import os
import sys
import numpy as np
import tensorflow as tf
from scipy import misc
import models
import utils
# process command arguments
print(sys.argv)
model, dped_dir, test_subset, iteration, resolution, use_gpu, run, kernel_size, depth, blocks, parametric, s_conv, convdeconv = utils.process_test_model_args(
sys.argv)
dirname = model + "_" + run
if run == "":
dirname = model
if model.endswith("_orig"):
phone = model.replace("_orig", "")
orig = True
if phone == "iphone":
dirname = "iphone_convdeconv16_iteration_40000"
else:
raise ValueError("No pre-trained model exists for '{}'".format(phone))
else:
phone = model
orig = False
# Copyright 2020 by Andrey Ignatov. All Rights Reserved.
import numpy as np
import tensorflow as tf
import imageio
import sys
import os
from model import PyNET
import utils
from load_dataset import extract_bayer_channels
IMAGE_HEIGHT, IMAGE_WIDTH = 1472, 1984
LEVEL, restore_iter, dataset_dir, use_gpu, orig_model = utils.process_test_model_args(
sys.argv)
DSLR_SCALE = float(1) / (2**(LEVEL - 1))
# Disable gpu if specified
config = tf.ConfigProto(
device_count={'GPU': 0}) if use_gpu == "false" else None
with tf.Session(config=config) as sess:
# Placeholders for test data
x_ = tf.placeholder(tf.float32, [1, IMAGE_HEIGHT, IMAGE_WIDTH, 4])
# generate enhanced image
output_l0, output_l1, output_l2, output_l3, output_l4, output_l5 =\
PyNET(x_, instance_norm=True, instance_norm_level_1=False)
import numpy as np
import tensorflow as tf
import imageio
import sys
import os
from model import PUNET
import utils
from datetime import datetime
from load_dataset import extract_bayer_channels
dataset_dir, test_dir, model_dir, result_dir, arch, LEVEL, inst_norm, num_maps_base,\
orig_model, rand_param, restore_iter, IMAGE_HEIGHT, IMAGE_WIDTH, use_gpu, save_model, test_image = \
utils.process_test_model_args(sys.argv)
DSLR_SCALE = float(1) / (2**(max(LEVEL, 0) - 1))
# Disable gpu if specified
config = tf.ConfigProto(device_count={'GPU': 0}) if not use_gpu else None
with tf.compat.v1.Session(config=config) as sess:
time_start = datetime.now()
# determine model name
if arch == "punet":
name_model = "punet"
# Placeholders for test data
x_ = tf.compat.v1.placeholder(tf.float32,
[1, IMAGE_HEIGHT // 2, IMAGE_WIDTH // 2, 4])
