os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # less logs # local libs from utils.plot_utils import save_val_samples from utils.data_utils import dataLoaderUSR, deprocess from utils.loss_utils import perceptual_distance, total_gen_loss ############################################################################# ## dataset and image information dataset_name = "USR_2x" # SCALE = 2 channels = 3 lr_width, lr_height = 320, 240 # low res hr_width, hr_height = 640, 480 # high res (2x) # input and output data lr_shape = (lr_height, lr_width, channels) hr_shape = (hr_height, hr_width, channels) data_loader = dataLoaderUSR(SCALE=2) # training parameters num_epochs = 40 batch_size = 2 sample_interval = 500 # per step ckpt_interval = 4 # per epoch steps_per_epoch = (data_loader.num_train // batch_size) num_step = num_epochs * steps_per_epoch ################################################################################### model_name = "srdrm" # ["res_sr", "image_sr", "sr_cnn", "denoise_sr", "srdrm"] if (model_name.lower() == "res_sr"): from nets.gen_models import ResNetSR model_loader = ResNetSR(lr_shape, hr_shape, SCALE=2) elif (model_name.lower() == "image_sr"):
from keras.models import Model import keras.backend as K os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # less logs # local libs from utils.plot_utils import save_val_samples from utils.data_utils import dataLoaderUSR, deprocess ##################################################################### ## dataset and image information dataset_name = "USR_4x" # SCALE = 4 channels = 3 lr_width, lr_height = 160, 120 # low res hr_width, hr_height = 640, 480 # high res (4x) # input and output data lr_shape = (lr_height, lr_width, channels) hr_shape = (hr_height, hr_width, channels) data_loader = dataLoaderUSR(DATA_PATH="./USR-248/", SCALE=4) # training parameters num_epochs = 100 batch_size = 8 sample_interval = 500 # per step ckpt_interval = 4 # per epoch steps_per_epoch = (data_loader.num_train // batch_size) num_step = num_epochs * steps_per_epoch ##################################################################### # choose which model to run model_name = "srdrm-gan" # options: ["srdrm-gan", "srgan", "esrgan", "edsrgan"] if model_name.lower() == "srgan": from nets.SRGAN import SRGAN_model gan_model = SRGAN_model(lr_shape, hr_shape, SCALE=4)
import keras.backend as K os.environ['TF_CPP_MIN_LOG_LEVEL']='2' # less logs # local libs from utils.plot_utils import save_val_samples from utils.data_utils import dataLoaderUSR, deprocess from utils.loss_utils import perceptual_distance, total_gen_loss ############################################################################# ## dataset and image information dataset_name = "USR_2x" # SCALE = 2 channels = 3 lr_width, lr_height = 320, 240 # low res hr_width, hr_height = 640, 480 # high res (2x) # input and output data lr_shape = (lr_height, lr_width, channels) hr_shape = (hr_height, hr_width, channels) data_loader = dataLoaderUSR(DATA_PATH="/mnt/data1/ImageSR/USR-248/", SCALE=2) # training parameters num_epochs = 40 batch_size = 2 sample_interval = 500 # per step ckpt_interval = 4 # per epoch steps_per_epoch = (data_loader.num_train//batch_size) num_step = num_epochs*steps_per_epoch ################################################################################### model_name = "srdrm" # ["res_sr", "image_sr", "sr_cnn", "denoise_sr", "srdrm"] if (model_name.lower()=="res_sr"): from nets.gen_models import ResNetSR model_loader = ResNetSR(lr_shape, hr_shape, SCALE=2) elif (model_name.lower()=="image_sr"):