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from .base_model import BaseModel
from . import networks
import torch
import numpy as np
import torchvision.transforms as transforms
import PIL
import torch.nn.functional as F
class TestModel(BaseModel):
def name(self):
return 'TestModel'
@staticmethod
def modify_commandline_options(parser, is_train=True):
assert not is_train, 'TestModel cannot be used in train mode'
parser.set_defaults(dataset_mode='aligned')
parser.add_argument('--model_suffix', type=str, default='',
help='In checkpoints_dir, [which_epoch]_net_G[model_suffix].pth will'
' be loaded as the generator of TestModel')
return parser
def initialize(self, opt):
assert(not opt.isTrain)
BaseModel.initialize(self, opt)
# specify the training losses you want to print out. The program will call base_model.get_current_losses
self.loss_names = []
# specify the images you want to save/display. The program will call base_model.get_current_visuals
self.visual_names = ['fake_A','real_A']
self.model_names = ['G']
self.netG = networks.define_G('UNetDictFace',self.gpu_ids)
def set_input(self, input):
self.real_A = input['A'].to(self.device) #degraded img
self.real_C = input['C'].to(self.device) #groundtruth
self.image_paths = input['A_paths']
self.Part_locations = input['Part_locations']
def forward(self):
self.fake_A = self.netG(self.real_A, self.Part_locations) #
# try:
# self.fake_A = self.netG(self.real_A, self.Part_locations) #็ๆๅพ
# except:
# self.fake_A = self.real_A
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