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A10G
Running
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A10G
| """Model class template | |
| This module provides a template for users to implement custom models. | |
| You can specify '--model template' to use this model. | |
| The class name should be consistent with both the filename and its model option. | |
| The filename should be <model>_dataset.py | |
| The class name should be <Model>Dataset.py | |
| It implements a simple image-to-image translation baseline based on regression loss. | |
| Given input-output pairs (data_A, data_B), it learns a network netG that can minimize the following L1 loss: | |
| min_<netG> ||netG(data_A) - data_B||_1 | |
| You need to implement the following functions: | |
| <modify_commandline_options>: Add model-specific options and rewrite default values for existing options. | |
| <__init__>: Initialize this model class. | |
| <set_input>: Unpack input data and perform data pre-processing. | |
| <forward>: Run forward pass. This will be called by both <optimize_parameters> and <test>. | |
| <optimize_parameters>: Update network weights; it will be called in every training iteration. | |
| """ | |
| import numpy as np | |
| import torch | |
| from .base_model import BaseModel | |
| from . import networks | |
| class TemplateModel(BaseModel): | |
| def modify_commandline_options(parser, is_train=True): | |
| """Add new model-specific options and rewrite default values for existing options. | |
| Parameters: | |
| parser -- the option parser | |
| is_train -- if it is training phase or test phase. You can use this flag to add training-specific or test-specific options. | |
| Returns: | |
| the modified parser. | |
| """ | |
| parser.set_defaults(dataset_mode='aligned') # You can rewrite default values for this model. For example, this model usually uses aligned dataset as its dataset. | |
| if is_train: | |
| parser.add_argument('--lambda_regression', type=float, default=1.0, help='weight for the regression loss') # You can define new arguments for this model. | |
| return parser | |
| def __init__(self, opt): | |
| """Initialize this model class. | |
| Parameters: | |
| opt -- training/test options | |
| A few things can be done here. | |
| - (required) call the initialization function of BaseModel | |
| - define loss function, visualization images, model names, and optimizers | |
| """ | |
| BaseModel.__init__(self, opt) # call the initialization method of BaseModel | |
| # specify the training losses you want to print out. The program will call base_model.get_current_losses to plot the losses to the console and save them to the disk. | |
| self.loss_names = ['loss_G'] | |
| # specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images. | |
| self.visual_names = ['data_A', 'data_B', 'output'] | |
| # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks. | |
| # you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them. | |
| self.model_names = ['G'] | |
| # define networks; you can use opt.isTrain to specify different behaviors for training and test. | |
| self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, gpu_ids=self.gpu_ids) | |
| if self.isTrain: # only defined during training time | |
| # define your loss functions. You can use losses provided by torch.nn such as torch.nn.L1Loss. | |
| # We also provide a GANLoss class "networks.GANLoss". self.criterionGAN = networks.GANLoss().to(self.device) | |
| self.criterionLoss = torch.nn.L1Loss() | |
| # define and initialize optimizers. You can define one optimizer for each network. | |
| # If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example. | |
| self.optimizer = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)) | |
| self.optimizers = [self.optimizer] | |
| # Our program will automatically call <model.setup> to define schedulers, load networks, and print networks | |
| def set_input(self, input): | |
| """Unpack input data from the dataloader and perform necessary pre-processing steps. | |
| Parameters: | |
| input: a dictionary that contains the data itself and its metadata information. | |
| """ | |
| AtoB = self.opt.direction == 'AtoB' # use <direction> to swap data_A and data_B | |
| self.data_A = input['A' if AtoB else 'B'].to(self.device) # get image data A | |
| self.data_B = input['B' if AtoB else 'A'].to(self.device) # get image data B | |
| self.image_paths = input['A_paths' if AtoB else 'B_paths'] # get image paths | |
| def forward(self): | |
| """Run forward pass. This will be called by both functions <optimize_parameters> and <test>.""" | |
| self.output = self.netG(self.data_A) # generate output image given the input data_A | |
| def backward(self): | |
| """Calculate losses, gradients, and update network weights; called in every training iteration""" | |
| # caculate the intermediate results if necessary; here self.output has been computed during function <forward> | |
| # calculate loss given the input and intermediate results | |
| self.loss_G = self.criterionLoss(self.output, self.data_B) * self.opt.lambda_regression | |
| self.loss_G.backward() # calculate gradients of network G w.r.t. loss_G | |
| def optimize_parameters(self): | |
| """Update network weights; it will be called in every training iteration.""" | |
| self.forward() # first call forward to calculate intermediate results | |
| self.optimizer.zero_grad() # clear network G's existing gradients | |
| self.backward() # calculate gradients for network G | |
| self.optimizer.step() # update gradients for network G | |