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| import os | |
| import torch, gc | |
| from modules import devices | |
| from collections import OrderedDict | |
| from abc import ABC, abstractmethod | |
| from . import networks | |
| class BaseModel(ABC): | |
| """This class is an abstract base class (ABC) for models. | |
| To create a subclass, you need to implement the following five functions: | |
| -- <__init__>: initialize the class; first call BaseModel.__init__(self, opt). | |
| -- <set_input>: unpack data from dataset and apply preprocessing. | |
| -- <forward>: produce intermediate results. | |
| -- <optimize_parameters>: calculate losses, gradients, and update network weights. | |
| -- <modify_commandline_options>: (optionally) add model-specific options and set default options. | |
| """ | |
| def __init__(self, opt): | |
| """Initialize the BaseModel class. | |
| Parameters: | |
| opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions | |
| When creating your custom class, you need to implement your own initialization. | |
| In this function, you should first call <BaseModel.__init__(self, opt)> | |
| Then, you need to define four lists: | |
| -- self.loss_names (str list): specify the training losses that you want to plot and save. | |
| -- self.model_names (str list): define networks used in our training. | |
| -- self.visual_names (str list): specify the images that you want to display and save. | |
| -- self.optimizers (optimizer list): 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.opt = opt | |
| self.gpu_ids = opt.gpu_ids | |
| self.isTrain = opt.isTrain | |
| self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') # get device name: CPU or GPU | |
| self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir | |
| if opt.preprocess != 'scale_width': # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark. | |
| torch.backends.cudnn.benchmark = True | |
| self.loss_names = [] | |
| self.model_names = [] | |
| self.visual_names = [] | |
| self.optimizers = [] | |
| self.image_paths = [] | |
| self.metric = 0 # used for learning rate policy 'plateau' | |
| def modify_commandline_options(parser, is_train): | |
| """Add new model-specific options, and rewrite default values for existing options. | |
| Parameters: | |
| parser -- original option parser | |
| is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. | |
| Returns: | |
| the modified parser. | |
| """ | |
| return parser | |
| def set_input(self, input): | |
| """Unpack input data from the dataloader and perform necessary pre-processing steps. | |
| Parameters: | |
| input (dict): includes the data itself and its metadata information. | |
| """ | |
| pass | |
| def forward(self): | |
| """Run forward pass; called by both functions <optimize_parameters> and <test>.""" | |
| pass | |
| def optimize_parameters(self): | |
| """Calculate losses, gradients, and update network weights; called in every training iteration""" | |
| pass | |
| def setup(self, opt): | |
| """Load and print networks; create schedulers | |
| Parameters: | |
| opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions | |
| """ | |
| if self.isTrain: | |
| self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers] | |
| if not self.isTrain or opt.continue_train: | |
| load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch | |
| self.load_networks(load_suffix) | |
| self.print_networks(opt.verbose) | |
| def eval(self): | |
| """Make models eval mode during test time""" | |
| for name in self.model_names: | |
| if isinstance(name, str): | |
| net = getattr(self, 'net' + name) | |
| net.eval() | |
| def test(self): | |
| """Forward function used in test time. | |
| This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop | |
| It also calls <compute_visuals> to produce additional visualization results | |
| """ | |
| with torch.no_grad(): | |
| self.forward() | |
| self.compute_visuals() | |
| def compute_visuals(self): | |
| """Calculate additional output images for visdom and HTML visualization""" | |
| pass | |
| def get_image_paths(self): | |
| """ Return image paths that are used to load current data""" | |
| return self.image_paths | |
| def update_learning_rate(self): | |
| """Update learning rates for all the networks; called at the end of every epoch""" | |
| old_lr = self.optimizers[0].param_groups[0]['lr'] | |
| for scheduler in self.schedulers: | |
| if self.opt.lr_policy == 'plateau': | |
| scheduler.step(self.metric) | |
| else: | |
| scheduler.step() | |
| lr = self.optimizers[0].param_groups[0]['lr'] | |
| print('learning rate %.7f -> %.7f' % (old_lr, lr)) | |
| def get_current_visuals(self): | |
| """Return visualization images. train.py will display these images with visdom, and save the images to a HTML""" | |
| visual_ret = OrderedDict() | |
| for name in self.visual_names: | |
| if isinstance(name, str): | |
| visual_ret[name] = getattr(self, name) | |
| return visual_ret | |
| def get_current_losses(self): | |
| """Return traning losses / errors. train.py will print out these errors on console, and save them to a file""" | |
| errors_ret = OrderedDict() | |
| for name in self.loss_names: | |
| if isinstance(name, str): | |
| errors_ret[name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number | |
| return errors_ret | |
| def save_networks(self, epoch): | |
| """Save all the networks to the disk. | |
| Parameters: | |
| epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) | |
| """ | |
| for name in self.model_names: | |
| if isinstance(name, str): | |
| save_filename = '%s_net_%s.pth' % (epoch, name) | |
| save_path = os.path.join(self.save_dir, save_filename) | |
| net = getattr(self, 'net' + name) | |
| if len(self.gpu_ids) > 0 and torch.cuda.is_available(): | |
| torch.save(net.module.cpu().state_dict(), save_path) | |
| net.cuda(self.gpu_ids[0]) | |
| else: | |
| torch.save(net.cpu().state_dict(), save_path) | |
| def unload_network(self, name): | |
| """Unload network and gc. | |
| """ | |
| if isinstance(name, str): | |
| net = getattr(self, 'net' + name) | |
| del net | |
| gc.collect() | |
| devices.torch_gc() | |
| return None | |
| def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0): | |
| """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)""" | |
| key = keys[i] | |
| if i + 1 == len(keys): # at the end, pointing to a parameter/buffer | |
| if module.__class__.__name__.startswith('InstanceNorm') and \ | |
| (key == 'running_mean' or key == 'running_var'): | |
| if getattr(module, key) is None: | |
| state_dict.pop('.'.join(keys)) | |
| if module.__class__.__name__.startswith('InstanceNorm') and \ | |
| (key == 'num_batches_tracked'): | |
| state_dict.pop('.'.join(keys)) | |
| else: | |
| self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1) | |
| def load_networks(self, epoch): | |
| """Load all the networks from the disk. | |
| Parameters: | |
| epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name) | |
| """ | |
| for name in self.model_names: | |
| if isinstance(name, str): | |
| load_filename = '%s_net_%s.pth' % (epoch, name) | |
| load_path = os.path.join(self.save_dir, load_filename) | |
| net = getattr(self, 'net' + name) | |
| if isinstance(net, torch.nn.DataParallel): | |
| net = net.module | |
| # print('Loading depth boost model from %s' % load_path) | |
| # if you are using PyTorch newer than 0.4 (e.g., built from | |
| # GitHub source), you can remove str() on self.device | |
| state_dict = torch.load(load_path, map_location=str(self.device)) | |
| if hasattr(state_dict, '_metadata'): | |
| del state_dict._metadata | |
| # patch InstanceNorm checkpoints prior to 0.4 | |
| for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop | |
| self.__patch_instance_norm_state_dict(state_dict, net, key.split('.')) | |
| net.load_state_dict(state_dict) | |
| def print_networks(self, verbose): | |
| """Print the total number of parameters in the network and (if verbose) network architecture | |
| Parameters: | |
| verbose (bool) -- if verbose: print the network architecture | |
| """ | |
| print('---------- Networks initialized -------------') | |
| for name in self.model_names: | |
| if isinstance(name, str): | |
| net = getattr(self, 'net' + name) | |
| num_params = 0 | |
| for param in net.parameters(): | |
| num_params += param.numel() | |
| if verbose: | |
| print(net) | |
| print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6)) | |
| print('-----------------------------------------------') | |
| def set_requires_grad(self, nets, requires_grad=False): | |
| """Set requies_grad=Fasle for all the networks to avoid unnecessary computations | |
| Parameters: | |
| nets (network list) -- a list of networks | |
| requires_grad (bool) -- whether the networks require gradients or not | |
| """ | |
| if not isinstance(nets, list): | |
| nets = [nets] | |
| for net in nets: | |
| if net is not None: | |
| for param in net.parameters(): | |
| param.requires_grad = requires_grad | |