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import os | |
import torch | |
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 fucntion, 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): specify the images that you want to display and save. | |
-- self.visual_names (str list): define networks used in our training. | |
-- 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 dict_grad_hook_factory(add_func=lambda x: x): | |
saved_dict = dict() | |
def hook_gen(name): | |
def grad_hook(grad): | |
saved_vals = add_func(grad) | |
saved_dict[name] = saved_vals | |
return grad_hook | |
return hook_gen, saved_dict | |
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 = opt.epoch | |
self.load_networks(load_suffix) | |
self.print_networks(opt.verbose) | |
def parallelize(self): | |
for name in self.model_names: | |
if isinstance(name, str): | |
net = getattr(self, 'net' + name) | |
setattr(self, 'net' + name, torch.nn.DataParallel(net, self.opt.gpu_ids)) | |
def data_dependent_initialize(self, data): | |
pass | |
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""" | |
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' % 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 __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) | |
if self.opt.isTrain and self.opt.pretrained_name is not None: | |
load_dir = os.path.join(self.opt.checkpoints_dir, self.opt.pretrained_name) | |
else: | |
load_dir = self.save_dir | |
load_path = os.path.join(load_dir, load_filename) | |
net = getattr(self, 'net' + name) | |
if isinstance(net, torch.nn.DataParallel): | |
net = net.module | |
print('loading the 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 | |
def generate_visuals_for_evaluation(self, data, mode): | |
return {} | |