| import functools | |
| import torch | |
| import torch.nn as nn | |
| from networks.base_model import BaseModel, init_weights | |
| import sys | |
| from models import get_model | |
| class Trainer(BaseModel): | |
| def name(self): | |
| return 'Trainer' | |
| def __init__(self, opt): | |
| super(Trainer, self).__init__(opt) | |
| self.opt = opt | |
| self.model = get_model(opt.arch) | |
| torch.nn.init.normal_(self.model.fc.weight.data, 0.0, opt.init_gain) | |
| if opt.fix_backbone: | |
| params = [] | |
| for name, p in self.model.named_parameters(): | |
| if name=="fc.weight" or name=="fc.bias": | |
| params.append(p) | |
| else: | |
| p.requires_grad = False | |
| else: | |
| print("Your backbone is not fixed. Are you sure you want to proceed? If this is a mistake, enable the --fix_backbone command during training and rerun") | |
| import time | |
| time.sleep(3) | |
| params = self.model.parameters() | |
| if opt.optim == 'adam': | |
| self.optimizer = torch.optim.AdamW(params, lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=opt.weight_decay) | |
| elif opt.optim == 'sgd': | |
| self.optimizer = torch.optim.SGD(params, lr=opt.lr, momentum=0.0, weight_decay=opt.weight_decay) | |
| else: | |
| raise ValueError("optim should be [adam, sgd]") | |
| self.loss_fn = nn.BCEWithLogitsLoss() | |
| self.model.to(opt.gpu_ids[0]) | |
| def adjust_learning_rate(self, min_lr=1e-6): | |
| for param_group in self.optimizer.param_groups: | |
| param_group['lr'] /= 10. | |
| if param_group['lr'] < min_lr: | |
| return False | |
| return True | |
| def set_input(self, input): | |
| self.input = input[0].to(self.device) | |
| self.label = input[1].to(self.device).float() | |
| def forward(self): | |
| self.output = self.model(self.input) | |
| self.output = self.output.view(-1).unsqueeze(1) | |
| def get_loss(self): | |
| return self.loss_fn(self.output.squeeze(1), self.label) | |
| def optimize_parameters(self): | |
| self.forward() | |
| self.loss = self.loss_fn(self.output.squeeze(1), self.label) | |
| self.optimizer.zero_grad() | |
| self.loss.backward() | |
| self.optimizer.step() | |