| | import functools |
| | import torch |
| | import torch.nn as nn |
| | from .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, opt) |
| | self.lr = opt.lr |
| | 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") |
| | |
| | |
| | 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(weight=torch.tensor([0.08, 0.92]).to('cuda')) |
| |
|
| | 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'] *= 0.8 |
| | self.lr = param_group['lr'] |
| | 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() |
| |
|
| |
|
| |
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| |
|
