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0788e19 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import torch
import torch.nn as nn
from networks.base_model import BaseModel
from networks.resnet import resnet50
class Trainer(BaseModel):
def name(self):
return 'Trainer'
def __init__(self, opt):
super(Trainer, self).__init__(opt)
if self.isTrain and not opt.continue_train:
self.model = resnet50(pretrained=True)
self.model.fc = nn.Linear(2048, 1)
torch.nn.init.normal_(self.model.fc.weight.data, 0.0, opt.init_gain)
if not self.isTrain or opt.continue_train:
self.model = resnet50(num_classes=1)
if self.isTrain:
self.loss_fn = nn.BCEWithLogitsLoss()
# initialize optimizers
if opt.optim == 'adam':
self.optimizer = torch.optim.Adam(
self.model.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999)
)
elif opt.optim == 'sgd':
self.optimizer = torch.optim.SGD(
self.model.parameters(), lr=opt.lr, momentum=0.0, weight_decay=0
)
else:
raise ValueError('optim should be [adam, sgd]')
if not self.isTrain or opt.continue_train:
self.load_networks(opt.epoch)
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'] /= 10.0
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)
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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