trainmodel.py

# import sys, time
# import numpy as np
# import torch
#
#
# torch.manual_seed(0)
# torch.cuda.manual_seed(0)
# np.random.seed(0)
#
#
#
# ########################################################################################################################
#
# class Appr(object):
#     def __init__(self, model,args=None):
#         self.model = model
#         self.nepochs = args.num_epochs
#         self.sbatch = args.batch_size
#         self.lr = args.learning_rate
#         self.ce = torch.nn.CrossEntropyLoss()
#         self.optimizer = self._get_optimizer()
#         self.gpu = args.gpu_id
#
#
#         return
#
#     def update_lr(self,optimizer):
#         for param_group in optimizer.param_groups:
#             param_group['lr'] = param_group['lr']/10.
#
#
#     def _get_optimizer(self, lr=None):
#         lr = self.lr
#         optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, self.model.parameters()), lr=lr,momentum = 0.5)
#         return optimizer
#
#     def train(self, train_loader):
#
#         lr = self.lr
#         self.optimizer = self._get_optimizer(lr)
#         nepochs = self.nepochs
#         # Loop epochs
#         try:
#             for e in range(nepochs):
#                 self.train_epoch(train_loader, cur_epoch=e, nepoch=nepochs)
#         except KeyboardInterrupt:
#             print()
#
#
#     def train_epoch(self,train_loader, cur_epoch=0, nepoch=0):
#         self.model.train()
#         for i, (images, labels) in enumerate(train_loader):
#
#             images = images.cuda(self.gpu)
#             targets = labels.cuda(self.gpu)
#             output,_= self.model.forward(images)
#             loss = self.ce(output, targets)
#             self.optimizer.zero_grad()
#             loss.backward()
#             self.optimizer.step()
#         return
#
#
#