def search(self, train_x, train_y, valid_x, valid_y, metadata): np.random.seed(self.seed) cudnn.benchmark = True torch.manual_seed(self.seed) cudnn.enabled = True torch.cuda.manual_seed(self.seed) is_multi_gpu = False helper_function() n_classes = metadata['n_classes'] # check torch available if not torch.cuda.is_available(): logging.info('no gpu device available') sys.exit(1) cudnn.benchmark = True cudnn.enabled = True # loading criterion criterion = nn.CrossEntropyLoss() criterion = criterion.cuda() train_pack = list(zip(train_x, train_y)) valid_pack = list(zip(valid_x, valid_y)) data_channel = np.array(train_x).shape[1] train_loader = torch.utils.data.DataLoader(train_pack, int(self.batch_size), pin_memory=True, num_workers=4) valid_loader = torch.utils.data.DataLoader(valid_pack, int(self.batch_size), pin_memory=True, num_workers=4) model = Network(self.init_channels, data_channel, n_classes, self.layers, criterion) model = model.cuda() # since submission server does not deal with multi-gpu if is_multi_gpu: print("Let's use", torch.cuda.device_count(), "GPUs!") # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs model = nn.DataParallel(model) arch_parameters = model.module.arch_parameters( ) if is_multi_gpu else model.arch_parameters() arch_params = list(map(id, arch_parameters)) parameters = model.module.parameters( ) if is_multi_gpu else model.parameters() weight_params = filter(lambda p: id(p) not in arch_params, parameters) optimizer = torch.optim.SGD(weight_params, self.learning_rate, momentum=self.momentum, weight_decay=self.weight_decay) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, float(self.epochs), eta_min=self.learning_rate_min) architect = Architect(is_multi_gpu, model, criterion, self.momentum, self.weight_decay, self.arch_learning_rate, self.arch_weight_decay) best_accuracy = 0 best_accuracy_different_cnn_counts = dict() for epoch in range(self.epochs): lr = scheduler.get_lr()[0] logging.info('epoch %d lr %e', epoch, lr) # training objs = utils.AvgrageMeter() top1 = utils.AvgrageMeter() top5 = utils.AvgrageMeter() train_batch = time.time() for step, (input, target) in enumerate(train_loader): # logging.info("epoch %d, step %d START" % (epoch, step)) model.train() n = input.size(0) input = input.cuda() target = target.cuda() # get a random minibatch from the search queue with replacement input_search, target_search = next(iter(valid_loader)) input_search = input_search.cuda() target_search = target_search.cuda() # Update architecture alpha by Adam-SGD # logging.info("step %d. update architecture by Adam. START" % step) # if args.optimization == "DARTS": # architect.step(input, target, input_search, target_search, lr, optimizer, unrolled=args.unrolled) # else: architect.step_milenas_2ndorder(input, target, input_search, target_search, lr, optimizer, 1, 1) # logging.info("step %d. update architecture by Adam. FINISH" % step) # Update weights w by SGD, ignore the weights that gained during architecture training # logging.info("step %d. update weight by SGD. START" % step) optimizer.zero_grad() logits = model(input) loss = criterion(logits, target) loss.backward() parameters = model.module.arch_parameters( ) if is_multi_gpu else model.arch_parameters() nn.utils.clip_grad_norm_(parameters, self.grad_clip) optimizer.step() # logging.info("step %d. update weight by SGD. FINISH\n" % step) prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) objs.update(loss.item(), n) top1.update(prec1.item(), n) top5.update(prec5.item(), n) # torch.cuda.empty_cache() if step % self.report_freq == 0: average_batch_t = (time.time() - train_batch) / (step + 1) print("Epoch: {}, Step: {}, Top1: {}, Top5: {}, T: {}". format( epoch, step, top1.avg, top5.avg, show_time(average_batch_t * (len(train_loader) - step)))) model.eval() # validation with torch.no_grad(): objs = utils.AvgrageMeter() top1 = utils.AvgrageMeter() top5 = utils.AvgrageMeter() for step, (input, target) in enumerate(valid_loader): input = input.cuda() target = target.cuda() logits = model(input) loss = criterion(logits, target) prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) n = input.size(0) objs.update(loss.item(), n) top1.update(prec1.item(), n) top5.update(prec5.item(), n) if step % self.report_freq == 0: print("Epoch: {}, Step: {}, Top1: {}, Top5: {}".format( epoch, step, top1.avg, top5.avg)) scheduler.step() # save the structure genotype, normal_cnn_count, reduce_cnn_count = model.module.genotype( ) if is_multi_gpu else model.genotype() print("(n:%d,r:%d)" % (normal_cnn_count, reduce_cnn_count)) # print(F.softmax(model.module.alphas_normal if is_multi_gpu else model.alphas_normal, dim=-1)) # print(F.softmax(model.module.alphas_reduce if is_multi_gpu else model.alphas_reduce, dim=-1)) # logging.info('genotype = %s', genotype) return model
def main(): if not torch.cuda.is_available(): logging.info('no gpu device available') sys.exit(1) random.seed(args.seed) np.random.seed(args.seed) torch.cuda.set_device(args.gpu) cudnn.benchmark = False torch.manual_seed(args.seed) cudnn.enabled = True cudnn.deterministic = True torch.cuda.manual_seed(args.seed) logging.info('gpu device = %d' % args.gpu) logging.info("args = %s", args) if args.loss_func == 'cce': criterion = nn.CrossEntropyLoss().cuda() elif args.loss_func == 'rll': criterion = utils.RobustLogLoss(alpha=args.alpha).cuda() else: assert False, "Invalid loss function '{}' given. Must be in {'cce', 'rll'}".format( args.loss_func) model = Network(args.init_channels, CIFAR_CLASSES, args.layers, criterion) model = model.cuda() model.train() model.apply(weights_init) nn.utils.clip_grad_norm(model.parameters(), args.grad_clip) logging.info("param size = %fMB", utils.count_parameters_in_MB(model)) train_transform, valid_transform = utils._data_transforms_cifar10(args) # Load dataset if args.dataset == 'cifar10': train_data = CIFAR10(root=args.data, train=True, gold=False, gold_fraction=0.0, corruption_prob=args.corruption_prob, corruption_type=args.corruption_type, transform=train_transform, download=True, seed=args.seed) gold_train_data = CIFAR10(root=args.data, train=True, gold=True, gold_fraction=1.0, corruption_prob=args.corruption_prob, corruption_type=args.corruption_type, transform=train_transform, download=True, seed=args.seed) elif args.dataset == 'cifar100': train_data = CIFAR100(root=args.data, train=True, gold=False, gold_fraction=0.0, corruption_prob=args.corruption_prob, corruption_type=args.corruption_type, transform=train_transform, download=True, seed=args.seed) gold_train_data = CIFAR100(root=args.data, train=True, gold=True, gold_fraction=1.0, corruption_prob=args.corruption_prob, corruption_type=args.corruption_type, transform=train_transform, download=True, seed=args.seed) num_train = len(train_data) indices = list(range(num_train)) split = int(np.floor(args.train_portion * num_train)) clean_train_queue = torch.utils.data.DataLoader( gold_train_data, batch_size=args.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]), pin_memory=True, num_workers=0) noisy_train_queue = torch.utils.data.DataLoader( train_data, batch_size=args.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]), pin_memory=True, num_workers=0) clean_valid_queue = torch.utils.data.DataLoader( gold_train_data, batch_size=args.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:]), pin_memory=True, num_workers=0) noisy_valid_queue = torch.utils.data.DataLoader( train_data, batch_size=args.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:]), pin_memory=True, num_workers=0) clean_train_list, clean_valid_list, noisy_train_list, noisy_valid_list = [], [], [], [] for dst_list, queue in [ (clean_train_list, clean_train_queue), (clean_valid_list, clean_valid_queue), (noisy_train_list, noisy_train_queue), (noisy_valid_list, noisy_valid_queue), ]: for input, target in queue: input = Variable(input, volatile=True).cuda() target = Variable(target, volatile=True).cuda(async=True) dst_list.append((input, target)) for epoch in range(args.epochs): logging.info('Epoch %d, random architecture with fix weights', epoch) genotype = model.genotype() logging.info('genotype = %s', genotype) logging.info(F.softmax(model.alphas_normal, dim=-1)) logging.info(F.softmax(model.alphas_reduce, dim=-1)) # training clean_train_acc, clean_train_obj = infer(clean_train_list, model, criterion, kind='clean_train') logging.info('clean_train_acc %f, clean_train_loss %f', clean_train_acc, clean_train_obj) noisy_train_acc, noisy_train_obj = infer(noisy_train_list, model, criterion, kind='noisy_train') logging.info('noisy_train_acc %f, noisy_train_loss %f', noisy_train_acc, noisy_train_obj) # validation clean_valid_acc, clean_valid_obj = infer(clean_valid_list, model, criterion, kind='clean_valid') logging.info('clean_valid_acc %f, clean_valid_loss %f', clean_valid_acc, clean_valid_obj) noisy_valid_acc, noisy_valid_obj = infer(noisy_valid_list, model, criterion, kind='noisy_valid') logging.info('noisy_valid_acc %f, noisy_valid_loss %f', noisy_valid_acc, noisy_valid_obj) utils.save(model, os.path.join(args.save, 'weights.pt')) # Randomly change the alphas k = sum(1 for i in range(model._steps) for n in range(2 + i)) num_ops = len(PRIMITIVES) model.alphas_normal.data.copy_(torch.randn(k, num_ops)) model.alphas_reduce.data.copy_(torch.randn(k, num_ops))
class neural_architecture_search(): def __init__(self, args): self.args = args if not torch.cuda.is_available(): logging.info('no gpu device available') sys.exit(1) if self.args.distributed: # Init distributed environment self.rank, self.world_size, self.device = init_dist( port=self.args.port) self.seed = self.rank * self.args.seed else: torch.cuda.set_device(self.args.gpu) self.device = torch.device("cuda") self.rank = 0 self.seed = self.args.seed self.world_size = 1 if self.args.fix_seedcudnn: random.seed(self.seed) torch.backends.cudnn.deterministic = True np.random.seed(self.seed) cudnn.benchmark = False torch.manual_seed(self.seed) cudnn.enabled = True torch.cuda.manual_seed(self.seed) torch.cuda.manual_seed_all(self.seed) else: np.random.seed(self.seed) cudnn.benchmark = True torch.manual_seed(self.seed) cudnn.enabled = True torch.cuda.manual_seed(self.seed) torch.cuda.manual_seed_all(self.seed) self.path = os.path.join(generate_date, self.args.save) if self.rank == 0: utils.create_exp_dir(generate_date, self.path, scripts_to_save=glob.glob('*.py')) logging.basicConfig(stream=sys.stdout, level=logging.INFO, format=log_format, datefmt='%m/%d %I:%M:%S %p') fh = logging.FileHandler(os.path.join(self.path, 'log.txt')) fh.setFormatter(logging.Formatter(log_format)) logging.getLogger().addHandler(fh) logging.info("self.args = %s", self.args) self.logger = tensorboardX.SummaryWriter( './runs/' + generate_date + '/nas_{}'.format(self.args.remark)) else: self.logger = None # set default resource_lambda for different methods if self.args.resource_efficient: if self.args.method == 'policy_gradient': if self.args.log_penalty: default_resource_lambda = 1e-4 else: default_resource_lambda = 1e-5 if self.args.method == 'reparametrization': if self.args.log_penalty: default_resource_lambda = 1e-2 else: default_resource_lambda = 1e-5 if self.args.method == 'discrete': if self.args.log_penalty: default_resource_lambda = 1e-2 else: default_resource_lambda = 1e-4 if self.args.resource_lambda == default_lambda: self.args.resource_lambda = default_resource_lambda #initialize loss function self.criterion = nn.CrossEntropyLoss().to(self.device) #initialize model self.init_model() #calculate model param size if self.rank == 0: logging.info("param size = %fMB", utils.count_parameters_in_MB(self.model)) self.model._logger = self.logger self.model._logging = logging #initialize optimizer self.init_optimizer() #iniatilize dataset loader self.init_loaddata() self.update_theta = True self.update_alpha = True def init_model(self): self.model = Network(self.args.init_channels, CIFAR_CLASSES, self.args.layers, self.criterion, self.args, self.rank, self.world_size) self.model.to(self.device) if self.args.distributed: broadcast_params(self.model) for v in self.model.parameters(): if v.requires_grad: if v.grad is None: v.grad = torch.zeros_like(v) self.model.normal_log_alpha.grad = torch.zeros_like( self.model.normal_log_alpha) self.model.reduce_log_alpha.grad = torch.zeros_like( self.model.reduce_log_alpha) def init_optimizer(self): if args.distributed: self.optimizer = torch.optim.SGD( [ param for name, param in self.model.named_parameters() if name != 'normal_log_alpha' and name != 'reduce_log_alpha' ], self.args.learning_rate, momentum=self.args.momentum, weight_decay=self.args.weight_decay) self.arch_optimizer = torch.optim.Adam( [ param for name, param in self.model.named_parameters() if name == 'normal_log_alpha' or name == 'reduce_log_alpha' ], lr=self.args.arch_learning_rate, betas=(0.5, 0.999), weight_decay=self.args.arch_weight_decay) else: self.optimizer = torch.optim.SGD(self.model.parameters(), self.args.learning_rate, momentum=self.args.momentum, weight_decay=args.weight_decay) self.arch_optimizer = torch.optim.SGD( self.model.arch_parameters(), lr=self.args.arch_learning_rate) def init_loaddata(self): train_transform, valid_transform = utils._data_transforms_cifar10( self.args) train_data = dset.CIFAR10(root=self.args.data, train=True, download=True, transform=train_transform) valid_data = dset.CIFAR10(root=self.args.data, train=False, download=True, transform=valid_transform) if self.args.seed: def worker_init_fn(): seed = self.seed np.random.seed(seed) random.seed(seed) torch.manual_seed(seed) return else: worker_init_fn = None if self.args.distributed: train_sampler = DistributedSampler(train_data) valid_sampler = DistributedSampler(valid_data) self.train_queue = torch.utils.data.DataLoader( train_data, batch_size=self.args.batch_size // self.world_size, shuffle=False, num_workers=0, pin_memory=False, sampler=train_sampler) self.valid_queue = torch.utils.data.DataLoader( valid_data, batch_size=self.args.batch_size // self.world_size, shuffle=False, num_workers=0, pin_memory=False, sampler=valid_sampler) else: self.train_queue = torch.utils.data.DataLoader( train_data, batch_size=self.args.batch_size, shuffle=True, pin_memory=False, num_workers=2) self.valid_queue = torch.utils.data.DataLoader( valid_data, batch_size=self.args.batch_size, shuffle=False, pin_memory=False, num_workers=2) def main(self): # lr scheduler: cosine annealing # temp scheduler: linear annealing (self-defined in utils) self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( self.optimizer, float(self.args.epochs), eta_min=self.args.learning_rate_min) self.temp_scheduler = utils.Temp_Scheduler(self.args.epochs, self.model._temp, self.args.temp, temp_min=self.args.temp_min) for epoch in range(self.args.epochs): if self.args.random_sample_pretrain: if epoch < self.args.random_sample_pretrain_epoch: self.args.random_sample = True else: self.args.random_sample = False self.scheduler.step() if self.args.temp_annealing: self.model._temp = self.temp_scheduler.step() self.lr = self.scheduler.get_lr()[0] if self.rank == 0: logging.info('epoch %d lr %e temp %e', epoch, self.lr, self.model._temp) self.logger.add_scalar('epoch_temp', self.model._temp, epoch) logging.info(self.model.normal_log_alpha) logging.info(self.model.reduce_log_alpha) logging.info( self.model._get_weights(self.model.normal_log_alpha[0])) logging.info( self.model._get_weights(self.model.reduce_log_alpha[0])) genotype_edge_all = self.model.genotype_edge_all() if self.rank == 0: logging.info('genotype_edge_all = %s', genotype_edge_all) # create genotypes.txt file txt_name = self.args.remark + '_genotype_edge_all_epoch' + str( epoch) utils.txt('genotype', self.args.save, txt_name, str(genotype_edge_all), generate_date) self.model.train() train_acc, loss, error_loss, loss_alpha = self.train( epoch, logging) if self.rank == 0: logging.info('train_acc %f', train_acc) self.logger.add_scalar("epoch_train_acc", train_acc, epoch) self.logger.add_scalar("epoch_train_error_loss", error_loss, epoch) if self.args.dsnas: self.logger.add_scalar("epoch_train_alpha_loss", loss_alpha, epoch) # validation self.model.eval() valid_acc, valid_obj = self.infer(epoch) if self.args.gen_max_child: self.args.gen_max_child_flag = True valid_acc_max_child, valid_obj_max_child = self.infer(epoch) self.args.gen_max_child_flag = False if self.rank == 0: logging.info('valid_acc %f', valid_acc) self.logger.add_scalar("epoch_valid_acc", valid_acc, epoch) if self.args.gen_max_child: logging.info('valid_acc_argmax_alpha %f', valid_acc_max_child) self.logger.add_scalar("epoch_valid_acc_argmax_alpha", valid_acc_max_child, epoch) utils.save(self.model, os.path.join(self.path, 'weights.pt')) if self.rank == 0: logging.info(self.model.normal_log_alpha) logging.info(self.model.reduce_log_alpha) genotype_edge_all = self.model.genotype_edge_all() logging.info('genotype_edge_all = %s', genotype_edge_all) def train(self, epoch, logging): objs = utils.AvgrageMeter() top1 = utils.AvgrageMeter() top5 = utils.AvgrageMeter() grad = utils.AvgrageMeter() normal_resource_gradient = 0 reduce_resource_gradient = 0 normal_loss_gradient = 0 reduce_loss_gradient = 0 normal_total_gradient = 0 reduce_total_gradient = 0 loss_alpha = None count = 0 for step, (input, target) in enumerate(self.train_queue): if self.args.alternate_update: if step % 2 == 0: self.update_theta = True self.update_alpha = False else: self.update_theta = False self.update_alpha = True n = input.size(0) input = input.to(self.device) target = target.to(self.device, non_blocking=True) if self.args.snas: logits, logits_aux, penalty, op_normal, op_reduce = self.model( input) error_loss = self.criterion(logits, target) if self.args.auxiliary: loss_aux = self.criterion(logits_aux, target) error_loss += self.args.auxiliary_weight * loss_aux if self.args.dsnas: logits, error_loss, loss_alpha, penalty = self.model( input, target, self.criterion) num_normal = self.model.num_normal num_reduce = self.model.num_reduce normal_arch_entropy = self.model._arch_entropy( self.model.normal_log_alpha) reduce_arch_entropy = self.model._arch_entropy( self.model.reduce_log_alpha) if self.args.resource_efficient: if self.args.method == 'policy_gradient': resource_penalty = (penalty[2]) / 6 + self.args.ratio * ( penalty[7]) / 2 log_resource_penalty = ( penalty[35]) / 6 + self.args.ratio * (penalty[36]) / 2 elif self.args.method == 'reparametrization': resource_penalty = (penalty[26]) / 6 + self.args.ratio * ( penalty[25]) / 2 log_resource_penalty = ( penalty[37]) / 6 + self.args.ratio * (penalty[38]) / 2 elif self.args.method == 'discrete': resource_penalty = (penalty[28]) / 6 + self.args.ratio * ( penalty[27]) / 2 log_resource_penalty = ( penalty[39]) / 6 + self.args.ratio * (penalty[40]) / 2 elif self.args.method == 'none': # TODo resource_penalty = torch.zeros(1).cuda() log_resource_penalty = torch.zeros(1).cuda() else: logging.info( "wrongly input of method, please re-enter --method from 'policy_gradient', 'discrete', " "'reparametrization', 'none'") sys.exit(1) else: resource_penalty = torch.zeros(1).cuda() log_resource_penalty = torch.zeros(1).cuda() if self.args.log_penalty: resource_loss = self.model._resource_lambda * log_resource_penalty else: resource_loss = self.model._resource_lambda * resource_penalty if self.args.loss: if self.args.snas: loss = resource_loss.clone() + error_loss.clone() elif self.args.dsnas: loss = resource_loss.clone() else: loss = resource_loss.clone() + -child_coef * ( torch.log(normal_one_hot_prob) + torch.log(reduce_one_hot_prob)).sum() else: if self.args.snas or self.args.dsnas: loss = error_loss.clone() if self.args.distributed: loss.div_(self.world_size) error_loss.div_(self.world_size) resource_loss.div_(self.world_size) if self.args.dsnas: loss_alpha.div_(self.world_size) # logging gradient count += 1 if self.args.resource_efficient: self.optimizer.zero_grad() self.arch_optimizer.zero_grad() resource_loss.backward(retain_graph=True) if not self.args.random_sample: normal_resource_gradient += self.model.normal_log_alpha.grad reduce_resource_gradient += self.model.reduce_log_alpha.grad if self.args.snas: self.optimizer.zero_grad() self.arch_optimizer.zero_grad() error_loss.backward(retain_graph=True) if not self.args.random_sample: normal_loss_gradient += self.model.normal_log_alpha.grad reduce_loss_gradient += self.model.reduce_log_alpha.grad self.optimizer.zero_grad() self.arch_optimizer.zero_grad() if self.args.snas or not self.args.random_sample and not self.args.dsnas: loss.backward() if not self.args.random_sample: normal_total_gradient += self.model.normal_log_alpha.grad reduce_total_gradient += self.model.reduce_log_alpha.grad if self.args.distributed: reduce_tensorgradients(self.model.parameters(), sync=True) nn.utils.clip_grad_norm_([ param for name, param in self.model.named_parameters() if name != 'normal_log_alpha' and name != 'reduce_log_alpha' ], self.args.grad_clip) arch_grad_norm = nn.utils.clip_grad_norm_([ param for name, param in self.model.named_parameters() if name == 'normal_log_alpha' or name == 'reduce_log_alpha' ], 10.) else: nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_clip) arch_grad_norm = nn.utils.clip_grad_norm_( self.model.arch_parameters(), 10.) grad.update(arch_grad_norm) if not self.args.fix_weight and self.update_theta: self.optimizer.step() self.optimizer.zero_grad() if not self.args.random_sample and self.update_alpha: self.arch_optimizer.step() self.arch_optimizer.zero_grad() if self.rank == 0: self.logger.add_scalar( "iter_train_loss", error_loss, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "normal_arch_entropy", normal_arch_entropy, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reduce_arch_entropy", reduce_arch_entropy, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "total_arch_entropy", normal_arch_entropy + reduce_arch_entropy, step + len(self.train_queue.dataset) * epoch) if self.args.dsnas: #reward_normal_edge self.logger.add_scalar( "reward_normal_edge_0", self.model.normal_edge_reward[0], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_1", self.model.normal_edge_reward[1], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_2", self.model.normal_edge_reward[2], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_3", self.model.normal_edge_reward[3], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_4", self.model.normal_edge_reward[4], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_5", self.model.normal_edge_reward[5], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_6", self.model.normal_edge_reward[6], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_7", self.model.normal_edge_reward[7], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_8", self.model.normal_edge_reward[8], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_9", self.model.normal_edge_reward[9], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_10", self.model.normal_edge_reward[10], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_11", self.model.normal_edge_reward[11], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_12", self.model.normal_edge_reward[12], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_normal_edge_13", self.model.normal_edge_reward[13], step + len(self.train_queue.dataset) * epoch) #reward_reduce_edge self.logger.add_scalar( "reward_reduce_edge_0", self.model.reduce_edge_reward[0], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_1", self.model.reduce_edge_reward[1], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_2", self.model.reduce_edge_reward[2], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_3", self.model.reduce_edge_reward[3], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_4", self.model.reduce_edge_reward[4], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_5", self.model.reduce_edge_reward[5], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_6", self.model.reduce_edge_reward[6], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_7", self.model.reduce_edge_reward[7], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_8", self.model.reduce_edge_reward[8], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_9", self.model.reduce_edge_reward[9], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_10", self.model.reduce_edge_reward[10], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_11", self.model.reduce_edge_reward[11], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_12", self.model.reduce_edge_reward[12], step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "reward_reduce_edge_13", self.model.reduce_edge_reward[13], step + len(self.train_queue.dataset) * epoch) #policy size self.logger.add_scalar( "iter_normal_size_policy", penalty[2] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_size_policy", penalty[7] / num_reduce, step + len(self.train_queue.dataset) * epoch) # baseline: discrete_probability self.logger.add_scalar( "iter_normal_size_baseline", penalty[3] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_flops_baseline", penalty[5] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_mac_baseline", penalty[6] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_size_baseline", penalty[8] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_flops_baseline", penalty[9] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_mac_baseline", penalty[10] / num_reduce, step + len(self.train_queue.dataset) * epoch) # R - median(R) self.logger.add_scalar( "iter_normal_size-avg", penalty[60] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_flops-avg", penalty[61] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_mac-avg", penalty[62] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_size-avg", penalty[63] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_flops-avg", penalty[64] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_mac-avg", penalty[65] / num_reduce, step + len(self.train_queue.dataset) * epoch) # lnR - ln(median) self.logger.add_scalar( "iter_normal_ln_size-ln_avg", penalty[66] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_ln_flops-ln_avg", penalty[67] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_ln_mac-ln_avg", penalty[68] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_ln_size-ln_avg", penalty[69] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_ln_flops-ln_avg", penalty[70] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_ln_mac-ln_avg", penalty[71] / num_reduce, step + len(self.train_queue.dataset) * epoch) ''' self.logger.add_scalar("iter_normal_size_normalized", penalty[17] / 6, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar("iter_normal_flops_normalized", penalty[18] / 6, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar("iter_normal_mac_normalized", penalty[19] / 6, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar("iter_reduce_size_normalized", penalty[20] / 2, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar("iter_reduce_flops_normalized", penalty[21] / 2, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar("iter_reduce_mac_normalized", penalty[22] / 2, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar("iter_normal_penalty_normalized", penalty[23] / 6, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar("iter_reduce_penalty_normalized", penalty[24] / 2, step + len(self.train_queue.dataset) * epoch) ''' # Monte_Carlo(R_i) self.logger.add_scalar( "iter_normal_size_mc", penalty[29] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_flops_mc", penalty[30] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_mac_mc", penalty[31] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_size_mc", penalty[32] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_flops_mc", penalty[33] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_mac_mc", penalty[34] / num_reduce, step + len(self.train_queue.dataset) * epoch) # log(|R_i|) self.logger.add_scalar( "iter_normal_log_size", penalty[41] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_log_flops", penalty[42] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_log_mac", penalty[43] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_log_size", penalty[44] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_log_flops", penalty[45] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_log_mac", penalty[46] / num_reduce, step + len(self.train_queue.dataset) * epoch) # log(P)R_i self.logger.add_scalar( "iter_normal_logP_size", penalty[47] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_logP_flops", penalty[48] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_logP_mac", penalty[49] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_logP_size", penalty[50] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_logP_flops", penalty[51] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_logP_mac", penalty[52] / num_reduce, step + len(self.train_queue.dataset) * epoch) # log(P)log(R_i) self.logger.add_scalar( "iter_normal_logP_log_size", penalty[53] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_logP_log_flops", penalty[54] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_normal_logP_log_mac", penalty[55] / num_normal, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_logP_log_size", penalty[56] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_logP_log_flops", penalty[57] / num_reduce, step + len(self.train_queue.dataset) * epoch) self.logger.add_scalar( "iter_reduce_logP_log_mac", penalty[58] / num_reduce, step + len(self.train_queue.dataset) * epoch) prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) if self.args.distributed: loss = loss.detach() dist.all_reduce(error_loss) dist.all_reduce(prec1) dist.all_reduce(prec5) prec1.div_(self.world_size) prec5.div_(self.world_size) #dist_util.all_reduce([loss, prec1, prec5], 'mean') objs.update(error_loss.item(), n) top1.update(prec1.item(), n) top5.update(prec5.item(), n) if step % self.args.report_freq == 0 and self.rank == 0: logging.info('train %03d %e %f %f', step, objs.avg, top1.avg, top5.avg) self.logger.add_scalar( "iter_train_top1_acc", top1.avg, step + len(self.train_queue.dataset) * epoch) if self.rank == 0: logging.info('-------resource gradient--------') logging.info(normal_resource_gradient / count) logging.info(reduce_resource_gradient / count) logging.info('-------loss gradient--------') logging.info(normal_loss_gradient / count) logging.info(reduce_loss_gradient / count) logging.info('-------total gradient--------') logging.info(normal_total_gradient / count) logging.info(reduce_total_gradient / count) return top1.avg, loss, error_loss, loss_alpha def infer(self, epoch): objs = utils.AvgrageMeter() top1 = utils.AvgrageMeter() top5 = utils.AvgrageMeter() self.model.eval() with torch.no_grad(): for step, (input, target) in enumerate(self.valid_queue): input = input.to(self.device) target = target.to(self.device) if self.args.snas: logits, logits_aux, resource_loss, op_normal, op_reduce = self.model( input) loss = self.criterion(logits, target) elif self.args.dsnas: logits, error_loss, loss_alpha, resource_loss = self.model( input, target, self.criterion) loss = error_loss prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) if self.args.distributed: loss.div_(self.world_size) loss = loss.detach() dist.all_reduce(loss) dist.all_reduce(prec1) dist.all_reduce(prec5) prec1.div_(self.world_size) prec5.div_(self.world_size) objs.update(loss.item(), input.size(0)) top1.update(prec1.item(), input.size(0)) top5.update(prec5.item(), input.size(0)) if step % self.args.report_freq == 0 and self.rank == 0: logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg) self.logger.add_scalar( "iter_valid_loss", loss, step + len(self.valid_queue.dataset) * epoch) self.logger.add_scalar( "iter_valid_top1_acc", top1.avg, step + len(self.valid_queue.dataset) * epoch) return top1.avg, objs.avg
def main(): utils.create_exp_dir(args.save, scripts_to_save=glob.glob('*.py')) print(args) seed = random.randint(1, 100000000) print(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.set_device(args.gpu) cudnn.benchmark = True cudnn.enabled = True n_channels = 3 n_bins = 2.**args.n_bits # Define model and loss criteria model = SearchNetwork(n_channels, args.n_flow, args.n_block, n_bins, affine=args.affine, conv_lu=not args.no_lu) model = nn.DataParallel(model, [args.gpu]) model.load_state_dict( torch.load("architecture.pt", map_location="cuda:{}".format(args.gpu))) model = model.module genotype = model.sample_architecture() with open(args.save + '/genotype.pkl', 'wb') as fp: pickle.dump(genotype, fp) model_single = EnsembleNetwork(n_channels, args.n_flow, args.n_block, n_bins, genotype, affine=args.affine, conv_lu=not args.no_lu) model = model_single model = model.to(device) optimizer = torch.optim.Adam(model.parameters(), args.learning_rate) dataset = iter(sample_cifar10(args.batch, args.img_size)) # Sample generated images z_sample = [] z_shapes = calc_z_shapes(n_channels, args.img_size, args.n_flow, args.n_block) for z in z_shapes: z_new = torch.randn(args.n_sample, *z) * args.temp z_sample.append(z_new.to(device)) with tqdm(range(args.iter)) as pbar: for i in pbar: # Training procedure model.train() # Get a random minibatch from the search queue with replacement input, _ = next(dataset) input = Variable(input, requires_grad=False).cuda(non_blocking=True) log_p, logdet, _ = model(input + torch.rand_like(input) / n_bins) logdet = logdet.mean() loss, _, _ = likelihood_loss(log_p, logdet, args.img_size, n_bins) # Optimize model optimizer.zero_grad() loss.backward() optimizer.step() pbar.set_description("Loss: {}".format(loss.item())) # Save generated samples if i % 100 == 0: with torch.no_grad(): tvutils.save_image( model_single.reverse(z_sample).cpu().data, "{}/samples/{}.png".format(args.save, str(i + 1).zfill(6)), normalize=False, nrow=10, ) # Save checkpoint if i % 1000 == 0: utils.save(model, os.path.join(args.save, 'latest_weights.pt'))
def search(self, train_x, train_y, valid_x, valid_y, metadata): np.random.seed(self.seed) cudnn.benchmark = True torch.manual_seed(self.seed) cudnn.enabled = True torch.cuda.manual_seed(self.seed) helpers.helper_function() n_classes = metadata['n_classes'] # reshape it to this dataset # model = torchvision.models.resnet18() # model.conv1 = nn.Conv2d(train_x.shape[1], 64, kernel_size=(7, 7), stride=1, padding=3) # model.fc = nn.Linear(model.fc.in_features, n_classes, bias=True) # return model if not torch.cuda.is_available(): logging.info('no gpu device available') sys.exit(1) cudnn.benchmark = True cudnn.enabled = True criterion = nn.CrossEntropyLoss() criterion = criterion.cuda() model = Network(self.init_channels, n_classes, self.layers, criterion) model = model.cuda() logging.info("param size = %fMB", utils.count_parameters_in_MB(model)) optimizer = torch.optim.SGD(model.parameters(), self.learning_rate, momentum=self.momentum, weight_decay=self.weight_decay) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, float(self.epochs), eta_min=self.learning_rate_min) architect = Architect(model) train_pack = list(zip(train_x, train_y)) valid_pack = list(zip(valid_x, valid_y)) train_loader = torch.utils.data.DataLoader(train_pack, int(self.batch_size), shuffle=False) valid_loader = torch.utils.data.DataLoader(valid_pack, int(self.batch_size)) for epoch in range(self.epochs): scheduler.step() lr = scheduler.get_lr()[0] logging.info('epoch %d lr %e', epoch, lr) genotype = model.genotype() logging.info('genotype = %s', genotype) # print(F.softmax(model.alphas_normal, dim=-1)) # print(F.softmax(model.alphas_reduce, dim=-1)) # training print("++++++Start training+++++++") for step, (input, target) in enumerate(train_loader): model.train() n = input.size(0) input = Variable(input, requires_grad=False).cuda() target = Variable(target, requires_grad=False).cuda(non_blocking=True) # get a random minibatch from the search queue with replacement input_search, target_search = next(iter(valid_loader)) input_search = Variable(input_search, requires_grad=False).cuda() target_search = Variable( target_search, requires_grad=False).cuda(non_blocking=True) architect.step(input, target, input_search, target_search, lr, optimizer, unrolled=self.unrolled) optimizer.zero_grad() logits = model(input) loss = criterion(logits, target) loss.backward() nn.utils.clip_grad_norm(model.parameters(), self.grad_clip) optimizer.step() if step % self.report_freq == 0: prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) print(step, loss, prec1, prec5) # validation print("++++++Start validation+++++++") with torch.no_grad(): for step, (input, target) in enumerate(valid_loader): input = Variable(input).cuda() target = Variable(target).cuda(non_blocking=True) model.eval() logits = model(input) loss = criterion(logits, target) if step % self.report_freq == 0: prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) print(step, loss, prec1, prec5) return model
class neural_architecture_search(): def __init__(self, args): self.args = args if not torch.cuda.is_available(): logging.info('no gpu device available') sys.exit(1) torch.cuda.set_device(self.args.gpu) self.device = torch.device("cuda") self.rank = 0 self.seed = self.args.seed self.world_size = 1 if self.args.fix_cudnn: random.seed(self.seed) torch.backends.cudnn.deterministic = True np.random.seed(self.seed) cudnn.benchmark = False torch.manual_seed(self.seed) cudnn.enabled = True torch.cuda.manual_seed(self.seed) torch.cuda.manual_seed_all(self.seed) else: np.random.seed(self.seed) cudnn.benchmark = True torch.manual_seed(self.seed) cudnn.enabled = True torch.cuda.manual_seed(self.seed) torch.cuda.manual_seed_all(self.seed) self.path = os.path.join(generate_date, self.args.save) if self.rank == 0: utils.create_exp_dir(generate_date, self.path, scripts_to_save=glob.glob('*.py')) logging.basicConfig(stream=sys.stdout, level=logging.INFO, format=log_format, datefmt='%m/%d %I:%M:%S %p') fh = logging.FileHandler(os.path.join(self.path, 'log.txt')) fh.setFormatter(logging.Formatter(log_format)) logging.getLogger().addHandler(fh) logging.info("self.args = %s", self.args) self.logger = tensorboardX.SummaryWriter('./runs/' + generate_date + '/' + self.args.save_log) else: self.logger = None #initialize loss function self.criterion = nn.CrossEntropyLoss().to(self.device) #initialize model self.init_model() if self.args.resume: self.reload_model() #calculate model param size if self.rank == 0: logging.info("param size = %fMB", utils.count_parameters_in_MB(self.model)) self.model._logger = self.logger self.model._logging = logging #initialize optimizer self.init_optimizer() #iniatilize dataset loader self.init_loaddata() self.update_theta = True self.update_alpha = True def init_model(self): self.model = Network(self.args.init_channels, CIFAR_CLASSES, self.args.layers, self.criterion, self.args, self.rank, self.world_size, self.args.steps, self.args.multiplier) self.model.to(self.device) for v in self.model.parameters(): if v.requires_grad: if v.grad is None: v.grad = torch.zeros_like(v) self.model.normal_log_alpha.grad = torch.zeros_like( self.model.normal_log_alpha) self.model.reduce_log_alpha.grad = torch.zeros_like( self.model.reduce_log_alpha) def reload_model(self): self.model.load_state_dict(torch.load(self.args.resume_path + '/weights.pt'), strict=True) def init_optimizer(self): self.optimizer = torch.optim.SGD(self.model.parameters(), self.args.learning_rate, momentum=self.args.momentum, weight_decay=args.weight_decay) self.arch_optimizer = torch.optim.Adam( self.model.arch_parameters(), lr=self.args.arch_learning_rate, betas=(0.5, 0.999), weight_decay=self.args.arch_weight_decay) def init_loaddata(self): train_transform, valid_transform = utils._data_transforms_cifar10( self.args) train_data = dset.CIFAR10(root=self.args.data, train=True, download=True, transform=train_transform) valid_data = dset.CIFAR10(root=self.args.data, train=False, download=True, transform=valid_transform) if self.args.seed: def worker_init_fn(): seed = self.seed np.random.seed(seed) random.seed(seed) torch.manual_seed(seed) return else: worker_init_fn = None num_train = len(train_data) indices = list(range(num_train)) self.train_queue = torch.utils.data.DataLoader( train_data, batch_size=self.args.batch_size, shuffle=True, pin_memory=False, num_workers=2) self.valid_queue = torch.utils.data.DataLoader( valid_data, batch_size=self.args.batch_size, shuffle=False, pin_memory=False, num_workers=2) def main(self): # lr scheduler: cosine annealing # temp scheduler: linear annealing (self-defined in utils) self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( self.optimizer, float(self.args.epochs), eta_min=self.args.learning_rate_min) self.temp_scheduler = utils.Temp_Scheduler(self.args.epochs, self.model._temp, self.args.temp, temp_min=self.args.temp_min) for epoch in range(self.args.epochs): if self.args.child_reward_stat: self.update_theta = False self.update_alpha = False if self.args.current_reward: self.model.normal_reward_mean = torch.zeros_like( self.model.normal_reward_mean) self.model.reduce_reward_mean = torch.zeros_like( self.model.reduce_reward_mean) self.model.count = 0 if epoch < self.args.resume_epoch: continue self.scheduler.step() if self.args.temp_annealing: self.model._temp = self.temp_scheduler.step() self.lr = self.scheduler.get_lr()[0] if self.rank == 0: logging.info('epoch %d lr %e temp %e', epoch, self.lr, self.model._temp) self.logger.add_scalar('epoch_temp', self.model._temp, epoch) logging.info(self.model.normal_log_alpha) logging.info(self.model.reduce_log_alpha) logging.info(F.softmax(self.model.normal_log_alpha, dim=-1)) logging.info(F.softmax(self.model.reduce_log_alpha, dim=-1)) genotype_edge_all = self.model.genotype_edge_all() if self.rank == 0: logging.info('genotype_edge_all = %s', genotype_edge_all) # create genotypes.txt file txt_name = remark + '_genotype_edge_all_epoch' + str(epoch) utils.txt('genotype', self.args.save, txt_name, str(genotype_edge_all), generate_date) self.model.train() train_acc, loss, error_loss, loss_alpha = self.train( epoch, logging) if self.rank == 0: logging.info('train_acc %f', train_acc) self.logger.add_scalar("epoch_train_acc", train_acc, epoch) self.logger.add_scalar("epoch_train_error_loss", error_loss, epoch) if self.args.dsnas: self.logger.add_scalar("epoch_train_alpha_loss", loss_alpha, epoch) if self.args.dsnas and not self.args.child_reward_stat: if self.args.current_reward: logging.info('reward mean stat') logging.info(self.model.normal_reward_mean) logging.info(self.model.reduce_reward_mean) logging.info('count') logging.info(self.model.count) else: logging.info('reward mean stat') logging.info(self.model.normal_reward_mean) logging.info(self.model.reduce_reward_mean) if self.model.normal_reward_mean.size(0) > 1: logging.info('reward mean total stat') logging.info(self.model.normal_reward_mean.sum(0)) logging.info(self.model.reduce_reward_mean.sum(0)) if self.args.child_reward_stat: logging.info('reward mean stat') logging.info(self.model.normal_reward_mean.sum(0)) logging.info(self.model.reduce_reward_mean.sum(0)) logging.info('reward var stat') logging.info( self.model.normal_reward_mean_square.sum(0) - self.model.normal_reward_mean.sum(0)**2) logging.info( self.model.reduce_reward_mean_square.sum(0) - self.model.reduce_reward_mean.sum(0)**2) # validation self.model.eval() valid_acc, valid_obj = self.infer(epoch) if self.args.gen_max_child: self.args.gen_max_child_flag = True valid_acc_max_child, valid_obj_max_child = self.infer(epoch) self.args.gen_max_child_flag = False if self.rank == 0: logging.info('valid_acc %f', valid_acc) self.logger.add_scalar("epoch_valid_acc", valid_acc, epoch) if self.args.gen_max_child: logging.info('valid_acc_argmax_alpha %f', valid_acc_max_child) self.logger.add_scalar("epoch_valid_acc_argmax_alpha", valid_acc_max_child, epoch) utils.save(self.model, os.path.join(self.path, 'weights.pt')) if self.rank == 0: logging.info(self.model.normal_log_alpha) logging.info(self.model.reduce_log_alpha) genotype_edge_all = self.model.genotype_edge_all() logging.info('genotype_edge_all = %s', genotype_edge_all) def train(self, epoch, logging): objs = utils.AvgrageMeter() top1 = utils.AvgrageMeter() top5 = utils.AvgrageMeter() grad = utils.AvgrageMeter() normal_loss_gradient = 0 reduce_loss_gradient = 0 normal_total_gradient = 0 reduce_total_gradient = 0 loss_alpha = None train_correct_count = 0 train_correct_cost = 0 train_correct_entropy = 0 train_correct_loss = 0 train_wrong_count = 0 train_wrong_cost = 0 train_wrong_entropy = 0 train_wrong_loss = 0 count = 0 for step, (input, target) in enumerate(self.train_queue): n = input.size(0) input = input.to(self.device) target = target.to(self.device, non_blocking=True) if self.args.snas: logits, logits_aux = self.model(input) error_loss = self.criterion(logits, target) if self.args.auxiliary: loss_aux = self.criterion(logits_aux, target) error_loss += self.args.auxiliary_weight * loss_aux if self.args.dsnas: logits, error_loss, loss_alpha = self.model( input, target, self.criterion, update_theta=self.update_theta, update_alpha=self.update_alpha) for i in range(logits.size(0)): index = logits[i].topk(5, 0, True, True)[1] if index[0].item() == target[i].item(): train_correct_cost += ( -logits[i, target[i].item()] + (F.softmax(logits[i]) * logits[i]).sum()) train_correct_count += 1 discrete_prob = F.softmax(logits[i], dim=-1) train_correct_entropy += -( discrete_prob * torch.log(discrete_prob)).sum(-1) train_correct_loss += -torch.log(discrete_prob)[ target[i].item()] else: train_wrong_cost += ( -logits[i, target[i].item()] + (F.softmax(logits[i]) * logits[i]).sum()) train_wrong_count += 1 discrete_prob = F.softmax(logits[i], dim=-1) train_wrong_entropy += -(discrete_prob * torch.log(discrete_prob)).sum(-1) train_wrong_loss += -torch.log(discrete_prob)[ target[i].item()] num_normal = self.model.num_normal num_reduce = self.model.num_reduce if self.args.snas or self.args.dsnas: loss = error_loss.clone() #self.update_lr() # logging gradient count += 1 if self.args.snas: self.optimizer.zero_grad() self.arch_optimizer.zero_grad() error_loss.backward(retain_graph=True) if not self.args.random_sample: normal_loss_gradient += self.model.normal_log_alpha.grad reduce_loss_gradient += self.model.reduce_log_alpha.grad self.optimizer.zero_grad() self.arch_optimizer.zero_grad() if self.args.snas and (not self.args.random_sample and not self.args.dsnas): loss.backward() if not self.args.random_sample: normal_total_gradient += self.model.normal_log_alpha.grad reduce_total_gradient += self.model.reduce_log_alpha.grad nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_clip) arch_grad_norm = nn.utils.clip_grad_norm_( self.model.arch_parameters(), 10.) grad.update(arch_grad_norm) if not self.args.fix_weight and self.update_theta: self.optimizer.step() self.optimizer.zero_grad() if not self.args.random_sample and self.update_alpha: self.arch_optimizer.step() self.arch_optimizer.zero_grad() prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) objs.update(error_loss.item(), n) top1.update(prec1.item(), n) top5.update(prec5.item(), n) if step % self.args.report_freq == 0 and self.rank == 0: logging.info('train %03d %e %f %f', step, objs.avg, top1.avg, top5.avg) self.logger.add_scalar( "iter_train_top1_acc", top1.avg, step + len(self.train_queue.dataset) * epoch) if self.rank == 0: logging.info('-------loss gradient--------') logging.info(normal_loss_gradient / count) logging.info(reduce_loss_gradient / count) logging.info('-------total gradient--------') logging.info(normal_total_gradient / count) logging.info(reduce_total_gradient / count) logging.info('correct loss ') logging.info((train_correct_loss / train_correct_count).item()) logging.info('correct entropy ') logging.info((train_correct_entropy / train_correct_count).item()) logging.info('correct cost ') logging.info((train_correct_cost / train_correct_count).item()) logging.info('correct count ') logging.info(train_correct_count) logging.info('wrong loss ') logging.info((train_wrong_loss / train_wrong_count).item()) logging.info('wrong entropy ') logging.info((train_wrong_entropy / train_wrong_count).item()) logging.info('wrong cost ') logging.info((train_wrong_cost / train_wrong_count).item()) logging.info('wrong count ') logging.info(train_wrong_count) logging.info('total loss ') logging.info(((train_correct_loss + train_wrong_loss) / (train_correct_count + train_wrong_count)).item()) logging.info('total entropy ') logging.info(((train_correct_entropy + train_wrong_entropy) / (train_correct_count + train_wrong_count)).item()) logging.info('total cost ') logging.info(((train_correct_cost + train_wrong_cost) / (train_correct_count + train_wrong_count)).item()) logging.info('total count ') logging.info(train_correct_count + train_wrong_count) return top1.avg, loss, error_loss, loss_alpha def infer(self, epoch): objs = utils.AvgrageMeter() top1 = utils.AvgrageMeter() top5 = utils.AvgrageMeter() self.model.eval() with torch.no_grad(): for step, (input, target) in enumerate(self.valid_queue): input = input.to(self.device) target = target.to(self.device) if self.args.snas: logits, logits_aux = self.model(input) loss = self.criterion(logits, target) elif self.args.dsnas: logits, error_loss, loss_alpha = self.model( input, target, self.criterion) loss = error_loss prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) objs.update(loss.item(), input.size(0)) top1.update(prec1.item(), input.size(0)) top5.update(prec5.item(), input.size(0)) if step % self.args.report_freq == 0 and self.rank == 0: logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg) self.logger.add_scalar( "iter_valid_loss", loss, step + len(self.valid_queue.dataset) * epoch) self.logger.add_scalar( "iter_valid_top1_acc", top1.avg, step + len(self.valid_queue.dataset) * epoch) return top1.avg, objs.avg
def train(): use_gpu = cfg.MODEL.DEVICE == "cuda" # 1、make dataloader train_loader, val_loader, test_loader, num_query, num_class = darts_make_data_loader( cfg) # print(num_query, num_class) # 2、make model model = Network(num_class, cfg) # tensor = torch.randn(2, 3, 256, 128) # res = model(tensor) # print(res[0].size()) [2, 751] # 3、make optimizer optimizer = make_optimizer(cfg, model) arch_optimizer = torch.optim.Adam( model._arch_parameters(), lr=cfg.SOLVER.ARCH_LR, betas=(0.5, 0.999), weight_decay=cfg.SOLVER.ARCH_WEIGHT_DECAY) # 4、make lr scheduler lr_scheduler = make_lr_scheduler(cfg, optimizer) # make lr scheduler arch_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR( arch_optimizer, [80, 160], 0.1) # 5、make loss loss_fn = darts_make_loss(cfg) # model._set_loss(loss_fn, compute_loss_acc) # 6、make architect # architect = Architect(model, cfg) # get parameters device = cfg.MODEL.DEVICE use_gpu = device == "cuda" pretrained = cfg.MODEL.PRETRAINED != "" log_period = cfg.OUTPUT.LOG_PERIOD ckpt_period = cfg.OUTPUT.CKPT_PERIOD eval_period = cfg.OUTPUT.EVAL_PERIOD output_dir = cfg.OUTPUT.DIRS ckpt_save_path = output_dir + cfg.OUTPUT.CKPT_DIRS epochs = cfg.SOLVER.MAX_EPOCHS batch_size = cfg.SOLVER.BATCH_SIZE grad_clip = cfg.SOLVER.GRAD_CLIP batch_num = len(train_loader) log_iters = batch_num // log_period if not os.path.exists(ckpt_save_path): os.makedirs(ckpt_save_path) # create *_result.xlsx # save the result for analyze name = (cfg.OUTPUT.LOG_NAME).split(".")[0] + ".xlsx" result_path = cfg.OUTPUT.DIRS + name wb = xl.Workbook() sheet = wb.worksheets[0] titles = [ 'size/M', 'speed/ms', 'final_planes', 'acc', 'mAP', 'r1', 'r5', 'r10', 'loss', 'acc', 'mAP', 'r1', 'r5', 'r10', 'loss', 'acc', 'mAP', 'r1', 'r5', 'r10', 'loss' ] sheet.append(titles) check_epochs = [40, 80, 120, 160, 200, 240, 280, 320, 360, epochs] values = [] logger = logging.getLogger("CSNet_Search.train") size = count_parameters(model) values.append(format(size, '.2f')) values.append(model.final_planes) logger.info("the param number of the model is {:.2f} M".format(size)) logger.info("Starting Search CDNetwork") best_mAP, best_r1 = 0., 0. is_best = False avg_loss, avg_acc = RunningAverageMeter(), RunningAverageMeter() avg_time, global_avg_time = AverageMeter(), AverageMeter() if use_gpu: model = model.to(device) if pretrained: logger.info("load self pretrained chekpoint to init") model.load_pretrained_model(cfg.MODEL.PRETRAINED) else: logger.info("use kaiming init to init the model") model.kaiming_init_() # exit(1) for epoch in range(epochs): model.set_tau(cfg.MODEL.TAU_MAX - (cfg.MODEL.TAU_MAX - cfg.MODEL.TAU_MIN) * epoch / (epochs - 1)) lr_scheduler.step() lr = lr_scheduler.get_lr()[0] # architect lr.step arch_lr_scheduler.step() # if save epoch_num k, then run k+1 epoch next if pretrained and epoch < model.start_epoch: continue # print(epoch) # exit(1) model.train() avg_loss.reset() avg_acc.reset() avg_time.reset() for i, batch in enumerate(train_loader): t0 = time.time() imgs, labels = batch val_imgs, val_labels = next(iter(val_loader)) if use_gpu: imgs = imgs.to(device) labels = labels.to(device) val_imgs = val_imgs.to(device) val_labels = val_labels.to(device) # 1、 update the weights optimizer.zero_grad() res = model(imgs) # loss = loss_fn(scores, feats, labels) loss, acc = compute_loss_acc(res, labels, loss_fn) loss.backward() if grad_clip != 0: nn.utils.clip_grad_norm_(model.parameters(), grad_clip) optimizer.step() # 2、update the alpha arch_optimizer.zero_grad() res = model(val_imgs) val_loss, val_acc = compute_loss_acc(res, val_labels, loss_fn) val_loss.backward() arch_optimizer.step() # compute the acc # acc = (scores.max(1)[1] == labels).float().mean() t1 = time.time() avg_time.update((t1 - t0) / batch_size) avg_loss.update(loss) avg_acc.update(acc) # log info if (i + 1) % log_iters == 0: logger.info( "epoch {}: {}/{} with loss is {:.5f} and acc is {:.3f}". format(epoch + 1, i + 1, batch_num, avg_loss.avg, avg_acc.avg)) logger.info( "end epochs {}/{} with lr: {:.5f} and avg_time is: {:.3f} ms". format(epoch + 1, epochs, lr, avg_time.avg * 1000)) global_avg_time.update(avg_time.avg) # test the model if (epoch + 1) % eval_period == 0 or (epoch + 1) in check_epochs: model.eval() metrics = R1_mAP(num_query, use_gpu=use_gpu) with torch.no_grad(): for vi, batch in enumerate(test_loader): # break # print(len(batch)) imgs, labels, camids = batch if use_gpu: imgs = imgs.to(device) feats = model(imgs) metrics.update((feats, labels, camids)) #compute cmc and mAP cmc, mAP = metrics.compute() logger.info("validation results at epoch {}".format(epoch + 1)) logger.info("mAP:{:2%}".format(mAP)) for r in [1, 5, 10]: logger.info("CMC curve, Rank-{:<3}:{:.2%}".format( r, cmc[r - 1])) # determine whether current model is the best if mAP > best_mAP: is_best = True best_mAP = mAP logger.info("Get a new best mAP") if cmc[0] > best_r1: is_best = True best_r1 = cmc[0] logger.info("Get a new best r1") # add the result to sheet if (epoch + 1) in check_epochs: val = [avg_acc.avg, mAP, cmc[0], cmc[4], cmc[9]] change = [format(v * 100, '.2f') for v in val] change.append(format(avg_loss.avg, '.3f')) values.extend(change) # whether to save the model if (epoch + 1) % ckpt_period == 0 or is_best: torch.save(model.state_dict(), ckpt_save_path + "checkpoint_{}.pth".format(epoch + 1)) model._parse_genotype(file=ckpt_save_path + "genotype_{}.json".format(epoch + 1)) logger.info("checkpoint {} was saved".format(epoch + 1)) if is_best: torch.save(model.state_dict(), ckpt_save_path + "best_ckpt.pth") model._parse_genotype(file=ckpt_save_path + "best_genotype.json") logger.info("best_checkpoint was saved") is_best = False # exit(1) values.insert(1, format(global_avg_time.avg * 1000, '.2f')) sheet.append(values) wb.save(result_path) logger.info("Ending Search GDAS_Search")