def main(): # load pre-trained model K.set_image_data_format('channels_first') model = load_model("nchw_model.h5") model.summary() # load mnist dataset x_test, y_test = data.get_test_dataset() img_h = x_test.shape[2] img_w = x_test.shape[3] helper.print_ascii(x_test[0], img_h, img_w) # use Keras to infer t0 = time.time() y_keras = model.predict(x_test) t1 = time.time() print('Keras time', t1 - t0) data.verify(y_keras, y_test) # use TensorRT to infer engine = TrtEngine(model, 1000) t0 = time.time() y_trt = engine.infer(x_test) t1 = time.time() print('TensorRT time', t1 - t0) data.verify(y_trt, y_test) engine.save('nchw_engine.bin')
def main(): # load pre-trained model model = load_model("nhwc_model.h5") model.summary() # load mnist dataset x_test, y_test = data.get_test_dataset() batch_size = 1000 img_h = x_test.shape[1] img_w = x_test.shape[2] helper.print_ascii(x_test[0], img_h, img_w) # use Keras to do infer t0 = time.time() y_keras = model.predict(x_test) t1 = time.time() print('Keras time', t1 - t0) data.verify(y_keras, y_test) frozen_graph = FrozenGraph(model, (img_h, img_w, 1)) tf_engine = TfEngine(frozen_graph) t0 = time.time() y_tf = tf_engine.infer(x_test) t1 = time.time() print('Tensorflow time', t1 - t0) verify(y_tf, y_keras) tftrt_engine = TftrtEngine(frozen_graph, batch_size, 'FP32') t0 = time.time() y_tftrt = tftrt_engine.infer(x_test) t1 = time.time() print('TFTRT time', t1 - t0) verify(y_tftrt, y_keras) tftrt_engine = TftrtEngine(frozen_graph, batch_size, 'FP16') t0 = time.time() y_tftrt = tftrt_engine.infer(x_test) t1 = time.time() print('TFTRT_FP16 time', t1 - t0) verify(y_tftrt, y_keras)
def main_worker(gpus, args): # 定义模型,损失函数,优化器 model = resnet18() torch.cuda.set_device('cuda:{}'.format(gpus[0])) model.cuda() criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=0.9, weight_decay=1e-4) # apex initialization model, optimizer = amp.initialize(model, optimizer) # 如果使用的GPU数量大于1,需要用nn.DataParallel来修饰模型 if len(gpus) > 1: model = nn.DataParallel(model, device_ids=gpus, output_device=gpus[0]) # Define Training Schedule and Dataloader train_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[60, 120, 160], gamma=0.2) train_dataset = get_train_dataset() train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, num_workers=4, pin_memory=True) test_dataset = get_test_dataset() test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, num_workers=4, pin_memory=True) # Training for epoch in range(args.epochs): start = time.time() model.train() # 设置 train_scheduler 来调整学习率 train_scheduler.step(epoch) for step, (images, labels) in enumerate(train_loader): # 将对应进程的数据放到 GPU 上 images = images.cuda(non_blocking=True) labels = labels.cuda(non_blocking=True) outputs = model(images) loss = criterion(outputs, labels) # 更新优化模型权重 optimizer.zero_grad() # loss.backward() with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() optimizer.step() print( 'Training Epoch: {epoch} [{trained_samples}/{total_samples}]\tLoss: {:0.4f}\tLR: {:0.6f}' .format(loss, optimizer.param_groups[0]['lr'], epoch=epoch + 1, trained_samples=step * args.batch_size + len(images), total_samples=len(train_loader.dataset))) finish = time.time() print('epoch {} training time consumed: {:.2f}s'.format( epoch, finish - start)) # validate after every epoch validate(test_loader, model, criterion)
def main_worker(local_rank, nprocs, args): args.local_rank = local_rank init_seeds(local_rank+1) # 获得init_method的通信端口 init_method = 'tcp://' + args.ip + ':' + args.port # 1. 分布式初始化,对于每一个进程都需要进行初始化,所以定义在 main_worker中 cudnn.benchmark = True dist.init_process_group(backend='nccl', init_method=init_method, world_size=args.nprocs, rank=local_rank) # 2. 基本定义,模型-损失函数-优化器 model = resnet18() torch.cuda.set_device(local_rank) model.cuda(local_rank) criterion = nn.CrossEntropyLoss().cuda(local_rank) optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=0.9, weight_decay=1e-4) train_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[60, 120, 160], gamma=0.2) # apex初始化 model = apex.parallel.convert_syncbn_model(model).to(local_rank) # 使用 apex 提供的 SyncBatchNorm 操作 model, optimizer = amp.initialize(model, optimizer) model = DDP(model) # 3. 加载数据, batch_size = int(args.batch_size / nprocs) train_dataset = get_train_dataset() train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=4, pin_memory=True, sampler=train_sampler) test_dataset = get_test_dataset() test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, num_workers=4, pin_memory=True, sampler=test_sampler) for epoch in range(args.epochs): start = time.time() model.train() train_sampler.set_epoch(epoch) train_scheduler.step(epoch) for step, (images, labels) in enumerate(train_loader): # 将对应进程的数据放到对应 GPU 上 images = images.cuda(local_rank, non_blocking=True) labels = labels.cuda(local_rank, non_blocking=True) outputs = model(images) loss = criterion(outputs, labels) torch.distributed.barrier() reduced_loss = reduce_mean(loss, args.nprocs) # 更新优化模型权重, 用scale_loss修饰loss optimizer.zero_grad() with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() optimizer.step() if args.local_rank == 0: print( 'Training Epoch: {epoch} [{trained_samples}/{total_samples}]\tLoss: {:0.4f}\tLR: {:0.6f}'.format( reduced_loss, optimizer.param_groups[0]['lr'], epoch=epoch+1, trained_samples=step * args.batch_size + len(images), total_samples=len(train_loader.dataset) )) finish = time.time() if args.local_rank == 0: print('epoch {} training time consumed: {:.2f}s'.format(epoch, finish - start)) # validate after every epoch validate(test_loader, model, criterion, local_rank, args)
def main_worker(local_rank, nprocs, args): args.local_rank = local_rank init_seeds(local_rank + 1) # set different seed for each worker # 获得init_method的通信端口 init_method = 'tcp://' + args.ip + ':' + args.port # 1. 分布式初始化,对于每一个进程都需要进行初始化,所以定义在 main_worker中 cudnn.benchmark = True dist.init_process_group(backend='nccl', init_method=init_method, world_size=args.nprocs, rank=local_rank) # 2. 基本定义,模型-损失函数-优化器 model = resnet18( ) # 定义模型,将对应进程放到对应的GPU上, .cuda(local_rank) / .set_device(local_rank) # 以下是需要加 local_rank 的部分:模型 # ================================ torch.cuda.set_device(local_rank) # 使用 set_device 和 cuda 来指定需要的 GPU model.cuda(local_rank) model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(local_rank) model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[local_rank]) # 将模型用 DistributedDataParallel 包裹 # ================================= criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=0.9, weight_decay=1e-4) train_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[60, 120, 160], gamma=0.2) # 3. 加载数据, batch_size = int(args.batch_size / nprocs) # 需要手动划分 batch_size 为 mini-batch_size train_dataset = get_train_dataset() train_sampler = torch.utils.data.distributed.DistributedSampler( train_dataset) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, num_workers=4, pin_memory=True, sampler=train_sampler) test_dataset = get_test_dataset() test_sampler = torch.utils.data.distributed.DistributedSampler( test_dataset) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, num_workers=4, pin_memory=True, sampler=test_sampler) for epoch in range(args.epochs): start = time.time() model.train() # 需要设置sampler的epoch为当前epoch来保证dataloader的shuffle的有效性 train_sampler.set_epoch(epoch) # 设置 train_scheduler 来调整学习率 train_scheduler.step(epoch) for step, (images, labels) in enumerate(train_loader): # 将对应进程的数据放到 GPU 上 images = images.cuda(non_blocking=True) labels = labels.cuda(non_blocking=True) outputs = model(images) loss = criterion(outputs, labels) # torch.distributed.barrier()的作用是,阻塞进程,保证每个进程运行完这一行代码之前的所有代码,才能继续执行,这样才计算平均loss和平均acc的时候不会出现因为进程执行速度不一致的错误 torch.distributed.barrier() reduced_loss = reduce_mean(loss, args.nprocs) # 更新优化模型权重 optimizer.zero_grad() loss.backward() optimizer.step() if args.local_rank == 0: print( 'Training Epoch: {epoch} [{trained_samples}/{total_samples}]\tLoss: {:0.4f}\tLR: {:0.6f}' .format(reduced_loss, optimizer.param_groups[0]['lr'], epoch=epoch + 1, trained_samples=step * args.batch_size + len(images), total_samples=len(train_loader.dataset))) finish = time.time() if args.local_rank == 0: print('epoch {} training time consumed: {:.2f}s'.format( epoch, finish - start)) # validate after every epoch validate(test_loader, model, criterion, local_rank, args)