def _main(): base_dir = os.path.dirname(__file__) request = { "dataset": "imagenet", "model_name": "resnet_50", "data_dir": os.path.join(base_dir, "./data/imagenet"), "batch_size": 256, "batch_size_val": 100, "learning_rate": 0.1, "epochs": 120, "checkpoint_path": os.path.join(base_dir, "./models_ckpt/resnet_50_imagenet_pruned"), "checkpoint_save_period": 5, # save a checkpoint every 5 epoch "checkpoint_eval_path": os.path.join(base_dir, "./models_eval_ckpt/resnet_50_imagenet_pruned"), "scheduler": "uniform_auto", "scheduler_file_name": "resnet_50_imagenet_bn.yaml" } prune_model(request)
def _main(): base_dir = os.path.dirname(__file__) request = { "dataset": "cifar10", "model_name": "vgg_m_16", "data_dir": os.path.join(base_dir, "./data/cifar10"), "batch_size": 128, "batch_size_val": 100, "learning_rate": 0.1, "epochs": 160, "checkpoint_path": os.path.join(base_dir, "./models_ckpt/vgg_m_16_cifar10"), "checkpoint_save_period": 20, # save a checkpoint every epoch "checkpoint_eval_path": os.path.join(base_dir, "./models_eval_ckpt/vgg_m_16_cifar10"), "scheduler": "train" } prune_model(request)
def _main(): base_dir = os.path.dirname(__file__) request = { "dataset": "imagenet", "model_name": "mobilenet_v2", "data_dir": os.path.join(base_dir, "./data/imagenet"), "batch_size": 256, "batch_size_val": 100, "learning_rate": 0.05, "epochs": 240, "checkpoint_path": os.path.join(base_dir, "./models_ckpt/mobilenet_v2_imagenet"), "checkpoint_save_period": 5, # save a checkpoint every 5 epoch "checkpoint_eval_path": os.path.join(base_dir, "./models_eval_ckpt/mobilenet_v2_imagenet"), "scheduler": "train" } prune_model(request)
def _main(): base_dir = os.path.dirname(__file__) request = { "dataset": "imagenet", "model_name": "resnet_50", "data_dir": os.path.join(base_dir, "/data/imagenet/tfrecord-dataset"), "batch_size": 256, "batch_size_val": 100, "learning_rate": 0.1, "epochs": 360, "checkpoint_path": os.path.join(base_dir, "./models_ckpt/resnet_50_imagenet_pruned"), "checkpoint_save_period": 5, # save a checkpoint every 5 epoch "checkpoint_eval_path": os.path.join(base_dir, "./models_eval_ckpt/resnet_50_imagenet_pruned"), "scheduler": "uniform_auto", "is_distill": True, "scheduler_file_name": "resnet_50_imagenet_0.5_distill.yaml" } os.environ['L2_WEIGHT_DECAY'] = "5e-5" prune_model(request)
def _main(): base_dir = os.path.dirname(__file__) request = { "dataset": "mnist", "model_name": "lenet", "data_dir": os.path.join(base_dir, "./data/mnist"), "batch_size": 120, "batch_size_val": 100, "learning_rate": 0.001, "epochs": 12, "checkpoint_path": os.path.join(base_dir, "./models_ckpt/lenet_mnist_pruned"), "checkpoint_save_period": 1, # save a checkpoint every n epoch "checkpoint_eval_path": os.path.join(base_dir, "./models_eval_ckpt/lenet_mnist_pruned"), "scheduler": "uniform_auto" } prune_model(request)