num_epochs: 10 checkpoint_interval: 5 validation_interval: 5 batch_size: 4 seed: 1234 num_gpus: 1 gpu_ids: [0] use_amp: True optim_momentum: 0.9 lr: 0.0000015 min_lr_rate: 0.2 wd: 0.0005 warmup_epochs: 1 crf_kernel_size: 3 crf_num_iter: 100 loss_mil_weight: 4 loss_crf_weigh...
patch_size=14, query_nums=64, batch_vision=False, max_length=2048, ) -> Dict: """Make dataset and collator for supervised fine-tuning.""" dataset_cls = SupervisedDataset rank0_print("Loading data...") train_json = json.load(open(data_args.data_path, "r")) train_dataset = dat...
uc = model.get_learned_conditioning(batch_size * [""]) samples_ddim, _ = sampler.sample(conditioning=c, unconditional_conditioning=uc, [...]) ``` 这将启动重复的采样器: - 去噪图片引导它看起来更像你的提示(调节) - 去噪图片引导它看起来更像一个空提示(unconditional_conditioning) - 查看它们...
val_img_dir: '/datasets/coco/raw-data/val2017' load_mask: True crop_size: 512 inference: ann_path: '/dataset/sample.json' img_dir: '/dataset/sample_dir' label_dump_path: '/dataset/sample_output.json' model: arch: 'vit-mae-base/16' train: num_epochs: 10 batch_size: 4 use_amp...
x = layers.Conv2D(32, kernel_size=3, strides= 2, padding='same', name='conv_1')(inputs) x = layers.BatchNormalization(name='bn_1')(x) x = layers.LeakyReLU(name='lrelu_1')(x) # Block-2 x = layers.Conv2D(64, kernel_size=3, strides= 2, padding='same', name='conv_2'...
x = layers.Conv2D(32, kernel_size=3, strides= 2, padding='same', name='conv_1')(inputs) x = layers.BatchNormalization(name='bn_1')(x) x = layers.LeakyReLU(name='lrelu_1')(x) # Block-2 x = layers.Conv2D(64, kernel_size=3, strides= 2, padding='same', name='conv_2'...
"layer2": {"_type": "int_quniform", "_value": [128, 384, 64],"_default": 256}, "use_bn": {"_type": "choice", "_value": [true, false],"_default": false}, "lr": 0.01, "max_epoch": 64, "max_epoch": 128, "random_state": 42, "batch_size": 1024, "optimizer": ...
{"lr":3e-5,"betas": [0.9,0.999],"eps":1e-8,"weight_decay":3e-7} },"scheduler": {"type":"WarmupLR","params": {"warmup_min_lr":0,"warmup_max_lr":3e-5,"warmup_num_steps":500} },"train_batch_size":24,"fp16": {"enabled":true,"loss_scale":0,"initial_scale_power...
batch_size: 64 num_workers: 8 shuffle: False pin_memory: True persistent_workers: False optimizer: _target_: torch.optim.AdamW lr: 1.0e-4 betas: [0.95, 0.999] eps: 1.0e-8 weight_decay: 1.0e-6 training: device: "cuda:0" seed: 42 debug: False resume: False # optimization lr_schedu...
Therefore, large-scale clusters are also possible to be novelty. • Our extensive experiment shows that DAE-DBC has a greater performance than other state-of-the-art unsupervised anomaly detection methods. This article consists of 5 sections. In Section 2, we provide a detailed survey of ...