DataLoader(dataset=test_data, batch_size=64, shuffle=True) cnn = torch.load("model/mnist_model.pkl") cnn = cnn.cuda() # loss # eval/test loss_test = 0 accuracy = 0 import cv2 # pip install opencv-python -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun....
train_dataset, eval_dataset = train_test_split(data, test_size=0.2, random_state=1024, stratify=data['isChurn']) print(train_dataset) train_dataset_distribution = train_dataset['isChurn'].value_counts() print("train_dataset isChurn Distribution:\n", train_dataset_distribution) print(eval_da...
eval_dataset = create_dataset(os.path.join(_mnist_path, "test"), 32) # save the network model and parameters for subsequence fine-tuning config_ck = CheckpointConfig(save_checkpoint_steps=375, keep_checkpoint_max=16) # group layers into an object with training and evaluation features ckpoint...
def train_and_eval(): """Train and evaluate the model.""" df_train = pd.read_csv( tf.gfile.Open("./train.csv"), skipinitialspace=True) df_test = pd.read_csv( tf.gfile.Open("./test.csv"), skipinitialspace=True) model_dir = "./models" print("model directory = %s" % model...
Hi, thank you very much for open source. I want to use my own Image and caption, and QA data to fine-tune the BLIP2 data. Should my process be to prepare the same data set for okvaq, and then run the /run_scripts/blip2/eval/eval_okvqa_ze...
if step != (train_ds_len // batch_size - 1) and not as_test: session.report( { "epoch": epoch, "step": step, "train_loss": loss.item(), "eval_loss": None, "perplexity": None, "number of iterations": step + 1, "Train time per epoch": None, ...
pipeline=test_pipeline), sampler=dict(type='DefaultSampler', shuffle=False), ) val_evaluator = dict(type='Accuracy', topk=(1, 2)) # 注意这里类别数较少时需要调整。 # If you want standard test, please manually configure the test dataset ...
data.test_transforms, is_training=False, augment=False, rect=args.rect, single_cls=args.single_cls, batch_size=args.per_batch_size, stride=max(args.network.stride), ) eval_dataloader = create_loader( dataset=eval_dataset, batch_collate_fn=eval_dataset.test_collate_fn, ...
train_loader = torch.utils.data.DataLoader(dataset=custom_dataset, batch_size=64, shuffle=True) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 这是一个标准的模板,我们就使用这个模板,来加载数据,定义标签,以及进...
test_data Yes The OSS path of the evaluation dataset. Example: oss://path/to/test_*.tfrecord N/A train_batch_size Yes The number of samples that are used to train the model per iteration. Type: INT. Example: 32 N/A test_batch_size ...