I am trying to loop over a set of graphs as shown in the snippet from the main script below: import pickle for timeOfDay in range(1440): # total number of minutes in a day=1440 with open("G_" + timeOfDay +’.pickle’, ‘rb’) as handle: G = pickle.load(handle) ## do something with G I could load all the ..
I’m quite new to programming and have now clue where my error comes from. I got the following code to set up my dataset for training my classifier: class cows_train(Dataset): def __init__(self, folder_path): self.image_list = glob.glob(folder_path+’/content/cows/train’) self.data_len = len(self.image_list) def __getitem__(self, index): single_image_path = self.image_list[index] im_as_im = Image.open(single_image_path) im_as_np = np.asarray(im_as_im)/255 im_as_np = np.expand_dims(im_as_np, 0) ..
I have a dataframe with some (35 columns) Variables and millions (rows) of timesteps. I would like to cut the data with the timeseriesgenerator (https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence/TimeseriesGenerator). I have done it before for some neural nets. I would like to stick to the generator, because I need to skip some samples, but how many isn’t clear at ..
The following is part of the code, epoch=300, each npz file is 2.73M, but the batch size of my dataloader gives 64, a total of 8 gpuss, so a mini batch should be 64×8×2.73M≈1.1G, my actual memory is 128G. Even if it becomes larger after decompression, it will not reach the size of 128G. The ..
The code below is part of the data set code I defined, which neglect the getitem(self, index) part. But when I train the model with the dataset, because the dataset is too large, cpu memory can not support the dataset. So I wondering how to modify my datset code! class VimeoDataset(Dataset): def __init__(self, dataset_name, batch_size=32): ..
I’m trying to think of a fast and efficient way to convert a Pytorch dataset into a sampler that samples at least one of each class. So far I’ve iterated through the Pytorch dataset sequentially and creates a 2D tensor for each class then returns random idxes of each tensor, but that’s pretty slow and ..
I’m doing: train_set = AudioLoader(time_mask_max=TIME_MASK_MAX, sequence_length=args.sequence_length) print(train_set.len) train_loader = torch.utils.data.DataLoader(train_set, shuffle=True, num_workers=1, batch_size=args.batch_size, collate_fn=collate_batch, **params) Where: def collate_batch(batch): ”’ Padds batch of variable length note: it converts things ToTensor manually here since the ToTensor transform assume it takes in images rather than arbitrary tensors. ”’ # get sequence lengths print(‘HHH’) lengths = torch.tensor([t.shape for t ..
I am writting a customed dataloader, while the returned value makes me confused. import torch import torch.nn as nn import numpy as np import torch.utils.data as data_utils class TestDataset: def __init__(self): self.db = np.random.randn(20, 3, 60, 60) def __getitem__(self, idx): img = self.db[idx] return img, img.shape[1:] def __len__(self): return self.db.shape if __name__ == ‘__main__’: test_dataset ..
So my issue is that when not using DataLoader, just creating 1000 epochs and doing the learning, the results are ok, and the losses drop to ~0.2. However, when trying to use DataLoader, the output is: |batch|index|loss| 8 0 0.6232748031616211 .. 8 23 0.6030591726303101 9 0 0.5626393556594849 9 1 0.6434788703918457 .. 9 20 0.6232720017433167 I ..
I have a test data same as the Figure here [![enter image description here]]. When I want to use the DataLoader in the torch, it gives me this error. I know that why this happens, but I want to use the data as input for a system that only has one input. Could you help ..