orobix / prototypical-networks-for-few-shot-learning-pytorch Goto Github PK

I have modified the codes a bit to run on mini_imagenet dataset from the link:
https://github.com/renmengye/few-shot-ssl-public
However, it is giving 0 error and 100% accuracy, which is strange for me.
Can you please help?

Thank you for your work!

I've trained a model for a few epochs, and now I'd like to make predictions with it. I load it:

model = ProtoNet().cuda()
model.load_state_dict(torch.load('./output/best_model.pth'))

I load 15 labeled data points, for a total of 3 labels:

# x.size() -> (15)
# y.size() -> (15, 64, 64)
x, y = load_data()

I load a single datapoint I want to predict

to_predict = torch.Tensor(1, 64, 64)

I now would like to few-shot train on 5 examples per class and then predict a class for my to_predict. How do I go about that?

Hi
I have a question about image loading in omniglot_dataset.py

Why use 1 - array & why use transpose and reshape as below?

https://github.com/orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch/blob/master/src/omniglot_dataset.py#L179-L180

Thanks

Hi,
Any Idea what parameters where used to produce the t-SNE plot i.e. Figure 3 in the Appendix of https://arxiv.org/pdf/1703.05175.pdf

How to train and test with own dataset?

In the original paper in "Algorithm 1", they mention that each batch is sampled "without replacement":

... RANDOMSAMPLE(S, N) denotes a set of N elements chosen uniformly at
random from set S, without replacement.

where as your sampler class clearly samples with replacement as you even pass the number of iterations as an argument to the class constructor. may i ask why?

paths, self.y = zip(*[self.get_path_label(pl) for pl in range(len(self))])

what is len(self)?

Running python train.py --cuda -nsTr 1 -nsVa 1 gives me a runtime error in euclidean_dist ("dimension out of range (expected to be in range of [-1, 0], but got 1)")

It seems the code is trying to compute a distance between query_samples (a matrix size 360x64) and prototypes (a vector length 60)- so yeah, I can see why it's producing this error. But why are query_samples and prototypes these sizes, to begin with? Shouldn't they have the same value in size(1)?

Hi guys! I am at this point and I do not just understand it, I already have the trained model. For the test, what I understand that should be done is to calculate the embedding of my sample and see which centroid is closest to classify ... If I have trained with 5000 classes I do not understand at all as in the test phase it is necessary to pass a set of support and another of query.

Taking the implementation of @ale316 , predict (support_x, support_y, query_x, query_y = None), we will pass support sets of the training set and querys of which we do not know its y (therefore we equate it to None) and ... as takes into account the 5000 classes if the support only includes 30 (to say some number)

My idea is to create all the training embeddings and then generate the centroids -> After that generate the embeddings for the test samples and get the class with the distance euclidean to the centroids... I am correct? But you dont do that,

Sorry for the spam here, but for posterity:

I wrote a function that should return predictions given:

• a tensor support_x of size (n_support, 1024)
• a tensor support_y of size (n_support,)
• a tensor query_x of size (n_query, 1024)

def predict(support_x, support_y, query_x, query_y=None):
    support_x = support_x.to('cpu')
    support_y = support_y.to('cpu')
    query_x = query_x.to('cpu')

    classes = torch.unique(support_y)
    n_classes = len(classes)
    n_query = len(query_x)

    # get a list of tensors of support_y for each class
    support_idxs = list(map(lambda c: support_y.eq(c).nonzero().squeeze(1), classes))

    # take the mean of tensors for each class to create a centroid
    prototypes = torch.stack([support_x[idx_list].mean(0) for idx_list in support_idxs])

    # finds the euclidean distances between each query_x and each centroid
    dists = euclidean_dist(query_x, prototypes)

    # run it through softmax
    log_p_y = F.log_softmax(-dists, dim=1)

    # lists the idx (label) of the closest centroid for each query_x
    _, y_hat = log_p_y.max(1)
    labels = [classes[i] for i in y_hat.squeeze()]

    return labels

Now, I still have some doubts:

• Is this correct? Edit: yes it is
• Why do we even softmax the distances, instead of just taking the min?

Originally posted by @MarioProjects in #8 (comment)

Do you mind share your code about miniimagenet dataloader and train? It will be appreciated.

I run your code with default settings, but get very pool results of accurcy (about 53%). What could be the reason for this?

可以尝试从此处获取txt文档，至于omniglot，网上很多，自己找一找吧
链接：https://pan.baidu.com/s/107kT-8A7jqzHnzIJD4Fm3A
提取码：iku1

Dear author

Thank you for your carefully written code.
I re-use your some codes, and I found out the error

please check the line 84 in prototypical_loss.py
I think y_hat should be sequeezed with squeeze()

y_hat and target_inds.squeeze() look like:

y_hat = torch.tensor([[0],[1],[2],[0],[4]])
target_inds.squeeze() = torch.tensor([0, 1, 2, 3, 4])

In this case,

y_hat.eq(target_inds.squeeze()).float()

tensor([[1., 0., 0., 0., 0.],
        [0., 1., 0., 0., 0.],
        [0., 0., 1., 0., 0.],
        [1., 0., 0., 0., 0.],
        [0., 0., 0., 0., 1.]])

In this case, accuracy is 0.2

It should be tensor([1., 1., 1., 0., 1.]).
In this case, accuracy is 0.8

When i run the programe: how can i solve it?

opt = get_parser().parse_args()
mode = "train"
OmniglotDataset(mode=mode, root=opt.dataset_root)

== Downloading https://raw.githubusercontent.com/jakesnell/prototypical-networks/master/data/omniglot/splits/vinyals/test.txt
Traceback (most recent call last):

File "", line 1, in
dataset = OmniglotDataset(mode=mode, root=opt.dataset_root)

File "C:\Users\lenovo\Desktop\Prototypical-Networks-for-Few-shot-Learning-PyTorch-master\src\omniglot_dataset.py", line 50, in init
self.download()

File "C:\Users\lenovo\Desktop\Prototypical-Networks-for-Few-shot-Learning-PyTorch-master\src\omniglot_dataset.py", line 113, in download
with open(file_path, 'wb') as f:

OSError: [Errno 22] Invalid argument: '..\dataset\splits\vinyals\https://raw.githubusercontent.com/jakesnell/prototypical-networks/master/data/omniglot/splits/vinyals/test.txt'

Dear author(s),

Can this algorithm be used in another context different than image classification?

Thank you in advance for your help,

Best regards,

WIP

Thanks for share of code, but I think the preprocessing code is too complex to understand, which could write much more simple.

During the learning stage, the loss isn't backpropagated to the model and I am obtaining the same accuracy and loss even after training for a huge number of epochs.

Thank you very much for sharing your code.

I want to train models with a dataset other than miniImageNet and Omniglot. Could you guide me how to arrange dataset (main folder , sub-folder), and how to train models on a new dataset?

Thanks a lot.

Hi.

Thanks for the repo. I have a couple of queries in the load_img function.

1. Is it necessary to do x = 1.0 - torch.from_numpy(x) ? I understand this is to have 1s in the region of interest (the character) but does it really help?

2. Why do you take the transpose (rotates the image again) at the end?

Thanks!

When testing, the network calculates loss using the number of supports specified for the training regime. This appears to be an error, but perhaps I've missed something?

_, acc = loss_fn(model_output, target=y, n_support=opt.num_support_tr)

To me, this line should be

_, acc = loss_fn(model_output, target=y, n_support=opt.num_support_val)

I use your 'python train.py --cuda' to evaluate your code.
But I can only get 65% test acc.
Can you tall me what happened?
Thank you very much

FileNotFoundError Traceback (most recent call last)
in
252
253 if name == 'main':
--> 254 main()

in main()
208 init_seed(options)
209
--> 210 tr_dataloader = init_dataloader(options, 'train')
211 val_dataloader = init_dataloader(options, 'val')
212 # trainval_dataloader = init_dataloader(options, 'trainval')

in init_dataloader(opt, mode)
47
48 def init_dataloader(opt, mode):
---> 49 dataset = init_dataset(opt, mode)
50 sampler = init_sampler(opt, dataset.y, mode)
51 dataloader = torch.utils.data.DataLoader(dataset, batch_sampler=sampler)

in init_dataset(opt, mode)
23
24 def init_dataset(opt, mode):
---> 25 dataset = OmniglotDataset(mode=mode, root=opt.dataset_root)
26 n_classes = len(np.unique(dataset.y))
27 if n_classes < opt.classes_per_it_tr or n_classes < opt.classes_per_it_val:

E:\学习\jupyter\prototypical net\Prototypical-Networks-for-Few-shot-Learning-PyTorch-master\src\omniglot_dataset.py in init(self, mode, root, transform, target_transform, download)
53 raise RuntimeError(
54 'Dataset not found. You can use download=True to download it')
---> 55 self.classes = get_current_classes(os.path.join(
56 self.root, self.splits_folder, mode + '.txt'))
57 self.all_items = find_items(os.path.join(

E:\学习\jupyter\prototypical net\Prototypical-Networks-for-Few-shot-Learning-PyTorch-master\src\omniglot_dataset.py in get_current_classes(fname)
159
160 def get_current_classes(fname):
--> 161 with open(fname) as f:
162 classes = f.read().replace('/', os.sep).splitlines()
163 return classes

FileNotFoundError: [Errno 2] No such file or directory: '..\dataset\splits\vinyals\train.txt'

Hello,

I have been trying to implement prototypical networks on a different dataset (audio), and I have been having some difficulty with training, my loss seems to be reducing very slowly.

I had a question about your batching function. If we assume there are 32 query points in a batch, and 3-5 support points per class.

Is each query point getting a label (1-32) and is this arbitrary? Or are datapoints given labels based on the whole training set?
Put in another way, Does a given query point get the same label (necessarily) in different mini-batches.
If you could give me an example that would be super helpful.

Thanks,
Gautam

Hello, thank you for the code provided.
When I set num_query_val to 1, why val_acc and test_acc will not change, and it is always about num_query_val / classes_per_it_val, can you tell me what went wrong?
Thank you! Looking forward to your reply.