pytorch-tutorial | PyTorch Tutorial for Deep Learning Researchers | Learning library
kandi X-RAY | pytorch-tutorial Summary
kandi X-RAY | pytorch-tutorial Summary
This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.
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Top functions reviewed by kandi - BETA
- Performs forward transformation
- Encodes the given x into a binary quadrature
- Reparameterize the model
- Decodes the input into a SSigmoid
- Resizes images in given directory
- Resize an image
- Create a new layer
- 3x3x3 Conv2d Conv2d
- Builds a vocabulary
- Adds a word to the vocabulary
- Decodes the input into a sigmoid
- Resets gradients
- Normalize x
- Updates the learning rate of the optimizer
- Adds a scalar summary
- Write a histogram summary
- Sample the model
- Load an image
- Return data loader
- Read data from file
- Writes image summary
- Detach from a list of states
pytorch-tutorial Key Features
pytorch-tutorial Examples and Code Snippets
Lines of Code : 111License : Non-SPDX (NOASSERTION)
Copyfrom torcheeg.datasets import DEAPDataset
from torcheeg.datasets.constants.emotion_recognition.deap import DEAP_CHANNEL_LOCATION_DICT
dataset = DEAPDataset(io_path=f'./tmp_out/deap',
root_path='./tmp_in/data_preprocessed_python from onnx_pytorch import code_gen
code_gen.gen("resnet18-v2-7.onnx", "./")
import numpy as np
import onnx
import onnxruntime
import torch
torch.set_printoptions(8)
from model import Model
model = Model()
model.eval()
inp = np.random.randn(1, 3, 22 import torch
import torchvision
import torch.onnx
# An instance of your model
model = torchvision.models.resnet18()
# An example input you would normally provide to your model's forward() method
x = torch.rand(1, 3, 224, 224)
# Export the model
to Community Discussions
Trending Discussions on pytorch-tutorial
QUESTION
I'm using a pre-trained image captioning model from this Repository, but I'm getting this error although I changed the type to long !!
Error :
File "caption.py", line 213, in seq, alphas = caption_image_beam_search(encoder, decoder, args.img, word_map, args.beam_size) File "caption.py", line 111, in caption_image_beam_search seqs = torch.cat([seqs[prev_word_inds].long(), next_word_inds.unsqueeze(1)], dim=1) # (s, step+1) IndexError: tensors used as indices must be long, byte or bool tensors
Code :
...ANSWER
Answered 2022-Feb-19 at 14:46You have cast the wrong part to long:
QUESTION
I'm trying to implement a neural network to generate sentences (image captions), and I'm using Pytorch's LSTM (nn.LSTM) for that.
The input I want to feed in the training is from size batch_size * seq_size * embedding_size, such that seq_size is the maximal size of a sentence. For example - 64*30*512.
After the LSTM there is one FC layer (nn.Linear).
As far as I understand, this type of networks work with hidden state (h,c in this case), and predict the next word each time.
My question is- in the training - do we have to manually feed the sentence word by word to the LSTM in the forward function, or the LSTM knows how to do it itself?
My forward function looks like this:
...ANSWER
Answered 2022-Jan-02 at 19:24The answer is, LSTM knows how to do it on its own. You do not have to manually feed each word one by one.
An intuitive way to understand is that the shape of the batch that you send, contains seq_length (batch.shape[1]), using which it decides the number of words in the sentence. The words are passed through LSTM Cell generating the hidden states and C.
QUESTION
Trying to figure out the issue with my cmd as it is getting stucked.
As I tried to run below commands to get the virtual env enabled..
...ANSWER
Answered 2021-Dec-23 at 04:50QUESTION
A model should be set in the evaluation mode for inference by calling model.eval().
Do we need to also do this during training before getting the model outputs? Like within a training epoch if the network contains one or more dropout and/or batch-normalization layers.
If this is not done then the output of the forward pass in the training epoch might be affected by the randomness in the dropout?
Many example codes do not do this and something along these lines is the common approach:
...ANSWER
Answered 2020-Jul-30 at 10:36TLDR:
Should this instead be?
No!
Why?
More explanation:
Different Modules behave differently depending on whether they are in training or evaluation/test mode.
BatchNorm and Dropout are only two examples of such modules, basically any module that has a training phase follows this rule.
When you do .eval(), you are signaling all modules in the model to shift operations accordingly.
Update
The answer is during training you should not use eval mode and yes, as long as you have not set the eval mode, the dropout will be active and act randomly in each forward passes. Similarly all other modules that have two phases, will perform accordingly. That is BN will always update the mean/var for each pass, and also if you use batch_size of 1, it will error out as it can not do BN with batch of 1
As it was pointed out in comments, it should be noted that during training, you should not do eval() before the forward pass, as it effectively disables all modules that has different phases for train/test mode such as BN and Dropout (basically any module that has updateable/learnable parameters, or impacts network topology like dropout) will be disabled and you will not see them contributing to your network learning. So don't code like that!
Let me explain a bit what happens during training:
When you are in training mode, all of your modules that make up your model may have two modes, training and test mode. These modules either have learnable parameters that need to be updated during training, like BN, or affect network topology in a sense like Dropout (by disabling some features during forward pass). some modules such as ReLU() only operate in one mode and thus do not have any change when modes change.
When you are in training mode, you feed an image, it passes trough layers until it faces a dropout and here, some features are disabled, thus theri responses to the next layer is omitted, the output goes to other layers until it reaches the end of the network and you get a prediction.
the network may have correct or wrong predictions, which will accordingly update the weights. if the answer was right, the features/combinations of features that resulted in the correct answer will be positively affected and vice versa. So during training you do not need and should not disable dropout, as it affects the output and should be affecting it so that the model learns a better set of features.
I hope this makes it a bit more clear for you. if you still feel you need more, say so in the comments.
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