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Introduction

Imports

import torch
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader

import torchvision
from torchvision import transforms
from torchvision.datasets import ImageFolder
import timm

import matplotlib.pyplot as plt ## For data viz
import pandas as pd
import numpy as np

import sys
from tqdm.notebook import tqdm

print('System Version:', sys.version)
print('PyTorch version', torch.__version__)
print('Torchvision version', torchvision.__version__)
print('Numpy version', np.__version__)
print('Pandas version', pd.__version__)

Init

Device

device = (
  "mps" if torch.backends.mps.is_available() and torch.backends.mps.is_built()
  else
  "cuda" if torch.cuda().is_available()
  else
  "cpu"
)

Seed

Disable these after debugging 

seed = 42

random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)

torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False # hit on performance

Tensors

torch.mean(image_data, axis=0) ## column-wise mean

luminance_approx = torch.mean(image_array, axis=-1) ## color_channel-wise mean

values, indices = torch.max(data, axis=-1)
  • int8 is an integer type, it can be used for any operation which needs integers
  • qint8 is a quantized tensor type which represents a compressed floating point tensor, it has an underlying int8 data layer, a scale, a zero_point and a qscheme

Creating Tensors

# ❌
tensor.tensor([2, 2]).cuda()
tensor.rand(2, 2).cuda()

# ✅
tensor.tensor([2, 2], device=device)
tensor.rand(2, 2, device=device)

Conversion To Tensor

# ❌ creates a copy
tensor = torch.tensor(array, device=device)

# ✅ avoids copying
tensor = torch.as_tensor(array, device=device)
tensor = torch.from_numpy(array, device=device)
# however, changing array will also affect tensor

Conversion From Tensor

# ❌
tensor.cpu()
tensor.numpy()
tensor.item() # causes synchronization

# ✅
tensor.detach()

API

Model Mode

model.train()

model.eval()
with torch.inference_mode(): # turn off history tracking
    pass

Sequential

nn.Sequential(
    nn.LazyLinear(100),
  nn.ReLU()
)

Lazy Layers

automatically detect the input size

Only specify output size

nn.Sequential(
  nn.LazyLinear(1000),
  nn.LazyLinear(10),
  nn.LazyLinear(100)
)

Save Model

torch.save(model, "model.pkl")

View Parameters

for param in model.parameters():
  print(name)

for name, param in model.named_parameters():
    if param.requires_grad:
        print(name, param.data)

Custom Loss Function

class loss(nn.module):
  def forward(self, pred, y):
    error = pred-y
    return torch.mean(
      torch.abs(error)
    )

IDK

Time-Series

class TimeseriesDataset(torch.utils.data.Dataset):   
    def __init__(self, X, y, seq_len=1):
        self.X = X
        self.y = y
        self.seq_len = seq_len

    def __len__(self):
        return self.X.__len__() - (self.seq_len-1)

    def __getitem__(self, index):
        return (self.X[index:index+self.seq_len], self.y[index+self.seq_len-1])

train_dataset = TimeseriesDataset(X_lstm, y_lstm, seq_len=4)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size = 3, shuffle = False)

for i, d in enumerate(train_loader):
    print(i, d[0].shape, d[1].shape)

>>>
# shape: tuple((batch_size, seq_len, n_features), (batch_size))
0 torch.Size([3, 4, 2]) torch.Size([3])

TorchServe

[!WARNING] Torchserve Shelltorch exploit

Last Updated: 2024-12-26 ; Contributors: AhmedThahir, web-flow

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