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reran and refined old tutorials

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Aladdin Persson
2022-12-19 15:57:59 +01:00
parent 088bdb63e9
commit cc0df999e2
9 changed files with 52 additions and 3124 deletions
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4
.gitignore vendored
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@@ -1,4 +1,6 @@
.idea/
ML/Pytorch/more_advanced/image_captioning/flickr8k/
ML/algorithms/svm/__pycache__/utils.cpython-38.pyc
__pycache__/
__pycache__/
*.pth.tar
*.DS_STORE

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ML/Pytorch/Basics/custom_dataset/custom_FCNN.py
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@@ -1,131 +0,0 @@
# Imports
import os
from typing import Union
import torch.nn.functional as F # All functions that don't have any parameters
import pandas as pd
import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torchvision
import torchvision.transforms as transforms # Transformations we can perform on our dataset
from pandas import io
# from skimage import io
from torch.utils.data import (
Dataset,
DataLoader,
) # Gives easier dataset managment and creates mini batches
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
# Create Fully Connected Network
class NN(nn.Module):
def __init__(self, input_size, num_classes):
super(NN, self).__init__()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50, num_classes)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
class SoloDataset(Dataset):
def __init__(self, csv_file, root_dir, transform=None):
self.annotations = pd.read_csv(csv_file)
self.root_dir = root_dir
self.transform = transform
def __len__(self):
return len(self.annotations)
def __getitem__(self, index):
x_data = self.annotations.iloc[index, 0:11]
x_data = torch.tensor(x_data)
y_label = torch.tensor(int(self.annotations.iloc[index, 11]))
return (x_data.float(), y_label)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Hyperparameters
num_classes = 26
learning_rate = 1e-3
batch_size = 5
num_epochs = 30
input_size = 11
# Load Data
dataset = SoloDataset(
csv_file="power.csv", root_dir="test123", transform=transforms.ToTensor()
)
train_set, test_set = torch.utils.data.random_split(dataset, [2900, 57])
train_loader = DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_set, batch_size=batch_size, shuffle=True)
# Model
model = NN(input_size=input_size, num_classes=num_classes).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
print(len(train_set))
print(len(test_set))
# Train Network
for epoch in range(num_epochs):
losses = []
for batch_idx, (data, targets) in enumerate(train_loader):
# Get data to cuda if possible
data = data.to(device=device)
targets = targets.to(device=device)
# forward
scores = model(data)
loss = criterion(scores, targets)
losses.append(loss.item())
# backward
optimizer.zero_grad()
loss.backward()
# gradient descent or adam step
optimizer.step()
print(f"Cost at epoch {epoch} is {sum(losses) / len(losses)}")
# Check accuracy on training to see how good our model is
def check_accuracy(loader, model):
num_correct = 0
num_samples = 0
model.eval()
with torch.no_grad():
for x, y in loader:
x = x.to(device=device)
y = y.to(device=device)
scores = model(x)
_, predictions = scores.max(1)
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
print(
f"Got {num_correct} / {num_samples} with accuracy {float(num_correct) / float(num_samples) * 100:.2f}"
)
model.train()
print("Checking accuracy on Training Set")
check_accuracy(train_loader, model)
print("Checking accuracy on Test Set")
check_accuracy(test_loader, model)

19
ML/Pytorch/Basics/custom_dataset/custom_dataset.py
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@@ -6,7 +6,7 @@ label (0 for cat, 1 for dog).
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-03 Initial coding
* 2022-12-19 Updated with better comments, improved code using PIL, and checked code still functions as intended.
"""
# Imports
@@ -17,7 +17,7 @@ import torchvision.transforms as transforms # Transformations we can perform on
import torchvision
import os
import pandas as pd
from skimage import io
from PIL import Image
from torch.utils.data import (
Dataset,
DataLoader,
@@ -35,7 +35,7 @@ class CatsAndDogsDataset(Dataset):
def __getitem__(self, index):
img_path = os.path.join(self.root_dir, self.annotations.iloc[index, 0])
image = io.imread(img_path)
image = Image.open(img_path)
y_label = torch.tensor(int(self.annotations.iloc[index, 1]))
if self.transform:
@@ -50,7 +50,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Hyperparameters
in_channel = 3
num_classes = 2
learning_rate = 1e-3
learning_rate = 3e-4
batch_size = 32
num_epochs = 10
@@ -69,12 +69,19 @@ train_loader = DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True
test_loader = DataLoader(dataset=test_set, batch_size=batch_size, shuffle=True)
# Model
model = torchvision.models.googlenet(pretrained=True)
model = torchvision.models.googlenet(weights="DEFAULT")
# freeze all layers, change final linear layer with num_classes
for param in model.parameters():
param.requires_grad = False
# final layer is not frozen
model.fc = nn.Linear(in_features=1024, out_features=num_classes)
model.to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-5)
# Train Network
for epoch in range(num_epochs):

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ML/Pytorch/Basics/custom_dataset/power.csv
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16
ML/Pytorch/Basics/pytorch_bidirectional_lstm.py
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@@ -1,15 +1,17 @@
"""
Example code of a simple bidirectional LSTM on the MNIST dataset.
Note that using RNNs on image data is not the best idea, but it is a
good example to show how to use RNNs that still generalizes to other tasks.
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-05-09 Initial coding
* 2022-12-16 Updated with more detailed comments, docstrings to functions, and checked code still functions as intended.
"""
# Imports
import torch
import torchvision
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F # All functions that don't have any parameters
@@ -18,9 +20,10 @@ from torch.utils.data import (
) # Gives easier dataset managment and creates mini batches
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
import torchvision.transforms as transforms # Transformations we can perform on our dataset
from tqdm import tqdm # progress bar
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = "cuda" if torch.cuda.is_available() else "cpu"
# Hyperparameters
input_size = 28
@@ -28,7 +31,7 @@ sequence_length = 28
num_layers = 2
hidden_size = 256
num_classes = 10
learning_rate = 0.001
learning_rate = 3e-4
batch_size = 64
num_epochs = 2
@@ -47,7 +50,7 @@ class BRNN(nn.Module):
h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
out, _ = self.lstm(x, (h0, c0))
out, _ = self.lstm(x)
out = self.fc(out[:, -1, :])
return out
@@ -74,7 +77,7 @@ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network
for epoch in range(num_epochs):
for batch_idx, (data, targets) in enumerate(train_loader):
for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
# Get data to cuda if possible
data = data.to(device=device).squeeze(1)
targets = targets.to(device=device)
@@ -90,9 +93,8 @@ for epoch in range(num_epochs):
# gradient descent or adam step
optimizer.step()
# Check accuracy on training & test to see how good our model
def check_accuracy(loader, model):
if loader.dataset.train:
print("Checking accuracy on training data")

18
ML/Pytorch/Basics/pytorch_init_weights.py
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@@ -1,12 +1,16 @@
"""
Example code of how to initialize weights for a simple CNN network.
Usually this is not needed as default initialization is usually good,
but sometimes it can be useful to initialize weights in a specific way.
This way of doing it should generalize to other network types just make
sure to specify and change the modules you wish to modify.
Video explanation: https://youtu.be/xWQ-p_o0Uik
Got any questions leave a comment on youtube :)
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-04-10 Initial coding
* 2022-12-16 Updated with more detailed comments, and checked code still functions as intended.
"""
# Imports
@@ -20,17 +24,17 @@ class CNN(nn.Module):
self.conv1 = nn.Conv2d(
in_channels=in_channels,
out_channels=6,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
kernel_size=3,
stride=1,
padding=1,
)
self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))
self.conv2 = nn.Conv2d(
in_channels=6,
out_channels=16,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
kernel_size=3,
stride=1,
padding=1,
)
self.fc1 = nn.Linear(16 * 7 * 7, num_classes)
self.initialize_weights()

7
ML/Pytorch/Basics/pytorch_loadsave.py
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@@ -9,7 +9,8 @@ Video explanation of code & how to save and load model: https://youtu.be/g6kQl_E
Got any questions leave a comment on youtube :)
Coded by Aladdin Persson <aladdin dot person at hotmail dot com>
- 2020-04-07 Initial programming
* 2020-04-07 Initial programming
* 2022-12-16 Updated with more detailed comments, and checked code still functions as intended.
"""
@@ -39,7 +40,9 @@ def load_checkpoint(checkpoint, model, optimizer):
def main():
# Initialize network
model = torchvision.models.vgg16(pretrained=False)
model = torchvision.models.vgg16(
weights=None
) # pretrained=False deprecated, use weights instead
optimizer = optim.Adam(model.parameters())
checkpoint = {"state_dict": model.state_dict(), "optimizer": optimizer.state_dict()}

16
ML/Pytorch/Basics/pytorch_rnn_gru_lstm.py
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@@ -3,22 +3,24 @@ Example code of a simple RNN, GRU, LSTM on the MNIST dataset.
Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
* 2020-05-09 Initial coding
* 2022-12-16 Updated with more detailed comments, docstrings to functions, and checked code still functions as intended.
"""
# Imports
import torch
import torchvision # torch package for vision related things
import torch.nn.functional as F # Parameterless functions, like (some) activation functions
import torchvision.datasets as datasets # Standard datasets
import torchvision.transforms as transforms # Transformations we can perform on our dataset for augmentation
from torch import optim # For optimizers like SGD, Adam, etc.
from torch import nn # All neural network modules
from torch.utils.data import DataLoader # Gives easier dataset managment by creating mini batches etc.
from torch.utils.data import (
DataLoader,
) # Gives easier dataset managment by creating mini batches etc.
from tqdm import tqdm # For a nice progress bar!
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = "cuda" if torch.cuda.is_available() else "cpu"
# Hyperparameters
input_size = 28
@@ -100,8 +102,12 @@ class RNN_LSTM(nn.Module):
# Load Data
train_dataset = datasets.MNIST(root="dataset/", train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root="dataset/", train=False, transform=transforms.ToTensor(), download=True)
train_dataset = datasets.MNIST(
root="dataset/", train=True, transform=transforms.ToTensor(), download=True
)
test_dataset = datasets.MNIST(
root="dataset/", train=False, transform=transforms.ToTensor(), download=True
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

7
ML/Pytorch/CNN_architectures/pytorch_efficientnet.py
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@@ -173,10 +173,3 @@ def test():
print(model(x).shape) # (num_examples, num_classes)
test()