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[0]: from __future__ import print_function
[1]: import torch
[2]: import torch.nn as nn
[3]: import torch.optim as optim
[4]: from torch.optim import lr_scheduler
[5]: import numpy as np
[6]: import torchvision
[7]: from torchvision import datasets, models, transforms
[8]: import matplotlib.pyplot as plt
[9]: import time
[10]: import os
[11]: import copy
[12]: import pandas as pd
[13]: # data augmentation and normalization for training
[14]: # Just normalization for validation
[15]: data_transforms = {
[16]: 'train': transforms.Compose([
[17]: transforms.RandomResizedCrop(224),
[18]: transforms.RandomHorizontalFlip(),
[19]: transforms.ToTensor(),
[20]: transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
[21]: ]),
[22]: 'val': transforms.Compose([
[23]: transforms.Resize(256),
[24]: transforms.CenterCrop(224),
[25]: transforms.ToTensor(),
[26]: transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
[27]: ]),
[28]: }
[29]: data_dir = 'D:/data/flowers'
[30]: image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
[31]: data_transforms[x])
[32]: for x in ['train', 'val']}
[33]: dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=64,
[34]: shuffle=True)
[35]: for x in ['train', 'val']}
[36]: dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
[37]: class_names = image_datasets['train'].classes
[38]: device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
***** Tag Data *****
ID: 1
description: Data augmentation and normalization pipeline setup using PyTorch's torchvision.transforms.
start line: 15
end line: 28
dependencies:
- type: Other
name: data_dir
start line: 29
end line: 29
context description: This snippet sets up the data transformations for training and
validation datasets using PyTorch's torchvision.transforms library. It includes
random cropping and flipping for training data augmentation and resizing and normalization
for both training and validation.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 2
advanced coding concepts: 3
interesting for students: 5
self contained: Y
*************
## Suggestions for complexity
1. **Dynamic Augmentation Strategies**: Implement dynamic augmentation strategies that change based on the epoch number or model performance metrics.
2. **Conditional Normalization**: Modify the normalization parameters conditionally based on the dataset characteristics or specific classes within the dataset.
3. **Custom Transformations**: Create custom transformation functions that apply more complex operations like elastic distortions or color jittering with constraints.
4. **Multi-Scale Training**: Implement multi-scale training where different scales of images are randomly chosen during each epoch to improve robustness.
5. **Integration with Other Libraries**: Integrate this transformation pipeline with another library like Albumentations to leverage more advanced augmentation techniques.
## Conversation
<|user|>How can I make the augmentations dynamic based on epoch number?<|end|>
<|assistant|>You can create a custom transform class that takes the current epoch number as an input and adjusts the transformations accordingly.
Here's an example:
python
class DynamicTransform:
def __init__(self):
self.epoch = None
def set_epoch(self, epoch):
self.epoch = epoch
def __call__(self, img):
if self.epoch is None:
raise ValueError("Epoch not set")
if self.epoch % 10 == 0:
return transforms.RandomResizedCrop(256)(img)
elif self.epoch % 10 == 1:
return transforms.RandomHorizontalFlip()(img)
return img
dynamic_transform = DynamicTransform()
data_transforms = {
'train': transforms.Compose([
dynamic_transform,
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
Then you can update `dynamic_transform.set_epoch(epoch)` at the start of each epoch during training.<|end|>
<|user|>I want to normalize differently based on classes in dataset.<|end|>
<|assistant|>You can create a custom transformation that applies different normalization parameters based on the class label of each image.
Here's how you can do it:
python
class ConditionalNormalize:
def __init__(self):
self.mean_std_dict = {
'class_1': ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
'class_2': ([0.5, 0.5, 0.5], [0.2, .2 , .2]),
# Add more classes and their normalization parameters here...
}
def __call__(self, img, label):
mean_std = self.mean_std_dict.get(label)
if mean_std is None:
raise ValueError(f"No normalization parameters found for class {label}")
normalize = transforms.Normalize(*mean_std)
return normalize(img)
# Assuming you have access to labels when calling your transform pipeline.
# This requires modifying your DataLoader to provide labels along with images.
def custom_transform(img_label_pair):
img, label = img_label_pair
if isinstance(label,str):
label
