Datasets in ``salad``
=====================

This is a test notebook that will later hold demos for a subpackage of
``salad``

Introduction
------------

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.. code:: ipython3

    import torch
    from torch import nn
    
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    %matplotlib inline
    #%config InlineBackend.figure_format = 'svg'

Digits Datasets
---------------

A standard benchmark for domain adaptation is digit classification. In
``salad.datasets``, it is easy to get access to this standard benchmark.

.. code:: ipython3

    from salad.datasets import DigitsLoader
    from salad.utils import panelize

We currently implemented unified loading functions for four different
benchmarks.

.. code:: ipython3

    from salad.datasets import MNIST, USPS, SVHN, Synth
    
    mnist = MNIST('/tmp/data')
    usps  = USPS('/tmp/data')
    svhn  = SVHN('/tmp/data')
    synth = Synth('/tmp/data')


.. parsed-literal::

    Extracting /tmp/data/zip.train.gz
    Extracting /tmp/data/synth_train_32x32.mat?raw=true


Accessing them is also possible directy by ``DigitsLoader``, which
subclasses the usual ``torch.utils.data.DataLoader``:

.. code:: ipython3

    dataset_names = ['mnist', 'usps', 'synth', 'svhn']
    data = DigitsLoader('/tmp/data', dataset_names, shuffle=True, batch_size = 64, normalize=False)


.. parsed-literal::

    Extracting /tmp/data/zip.train.gz
    Extracting /tmp/data/synth_train_32x32.mat?raw=true
    Using downloaded and verified file: /tmp/data/train_32x32.mat


.. code:: ipython3

    for batch in data:
        
        for (x,y), name in zip(batch, dataset_names):
            
            print(name, x.size(), y.dtype, np.unique(y.numpy()))
        
        break


.. parsed-literal::

    mnist torch.Size([64, 3, 32, 32]) torch.int64 [0 1 2 3 4 5 6 7 8 9]
    usps torch.Size([64, 3, 32, 32]) torch.int64 [0 1 2 3 4 5 6 7 8 9]
    synth torch.Size([64, 3, 32, 32]) torch.int64 [0 1 2 3 4 5 6 7 8 9]
    svhn torch.Size([64, 3, 32, 32]) torch.int64 [0 1 2 3 4 5 6 7 8 9]


.. code:: ipython3

    fig, axes = plt.subplots(2,2,figsize=(10,10))
    axes = axes.flatten()
    
    for batch in data:
        
        for (x,y), ax, name in zip(batch, axes, dataset_names):
            
            ax.imshow(panelize(x.numpy()))
            ax.set_title(name.upper())
            ax.axis('off')
            
        break
        
    plt.show()



.. image:: salad.datasets_files/salad.datasets_9_0.svg


For training adaptation models, it is generally a good idea to normalize
the images in some way prior to feeding them into a neural network.
``salad`` provide some means of doing this in a standardized way. In
particular, it is easy to check statistics of the dataset:

.. code:: ipython3

    default_normalization = {}
    
    data_normalized = DigitsLoader('/tmp/data', dataset_names, shuffle=True, batch_size = 200, normalize = True)
    
    for dataset in data_normalized.datasets:
        
        samples = np.concatenate([x.numpy() for x,_ in dataset])
        print(samples.mean(axis=(0,2,3)), samples.std(axis=(0,2,3)))


.. parsed-literal::

    Normalize data
    Normalize data
    Extracting /tmp/data/zip.train.gz
    Normalize data
    Downloading https://github.com/domainadaptation/datasets/blob/master/synth/synth_train_32x32_small.mat?raw=true to /tmp/data/synth_train_32x32_small.mat?raw=true
    Downloading https://github.com/domainadaptation/datasets/blob/master/synth/synth_test_32x32_small.mat?raw=true to /tmp/data/synth_test_32x32_small.mat?raw=true
    Done!
    Extracting /tmp/data/synth_train_32x32_small.mat?raw=true
    Normalize data
    Using downloaded and verified file: /tmp/data/train_32x32.mat
    [9.4225504e-07 9.4225504e-07 9.4225504e-07] [1.0009977 1.0009977 1.0009977]
    [-3.891881e-05 -3.891881e-05 -3.891881e-05] [1.0014582 1.0014582 1.0014582]
    [1.4131116e-05 7.6292954e-06 4.8976064e-05] [1.0168213 1.0188137 1.0175495]
    [-9.3011549e-05 -1.5506116e-04  2.3364362e-05] [1.0297976 1.0292693 1.0348536]


The normalization values can be found in ``salad.datasets.transforms``.

Toy Datasets
------------

The ``salad.datasets`` package also provides a loader for toy datasets.

.. code:: ipython3

    from salad.datasets import ToyDatasetLoader
    
    loader_stacked = ToyDatasetLoader(augment = False, collate='stack', batch_size = 2048, seed=1306)
    loader_concat  = ToyDatasetLoader(augment = False, collate='cat', batch_size = 2048, seed=1306)

A stacked loader returns a tuples containing the return values of the
individual datasets:

.. code:: ipython3

    for (xs,ys), (xt,yt) in loader_stacked:
        
        plt.figure(figsize=(12,3))
        plt.subplot(1,3,1)
        plt.scatter(*xs.transpose(1,0), c = ys, s= 1)
        plt.axis('off')
        plt.subplot(1,3,2)
        plt.scatter(*xt.transpose(1,0), c = yt, s = 1)
        plt.axis('off')
        plt.subplot(1,3,3)
        plt.scatter(*xs.transpose(1,0), s=1, alpha = .75)
        plt.scatter(*xt.transpose(1,0), s=1, alpha = .75)  
        plt.axis('off')
        
        break
    
    plt.show()



.. image:: salad.datasets_files/salad.datasets_16_0.png