CORe50 Dataset added. Utils imporved with reporthook during download.

docs/source/datasets.rst

@@ -30,6 +30,11 @@ MNIST
 
 .. autoclass:: MNIST
 
+CORe50
+~~~~~~
+
+.. autoclass:: CORE50
+
 COCO
 ~~~~
 
@@ -95,7 +100,7 @@ STL10
   :special-members:
 
 SVHN
-~~~~~
+~~~~
 
 
 .. autoclass:: SVHN

torchvision/datasets/__init__.py

@@ -1,3 +1,4 @@
+from .core50 import CORE50
 from .lsun import LSUN, LSUNClass
 from .folder import ImageFolder
 from .coco import CocoCaptions, CocoDetection
@@ -8,7 +9,7 @@
 from .phototour import PhotoTour
 from .fakedata import FakeData
 
-__all__ = ('LSUN', 'LSUNClass',
+__all__ = ('CORE50', 'LSUN', 'LSUNClass',
            'ImageFolder', 'FakeData',
            'CocoCaptions', 'CocoDetection',
            'CIFAR10', 'CIFAR100', 'FashionMNIST',

torchvision/datasets/core50.py

@@ -0,0 +1,229 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+################################################################################
+# Date: 22-11-2017                                                             #
+# Author: Vincenzo Lomonaco                                                    #
+# E-mail: vincenzo.lomonaco@unibo.it                                           #
+# Website: vincenzolomonaco.com                                                #
+################################################################################
+
+""" CORe50 Dataset class """
+
+from __future__ import print_function
+import os
+import os.path
+import torch.utils.data as data
+from .utils import download_url, check_integrity
+from torchvision.datasets.folder import pil_loader
+
+
+class CORE50(data.Dataset):
+    """`CORE50 <https://vlomonaco.github.io/core50/>`_ Dataset, specifically
+        designed for Continuous Learning and Robotic Vision applications.
+        For more information and additional materials visit the official
+        website `CORE50 <https://vlomonaco.github.io/core50/>`
+
+    Args:
+        root (string): Root directory of the dataset where the ``CORe50``
+            dataset exists or should be downloaded.
+        check_integrity (bool, optional): If True check the integrity of the
+            Dataset before trying to load it.
+        scenario (string, optional): One of the three scenarios of the CORe50
+            benchmark ``ni``, ``nc`` or ``nic``.
+        train (bool, optional): If True, creates the dataset from the training
+            set, otherwise creates from test set.
+        img_size (string, optional): One of the two img sizes available among
+            ``128x128`` or ``350x350``.
+        cumul (bool, optional): If True the cumulative scenario is assumed, the
+            incremental scenario otherwise. Practically speaking ``cumul=True``
+            means that for batch=i also batch=0,...i-1 will be added to the
+            available training data.
+        run (int, optional): One of the 10 runs (from 0 to 9) in which the
+            training batch order is changed as in the official benchmark.
+        batch (int, optional): One of the training incremental batches from 0 to
+            max-batch - 1. Remember that for the ``ni``, ``nc`` and ``nic`` we
+            have respectively 8, 9 and 79 incremental batches. If
+            ``train=False`` this parameter will be ignored.
+        transform (callable, optional): A function/transform that takes in an
+            PIL image and returns a transformed version. E.g,
+            ``transforms.ToTensor()``
+        target_transform (callable, optional): A function/transform that takes
+            in the target and transforms it.
+        download (bool, optional): If true, downloads the dataset from the
+            internet and puts it in root directory. If dataset is already
+            downloaded, it is not downloaded again.
+
+    Example:
+
+        .. code:: python
+
+            training_data = datasets.CORE50(
+                '~/data/core50', transform=transforms.ToTensor(), download=True
+            )
+            training_loader = torch.utils.data.DataLoader(
+                training_data, batch_size=128, shuffle=True, num_workers=4
+            )
+            test_data = datasets.CORE50(
+                '~/data/core50', transform=transforms.ToTensor(), train=False,
+                download=True
+            )
+            test_loader = torch.utils.data.DataLoader(
+                training_data, batch_size=128, shuffle=True, num_workers=4
+            )
+
+            for batch in training_loader:
+                imgs, labels = batch
+                ...
+
+        This is the simplest way of using the Dataset with the common Train/Test
+        split. If you want to use the benchmark as in the original CORe50 paper
+        (that is for continuous learning) you need to play with the parameters
+        ``scenario``, ``cumul``, ``run`` and ``batch`` hence creating a number
+        of Dataset objects (one for each incremental training batch and one for
+        the test set).
+
+    """
+    ntrain_batch = {
+        'ni': 8,
+        'nc': 9,
+        'nic': 79
+    }
+    urls = {
+        '128x128': 'http://bias.csr.unibo.it/maltoni/download/core50/'
+                   'core50_128x128.zip',
+        '350x350': 'http://bias.csr.unibo.it/maltoni/download/core50/'
+                   'core50_350x350.zip',
+        'filelists': 'https://vlomonaco.github.io/core50/data/'
+                     'batches_filelists.zip'
+    }
+    filenames = {
+        '128x128': 'core50_128x128.zip',
+        '350x350': 'core50_350x350.zip',
+        'filelists': 'batches_filelists.zip'
+    }
+    md5s = {
+        'core50_128x128.zip': '745f3373fed08d69343f1058ee559e13',
+        'core50_350x350.zip': 'e304258739d6cd4b47e19adfa08e7571',
+        'batches_filelists.zip': 'e3297508a8998ba0c99a83d6b36bde62'
+    }
+
+    def __init__(self, root, check_integrity=True, scenario='ni', train=True,
+                 img_size='128x128', run=0, batch=7, cumul=True, transform=None,
+                 target_transform=None, download=False):
+
+        self.root = os.path.expanduser(root)
+        self.img_size = img_size
+        self.scenario = scenario
+        self.run = run
+        self.batch = batch
+        self.transform = transform
+        self.target_transform = target_transform
+
+        # To be filled
+        self.fpath = None
+        self.img_paths = []
+        self.labels = []
+
+        # Downloading files if needed
+        if download:
+            self.download()
+
+        if check_integrity:
+            print("Making sure CORe50 exists and it's not corrupted...")
+            if not self._check_integrity():
+                raise RuntimeError('Dataset not found or corrupted.' +
+                                   ' You can use download=True to download it')
+
+        if cumul:
+            suffix = 'cum'
+        else:
+            suffix = 'inc'
+
+        if train:
+            self.fpath = os.path.join(
+                scenario.upper() + '_' + suffix, 'run' + str(run),
+                'train_batch_' + str(batch).zfill(2) + '_filelist.txt'
+            )
+        else:
+            # it's the last one, hence the test batch
+            self.fpath = os.path.join(
+                scenario.upper() + '_' + suffix, 'run' + str(run),
+                'test_filelist.txt'
+            )
+
+        # Loading the filelist
+        path = os.path.join(self.root, self.filenames['filelists'][:-4],
+                            self.fpath)
+        with open(path, 'r') as f:
+            for i, line in enumerate(f):
+                if line.strip():
+                    path, label = line.split()
+                    self.labels.append(int(label))
+                    self.img_paths.append(path)
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+
+        fpath = self.img_paths[index]
+        target = self.labels[index]
+        img = pil_loader(
+            os.path.join(self.root, self.filenames[self.img_size][:-4], fpath)
+        )
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+
+        return len(self.labels)
+
+    def _check_integrity(self):
+
+        # removing files we have not downloaded
+        if self.img_size == '128x128':
+            del self.md5s['core50_350x350.zip']
+        else:
+            del self.md5s['core50_128x128.zip']
+
+        root = self.root
+        for filename, md5 in self.md5s.items():
+            fpath = os.path.join(root, filename)
+            if not check_integrity(fpath, md5):
+                return False
+        return True
+
+    def download(self):
+        import zipfile
+
+        if self._check_integrity():
+            print('Files already downloaded and verified')
+            return
+
+        root = self.root
+
+        # Downloading the dataset and filelists
+        for name in (self.img_size, 'filelists'):
+            download_url(
+                self.urls[name], root, self.filenames[name],
+                self.md5s[self.filenames[name]]
+            )
+
+            # extract file
+            cwd = os.getcwd()
+            zip = zipfile.ZipFile(os.path.join(root, self.filenames[name]), "r")
+            os.chdir(root)
+            zip.extractall()
+            zip.close()
+            os.chdir(cwd)

torchvision/datasets/utils.py

@@ -2,6 +2,8 @@
 import os.path
 import hashlib
 import errno
+import sys
+import time
 
 
 def check_integrity(fpath, md5):
@@ -18,6 +20,20 @@ def check_integrity(fpath, md5):
     return True
 
 
+def reporthook(count, block_size, total_size):
+    global start_time
+    if count == 0:
+        start_time = time.time()
+        return
+    duration = time.time() - start_time
+    progress_size = int(count * block_size)
+    speed = int(progress_size / (1024 * duration))
+    percent = min(int(count * block_size * 100 / total_size), 100)
+    sys.stdout.write("\r...%d%%, %d MB, %d KB/s, %d seconds passed" %
+                    (percent, progress_size / (1024 * 1024), speed, duration))
+    sys.stdout.flush()
+
+
 def download_url(url, root, filename, md5):
     from six.moves import urllib
 
@@ -37,4 +53,4 @@ def download_url(url, root, filename, md5):
         print('Using downloaded and verified file: ' + fpath)
     else:
         print('Downloading ' + url + ' to ' + fpath)
-        urllib.request.urlretrieve(url, fpath)
+        urllib.request.urlretrieve(url, fpath, reporthook)