Add MNIST dataset & drop torchvision dep. from tests (#986)

* added custom mnist without torchvision dep

* move files so it does not conflict with mnist gitignore

* mock torchvision for tests

* fix line too long

* fix line too long

* fix "module level import not at top of file" warning

* move mock imports to __init__.py

* simplify MNIST a lot and download directly the .pt files

* further simplify and clean up mnist

* revert import overrides

* make as before

* drop  PIL requirement

* move mnist.py to datasets subfolder

* use logging instead of print

* choose same name as in torchvision

* remove torchvision and pillow also from yml file

* refactor if train

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* capitalized class attr

* moved mnist to models

* re-added datsets ignore

* better name for file variable

* Update mnist.py

* move dataset classes to datasets.py

* new line

* update

* update

* fix automerge

* move to base folder

* adapt testingmnist to new mnist base class

* remove temporal fix

* fix datatype

* remove old testingmnist

* readable

* fix import

* fix whitespace

* docstring

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* Update tests/base/datasets.py

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* changelog

* added types

* Update CHANGELOG.md

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* exist->isfile

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* index -> idx

* temporary fix for trains error

* better changelog message

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

tests/base/models.py

@@ -7,8 +7,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch import optim
 from torch.utils.data import DataLoader
-from torchvision import transforms
-from torchvision.datasets import MNIST
+
+from tests.base.datasets import TestingMNIST
 
 try:
     from test_tube import HyperOptArgumentParser
@@ -18,29 +18,6 @@ except ImportError:
 
 from pytorch_lightning.core.lightning import LightningModule
 
-# TODO: remove after getting own MNIST
-# TEMPORAL FIX, https://github.com/pytorch/vision/issues/1938
-import urllib.request
-opener = urllib.request.build_opener()
-opener.addheaders = [('User-agent', 'Mozilla/5.0')]
-urllib.request.install_opener(opener)
-
-
-class TestingMNIST(MNIST):
-
-    def __init__(self, root, train=True, transform=None, target_transform=None,
-                 download=False, num_samples=8000):
-        super().__init__(
-            root,
-            train=train,
-            transform=transform,
-            target_transform=target_transform,
-            download=download
-        )
-        # take just a subset of MNIST dataset
-        self.data = self.data[:num_samples]
-        self.targets = self.targets[:num_samples]
-
 
 class DictHparamsModel(LightningModule):
 
@@ -61,8 +38,7 @@ class DictHparamsModel(LightningModule):
         return torch.optim.Adam(self.parameters(), lr=0.02)
 
     def train_dataloader(self):
-        return DataLoader(TestingMNIST(os.getcwd(), train=True, download=True,
-                                       transform=transforms.ToTensor()), batch_size=32)
+        return DataLoader(TestingMNIST(os.getcwd(), train=True, download=True), batch_size=32)
 
 
 class TestModelBase(LightningModule):
@@ -178,17 +154,13 @@ class TestModelBase(LightningModule):
         return [optimizer], [scheduler]
 
     def prepare_data(self):
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize((0.5,), (1.0,))])
         _ = TestingMNIST(root=self.hparams.data_root, train=True,
-                         transform=transform, download=True, num_samples=2000)
+                         download=True, num_samples=2000)
 
     def _dataloader(self, train):
         # init data generators
-        transform = transforms.Compose([transforms.ToTensor(),
-                                        transforms.Normalize((0.5,), (1.0,))])
         dataset = TestingMNIST(root=self.hparams.data_root, train=train,
-                               transform=transform, download=False, num_samples=2000)
+                               download=False, num_samples=2000)
 
         # when using multi-node we need to add the datasampler
         batch_size = self.hparams.batch_size