added train step return

.circleci/config.yml

@@ -53,14 +53,13 @@ references:
        flake8
 
   make_docs: &make_docs
-    run:
-      name: Make Documentation
-      command: |
-        # sudo apt-get install pandoc
-        pip install -r requirements.txt --user
-        sudo pip install -r docs/requirements.txt
-        # sphinx-apidoc -o ./docs/source ./pytorch_lightning **/test_* --force --follow-links
-        cd docs; make clean ; make html --debug --jobs 2 SPHINXOPTS="-W"
+   run:
+     name: Make Documentation
+     command: |
+       # First run the same pipeline as Read-The-Docs
+       sudo apt-get update && sudo apt-get install -y cmake
+       sudo pip install -r docs/requirements.txt
+       cd docs; make clean; make html --debug --jobs 2 SPHINXOPTS="-W"
 
 jobs:
 

.github/workflows/ci-testing.yml

@@ -0,0 +1,122 @@
+name: CI testing
+
+# see: https://help.github.com/en/actions/reference/events-that-trigger-workflows
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the master branch
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+
+jobs:
+  build:
+
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      # max-parallel: 6
+      matrix:
+        # PyTorch 1.5 is failing on Win and bolts requires torchvision>=0.5
+        os: [ubuntu-18.04, macOS-10.15]  # , windows-2019
+        python-version: [3.6, 3.7, 3.8]
+        requires: ['minimal', 'latest']
+        # exclude:
+        #   # excludes PT 1.3 as it is missing on pypi
+        #   - python-version: 3.8
+        #     requires: 'minimal'
+
+    # Timeout: https://stackoverflow.com/a/59076067/4521646
+    timeout-minutes: 15
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    # Github Actions: Run step on specific OS: https://stackoverflow.com/a/57948488/4521646
+    - name: Setup macOS
+      if: runner.os == 'macOS'
+      run: |
+        brew install libomp  # https://github.com/pytorch/pytorch/issues/20030
+
+    #- name: Setup Windows
+    #  if: runner.os == 'windows'
+    #  run: |
+    #    python -c "lines = open('requirements.txt').readlines() + ['torch<1.5\n']; open('requirements.txt', 'w').writelines(lines)"
+
+    - name: Set min. dependencies
+      if: matrix.requires == 'minimal'
+      run: |
+        python -c "req = open('requirements.txt').read().replace('>', '=') ; open('requirements.txt', 'w').write(req)"
+
+    # Note: This uses an internal pip API and may not always work
+    # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
+    - name: Get pip cache
+      id: pip-cache
+      run: |
+        python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)"
+
+    - name: Cache pip
+      uses: actions/cache@v1
+      with:
+        path: ${{ steps.pip-cache.outputs.dir }}
+        key: ${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}-pip-${{ hashFiles('requirements.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}-pip-
+
+    - name: Install dependencies
+      run: |
+        # python -m pip install --upgrade --user pip
+        pip install -r requirements.txt -U -f https://download.pytorch.org/whl/torch_stable.html -q
+        pip install -r ./tests/requirements.txt -q
+        # pip install tox coverage
+        python --version
+        pip --version
+        pip list
+      shell: bash
+
+    - name: Cache datasets
+      uses: actions/cache@v1
+      with:
+        path: tests/Datasets # This path is specific to Ubuntu
+        # Look to see if there is a cache hit for the corresponding requirements file
+        key: mnist-dataset
+
+    - name: Tests
+      # env:
+      #   TOXENV: py${{ matrix.python-version }}
+      run: |
+        # tox --sitepackages
+        coverage run --source pl_bolts -m py.test pl_bolts tests -v --doctest-modules --junitxml=junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
+
+    - name: Upload pytest test results
+      uses: actions/upload-artifact@master
+      with:
+        name: pytest-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}
+        path: junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
+      # Use always() to always run this step to publish test results when there are test failures
+      if: always()
+
+    - name: Package Setup
+      run: |
+        check-manifest
+        python setup.py check --metadata --strict
+        python setup.py sdist
+        twine check dist/*
+
+    #- name: Try install package
+    #  if: ! startsWith(matrix.os, 'windows')
+    #  run: |
+    #    virtualenv vEnv ; source vEnv/bin/activate
+    #    pip install --editable . ; cd .. & python -c "import pytorch_lightning ; print(pytorch_lightning.__version__)"
+    #    deactivate ; rm -rf vEnv
+
+    - name: Statistics
+      if: success()
+      run: |
+         coverage report

README.md

@@ -1,6 +1,7 @@
 # PyTorchLightning Bolts
 
 [![CircleCI](https://circleci.com/gh/PyTorchLightning/pytorch-lightning-bolts/tree/master.svg?style=svg)](https://circleci.com/gh/PyTorchLightning/pytorch-lightning-bolts/tree/master)
+![CI testing](https://github.com/PyTorchLightning/pytorch-lightning-bolts/workflows/CI%20testing/badge.svg?branch=master)
 [![codecov](https://codecov.io/gh/PyTorchLightning/pytorch-lightning-bolts/branch/master/graph/badge.svg)](https://codecov.io/gh/PyTorchLightning/pytorch-lightning-bolts)
 [![CodeFactor](https://www.codefactor.io/repository/github/pytorchlightning/pytorch-lightning-bolts/badge)](https://www.codefactor.io/repository/github/pytorchlightning/pytorch-lightning-bolts)
 [![Documentation Status](https://readthedocs.org/projects/pytorch-lightning-bolts/badge/?version=latest)](https://pytorch-lightning-bolts.readthedocs.io/en/latest/)

docs/source/conf.py

@@ -27,6 +27,8 @@
 
 builtins.__LIGHTNING_BOLT_SETUP__ = True
 
+SPHINX_MOCK_REQUIREMENTS = int(os.environ.get('SPHINX_MOCK_REQUIREMENTS', True))
+
 import pl_bolts  # noqa: E402
 
 # -- Project information -----------------------------------------------------
@@ -130,8 +132,7 @@
 exclude_patterns = [
     'api/pl_bolts.rst',
     'api/modules.rst',
-    'api/pl_bolts.datamodules.*',
-    'api/pl_bolts.metrics.*'
+    'api/pl_bolts.submit.rst',
 ]
 
 # The name of the Pygments (syntax highlighting) style to use.
@@ -279,24 +280,35 @@ def setup(app):
     path_ipynb2 = os.path.join(path_nbs, os.path.basename(path_ipynb))
     shutil.copy(path_ipynb, path_ipynb2)
 
+
 # Ignoring Third-party packages
 # https://stackoverflow.com/questions/15889621/sphinx-how-to-exclude-imports-in-automodule
+def package_list_from_file(file):
+    mocked_packages = []
+    with open(file, 'r') as fp:
+        for ln in fp.readlines():
+            found = [ln.index(ch) for ch in list(',=<>#') if ch in ln]
+            pkg = ln[:min(found)] if found else ln
+            if pkg.rstrip():
+                mocked_packages.append(pkg.rstrip())
+    return mocked_packages
+
+
+MOCK_PACKAGES = []
+if SPHINX_MOCK_REQUIREMENTS:
+    # mock also base packages when we are on RTD since we don't install them there
+    MOCK_PACKAGES += package_list_from_file(os.path.join(PATH_ROOT, 'requirements.txt'))
 
-MOCK_REQUIRE_PACKAGES = []
-with open(os.path.join(PATH_ROOT, 'requirements.txt'), 'r') as fp:
-    for ln in fp.readlines():
-        found = [ln.index(ch) for ch in list(',=<>#') if ch in ln]
-        pkg = ln[:min(found)] if found else ln
-        if pkg.rstrip():
-            MOCK_REQUIRE_PACKAGES.append(pkg.rstrip())
-
-# TODO: better parse from package since the import name and package name may differ
 MOCK_MANUAL_PACKAGES = [
-    'torch',
     'pytorch_lightning',
+    'numpy',
+    'torch',
     'torchvision',
+    'sklearn',
+    'PIL',
+    'cv2',
 ]
-autodoc_mock_imports = MOCK_REQUIRE_PACKAGES + MOCK_MANUAL_PACKAGES
+autodoc_mock_imports = MOCK_PACKAGES + MOCK_MANUAL_PACKAGES
 # for mod_name in MOCK_REQUIRE_PACKAGES:
 #     sys.modules[mod_name] = mock.Mock()
 

docs/source/index.rst

@@ -63,7 +63,12 @@ Indices and tables
    :hidden:
 
    readme
+   api/pl_bolts.callbacks
+   api/pl_bolts.datamodules
+   api/pl_bolts.metrics
    api/pl_bolts.models
    api/pl_bolts.callbacks
+   api/pl_bolts.losses
    api/pl_bolts.loggers
+   api/pl_bolts.optimizers
    api/pl_bolts.transforms

pl_bolts/datamodules/imagenet_dataset.py

@@ -134,9 +134,9 @@ def generate_meta_bins(cls, devkit_dir):
 
 def _verify_archive(root, file, md5):
     if not _check_integrity(os.path.join(root, file), md5):
-        msg = ("The archive {} is not present in the root directory or is corrupted. "
-               "You need to download it externally and place it in {}.")
-        raise RuntimeError(msg.format(file, root))
+        raise RuntimeError(
+            f"The archive {file} is not present in the root directory or is corrupted."
+            f" You need to download it externally and place it in {root}.")
 
 
 def _check_integrity(fpath, md5=None):
@@ -240,7 +240,7 @@ def extract_archive(from_path, to_path=None, remove_finished=False):
         with zipfile.ZipFile(from_path, 'r') as z:
             z.extractall(to_path)
     else:
-        raise ValueError("Extraction of {} not supported".format(from_path))
+        raise ValueError(f"Extraction of {from_path} not supported")
 
     if remove_finished:
         os.remove(from_path)

pl_bolts/datamodules/stl10_dataloaders.py

@@ -2,8 +2,8 @@
 from torchvision import transforms as transform_lib
 from torchvision.datasets import STL10
 
-from pl_bolts.datamodules.concat_dataset import ConcatDataset
 from pl_bolts.datamodules.bolts_dataloaders_base import BoltDataLoaders
+from pl_bolts.datamodules.concat_dataset import ConcatDataset
 from pl_bolts.transforms.dataset_normalizations import stl10_normalization
 
 
@@ -31,7 +31,9 @@ def train_dataloader(self, batch_size, transforms=None):
 
         dataset = STL10(self.save_path, split='unlabeled', download=False, transform=transforms)
         train_length = len(dataset)
-        dataset_train, _ = random_split(dataset, [train_length - self.unlabeled_val_split, self.unlabeled_val_split])
+        dataset_train, _ = random_split(dataset,
+                                        [train_length - self.unlabeled_val_split,
+                                         self.unlabeled_val_split])
         loader = DataLoader(
             dataset_train,
             batch_size=batch_size,
@@ -46,14 +48,20 @@ def train_dataloader_mixed(self, batch_size, transforms=None):
         if transforms is None:
             transforms = self._default_transforms()
 
-        unlabeled_dataset = STL10(self.save_path, split='unlabeled', download=False, transform=transforms)
+        unlabeled_dataset = STL10(self.save_path,
+                                  split='unlabeled',
+                                  download=False,
+                                  transform=transforms)
         unlabeled_length = len(unlabeled_dataset)
         unlabeled_dataset, _ = random_split(unlabeled_dataset,
-                                            [unlabeled_length - self.unlabeled_val_split, self.unlabeled_val_split])
+                                            [unlabeled_length - self.unlabeled_val_split,
+                                             self.unlabeled_val_split])
 
         labeled_dataset = STL10(self.save_path, split='train', download=False, transform=transforms)
         labeled_length = len(labeled_dataset)
-        labeled_dataset, _ = random_split(labeled_dataset, [labeled_length - self.train_val_split, self.train_val_split])
+        labeled_dataset, _ = random_split(labeled_dataset,
+                                          [labeled_length - self.train_val_split,
+                                           self.train_val_split])
 
         dataset = ConcatDataset(unlabeled_dataset, labeled_dataset)
         loader = DataLoader(
@@ -72,7 +80,9 @@ def val_dataloader(self, batch_size, transforms=None):
 
         dataset = STL10(self.save_path, split='unlabeled', download=False, transform=transforms)
         train_length = len(dataset)
-        _, dataset_val = random_split(dataset, [train_length - self.unlabeled_val_split, self.unlabeled_val_split])
+        _, dataset_val = random_split(dataset,
+                                      [train_length - self.unlabeled_val_split,
+                                       self.unlabeled_val_split])
         loader = DataLoader(
             dataset_val,
             batch_size=batch_size,
@@ -86,14 +96,20 @@ def val_dataloader_mixed(self, batch_size, transforms=None):
         if transforms is None:
             transforms = self._default_transforms()
 
-        unlabeled_dataset = STL10(self.save_path, split='unlabeled', download=False, transform=transforms)
+        unlabeled_dataset = STL10(self.save_path,
+                                  split='unlabeled',
+                                  download=False,
+                                  transform=transforms)
         unlabeled_length = len(unlabeled_dataset)
         _, unlabeled_dataset = random_split(unlabeled_dataset,
-                                            [unlabeled_length - self.unlabeled_val_split, self.unlabeled_val_split])
+                                            [unlabeled_length - self.unlabeled_val_split,
+                                             self.unlabeled_val_split])
 
         labeled_dataset = STL10(self.save_path, split='train', download=False, transform=transforms)
         labeled_length = len(labeled_dataset)
-        _, labeled_dataset = random_split(labeled_dataset, [labeled_length - self.train_val_split, self.train_val_split])
+        _, labeled_dataset = random_split(labeled_dataset,
+                                          [labeled_length - self.train_val_split,
+                                           self.train_val_split])
 
         dataset = ConcatDataset(unlabeled_dataset, labeled_dataset)
         loader = DataLoader(

pl_bolts/losses/self_supervised_learning.py

@@ -1,6 +1,6 @@
+import numpy as np
 import torch
 from torch import nn
-import numpy as np
 
 from pl_bolts.models.vision import PixelCNN
 

pl_bolts/models/self_supervised/amdim/networks.py

@@ -19,7 +19,7 @@ def __init__(self, dummy_batch, num_channels=3, ndf=64, n_rkhs=512,
         self.encoder_size = encoder_size
 
         # encoding block for local features
-        print('Using a {}x{} encoder'.format(encoder_size, encoder_size))
+        print(f'Using a {encoder_size}x{encoder_size} encoder')
         if encoder_size == 32:
             self.layer_list = nn.ModuleList([
                 Conv3x3(num_channels, ndf, 3, 1, 0, False),
@@ -58,8 +58,7 @@ def __init__(self, dummy_batch, num_channels=3, ndf=64, n_rkhs=512,
                 MaybeBatchNorm2d(n_rkhs, True, True)
             ])
         else:
-            raise RuntimeError("Could not build encoder."
-                               "Encoder size {} is not supported".format(encoder_size))
+            raise RuntimeError(f"Could not build encoder. Encoder size {encoder_size} is not supported")
         self._config_modules(
             dummy_batch,
             output_widths=[1, 5, 7],

pl_bolts/models/self_supervised/cpc/cpc_module.py

@@ -14,10 +14,10 @@
 from pl_bolts import metrics
 from pl_bolts.datamodules import CIFAR10DataLoaders, STL10DataLoaders
 from pl_bolts.datamodules.ssl_imagenet_dataloaders import SSLImagenetDataLoaders
+from pl_bolts.losses.self_supervised_learning import InfoNCE
 from pl_bolts.models.self_supervised.cpc import transforms as cpc_transforms
 from pl_bolts.models.self_supervised.cpc.networks import CPCResNet101
 from pl_bolts.models.self_supervised.evaluator import SSLEvaluator
-from pl_bolts.losses.self_supervised_learning import InfoNCE
 
 __all__ = [
     'CPCV2'
@@ -285,7 +285,11 @@ def add_model_specific_args(parent_parser):
             'dataset': 'imagenet128',
             'depth': 10,
             'patch_size': 32,
+<<<<<<< HEAD
             'batch_size': 48,
+=======
+            'batch_size': 52,
+>>>>>>> cee9bdd88cdbec912af7c419925b5d8c0fa47f58
             'nb_classes': 1000,
             'patch_overlap': 32 // 2,
             'lr_options': [

pl_bolts/models/self_supervised/cpc/networks.py

@@ -23,8 +23,8 @@ def __init__(self, sample_batch, zero_init_residual=False,
             # the 2x2 stride with a dilated convolution instead
             replace_stride_with_dilation = [False, False, False]
         if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+            raise ValueError("replace_stride_with_dilation should be None"
+                             f" or a 3-element tuple, got {replace_stride_with_dilation}")
         self.groups = groups
         self.base_width = width_per_group
         self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,