update min requirements - PL 1.1.1 (Lightning-Universe#448)

* update min requirements

* rc0

* imports

* isort

* flake8

* 1.1.1

* flake8

* docs

docs/source/conf.py

@@ -339,8 +339,8 @@ def package_list_from_file(file):
         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())
+            if pkg.strip():
+                mocked_packages.append(pkg.strip())
     return mocked_packages
 
 
@@ -360,8 +360,7 @@ def package_list_from_file(file):
 # replace PyPI packages by importing ones
 MOCK_PACKAGES = [PACKAGE_MAPPING.get(pkg, pkg) for pkg in MOCK_PACKAGES]
 
-MOCK_MANUAL_PACKAGES = []
-autodoc_mock_imports = MOCK_PACKAGES + MOCK_MANUAL_PACKAGES
+autodoc_mock_imports = MOCK_PACKAGES
 # for mod_name in MOCK_REQUIRE_PACKAGES:
 #     sys.modules[mod_name] = mock.Mock()
 
@@ -441,10 +440,6 @@ def find_source():
 
 import pytorch_lightning as pl
 from pytorch_lightning import Trainer, LightningModule
-from pytorch_lightning.utilities import NATIVE_AMP_AVALAIBLE
-APEX_AVAILABLE = importlib.util.find_spec("apex") is not None
-XLA_AVAILABLE = importlib.util.find_spec("torch_xla") is not None
-TORCHVISION_AVAILABLE = importlib.util.find_spec("torchvision") is not None
 
 """
 coverage_skip_undoc_in_source = True

pl_bolts/callbacks/variational.py

@@ -1,6 +1,6 @@
 import math
-import numpy as np
 
+import numpy as np
 import torch
 from pytorch_lightning.callbacks import Callback
 

pl_bolts/datamodules/binary_mnist_datamodule.py

@@ -8,8 +8,7 @@
 
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
-    from torchvision.datasets import MNIST
-else:  # pragma: no cover
+else:  # pragma: no-cover
     warn_missing_pkg('torchvision')
 
 
@@ -64,8 +63,8 @@ def __init__(
         """
         super().__init__(*args, **kwargs)
 
-        if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
+        if not _TORCHVISION_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use MNIST dataset loaded from `torchvision` which is not installed yet.'
             )
 

pl_bolts/datamodules/cifar10_datamodule.py

@@ -13,8 +13,8 @@
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
     from torchvision.datasets import CIFAR10
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class CIFAR10DataModule(LightningDataModule):

pl_bolts/datamodules/cityscapes_datamodule.py

@@ -7,8 +7,8 @@
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
     from torchvision.datasets import Cityscapes
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class CityscapesDataModule(LightningDataModule):
@@ -80,8 +80,8 @@ def __init__(
         """
         super().__init__(*args, **kwargs)
 
-        if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
+        if not _TORCHVISION_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use CityScapes dataset loaded from `torchvision` which is not installed yet.'
             )
 

pl_bolts/datamodules/experience_source.py

@@ -2,7 +2,6 @@
 Datamodules for RL models that rely on experiences generated during training
 Based on implementations found here: https://github.com/Shmuma/ptan/blob/master/ptan/experience.py
 """
-import importlib
 from abc import ABC
 from collections import deque, namedtuple
 from typing import Callable, Iterable, List, Tuple

pl_bolts/datamodules/fashion_mnist_datamodule.py

@@ -8,8 +8,8 @@
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
     from torchvision.datasets import FashionMNIST
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class FashionMNISTDataModule(LightningDataModule):
@@ -62,8 +62,8 @@ def __init__(
         """
         super().__init__(*args, **kwargs)
 
-        if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
+        if not _TORCHVISION_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use fashion MNIST dataset loaded from `torchvision` which is not installed yet.'
             )
 

pl_bolts/datamodules/imagenet_datamodule.py

@@ -11,8 +11,8 @@
 
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class ImagenetDataModule(LightningDataModule):
@@ -69,8 +69,8 @@ def __init__(
         """
         super().__init__(*args, **kwargs)
 
-        if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
+        if not _TORCHVISION_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use ImageNet dataset loaded from `torchvision` which is not installed yet.'
             )
 

pl_bolts/datamodules/mnist_datamodule.py

@@ -8,8 +8,8 @@
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
     from torchvision.datasets import MNIST
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class MNISTDataModule(LightningDataModule):
@@ -63,8 +63,8 @@ def __init__(
         """
         super().__init__(*args, **kwargs)
 
-        if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
+        if not _TORCHVISION_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use MNIST dataset loaded from `torchvision` which is not installed yet.'
             )
 

pl_bolts/datamodules/sklearn_datamodule.py

@@ -11,8 +11,8 @@
 
 if _SKLEARN_AVAILABLE:
     from sklearn.utils import shuffle as sk_shuffle
-else:  # pragma: no cover
-    warn_missing_pkg("sklearn")
+else:
+    warn_missing_pkg("sklearn")  # pragma: no-cover
 
 
 class SklearnDataset(Dataset):
@@ -160,8 +160,8 @@ def __init__(
         # shuffle x and y
         if shuffle and _SKLEARN_AVAILABLE:
             X, y = sk_shuffle(X, y, random_state=random_state)
-        elif shuffle and not _SKLEARN_AVAILABLE:   # pragma: no cover
-            raise ModuleNotFoundError(
+        elif shuffle and not _SKLEARN_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use shuffle function from `scikit-learn` which is not installed yet.'
             )
 

pl_bolts/datamodules/ssl_imagenet_datamodule.py

@@ -10,8 +10,8 @@
 
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class SSLImagenetDataModule(LightningDataModule):  # pragma: no cover
@@ -29,8 +29,8 @@ def __init__(
     ):
         super().__init__(*args, **kwargs)
 
-        if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
+        if not _TORCHVISION_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use ImageNet dataset loaded from `torchvision` which is not installed yet.'
             )
 

pl_bolts/datamodules/stl10_datamodule.py

@@ -13,8 +13,8 @@
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms as transform_lib
     from torchvision.datasets import STL10
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class STL10DataModule(LightningDataModule):  # pragma: no cover
@@ -73,8 +73,8 @@ def __init__(
         """
         super().__init__(*args, **kwargs)
 
-        if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
+        if not _TORCHVISION_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use STL10 dataset loaded from `torchvision` which is not installed yet.'
             )
 

pl_bolts/datamodules/vocdetection_datamodule.py

@@ -8,8 +8,8 @@
 if _TORCHVISION_AVAILABLE:
     import torchvision.transforms as T
     from torchvision.datasets import VOCDetection
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class Compose(object):
@@ -114,8 +114,8 @@ def __init__(
         *args,
         **kwargs,
     ):
-        if not _TORCHVISION_AVAILABLE:  # pragma: no cover
-            raise ModuleNotFoundError(
+        if not _TORCHVISION_AVAILABLE:
+            raise ModuleNotFoundError(  # pragma: no-cover
                 'You want to use VOC dataset loaded from `torchvision` which is not installed yet.'
             )
 

pl_bolts/datasets/cifar10_dataset.py

@@ -12,8 +12,8 @@
 
 if _PIL_AVAILABLE:
     from PIL import Image
-else:  # pragma: no cover
-    warn_missing_pkg('PIL', pypi_name='Pillow')
+else:
+    warn_missing_pkg('PIL', pypi_name='Pillow')  # pragma: no-cover
 
 
 class CIFAR10(LightDataset):

pl_bolts/datasets/mnist_dataset.py

@@ -2,16 +2,15 @@
 from pl_bolts.utils.warnings import warn_missing_pkg
 
 if _TORCHVISION_AVAILABLE:
-    from torchvision import transforms as transform_lib
     from torchvision.datasets import MNIST
-else:  # pragma: no cover
-    warn_missing_pkg('torchvision')
+else:
+    warn_missing_pkg('torchvision')  # pragma: no-cover
     MNIST = object
 
 if _PIL_AVAILABLE:
     from PIL import Image
-else:  # pragma: no cover
-    warn_missing_pkg('PIL', pypi_name='Pillow')
+else:
+    warn_missing_pkg('PIL', pypi_name='Pillow')  # pragma: no-cover
 
 
 class BinaryMNIST(MNIST):

pl_bolts/models/rl/common/gym_wrappers.py

@@ -9,15 +9,13 @@
 import numpy as np
 import torch
 
+from pl_bolts.utils import _OPENCV_AVAILABLE
 from pl_bolts.utils.warnings import warn_missing_pkg
 
-try:
+if _OPENCV_AVAILABLE:
     import cv2
-except ModuleNotFoundError:
-    warn_missing_pkg('cv2', pypi_name='opencv-python')  # pragma: no-cover
-    _OPENCV_AVAILABLE = False
 else:
-    _OPENCV_AVAILABLE = True
+    warn_missing_pkg('cv2', pypi_name='opencv-python')  # pragma: no-cover
 
 
 class ToTensor(gym.Wrapper):

pl_bolts/models/rl/dqn_model.py

@@ -20,15 +20,13 @@
 from pl_bolts.models.rl.common.agents import ValueAgent
 from pl_bolts.models.rl.common.memory import MultiStepBuffer
 from pl_bolts.models.rl.common.networks import CNN
+from pl_bolts.utils import _GYM_AVAILABLE
 from pl_bolts.utils.warnings import warn_missing_pkg
 
-try:
+if _GYM_AVAILABLE:
     from pl_bolts.models.rl.common.gym_wrappers import gym, make_environment
-except ModuleNotFoundError:
-    warn_missing_pkg('gym')  # pragma: no-cover
-    _GYM_AVAILABLE = False
 else:
-    _GYM_AVAILABLE = True
+    warn_missing_pkg('gym')  # pragma: no-cover
 
 
 class DQN(pl.LightningModule):

pl_bolts/models/rl/reinforce_model.py

@@ -16,15 +16,13 @@
 from pl_bolts.datamodules.experience_source import Experience
 from pl_bolts.models.rl.common.agents import PolicyAgent
 from pl_bolts.models.rl.common.networks import MLP
+from pl_bolts.utils import _GYM_AVAILABLE
 from pl_bolts.utils.warnings import warn_missing_pkg
 
-try:
+if _GYM_AVAILABLE:
     import gym
-except ModuleNotFoundError:
-    warn_missing_pkg('gym')  # pragma: no-cover
-    _GYM_AVAILABLE = False
 else:
-    _GYM_AVAILABLE = True
+    warn_missing_pkg('gym')  # pragma: no-cover
 
 
 class Reinforce(pl.LightningModule):

pl_bolts/models/rl/vanilla_policy_gradient_model.py

@@ -15,15 +15,13 @@
 from pl_bolts.datamodules import ExperienceSourceDataset
 from pl_bolts.models.rl.common.agents import PolicyAgent
 from pl_bolts.models.rl.common.networks import MLP
+from pl_bolts.utils import _GYM_AVAILABLE
 from pl_bolts.utils.warnings import warn_missing_pkg
 
-try:
+if _GYM_AVAILABLE:
     import gym
-except ModuleNotFoundError:
-    warn_missing_pkg('gym')  # pragma: no-cover
-    _GYM_AVAILABLE = False
 else:
-    _GYM_AVAILABLE = True
+    warn_missing_pkg('gym')  # pragma: no-cover
 
 
 class VanillaPolicyGradient(pl.LightningModule):

pl_bolts/models/self_supervised/amdim/transforms.py

@@ -1,13 +1,11 @@
 from pl_bolts.transforms.self_supervised import RandomTranslateWithReflect
+from pl_bolts.utils import _TORCHVISION_AVAILABLE
 from pl_bolts.utils.warnings import warn_missing_pkg
 
-try:
+if _TORCHVISION_AVAILABLE:
     from torchvision import transforms
-except ModuleNotFoundError:
-    warn_missing_pkg('torchvision')  # pragma: no-cover
-    _TORCHVISION_AVAILABLE = False
 else:
-    _TORCHVISION_AVAILABLE = True
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class AMDIMTrainTransformsCIFAR10:

pl_bolts/models/self_supervised/cpc/transforms.py

@@ -1,13 +1,11 @@
 from pl_bolts.transforms.self_supervised import Patchify, RandomTranslateWithReflect
+from pl_bolts.utils import _TORCHVISION_AVAILABLE
 from pl_bolts.utils.warnings import warn_missing_pkg
 
-try:
+if _TORCHVISION_AVAILABLE:
     from torchvision import transforms
-except ModuleNotFoundError:
-    warn_missing_pkg('torchvision')  # pragma: no-cover
-    _TORCHVISION_AVAILABLE = False
 else:
-    _TORCHVISION_AVAILABLE = True
+    warn_missing_pkg('torchvision')  # pragma: no-cover
 
 
 class CPCTrainTransformsCIFAR10: