ci/cd with 1.7.1

.github/workflows/integration.yml

@@ -34,7 +34,7 @@ jobs:
         which python
         python -m pip install --upgrade pip wheel
         python -m pip uninstall -y torch torchvision
-        python -m pip install torch==1.7.0 torchvision==0.8.1
+        python -m pip install torch==1.7.1 torchvision==0.8.2
         python -m pip install -r requirements-dev.txt
     - name: Run integration tests
       run: |

.github/workflows/pythonapp.yml

@@ -80,13 +80,13 @@ jobs:
     - if: runner.os == 'windows'
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
-        python -m pip install torch==1.7.0+cpu torchvision==0.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        python -m pip install torch==1.7.1+cpu torchvision==0.8.2+cpu -f https://download.pytorch.org/whl/torch_stable.html
         # min. requirements for windows instances
         python -c "f=open('requirements-dev.txt', 'r'); txt=f.readlines(); f.close(); print(txt); f=open('requirements-dev.txt', 'w'); f.writelines(txt[1:12]); f.close()"
     - name: Install the dependencies
       run: |
-        python -m pip install torch==1.7.0
-        python -m pip install torchvision==0.8.1
+        python -m pip install torch==1.7.1
+        python -m pip install torchvision==0.8.2
         cat "requirements-dev.txt"
         python -m pip install -r requirements-dev.txt
         python -m pip list
@@ -108,7 +108,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [windows-latest, macOS-latest, ubuntu-latest]
-    timeout-minutes: 20
+    timeout-minutes: 40
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python 3.8
@@ -134,11 +134,11 @@ jobs:
     - if: runner.os == 'windows'
       name: Install torch cpu from pytorch.org (Windows only)
       run: |
-        python -m pip install torch==1.7.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+        python -m pip install torch==1.7.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
     - name: Install the dependencies
       run: |
         # min. requirements
-        python -m pip install torch==1.7.0
+        python -m pip install torch==1.7.1
         python -m pip install -r requirements-min.txt
         python -m pip list
         BUILD_MONAI=0 python setup.py develop  # no compile of extensions
@@ -173,7 +173,7 @@ jobs:
             pytorch: "-h"
             base: "nvcr.io/nvidia/pytorch:20.07-py3"
           - environment: PT17+CUDA102
-            pytorch: "torch==1.7.0 torchvision==0.8.1"
+            pytorch: "torch==1.7.1 torchvision==0.8.2"
             base: "nvcr.io/nvidia/cuda:10.2-devel-ubuntu18.04"
           - environment: PT17+CUDA110
             # we explicitly set pytorch to -h to avoid pip install error

.github/workflows/setupapp.yml

@@ -36,7 +36,7 @@ jobs:
         which python
         python -m pip install --upgrade pip wheel
         python -m pip uninstall -y torch torchvision
-        python -m pip install torch==1.7.0 torchvision==0.8.1
+        python -m pip install torch==1.7.1 torchvision==0.8.2
         python -m pip install -r requirements-dev.txt
     - name: Run unit tests report coverage
       run: |
@@ -82,7 +82,7 @@ jobs:
     - name: Install the dependencies
       run: |
         python -m pip install --upgrade pip wheel
-        python -m pip install torch==1.7.0 torchvision==0.8.1
+        python -m pip install torch==1.7.1 torchvision==0.8.2
         python -m pip install -r requirements-dev.txt
     - name: Run quick tests CPU ubuntu
       run: |
@@ -151,7 +151,7 @@ jobs:
       run: |
         docker build -t localhost:5000/local_monai:latest -f Dockerfile .
         docker push localhost:5000/local_monai:latest
-        sed -i '/flake/d' requirements-dev.txt 
+        sed -i '/flake/d' requirements-dev.txt
         docker build -t projectmonai/monai:latest -f Dockerfile .
         docker login -u projectmonai -p ${{ secrets.DOCKER_PW }}
         docker push projectmonai/monai:latest

docs/source/utils.rst

@@ -26,3 +26,8 @@ Misc
 ----
 .. automodule:: monai.utils.misc
   :members:
+
+Profiling
+---------
+.. automodule:: monai.utils.profiling
+  :members:

monai/config/deviceconfig.py

@@ -100,14 +100,6 @@ def set_visible_devices(*dev_inds):
     os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, dev_inds))
 
 
-def get_torch_version_tuple():
-    """
-    Returns:
-        tuple of ints represents the pytorch major/minor version.
-    """
-    return tuple((int(x) for x in torch.__version__.split(".")[:2]))
-
-
 def _dict_append(in_dict, key, fn):
     try:
         in_dict[key] = fn()

monai/networks/layers/simplelayers.py

@@ -17,12 +17,11 @@
 from torch import nn
 from torch.autograd import Function
 
-from monai.config import get_torch_version_tuple
 from monai.networks.layers.convutils import gaussian_1d, same_padding
-from monai.utils import InvalidPyTorchVersionError, SkipMode, ensure_tuple_rep, optional_import
+from monai.utils import PT_BEFORE_1_7, InvalidPyTorchVersionError, SkipMode, ensure_tuple_rep, optional_import
 
 _C, _ = optional_import("monai._C")
-if tuple(int(s) for s in torch.__version__.split(".")[0:2]) >= (1, 7):
+if not PT_BEFORE_1_7:
     fft, _ = optional_import("torch.fft")
 
 __all__ = ["SkipConnection", "Flatten", "GaussianFilter", "LLTM", "Reshape", "separable_filtering", "HilbertTransform"]
@@ -145,7 +144,7 @@ class HilbertTransform(nn.Module):
 
     def __init__(self, axis: int = 2, n: Union[int, None] = None) -> None:
 
-        if get_torch_version_tuple() < (1, 7):
+        if PT_BEFORE_1_7:
             raise InvalidPyTorchVersionError("1.7.0", self.__class__.__name__)
 
         super().__init__()

monai/transforms/intensity/array.py

@@ -20,11 +20,10 @@
 import numpy as np
 import torch
 
-from monai.config import get_torch_version_tuple
 from monai.networks.layers import GaussianFilter, HilbertTransform
 from monai.transforms.compose import Randomizable, Transform
 from monai.transforms.utils import rescale_array
-from monai.utils import InvalidPyTorchVersionError, dtype_torch_to_numpy, ensure_tuple_size
+from monai.utils import PT_BEFORE_1_7, InvalidPyTorchVersionError, dtype_torch_to_numpy, ensure_tuple_size
 
 
 class RandGaussianNoise(Randomizable, Transform):
@@ -524,7 +523,7 @@ class DetectEnvelope(Transform):
 
     def __init__(self, axis: int = 1, n: Union[int, None] = None) -> None:
 
-        if get_torch_version_tuple() < (1, 7):
+        if PT_BEFORE_1_7:
             raise InvalidPyTorchVersionError("1.7.0", self.__class__.__name__)
 
         if axis < 0:

monai/utils/misc.py

@@ -12,7 +12,6 @@
 import collections.abc
 import itertools
 import random
-import time
 from ast import literal_eval
 from distutils.util import strtobool
 from typing import Any, Callable, Optional, Sequence, Tuple, Union
@@ -287,23 +286,3 @@ def dtype_torch_to_numpy(dtype):
 def dtype_numpy_to_torch(dtype):
     """Convert a numpy dtype to its torch equivalent."""
     return _np_to_torch_dtype[dtype]
-
-
-class PerfContext:
-    """
-    Context manager for tracking how much time is spent within context blocks. This uses `time.perf_counter` to
-    accumulate the total amount of time in seconds in the attribute `total_time` over however many context blocks
-    the object is used in.
-    """
-
-    def __init__(self):
-        self.total_time = 0
-        self.start_time = None
-
-    def __enter__(self):
-        self.start_time = time.perf_counter()
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        self.total_time += time.perf_counter() - self.start_time
-        self.start_time = None

monai/utils/module.py

@@ -16,6 +16,8 @@
 from re import match
 from typing import Any, Callable, List, Sequence, Tuple, Union
 
+import torch
+
 from .misc import ensure_tuple
 
 OPTIONAL_IMPORT_MSG_FMT = "{}"
@@ -31,6 +33,8 @@
     "get_full_type_name",
     "has_option",
     "get_package_version",
+    "get_torch_version_tuple",
+    "PT_BEFORE_1_7",
 ]
 
 
@@ -264,3 +268,22 @@ def get_package_version(dep_name, default="NOT INSTALLED or UNKNOWN VERSION."):
             del dep
             del sys.modules[dep_name]
     return dep_ver
+
+
+def get_torch_version_tuple():
+    """
+    Returns:
+        tuple of ints represents the pytorch major/minor version.
+    """
+    return tuple((int(x) for x in torch.__version__.split(".")[:2]))
+
+
+PT_BEFORE_1_7 = True
+ver, has_ver = optional_import("pkg_resources", name="parse_version")
+try:
+    if has_ver:
+        PT_BEFORE_1_7 = ver(torch.__version__) < ver("1.7")
+    else:
+        PT_BEFORE_1_7 = get_torch_version_tuple() < (1, 7)
+except (AttributeError, TypeError):
+    pass

monai/utils/profiling.py

@@ -14,7 +14,7 @@
 
 import torch
 
-__all__ = ["torch_profiler_full", "torch_profiler_time_cpu_gpu", "torch_profiler_time_end_to_end"]
+__all__ = ["torch_profiler_full", "torch_profiler_time_cpu_gpu", "torch_profiler_time_end_to_end", "PerfContext"]
 
 
 def torch_profiler_full(func):
@@ -88,3 +88,23 @@ def wrapper(*args, **kwargs):
         return result
 
     return wrapper
+
+
+class PerfContext:
+    """
+    Context manager for tracking how much time is spent within context blocks. This uses `time.perf_counter` to
+    accumulate the total amount of time in seconds in the attribute `total_time` over however many context blocks
+    the object is used in.
+    """
+
+    def __init__(self):
+        self.total_time = 0
+        self.start_time = None
+
+    def __enter__(self):
+        self.start_time = time.perf_counter()
+        return self
+
+    def __exit__(self, exc_type, exc_value, exc_traceback):
+        self.total_time += time.perf_counter() - self.start_time
+        self.start_time = None

tests/min_tests.py

@@ -32,6 +32,7 @@ def run_testsuit():
         "test_cachedataset",
         "test_cachedataset_parallel",
         "test_dataset",
+        "test_detect_envelope",
         "test_iterable_dataset",
         "test_ensemble_evaluator",
         "test_handler_checkpoint_loader",
@@ -51,6 +52,7 @@ def run_testsuit():
         "test_handler_validation",
         "test_hausdorff_distance",
         "test_header_correct",
+        "test_hilbert_transform",
         "test_img2tensorboard",
         "test_integration_segmentation_3d",
         "test_integration_sliding_window",

tests/test_detect_envelope.py

@@ -156,9 +156,8 @@ def test_no_fft_module_error(self):
 @SkipIfAtLeastPyTorchVersion((1, 7))
 class TestDetectEnvelopeInvalidPyTorch(unittest.TestCase):
     def test_invalid_pytorch_error(self):
-        with self.assertRaises(InvalidPyTorchVersionError) as cm:
+        with self.assertRaisesRegexp(InvalidPyTorchVersionError, "version"):
             DetectEnvelope()
-            self.assertEqual("DetectEnvelope requires PyTorch version 1.7.0 or later", str(cm.exception))
 
 
 if __name__ == "__main__":

tests/test_highresnet.py

@@ -53,7 +53,7 @@ def test_shape(self, input_param, input_shape, expected_shape):
             result = net.forward(torch.randn(input_shape).to(device))
             self.assertEqual(result.shape, expected_shape)
 
-    @TimedCall(seconds=100, force_quit=True)
+    @TimedCall(seconds=200, force_quit=True)
     def test_script(self):
         input_param, input_shape, expected_shape = TEST_CASE_1
         net = HighResNet(**input_param)

tests/test_hilbert_transform.py

@@ -217,9 +217,8 @@ def test_no_fft_module_error(self):
 @SkipIfAtLeastPyTorchVersion((1, 7))
 class TestHilbertTransformInvalidPyTorch(unittest.TestCase):
     def test_invalid_pytorch_error(self):
-        with self.assertRaises(InvalidPyTorchVersionError) as cm:
+        with self.assertRaisesRegex(InvalidPyTorchVersionError, "version"):
             HilbertTransform()
-            self.assertEqual("HilbertTransform requires PyTorch version 1.7.0 or later", str(cm.exception))
 
 
 if __name__ == "__main__":

tests/test_integration_workflows.py

@@ -296,7 +296,7 @@ def train_and_infer(self, idx=0):
 
     def test_training(self):
         repeated = []
-        test_rounds = 3 if monai.config.get_torch_version_tuple() >= (1, 6) else 2
+        test_rounds = 3 if monai.utils.module.get_torch_version_tuple() >= (1, 6) else 2
         for i in range(test_rounds):
             results = self.train_and_infer(idx=i)
             repeated.append(results)
@@ -308,7 +308,7 @@ def test_training(self):
         daemon=False,
     )
     def test_timing(self):
-        if monai.config.get_torch_version_tuple() >= (1, 6):
+        if monai.utils.module.get_torch_version_tuple() >= (1, 6):
             self.train_and_infer(idx=2)
 
 

tests/utils.py

@@ -28,11 +28,12 @@
 import torch
 import torch.distributed as dist
 
-from monai.config import get_torch_version_tuple
 from monai.data import create_test_image_2d, create_test_image_3d
 from monai.utils import ensure_tuple, optional_import, set_determinism
+from monai.utils.module import get_torch_version_tuple
 
 nib, _ = optional_import("nibabel")
+ver, has_pkg_res = optional_import("pkg_resources", name="parse_version")
 
 quick_test_var = "QUICKTEST"
 
@@ -99,7 +100,10 @@ class SkipIfBeforePyTorchVersion(object):
 
     def __init__(self, pytorch_version_tuple):
         self.min_version = pytorch_version_tuple
-        self.version_too_old = get_torch_version_tuple() < self.min_version
+        if has_pkg_res:
+            self.version_too_old = ver(torch.__version__) < ver(".".join(map(str, self.min_version)))
+        else:
+            self.version_too_old = get_torch_version_tuple() < self.min_version
 
     def __call__(self, obj):
         return unittest.skipIf(
@@ -113,7 +117,10 @@ class SkipIfAtLeastPyTorchVersion(object):
 
     def __init__(self, pytorch_version_tuple):
         self.max_version = pytorch_version_tuple
-        self.version_too_new = get_torch_version_tuple() >= self.max_version
+        if has_pkg_res:
+            self.version_too_new = ver(torch.__version__) >= ver(".".join(map(str, self.max_version)))
+        else:
+            self.version_too_new = get_torch_version_tuple() >= self.max_version
 
     def __call__(self, obj):
         return unittest.skipIf(