Add conda badge to readme (#5661)

Add conda badge to readme

Signed-off-by: Wenqi Li <wenqil@nvidia.com>
Co-authored-by: Wenqi Li <wenqil@nvidia.com>

Author: rijobro

README.md

@@ -9,6 +9,7 @@
 [![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/?badge=latest)
 [![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg)](https://codecov.io/gh/Project-MONAI/MONAI)
 [![PyPI version](https://badge.fury.io/py/monai.svg)](https://badge.fury.io/py/monai)
+[![conda](https://img.shields.io/conda/vn/conda-forge/monai)](https://anaconda.org/conda-forge/monai)
 
 MONAI is a [PyTorch](https://pytorch.org/)-based, [open-source](https://github.com/Project-MONAI/MONAI/blob/dev/LICENSE) framework for deep learning in healthcare imaging, part of [PyTorch Ecosystem](https://pytorch.org/ecosystem/).
 Its ambitions are:

tests/test_dynunet.py

@@ -17,8 +17,7 @@
 
 from monai.networks import eval_mode
 from monai.networks.nets import DynUNet
-from monai.utils.module import pytorch_after
-from tests.utils import skip_if_no_cuda, skip_if_windows, test_script_save
+from tests.utils import assert_allclose, skip_if_no_cuda, skip_if_windows, test_script_save
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -144,11 +143,7 @@ def test_consistency(self, input_param, input_shape, _):
                         with eval_mode(net_fuser):
                             result_fuser = net_fuser(input_tensor)
 
-                        # torch.testing.assert_allclose() is deprecated since 1.12 and will be removed in 1.14
-                        if pytorch_after(1, 12):
-                            torch.testing.assert_close(result, result_fuser)
-                        else:
-                            torch.testing.assert_allclose(result, result_fuser)
+                        assert_allclose(result, result_fuser, rtol=1e-4, atol=1e-4)
 
 
 class TestDynUNetDeepSupervision(unittest.TestCase):

tests/test_vitautoenc.py

@@ -15,6 +15,7 @@
 
 from monai.networks import eval_mode
 from monai.networks.nets.vitautoenc import ViTAutoEnc
+from tests.utils import skip_if_windows
 
 TEST_CASE_Vitautoenc = []
 for in_channels in [1, 4]:
@@ -63,6 +64,7 @@
 
 class TestPatchEmbeddingBlock(unittest.TestCase):
     @parameterized.expand(TEST_CASE_Vitautoenc)
+    @skip_if_windows
     def test_shape(self, input_param, input_shape, expected_shape):
         net = ViTAutoEnc(**input_param)
         with eval_mode(net):

tests/test_weighted_random_sampler_dist.py

@@ -16,9 +16,10 @@
 import torch.distributed as dist
 
 from monai.data import DistributedWeightedRandomSampler
-from tests.utils import DistCall, DistTestCase
+from tests.utils import DistCall, DistTestCase, skip_if_windows
 
 
+@skip_if_windows
 class DistributedWeightedRandomSamplerTest(DistTestCase):
     @DistCall(nnodes=1, nproc_per_node=2)
     def test_sampling(self):