Updates to test precision to account for float32 conversion, this should be removed when PyTorch is updated.

Signed-off-by: Eric Kerfoot <[email protected]>

Author: ericspod

environment-dev-test.yml

@@ -0,0 +1,14 @@
+name: monai
+channels:
+  - pytorch
+  - defaults
+  - nvidia
+  - conda-forge
+dependencies:
+  - numpy>=1.17
+  - pytorch>=1.8
+  - torchvision
+  - pytorch-cuda=11.6
+  - pip
+  - pip:
+    - -r requirements-dev.txt

tests/integration/test_pad_collation.py

@@ -11,8 +11,10 @@
 
 from __future__ import annotations
 
+import os
 import random
 import unittest
+from contextlib import redirect_stderr
 from functools import wraps
 
 import numpy as np
@@ -35,7 +37,7 @@
     RandZoomd,
     ToTensor,
 )
-from monai.utils import set_determinism
+from monai.utils import first, set_determinism
 
 
 @wraps(pad_list_data_collate)
@@ -97,8 +99,9 @@ def test_pad_collation(self, t_type, collate_method, transform):
         # Default collation should raise an error
         loader_fail = DataLoader(dataset, batch_size=10)
         with self.assertRaises(RuntimeError):
-            for _ in loader_fail:
-                pass
+            # stifle PyTorch error reporting, we expect failure so don't need to look at it
+            with open(os.devnull) as f, redirect_stderr(f):
+                _ = first(loader_fail)
 
         # Padded collation shouldn't
         loader = DataLoader(dataset, batch_size=10, collate_fn=collate_method)

tests/lazy_transforms_utils.py

@@ -12,6 +12,7 @@
 from __future__ import annotations
 
 from copy import deepcopy
+import sys
 
 from monai.data import MetaTensor, set_track_meta
 from monai.transforms import InvertibleTransform, MapTransform, Randomizable
@@ -62,6 +63,13 @@ def test_resampler_lazy(
         resampler.set_random_state(seed=seed)
     set_track_meta(True)
     resampler.lazy = True
+
+    # FIXME: this is a fix for https://github.com/Project-MONAI/MONAI/pull/8429, remove when PyTorch has
+    # fixed the underlying issue
+    if sys.platform == "win32":  
+        atol=1e-4
+        rtol=1e-4
+
     pending_output = resampler(**deepcopy(call_param))
     if output_idx is not None:
         expected_output, pending_output = (expected_output[output_idx], pending_output[output_idx])