5971-fix the pixelshuffle upsample shape mismatch problem.

.pre-commit-config.yaml

@@ -54,7 +54,8 @@ repos:
         exclude: |
           (?x)^(
               monai/__init__.py|
-              docs/source/conf.py
+              docs/source/conf.py|
+              tests/utils.py
           )$
 
   - repo: https://github.com/hadialqattan/pycln

monai/networks/nets/flexible_unet.py

@@ -309,7 +309,7 @@ def __init__(
             bias=decoder_bias,
             upsample=upsample,
             interp_mode=interp_mode,
-            pre_conv=None,
+            pre_conv="default",
             align_corners=None,
             is_pad=is_pad,
         )

tests/test_flexible_unet.py

@@ -173,29 +173,32 @@ def make_shape_cases(
     num_classes=10,
     input_shape=64,
     norm=("batch", {"eps": 1e-3, "momentum": 0.01}),
+    upsample=("nontrainable", "deconv", "pixelshuffle"),
 ):
     ret_tests = []
     for spatial_dim in spatial_dims:  # selected spatial_dims
         for batch in batches:  # check single batch as well as multiple batch input
             for model in models:  # selected models
                 for is_pretrained in pretrained:  # pretrained or not pretrained
-                    if ("resnet" in model) and is_pretrained:
-                        continue
-                    kwargs = {
-                        "in_channels": in_channels,
-                        "out_channels": num_classes,
-                        "backbone": model,
-                        "pretrained": is_pretrained,
-                        "spatial_dims": spatial_dim,
-                        "norm": norm,
-                    }
-                    ret_tests.append(
-                        [
-                            kwargs,
-                            (batch, in_channels) + (input_shape,) * spatial_dim,
-                            (batch, num_classes) + (input_shape,) * spatial_dim,
-                        ]
-                    )
+                    for upsample_method in upsample:
+                        if ("resnet" in model) and is_pretrained:
+                            continue
+                        kwargs = {
+                            "in_channels": in_channels,
+                            "out_channels": num_classes,
+                            "backbone": model,
+                            "pretrained": is_pretrained,
+                            "spatial_dims": spatial_dim,
+                            "norm": norm,
+                            "upsample": upsample_method,
+                        }
+                        ret_tests.append(
+                            [
+                                kwargs,
+                                (batch, in_channels) + (input_shape,) * spatial_dim,
+                                (batch, num_classes) + (input_shape,) * spatial_dim,
+                            ]
+                        )
     return ret_tests
 
 

tests/utils.py

@@ -49,7 +49,7 @@
 from monai.utils.type_conversion import convert_data_type
 
 nib, _ = optional_import("nibabel")
-http_error, has_requests = optional_import("requests", name="HTTPError")
+http_error, has_req = optional_import("requests", name="HTTPError")
 
 quick_test_var = "QUICKTEST"
 _tf32_enabled = None
@@ -126,7 +126,7 @@ def assert_allclose(
 def skip_if_downloading_fails():
     try:
         yield
-    except (ContentTooShortError, HTTPError, ConnectionError) + (http_error,) if has_requests else () as e:
+    except (ContentTooShortError, HTTPError, ConnectionError) + (http_error,) if has_req else () as e:  # noqa: B030
         raise unittest.SkipTest(f"error while downloading: {e}") from e
     except ssl.SSLError as ssl_e:
         if "decryption failed" in str(ssl_e):