WIP fix tests

Author: FynnBe

bioimageio/core/digest_spec.py

@@ -127,14 +127,14 @@ def _import_from_file_impl(
             )
 
             if importlib_spec is None:
-                raise ImportError(f"Failed to import {source}.")
+                raise ImportError(f"Failed to import {source}")
 
             module = importlib.util.module_from_spec(importlib_spec)
             assert importlib_spec.loader is not None
             importlib_spec.loader.exec_module(module)
 
         except Exception as e:
-            raise ImportError(f"Failed to import {source} .") from e
+            raise ImportError(f"Failed to import {source}") from e
         else:
             sys.modules[module_name] = module  # cache this module
 

tests/test_bioimageio_collection.py

@@ -4,7 +4,7 @@
 import requests
 from pydantic import HttpUrl
 
-from bioimageio.spec import InvalidDescr, ValidationContext
+from bioimageio.spec import InvalidDescr
 from bioimageio.spec.common import Sha256
 from tests.utils import ParameterSet, expensive_test
 
@@ -42,25 +42,34 @@ def yield_bioimageio_yaml_urls() -> Iterable[ParameterSet]:
     "affable-shark/1.1",  # onnx weights expect fixed input shape
     "affectionate-cow/0.1.0",  # custom dependencies
     "ambitious-sloth/1.2",  # requires inferno
+    "committed-turkey/1.2",  # error deserializing VarianceScaling
+    "creative-panda/1",  # error deserializing Conv2D
     "dazzling-spider/0.1.0",  # requires careamics
-    "dynamic-t-rex/1",  # model.v0_4.ScaleLinearKwargs with axes
+    "discreete-rooster/1",  # error deserializing VarianceScaling
+    "dynamic-t-rex/1",  # needs update to 0.5 for scale_linear with axes processing
+    "easy-going-sauropod/1",  # CPU implementation of Conv3D currently only supports the NHWC tensor format.
     "efficient-chipmunk/1",  # needs plantseg
     "famous-fish/0.1.0",  # list index out of range `fl[3]`
     "greedy-whale/1",  # batch size is actually limited to 1
     "happy-elephant/0.1.0",  # list index out of range `fl[3]`
+    "happy-honeybee/0.1.0",  # requires biapy
+    "heroic-otter/0.1.0",  # requires biapy
     "humorous-crab/1",  # batch size is actually limited to 1
     "humorous-fox/0.1.0",  # requires careamics
     "humorous-owl/1",  # error deserializing GlorotUniform
-    "noisy-ox/1",  # batch size is actually limited to 1
-    "stupendous-sheep/1.2",
-    "wild-rhino/0.1.0",  # requires careamics
     "idealistic-turtle/0.1.0",  # requires biapy
+    "impartial-shark/1",  # error deserializing VarianceScaling
     "intelligent-lion/0.1.0",  # requires biapy
+    "joyful-deer/1",  # needs update to 0.5 for scale_linear with axes processing
     "merry-water-buffalo/0.1.0",  # requires biapy
-    "venomous-swan/0.1.0",  # requires biapy
-    "heroic-otter/0.1.0",  # requires biapy
+    "naked-microbe/1",  # unknown layer Convolution2D
+    "noisy-ox/1",  # batch size is actually limited to 1
+    "non-judgemental-eagle/1",  # error deserializing GlorotUniform
+    "straightforward-crocodile/1",  # needs update to 0.5 for scale_linear with axes processing
     "stupendous-sheep/1.1",  # requires relativ import of attachment
-    "commited-turkey/1.2",  # error deserializng VarianceScaling
+    "stupendous-sheep/1.2",
+    "venomous-swan/0.1.0",  # requires biapy
+    "wild-rhino/0.1.0",  # requires careamics
 }
 
 
@@ -76,8 +85,7 @@ def test_rdf(
 
     from bioimageio.core import load_description_and_test
 
-    with ValidationContext():
-        descr = load_description_and_test(descr_url, sha256=sha, stop_early=True)
+    descr = load_description_and_test(descr_url, sha256=sha, stop_early=True)
 
     assert not isinstance(descr, InvalidDescr)
     assert (

tests/test_proc_ops.py

@@ -21,15 +21,17 @@ def tid():
 def test_scale_linear(tid: MemberId):
     from bioimageio.core.proc_ops import ScaleLinear
 
-    offset = xr.DataArray([1, 2, 42], dims=("c"))
-    gain = xr.DataArray([1, 2, 3], dims=("c"))
-    data = xr.DataArray(np.arange(6).reshape((1, 2, 3)), dims=("x", "y", "c"))
+    offset = xr.DataArray([1, 2, 42], dims=("channel",))
+    gain = xr.DataArray([1, 2, 3], dims=("channel",))
+    data = xr.DataArray(np.arange(6).reshape((1, 2, 3)), dims=("x", "y", "channel"))
     sample = Sample(members={tid: Tensor.from_xarray(data)}, stat={}, id=None)
 
     op = ScaleLinear(input=tid, output=tid, offset=offset, gain=gain)
     op(sample)
 
-    expected = xr.DataArray(np.array([[[1, 4, 48], [4, 10, 57]]]), dims=("x", "y", "c"))
+    expected = xr.DataArray(
+        np.array([[[1, 4, 48], [4, 10, 57]]]), dims=("x", "y", "channel")
+    )
     xr.testing.assert_allclose(expected, sample.members[tid].data, rtol=1e-5, atol=1e-7)
 
 
@@ -84,10 +86,10 @@ def test_zero_mean_unit_variance_fixed(tid: MemberId):
     op = FixedZeroMeanUnitVariance(
         tid,
         tid,
-        mean=xr.DataArray([3, 4, 5], dims=("c")),
-        std=xr.DataArray([2.44948974, 2.44948974, 2.44948974], dims=("c")),
+        mean=xr.DataArray([3, 4, 5], dims=("channel",)),
+        std=xr.DataArray([2.44948974, 2.44948974, 2.44948974], dims=("channel",)),
     )
-    data = xr.DataArray(np.arange(9).reshape((1, 3, 3)), dims=("b", "c", "x"))
+    data = xr.DataArray(np.arange(9).reshape((1, 3, 3)), dims=("b", "channel", "x"))
     expected = xr.DataArray(
         np.array(
             [
@@ -124,7 +126,7 @@ def test_zero_mean_unit_variance_fixed2(tid: MemberId):
 def test_zero_mean_unit_across_axes(tid: MemberId):
     from bioimageio.core.proc_ops import ZeroMeanUnitVariance
 
-    data = xr.DataArray(np.arange(18).reshape((2, 3, 3)), dims=("c", "x", "y"))
+    data = xr.DataArray(np.arange(18).reshape((2, 3, 3)), dims=("channel", "x", "y"))
 
     op = ZeroMeanUnitVariance(
         tid,
@@ -136,7 +138,8 @@ def test_zero_mean_unit_across_axes(tid: MemberId):
     sample.stat = compute_measures(op.required_measures, [sample])
 
     expected = xr.concat(
-        [(data[i : i + 1] - data[i].mean()) / data[i].std() for i in range(2)], dim="c"
+        [(data[i : i + 1] - data[i].mean()) / data[i].std() for i in range(2)],
+        dim="channel",
     )
     op(sample)
     xr.testing.assert_allclose(expected, sample.members[tid].data, rtol=1e-5, atol=1e-7)
@@ -146,7 +149,7 @@ def test_binarize(tid: MemberId):
     from bioimageio.core.proc_ops import Binarize
 
     op = Binarize(tid, tid, threshold=14)
-    data = xr.DataArray(np.arange(30).reshape((2, 3, 5)), dims=("x", "y", "c"))
+    data = xr.DataArray(np.arange(30).reshape((2, 3, 5)), dims=("x", "y", "channel"))
     sample = Sample(members={tid: Tensor.from_xarray(data)}, stat={}, id=None)
     expected = xr.zeros_like(data)
     expected[{"x": slice(1, None)}] = 1
@@ -158,7 +161,7 @@ def test_binarize2(tid: MemberId):
     from bioimageio.core.proc_ops import Binarize
 
     shape = (3, 32, 32)
-    axes = ("c", "y", "x")
+    axes = ("channel", "y", "x")
     np_data = np.random.rand(*shape)
     data = xr.DataArray(np_data, dims=axes)
 
@@ -188,7 +191,7 @@ def test_clip(tid: MemberId):
 def test_combination_of_op_steps_with_dims_specified(tid: MemberId):
     from bioimageio.core.proc_ops import ZeroMeanUnitVariance
 
-    data = xr.DataArray(np.arange(18).reshape((2, 3, 3)), dims=("c", "x", "y"))
+    data = xr.DataArray(np.arange(18).reshape((2, 3, 3)), dims=("channel", "x", "y"))
     sample = Sample(members={tid: Tensor.from_xarray(data)}, stat={}, id=None)
     op = ZeroMeanUnitVariance(
         tid,
@@ -239,7 +242,7 @@ def test_scale_mean_variance(tid: MemberId, axes: Optional[Tuple[AxisId, ...]]):
     from bioimageio.core.proc_ops import ScaleMeanVariance
 
     shape = (3, 32, 46)
-    ipt_axes = ("c", "y", "x")
+    ipt_axes = ("channel", "y", "x")
     np_data = np.random.rand(*shape)
     ipt_data = xr.DataArray(np_data, dims=ipt_axes)
     ref_data = xr.DataArray((np_data * 2) + 3, dims=ipt_axes)