Merge pull request #1609 from anuprulez/fix_axes

Fix transposed dimensions

Author: kostrykin

tools/bioimaging/bioimage_inference.xml

@@ -2,7 +2,7 @@
     <description>with PyTorch</description>
     <macros>
         <token name="@TOOL_VERSION@">2.4.1</token>
-        <token name="@VERSION_SUFFIX@">2</token>
+        <token name="@VERSION_SUFFIX@">3</token>
     </macros>
     <creator>
         <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" />
@@ -38,9 +38,8 @@
         <param name="input_image_file" type="data" format="tiff,png" label="Input image" help="Please provide an input image for the analysis."/>
         <param name="input_image_input_size" type="text" optional="false" label="Size of the input image" help="Provide the size of the input image. See the chosen model's RDF file to find the correct input size. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input size is 256 x 256 x 32 x 1. Enter the size as 256,256,32,1."/>
         <param name="input_image_input_axes" type="select" label="Axes of the input image" optional="false" help="Provide the input axes of the input image. See the chosen model's RDF file to find the correct axes. For example: for the BioImage.IO model MitochondriaEMSegmentationBoundaryModel, the input axes is 'bczyx'">
-            <option value="bczyx">bczyx</option>
-            <option value="bcyx">bcyx</option>
-            <option value="byxc">byxc</option>
+            <option value="bcyx">Four axes (e.g., bcyx, byxc)</option>
+            <option value="bczyx">Five axes (e.g., bczyx)</option>
         </param>
     </inputs>
     <outputs>
@@ -53,11 +52,7 @@
             <param name="input_image_file" value="input_image_file.tif" location="https://zenodo.org/api/records/6647674/files/sample_input_0.tif/content"/>
             <param name="input_image_input_size" value="256,256,1,1"/>
             <param name="input_image_input_axes" value="bcyx"/>
-            <output name="output_predicted_image" ftype="tiff">
-                <assert_contents>
-                    <has_size size="524846" delta="110" />
-                </assert_contents>
-            </output>
+            <output name="output_predicted_image" ftype="tiff" file="output_nucleisegboundarymodel.tiff" compare="image_diff" />
             <output name="output_predicted_image_matrix" ftype="npy">
                 <assert_contents>
                     <has_size size="524416" delta="110" />
@@ -69,11 +64,7 @@
             <param name="input_image_file" value="input_nucleisegboundarymodel.png"/>
             <param name="input_image_input_size" value="256,256,1,1"/>
             <param name="input_image_input_axes" value="bcyx"/>
-            <output name="output_predicted_image" ftype="tiff">
-                <assert_contents>
-                    <has_size size="524846" delta="110" />
-                </assert_contents>
-            </output>
+            <output name="output_predicted_image" ftype="tiff" file="output_nucleisegboundarymodel.tiff" compare="image_diff" />
             <output name="output_predicted_image_matrix" ftype="npy">
                 <assert_contents>
                     <has_size size="524416" delta="110" />

tools/bioimaging/main.py

@@ -108,8 +108,9 @@ def dynamic_resize(image: torch.Tensor, target_shape: tuple):
     # check if image dimensions are reversed
     reversed_order = list(reversed(range(exp_test_data.dim())))
     exp_test_data_T = exp_test_data.permute(*reversed_order)
-    if exp_test_data_T.shape == target_image_dim:
+    if exp_test_data_T.shape == target_image_dim and exp_test_data.shape != target_image_dim:
         exp_test_data = exp_test_data_T
+    # check if image dimensions are not equal to target image dimensions
     if exp_test_data.shape != target_image_dim:
         for i in range(len(target_image_dim) - exp_test_data.dim()):
             exp_test_data = exp_test_data.unsqueeze(i)