Mercurial > repos > bgruening > bioimage_inference
changeset 3:bc28236f407b draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/bioimaging commit e08711c242a340a1671dfca35f52d3724086e968
| author | bgruening |
|---|---|
| date | Wed, 26 Feb 2025 10:27:28 +0000 |
| parents | 0c0de5546fe1 |
| children | 2b61d8fcfa52 |
| files | bioimage_inference.xml main.py test-data/output_nucleisegboundarymodel.tif test-data/output_nucleisegboundarymodel_matrix.npy |
| diffstat | 4 files changed, 199 insertions(+), 52 deletions(-) [+] |
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--- a/bioimage_inference.xml Tue Oct 15 12:57:33 2024 +0000 +++ b/bioimage_inference.xml Wed Feb 26 10:27:28 2025 +0000 @@ -2,7 +2,7 @@ <description>with PyTorch</description> <macros> <token name="@TOOL_VERSION@">2.4.1</token> - <token name="@VERSION_SUFFIX@">0</token> + <token name="@VERSION_SUFFIX@">1</token> </macros> <creator> <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" /> @@ -30,12 +30,18 @@ --imaging_model '$input_imaging_model' --image_file '$input_image_file' --image_size '$input_image_input_size' + --image_axes '$input_image_input_axes' ]]> </command> <inputs> <param name="input_imaging_model" type="data" format="zip" label="BioImage.IO model" help="Please upload a BioImage.IO model."/> <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" 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_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> + </param> </inputs> <outputs> <data format="tif" name="output_predicted_image" from_work_dir="output_predicted_image.tif" label="Predicted image"></data> @@ -46,15 +52,97 @@ <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/> <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"/> - <output name="output_predicted_image" file="output_nucleisegboundarymodel.tif" compare="sim_size" delta="100" /> - <output name="output_predicted_image_matrix" file="output_nucleisegboundarymodel_matrix.npy" compare="sim_size" delta="100" /> + <param name="input_image_input_axes" value="bcyx"/> + <output name="output_predicted_image" ftype="tif"> + <assert_contents> + <has_size size="524846" delta="110" /> + </assert_contents> + </output> + <output name="output_predicted_image_matrix" ftype="npy"> + <assert_contents> + <has_size size="524416" delta="110" /> + </assert_contents> + </output> </test> <test> <param name="input_imaging_model" value="input_imaging_model.zip" location="https://zenodo.org/api/records/6647674/files/weights-torchscript.pt/content"/> <param name="input_image_file" value="input_nucleisegboundarymodel.png"/> <param name="input_image_input_size" value="256,256,1,1"/> - <output name="output_predicted_image" file="output_nucleisegboundarymodel.tif" compare="sim_size" delta="100" /> - <output name="output_predicted_image_matrix" file="output_nucleisegboundarymodel_matrix.npy" compare="sim_size" delta="100" /> + <param name="input_image_input_axes" value="bcyx"/> + <output name="output_predicted_image" ftype="tif"> + <assert_contents> + <has_size size="524846" delta="110" /> + </assert_contents> + </output> + <output name="output_predicted_image_matrix" ftype="npy"> + <assert_contents> + <has_size size="524416" delta="110" /> + </assert_contents> + </output> + </test> + <test> + <param name="input_imaging_model" value="input_imaging_model.zip" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/emotional-cricket/1.1/files/torchscript_tracing.pt"/> + <param name="input_image_file" value="input_image_file.tif" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/emotional-cricket/1.1/files/sample_input_0.tif"/> + <param name="input_image_input_size" value="128,128,100,1"/> + <param name="input_image_input_axes" value="bczyx"/> + <output name="output_predicted_image" ftype="tif"> + <assert_contents> + <has_size size="6572778" delta="100" /> + </assert_contents> + </output> + <output name="output_predicted_image_matrix" ftype="npy"> + <assert_contents> + <has_size size="6572778" delta="100" /> + </assert_contents> + </output> + </test> + <test> + <param name="input_imaging_model" value="input_imaging_model.zip" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/emotional-cricket/1.1/files/torchscript_tracing.pt"/> + <param name="input_image_file" value="input_3d-unet-arabidopsis-apical-stem-cells.png" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/emotional-cricket/1.1/files/raw.png"/> + <param name="input_image_input_size" value="128,128,100,1"/> + <param name="input_image_input_axes" value="bczyx"/> + <output name="output_predicted_image" ftype="tif"> + <assert_contents> + <has_size size="6572778" delta="100" /> + </assert_contents> + </output> + <output name="output_predicted_image_matrix" ftype="npy"> + <assert_contents> + <has_size size="6572778" delta="100" /> + </assert_contents> + </output> + </test> + <test> + <param name="input_imaging_model" value="input_imaging_model.zip" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/organized-badger/1/files/weights-torchscript.pt"/> + <param name="input_image_file" value="input_platynereisemnucleisegmentationboundarymodel.tif" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/organized-badger/1/files/sample_input_0.tif"/> + <param name="input_image_input_size" value="256,256,32,1"/> + <param name="input_image_input_axes" value="bczyx"/> + <output name="output_predicted_image" ftype="tif"> + <assert_contents> + <has_size size="16789714" delta="100" /> + </assert_contents> + </output> + <output name="output_predicted_image_matrix" ftype="npy"> + <assert_contents> + <has_size size="16777344" delta="100" /> + </assert_contents> + </output> + </test> + <test> + <param name="input_imaging_model" value="input_imaging_model.zip" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/thoughtful-turtle/1/files/torchscript_tracing.pt"/> + <param name="input_image_file" value="input_3d-unet-lateral-root-primordia-cells.tif" location="https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/thoughtful-turtle/1/files/sample_input_0.tif"/> + <param name="input_image_input_size" value="128,128,100,1"/> + <param name="input_image_input_axes" value="bczyx"/> + <output name="output_predicted_image" ftype="tif"> + <assert_contents> + <has_size size="6572778" delta="100" /> + </assert_contents> + </output> + <output name="output_predicted_image_matrix" ftype="npy"> + <assert_contents> + <has_size size="6553728" delta="100" /> + </assert_contents> + </output> </test> </tests> <help> @@ -64,13 +152,14 @@ The tool takes a BioImage.IO model and an image (as TIF or PNG) to be analyzed. The analysis is performed by the model. The model is used to obtain a prediction of the result of the analysis, and the predicted image becomes available as a TIF file in the Galaxy history. **Input files** - - BioImage.IO model: Add one of the model from Galaxy file uploader by choosing a "remote" file at "ML Models/bioimaging-models" - - Image to be analyzed: Provide an image as TIF/PNG file - - Provide the necessary input size for the model. This information can be found in the RDF file of each model (RDF file > config > test_information > inputs > size) + - BioImage.IO model: Add one of the model from Galaxy file uploader by choosing a "remote" file at "ML Models/bioimaging-models" + - Image to be analyzed: Provide an image as TIF/PNG file + - Provide the necessary input size for the model. This information can be found in the RDF file of each model (RDF file > config > test_information > inputs > size) + - Provide axes of input image. This information can also be found in the RDF file of each model (RDF file > inputs > axes). An example value of axes is 'bczyx' for 3D U-Net Arabidopsis Lateral Root Primordia model **Output files** - - Predicted image: Predicted image using the BioImage.IO model - - Predicted image matrix: Predicted image matrix in original dimensions + - Predicted image: Predicted image using the BioImage.IO model + - Predicted image matrix: Predicted image matrix in original dimensions ]]> </help> <citations>
--- a/main.py Tue Oct 15 12:57:33 2024 +0000 +++ b/main.py Wed Feb 26 10:27:28 2025 +0000 @@ -7,70 +7,128 @@ import imageio import numpy as np import torch +import torch.nn.functional as F -def find_dim_order(user_in_shape, input_image): +def dynamic_resize(image: torch.Tensor, target_shape: tuple): """ - Find the correct order of input image's - shape. For a few models, the order of input size - mentioned in the RDF.yaml file is reversed compared - to the input image's original size. If it is reversed, - transpose the image to find correct order of image's - dimensions. + Resize an input tensor dynamically to the target shape. + + Parameters: + - image: Input tensor with shape (C, D1, D2, ..., DN) (any number of spatial dims) + - target_shape: Tuple specifying the target shape (C', D1', D2', ..., DN') + + Returns: + - Resized tensor with target shape target_shape. """ - image_shape = list(input_image.shape) - # reverse the input shape provided from RDF.yaml file - correct_order = user_in_shape.split(",")[::-1] - # remove 1s from the original dimensions - correct_order = [int(i) for i in correct_order if i != "1"] - if (correct_order[0] == image_shape[-1]) and (correct_order != image_shape): - input_image = torch.tensor(input_image.transpose()) - return input_image, correct_order + # Extract input shape + input_shape = image.shape + num_dims = len(input_shape) # Includes channels and spatial dimensions + + # Ensure target shape matches the number of dimensions + if len(target_shape) != num_dims: + raise ValueError( + f"Target shape {target_shape} must match input dimensions {num_dims}" + ) + + # Extract target channels and spatial sizes + target_channels = target_shape[0] # First element is the target channel count + target_spatial_size = target_shape[1:] # Remaining elements are spatial dimensions + + # Add batch dim (N=1) for resizing + image = image.unsqueeze(0) + + # Choose the best interpolation mode based on dimensionality + if num_dims == 4: + interp_mode = "trilinear" + elif num_dims == 3: + interp_mode = "bilinear" + elif num_dims == 2: + interp_mode = "bicubic" + else: + interp_mode = "nearest" + + # Resize spatial dimensions dynamically + image = F.interpolate( + image, size=target_spatial_size, mode=interp_mode, align_corners=False + ) + + # Adjust channels if necessary + current_channels = image.shape[1] + + if target_channels > current_channels: + # Expand channels by repeating existing ones + expand_factor = target_channels // current_channels + remainder = target_channels % current_channels + image = image.repeat(1, expand_factor, *[1] * (num_dims - 1)) + + if remainder > 0: + extra_channels = image[ + :, :remainder, ... + ] # Take the first few channels to match target + image = torch.cat([image, extra_channels], dim=1) + + elif target_channels < current_channels: + # Reduce channels by averaging adjacent ones + image = image[:, :target_channels, ...] # Simply slice to reduce channels + return image.squeeze(0) # Remove batch dimension before returning if __name__ == "__main__": arg_parser = argparse.ArgumentParser() - arg_parser.add_argument("-im", "--imaging_model", required=True, help="Input BioImage model") - arg_parser.add_argument("-ii", "--image_file", required=True, help="Input image file") - arg_parser.add_argument("-is", "--image_size", required=True, help="Input image file's size") + arg_parser.add_argument( + "-im", "--imaging_model", required=True, help="Input BioImage model" + ) + arg_parser.add_argument( + "-ii", "--image_file", required=True, help="Input image file" + ) + arg_parser.add_argument( + "-is", "--image_size", required=True, help="Input image file's size" + ) + arg_parser.add_argument( + "-ia", "--image_axes", required=True, help="Input image file's axes" + ) # get argument values args = vars(arg_parser.parse_args()) model_path = args["imaging_model"] input_image_path = args["image_file"] + input_size = args["image_size"] # load all embedded images in TIF file test_data = imageio.v3.imread(input_image_path, index="...") + test_data = test_data.astype(np.float32) test_data = np.squeeze(test_data) - test_data = test_data.astype(np.float32) + + target_image_dim = input_size.split(",")[::-1] + target_image_dim = [int(i) for i in target_image_dim if i != "1"] + target_image_dim = tuple(target_image_dim) - # assess the correct dimensions of TIF input image - input_image_shape = args["image_size"] - im_test_data, shape_vals = find_dim_order(input_image_shape, test_data) + exp_test_data = torch.tensor(test_data) + # 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: + exp_test_data = exp_test_data_T + 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) + try: + exp_test_data = dynamic_resize(exp_test_data, target_image_dim) + except Exception as e: + raise RuntimeError(f"Error during resizing: {e}") from e + + current_dimension = len(exp_test_data.shape) + input_axes = args["image_axes"] + target_dimension = len(input_axes) + # expand input image based on the number of target dimensions + for i in range(target_dimension - current_dimension): + exp_test_data = torch.unsqueeze(exp_test_data, i) # load model model = torch.load(model_path) model.eval() - # find the number of dimensions required by the model - target_dimension = 0 - for param in model.named_parameters(): - target_dimension = len(param[1].shape) - break - current_dimension = len(list(im_test_data.shape)) - - # update the dimensions of input image if the required image by - # the model is smaller - slices = tuple(slice(0, s_val) for s_val in shape_vals) - - # apply the slices to the reshaped_input - im_test_data = im_test_data[slices] - exp_test_data = torch.tensor(im_test_data) - - # expand input image's dimensions - for i in range(target_dimension - current_dimension): - exp_test_data = torch.unsqueeze(exp_test_data, i) - # make prediction pred_data = model(exp_test_data) pred_data_output = pred_data.detach().numpy()