Gaby neubias (#80)

* Add ModelZoo comfort export, see Issue #71.

* Add test and sample tzw compressed file, see Issue #44 and specify version of tifffile so it handles LZW compressed files.

* Update requirements. See Issue #82.

* Update README.md

Co-authored-by: tibuch <[email protected]>

.gitignore

@@ -1,7 +1,6 @@
 __pycache__/
 *.py[cod]
 .idea
-
 .DS_Store
 .ipynb_checkpoints
 .pytest_cache
@@ -10,18 +9,27 @@ build/
 dist/
 docs/build/
 examples/**/models/
-
 *.png
 *.npz
 *.npy
 *.tif*
+!flybrain_lzw.tiff
 *.hdf5
 *.h5
 *.zip
 *.tfevents.*
 config.json
-
+!tests/test_data/config.json
+model.yaml
 /.project
 history/
 
-.cache/
+.cache/
+.pydevproject
+.settings/org.python.pydev.yaml
+.settings/org.eclipse.core.resources.prefs
+.pydevproject
+/.settings
+.pydevproject
+test.yml
+test*.yml

README.md

@@ -71,3 +71,8 @@ Have a look at our jupyter notebook:
 
 see [here](https://github.com/mpicbg-csbd/structured_N2V) for more info on `StructN2V`.
 
+## Note on functional tests
+
+The functional "tests" are meant to be run as regular programs. They are there to make sure that
+examples are still running after changes.
+

n2v/models/n2v_standard.py

@@ -1,6 +1,8 @@
 from csbdeep.models import CARE
 from csbdeep.utils import _raise, axes_check_and_normalize, axes_dict, load_json, save_json
-from csbdeep.internals import nets
+from csbdeep.internals import nets, predict
+from csbdeep.models.base_model import suppress_without_basedir
+from csbdeep.version import __version__ as package_version
 
 from six import string_types
 from csbdeep.utils.six import Path, FileNotFoundError
@@ -9,17 +11,24 @@
 from keras.callbacks import TerminateOnNaN
 import tensorflow as tf
 from keras import backend as K
-
+from ruamel.yaml import YAML
+import json
+import os
 import datetime
 import warnings
-
+from zipfile import ZipFile
 from .n2v_config import N2VConfig
-from ..utils import n2v_utils
 from ..internals.N2V_DataWrapper import N2V_DataWrapper
-from ..internals.n2v_losses import loss_mae, loss_mse
+from ..internals.n2v_losses import loss_mse, loss_mae 
+from ..utils import n2v_utils
 from ..utils.n2v_utils import pm_identity, pm_normal_additive, pm_normal_fitted, pm_normal_withoutCP, pm_uniform_withCP
 from n2v.nets.unet import build_single_unet_per_channel
 
+from tifffile import imsave
+import keras
+from csbdeep.utils.six import tempfile
+import shutil
+
 import numpy as np
 
 class N2V(CARE):
@@ -420,6 +429,227 @@ def _set_logdir(self):
             self.logdir.mkdir(parents=True, exist_ok=True)
             save_json(vars(self.config), str(config_file))
 
+    @suppress_without_basedir(warn=True)
+    def export_TF(self, name, description, authors, test_img, axes, patch_shape, fname=None):
+        """
+        name: String
+            Name of the model. 
+        description: String
+            A short description of the model e.g. on what data it was trained.
+        authors: String
+            Comma seperated list of author names.
+        patch_shape: The shape of the patches used in model.train().
+        """
+        if fname is None:
+            fname = self.logdir / 'export.bioimage.io.zip'
+        else:
+            fname = Path(fname)
+
+        input_n_dims = len(test_img.shape)
+        if 'C' in axes:
+            input_n_dims -=1 
+        assert input_n_dims == self.config.n_dim, 'Input and network dimensions do not match.'
+        assert test_img.shape[axes.index('X')] == test_img.shape[axes.index('Y')], 'X and Y dimensions are not of same length.'    
+        test_output = self.predict(test_img, axes) 
+        # Extract central slice of Z-Stack
+        if 'Z' in axes:
+            z_dim = axes.index('Z')
+            if z_dim != 0:
+                test_output = np.moveaxis(test_output, z_dim, 0)
+            test_output = test_output[int(test_output.shape[0]/2)]
+
+        # CSBDeep Export
+        meta = {
+            'type':          self.__class__.__name__,
+            'version':       package_version,
+            'probabilistic': self.config.probabilistic,
+            'axes':          self.config.axes,
+            'axes_div_by':   self._axes_div_by(self.config.axes),
+            'tile_overlap':  self._axes_tile_overlap(self.config.axes),
+        }
+        export_SavedModel(self.keras_model, str(fname), meta=meta)
+        # CSBDeep Export Done
+
+        # Replace : with -
+        name = name.replace(':', ' -')
+        yml_dict = self.get_yml_dict(name, description, authors, test_img, axes, patch_shape=patch_shape)
+        yml_file = self.logdir / 'model.yaml'
+
+        '''default_flow_style must be set to TRUE in order for the output to display arrays as [x,y,z]'''
+        yaml = YAML(typ='rt') 
+        yaml.default_flow_style = False
+        with open(yml_file, 'w') as outfile:
+            yaml.dump(yml_dict, outfile)
+
+        input_file = self.logdir / 'testinput.tif'
+        output_file = self.logdir / 'testoutput.tif'
+        imsave(input_file, test_img)
+        imsave(output_file, test_output)
+
+        with ZipFile(fname, 'a') as myzip:
+            myzip.write(yml_file, arcname=os.path.basename(yml_file))
+            myzip.write(input_file, arcname=os.path.basename(input_file))
+            myzip.write(output_file, arcname=os.path.basename(output_file))
+
+        print("\nModel exported in BioImage ModelZoo format:\n%s" % str(fname.resolve()))
+
+
+    def get_yml_dict(self, name, description, authors, test_img, axes, patch_shape=None):
+        if (patch_shape != None):
+            self.config.patch_shape = patch_shape
+
+        ''' Repeated values to avoid reference tags of the form &id002 in yml output when the same variable is used more than
+        once in the dictionary''' 
+        mean_val = [] 
+        mean_val1 = [] 
+        for ele in self.config.means:
+            mean_val.append(float(ele))
+            mean_val1.append(float(ele))
+        std_val = [] 
+        std_val1 = [] 
+        for ele in self.config.stds:
+            std_val.append(float(ele))
+            std_val1.append(float(ele))
+        in_data_range_val = ['-inf', 'inf']
+        out_data_range_val = ['-inf', 'inf']
+
+        axes_val = 'b' + self.config.axes
+        axes_val = axes_val.lower()
+        val = 2**self.config.unet_n_depth
+        val1 = predict.tile_overlap(self.config.unet_n_depth, self.config.unet_kern_size)
+        min_val = [1, val, val, self.config.n_channel_in ]
+        step_val = [1, val, val, 0]
+        halo_val = [0, val1, val1, 0]
+        scale_val = [1, 1, 1, 1]
+        offset_val = [0, 0, 0, 0]
+
+        yaml = YAML(typ='rt')
+        with open(self.logdir/'config.json','r') as f:
+            tr_kwargs_val = yaml.load(f)
+
+        if (self.config.n_dim == 3):
+            min_val = [1, val, val, val, self.config.n_channel_in ]
+            step_val = [1, val, val, val, 0]
+            halo_val = [0, val1, val1, val1, 0]
+            scale_val = [1, 1, 1, 1, 1]
+            offset_val = [0, 0, 0, 0, 0]
+
+        yml_dict = {
+            'name': name,
+            'description': description,
+            'cite': [{
+                'text': 'Krull, A. and Buchholz, T. and Jug, F. Noise2void - learning denoising from single noisy images.\nProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)',
+                'doi': '10.1109/CVPR.2019.00223'
+            }],
+            'authors': authors,
+            'language': 'python',
+            'framework': 'tensorflow',
+            'format_version': '0.2.0-csbdeep',
+            'source': 'n2v',
+            'test_input': 'testinput.tif',
+            'test_output': 'testoutput.tif',
+            'inputs': [{
+                'name': 'input',
+                'axes': axes_val,
+                'data_type': 'float32',
+                'data_range': in_data_range_val,
+                'halo': halo_val,
+                'shape': {
+                    'min': min_val,
+                    'step': step_val
+                }
+            }],
+            'outputs': [{ 
+                'name': self.keras_model.layers[-1].output.name , 
+                'axes': axes_val,
+                'data_type': 'float32',
+                'data_range': out_data_range_val,
+                'shape': {
+                    'reference_input': 'input',
+                    'scale': scale_val,
+                    'offset': offset_val
+                }
+            }],
+            'training': {
+                'source': 'n2v.train()',
+                'kwargs': tr_kwargs_val
+            },
+            'prediction': {
+                'weights': {'source': './variables/variables'},
+                'preprocess': [{
+                    'kwargs': { 
+                        'mean': mean_val,
+                        'stdDev': std_val
+                    }
+                }],
+                'postprocess': [{
+                    'kwargs': { 
+                        'mean': mean_val1,
+                        'stdDev': std_val1
+                    }
+                }]
+            }
+        }
+
+        return yml_dict
+
     @property
     def _config_class(self):
         return N2VConfig
+
+
+def export_SavedModel(model, outpath, meta={}, format='zip'):
+    """Export Keras model in TensorFlow's SavedModel_ format.
+    See `Your Model in Fiji`_ to learn how to use the exported model with our CSBDeep Fiji plugins.
+    .. _SavedModel: https://www.tensorflow.org/programmers_guide/saved_model#structure_of_a_savedmodel_directory
+    .. _`Your Model in Fiji`: https://github.com/CSBDeep/CSBDeep_website/wiki/Your-Model-in-Fiji
+    Parameters
+    ----------
+    model : :class:`keras.models.Model`
+        Keras model to be exported.
+    outpath : str
+        Path of the file/folder that the model will exported to.
+    meta : dict, optional
+        Metadata to be saved in an additional ``meta.json`` file.
+    format : str, optional
+        Can be 'dir' to export as a directory or 'zip' (default) to export as a ZIP file.
+    Raises
+    ------
+    ValueError
+        Illegal arguments.
+    """
+
+    def export_to_dir(dirname):
+        if len(model.inputs) > 1 or len(model.outputs) > 1:
+            warnings.warn('Found multiple input or output layers.')
+        builder = tf.saved_model.builder.SavedModelBuilder(dirname)
+        # use name 'input'/'output' if there's just a single input/output layer
+        inputs  = dict(zip(model.input_names,model.inputs))   if len(model.inputs)  > 1 else dict(input=model.input)
+        outputs = dict(zip(model.output_names,model.outputs)) if len(model.outputs) > 1 else dict(output=model.output)
+        signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs=inputs, outputs=outputs)
+        signature_def_map = { tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature }
+        builder.add_meta_graph_and_variables(K.get_session(),
+                                             [tf.saved_model.tag_constants.SERVING],
+                                             signature_def_map=signature_def_map)
+        builder.save()
+        if meta is not None and len(meta) > 0:
+            save_json(meta, os.path.join(dirname,'meta.json'))
+
+
+    ## checks
+    isinstance(model,keras.models.Model) or _raise(ValueError("'model' must be a Keras model."))
+    # supported_formats = tuple(['dir']+[name for name,description in shutil.get_archive_formats()])
+    supported_formats = 'dir','zip'
+    format in supported_formats or _raise(ValueError("Unsupported format '%s', must be one of %s." % (format,str(supported_formats))))
+
+    # remove '.zip' file name extension if necessary
+    if format == 'zip' and outpath.endswith('.zip'):
+        outpath = os.path.splitext(outpath)[0]
+
+    if format == 'dir':
+        export_to_dir(outpath)
+    else:
+        with tempfile.TemporaryDirectory() as tmpdir:
+            tmpsubdir = os.path.join(tmpdir,'model')
+            export_to_dir(tmpsubdir)
+            shutil.make_archive(outpath, format, tmpsubdir, './')

setup.py

@@ -49,11 +49,13 @@
           "matplotlib",
           "six",
           "keras>=2.2.4,<2.3.0",
-          "tifffile",
+          "tifffile>=2020.5.11",
+          "imagecodecs>=2020.2.18",
           "tqdm",
           "pathlib2;python_version<'3'",
           "backports.tempfile;python_version<'3.4'",
-          "csbdeep>=0.4.0,<0.5.0",
-          "Pillow"
-      ]
+          "csbdeep>=0.4.0,<0.6.0",
+          "Pillow",
+          "ruamel.yaml>=0.16.10"
+        ]
       )

tests/test_Noise2VoidDataGenerator.py

@@ -9,7 +9,7 @@ def test_generate_patches_2D():
     if not os.path.isdir('data'):
         os.mkdir('data')
     zip_path = "data/RGB.zip"
-    if not os.path.exists(zip_path):
+    if not os.path.isfile(zip_path):
         data = urllib.request.urlretrieve('https://cloud.mpi-cbg.de/index.php/s/Frru2hsjjAljpfW/download', zip_path)
         with zipfile.ZipFile(zip_path, 'r') as zip_ref:
             zip_ref.extractall('data')
@@ -30,15 +30,15 @@ def test_generate_patches_3D():
     if not os.path.isdir('data'):
         os.mkdir('data')
     zip_path = 'data/flywing-data.zip'
-    if not os.path.exists(zip_path):
+    if not os.path.isfile(zip_path):
         # download and unzip data
         data = urllib.request.urlretrieve('https://cloud.mpi-cbg.de/index.php/s/RKStdwKo4FlFrxE/download', zip_path)
         with zipfile.ZipFile(zip_path, 'r') as zip_ref:
             zip_ref.extractall('data')
 
     datagen = N2V_DataGenerator()
 
-    imgs = datagen.load_imgs_from_directory(directory="data", filter='*.tif', dims='ZYX')
+    imgs = datagen.load_imgs_from_directory(directory="data", filter='flywing.tif', dims='ZYX')
     print(imgs[0].shape)
     patches = datagen.generate_patches_from_list(imgs[:1], shape=(35, 520, 692))
     assert len(patches) == 1

tests/test_data/config.json

@@ -0,0 +1 @@
+{"means": ["0.55066955", "0.5919245", "0.5703182"], "stds": ["0.2943783", "0.30203635", "0.31610382"], "n_dim": 2, "axes": "YXC", "n_channel_in": 3, "n_channel_out": 3, "unet_residual": false, "unet_n_depth": 3, "unet_kern_size": 3, "unet_n_first": 64, "unet_last_activation": "linear", "unet_input_shape": [null, null, 3], "train_loss": "mse", "train_epochs": 2, "train_steps_per_epoch": 2, "train_learning_rate": 0.0004, "train_batch_size": 128, "train_tensorboard": true, "train_checkpoint": "weights_best.h5", "train_reduce_lr": {"factor": 0.5, "patience": 10}, "batch_norm": true, "n2v_perc_pix": 5, "n2v_patch_shape": [64, 64], "n2v_manipulator": "uniform_withCP", "n2v_neighborhood_radius": 5, "probabilistic": false, "patch_shape": [64, 64]}