Added Environment Files

.github/workflows/test.yml

@@ -16,10 +16,10 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Set up Python 3.9
+      - name: Set up Python 3.10
         uses: actions/setup-python@v4
         with:
-          python-version: 3.9
+          python-version: 3.10
           cache-dependency-path: "**/pyproject.toml"
           cache: "pip"
 

.gitignore

@@ -161,3 +161,5 @@ cython_debug/
 .vscode
 .DS_Store
 coverage.lcov
+data/
+*.h5

environment-linux.yml

@@ -0,0 +1,14 @@
+name: Nimbus_macOS
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+  - cudatoolkit=11.8.0
+  - python=3.11
+  - pip
+  - hdf5
+  - numpy
+  - pip:
+      - nvidia-cudnn-cu11==8.6.0.163
+      - tensorflow==2.13.*
+      - .

environment-macOS.yml

@@ -0,0 +1,13 @@
+name: Nimbus_macOS
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+  - python=3.11
+  - pip
+  - hdf5
+  - numpy
+  - pip:
+      - tensorflow==2.13.*
+      - tensorflow-metal==1.0.1
+      - .

pyproject.toml

@@ -21,19 +21,19 @@ dependencies = [
     "seaborn>=0.12",
     "alpineer>=0.1.5",
     "natsort>=7.1",
-    "tensorflow==2.8",
-    "protobuf<=3.20",
+    "tensorflow>=2.12",
+    "protobuf",
 ]
 name = "cell_classification"
 authors = [{ name = "Angelo Lab", email = "[email protected]" }]
 description = "Cell classification tool for classifying cells into marker positive and negative for arbitrary markers."
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = "==3.10"
 license = { text = "Modified Apache License 2.0" }
 classifiers = [
     "Development Status :: 4 - Beta",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
     "License :: OSI Approved :: Apache Software License",
     "Topic :: Scientific/Engineering :: Bio-Informatics",
     "Topic :: Scientific/Engineering :: Image Processing",

src/deepcell/layers.py

@@ -31,8 +31,6 @@
 from tensorflow.keras import constraints
 from tensorflow.keras import initializers
 from tensorflow.keras import regularizers
-from keras.utils import conv_utils
-
 logger = tf.get_logger()
 
 
@@ -52,7 +50,7 @@ class UpsampleLike(Layer):
 
     def __init__(self, data_format=None, **kwargs):
         super().__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = data_format
 
     def _resize_drop_axis(self, image, size, axis):
         image_shape = tf.shape(image)
@@ -211,7 +209,7 @@ def __init__(self,
 
         self.filter_size = filter_size
         self.norm_method = norm_method
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = data_format
 
         if self.data_format == 'channels_first':
             self.channel_axis = 1
@@ -343,7 +341,7 @@ def __init__(self, data_format=None, **kwargs):
             logger.warn('in_shape (from deepcell.layerse.location) is '
                         'deprecated and will be removed in a future version.')
         super().__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = data_format
 
     def compute_output_shape(self, input_shape):
         input_shape = tensor_shape.TensorShape(input_shape).as_list()
@@ -406,7 +404,7 @@ def __init__(self, data_format=None, **kwargs):
             logger.warn('in_shape (from deepcell.layerse.location) is '
                         'deprecated and will be removed in a future version.')
         super().__init__(**kwargs)
-        self.data_format = conv_utils.normalize_data_format(data_format)
+        self.data_format = data_format
 
     def compute_output_shape(self, input_shape):
         input_shape = tensor_shape.TensorShape(input_shape).as_list()