Merge b6c2a77 into a1aad5b

deepcell/__init__.py

@@ -29,6 +29,7 @@
 from __future__ import division
 from __future__ import print_function
 
+from deepcell import applications
 from deepcell import datasets
 from deepcell import layers
 from deepcell import losses

deepcell/applications/__init__.py

@@ -0,0 +1,37 @@
+# Copyright 2016-2019 The Van Valen Lab at the California Institute of
+# Technology (Caltech), with support from the Paul Allen Family Foundation,
+# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
+# All rights reserved.
+#
+# Licensed under a modified Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.github.com/vanvalenlab/deepcell-tf/LICENSE
+#
+# The Work provided may be used for non-commercial academic purposes only.
+# For any other use of the Work, including commercial use, please contact:
+# vanvalenlab@gmail.com
+#
+# Neither the name of Caltech nor the names of its contributors may be used
+# to endorse or promote products derived from this software without specific
+# prior written permission.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Deepcell Applications - Pre-trained models for specific functions"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from deepcell.applications.label_detection import LabelDetectionModel
+from deepcell.applications.scale_detection import ScaleDetectionModel
+
+del absolute_import
+del division
+del print_function

deepcell/applications/label_detection.py

@@ -0,0 +1,105 @@
+# Copyright 2016-2019 The Van Valen Lab at the California Institute of
+# Technology (Caltech), with support from the Paul Allen Family Foundation,
+# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
+# All rights reserved.
+#
+# Licensed under a modified Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.github.com/vanvalenlab/deepcell-tf/LICENSE
+#
+# The Work provided may be used for non-commercial academic purposes only.
+# For any other use of the Work, including commercial use, please contact:
+# vanvalenlab@gmail.com
+#
+# Neither the name of Caltech nor the names of its contributors may be used
+# to endorse or promote products derived from this software without specific
+# prior written permission.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Classify the type of an input image to send the data to the correct model"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python import keras
+
+try:
+    from tensorflow.python.keras.utils.data_utils import get_file
+except ImportError:  # tf v1.9 moves conv_utils from _impl to keras.utils
+    from tensorflow.python.keras._impl.keras.utils.data_utils import get_file
+
+from deepcell.layers import ImageNormalization2D, TensorProduct
+from deepcell.utils.backbone_utils import get_backbone
+
+
+WEIGHTS_PATH = ('https://deepcell-data.s3-us-west-1.amazonaws.com/'
+                'model-weights/LabelDetectionModel_VGG16.h5')
+
+
+def LabelDetectionModel(input_shape=(None, None, 1),
+                        inputs=None,
+                        backbone='VGG16',
+                        use_pretrained_weights=True):
+    """Classify a microscopy image as Nuclear, Cytoplasm, or Phase.
+
+    This can be helpful in determining the type of data (nuclear, cytoplasm,
+    etc.) so that this data can be forwared to the correct segmenation model.
+    """
+    required_channels = 3  # required for most backbones
+
+    if inputs is None:
+        inputs = keras.layers.Input(shape=input_shape)
+
+    if keras.backend.image_data_format() == 'channels_first':
+        channel_axis = 0
+    else:
+        channel_axis = -1
+
+    norm = ImageNormalization2D(norm_method='whole_image')(inputs)
+    fixed_inputs = TensorProduct(required_channels)(norm)
+
+    # force the input shape
+    fixed_input_shape = list(input_shape)
+    fixed_input_shape[channel_axis] = required_channels
+    fixed_input_shape = tuple(fixed_input_shape)
+
+    backbone_model = get_backbone(
+        backbone,
+        fixed_inputs,
+        use_imagenet=False,
+        return_dict=False,
+        include_top=False,
+        weights=None,
+        input_shape=fixed_input_shape,
+        pooling=None)
+
+    x = keras.layers.AveragePooling2D(4)(backbone_model.outputs[0])
+    x = TensorProduct(256)(x)
+    x = TensorProduct(3)(x)
+    x = keras.layers.Flatten()(x)
+    outputs = keras.layers.Activation('softmax')(x)
+
+    model = keras.Model(inputs=backbone_model.inputs, outputs=outputs)
+
+    if use_pretrained_weights:
+        if backbone.upper() == 'VGG16':
+            weights_path = get_file(
+                'LabelDetectionModel_{}.h5'.format(backbone),
+                WEIGHTS_PATH,
+                cache_subdir='models',
+                md5_hash='090a0de7a33dceff7ad690b3c9852938')
+        else:
+            raise ValueError('Backbone %s does not have a weights file.' %
+                             backbone)
+
+        model.load_weights(weights_path)
+
+    return model

deepcell/applications/label_detection_test.py

@@ -0,0 +1,73 @@
+# Copyright 2016-2019 The Van Valen Lab at the California Institute of
+# Technology (Caltech), with support from the Paul Allen Family Foundation,
+# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
+# All rights reserved.
+#
+# Licensed under a modified Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.github.com/vanvalenlab/deepcell-tf/LICENSE
+#
+# The Work provided may be used for non-commercial academic purposes only.
+# For any other use of the Work, including commercial use, please contact:
+# vanvalenlab@gmail.com
+#
+# Neither the name of Caltech nor the names of its contributors may be used
+# to endorse or promote products derived from this software without specific
+# prior written permission.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for LabelDetectionModel"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.keras.layers import Input
+from tensorflow.python.platform import test
+
+from deepcell.applications import LabelDetectionModel
+
+
+class TestLabelDetectionModel(test.TestCase):
+
+    def test_label_detection_model(self):
+
+        valid_backbones = ['VGG16']
+        input_shape = (256, 256, 1)  # channels will be set to 3
+
+        batch_shape = tuple([8] + list(input_shape))
+
+        X = np.random.random(batch_shape)
+
+        for backbone in valid_backbones:
+            with self.test_session(use_gpu=True):
+                inputs = Input(shape=input_shape)
+                model = LabelDetectionModel(
+                    inputs=inputs,
+                    backbone=backbone,
+                    use_pretrained_weights=False)
+
+                y = model.predict(X)
+
+                assert y.shape[0] == X.shape[0]
+                assert len(y.shape) == 2
+
+            with self.test_session(use_gpu=True):
+                model = LabelDetectionModel(
+                    input_shape=input_shape,
+                    backbone=backbone,
+                    use_pretrained_weights=False)
+
+                y = model.predict(X)
+
+                assert y.shape[0] == X.shape[0]
+                assert len(y.shape) == 2

deepcell/applications/scale_detection.py

@@ -0,0 +1,105 @@
+# Copyright 2016-2019 The Van Valen Lab at the California Institute of
+# Technology (Caltech), with support from the Paul Allen Family Foundation,
+# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
+# All rights reserved.
+#
+# Licensed under a modified Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.github.com/vanvalenlab/deepcell-tf/LICENSE
+#
+# The Work provided may be used for non-commercial academic purposes only.
+# For any other use of the Work, including commercial use, please contact:
+# vanvalenlab@gmail.com
+#
+# Neither the name of Caltech nor the names of its contributors may be used
+# to endorse or promote products derived from this software without specific
+# prior written permission.
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Detect the scale of input data for rescaling for other models"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python import keras
+
+try:
+    from tensorflow.python.keras.utils.data_utils import get_file
+except ImportError:  # tf v1.9 moves conv_utils from _impl to keras.utils
+    from tensorflow.python.keras._impl.keras.utils.data_utils import get_file
+
+from deepcell.layers import ImageNormalization2D, TensorProduct
+from deepcell.utils.backbone_utils import get_backbone
+
+
+WEIGHTS_PATH = ('https://deepcell-data.s3-us-west-1.amazonaws.com/'
+                'model-weights/ScaleDetectionModel_VGG16.h5')
+
+
+def ScaleDetectionModel(input_shape=(None, None, 1),
+                        inputs=None,
+                        backbone='VGG16',
+                        use_pretrained_weights=True):
+    """Create a ScaleDetectionModel for detecting scales of input data.
+
+    This enables data to be scaled appropriately for other segmentation models
+    which may not be resolution tolerant.
+    """
+    required_channels = 3  # required for most backbones
+
+    if inputs is None:
+        inputs = keras.layers.Input(shape=input_shape)
+
+    if keras.backend.image_data_format() == 'channels_first':
+        channel_axis = 0
+    else:
+        channel_axis = -1
+
+    norm = ImageNormalization2D(norm_method='whole_image')(inputs)
+    fixed_inputs = TensorProduct(required_channels)(norm)
+
+    # force the input shape
+    fixed_input_shape = list(input_shape)
+    fixed_input_shape[channel_axis] = required_channels
+    fixed_input_shape = tuple(fixed_input_shape)
+
+    backbone_model = get_backbone(
+        backbone,
+        fixed_inputs,
+        use_imagenet=False,
+        return_dict=False,
+        include_top=False,
+        weights=None,
+        input_shape=fixed_input_shape,
+        pooling=None)
+
+    x = keras.layers.AveragePooling2D(4)(backbone_model.outputs[0])
+    x = TensorProduct(256)(x)
+    x = TensorProduct(1)(x)
+    x = keras.layers.Flatten()(x)
+    outputs = keras.layers.Activation('relu')(x)
+
+    model = keras.Model(inputs=backbone_model.inputs, outputs=outputs)
+
+    if use_pretrained_weights:
+        if backbone.upper() == 'VGG16':
+            weights_path = get_file(
+                'ScaleDetectionModel_{}.h5'.format(backbone),
+                WEIGHTS_PATH,
+                cache_subdir='models',
+                md5_hash='ab23e35676ffcdf1c72d3804cc65ea1d')
+        else:
+            raise ValueError('Backbone %s does not have a weights file.' %
+                             backbone)
+
+        model.load_weights(weights_path)
+
+    return model