text: # Copyright 2016-2021 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.
  # ==============================================================================
  """Test the featurenet models."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl.testing import parameterized
  
  from tensorflow.python.framework import test_util as tf_test_util
  from tensorflow.python.keras import keras_parameterized
  
  from tensorflow.keras import backend as K
  
  from deepcell.model_zoo import featurenet
  
  
  class FeatureNetTest(keras_parameterized.TestCase):
  
      @keras_parameterized.run_all_keras_modes
      @parameterized.named_parameters([
          {
              'testcase_name': 'dilated_include_top',
              'include_top': True,
              'dilated': True,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (32, 32, 1),
              'data_format': 'channels_last'
          },
          {
              'testcase_name': 'dilated_include_top_cf',
              'include_top': True,
              'dilated': True,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (32, 32, 1),
              'data_format': 'channels_first'
          },
          # {
          #     'testcase_name': 'not_dilated_include_top',
          #     'include_top': True,
          #     'dilated': False,
          #     'padding_mode': 'reflect',
          #     'padding': True,
          #     'multires': False,
          #     'location': False,
          #     'shape': (32, 32, 1),
          #     'data_format': 'channels_last',
          # },
          # {
          #     'testcase_name': 'not_dilated_include_top_cf',
          #     'include_top': True,
          #     'dilated': False,
          #     'padding_mode': 'reflect',
          #     'padding': True,
          #     'multires': False,
          #     'location': False,
          #     'shape': (32, 32, 1),
          #     'data_format': 'channels_first',
          # },
          {
              'testcase_name': 'dilated_no_top',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (33, 33, 1),
              'data_format': 'channels_last',
          },
          {
              'testcase_name': 'dilated_no_top_cf',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (33, 33, 1),
              'data_format': 'channels_first',
          },
          {
              'testcase_name': 'not_dilated_no_top',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (32, 32, 1),
              'data_format': 'channels_last',
          },
          {
              'testcase_name': 'not_dilated_no_top_cf',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (32, 32, 1),
              'data_format': 'channels_first',
          },
          {
              'testcase_name': 'not_dilated_no_top_location',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': True,
              'location': True,
              'shape': (32, 32, 1),
              'data_format': 'channels_last',
          },
          {
              'testcase_name': 'not_dilated_no_top_location_cf',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': True,
              'location': True,
              'shape': (32, 32, 1),
              'data_format': 'channels_first',
          },
          {
              'testcase_name': 'not_dilated_no_top_multires',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': True,
              'location': False,
              'shape': (32, 32, 1),
              'data_format': 'channels_last',
          },
          {
              'testcase_name': 'not_dilated_no_top_multires_cf',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': True,
              'location': False,
              'shape': (32, 32, 1),
              'data_format': 'channels_first',
          },
      ])
      def test_bn_feature_net_2D(self, include_top, padding, padding_mode, shape,
                                 dilated, multires, location, data_format):
          n_features = 3
          n_dense_filters = 200
  
          # BAD: dilated=True, include_top=False
          # BAD: inputs != None
  
          with self.cached_session():
              K.set_image_data_format(data_format)
              model = featurenet.bn_feature_net_2D(
                  include_top=include_top,
                  dilated=dilated,
                  input_shape=shape,
                  n_features=n_features,
                  n_dense_filters=n_dense_filters,
                  padding=padding,
                  padding_mode=padding_mode,
                  multires=multires,
                  VGG_mode=multires,
                  location=location)
              self.assertEqual(len(model.output_shape), 4)
              output = n_features if include_top else n_dense_filters
              axis = 1 if data_format == 'channels_first' else -1
              self.assertEqual(model.output_shape[axis], output)
  
      @keras_parameterized.run_all_keras_modes
      @parameterized.named_parameters(
          *tf_test_util.generate_combinations_with_testcase_name(
              data_format=['channels_first', 'channels_last']))
      def test_bn_feature_net_2D_skip(self, data_format):
          receptive_field = 61
          n_features = 3
          n_dense_filters = 300
          input_shape = (256, 256, 1)
          n_skips = 1
  
          with self.cached_session():
              K.set_image_data_format(data_format)
              axis = 1 if data_format == 'channels_first' else -1
  
              fgbg_model = featurenet.bn_feature_net_skip_2D(
                  receptive_field=receptive_field,
                  input_shape=input_shape,
                  n_features=n_features,
                  n_dense_filters=n_dense_filters,
                  n_skips=n_skips,
                  last_only=False)
  
              self.assertIsInstance(fgbg_model.output, list)
              self.assertEqual(len(fgbg_model.output), n_skips + 1)
  
              model = featurenet.bn_feature_net_skip_2D(
                  receptive_field=receptive_field,
                  input_shape=input_shape,
                  fgbg_model=fgbg_model,
                  n_features=n_features,
                  n_dense_filters=n_dense_filters,
                  n_skips=n_skips,
                  last_only=True)
  
              self.assertEqual(len(model.output_shape), 4)
              self.assertEqual(model.output_shape[axis], n_features)
  
      @keras_parameterized.run_all_keras_modes
      @parameterized.named_parameters([
          {
              'testcase_name': 'dilated_include_top',
              'include_top': True,
              'dilated': True,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 33, 33, 1),
              'data_format': 'channels_last',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_include_top_cf',
              'include_top': True,
              'dilated': True,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 33, 33, 1),
              'data_format': 'channels_first',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          # {
          #     'testcase_name': 'not_dilated_include_top',
          #     'include_top': True,
          #     'dilated': False,
          #     'padding_mode': 'reflect',
          #     'padding': True,
          #     'multires': False,
          #     'location': False,
          #     'data_format': 'channels_last',
          # },
          # {
          #     'testcase_name': 'not_dilated_include_top_cf',
          #     'include_top': True,
          #     'dilated': False,
          #     'padding_mode': 'reflect',
          #     'padding': True,
          #     'multires': False,
          #     'location': False,
          #     'data_format': 'channels_first',
          # },
          {
              'testcase_name': 'dilated_no_top',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_cf',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_first',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'not_dilated_no_top',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 33, 33, 1),
              'data_format': 'channels_last',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'not_dilated_no_top_cf',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 33, 33, 1),
              'data_format': 'channels_first',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'not_dilated_no_top_location',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': True,
              'location': True,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'not_dilated_no_top_location_cf',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': True,
              'location': True,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_first',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'not_dilated_no_top_multires',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': True,
              'location': False,
              'shape': (10, 33, 33, 1),
              'data_format': 'channels_last',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'not_dilated_no_top_multires_cf',
              'include_top': False,
              'dilated': False,
              'padding_mode': 'reflect',
              'padding': False,
              'multires': True,
              'location': False,
              'shape': (10, 33, 33, 1),
              'data_format': 'channels_first',
              'temporal': None,
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_conv3D_nonresidual',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': 'conv',
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_conv3D_residual',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': 'conv',
              'residual': True,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_convGRU2D_nonresidual',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': 'gru',
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_convgru2D_nonresidual',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': 'GRU',
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_convGRU2D_residual',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': 'gru',
              'residual': True,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_convlstm2D_nonresidual',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': 'lstm',
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_convLSTM2D_nonresidual',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': 'LSTM',
              'residual': False,
              'temporal_kernel_size': 3,
          },
          {
              'testcase_name': 'dilated_no_top_convLSTM2D_residual',
              'include_top': False,
              'dilated': True,
              'padding_mode': 'zero',
              'padding': False,
              'multires': False,
              'location': False,
              'shape': (10, 32, 32, 1),
              'data_format': 'channels_last',
              'temporal': 'lstm',
              'residual': True,
              'temporal_kernel_size': 3,
          },
      ])
      def test_bn_feature_net_3D(self, include_top, padding, padding_mode, shape,
                                 dilated, multires, location, data_format,
                                 temporal, residual, temporal_kernel_size):
          n_features = 3
          n_dense_filters = 200
          n_frames = 5
          # input_shape = (10, 32, 32, 1)
  
          with self.cached_session():
              K.set_image_data_format(data_format)
              model = featurenet.bn_feature_net_3D(
                  include_top=include_top,
                  dilated=dilated,
                  n_frames=n_frames,
                  input_shape=shape,
                  n_features=n_features,
                  n_dense_filters=n_dense_filters,
                  padding=padding,
                  padding_mode=padding_mode,
                  multires=multires,
                  VGG_mode=multires,
                  location=location,
                  temporal=temporal,
                  residual=residual,
                  temporal_kernel_size=temporal_kernel_size)
              self.assertEqual(len(model.output_shape), 5 if dilated else 2)
              channel_axis = 1 if data_format == 'channels_first' else -1
              self.assertEqual(model.output_shape[channel_axis], n_features)
  
      @keras_parameterized.run_all_keras_modes
      @parameterized.named_parameters(
          *tf_test_util.generate_combinations_with_testcase_name(
              data_format=['channels_first', 'channels_last']))
      def test_bn_feature_net_3D_skip(self, data_format):
          receptive_field = 61
          n_features = 3
          n_dense_filters = 300
          input_shape = (10, 32, 32, 1)
          n_skips = 1
          temporal = None
          residual = False
          temporal_kernel_size = 3
  
          with self.cached_session():
              K.set_image_data_format(data_format)
              axis = 1 if data_format == 'channels_first' else -1
  
              fgbg_model = featurenet.bn_feature_net_skip_3D(
                  receptive_field=receptive_field,
                  input_shape=input_shape,
                  n_features=n_features,
                  n_dense_filters=n_dense_filters,
                  n_skips=n_skips,
                  last_only=False,
                  temporal=temporal,
                  residual=residual,
                  temporal_kernel_size=temporal_kernel_size)
  
              self.assertIsInstance(fgbg_model.output, list)
              self.assertEqual(len(fgbg_model.output), n_skips + 1)
  
              model = featurenet.bn_feature_net_skip_3D(
                  receptive_field=receptive_field,
                  input_shape=input_shape,
                  fgbg_model=fgbg_model,
                  n_features=n_features,
                  n_dense_filters=n_dense_filters,
                  n_skips=n_skips,
                  last_only=True,
                  temporal=temporal,
                  residual=residual,
                  temporal_kernel_size=temporal_kernel_size)
  
              self.assertEqual(len(model.output_shape), 5)
              self.assertEqual(model.output_shape[axis], n_features)
  