# coding=utf-8 # Copyright 2021 The Deeplab2 Authors. # # Licensed under the 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.apache.org/licenses/LICENSE-2.0 # # 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 axial_resnet_instances.""" import os from absl import flags from absl.testing import parameterized import numpy as np import tensorflow as tf from deeplab2.model import test_utils from deeplab2.model.encoder import axial_resnet_instances FLAGS = flags.FLAGS class AxialResnetInstancesTest(tf.test.TestCase, parameterized.TestCase): # The parameter count does not include the classification head. @parameterized.parameters( ('resnet50', 1, 23508032), ('resnet50_beta', 1, 23631808), # 123776 more than resnet50 ('max_deeplab_s_backbone', 1, 41343424), ('max_deeplab_l_backbone', 1, 175115392), ('axial_resnet_s', 1, 11466912), ('axial_resnet_l', 1, 43714048), # 127872 fewer than axial_deeplab_l ('axial_deeplab_s', 1, 11565856), ('axial_deeplab_l', 1, 43841920), ('swidernet', 1, 109014080), # SWideRNet-(1,1,1) without SE or SAC ('swidernet', 3, 333245504), # Should be more than 3 x 109014080 ('swidernet', 4.5, 487453760), # Rounded down to [13, 27, 13, 13] ('axial_swidernet', 1, 136399392), ('axial_swidernet', 3, 393935520), ('axial_swidernet', 4.5, 570346912), ) def test_model_output_shape_and_num_params( self, model_name, backbone_layer_multiplier, expected_num_params): model = axial_resnet_instances.get_model( model_name, backbone_layer_multiplier=backbone_layer_multiplier, bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001) output = model(tf.keras.Input(shape=(224, 224, 3))) if model_name in ('axial_resnet_s', 'axial_deeplab_s'): self.assertListEqual(output['res5'].get_shape().as_list(), [None, 14, 14, 1024]) else: self.assertListEqual(output['res5'].get_shape().as_list(), [None, 14, 14, 2048]) num_params = np.sum( [np.prod(v.get_shape().as_list()) for v in model.trainable_weights]) self.assertEqual(num_params, expected_num_params) def test_resnet50_variable_checkpoint_names(self): model = axial_resnet_instances.get_model( 'resnet50', bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001) model(tf.keras.Input(shape=(224, 224, 3))) variable_names = [w.name for w in model.trainable_weights] test_variable_name = 'resnet50/stage4/block6/conv3_bn/batch_norm/beta:0' self.assertIn(test_variable_name, variable_names) temp_dir = self.create_tempdir() temp_path = os.path.join(temp_dir, 'ckpt') checkpoint = tf.train.Checkpoint(encoder=model) checkpoint.save(temp_path) latest_checkpoint = tf.train.latest_checkpoint(temp_dir) reader = tf.train.load_checkpoint(latest_checkpoint) checkpoint_names = reader.get_variable_to_shape_map().keys() test_checkpoint_name = 'encoder/_stage4/_block6/_conv3_bn/_batch_norm/gamma/.ATTRIBUTES/VARIABLE_VALUE' self.assertIn(test_checkpoint_name, checkpoint_names) def test_max_deeplab_s_output_shape_and_num_params(self): model = axial_resnet_instances.get_model( 'max_deeplab_s', bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001) endpoints = model(tf.keras.Input(shape=(65, 65, 3))) self.assertListEqual(endpoints['backbone_output'].get_shape().as_list(), [None, 5, 5, 2048]) self.assertListEqual( endpoints['transformer_class_feature'].get_shape().as_list(), [None, 128, 256]) self.assertListEqual( endpoints['transformer_mask_feature'].get_shape().as_list(), [None, 128, 256]) self.assertListEqual(endpoints['feature_panoptic'].get_shape().as_list(), [None, 17, 17, 256]) self.assertListEqual(endpoints['feature_semantic'].get_shape().as_list(), [None, 5, 5, 2048]) num_params = np.sum( [np.prod(v.get_shape().as_list()) for v in model.trainable_weights]) self.assertEqual(num_params, 61726624) def test_max_deeplab_l_output_shape_and_num_params(self): model = axial_resnet_instances.get_model( 'max_deeplab_l', bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001) endpoints = model(tf.keras.Input(shape=(65, 65, 3))) self.assertListEqual(endpoints['backbone_output'].get_shape().as_list(), [None, 5, 5, 2048]) self.assertListEqual( endpoints['transformer_class_feature'].get_shape().as_list(), [None, 128, 512]) self.assertListEqual( endpoints['transformer_mask_feature'].get_shape().as_list(), [None, 128, 512]) self.assertListEqual(endpoints['feature_panoptic'].get_shape().as_list(), [None, 17, 17, 256]) self.assertListEqual(endpoints['feature_semantic'].get_shape().as_list(), [None, 17, 17, 256]) num_params = np.sum( [np.prod(v.get_shape().as_list()) for v in model.trainable_weights]) self.assertEqual(num_params, 450523232) def test_global_attention_absolute_positional_encoding_names(self): model = axial_resnet_instances.get_model( 'max_deeplab_s_backbone', block_group_config={'use_global_beyond_stride': 16, 'positional_encoding_type': '1D', 'axial_layer_config': { 'use_query_rpe_similarity': False, 'use_key_rpe_similarity': False, 'retrieve_value_rpe': False}}, bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001) model(tf.keras.Input(shape=(224, 224, 3))) variable_names = [w.name for w in model.trainable_weights] test_variable_name1 = 'max_deeplab_s_backbone/stage4/add_absolute_positional_encoding/height_axis_embeddings:0' test_variable_name2 = 'max_deeplab_s_backbone/stage4/block2/attention/global/qkv_kernel:0' self.assertIn(test_variable_name1, variable_names) self.assertIn(test_variable_name2, variable_names) @parameterized.product( (dict(model_name='resnet50', backbone_layer_multiplier=1), dict(model_name='resnet50_beta', backbone_layer_multiplier=1), dict(model_name='wide_resnet41', backbone_layer_multiplier=1), dict(model_name='swidernet', backbone_layer_multiplier=2)), output_stride=[4, 8, 16, 32]) def test_model_atrous_consistency_with_output_stride_four( self, model_name, backbone_layer_multiplier, output_stride): tf.random.set_seed(0) # Create the input. pixel_inputs = test_utils.create_test_input(1, 225, 225, 3) # Create the model and the weights. model_1 = axial_resnet_instances.get_model( model_name, backbone_layer_multiplier=backbone_layer_multiplier, bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001, output_stride=4) # Create the weights. model_1(pixel_inputs, training=False) # Set the batch norm gamma as non-zero so that the 3x3 convolution affects # the output. for weight in model_1.trainable_weights: if '/gamma:0' in weight.name: weight.assign(tf.ones_like(weight)) # Dense feature extraction followed by subsampling. pixel_outputs = model_1(pixel_inputs, training=False)['res5'] downsampling_stride = output_stride // 4 expected = pixel_outputs[:, ::downsampling_stride, ::downsampling_stride, :] # Feature extraction at the nominal network rate. model_2 = axial_resnet_instances.get_model( model_name, backbone_layer_multiplier=backbone_layer_multiplier, bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001, output_stride=output_stride) # Create the weights. model_2(pixel_inputs, training=False) # Make the two networks use the same weights. model_2.set_weights(model_1.get_weights()) output = model_2(pixel_inputs, training=False)['res5'] # Normalize the outputs. Since we set batch_norm gamma to 1, the output # activations can explode to a large standard deviation, which sometimes # cause numerical errors beyond the tolerances. normalizing_factor = tf.math.reduce_std(expected) # Compare normalized outputs. self.assertAllClose(output / normalizing_factor, expected / normalizing_factor, atol=1e-4, rtol=1e-4) @parameterized.parameters( ('resnet50',), ('resnet50_beta',), ('max_deeplab_s_backbone',), ('max_deeplab_l_backbone',), ('axial_resnet_s',), ('axial_resnet_l',), ('axial_deeplab_s',), ('axial_deeplab_l',), ('swidernet',), ('axial_swidernet',), ) def test_model_export(self, model_name): model = axial_resnet_instances.get_model( model_name, output_stride=16, backbone_layer_multiplier=1.0, bn_layer=tf.keras.layers.BatchNormalization, conv_kernel_weight_decay=0.0001, # Disable drop path as it is not compatible with model exporting. block_group_config={'drop_path_keep_prob': 1.0}) model(tf.keras.Input([257, 257, 3], batch_size=1), training=False) export_dir = os.path.join( FLAGS.test_tmpdir, 'test_model_export', model_name) model.save(export_dir) if __name__ == '__main__': tf.test.main()