diff --git a/keras_hub/src/layers/preprocessing/image_converter_test.py b/keras_hub/src/layers/preprocessing/image_converter_test.py index 5e0fd940c2..d638ccf9ab 100644 --- a/keras_hub/src/layers/preprocessing/image_converter_test.py +++ b/keras_hub/src/layers/preprocessing/image_converter_test.py @@ -6,12 +6,10 @@ from keras import ops from keras_hub.src.layers.preprocessing.image_converter import ImageConverter -from keras_hub.src.models.pali_gemma.pali_gemma_backbone import ( - PaliGemmaBackbone, -) from keras_hub.src.models.pali_gemma.pali_gemma_image_converter import ( PaliGemmaImageConverter, ) +from keras_hub.src.models.resnet.resnet_backbone import ResNetBackbone from keras_hub.src.tests.test_case import TestCase @@ -86,24 +84,19 @@ def test_from_preset_errors(self): def test_save_to_preset(self): save_dir = self.get_temp_dir() converter = ImageConverter.from_preset( - "pali_gemma_3b_mix_224", + "resnet_50_imagenet", interpolation="nearest", ) converter.save_to_preset(save_dir) # Save a tiny backbone so the preset is valid. - backbone = PaliGemmaBackbone( - vocabulary_size=100, - image_size=224, - num_layers=1, - num_query_heads=1, - num_key_value_heads=1, - hidden_dim=8, - intermediate_dim=16, - head_dim=8, - vit_patch_size=14, - vit_num_heads=1, - vit_hidden_dim=8, - vit_num_layers=1, + backbone = ResNetBackbone( + input_conv_filters=[64], + input_conv_kernel_sizes=[7], + stackwise_num_filters=[64, 64, 64], + stackwise_num_blocks=[2, 2, 2], + stackwise_num_strides=[1, 2, 2], + block_type="basic_block", + use_pre_activation=True, ) backbone.save_to_preset(save_dir) diff --git a/keras_hub/src/models/resnet/resnet_backbone.py b/keras_hub/src/models/resnet/resnet_backbone.py index aee033cdda..407ce44f5b 100644 --- a/keras_hub/src/models/resnet/resnet_backbone.py +++ b/keras_hub/src/models/resnet/resnet_backbone.py @@ -80,7 +80,6 @@ class ResNetBackbone(FeaturePyramidBackbone): stackwise_num_strides=[1, 2, 2], block_type="basic_block", use_pre_activation=True, - pooling="avg", ) model(input_data) ```