Internal change

PiperOrigin-RevId: 489221731

official/vision/configs/experiments/image_classification/imagenet_resnet26_tpu.yaml

+runtime:
+  distribution_strategy: 'tpu'
+  mixed_precision_dtype: 'bfloat16'
+task:
+  model:
+    num_classes: 1001
+    input_size: [224, 224, 3]
+    backbone:
+      type: 'resnet'
+      resnet:
+        model_id: 26
+  losses:
+    l2_weight_decay: 0.0001
+    one_hot: true
+    label_smoothing: 0.1
+  train_data:
+    input_path: 'gs://mlcompass-data/imagenet/imagenet-2012-tfrecord/train*'
+    is_training: true
+    global_batch_size: 4096
+    dtype: 'bfloat16'
+  validation_data:
+    input_path: 'gs://mlcompass-data/imagenet/imagenet-2012-tfrecord/valid*'
+    is_training: false
+    global_batch_size: 4096
+    dtype: 'bfloat16'
+    drop_remainder: false
+trainer:
+  train_steps: 28080
+  validation_steps: 13
+  validation_interval: 312
+  steps_per_loop: 312
+  summary_interval: 312
+  checkpoint_interval: 312
+  optimizer_config:
+    optimizer:
+      type: 'sgd'
+      sgd:
+        momentum: 0.9
+    learning_rate:
+      type: 'stepwise'
+      stepwise:
+        boundaries: [9360, 18720, 24960]
+        values: [1.6, 0.16, 0.016, 0.0016]
+    warmup:
+      type: 'linear'
+      linear:
+        warmup_steps: 1560

official/vision/modeling/backbones/resnet.py

@@ -44,6 +44,12 @@ RESNET_SPECS = {
         ('residual', 256, 2),
         ('residual', 512, 2),
     ],
+    26: [
+        ('residual', 64, 3),
+        ('residual', 128, 3),
+        ('residual', 256, 3),
+        ('residual', 512, 3),
+    ],
     34: [
         ('residual', 64, 3),
         ('residual', 128, 4),

official/vision/modeling/backbones/resnet_test.py

@@ -29,6 +29,7 @@ class ResNetTest(parameterized.TestCase, tf.test.TestCase):
   @parameterized.parameters(
       (128, 10, 1),
       (128, 18, 1),
+      (128, 26, 1),
       (128, 34, 1),
       (128, 50, 4),
       (128, 101, 4),
@@ -40,6 +41,7 @@ class ResNetTest(parameterized.TestCase, tf.test.TestCase):
     resnet_params = {
         10: 4915904,
         18: 11190464,
+        26: 17465024,
         34: 21306048,
         50: 23561152,
         101: 42605504,