No public description

PiperOrigin-RevId: 658525407

Author: hawkinsp

official/projects/mosaic/configs/mosaic_config.py

@@ -14,11 +14,10 @@
 
 """Configuration definition for Semantic Segmentation with MOSAIC."""
 import dataclasses
+import math
 import os
 from typing import List, Optional, Union
 
-import numpy as np
-
 from official.core import config_definitions as cfg
 from official.core import exp_factory
 from official.modeling import hyperparams
@@ -133,7 +132,7 @@ def mosaic_mnv35_cityscapes() -> cfg.ExperimentConfig:
   steps_per_epoch = CITYSCAPES_TRAIN_EXAMPLES // train_batch_size
   output_stride = 16
 
-  backbone_output_level = int(np.math.log2(output_stride))
+  backbone_output_level = int(math.log2(output_stride))
   config = cfg.ExperimentConfig(
       task=MosaicSemanticSegmentationTask(
           model=MosaicSemanticSegmentationModel(

official/projects/panoptic/configs/panoptic_deeplab.py

@@ -14,11 +14,10 @@
 
 """Panoptic Deeplab configuration definition."""
 import dataclasses
+import math
 import os
 from typing import List, Optional, Union
 
-import numpy as np
-
 from official.core import config_definitions as cfg
 from official.core import exp_factory
 from official.modeling import hyperparams
@@ -220,7 +219,7 @@ def panoptic_deeplab_resnet_coco() -> cfg.ExperimentConfig:
   aspp_dilation_rates = [6, 12, 18]
   multigrid = [1, 2, 4]
   stem_type = 'v1'
-  level = int(np.math.log2(output_stride))
+  level = int(math.log2(output_stride))
 
   config = cfg.ExperimentConfig(
       runtime=cfg.RuntimeConfig(
@@ -385,7 +384,7 @@ def panoptic_deeplab_mobilenetv3_large_coco() -> cfg.ExperimentConfig:
   input_size = [640, 640, 3]
   output_stride = 16
   aspp_dilation_rates = [6, 12, 18]
-  level = int(np.math.log2(output_stride))
+  level = int(math.log2(output_stride))
 
   config = cfg.ExperimentConfig(
       runtime=cfg.RuntimeConfig(
@@ -547,7 +546,7 @@ def panoptic_deeplab_mobilenetv3_small_coco() -> cfg.ExperimentConfig:
   input_size = [640, 640, 3]
   output_stride = 16
   aspp_dilation_rates = [6, 12, 18]
-  level = int(np.math.log2(output_stride))
+  level = int(math.log2(output_stride))
 
   config = cfg.ExperimentConfig(
       runtime=cfg.RuntimeConfig(

official/vision/configs/semantic_segmentation.py

@@ -14,11 +14,10 @@
 
 """Semantic segmentation configuration definition."""
 import dataclasses
+import math
 import os
 from typing import List, Optional, Sequence, Union
 
-import numpy as np
-
 from official.core import config_definitions as cfg
 from official.core import exp_factory
 from official.modeling import hyperparams
@@ -233,7 +232,7 @@ def seg_deeplabv3_pascal() -> cfg.ExperimentConfig:
   aspp_dilation_rates = [12, 24, 36]  # [6, 12, 18] if output_stride = 16
   multigrid = [1, 2, 4]
   stem_type = 'v1'
-  level = int(np.math.log2(output_stride))
+  level = int(math.log2(output_stride))
   config = cfg.ExperimentConfig(
       task=SemanticSegmentationTask(
           model=SemanticSegmentationModel(
@@ -325,7 +324,7 @@ def seg_deeplabv3plus_pascal() -> cfg.ExperimentConfig:
   aspp_dilation_rates = [6, 12, 18]
   multigrid = [1, 2, 4]
   stem_type = 'v1'
-  level = int(np.math.log2(output_stride))
+  level = int(math.log2(output_stride))
   config = cfg.ExperimentConfig(
       task=SemanticSegmentationTask(
           model=SemanticSegmentationModel(
@@ -492,7 +491,7 @@ def mnv2_deeplabv3_pascal() -> cfg.ExperimentConfig:
   steps_per_epoch = PASCAL_TRAIN_EXAMPLES // train_batch_size
   output_stride = 16
   aspp_dilation_rates = []
-  level = int(np.math.log2(output_stride))
+  level = int(math.log2(output_stride))
   pool_kernel_size = []
 
   config = cfg.ExperimentConfig(
@@ -593,7 +592,7 @@ def seg_deeplabv3plus_cityscapes() -> cfg.ExperimentConfig:
   aspp_dilation_rates = [6, 12, 18]
   multigrid = [1, 2, 4]
   stem_type = 'v1'
-  level = int(np.math.log2(output_stride))
+  level = int(math.log2(output_stride))
   config = cfg.ExperimentConfig(
       task=SemanticSegmentationTask(
           model=SemanticSegmentationModel(
@@ -694,7 +693,7 @@ def mnv2_deeplabv3_cityscapes() -> cfg.ExperimentConfig:
   aspp_dilation_rates = []
   pool_kernel_size = [512, 1024]
 
-  level = int(np.math.log2(output_stride))
+  level = int(math.log2(output_stride))
   config = cfg.ExperimentConfig(
       task=SemanticSegmentationTask(
           model=SemanticSegmentationModel(

official/vision/modeling/backbones/resnet_deeplab.py

@@ -14,10 +14,11 @@
 
 """Contains definitions of Residual Networks with Deeplab modifications."""
 
-from typing import Callable, Optional, Tuple, List
+import math
+from typing import Callable, List, Optional, Tuple
 
-import numpy as np
 import tensorflow as tf, tf_keras
+
 from official.modeling import hyperparams
 from official.modeling import tf_utils
 from official.vision.modeling.backbones import factory
@@ -238,7 +239,7 @@ def __init__(
     else:
       x = layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)
 
-    normal_resnet_stage = int(np.math.log2(self._output_stride)) - 2
+    normal_resnet_stage = int(math.log2(self._output_stride)) - 2
 
     endpoints = {}
     for i in range(normal_resnet_stage + 1):

official/vision/modeling/backbones/resnet_deeplab_test.py

@@ -14,7 +14,10 @@
 
 """Tests for resnet_deeplab models."""
 
+import math
+
 # Import libraries
+
 from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf, tf_keras
@@ -46,10 +49,15 @@ def test_network_creation(self, input_size, model_id,
     inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
     endpoints = network(inputs)
     print(endpoints)
-    self.assertAllEqual([
-        1, input_size / output_stride, input_size / output_stride,
-        512 * endpoint_filter_scale
-    ], endpoints[str(int(np.math.log2(output_stride)))].shape.as_list())
+    self.assertAllEqual(
+        [
+            1,
+            input_size / output_stride,
+            input_size / output_stride,
+            512 * endpoint_filter_scale,
+        ],
+        endpoints[str(int(math.log2(output_stride)))].shape.as_list(),
+    )
 
   @parameterized.parameters(
       ('v0', None, 0.0, False, False),
@@ -82,10 +90,15 @@ def test_network_features(self, stem_type, se_ratio,
     inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
     endpoints = network(inputs)
     print(endpoints)
-    self.assertAllEqual([
-        1, input_size / output_stride, input_size / output_stride,
-        512 * endpoint_filter_scale
-    ], endpoints[str(int(np.math.log2(output_stride)))].shape.as_list())
+    self.assertAllEqual(
+        [
+            1,
+            input_size / output_stride,
+            input_size / output_stride,
+            512 * endpoint_filter_scale,
+        ],
+        endpoints[str(int(math.log2(output_stride)))].shape.as_list(),
+    )
 
   @combinations.generate(
       combinations.combine(