GPU experiment

gpu.py

@@ -0,0 +1,23 @@
+import tensorflow as tf
+
+# Disable all GPUs
+tf.config.set_visible_devices([], "GPU")
+
+# Verify that the GPU is disabled
+print("Available GPUs:", tf.config.list_physical_devices("GPU"))
+
+# Your TensorFlow code here
+
+
+# import tensorflow as tf
+
+
+# # Create a simple computation to verify GPU usage
+# a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
+# b = tf.constant([[1.0, 1.0], [0.0, 1.0]])
+# c = tf.matmul(a, b)
+
+# print("\n\n")
+# # Check if TensorFlow is using the GPU
+# print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices("GPU")))
+# print("Result of matrix multiplication:\n", c)

simple.json

@@ -1 +1 @@
-{"training_data":6.0,"evaluation_data":5.5}
+{"training_data":None,"evaluation_data":100.0}

src/biabduction.py

@@ -13,13 +13,21 @@
 from load_data import Task, Tasks, evaluation_data, training_data
 from logger import logger
 from match_colored_objects import match_colored_objects
+from match_two_objects_with_output import match_two_objects_with_output
 from match_objects_in_grid import match_rectangular_objects_in_grid
 from match_subgrids_in_lattice import match_subgrids_in_lattice
 from objects import Object, display_multiple
 from primitives import primitive_to_xform, translate_down_1, xform_identity
 from split_mirrot_match import frame_split_and_mirror_xform
 
 
+import tensorflow as tf
+# turn off gpu
+#tf.config.set_visible_devices([], "GPU")
+print("Available GPUs:", tf.config.list_physical_devices("GPU"))
+
+
+
 def filter_simple_xforms(task: Task, task_name: str):
     examples = task.train
     for example in examples:
@@ -60,6 +68,7 @@ def filter_complex_xforms(task: Task, task_name: str):
     XformEntry(match_rectangular_objects_in_grid, 3),
     XformEntry(inpainting_xform_no_mask, 2),
     XformEntry(inpainting_xform_output_is_block, 2),
+    XformEntry(match_two_objects_with_output, 3),
 ] + (
     [
         XformEntry(inpainting_xform_with_mask, 2),

src/config.py

@@ -13,10 +13,13 @@
 # task_name = "f9d67f8b.json" # maybe a mistake in the task
 
 # task_name = "72ca375d.json"
-# task_name = "0934a4d8.json"
+task_name = "0934a4d8.json"
 
 # task_name = "1e97544e.json"
 
+# task_name = "6ecd11f4.json"
+
+
 task_fractal = "8f2ea7aa.json"  # fractal expansion
 task_puzzle = "97a05b5b.json"  # puzzle-like, longest in DSL (59 lines)
 
@@ -54,7 +57,7 @@
 max_colors = 4
 
 only_complex_examples = False
-min_size = 23
+min_size = 21
 min_colors = 9
 
 find_xform = True

src/match_two_objects_with_output.py

@@ -0,0 +1,44 @@
+from typing import List, Optional
+
+from bi_types import Example, Match, Object
+from logger import logger
+from objects import display_multiple, display
+from visual_cortex import find_rectangular_objects
+import config
+
+
+def match_two_objects_with_output(
+    examples: List[Example[Object]],
+    task_name: str,
+    nesting_level: int,
+) -> Optional[Match[Object, Object]]:
+    logger.info(
+        f"{'  ' * nesting_level}match_two_objects_with_output examples:{len(examples)} task_name:{task_name} nesting_level:{nesting_level}"
+    )
+    for input, output in examples:
+        background_color = input.main_color(allow_black=True)
+        objects = input.detect_objects(
+            diagonals=True, background_color=background_color, multicolor=True
+        )
+        if len(objects) == 0:
+            return None
+        largest_object = max(objects, key=lambda o: o.area)
+        other_objects = [o for o in objects if o != largest_object]
+        if len(other_objects) < 1:
+            return None
+
+        if largest_object.size == input.size:
+            return None
+        if all(output.size != o.size for o in objects):
+            return None
+
+        if len(objects) <= 2:
+            return None
+
+        # display_multiple(
+        #     [largest_object, other_object, output], title=f"objects:{len(objects)}"
+        # )
+
+    config.display_this_task = True
+
+    return None