Add SuperSimpleNorm and update synthetic env

tests/test_super_norm.py

@@ -0,0 +1,53 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+# pytest ./tests/test_super_norm.py -s              #
+#####################################################
+import sys, random
+import unittest
+import pytest
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
+print("library path: {:}".format(lib_dir))
+if str(lib_dir) not in sys.path:
+    sys.path.insert(0, str(lib_dir))
+
+import torch
+from xlayers import super_core
+import spaces
+
+
+class TestSuperSimpleNorm(unittest.TestCase):
+    """Test the super simple norm."""
+
+    def test_super_simple_norm(self):
+        out_features = spaces.Categorical(12, 24, 36)
+        bias = spaces.Categorical(True, False)
+        model = super_core.SuperSequential(
+            super_core.SuperSimpleNorm(5, 0.5),
+            super_core.SuperLinear(10, out_features, bias=bias),
+        )
+        print("The simple super module is:\n{:}".format(model))
+        model.apply_verbose(True)
+
+        print(model.super_run_type)
+        self.assertTrue(model[1].bias)
+
+        inputs = torch.rand(20, 10)
+        print("Input shape: {:}".format(inputs.shape))
+        outputs = model(inputs)
+        self.assertEqual(tuple(outputs.shape), (20, 36))
+
+        abstract_space = model.abstract_search_space
+        abstract_space.clean_last()
+        abstract_child = abstract_space.random()
+        print("The abstract searc space:\n{:}".format(abstract_space))
+        print("The abstract child program:\n{:}".format(abstract_child))
+
+        model.set_super_run_type(super_core.SuperRunMode.Candidate)
+        model.apply_candidate(abstract_child)
+
+        output_shape = (20, abstract_child["1"]["_out_features"].value)
+        outputs = model(inputs)
+        self.assertEqual(tuple(outputs.shape), output_shape)