[ENH] move hed specific method in utils to examples/hed/utils.py

abl/utils/utils.py

@@ -1,8 +1,5 @@
-import torch
-import torch.nn as nn
 import numpy as np
 from .plog import INFO
-from collections import OrderedDict
 from itertools import chain
 
 def flatten(l):
@@ -51,31 +48,6 @@ def block_sample(X, Z, Y, sample_num, epoch_idx):
 
     return X, Z, Y
 
-def gen_mappings(chars, symbs):
-    n_char = len(chars)
-    n_symbs = len(symbs)
-    if n_char != n_symbs:
-        print("Characters and symbols size dosen't match.")
-        return
-    from itertools import permutations
-
-    mappings = []
-    # returned mappings
-    perms = permutations(symbs)
-    for p in perms:
-        mappings.append(dict(zip(chars, list(p))))
-    return mappings
-
-
-def mapping_res(original_pred_res, m):
-    return [[m[symbol] for symbol in formula] for formula in original_pred_res]
-
-
-def remapping_res(pred_res, m):
-    remapping = {}
-    for key, value in m.items():
-        remapping[value] = key
-    return [[remapping[symbol] for symbol in formula] for formula in pred_res]
 
 def check_equal(a, b, max_err=0):
     if isinstance(a, (int, float)) and isinstance(b, (int, float)):
@@ -90,27 +62,7 @@ def check_equal(a, b, max_err=0):
         return True
 
     else:
-        return a == b
-
-
-def extract_feature(img):
-    extractor = nn.AvgPool2d(2, stride=2)
-    feature_map = np.array(extractor(torch.Tensor(img)))
-    return feature_map.reshape((-1,))
-    return np.concatenate(
-        (np.squeeze(np.sum(img, axis=1)), np.squeeze(np.sum(img, axis=2))), axis=0
-    )
-
-
-def reduce_dimension(data):
-    for truth_value in [0, 1]:
-        for equation_len in range(5, 27):
-            equations = data[truth_value][equation_len]
-            reduced_equations = [
-                [extract_feature(symbol_img) for symbol_img in equation]
-                for equation in equations
-            ]
-            data[truth_value][equation_len] = reduced_equations       
+        return a == b      
    
 
 def to_hashable(l):

examples/hed/utils.py

@@ -0,0 +1,47 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+
+def gen_mappings(chars, symbs):
+    n_char = len(chars)
+    n_symbs = len(symbs)
+    if n_char != n_symbs:
+        print("Characters and symbols size dosen't match.")
+        return
+    from itertools import permutations
+
+    mappings = []
+    # returned mappings
+    perms = permutations(symbs)
+    for p in perms:
+        mappings.append(dict(zip(chars, list(p))))
+    return mappings
+
+
+def mapping_res(original_pred_res, m):
+    return [[m[symbol] for symbol in formula] for formula in original_pred_res]
+
+
+def remapping_res(pred_res, m):
+    remapping = {}
+    for key, value in m.items():
+        remapping[value] = key
+    return [[remapping[symbol] for symbol in formula] for formula in pred_res]
+
+
+def extract_feature(img):
+    extractor = nn.AvgPool2d(2, stride=2)
+    feature_map = np.array(extractor(torch.Tensor(img)))
+    return feature_map.reshape((-1,))
+
+
+def reduce_dimension(data):
+    for truth_value in [0, 1]:
+        for equation_len in range(5, 27):
+            equations = data[truth_value][equation_len]
+            reduced_equations = [
+                [extract_feature(symbol_img) for symbol_img in equation]
+                for equation in equations
+            ]
+            data[truth_value][equation_len] = reduced_equations