Fix typos (#2018)

* Fix typos

* Fix lambda variable

* Use only ascii characters

---------

Co-authored-by: Alejandro Gaston Alvarez Franceschi <alejandro.alvarez@projectx.ai>

Author: alealv

coremltools/_deps/__init__.py

@@ -4,7 +4,7 @@
 # found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
 
 """
-List of all external dependancies for this package. Imported as
+List of all external dependencies for this package. Imported as
 optional includes
 """
 import platform as _platform

coremltools/converters/_converters_entry.py

@@ -163,7 +163,7 @@ def convert(
               in the TF model.
             - If ``name`` is specified with ``TensorType`` and ``ImageType``, it
               must correspond to a placeholder op in the TF graph. The input names
-              in the converted Core ML model can later be modifed using the
+              in the converted Core ML model can later be modified using the
               ``ct.utils.rename_feature`` API.
             - If ``dtype`` is not specified, it defaults to the ``dtype`` of the
               inputs in the TF model.

coremltools/converters/libsvm/_libsvm_converter.py

@@ -77,7 +77,7 @@ def convert(libsvm_model, feature_names, target, input_length, probability):
         # input will be a single array
         if input_length == "auto":
             print(
-                "[WARNING] Infering an input length of %d. If this is not correct,"
+                "[WARNING] Inferring an input length of %d. If this is not correct,"
                 " use the 'input_length' parameter." % inferred_length
             )
             input_length = inferred_length
@@ -167,7 +167,7 @@ def convert(libsvm_model, feature_names, target, input_length, probability):
     else:
         svm.rho = libsvm_model.rho[0]
 
-    # set coefficents
+    # set coefficients
     if svm_type_enum == _svm.C_SVC or svm_type_enum == _svm.NU_SVC:
         for _ in range(nr_class - 1):
             svm.coefficients.add()

coremltools/converters/mil/backend/mil/load.py

@@ -226,7 +226,7 @@ def convert_function(function, parameters, blob_writer, opset):
     return pm.Function(inputs=inputs, opset=opset, block_specializations={opset: block})
 
 # Add a classify op to the output.
-# Replaces the original probabilites output (in the containing MIL block)
+# Replaces the original probabilities output (in the containing MIL block)
 # with the outputs of the classifier op. Returns the name of the original
 # probabilities output variable.
 def _add_classify_op(prog, classifier_config):

coremltools/converters/mil/backend/mil/passes/fuse_pow2_sqrt.py

@@ -10,7 +10,7 @@
 def _match_pattern(op):
     pow_op, sqrt_op = None, None
 
-    # check the curernt op is pow(2) or sqrt
+    # check the current op is pow(2) or sqrt
     if op.op_type == "pow" and op.y.val == 2:
         pow_op = op
     if op.op_type == "sqrt":

coremltools/converters/mil/backend/nn/load.py

@@ -157,7 +157,7 @@ def _set_user_inputs(proto, inputs):
 
 
 def _set_symbolic_inputs(proto, symbolic_inputs):
-    # Set symbolic input shapes by -1 infered from graph
+    # Set symbolic input shapes by -1 inferred from graph
     for input_name, shape in symbolic_inputs.items():
         lb = [1 if is_symbolic(d) else d for d in shape]
         ub = [-1 if is_symbolic(d) else d for d in shape]

coremltools/converters/mil/backend/nn/op_mapping.py

@@ -557,7 +557,7 @@ def conv_helper(const_context, builder, op):
 
     if is_conv1d or is_conv2d:
         if weights is None and has_bias:
-            # weights are dyanmic.
+            # weights are dynamic.
             # In this case, bias, if present, cannot be part of the conv op
             # it needs to be added separately via an add op
             out_name += "_without_bias"
@@ -3537,7 +3537,7 @@ def _realloc_list(const_context, builder, ls_var, index_var, value_var, mode):
     # (1)
     # check if we need to re-initialize the tensorarray:
     # it happens when the elem_shape is runtime determined and the runtime shape is not equal to
-    # the default shape. Ex: elem_shape is = [i0, 10] (initilized with [1, 10]) and at the runtime we get [2, 10].
+    # the default shape. Ex: elem_shape is = [i0, 10] (initialized with [1, 10]) and at the runtime we get [2, 10].
 
     # (2)
     # If index_var >= len(ls_var), reallocate the array and copy over existing

coremltools/converters/mil/backend/nn/passes/mlmodel_passes.py

@@ -175,7 +175,7 @@ def _remove_layers_from_spec(nn_spec, layers_to_delete):
 
     def _get_disconnected_layers_rec(nn_spec):
         """
-        - Iteraters over layers in bottom-up fashion
+        - Iterates over layers in bottom-up fashion
         - Collect layers if it's output is not being used (marks and does lazy deletion)
         - Recursively iterates over NN Spec if layer is Loop or Branch
         """
@@ -245,7 +245,7 @@ def _decrease_input_degree(layer):
                     and len(else_layers_to_delete) == total_else_layers
                 ):
                     # If both branches are empty after dead-layer elimination
-                    # remove branch layer altogehter
+                    # remove branch layer altogether
                     layers_to_delete.append(_layer)
                     _decrease_input_degree(_layer)
                 continue
@@ -422,7 +422,7 @@ def solve_dp(layers):
             For example, if sol_num[10] = 5, this means after index 10, we can at most remove 5 nodes.
             sol_bt[i] keeps the first starting point of identity sequence which results in the
             optimal solution after index i.
-            For example, if sol_num[10] = 12, means that in order to get rid of the maxium number of
+            For example, if sol_num[10] = 12, means that in order to get rid of the maximum number of
             nodes after 10, the first starting point is index 12.
             After construct sol_num and sol_bt by dynamic programming, we backtrack for the optimal
             solution using sol_bt.

coremltools/converters/mil/backend/nn/passes/test_mlmodel_passes.py

@@ -350,7 +350,7 @@ def _test_builder(self, builder, input_shape, expected_layer_num=None):
 
     def test_output_edge_case(self):
 
-        # For now for safety purpose, the node which are output should't be merged
+        # For now for safety purpose, the node which are output shouldn't be merged
         input_shape = (1, 10, 5)
         input_features = [("data", datatypes.Array(*input_shape))]
         output_features = [("out", None)]
@@ -374,7 +374,7 @@ def test_output_edge_case(self):
 
     def test_output_edge_case_2(self):
 
-        # For now for safety purpose, the node which are output should't be merged
+        # For now for safety purpose, the node which are output shouldn't be merged
         input_shape = (1, 10, 5)
         input_features = [("data", datatypes.Array(*input_shape))]
         output_features = [("out", None)]
@@ -675,7 +675,7 @@ def test_branch_structure(self):
                 RELU_2
         t_0, t_1, t_3 can be merged.
         t_4, t_5 can be merged.
-        The output shuld be
+        The output should be
                 INPUT
                  |
                  .------.

coremltools/converters/mil/experimental/passes/generic_conv_bias_fusion.py

@@ -46,7 +46,7 @@
        ...
 
 When taking all of the conv/conv_tranpose, transpose/no transpose, and add/sub into account,
-We end up with a total of 8 patterns (2^3). These patterns are paramaterized by the pattern_to_detect
+We end up with a total of 8 patterns (2^3). These patterns are parameterized by the pattern_to_detect
 function below.
 """