Revert "perf: Prefer generator expressions over list comprehensions (#2486)" (#2491)

This reverts commit 37b9aeb.

Author: DawerG

coremltools/converters/_converters_entry.py

@@ -750,14 +750,14 @@ def _validate_outputs_argument(outputs):
                                  'or of types ct.ImageType/ct.TensorType'
         if isinstance(outputs[0], str):
             # if one of the elements is a string, all elements must be strings
-            if not all(isinstance(t, str) for t in outputs):
+            if not all([isinstance(t, str) for t in outputs]):
                 raise ValueError(msg_inconsistent_types)
             return outputs, [TensorType(name=name) for name in outputs]
 
         if isinstance(outputs[0], InputType):
-            if not all(isinstance(t, TensorType) or isinstance(t, ImageType) for t in outputs):
+            if not all([isinstance(t, TensorType) or isinstance(t, ImageType) for t in outputs]):
                 raise ValueError(msg_inconsistent_types)
-            if any(t.shape is not None for t in outputs):
+            if any([t.shape is not None for t in outputs]):
                 msg = "The 'shape' argument must not be specified for the outputs, since it is " \
                       "automatically inferred from the input shapes and the ops in the model"
                 raise ValueError(msg)
@@ -777,9 +777,9 @@ def _validate_outputs_argument(outputs):
             output_names = [t.name for t in outputs]
             # verify that either all of the entries in output_names is "None" or none of them is "None"
             msg_consistent_names = 'Either none or all the outputs must have the "name" argument specified'
-            if output_names[0] is None and not all(name is None for name in output_names):
+            if output_names[0] is None and not all([name is None for name in output_names]):
                 raise ValueError(msg_consistent_names)
-            if output_names[0] is not None and not all(name is not None for name in output_names):
+            if output_names[0] is not None and not all([name is not None for name in output_names]):
                 raise ValueError(msg_consistent_names)
             if output_names[0] is not None:
                 if len(set(output_names)) != len(output_names):
@@ -914,7 +914,7 @@ def _flatten_list(_inputs):
         if inputs is not None:
             raise_if_duplicated(inputs)
 
-        if inputs is not None and not all(isinstance(_input, InputType) for _input in inputs):
+        if inputs is not None and not all([isinstance(_input, InputType) for _input in inputs]):
             raise ValueError("Input should be a list of TensorType or ImageType")
 
     elif exact_source == "pytorch":

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

@@ -551,7 +551,7 @@ def remove_output(block, prob_var):
         classes = classes.splitlines()
     elif isinstance(classes_in, list):  # list[int or str]
         classes = classes_in
-        assert all(isinstance(x, (int, str)) for x in classes), message
+        assert all([isinstance(x, (int, str)) for x in classes]), message
     else:
         raise ValueError(message)
 

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

@@ -266,7 +266,7 @@ def load(prog, **kwargs):
 
     proto = builder.spec
     # image input
-    has_image_input = any(isinstance(s, ImageType) for s in input_types)
+    has_image_input = any([isinstance(s, ImageType) for s in input_types])
     if has_image_input:
         proto = _convert_to_image_input(proto, input_types,
                                         skip_model_load=kwargs.get("skip_model_load", False))
@@ -284,7 +284,7 @@ def load(prog, **kwargs):
                 shape = var.sym_type.get_shape()
                 if any_variadic(shape):
                     raise ValueError("Variable rank model outputs, that are ImageTypes, are not supported")
-                if any(is_symbolic(d) for d in shape):
+                if any([is_symbolic(d) for d in shape]):
                     raise NotImplementedError("Image output '{}' has symbolic dimensions in its shape".
                                               format(var.name))
                 _validate_image_input_output_shapes(output_types[i].color_layout, shape, var.name, is_input=False)

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

@@ -196,7 +196,7 @@ def _try_convert_global_pool(const_context, builder, op, mode):
 
     if tuple(op.outputs[0].shape[:-2]) != tuple(op.inputs["x"].shape[:-2]):
         return False
-    if not all(s == 1 for s in op.outputs[0].shape[-2:]):
+    if not all([s == 1 for s in op.outputs[0].shape[-2:]]):
         return False
 
     builder.add_pooling(
@@ -795,11 +795,11 @@ def _add_elementwise_binary(
                 # INTERNAL_MUL_XYKN not implemented
                 continue
             if all(shape_x[indices] == shape_y[indices]):
-                if all(True if i in indices else s == 1 for i, s in enumerate(shape_x)):
+                if all([True if i in indices else s == 1 for i, s in enumerate(shape_x)]):
                     internal_y = op.x
                     internal_x = op.y
                     break
-                if all(True if i in indices else s == 1 for i, s in enumerate(shape_y)):
+                if all([True if i in indices else s == 1 for i, s in enumerate(shape_y)]):
                     internal_x = op.x
                     internal_y = op.y
                     break
@@ -3323,7 +3323,7 @@ def stack(const_context, builder, op):
 def split(const_context, builder, op):
     split = op.sizes
     split = [size for size in split if size != 0]
-    has_equal_splits = all(size == split[0] for size in split)
+    has_equal_splits = all([size == split[0] for size in split])
     num_splits = len(split)
     output_names = [op.outputs[i].name for i in range(len(op.sizes)) if op.sizes[i] != 0]
 
@@ -3545,7 +3545,7 @@ def _realloc_list(const_context, builder, ls_var, index_var, value_var, mode):
 
     # check if elem_shape is runtime-determined
     elem_shape = tuple(value_var.shape)
-    has_dynamic_shape = any(is_symbolic(i) for i in elem_shape)
+    has_dynamic_shape = any([is_symbolic(i) for i in elem_shape])
 
     # get the fill shape of the tensor array
     # [length, elem_dim1, elem_dim2, ...]

coremltools/converters/mil/converter.py

@@ -58,7 +58,7 @@ def __call__(self, model, *args, **kwargs):
                         type(inputs)
                     )
                 )
-            if not all(isinstance(i, input_types.InputType) for i in inputs):
+            if not all([isinstance(i, input_types.InputType) for i in inputs]):
                 raise ValueError(
                     "Type of inputs should be list or tuple of TensorType or ImageType, got {} instead.".format(
                         [type(i) for i in inputs]

coremltools/converters/mil/debugging_utils.py

@@ -84,7 +84,7 @@ def validate_inputs(func, input_vars):
                 else:
                     input_values.append(v)
 
-            if all(x in reachable_vars for x in input_values):
+            if all([x in reachable_vars for x in input_values]):
                 reachable_vars.update(op.outputs)
 
         for out in func.outputs:

coremltools/converters/mil/frontend/_utils.py

@@ -293,7 +293,7 @@ def _swap(a, b):
         return b, a
 
     a_var, b_var = vars
-    is_dynamic = any(any_symbolic(var.shape) for var in vars)
+    is_dynamic = any([any_symbolic(var.shape) for var in vars])
     # list of equations supported for explicit mil translations
     vec_bnqd_bnkd_bnqk = (
         [0, 1, 2, 3],
@@ -436,10 +436,10 @@ def get_output_names(outputs) -> Optional[List[str]]:
 
     output_names = None
     if outputs is not None:
-        assert all(isinstance(t, InputType) for t in outputs), \
+        assert all([isinstance(t, InputType) for t in outputs]), \
             "outputs must be a list of ct.ImageType or ct.TensorType"
         output_names = [t.name for t in outputs]
-        if all(name is None for name in output_names):
+        if all([name is None for name in output_names]):
             output_names = None
     return output_names
 

coremltools/converters/mil/frontend/tensorflow/converter.py

@@ -179,7 +179,7 @@ def __init__(
                         type(inputs)
                     )
                 )
-            if not all(isinstance(i, InputType) for i in inputs):
+            if not all([isinstance(i, InputType) for i in inputs]):
                 raise ValueError(
                     "Type of inputs should be list or tuple of TensorType or ImageType, got {} instead.".format(
                         [type(i) for i in inputs]
@@ -238,7 +238,7 @@ def __init__(
         for inputtype in self.inputs:
             if not isinstance(inputtype.shape, InputShape):
                 continue
-            if any(isinstance(s, RangeDim) for s in inputtype.shape.shape):
+            if any([isinstance(s, RangeDim) for s in inputtype.shape.shape]):
                 continue
             if inputtype.name not in graph:
                 raise ValueError(

coremltools/converters/mil/frontend/tensorflow/ops.py

@@ -633,7 +633,7 @@ def ExtractImagePatches(context, node):
     padding = node.attr.get("padding")
     if x.rank != 4:
         raise ValueError("input for ExtractImagePatches should be a 4D tensor.")
-    if not all(rate == 1 for rate in rates):
+    if not all([rate == 1 for rate in rates]):
         raise NotImplementedError(
             "only rates with all 1s is implemented for ExtractImagePatches."
         )
@@ -3022,7 +3022,7 @@ def Pack(context, node):
         else:
             x = mb.expand_dims(x=values[0], axes=[axis], name=node.name)
     else:
-        if all(_is_scalar(input.sym_type) for input in values):
+        if all([_is_scalar(input.sym_type) for input in values]):
             x = mb.concat(values=values, axis=axis, name=node.name)
         else:
             x = mb.stack(values=values, axis=axis, name=node.name)

coremltools/converters/mil/frontend/tensorflow/ssa_passes/tf_lstm_to_core_lstm.py

@@ -106,7 +106,7 @@ def _try_get_last_cell_state_in_tf_lstm_block(op: Operation) -> Var:
         return cs
     if len(cs.consuming_blocks) > 1:
         return None
-    if not all(child_op.op_type == "slice_by_index" for child_op in cs.child_ops):
+    if not all([child_op.op_type == "slice_by_index" for child_op in cs.child_ops]):
         return None
     child_ops = cs.child_ops[:]
     block = op.enclosing_block