[GraphQnt] some cleanup and renaming

src/qonnx/transformation/introduce_quantnode.py → src/qonnx/transformation/quantize_graph.py

@@ -180,32 +180,30 @@ def adjust_graph(self, model, input_positions, node_in_focus, quantized_nodes, n
         return model
 
 
-class IntroduceQuantnode(Transformation):
-    """This transformation can be used to introduce a Quant node for a specific type of node in the graph.
-    Users would be able to specify the location of the quant node by providing the input and output indexs
-    as the parameters.
+class QuantizeGraph(Transformation):
+    """This transformation can be used to introduce a Quant node for particular nodes in the graph,
+    determined based on either op_type or node name.
+    For the particular nodes identified, users can specify the location of the Quant nodes by providing
+    the input and output indices where Quant nodes are to be inserted.
+    Assumes the input model is cleaned-up with all intermediate shapes specified and nodes given
+    unique names already.
 
-             1) Expectations:
-                a) Onnx model in the modelwraper format.
-                b) Model must be cleaned using cleanup_model qonnx.util.cleanup.cleanup_model()
-                c) Batchsize to be set.
+        2) Steps to transform are
+            Step1: Finding the input for the quant node.
+            Step2: Finding the consumer of the quant node output.
+            Step3: Finding the shape for the output tensor of quant node.
+            Note: The output tensor of the quant node must have the same shape as the
+                    consumer of the input to the quant node.
 
-            2) Steps to transform are
-                Step1: Finding the input for the quant node.
-                Step2: Finding the consumer of the quant node output.
-                Step3: Finding the shape for the output tensor of quant node.
-                Note: The output tensor of the quant node must have the same shape as the
-                      consumer of the input to the quant node.
+        3) Introduction to quantnodes will be done with precedence to "Name" in comparison to "op_type".
 
-            3) Introduction to quantnodes will be done with precedence to "Name" in comparison to "op_type".
+        4) Assert:
+            a) The input is a dictionary representing the node names as keys and a list of quant positions
+                as values.
+            b) The input dictionary must have atleast one mac node (Conv, gemm, matmul) for the transformation.
 
-            4) Assert:
-                a) The input is a dictionary representing the node names as keys and a list of quant positions
-                   as values.
-                b) The input dictionary must have atleast one mac node (Conv, gemm, matmul) for the transformation.
-
-            5) Return:
-                Returns a cleaned version of the model.
+        5) Return:
+            Returns a cleaned version of the model.
 
     """
 

tests/transformation/test_introduce_quantnode.py → tests/transformation/test_quantize_graph.py

@@ -33,21 +33,21 @@
 import urllib.request
 
 from qonnx.core.modelwrapper import ModelWrapper
-from qonnx.transformation.introduce_quantnode import IntroduceQuantnode, graph_util
+from qonnx.transformation.quantize_graph import QuantizeGraph, graph_util
 from qonnx.util.cleanup import cleanup
 from qonnx.util.inference_cost import inference_cost
 
 random.seed(42)
 
 graph_util = graph_util()
 
-a = "https://github.com/onnx/models/raw/main/validated/vision/"
-b = "classification/resnet/model/resnet18-v1-7.onnx?download="
+download_url = "https://github.com/onnx/models/raw/main/validated/vision/"
+download_url += "classification/resnet/model/resnet18-v1-7.onnx?download="
 
 model_details = {
     "resnet18-v1-7": {
         "description": "Resnet18 Opset version 7.",
-        "url": (a + b),
+        "url": download_url,
         "test_input": {
             "name": {
                 "Conv_0": [
@@ -124,7 +124,7 @@ def test_introduce_quantnode(test_model):
     model = download_model(test_model, do_cleanup=True, return_modelwrapper=True)
     original_model_inf_cost = inference_cost(model, discount_sparsity=False)
     nodes_pos = test_details["test_input"]
-    model = model.transform(IntroduceQuantnode(nodes_pos))
+    model = model.transform(QuantizeGraph(nodes_pos))
     quantnodes_added = len(model.get_nodes_by_op_type("Quant"))
     assert quantnodes_added == 10  # 10 positions are specified.
     verification = to_verify(model, nodes_pos)