MLBEDSW-3148: Refactor Operation

- op.type is now an enum instead of a string
- Removed unused operator codes
- Refactored some attributes like npu_block_type, fused_activation_function
- Refactored operator index calculation
- Refactored a number of operator sets

Change-Id: I641f65ee375794b7aec42abc0664251ae37d78e8
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>

1 files changed, 5 insertions, 4 deletions

diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py
index c0786bfc..98dfa3d3 100644
--- a/ethosu/vela/tensor.py
+++ b/ethosu/vela/tensor.py
@@ -25,6 +25,7 @@ import numpy as np
 from . import numeric_util
 from .data_type import DataType
 from .ethos_u55_regs.ethos_u55_regs import resampling_mode
+from .operation import Op
 from .operation import Operation
 from .range_set import MemoryRangeSet
 
@@ -242,7 +243,7 @@ def create_const_tensor(name, shape, dtype, values, value_dtype=None, purpose=Te
     const_tensor.values = np.array(values, dtype=value_dtype)
     const_tensor.quant_values = np.frombuffer(const_tensor.values.tobytes(), dtype=np.uint8)
     # Operator
-    const_op = Operation("Const", name)
+    const_op = Operation(Op.Const, name)
     const_op.set_output_tensor(const_tensor)
     return const_tensor
 
@@ -258,7 +259,7 @@ def create_reshape_tensor(tens, shape, ifm_reshape=True):
     if not ifm_reshape:
         reshape_ifm, reshape_ofm = reshape_ofm, reshape_ifm
     # Operator
-    reshape_op = Operation("Reshape", name)
+    reshape_op = Operation(Op.Reshape, name)
     reshape_op.attrs["new_shape"] = shape
     reshape_op.add_input_tensor(reshape_ifm)
     reshape_op.add_input_tensor(create_const_tensor(name + "_shape", [1], DataType.int32, shape))
@@ -649,7 +650,7 @@ class Tensor:
         return strides
 
     def needs_dma(self):
-        return len(self.ops) == 1 and self.ops[0].type == "DMA"
+        return len(self.ops) == 1 and self.ops[0].type == Op.DMA
 
     def get_dma_src_tensor(self):
         # For weight tensors that need DMA: returns the source tensor in Flash, else None
@@ -659,7 +660,7 @@ class Tensor:
     def find_npu_op(self):
         # Returns the NPU operator that uses this tensor, excluding DMA operators.
         for op in self.consumers():
-            if op.type == "DMA":
+            if op.type == Op.DMA:
                 return op.outputs[0].find_npu_op()
             if op.run_on_npu:
                 return op