diff --git a/hpvm/projects/onnx/frontend/config.py b/hpvm/projects/onnx/frontend/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..b86adfa1ce7e7f5cbf6bebb62efdd3826d05fcf7
--- /dev/null
+++ b/hpvm/projects/onnx/frontend/config.py
@@ -0,0 +1,3 @@
+onnx_file_dir = "../models/keras/alexnet.onnx"
+src_emit_dir = "./test_src"
+opset_version_default = 11
\ No newline at end of file
diff --git a/hpvm/projects/onnx/frontend/graph_builder.py b/hpvm/projects/onnx/frontend/graph_builder.py
index 0659fe3fcd5fbae5a3cee124235916fa9692ca0e..063c8d83e3c3b87f42fc7874a5afb2d4532b1133 100644
--- a/hpvm/projects/onnx/frontend/graph_builder.py
+++ b/hpvm/projects/onnx/frontend/graph_builder.py
@@ -2,20 +2,7 @@ import sys
from onnx import numpy_helper
from tensor import InputTensor, WeightTensor
from graph_ir import *
-
-support_onnx_ops = {#"DepthwiseConv" : [2],
- "Conv" : [2], # only 2d supported here
- "MatMul" : None,
- "MaxPool": [2], # only 2d supported here
- "BatchNormalization" : None,
- "Flatten" : None,
- "Add" : None,
- "Relu" : None,
- "Softmax" : None,
- "Identity": None,
- "Pad": None,
- "AveragePool": None,
- "Tanh": None}
+from utils import support_onnx_ops
class GraphBuilder(object):
def __init__(self, model, shape, dtype, weight_dir):
@@ -151,14 +138,11 @@ class GraphBuilder(object):
# Dump weights
for tensor in self.tensors.values():
if isinstance(tensor, WeightTensor):
- print("Dump weight: {0}".format(weight_tensor.name))
- #tensor.dump_weight(self.weight_dir + "/" + tensor.get_mapped_name() + "_path.bin")
+ print("Dump weight: {0}".format(tensor.name))
+ tensor.dump_weight(self.weight_dir + "/" + tensor.get_mapped_name() + "_path.bin")
return DFG(self.graph, self.tensors)
class DFG(object):
-
- root_set = False
-
def __init__(self, graph, tensors):
self.graph = graph
self.tensors = tensors
diff --git a/hpvm/projects/onnx/frontend/graph_codegen.py b/hpvm/projects/onnx/frontend/graph_codegen.py
index f985ae810f9efa4744fa68760a4f0b490c7f55f0..b32132959ce956673e2161c676005fa7fc1ae2b9 100644
--- a/hpvm/projects/onnx/frontend/graph_codegen.py
+++ b/hpvm/projects/onnx/frontend/graph_codegen.py
@@ -2,18 +2,15 @@ import sys
import numpy as np
import os
-from graph_builder import *
-#from graph_ir import *
-from tensor import *
-
-skip_layer = ["Identity", "Flatten", "Pad"]
+from tensor import WeightTensor
+from utils import skip_layer
class GraphCodeGen(object):
- def __init__(self, DFG, weights_dir, test_data=None, test_labels=None):
+ def __init__(self, dfg, weights_dir, test_data=None, test_labels=None):
self.program_str = ""
- self.graph = DFG.graph
- self.tensors = DFG.tensors
- self.nodes = DFG.nodes
+ self.graph = dfg.graph
+ self.tensors = dfg.tensors
+ self.nodes = dfg.nodes
self.var_cnt = 0
self.weights_dir = weights_dir
self.test_data = test_data
diff --git a/hpvm/projects/onnx/frontend/graph_ir.py b/hpvm/projects/onnx/frontend/graph_ir.py
index 64796514bc644da0ecb708aa70256bd67e2dceba..745e5d0429fc7d8c6c11c8efb426160f5a6ccabd 100644
--- a/hpvm/projects/onnx/frontend/graph_ir.py
+++ b/hpvm/projects/onnx/frontend/graph_ir.py
@@ -42,8 +42,6 @@ class LogicalOpNode(DFGNode):
class AddNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -56,8 +54,6 @@ class AddNode(DFGNode):
class MatMulNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -71,8 +67,6 @@ class MatMulNode(DFGNode):
class SoftMaxNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -84,8 +78,6 @@ class SoftMaxNode(DFGNode):
class Conv2DNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -122,8 +114,6 @@ class Conv2DNode(DFGNode):
class MaxPool2DNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -152,8 +142,6 @@ class MaxPool2DNode(DFGNode):
class AveragePool2DNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -182,8 +170,6 @@ class AveragePool2DNode(DFGNode):
class ReluNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -194,8 +180,6 @@ class ReluNode(DFGNode):
return self.inst_str
class TanhNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -207,8 +191,6 @@ class TanhNode(DFGNode):
class BatchNormalizationNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
def codegen(self, tensors):
cur_node = self.onnx_node
@@ -230,25 +212,19 @@ class BatchNormalizationNode(DFGNode):
class PadNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
+ pass
class IdentityNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
+ pass
class FlattenNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
+ pass
class ZeroPadding2DNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
+ pass
class DepthwiseConv2DNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
+ pass
class DenseNode(DFGNode):
- def __init__(self, layer):
- DFGNode.__init__(self, layer)
+ pass
diff --git a/hpvm/projects/onnx/frontend/main.py b/hpvm/projects/onnx/frontend/main.py
index b9fefdecd7b04bbc94bf1d898e3d05a8c0bc04f8..16bf8868b09a6a5316480a1d3e5f49fd63898a76 100644
--- a/hpvm/projects/onnx/frontend/main.py
+++ b/hpvm/projects/onnx/frontend/main.py
@@ -3,24 +3,15 @@ import sys
import numpy as np
import onnx
import glob
-#from onnxruntime.backend.backend import OnnxRuntimeBackend as backend
-
-onnx_file_dir = "../models/keras/alexnet.onnx"
-src_emit_dir = "./test_src"
-opset_version_default = 10
def check_version(model, new_version):
try:
- opset = model.opset_import[0].version if model.opset_import else 1
+ opset = model.opset_import[0].version + 1 if model.opset_import else 1
except AttributeError:
opset = 1 # default opset version set to 1 if not specified
print("opset version: ", opset)
if opset != new_version:
#print('The model before conversion:\n{}'.format(model))
-
- # A full list of supported adapters can be found here:
- # https://github.com/onnx/onnx/blob/master/onnx/version_converter.py#L21
- # Apply the version conversion on the original model
from onnx import version_converter
try:
converted_model = version_converter.convert_version(model, new_version)
@@ -33,19 +24,21 @@ def check_version(model, new_version):
return model
def compile(model):
+ from config import opset_version_default, src_emit_dir
weights_dir = src_emit_dir
model = check_version(model, opset_version_default)
from graph_builder import GraphBuilder
from graph_codegen import GraphCodeGen
from hpvm_codegen import HpvmCodeGen
graphBuilder = GraphBuilder(model, None, "float32", weights_dir)
- #graphCodeGen = GraphCodeGen(gBuilder.build_graph(), weights_dir)
- #graphCodeGen.compile()
- hpvmCodeGen = HpvmCodeGen(graphBuilder.build_graph(), weights_dir)
- hpvmCodeGen.compile()
+ graphCodeGen = GraphCodeGen(graphBuilder.build_graph(), weights_dir)
+ graphCodeGen.compile()
+ #hpvmCodeGen = HpvmCodeGen(graphBuilder.build_graph(), weights_dir)
+ #hpvmCodeGen.compile()
def main():
# TODO: Put it in args
+ from config import onnx_file_dir
model = onnx.load(onnx_file_dir)
compile(model)
diff --git a/hpvm/projects/onnx/frontend/utils.py b/hpvm/projects/onnx/frontend/utils.py
index c05abe77a1bbcc63a1444a173be830a69964ba71..ef8aab545c62dc935e32eba3faac33074c636307 100644
--- a/hpvm/projects/onnx/frontend/utils.py
+++ b/hpvm/projects/onnx/frontend/utils.py
@@ -2,6 +2,21 @@ import numpy as np
import struct
import random
+support_onnx_ops = {#"DepthwiseConv" : [2],
+ "Conv" : [2], # only 2d supported here
+ "MatMul" : None,
+ "MaxPool": [2], # only 2d supported here
+ "BatchNormalization" : None,
+ "Flatten" : None,
+ "Add" : None,
+ "Relu" : None,
+ "Softmax" : None,
+ "Identity": None,
+ "Pad": None,
+ "AveragePool": None,
+ "Tanh": None}
+
+skip_layer = ["Identity", "Flatten", "Pad"]
def dumpLabels(file_name, Y_test):