From 40712a3cd23566fe5df7cd23a14909b21c645fc8 Mon Sep 17 00:00:00 2001
From: shingjan <yjshi03@gmail.com>
Date: Sun, 31 May 2020 15:47:25 -0500
Subject: [PATCH] some minor changes on main
---
hpvm/projects/onnx/frontend/graph_builder.py | 8 ++++----
hpvm/projects/onnx/frontend/graph_ir.py | 12 +++++++++++
hpvm/projects/onnx/frontend/main.py | 21 ++++++++------------
3 files changed, 24 insertions(+), 17 deletions(-)
diff --git a/hpvm/projects/onnx/frontend/graph_builder.py b/hpvm/projects/onnx/frontend/graph_builder.py
index 7ffe32c538..0659fe3fcd 100644
--- a/hpvm/projects/onnx/frontend/graph_builder.py
+++ b/hpvm/projects/onnx/frontend/graph_builder.py
@@ -3,11 +3,10 @@ from onnx import numpy_helper
from tensor import InputTensor, WeightTensor
from graph_ir import *
-support_onnx_ops = {"DepthwiseConv" : [2],
+support_onnx_ops = {#"DepthwiseConv" : [2],
"Conv" : [2], # only 2d supported here
"MatMul" : None,
"MaxPool": [2], # only 2d supported here
- "Activation" : None,
"BatchNormalization" : None,
"Flatten" : None,
"Add" : None,
@@ -187,6 +186,8 @@ class DFG(object):
return SoftMaxNode(onnx_node)
elif onnx_node.op_type == "Relu":
return ReluNode(onnx_node)
+ elif onnx_node.op_type == "Tanh":
+ return TanhNode(onnx_node)
elif onnx_node.op_type == "BatchNormalization":
return BatchNormalizationNode(onnx_node)
elif onnx_node.op_type == "Pad":
@@ -196,5 +197,4 @@ class DFG(object):
elif onnx_node.op_type == "Flatten":
return FlattenNode(onnx_node)
else:
- raise ValueError("Unsupported operator type!")
- sys.exit("Unsupported operator type!")
+ raise ValueError("Unsupported operator type: {}!".format(onnx_node.op_type))
diff --git a/hpvm/projects/onnx/frontend/graph_ir.py b/hpvm/projects/onnx/frontend/graph_ir.py
index 2cfed0c78f..64796514bc 100644
--- a/hpvm/projects/onnx/frontend/graph_ir.py
+++ b/hpvm/projects/onnx/frontend/graph_ir.py
@@ -193,6 +193,18 @@ class ReluNode(DFGNode):
self.inst_str += "tensorRelu(" + mapped_input_name + "); \n"
return self.inst_str
+class TanhNode(DFGNode):
+ def __init__(self, layer):
+ DFGNode.__init__(self, layer)
+
+ def codegen(self, tensors):
+ cur_node = self.onnx_node
+ mapped_input_name = tensors[cur_node.input[0]].get_mapped_name()
+ mapped_output_name = tensors[cur_node.output[0]].get_mapped_name()
+ self.inst_str += "void* " + mapped_output_name + " = "
+ self.inst_str += "tensorTanh(" + mapped_input_name + "); \n"
+ return self.inst_str
+
class BatchNormalizationNode(DFGNode):
def __init__(self, layer):
diff --git a/hpvm/projects/onnx/frontend/main.py b/hpvm/projects/onnx/frontend/main.py
index 258e1d23a5..b9fefdecd7 100644
--- a/hpvm/projects/onnx/frontend/main.py
+++ b/hpvm/projects/onnx/frontend/main.py
@@ -5,9 +5,9 @@ import onnx
import glob
#from onnxruntime.backend.backend import OnnxRuntimeBackend as backend
-onnx_file_dir = "../models/keras/lenet.onnx"
+onnx_file_dir = "../models/keras/alexnet.onnx"
src_emit_dir = "./test_src"
-opset_version_default = 11
+opset_version_default = 10
def check_version(model, new_version):
try:
@@ -33,25 +33,20 @@ def check_version(model, new_version):
return model
def compile(model):
- # TODO: make this in constant
- # make a cmd option, default value -> constant
weights_dir = src_emit_dir
- # test_data_dir = '../models/mnist/test_data_set_0'
- # converted_model = convert_version(model)
- # model = check_version(model, 11)
+ model = check_version(model, opset_version_default)
from graph_builder import GraphBuilder
from graph_codegen import GraphCodeGen
from hpvm_codegen import HpvmCodeGen
- gBuilder = GraphBuilder(model, None, "float32", weights_dir)
- #gCodegen = GraphCodeGen(gBuilder.build_graph(), weights_dir)
- hCodegen = HpvmCodeGen(gBuilder.build_graph(), weights_dir)
- #gCodegen.compile()
- hCodegen.compile()
+ 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()
def main():
# TODO: Put it in args
model = onnx.load(onnx_file_dir)
- # model = onnx.load('../models/keras/vgg16_cifar10.onnx')
compile(model)
if __name__ == "__main__":
--
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