tvm 0.6 , onnx 1.6.0 ,python3.5 .llvm 4.0
先发个正确的版本，这应该能说明我的环境没有问题
使用from_onnx.py里面的模型，super_resolution_0.2.onnx
import onnx
import numpy as np
import tvm
import tvm.relay as relay
onnx_model = onnx.load('super_resolution_0.2.onnx')
target = tvm.target.create('llvm')
input_name = '1' # change '1' to '0'
shape_dict = {input_name: (1, 1, 224, 224)}
sym, params = relay.frontend.from_onnx(onnx_model, shape_dict)
print("onnx model files")
zcc@zcc-X10SRA:~/下载/tvm$ python3 testonnx.py
/home/zcc/.local/lib/python3.5/site-packages/xgboost/__init__.py:28:
FutureWarning: Python 3.5 support is deprecated; XGBoost will require Python
3.6+ in the near future. Consider upgrading to Python 3.6+.
FutureWarning)
/home/zcc/App/incubator-tvm/incubator-tvm/python/tvm/relay/frontend/onnx.py:1487:
UserWarning: Mismatched attribute type in ' : kernel_shape'
==> Context: Bad node spec: input: "1" input: "2" output: "11" op_type: "Conv"
attribute { name: "kernel_shape" ints: 5 ints: 5 } attribute { name: "strides"
ints: 1 ints: 1 } attribute { name: "pads" ints: 2 ints: 2 ints: 2 ints: 2 }
attribute { name: "dilations" ints: 1 ints: 1 } attribute { name: "group" i: 1 }
warnings.warn(str(e))
onnx model files
下面是一个错误的示例，使用的是自己的模型，看了一个帖子，打算简化一下onnx
简单测试的代码：
import onnx
import numpy as np
import tvm
import tvm.relay as relay
onnx_model = onnx.load('mnas025.onnx')
target = tvm.target.create('llvm')
input_name = 'data' # change '1' to '0'
shape_dict = {input_name: (1, 3, 112, 112)}
sym, params = relay.frontend.from_onnx(onnx_model, shape_dict)
print("onnx model files")
#with relay.build_config(opt_level=2):
# graph, lib, params = relay.build_module.build(sym, target, params=params)
#dtype = 'float32'
#from tvm.contrib import graph_runtime
#print("Output model files")
#libpath = "./test.so"
#lib.export_library(libpath)
#graph_json_path = "./test.json"
#with open(graph_json_path, 'w') as fo:
# fo.write(graph)
#param_path = "./test.params"
#with open(param_path, 'wb') as fo:
# fo.write(relay.save_param_dict(params))
错误：
zcc@zcc-X10SRA:~/下载/tvm$ python3 testonnx.py
\/home/zcc/.local/lib/python3.5/site-packages/xgboost/__init__.py:28:
FutureWarning: Python 3.5 support is deprecated; XGBoost will require Python
3.6+ in the near future. Consider upgrading to Python 3.6+.
FutureWarning)
WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm
WARNING:root:Attribute spatial is ignored in relay.sym.batch_norm
. ..
...
...
WARNING:root:Attribute spatial is ignored in relay.sym.batch_norm
WARNING:root:Attribute momentum is ignored in relay.sym.batch_norm
WARNING:root:Attribute spatial is ignored in relay.sym.batch_norm
Traceback (most recent call last):
File "testonnx.py", line 13, in <module>
sym, params = relay.frontend.from_onnx(onnx_model, shape_dict)
File
"/home/zcc/App/incubator-tvm/incubator-tvm/python/tvm/relay/frontend/onnx.py",
line 1497, in from_onnx
mod, params = g.from_onnx(graph, opset)
File
"/home/zcc/App/incubator-tvm/incubator-tvm/python/tvm/relay/frontend/onnx.py",
line 1344, in from_onnx
return _module.Module.from_expr(func), self._params
File "/home/zcc/App/incubator-tvm/incubator-tvm/python/tvm/relay/module.py",
line 233, in from_expr
return _module.Module_FromExpr(expr, funcs, defs)
File
"/home/zcc/App/incubator-tvm/incubator-tvm/python/tvm/_ffi/_ctypes/function.py",
line 207, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
[bt] (7)
/home/zcc/App/incubator-tvm/incubator-tvm/build/libtvm.so(TVMFuncCall+0x61)
[0x7f6881245e71]
[bt] (6) /home/zcc/App/incubator-tvm/incubator-tvm/build/libtvm.so(+0xa888a1)
[0x7f68811648a1]
[bt] (5)
/home/zcc/App/incubator-tvm/incubator-tvm/build/libtvm.so(tvm::relay::ModuleNode::FromExpr(tvm::relay::Expr
const&, tvm::Map<tvm::relay::GlobalVar, tvm::relay::Function, void, void>
const&, tvm::Map<tvm::relay::GlobalTypeVar, tvm::relay::TypeData, void, void>
const&)+0x1d5) [0x7f6881163815]
[bt] (4)
/home/zcc/App/incubator-tvm/incubator-tvm/build/libtvm.so(tvm::relay::ModuleNode::Add(tvm::relay::GlobalVar
const&, tvm::relay::Function const&, bool)+0x28c) [0x7f68811613bc]
[bt] (3)
/home/zcc/App/incubator-tvm/incubator-tvm/build/libtvm.so(tvm::relay::InferType(tvm::relay::Function
const&, tvm::relay::Module const&, tvm::relay::GlobalVar const&)+0x1d7)
[0x7f6881084a97]
[bt] (2)
/home/zcc/App/incubator-tvm/incubator-tvm/build/libtvm.so(tvm::relay::TypeInferencer::Infer(tvm::relay::Expr)+0x86)
[0x7f6881084316]
[bt] (1)
/home/zcc/App/incubator-tvm/incubator-tvm/build/libtvm.so(tvm::relay::ErrorReporter::RenderErrors(tvm::relay::Module
const&, bool)+0x230c) [0x7f688114174c]
[bt] (0)
/home/zcc/App/incubator-tvm/incubator-tvm/build/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x32)
[0x7f6880a6fae2]
File "/home/zcc/App/incubator-tvm/incubator-tvm/src/relay/ir/error.cc", line
132
TVMError:
Error(s) have occurred. The program has been annotated with them:
In `main`:
v0.0.4
fn (%data: Tensor[(1, 3, 112, 112), float32], %scalar_op1: Tensor[(1),
float32], %scalar_op2: Tensor[(1), float32],
%mnasnet0_stage1_conv0_conv0_weight: Tensor[(8, 3, 3, 3), float32],
%mnasnet0_stage1_conv0_batchnorm0_gamma: Tensor[(8), float32],
%mnasnet0_stage1_conv0_batchnorm0_beta: Tensor[(8), float32],
...
...
...
%mnasnet0_stage5_3_batchnorm0_running_mean: Tensor[(256), float32],
%mnasnet0_stage5_3_batchnorm0_running_var: Tensor[(256), float32],
%mnasnet0_stage5_3_prelu0_alpha: Tensor[(1), float32],
%conv_6dw7_7_conv2d_weight: Tensor[(256, 1, 7, 7), float32],
%conv_6dw7_7_batchnorm_gamma: Tensor[(256), float32],
%conv_6dw7_7_batchnorm_beta: Tensor[(256), float32],
%conv_6dw7_7_batchnorm_moving_mean: Tensor[(256), float32],
%conv_6dw7_7_batchnorm_moving_var: Tensor[(256), float32], %pre_fc1_weight:
Tensor[(256, 256), float32], %pre_fc1_bias: Tensor[(256), float32], %fc1_gamma:
Tensor[(256), float32], %fc1_beta: Tensor[(256), float32], %fc1_moving_mean:
Tensor[(256), float32], %fc1_moving_var: Tensor[(256), float32]) {
%0 = subtract(%data, %scalar_op1);
%1 = multiply(%0, %scalar_op2);
%2 = nn.conv2d(%1, %mnasnet0_stage1_conv0_conv0_weight, padding=[1, 1],
kernel_size=[3, 3]);
..下面还有
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