yueyihua opened a new issue #4654: convert onnx to tvm model error URL: https://github.com/apache/incubator-tvm/issues/4654 When I convert my onnx model to tvm，some error occur： WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d WARNING:root:Attribute auto_pad is ignored in relay.sym.max_pool2d [15:48:29] /home/yyh/3rdparty/tvm/src/relay/ir/doc.h:50: text node: ' an internal invariant was violated while typechecking your program [15:48:29] /home/yyh/3rdparty/tvm/src/relay/op/nn/convolution.h:112: Check failed: reporter->AssertEQ(indexdiv(dshape_nchw[1], param->groups), wshape[1]): Stack trace: [bt] (0) /home/yyh/3rdparty/tvm/build/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x22) [0x7f1a82cd4b22] [bt] (1) /home/yyh/3rdparty/tvm/build/libtvm.so(bool tvm::relay::Conv2DRel<tvm::relay::Conv2DAttrs>(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)+0x12ca) [0x7f1a8303b62a] [bt] (2) /home/yyh/3rdparty/tvm/build/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<bool (tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)>::AssignTypedLambda<bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)>(bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&))::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)+0xd2) [0x7f1a82ff9fd2] [bt] (3) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::TypeSolver::Solve()+0x395) [0x7f1a8325e265] [bt] (4) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::TypeInferencer::Infer(tvm::relay::Expr)+0x45) [0x7f1a83249cb5] [bt] (5) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::InferType(tvm::relay::Function const&, tvm::relay::Module const&, tvm::relay::GlobalVar const&)+0x1f5) [0x7f1a8324a525] [bt] (6) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::ModuleNode::Add(tvm::relay::GlobalVar const&, tvm::relay::Function const&, bool)+0x25d) [0x7f1a8331602d] [bt] (7) /home/yyh/3rdparty/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::GlobalTypeVar, tvm::relay::TypeData, void, void> const&)+0x1b8) [0x7f1a833173d8] [bt] (8) /home/yyh/3rdparty/tvm/build/libtvm.so(+0x9ccac5) [0x7f1a83318ac5] ; ' should not has tab or newline. Traceback (most recent call last): File "traffic_cnn_keras_tvm_dbg.py", line 224, in <module> mod, params = relay.frontend.from_onnx(onnx_model, shape_dict) File "/home/yyh/3rdparty/tvm/python/tvm/relay/frontend/onnx.py", line 1555, in from_onnx mod, params = g.from_onnx(graph, opset) File "/home/yyh/3rdparty/tvm/python/tvm/relay/frontend/onnx.py", line 1383, in from_onnx op = self._convert_operator(op_name, inputs, attr, opset) File "/home/yyh/3rdparty/tvm/python/tvm/relay/frontend/onnx.py", line 1483, in _convert_operator sym = convert_map[op_name](inputs, attrs, self._params) File "/home/yyh/3rdparty/tvm/python/tvm/relay/frontend/onnx.py", line 351, in _impl_v1 a_shape = infer_shape(inputs[0]) File "/home/yyh/3rdparty/tvm/python/tvm/relay/frontend/common.py", line 465, in infer_shape out_type = infer_type(inputs, mod=mod) File "/home/yyh/3rdparty/tvm/python/tvm/relay/frontend/common.py", line 456, in infer_type new_mod = _module.Module.from_expr(node) File "/home/yyh/3rdparty/tvm/python/tvm/relay/module.py", line 233, in from_expr return _module.Module_FromExpr(expr, funcs, defs) File "/home/yyh/3rdparty/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/yyh/3rdparty/tvm/build/libtvm.so(TVMFuncCall+0x46) [0x7f1a83395266] [bt] (6) /home/yyh/3rdparty/tvm/build/libtvm.so(+0x9ccac5) [0x7f1a83318ac5] [bt] (5) /home/yyh/3rdparty/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::GlobalTypeVar, tvm::relay::TypeData, void, void> const&)+0x1b8) [0x7f1a833173d8] [bt] (4) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::ModuleNode::Add(tvm::relay::GlobalVar const&, tvm::relay::Function const&, bool)+0x25d) [0x7f1a8331602d] [bt] (3) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::InferType(tvm::relay::Function const&, tvm::relay::Module const&, tvm::relay::GlobalVar const&)+0x1f5) [0x7f1a8324a525] [bt] (2) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::TypeInferencer::Infer(tvm::relay::Expr)+0x76) [0x7f1a83249ce6] [bt] (1) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::ErrorReporter::RenderErrors(tvm::relay::Module const&, bool)+0x1706) [0x7f1a832f6ca6] [bt] (0) /home/yyh/3rdparty/tvm/build/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x22) [0x7f1a82cd4b22] [bt] (8) /home/yyh/3rdparty/tvm/build/libtvm.so(+0x9ccac5) [0x7f1a83318ac5] [bt] (7) /home/yyh/3rdparty/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::GlobalTypeVar, tvm::relay::TypeData, void, void> const&)+0x1b8) [0x7f1a833173d8] [bt] (6) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::ModuleNode::Add(tvm::relay::GlobalVar const&, tvm::relay::Function const&, bool)+0x25d) [0x7f1a8331602d] [bt] (5) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::InferType(tvm::relay::Function const&, tvm::relay::Module const&, tvm::relay::GlobalVar const&)+0x1f5) [0x7f1a8324a525] [bt] (4) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::TypeInferencer::Infer(tvm::relay::Expr)+0x45) [0x7f1a83249cb5] [bt] (3) /home/yyh/3rdparty/tvm/build/libtvm.so(tvm::relay::TypeSolver::Solve()+0x395) [0x7f1a8325e265] [bt] (2) /home/yyh/3rdparty/tvm/build/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<bool (tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)>::AssignTypedLambda<bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)>(bool (*)(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&))::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)+0xd2) [0x7f1a82ff9fd2] [bt] (1) /home/yyh/3rdparty/tvm/build/libtvm.so(bool tvm::relay::Conv2DRel<tvm::relay::Conv2DAttrs>(tvm::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::relay::TypeReporter const&)+0x12ca) [0x7f1a8303b62a] [bt] (0) /home/yyh/3rdparty/tvm/build/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x22) [0x7f1a82cd4b22] File "/home/yyh/3rdparty/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 (%conv2d_input: Tensor[(1, 1, 28, 28), float32], %conv2d_3/kernel:0: Tensor[(32, 1, 5, 5), float32], %conv2d_3/bias:0: Tensor[(32), float32], %conv2d_1_1/kernel:0: Tensor[(64, 32, 5, 5), float32], %conv2d_1_1/bias:0: Tensor[(64), float32]) { %0 = transpose(%conv2d_input, axes=[0, 3, 1, 2]); %1 = nn.conv2d(%0, %conv2d_3/kernel:0, padding=[2, 2], kernel_size=[5, 5]) an internal invariant was violated while typechecking your program [15:48:29] /home/yyh/3rdparty/tvm/src/relay/op/nn/convolution.h:112: Check failed: reporter->AssertEQ(indexdiv(dshape_nchw[1], param->groups), wshape[1]): ; ; %2 = nn.bias_add(%1, %conv2d_3/bias:0); %3 = nn.relu(%2); %4 = nn.max_pool2d(%3, pool_size=[2, 2], strides=[2, 2]); %5 = nn.conv2d(%4, %conv2d_1_1/kernel:0, padding=[2, 2], kernel_size=[5, 5]); %6 = nn.bias_add(%5, %conv2d_1_1/bias:0); %7 = nn.relu(%6); %8 = nn.max_pool2d(%7, pool_size=[2, 2], strides=[2, 2]); %9 = transpose(%8, axes=[0, 2, 3, 1]); nn.batch_flatten(%9) } This is my code： onnx_model = onnxmltools.convert_keras(keras_model) onnxmltools.utils.save_model(onnx_model, output_onnx_model) """ # Debug onnx sess = rt.InferenceSession(output_onnx_model) input_name = sess.get_inputs()[0].name output_name = sess.get_outputs()[0].name print(sess.get_inputs()) print(sess.get_outputs()) print('input_name: ' + input_name) print('label_name: ' + output_name) """ # convert the keras model(NHWC layout) to Relay format(NCHW layout). x = np.ones([1, 1, IMAGE_SIZE, IMAGE_SIZE]) shape_dict = {'conv2d_input': x.shape} mod, params = relay.frontend.from_onnx(onnx_model, shape_dict) # compile the model target = 'cuda' ctx = tvm.gpu(0) with relay.build_config(opt_level=3): executor = relay.build_module.create_executor('graph', mod, ctx, target) graph, lib, params = relay.build_module.build(mod, target, params=params) lib.export_library("./deploy_lib.so") print('lib export succeefully') with open("./deploy_graph.json", "w") as fo: fo.write(graph.json()) with open("./deploy_param.params", "wb") as fo: fo.write(nnvm.compiler.save_param_dict(params))
---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
