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未验证 提交 3a6201af 编写于 作者: 王 王明冬 提交者： GitHub
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[infrt] add resnet50 unit test. test=develop (#40950)

上级 7e05680c
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Showing with 548 addition and 25 deletion
paddle/infrt/host_context/paddle_mlir.cc
浏览文件 @ 3a6201af
......@@ -13,6 +13,9 @@
// limitations under the License.
#include "paddle/infrt/host_context/paddle_mlir.h"
#include <mlir/IR/OpDefinition.h>
#include "paddle/infrt/dialect/infrt/ir/basic_kernels.h"
#include "paddle/infrt/dialect/infrt/ir/infrt_dialect.h"
#include "paddle/infrt/dialect/pd/common/pd_ops_info.h"
......@@ -95,13 +98,13 @@ llvm::SmallVector<mlir::Type, 4> MLIRModelGenImpl::GetModelInputsType(
std::vector<int64_t> dims = RepeatedToVector<int64_t>(
var_desc.type().lod_tensor().tensor().dims());
infrt::PrecisionType precision_;
ConvertDataTypeToPhi(
ConvertDataTypeToInfrt(
var_desc.type().lod_tensor().tensor().data_type(), &precision_);
mlir::Type type_ =
infrt::DenseTensorType::get(context_,
infrt::TargetType::CPU,
precision_,
infrt::LayoutType::ANY);
infrt::LayoutType::NCHW);
operandTypes.push_back(type_);
}
......@@ -125,13 +128,13 @@ llvm::SmallVector<mlir::Type, 4> MLIRModelGenImpl::GetModelOutputsType(
std::vector<int64_t> dims = RepeatedToVector<int64_t>(
var_desc.type().lod_tensor().tensor().dims());
infrt::PrecisionType precision_;
ConvertDataTypeToPhi(
ConvertDataTypeToInfrt(
var_desc.type().lod_tensor().tensor().data_type(), &precision_);
mlir::Type type_ =
infrt::DenseTensorType::get(context_,
infrt::TargetType::CPU,
precision_,
infrt::LayoutType::ANY);
infrt::LayoutType::NCHW);
resultTypes.push_back(type_);
}
}
......@@ -179,10 +182,12 @@ void MLIRModelGenImpl::UpdateModelParams(
std::vector<int64_t> dims = RepeatedToVector<int64_t>(
var_desc.type().lod_tensor().tensor().dims());
infrt::PrecisionType precision_;
ConvertDataTypeToPhi(var_desc.type().lod_tensor().tensor().data_type(),
ConvertDataTypeToInfrt(var_desc.type().lod_tensor().tensor().data_type(),
&precision_);
mlir::Type type_ = infrt::DenseTensorType::get(
context_, infrt::TargetType::CPU, precision_, infrt::LayoutType::ANY);
mlir::Type type_ = infrt::DenseTensorType::get(context_,
infrt::TargetType::CPU,
precision_,
infrt::LayoutType::NCHW);
auto op = builder_.create<::infrt::phi::TensorMapGetTensorOp>(
mlir::UnknownLoc::get(context_), type_, map, name);
params_map_.insert(std::pair<std::string, mlir::Value>(
......@@ -222,12 +227,42 @@ void MLIRModelGenImpl::UpdateModelOutputs(
void MLIRModelGenImpl::buildOperation(
const infrt::paddle::framework_proto::OpDesc &op_) {
const std::string &op_name = "pd." + op_.type();
const std::string op_name = "pd." + op_.type();
mlir::Location loc = mlir::UnknownLoc::get(context_);
mlir::OperationState result(loc, op_name);
if (!result.name.isRegistered()) {
LOG(FATAL) << "Find unregistered operation: " << op_name;
return;
}
if (result.name.hasTrait<mlir::OpTrait::AttrSizedOperandSegments>()) {
LOG(FATAL) << "Find operation: " << op_name
<< "has trait: AttrSizedOperandSegments. Current not support!";
return;
}
llvm::SmallVector<mlir::Value, 4> operands = GetOpInputValue(op_);
int empty_operand_cnt = 0;
for (auto it = operands.begin(); it != operands.end();) {
if (*it) {
++it;
} else {
operands.erase(it);
++empty_operand_cnt;
}
}
if (empty_operand_cnt > 1) {
LOG(FATAL)
<< "Find operation: " << op_name << ", has " << empty_operand_cnt
<< " empty operands. current not support empty operands more than one!";
return;
}
result.addOperands(operands);
llvm::SmallVector<mlir::Type, 4> resultTypes = GetOpOutputType(op_);
llvm::SmallVector<mlir::NamedAttribute, 4> attrs = GetOpAttributes(op_);
mlir::OperationState result(loc, op_name, operands, resultTypes, attrs);
result.addTypes(resultTypes);
result.addAttributes(attrs);
mlir::Operation *mlir_op_ = builder_.createOperation(result);
RegisterOpOutputVars(op_, mlir_op_);
}
......@@ -239,11 +274,13 @@ llvm::SmallVector<mlir::Value, 4> MLIRModelGenImpl::GetOpInputValue(
std::unordered_map<std::string, uint8_t> inputs_info = {};
if (pd_dialect_inputs_info_map_.count(op_.type()))
inputs_info = pd_dialect_inputs_info_map_.at(op_.type());
operands.resize(inputs_info.size());
for (int var_idx = 0; var_idx < op_.inputs_size(); ++var_idx) {
auto &var = op_.inputs(var_idx);
if (!var.arguments().empty()) {
if (!inputs_info.count(var.parameter())) continue;
operands.push_back((params_map_[var.arguments()[0]]));
operands[inputs_info.at(var.parameter())] =
params_map_[var.arguments()[0]];
}
}
return operands;
......@@ -256,7 +293,7 @@ llvm::SmallVector<mlir::Type, 4> MLIRModelGenImpl::GetOpOutputType(
std::unordered_map<std::string, uint8_t> pd_dialect_outputs_info = {};
if (pd_dialect_outputs_info_map_.count(op_.type()))
pd_dialect_outputs_info = pd_dialect_outputs_info_map_.at(op_.type());
resultTypes.resize(pd_dialect_outputs_info.size());
// update op outputs info
for (int var_idx = 0; var_idx < op_.outputs_size(); ++var_idx) {
auto &var_name = op_.outputs(var_idx).arguments()[0];
......@@ -269,13 +306,14 @@ llvm::SmallVector<mlir::Type, 4> MLIRModelGenImpl::GetOpOutputType(
std::vector<int64_t> dims = RepeatedToVector<int64_t>(
var_desc.type().lod_tensor().tensor().dims());
infrt::PrecisionType precision_;
ConvertDataTypeToPhi(var_desc.type().lod_tensor().tensor().data_type(),
&precision_);
ConvertDataTypeToInfrt(
var_desc.type().lod_tensor().tensor().data_type(), &precision_);
mlir::Type type_ = infrt::DenseTensorType::get(context_,
infrt::TargetType::CPU,
precision_,
infrt::LayoutType::ANY);
resultTypes.push_back(type_);
infrt::LayoutType::NCHW);
resultTypes[pd_dialect_outputs_info.at(
op_.outputs(var_idx).parameter())] = type_;
}
}
}
......@@ -412,7 +450,7 @@ bool ConvertDataType(infrt::paddle::framework_proto::VarType::Type dtype,
}
}
bool ConvertDataTypeToPhi(infrt::paddle::framework_proto::VarType::Type dtype,
bool ConvertDataTypeToInfrt(infrt::paddle::framework_proto::VarType::Type dtype,
infrt::PrecisionType *type) {
switch (dtype) {
case infrt::paddle::framework_proto::VarType::Type::VarType_Type_FP16:
......
paddle/infrt/host_context/paddle_mlir.h
浏览文件 @ 3a6201af
......@@ -102,7 +102,7 @@ inline std::vector<T> RepeatedToVector(
bool ConvertDataType(infrt::paddle::framework_proto::VarType::Type dtype,
mlir::Builder builder,
mlir::Type *type);
bool ConvertDataTypeToPhi(infrt::paddle::framework_proto::VarType::Type dtype,
bool ConvertDataTypeToInfrt(infrt::paddle::framework_proto::VarType::Type dtype,
infrt::PrecisionType *type);
#endif // PADDLE_INFRT_HOST_CONTEXT_PADDLE_MLIR_H_
paddle/infrt/kernel/phi/infershaped/phi_kernel_launcher.h
浏览文件 @ 3a6201af
......@@ -37,8 +37,7 @@ template <typename KernelFunc,
typename InferShapedFunc,
InferShapedFunc infershape>
void KernelLauncherFunc(host_context::KernelFrame* frame) {
static InferShapedKernelLauncher launcher(
FuncArgStatics<InferShapedFunc>::arg_size);
InferShapedKernelLauncher launcher(FuncArgStatics<InferShapedFunc>::arg_size);
static const uint16_t num_input_tensors{InferShapeHelper<KernelFunc>::count};
static const bool turn_on_infer_shape_cache{true};
......
paddle/infrt/tests/CMakeLists.txt
浏览文件 @ 3a6201af
cc_test_tiny(test_abs_model SRCS model/test_abs.cc DEPS infrt ${MLIR_IR_LIBS})
cc_test_tiny(test_abs_model SRCS models/test_abs.cc DEPS infrt ${MLIR_IR_LIBS})
configure_file(lit.cfg.py.in "${CMAKE_SOURCE_DIR}/paddle/infrt/tests/lit.cfg.py")
......@@ -7,4 +7,5 @@ add_test(NAME test_infrt_by_lit COMMAND sh -c "lit -v ${CMAKE_SOURCE_DIR}/paddle
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/dialect/tensor/tensor_map.mlir.in ${CMAKE_CURRENT_SOURCE_DIR}/dialect/tensor/tensor_map.mlir)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/dialect/phi/linear_cpu.mlir.in ${CMAKE_CURRENT_SOURCE_DIR}/dialect/phi/linear_cpu.mlir)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/dialect/phi/resnet50.mlir.in ${CMAKE_CURRENT_SOURCE_DIR}/dialect/phi/resnet50.mlir)
configure_file(${CMAKE_CURRENT_SOURCE_DIR}/dialect/tensorrt/disabled_linear.mlir.in ${CMAKE_CURRENT_SOURCE_DIR}/dialect/tensorrt/disabled_linear.mlir)
paddle/infrt/tests/dialect/phi/resnet50.mlir.in 0 → 100644
浏览文件 @ 3a6201af
// RUN: infrtexec -i %s
module {
func @main_graph(%arg0: !phi.dense_tensor_map, %arg1: !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW> {
%0 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_37.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%1 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_47.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%2 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_6.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%3 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_13.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%4 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_5.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%5 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_40.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%6 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_6.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%7 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_27.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%8 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_11.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%9 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_40.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%10 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_38.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%11 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_2.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%12 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_21.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%13 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_15.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%14 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_8.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%15 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_29.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%16 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_35.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%17 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_26.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%18 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_50.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%19 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_31.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%20 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_22.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%21 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_27.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%22 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_28.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%23 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_46.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%24 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_37.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%25 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_18.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%26 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_38.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%27 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_39.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%28 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_43.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%29 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_3.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%30 = phi_dt.tensor_map_get_tensor(%arg0) {name = "linear_0.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%31 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_34.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%32 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_49.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%33 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_52.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%34 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_8.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%35 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_45.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%36 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_43.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%37 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_5.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%38 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_29.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%39 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_33.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%40 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_10.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%41 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_43.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%42 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_9.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%43 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_7.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%44 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_7.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%45 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_50.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%46 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_40.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%47 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_42.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%48 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_42.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%49 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_31.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%50 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_7.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%51 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_12.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%52 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_39.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%53 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_30.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%54 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_13.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%55 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_46.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%56 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_36.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%57 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_29.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%58 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_36.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%59 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_49.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%60 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_29.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%61 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_28.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%62 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_51.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%63 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_27.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%64 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_47.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%65 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_30.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%66 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_33.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%67 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_24.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%68 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_22.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%69 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_1.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%70 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_32.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%71 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_20.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%72 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_16.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%73 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_23.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%74 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_11.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%75 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_30.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%76 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_37.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%77 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_16.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%78 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_36.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%79 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_1.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%80 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_31.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%81 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_0.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%82 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_10.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%83 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_1.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%84 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_13.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%85 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_12.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%86 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_0.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%87 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_30.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%88 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_13.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%89 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_3.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%90 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_52.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%91 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_26.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%92 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_48.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%93 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_25.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%94 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_33.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%95 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_30.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%96 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_35.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%97 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_8.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%98 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_18.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%99 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_4.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%100 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_15.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%101 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_16.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%102 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_32.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%103 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_50.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%104 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_44.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%105 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_24.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%106 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_11.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%107 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_2.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%108 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_20.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%109 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_15.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%110 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_44.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%111 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_23.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%112 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_17.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%113 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_25.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%114 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_3.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%115 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_0.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%116 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_38.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%117 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_20.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%118 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_47.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%119 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_50.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%120 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_48.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%121 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_14.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%122 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_47.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%123 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_46.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%124 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_34.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%125 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_45.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%126 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_25.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%127 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_22.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%128 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_21.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%129 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_17.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%130 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_19.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%131 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_1.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%132 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_52.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%133 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_21.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%134 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_9.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%135 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_4.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%136 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_9.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%137 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_45.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%138 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_8.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%139 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_35.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%140 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_39.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%141 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_44.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%142 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_19.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%143 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_27.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%144 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_2.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%145 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_19.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%146 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_23.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%147 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_32.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%148 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_51.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%149 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_17.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%150 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_2.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%151 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_15.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%152 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_23.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%153 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_18.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%154 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_1.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%155 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_21.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%156 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_37.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%157 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_28.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%158 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_31.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%159 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_3.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%160 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_19.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%161 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_38.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%162 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_7.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%163 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_33.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%164 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_44.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%165 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_25.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%166 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_32.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%167 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_26.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%168 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_4.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%169 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_40.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%170 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_17.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%171 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_5.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%172 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_28.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%173 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_27.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%174 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_20.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%175 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_15.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%176 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_18.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%177 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_41.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%178 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_42.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%179 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_25.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%180 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_22.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%181 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_35.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%182 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_24.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%183 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_49.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%184 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_22.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%185 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_26.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%186 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_12.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%187 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_43.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%188 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_38.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%189 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_0.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%190 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_50.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%191 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_10.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%192 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_19.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%193 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_41.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%194 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_10.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%195 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_14.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%196 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_14.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%197 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_12.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%198 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_9.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%199 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_16.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%200 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_29.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%201 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_42.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%202 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_2.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%203 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_48.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%204 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_14.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%205 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_3.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%206 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_6.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%207 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_20.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%208 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_39.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%209 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_34.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%210 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_16.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%211 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_36.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%212 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_48.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%213 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_7.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%214 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_32.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%215 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_52.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%216 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_18.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%217 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_44.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%218 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_6.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%219 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_10.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%220 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_47.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%221 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_51.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%222 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_9.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%223 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_52.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%224 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_45.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%225 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_8.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%226 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_13.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%227 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_46.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%228 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_49.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%229 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_12.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%230 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_4.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%231 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_5.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%232 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_51.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%233 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_33.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%234 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_46.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%235 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_45.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%236 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_6.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%237 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_48.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%238 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_37.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%239 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_14.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%240 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_21.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%241 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_28.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%242 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_26.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%243 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_23.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%244 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_49.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%245 = phi_dt.tensor_map_get_tensor(%arg0) {name = "linear_0.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%246 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_51.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%247 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_41.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%248 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_35.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%249 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_42.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%250 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_43.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%251 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_24.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%252 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_31.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%253 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_41.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%254 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_11.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%255 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_41.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%256 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_34.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%257 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_4.w_1"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%258 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_40.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%259 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_0.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%260 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_36.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%261 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_5.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%262 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_11.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%263 = phi_dt.tensor_map_get_tensor(%arg0) {name = "conv2d_17.w_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%264 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_39.w_2"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%265 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_34.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%266 = phi_dt.tensor_map_get_tensor(%arg0) {name = "batch_norm2d_24.b_0"} -> !infrt.dense_tensor<CPU, FP32, NCHW>
%267 = "pd.conv2d"(%arg1, %86) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [3 : i32, 3 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y, %MeanOut, %VarianceOut = "pd.batch_norm"(%267, %259, %189, %115, %81) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%268 = "pd.relu"(%Y) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%269 = "pd.pool2d"(%268) {adaptive = false, ceil_mode = false, data_format = "NCHW", exclusive = true, global_pooling = false, ksize = [3 : i32, 3 : i32], padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], pooling_type = "max", strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%270 = "pd.conv2d"(%269, %11) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_0, %MeanOut_1, %VarianceOut_2 = "pd.batch_norm"(%270, %150, %107, %144, %202) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%271 = "pd.relu"(%Y_0) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%272 = "pd.conv2d"(%271, %29) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_3, %MeanOut_4, %VarianceOut_5 = "pd.batch_norm"(%272, %205, %159, %89, %114) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%273 = "pd.relu"(%Y_3) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%274 = "pd.conv2d"(%273, %99) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_6, %MeanOut_7, %VarianceOut_8 = "pd.batch_norm"(%274, %168, %135, %257, %230) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%275 = "pd.conv2d"(%269, %154) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_9, %MeanOut_10, %VarianceOut_11 = "pd.batch_norm"(%275, %79, %131, %69, %83) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%276 = "pd.elementwise_add"(%Y_6, %Y_9) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%277 = "pd.relu"(%276) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%278 = "pd.conv2d"(%277, %231) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_12, %MeanOut_13, %VarianceOut_14 = "pd.batch_norm"(%278, %37, %261, %171, %4) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%279 = "pd.relu"(%Y_12) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%280 = "pd.conv2d"(%279, %2) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_15, %MeanOut_16, %VarianceOut_17 = "pd.batch_norm"(%280, %206, %218, %236, %6) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%281 = "pd.relu"(%Y_15) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%282 = "pd.conv2d"(%281, %44) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_18, %MeanOut_19, %VarianceOut_20 = "pd.batch_norm"(%282, %162, %50, %43, %213) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%283 = "pd.elementwise_add"(%Y_18, %277) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%284 = "pd.relu"(%283) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%285 = "pd.conv2d"(%284, %34) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_21, %MeanOut_22, %VarianceOut_23 = "pd.batch_norm"(%285, %97, %14, %225, %138) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%286 = "pd.relu"(%Y_21) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%287 = "pd.conv2d"(%286, %134) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_24, %MeanOut_25, %VarianceOut_26 = "pd.batch_norm"(%287, %222, %198, %42, %136) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%288 = "pd.relu"(%Y_24) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%289 = "pd.conv2d"(%288, %219) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_27, %MeanOut_28, %VarianceOut_29 = "pd.batch_norm"(%289, %40, %194, %191, %82) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%290 = "pd.elementwise_add"(%Y_27, %284) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%291 = "pd.relu"(%290) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%292 = "pd.conv2d"(%291, %197) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_30, %MeanOut_31, %VarianceOut_32 = "pd.batch_norm"(%292, %229, %85, %51, %186) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%293 = "pd.relu"(%Y_30) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%294 = "pd.conv2d"(%293, %84) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_33, %MeanOut_34, %VarianceOut_35 = "pd.batch_norm"(%294, %3, %54, %88, %226) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%295 = "pd.relu"(%Y_33) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%296 = "pd.conv2d"(%295, %239) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_36, %MeanOut_37, %VarianceOut_38 = "pd.batch_norm"(%296, %196, %121, %204, %195) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%297 = "pd.conv2d"(%291, %74) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_39, %MeanOut_40, %VarianceOut_41 = "pd.batch_norm"(%297, %254, %262, %8, %106) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%298 = "pd.elementwise_add"(%Y_36, %Y_39) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%299 = "pd.relu"(%298) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%300 = "pd.conv2d"(%299, %175) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_42, %MeanOut_43, %VarianceOut_44 = "pd.batch_norm"(%300, %151, %100, %13, %109) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%301 = "pd.relu"(%Y_42) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%302 = "pd.conv2d"(%301, %199) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_45, %MeanOut_46, %VarianceOut_47 = "pd.batch_norm"(%302, %72, %77, %210, %101) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%303 = "pd.relu"(%Y_45) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%304 = "pd.conv2d"(%303, %263) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_48, %MeanOut_49, %VarianceOut_50 = "pd.batch_norm"(%304, %129, %149, %170, %112) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%305 = "pd.elementwise_add"(%Y_48, %299) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%306 = "pd.relu"(%305) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%307 = "pd.conv2d"(%306, %25) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_51, %MeanOut_52, %VarianceOut_53 = "pd.batch_norm"(%307, %98, %176, %153, %216) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%308 = "pd.relu"(%Y_51) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%309 = "pd.conv2d"(%308, %160) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_54, %MeanOut_55, %VarianceOut_56 = "pd.batch_norm"(%309, %145, %130, %192, %142) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%310 = "pd.relu"(%Y_54) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%311 = "pd.conv2d"(%310, %108) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_57, %MeanOut_58, %VarianceOut_59 = "pd.batch_norm"(%311, %117, %207, %174, %71) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%312 = "pd.elementwise_add"(%Y_57, %306) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%313 = "pd.relu"(%312) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%314 = "pd.conv2d"(%313, %155) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_60, %MeanOut_61, %VarianceOut_62 = "pd.batch_norm"(%314, %240, %12, %133, %128) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%315 = "pd.relu"(%Y_60) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%316 = "pd.conv2d"(%315, %20) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_63, %MeanOut_64, %VarianceOut_65 = "pd.batch_norm"(%316, %184, %180, %68, %127) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%317 = "pd.relu"(%Y_63) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%318 = "pd.conv2d"(%317, %111) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_66, %MeanOut_67, %VarianceOut_68 = "pd.batch_norm"(%318, %152, %243, %73, %146) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%319 = "pd.elementwise_add"(%Y_66, %313) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%320 = "pd.relu"(%319) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%321 = "pd.conv2d"(%320, %113) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_69, %MeanOut_70, %VarianceOut_71 = "pd.batch_norm"(%321, %179, %93, %126, %165) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%322 = "pd.relu"(%Y_69) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%323 = "pd.conv2d"(%322, %242) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_72, %MeanOut_73, %VarianceOut_74 = "pd.batch_norm"(%323, %17, %91, %185, %167) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%324 = "pd.relu"(%Y_72) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%325 = "pd.conv2d"(%324, %21) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_75, %MeanOut_76, %VarianceOut_77 = "pd.batch_norm"(%325, %143, %63, %7, %173) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%326 = "pd.conv2d"(%320, %105) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_78, %MeanOut_79, %VarianceOut_80 = "pd.batch_norm"(%326, %182, %266, %251, %67) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%327 = "pd.elementwise_add"(%Y_75, %Y_78) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%328 = "pd.relu"(%327) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%329 = "pd.conv2d"(%328, %157) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_81, %MeanOut_82, %VarianceOut_83 = "pd.batch_norm"(%329, %241, %22, %61, %172) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%330 = "pd.relu"(%Y_81) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%331 = "pd.conv2d"(%330, %15) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_84, %MeanOut_85, %VarianceOut_86 = "pd.batch_norm"(%331, %60, %200, %57, %38) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%332 = "pd.relu"(%Y_84) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%333 = "pd.conv2d"(%332, %75) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_87, %MeanOut_88, %VarianceOut_89 = "pd.batch_norm"(%333, %65, %87, %53, %95) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%334 = "pd.elementwise_add"(%Y_87, %328) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%335 = "pd.relu"(%334) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%336 = "pd.conv2d"(%335, %158) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_90, %MeanOut_91, %VarianceOut_92 = "pd.batch_norm"(%336, %80, %19, %49, %252) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%337 = "pd.relu"(%Y_90) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%338 = "pd.conv2d"(%337, %214) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_93, %MeanOut_94, %VarianceOut_95 = "pd.batch_norm"(%338, %70, %166, %102, %147) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%339 = "pd.relu"(%Y_93) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%340 = "pd.conv2d"(%339, %233) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_96, %MeanOut_97, %VarianceOut_98 = "pd.batch_norm"(%340, %66, %94, %39, %163) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%341 = "pd.elementwise_add"(%Y_96, %335) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%342 = "pd.relu"(%341) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%343 = "pd.conv2d"(%342, %124) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_99, %MeanOut_100, %VarianceOut_101 = "pd.batch_norm"(%343, %256, %265, %31, %209) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%344 = "pd.relu"(%Y_99) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%345 = "pd.conv2d"(%344, %16) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_102, %MeanOut_103, %VarianceOut_104 = "pd.batch_norm"(%345, %139, %248, %96, %181) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%346 = "pd.relu"(%Y_102) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%347 = "pd.conv2d"(%346, %211) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_105, %MeanOut_106, %VarianceOut_107 = "pd.batch_norm"(%347, %260, %78, %56, %58) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%348 = "pd.elementwise_add"(%Y_105, %342) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%349 = "pd.relu"(%348) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%350 = "pd.conv2d"(%349, %24) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_108, %MeanOut_109, %VarianceOut_110 = "pd.batch_norm"(%350, %76, %156, %238, %0) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%351 = "pd.relu"(%Y_108) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%352 = "pd.conv2d"(%351, %26) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_111, %MeanOut_112, %VarianceOut_113 = "pd.batch_norm"(%352, %10, %161, %116, %188) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%353 = "pd.relu"(%Y_111) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%354 = "pd.conv2d"(%353, %27) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_114, %MeanOut_115, %VarianceOut_116 = "pd.batch_norm"(%354, %52, %208, %140, %264) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%355 = "pd.elementwise_add"(%Y_114, %349) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%356 = "pd.relu"(%355) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%357 = "pd.conv2d"(%356, %46) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_117, %MeanOut_118, %VarianceOut_119 = "pd.batch_norm"(%357, %258, %9, %5, %169) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%358 = "pd.relu"(%Y_117) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%359 = "pd.conv2d"(%358, %247) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_120, %MeanOut_121, %VarianceOut_122 = "pd.batch_norm"(%359, %177, %255, %253, %193) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%360 = "pd.relu"(%Y_120) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%361 = "pd.conv2d"(%360, %178) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_123, %MeanOut_124, %VarianceOut_125 = "pd.batch_norm"(%361, %47, %201, %48, %249) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%362 = "pd.elementwise_add"(%Y_123, %356) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%363 = "pd.relu"(%362) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%364 = "pd.conv2d"(%363, %104) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_126, %MeanOut_127, %VarianceOut_128 = "pd.batch_norm"(%364, %164, %217, %110, %141) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%365 = "pd.relu"(%Y_126) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%366 = "pd.conv2d"(%365, %235) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_129, %MeanOut_130, %VarianceOut_131 = "pd.batch_norm"(%366, %137, %125, %224, %35) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%367 = "pd.relu"(%Y_129) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%368 = "pd.conv2d"(%367, %234) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_132, %MeanOut_133, %VarianceOut_134 = "pd.batch_norm"(%368, %227, %55, %123, %23) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%369 = "pd.conv2d"(%363, %28) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [2 : i32, 2 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_135, %MeanOut_136, %VarianceOut_137 = "pd.batch_norm"(%369, %187, %36, %41, %250) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%370 = "pd.elementwise_add"(%Y_132, %Y_135) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%371 = "pd.relu"(%370) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%372 = "pd.conv2d"(%371, %64) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_138, %MeanOut_139, %VarianceOut_140 = "pd.batch_norm"(%372, %1, %122, %118, %220) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%373 = "pd.relu"(%Y_138) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%374 = "pd.conv2d"(%373, %203) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_141, %MeanOut_142, %VarianceOut_143 = "pd.batch_norm"(%374, %237, %120, %212, %92) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%375 = "pd.relu"(%Y_141) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%376 = "pd.conv2d"(%375, %32) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_144, %MeanOut_145, %VarianceOut_146 = "pd.batch_norm"(%376, %244, %59, %183, %228) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%377 = "pd.elementwise_add"(%Y_144, %371) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%378 = "pd.relu"(%377) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%379 = "pd.conv2d"(%378, %190) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_147, %MeanOut_148, %VarianceOut_149 = "pd.batch_norm"(%379, %119, %103, %18, %45) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%380 = "pd.relu"(%Y_147) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%381 = "pd.conv2d"(%380, %246) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [1 : i32, 1 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_150, %MeanOut_151, %VarianceOut_152 = "pd.batch_norm"(%381, %148, %232, %221, %62) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%382 = "pd.relu"(%Y_150) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%383 = "pd.conv2d"(%382, %132) {data_format = "NCHW", dilations = [1 : i32, 1 : i32], groups = 1 : si32, padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%Y_153, %MeanOut_154, %VarianceOut_155 = "pd.batch_norm"(%383, %215, %90, %33, %223) {data_layout = "NCHW", epsilon = 9.99999974E-6 : f32, momentum = 0.899999976 : f32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>)
%384 = "pd.elementwise_add"(%Y_153, %378) {axis = -1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%385 = "pd.relu"(%384) : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%386 = "pd.pool2d"(%385) {adaptive = true, ceil_mode = false, data_format = "NCHW", exclusive = true, global_pooling = false, ksize = [1 : i32, 1 : i32], padding_algorithm = "EXPLICIT", paddings = [0 : i32, 0 : i32], pooling_type = "avg", strides = [1 : i32, 1 : i32]} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%387 = "pd.flatten_contiguous_range"(%386) {start_axis = 1 : si32, stop_axis = 3 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%388 = "pd.matmul_v2"(%387, %245) {trans_x = false, trans_y = false} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
%389 = "pd.elementwise_add"(%388, %30) {axis = 1 : si32} : (!infrt.dense_tensor<CPU, FP32, NCHW>, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
infrt.return %270 : !infrt.dense_tensor<CPU, FP32, NCHW>
}
func @main() {
%ctx = "phi_dt.create_context.cpu" (): () -> !phi.context<CPU>
%1 = "phi_dt.create_inited_dense_tensor.cpu.f32" (%ctx) {value = 12.0 : f32, layout=#infrt.layout<NCHW>, lod=[1:i64], dims=[1, 3, 256, 256]}: (!phi.context<CPU>) -> (!infrt.dense_tensor<CPU, FP32, NCHW>)
%map = phi_dt.load_combined_params(){model_path="@CMAKE_BINARY_DIR@/models/resnet50/model.pdmodel",params_path="@CMAKE_BINARY_DIR@/models/resnet50/model.pdiparams"}
%2 = infrt.call@main_graph(%map, %1) : (!phi.dense_tensor_map, !infrt.dense_tensor<CPU, FP32, NCHW>) -> !infrt.dense_tensor<CPU, FP32, NCHW>
phi_dt.print_tensor (%2 : !infrt.dense_tensor<CPU, FP32, NCHW>)
infrt.return
}
}
paddle/infrt/tests/models/resnet50_model.py 0 → 100644
浏览文件 @ 3a6201af
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
from paddle.vision.models import resnet50
from paddle.jit import to_static
from paddle.static import InputSpec
import sys
model = resnet50(True)
net = to_static(
model, input_spec=[InputSpec(
shape=[None, 3, 256, 256], name='x')])
paddle.jit.save(net, sys.argv[1])
paddle/scripts/infrt_build.sh
浏览文件 @ 3a6201af
......@@ -46,7 +46,9 @@ function update_pd_ops() {
python3 generate_phi_kernel_dialect.py
# generate test model
cd ${PADDLE_ROOT}
python3 paddle/infrt/tests/model/abs_model.py ${PADDLE_ROOT}/build/paddle/infrt/tests/abs
mkdir -p ${PADDLE_ROOT}/build/models
python3 paddle/infrt/tests/models/abs_model.py ${PADDLE_ROOT}/build/paddle/infrt/tests/abs
python3 paddle/infrt/tests/models/resnet50_model.py ${PADDLE_ROOT}/build/models/resnet50/model
}
function init() {
......@@ -114,7 +116,7 @@ function create_fake_models() {
python3 -m pip install *whl
cd ${PADDLE_ROOT}/build
python3 ${PADDLE_ROOT}/tools/infrt/fake_models/multi_fc.py
python3 ${PADDLE_ROOT}/paddle/infrt/tests/model/linear.py
python3 ${PADDLE_ROOT}/paddle/infrt/tests/models/linear.py
}
function test_infrt() {
......
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