experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

replace alternative operator to fix msvc build break

Browse Source
This commit is contained in:
fo40225 2021-01-24 14:15:27 +08:00
parent 2cff21749d
commit 6dd2011412
4 changed files with 7 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
tensorflow/core/framework/common_shape_fns.cc
View File

@ -1137,7 +1137,7 @@ Status FusedBatchNormShape(shape_inference::InferenceContext* c) {
data_format_str);
}
const int rank =
(data_format_str == "NDHWC" or data_format_str == "NCDHW") ? 5 : 4;
(data_format_str == "NDHWC" || data_format_str == "NCDHW") ? 5 : 4;
ShapeHandle x;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), rank, &x));
@ -1210,7 +1210,7 @@ Status FusedBatchNormGradShape(shape_inference::InferenceContext* c) {
data_format_str);
}
const int rank =
(data_format_str == "NDHWC" or data_format_str == "NCDHW") ? 5 : 4;
(data_format_str == "NDHWC" || data_format_str == "NCDHW") ? 5 : 4;
ShapeHandle y_backprop;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), rank, &y_backprop));
ShapeHandle x;

2
tensorflow/core/grappler/optimizers/generic_layout_optimizer.cc
View File

@ -77,7 +77,7 @@ inline bool NumConvOnDeviceWithDataTypeOverThreshold(
for (const auto& node : context.graph_view->GetNodes()) {
const auto* node_def = node.node();
if (!IsConv2D(*node_def) and !IsConv3D(*node_def)) {
if (!IsConv2D(*node_def) && !IsConv3D(*node_def)) {
continue;
}
const string& device_name =

2
tensorflow/core/grappler/optimizers/remapper.cc
View File

@ -1457,7 +1457,7 @@ Status AddBatchNormNodes(RemapperContext* ctx, const FusedBatchNorm& matched) {
Status status;
string x_format = fused_node.attr().at(kDataFormat).s();
if (x_format == "NCHW" or x_format == "NCDHW") {
if (x_format == "NCHW" || x_format == "NCDHW") {
// Need to reshape the last 4 inputs
NodeDef new_shape;
const string new_shape_name =

6
tensorflow/core/kernels/fused_batch_norm_op.cc
View File

@ -1248,7 +1248,7 @@ class FusedBatchNormOpBase : public OpKernel {
const Tensor& estimated_variance = context->input(4);
const Tensor* side_input = has_side_input_ ? &context->input(5) : nullptr;
OP_REQUIRES(context, x.dims() == 4 or x.dims() == 5,
OP_REQUIRES(context, x.dims() == 4 || x.dims() == 5,
errors::InvalidArgument("input must be 4 or 5-dimensional",
x.shape().DebugString()));
OP_REQUIRES(context, scale.dims() == 1,
@ -1408,10 +1408,10 @@ class FusedBatchNormGradOpBase : public OpKernel {
// saves inverted variance.
const Tensor& saved_maybe_inv_var_or_pop_var = context->input(4);
OP_REQUIRES(context, y_backprop.dims() == 4 or y_backprop.dims() == 5,
OP_REQUIRES(context, y_backprop.dims() == 4 || y_backprop.dims() == 5,
errors::InvalidArgument("input must be 4 or 5-dimensional",
y_backprop.shape().DebugString()));
OP_REQUIRES(context, x.dims() == 4 or x.dims() == 5,
OP_REQUIRES(context, x.dims() == 4 || x.dims() == 5,
errors::InvalidArgument("input must be 4 or 5-dimensional",
x.shape().DebugString()));
OP_REQUIRES(context, scale.dims() == 1,