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Slice and Unsqueeze ops bug fixes in v5.0 #293

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Slice and Unsqueeze ops bug fixes in v5.0 #293

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22 changes: 14 additions & 8 deletions builtin_op_importers.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -927,7 +927,7 @@ DEFINE_BUILTIN_OP_IMPORTER(Dropout) {
{
RETURN_IDENTITY(inputs.at(0));
}
else
else
{
// Return both Dropout outputs: (output + mask)
std::vector<TensorOrWeights> outputs;
Expand Down Expand Up @@ -1246,7 +1246,7 @@ DEFINE_BUILTIN_OP_IMPORTER(MaxPool) {

// TODO: Since TensorRT only 0 pads, need to do an elementwise ADD with
// -INFINITY on the padded dimensions to ensure max pooling functions as expected.
// This negatively impacts performance. Update when non-zero padding is supported in a
// This negatively impacts performance. Update when non-zero padding is supported in a
// future TRT version.

nvinfer1::Dims padded_dims = tensor_ptr->getDimensions();
Expand Down Expand Up @@ -1562,7 +1562,7 @@ DEFINE_BUILTIN_OP_IMPORTER(Reshape) {
new_shape = set_dims_CHW(remove_dim(new_shape, BATCH_DIM));
// Check for -1 dimension in new shape
TRT_CHECK(get_infer_dim(infer_dim,new_shape));

if (infer_dim < 0) {
ASSERT(get_shape_size(new_shape) ==
get_shape_size(tensor.getDimensions()),
Expand Down Expand Up @@ -1720,17 +1720,17 @@ DEFINE_BUILTIN_OP_IMPORTER(Slice) {
// TRT only supports slicing HW dims when using padding layer,
// so if user wants to slice some other axis, we check whether
// slice contains full dimension
if (axes[i] != nbDims-1 && axes[i] != nbDims)
if (axis != nbDims-2 && axis != nbDims-1)
{
ASSERT((ends[i] - starts[i]) == dims.d[axis], ErrorCode::kUNSUPPORTED_NODE);
}
else
{
if (axes[i] == nbDims-1)
if (axis == nbDims-2)
{
H_idx = i;
}
else if (axes[i] == nbDims)
else if (axis == nbDims-1)
{
W_idx = i;
}
Expand Down Expand Up @@ -2054,19 +2054,25 @@ DEFINE_BUILTIN_OP_IMPORTER(Unsqueeze) {
int ndim_in = old_shape.nbDims;
OnnxAttrs attrs(node);
auto axes = attrs.get<std::vector<int>>("axes");

std::set<int> axes_set_tmp(axes.begin(), axes.end());
int ndim_out = ndim_in + axes_set_tmp.size();

// If the input was already a tensor, then we're dealing with a TRT shape,
// so subtract 1 from the axes. Otherwise, this is an ONNX shape.
if (inputs.at(0).is_tensor())
{
for (auto& axis : axes)
{
ASSERT(axis != BATCH_DIM, ErrorCode::kUNSUPPORTED_NODE);
--axis;
convert_axis(axis, ndim_out);
// convert_axis already subtracts batch dimension
//--axis;
}
}

std::set<int> axes_set(axes.begin(), axes.end());
int ndim_out = ndim_in + axes_set.size();
//int ndim_out = ndim_in + axes_set.size();
ASSERT(ndim_out <= nvinfer1::Dims::MAX_DIMS, ErrorCode::kUNSUPPORTED_NODE);
nvinfer1::Dims new_shape;
new_shape.nbDims = ndim_out;
Expand Down