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authorTamas Nyiri <tamas.nyiri@arm.com>2021-11-23 17:37:22 +0000
committerTamas Nyiri <tamas.nyiri@arm.com>2021-11-26 12:07:47 +0000
commit9fc259682c8b85af58bda42e87703bbec5ae57ce (patch)
tree3376bd2ef3a5b7f0792f52de38b4ad8fc9d09af4 /src/armnnTfLiteParser/TfLiteParser.cpp
parent6f1c67d6bfcb61fe3bc920180c9fd40a6b40181f (diff)
downloadarmnn-9fc259682c8b85af58bda42e87703bbec5ae57ce.tar.gz
IVGCVSW-6252 Armnn Error: Failed to parse operator #1 within subgraph #0 error: Operator not supported
* Added missing support for reshape operator in tflite parser when the target shape is dynamic and batch size is unknown * Added corresponding unit test Change-Id: I78f4b386f2e5e0d08ecacde17e33e304f6afbb70 Signed-off-by: Tamas Nyiri <tamas.nyiri@arm.com>
Diffstat (limited to 'src/armnnTfLiteParser/TfLiteParser.cpp')
-rw-r--r--src/armnnTfLiteParser/TfLiteParser.cpp51
1 files changed, 42 insertions, 9 deletions
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index 15ca36d906..4e0efbbf3c 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -2545,9 +2545,11 @@ void TfLiteParserImpl::ParseReshape(size_t subgraphIndex, size_t operatorIndex)
// If there is no built-in option given or if the built-in new_shape parameter was empty
if (!targetShapeFound)
{
- // Check for a second input tensor
- if (inputs.size() > 1 && inputs[1] != nullptr)
+ bool secondInputTensorExists = inputs.size() > 1 && inputs[1] != nullptr;
+ if (secondInputTensorExists)
{
+ armnn::TensorInfo shapeInfo = ToTensorInfo(inputs[1]);
+
if (inputs[1]->is_variable)
{
ARMNN_THROW_PARSE_EXCEPTION( "Target shapes defined in non-const input tensors is not supported");
@@ -2563,16 +2565,47 @@ void TfLiteParserImpl::ParseReshape(size_t subgraphIndex, size_t operatorIndex)
ARMNN_THROW_PARSE_EXCEPTION("Target 'shape' input is not an int32 type");
}
- // Extract target shape from input
- auto bufferPtr = GetBuffer(m_Model, inputs[1]->buffer);
- auto values = reinterpret_cast<const int32_t*>(bufferPtr->data.data());
- if (!values)
+ // If the provided shape is constant, we can load in from the buffer ...
+ if (shapeInfo.IsConstant())
{
- ARMNN_THROW_PARSE_EXCEPTION("Reshape operator target shape input buffer data is null");
+ // Extract target shape from input
+ auto bufferPtr = GetBuffer(m_Model, inputs[1]->buffer);
+ auto values = reinterpret_cast<const int32_t*>(bufferPtr->data.data());
+ if (!values)
+ {
+ ARMNN_THROW_PARSE_EXCEPTION("Reshape operator target shape input buffer data is null");
+ }
+ for (int i=0; i < inputs[1]->shape[0]; ++i)
+ {
+ targetShape.push_back(values[i]);
+ }
}
- for (int i=0; i < inputs[1]->shape[0]; ++i)
+ // ... otherwise, we have to infer it during Runtime.
+ else
{
- targetShape.push_back(values[i]);
+ // The parser only supports shape (batch, -1) or (-1) for non-constant shape input.
+ unsigned int dims = shapeInfo.GetNumDimensions();
+ TensorShape shapes = shapeInfo.GetShape();
+ if (dims != 1 || shapes[0] > 2)
+ {
+ throw ParseException(fmt::format("Invalid input shape '{}' in Reshape layer '{}' {}. "
+ "The parser only supports shape (batch, -1) or (-1) for "
+ "non-constant shape input.",
+ shapes[0],
+ layerName,
+ CHECK_LOCATION().AsString()));
+ }
+
+ const int32_t numInputElements = inputTensorInfo.GetNumElements();
+ const int32_t inputTensorShape = inputTensorInfo.GetShape()[0];
+ if (shapes[0] == 1)
+ {
+ targetShape = { numInputElements };
+ }
+ else if (shapes[0] == 2)
+ {
+ targetShape = { inputTensorShape , numInputElements / inputTensorShape };
+ }
}
}
else