CHECK(IsScalarType(types[2], DataType::Int(32))); // input_zero_point
CHECK(IsScalarType(types[3], DataType::Int(32))); // kernel_zero_point
CHECK(IsScalarType(types[4], DataType::Float(32))); // input_scale
- CHECK(IsScalarType(types[5], DataType::Float(32))); // kernel_scale
+ // Kernel scale can be a vector of length output_channels or a scalar.
+ size_t axis = param->kernel_layout.find('O');
+ CHECK(axis != std::string::npos) << "Kernel layout attribute is not defined";
+ AssignType(types[5], DataType::Float(32), weight->shape[axis], reporter); // kernel scale
// Collect the input tensor and output tensor devoid of scale and zero points to reuse Relay
// Conv2D infer type function.
*/
static inline bool IsScalarType(const Type& expr_type, const DataType& dtype) {
const auto* tensor_type = expr_type.as<TensorTypeNode>();
+ CHECK(tensor_type) << "Only tensor type can be checked for scalar values. But got"
+ << AsText(expr_type, false);
CHECK_EQ(tensor_type->shape.size(), 0);
CHECK(tensor_type->dtype == dtype) << "Expected " << dtype << " but got " << tensor_type->dtype;
return true;
const TypeReporter& reporter) {
// Scale/Zero_points can be either const scalar or a vector with C axis num elems.
const auto* tensor_type = expr_type.as<TensorTypeNode>();
+ CHECK(tensor_type) << "Can assign type to Tensor type only. But got "
+ << AsText(expr_type, false);
const auto tensor_dtype = tensor_type->dtype;
CHECK(tensor_dtype == dtype) << "Expected type is " << dtype << " but received " << tensor_dtype;
if (tensor_type->shape.size() != 0) {
channels=4)
verify(ref_func, qnn_func, data_shape, data_dtype,
kernel_shape, kernel_dtype)
-
-
+
+
# Depthwise multiplier = 2
data_shape = (10, 4, 16, 16)
data_dtype = 'uint8'
channels=8)
verify(ref_func, qnn_func, data_shape, data_dtype,
kernel_shape, kernel_dtype)
-
+
# uint8 input, NHWC and HWOI
# Depthwise multiplier = 1
data_shape = (2, 16, 16, 4)
channels=4)
verify(ref_func, qnn_func, data_shape, data_dtype,
kernel_shape, kernel_dtype)
-
+
# Depthwise multiplier = 2
data_shape = (2, 16, 16, 4)
data_dtype = 'uint8'
verify(ref_func, qnn_func, data_shape, data_dtype,
kernel_shape, kernel_dtype)
+def test_per_channel_kernel_scale():
+ with TempOpAttr("qnn.conv2d", "FTVMQnnLegalize", legalize_qnn_conv2d):
+ data_shape = (2, 1, 2, 4)
+ data_dtype = 'uint8'
+ kernel_shape = (3, 1, 2, 2)
+ kernel_dtype = 'uint8'
+ data = relay.var("data", shape=data_shape,
+ dtype=data_dtype)
+ kernel = relay.var("kernel", shape=kernel_shape,
+ dtype=kernel_dtype)
+ kernel_scales = [2, 2, 2]
+ kernel_scales = relay.const(np.array(kernel_scales).astype('float32'))
+ func = relay.qnn.op.conv2d(
+ data, kernel,
+ input_zero_point=relay.const(0, 'int32'),
+ kernel_zero_point=relay.const(0, 'int32'),
+ input_scale=relay.const(2.0, 'float32'),
+ kernel_scale=kernel_scales,
+ kernel_size=(2, 2),
+ padding=(0, 0),
+ strides=(1, 1),
+ dilation=(1, 1),
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ out_dtype="int32")
+
+ mod = relay.Function(relay.analysis.free_vars(func), func)
+ mod = relay.Module.from_expr(mod)
+
if __name__ == "__main__":
test_no_zero_point()
test_input_zero_point()
test_tflite_output_multiplier_greater_than_one()
test_tflite_anistropic_strides()
test_depthwise_depth_multiplier()
+ test_per_channel_kernel_scale()