def test_constraint_tconv_valid():
# Valid
op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[4, 4, 1, 1])
op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"VALID"}
ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
ifm.quantization = testutil.default_quant_params()
op.add_input_tensor(ifm)
assert support.is_operator_supported(op)
# Invalid
op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[2, 2, 1, 1])
op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"VALID"}
ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
ifm.quantization = testutil.default_quant_params()
op.add_input_tensor(ifm)
assert not support.is_operator_supported(op)
def test_constraint_axis_exists():
# Missing axis attribute
op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2")
ifm2.quantization = testutil.default_quant_params()
op.add_input_tensor(ifm2)
assert not support.is_operator_supported(op)
def test_constraint_matching_dimensionality():
# Mismatching dimensionality: 4D+2D=4D
op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
ifm2 = Tensor([1, 4], DataType.uint8, "in2")
ifm2.quantization = testutil.default_quant_params()
op.add_input_tensor(ifm2)
op.attrs["axis"] = 3
assert not support.is_operator_supported(op)
def test_constraint_concat_pass():
# A working concat
op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2")
ifm2.quantization = testutil.default_quant_params()
op.add_input_tensor(ifm2)
op.attrs["axis"] = 3
assert support.is_operator_supported(op)
def test_constraint_tconv_stride():
# Strides must be 2
op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1])
op.attrs = {"stride_w": 1, "stride_h": 1, "padding": b"SAME"}
ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
ifm.quantization = testutil.default_quant_params()
op.add_input_tensor(ifm)
assert not support.is_operator_supported(op)
def test_constraint_weights_const():
# Weight tensor cannot be non-const tensors
op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8])
op.attrs = {"stride_w": 1, "stride_h": 1}
weights = Tensor([64, 64, 1, 1], DataType.uint8, "weights")
weights.quantization = testutil.default_quant_params()
op.add_input_tensor(weights)
assert not support.is_operator_supported(op)
def test_constraint_valid_dimensions():
# Mismatching dimension value:
# ifm2 has w and h as 2, which is not the axis to concat and doesnt match ifm1 or ofm
op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
ifm2 = Tensor([1, 2, 2, 4], DataType.uint8, "in2")
ifm2.quantization = testutil.default_quant_params()
op.add_input_tensor(ifm2)
op.attrs["axis"] = 3
assert not support.is_operator_supported(op)
def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets):
qp = testutil.default_quant_params()
in0 = Tensor(in_shape, DataType.uint8, "in")
in0.quantization = qp
in1 = create_const_tensor("begin", [len(start_offsets)], DataType.uint8, start_offsets, quantization=qp)
in2 = create_const_tensor("end", [len(end_offsets)], DataType.uint8, end_offsets, quantization=qp)
in3 = create_const_tensor("strides", [len(end_offsets)], DataType.uint8, len(end_offsets) * [1], quantization=qp)
out = Tensor(out_shape, DataType.uint8, "out")
out.quantization = qp
attrs = {"ellipsis_mask": 0, "new_axis_mask": 0, "shrink_axis_mask": 0, "begin_mask": 0, "end_mask": 0}
return testutil.create_op(Op.StridedSlice, [in0, in1, in2, in3], out, attrs=attrs)
def test_constraint_splitv_inferred():
# SplitV requires a maximum of one inferred shape (-1)
qp = testutil.default_quant_params()
op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8])
sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, -1, 2, -1]]]], np.int16, quantization=qp)
op.add_input_tensor(sizes)
assert not support.is_operator_supported(op)
op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8])
sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, 1, 2, -1]]]], np.int16, quantization=qp)
op.add_input_tensor(sizes)
assert support.is_operator_supported(op)