diff options
| -rw-r--r-- | ethosu/vela/test/test_tflite_supported_operators.py | 64 | ||||
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 25 | ||||
| -rw-r--r-- | ethosu/vela/tflite_supported_operators.py | 57 |
3 files changed, 118 insertions, 28 deletions
diff --git a/ethosu/vela/test/test_tflite_supported_operators.py b/ethosu/vela/test/test_tflite_supported_operators.py index 04d3cba1..ab12e417 100644 --- a/ethosu/vela/test/test_tflite_supported_operators.py +++ b/ethosu/vela/test/test_tflite_supported_operators.py @@ -306,30 +306,82 @@ def test_constraint_filter_product_height_range(): assert not support.is_operator_supported(op) -def test_constraint_resize(): +def test_constraint_bilinear_resize(): # IFM W and H == 1 op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 1, 1, 8], [1, 8, 8, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) assert support.is_operator_supported(op) + # IFM == OFM op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 8, 8, 8], [1, 8, 8, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) assert support.is_operator_supported(op) + # IFM x2 == OFM ; align_corners = False op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 8, 8, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) + assert support.is_operator_supported(op) + + # IFM x4 == OFM ; align_corners = False + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 16, 16, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [16, 16], np.int32)) assert support.is_operator_supported(op) - # IFM x2 -1 == OFM ; align_corners = True + + # IFM x8 == OFM ; align_corners = False + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 32, 32, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [32, 32], np.int32)) + assert support.is_operator_supported(op) + + # IFM -1 x2 == OFM -1 ; align_corners = True op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 7, 7, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [7, 7], np.int32)) op.attrs["align_corners"] = True assert support.is_operator_supported(op) - # Invalid cases - op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 20, 20, 8]) + + # IFM -1 x4 == OFM -1 ; align_corners = True + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 13, 13, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [13, 13], np.int32)) + op.attrs["align_corners"] = True + assert support.is_operator_supported(op) + + # IFM -1 x8 == OFM -1 ; align_corners = True + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 25, 25, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [25, 25], np.int32)) + op.attrs["align_corners"] = True + assert support.is_operator_supported(op) + + # Invalid case - upscale size + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 17, 17, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [17, 17], np.int32)) assert not support.is_operator_supported(op) + + # Invalid case - upscale size with align corners + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 15, 15, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [15, 15], np.int32)) op.attrs["align_corners"] = True assert not support.is_operator_supported(op) +def test_constraint_bilinear_resize_size(): + # Invalid case - size != ofm size + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 8, 8, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [7, 7], np.int32)) + assert not support.is_operator_supported(op) + + def test_constraint_bilinear_resize_attrs(): - op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 1, 1, 8], [1, 8, 8, 8]) - assert support.is_operator_supported(op) + # Invalid case - both align corners and half-pixel centers + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 8, 8, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) + op.attrs["align_corners"] = True + op.attrs["half_pixel_centers"] = True + assert not support.is_operator_supported(op) + + +def test_constraint_bilinear_resize_hpc(): + # Invalid case - half-pixel centers (not supported) + op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 8, 8, 8]) + op.add_input_tensor(create_const_tensor("size", [2], DataType.int32, [8, 8], np.int32)) op.attrs["half_pixel_centers"] = True assert not support.is_operator_supported(op) diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index b1a56605..d2899c4c 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -303,26 +303,22 @@ def convert_resizebilinear_1x1_to_add(op): # Convert ResizeBilinear to a number of 2x2 nearest neighbor upscaling and one avgpool op with kernel size dependent # on the upscaling factor. Avgpool kernel limit of 8x8 when padding is applied limits upscaling to 8x8. -def convert_resizebilinear_to_nearest_neighbor_upscaling_and_pool(op): +def convert_resizebilinear_to_upscale_and_average_pool(op): pre_op = op outputs = op.outputs dtype = op.ifm.dtype op.attrs.update({"strides": (1, 1, 1, 1), "ksize": (1, 1, 1, 1)}) - if op.attrs["align_corners"]: - shape_modifier = 1 - op.attrs["padding"] = Padding.VALID - else: - shape_modifier = 0 - op.attrs["padding"] = Padding.SAME + op.attrs["padding"] = Padding.SAME # doesn't really matter as the kernel is 1x1 op.ifm_resampling_mode = resampling_mode.NEAREST upscaled_shape = np.array(op.ifm_shapes[0].get_hw_as_list()) - out_shape = np.array(op.ofm_shapes[0].get_hw_as_list()) + + # Get upscale factor that was calculated in the supported operators check + upscale_factor = op.attrs["upscale_factor"] # Calculate how many times 2x2 upscaling needs to be performed # Force the result of round to be an integer. This is because the behaviour of rounding numpy.float64 values changed # between different versions of numpy. This consistency ensures that the kernel dimensions are kept integral - upscale_factor = int(round(out_shape[1] / upscaled_shape[1])) n = int(np.log2(upscale_factor)) # Perform 2x2 upscaling n-1 times @@ -333,7 +329,7 @@ def convert_resizebilinear_to_nearest_neighbor_upscaling_and_pool(op): scaled_op.inputs[0] = pre_op.outputs[0] # Nearest neighbor 2x2 upscaling - upscaled_shape = upscaled_shape * 2 - shape_modifier + upscaled_shape = upscaled_shape * 2 shape = op.ofm_shapes[0].as_list() shape[1:3] = upscaled_shape out_tens = Tensor(shape, dtype, f"{op.outputs[0].name}_{count}") @@ -348,8 +344,11 @@ def convert_resizebilinear_to_nearest_neighbor_upscaling_and_pool(op): if n > 1: scaled_op = op.clone(f"_{n-1}") scaled_op.inputs[0] = pre_op.outputs[0] - scaled_op.attrs["padding"] = Padding.EXPLICIT - scaled_op.attrs["explicit_padding"] = [0, 0, upscale_factor - 1, upscale_factor - 1] + if op.attrs["align_corners"]: + scaled_op.attrs["padding"] = Padding.VALID + else: + scaled_op.attrs["padding"] = Padding.EXPLICIT + scaled_op.attrs["explicit_padding"] = [0, 0, upscale_factor - 1, upscale_factor - 1] scaled_op.attrs.update({"ksize": (1, upscale_factor, upscale_factor, 1)}) scaled_op.outputs = outputs scaled_op.outputs[0].ops = [scaled_op] @@ -367,7 +366,7 @@ def fixup_resizebilinear(op, arch, nng): elif op.ifm_shapes[0].height == 1 and op.ifm_shapes[0].width == 1: convert_resizebilinear_1x1_to_add(op) else: - convert_resizebilinear_to_nearest_neighbor_upscaling_and_pool(op) + convert_resizebilinear_to_upscale_and_average_pool(op) return op diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py index 25a34e82..01d2e61f 100644 --- a/ethosu/vela/tflite_supported_operators.py +++ b/ethosu/vela/tflite_supported_operators.py @@ -242,8 +242,10 @@ class TFLiteSupportedOperators: # Resizing specific checks: for op_type in TFLiteSupportedOperators.resizing_ops: - self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_resize) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bilinear_resize) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bilinear_resize_size) self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bilinear_resize_attrs) + self.specific_constraints[op_type].append(TFLiteSupportedOperators.constraint_bilinear_resize_hpc) # Vector Product specific checks: for op_type in TFLiteSupportedOperators.fc_vector_products: @@ -587,35 +589,72 @@ class TFLiteSupportedOperators: return True, "Op has padding=SAME" @staticmethod - def constraint_resize(op): + def constraint_bilinear_resize(op): """The width and height of the IFM and OFM must match one of the following criteria: IFM W and H must both be 1 IFM must match OFM - OFM W and H must be equal and 2/4/8x IFM -1, if align_corners is True - OFM W and H must be equal and 2/4/8x IFM, if align_corners is False""" + OFM W and H must be equal and OFM W-1 and H-1 must be 2x/4x/8x IFM W-1 and H-1, if align_corners is True + OFM W and H must be equal and OFM W and H must be 2x/4x/8x IFM W and H, if align_corners is False""" # Easier to start with False condition as very few cases result in a supported resize valid = False ifm_shape = op.ifm.shape + ifm_shape_h = ifm_shape[1] + ifm_shape_w = ifm_shape[2] ofm_shape = op.ofm.shape + ofm_shape_h = ofm_shape[1] + ofm_shape_w = ofm_shape[2] + align_corners = op.attrs.get("align_corners", False) if len(ifm_shape) == 4: # Valid if IFM W and H are both 1, or IFM and OFM shape are the same - if ((ifm_shape[1] == 1) and (ifm_shape[2] == 1)) or (ifm_shape == ofm_shape): + if ((ifm_shape_h == 1) and (ifm_shape_w == 1)) or (ifm_shape == ofm_shape): valid = True else: # Valid if OFM is 2/4/8x IFM (-1 for align corners) - w_upscale_factor = (ofm_shape[1] + 1) / ifm_shape[1] if align_corners else ofm_shape[1] / ifm_shape[1] - h_upscale_factor = (ofm_shape[2] + 1) / ifm_shape[2] if align_corners else ofm_shape[2] / ifm_shape[2] + if align_corners: + h_upscale_factor = (ofm_shape_h - 1) / (ifm_shape_h - 1) + w_upscale_factor = (ofm_shape_w - 1) / (ifm_shape_w - 1) + else: + h_upscale_factor = ofm_shape_h / ifm_shape_h + w_upscale_factor = ofm_shape_w / ifm_shape_w - valid = w_upscale_factor == h_upscale_factor and w_upscale_factor in [2, 4, 8] + # could use either height or width. save as int because it is more usable later in graph optimiser + op.attrs["upscale_factor"] = int(h_upscale_factor) + valid = h_upscale_factor == w_upscale_factor and h_upscale_factor in (2.0, 4.0, 8.0) return valid, f"Op has ifm_shape={ifm_shape}, ofm_shape={ofm_shape} and align_corners={align_corners}" @staticmethod + def constraint_bilinear_resize_size(op): + "The size tensor must match the output tensor shape" + valid = False + ofm_shape = op.ofm.shape + size_h, size_w = None, None + # check that the size tensor (the second input) exists, is not none, and has the correct values + if len(op.inputs) == 2 and op.inputs[1] is not None and len(op.inputs[1].values) == 2: + size_h, size_w = op.inputs[1].values + # check size and output size match + if size_h == ofm_shape[1] and size_w == ofm_shape[2]: + valid = True + + return valid, f"Op has size={size_h}x{size_w} and ofm_shape={ofm_shape}." + + @staticmethod def constraint_bilinear_resize_attrs(op): + "Both align_corners and half_pixel_centers can't be True" + valid = True + align_corners = op.attrs.get("align_corners", False) + half_pixel_centers = op.attrs.get("half_pixel_centers", False) + + if align_corners and half_pixel_centers: + valid = False + return valid, "Op has both align_corners and half_pixel_centers set to True." + + @staticmethod + def constraint_bilinear_resize_hpc(op): "half_pixel_centers are not supported" valid = True - if op.attrs.get("half_pixel_centers"): + if op.attrs.get("half_pixel_centers", False): valid = False return valid, f"Op has half_pixel_centers set to {not valid}." |