diff options
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 35 |
1 files changed, 9 insertions, 26 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index 242f0ea6..ff7b4863 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright 2020-2022 Arm Limited and/or its affiliates <open-source-office@arm.com> +# SPDX-FileCopyrightText: Copyright 2020-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> # # SPDX-License-Identifier: Apache-2.0 # @@ -343,17 +343,10 @@ def convert_resizenn_ac_to_depthwise_conv(op, upscale_factor): weight_quant.zero_point = 0 weight_quant.quant_dim = 0 ofm_dtype = ofm.dtype - if ofm_dtype == DataType.uint8: - weight_value_dtype = np.uint8 + if ofm_dtype.type == BaseType.UnsignedInt: weight_quant.quant_min = 0 weight_quant.quant_max = (1 << ofm_dtype.bits) - 1 else: - if ofm_dtype == DataType.int8: - weight_value_dtype = np.int8 - else: - assert ofm_dtype == DataType.int16 - weight_value_dtype = np.int16 - weight_quant.quant_min = -(1 << (ofm_dtype.bits - 1)) weight_quant.quant_max = (1 << (ofm_dtype.bits - 1)) - 1 @@ -376,9 +369,8 @@ def convert_resizenn_ac_to_depthwise_conv(op, upscale_factor): create_const_tensor( "weights", weight_shape, - ofm.dtype, + ofm_dtype, np.array(weight_values).reshape(weight_shape), - value_dtype=weight_value_dtype, quantization=weight_quant, ), 1, # inputs tensor weight index @@ -586,7 +578,6 @@ def convert_resizebilinear_to_depthwise_convolutions(op, half_pixel_centers=True shape, intermediate_tens.dtype, np.array(kernel).reshape(shape), - value_dtype=np.int8, quantization=quant, ), ) @@ -1227,9 +1218,7 @@ def convert_lrelu_to_mul_max(op, arch): scalar, _ = scaling.elementwise_mul_scale(ifm.quantization.scale_f32, alpha, ofm.quantization.scale_f32) else: scalar = 1 - alpha_tens = create_const_tensor( - op.name + "_alpha_scalar", [1], alpha_dtype, [scalar], alpha_dtype.as_numpy_type(), quantization=quantization - ) + alpha_tens = create_const_tensor(op.name + "_alpha_scalar", [1], alpha_dtype, [scalar], quantization=quantization) mul_alpha.add_input_tensor(alpha_tens) fm_alpha = ofm.clone(op.name + "_alpha", set_unique=True) mul_alpha.set_output_tensor(fm_alpha) @@ -1256,9 +1245,7 @@ def convert_lrelu_to_mul_max(op, arch): quantization.max = quantization.quant_max - quantization.quant_min quantization.scale_f32 = np.float32(1) quantization.zero_point = 0 - identity_tens = create_const_tensor( - op.name + "_id_scalar", [], ifm.dtype, [1], np.uint8, quantization=quantization - ) + identity_tens = create_const_tensor(op.name + "_id_scalar", [], ifm.dtype, [1], quantization=quantization) mul_identity.add_input_tensor(identity_tens) # Make sure that fm_id is allocated to a different address than fm_alpha fm_id = ofm.clone(op.name + "_id", set_unique=True) @@ -1470,7 +1457,6 @@ def replace_pad_by_hw_pad(op: Operation, arch, nng): shape, op.ifm.dtype, weights, - np.uint8, purpose=TensorPurpose.Weights, quantization=quantization, ) @@ -1526,7 +1512,7 @@ def convert_pad(op: Operation, arch, nng): if top > 0: shape = Shape4D(1, top, ofm_shape.width, ofm_shape.depth) zero_tens = create_const_tensor( - op.name + "_top", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], np.uint8, quantization=quant + op.name + "_top", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], quantization=quant ) # If top/bottom or left/right are equal, the const tensors can be allocated to the same address zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values)) @@ -1538,7 +1524,6 @@ def convert_pad(op: Operation, arch, nng): shape.as_list(), ofm.dtype, shape.elements() * [pad_value], - np.uint8, quantization=quant, ) zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values)) @@ -1548,14 +1533,14 @@ def convert_pad(op: Operation, arch, nng): if left > 0: shape = Shape4D(1, ifm_shape.height, left, ofm_shape.depth) zero_tens = create_const_tensor( - op.name + "_left", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], np.uint8, quantization=quant + op.name + "_left", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], quantization=quant ) zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values)) create_avg_pool_for_concat(op, op.name + "_left", zero_tens, shape, shp_top) if right > 0: shape = Shape4D(1, ifm_shape.height, right, ofm_shape.depth) zero_tens = create_const_tensor( - op.name + "_right", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], np.uint8, quantization=quant + op.name + "_right", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], quantization=quant ) zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values)) create_avg_pool_for_concat( @@ -1715,7 +1700,6 @@ def convert_mean_to_depthwise_conv_or_avgpool(op, arch, nng): weight_shape, inp.dtype, np.ones(weight_shape), - value_dtype=np.uint8, quantization=weight_quant, ), 1, @@ -2008,8 +1992,7 @@ def tflite_optimise_graph(nng, arch): ofm_clone = ofm.clone() ofm_clone.values = ofm.values ofm.values = None - np_dtype = ofm.dtype.as_numpy_type() - zero = create_const_tensor("zero", [1], ofm.dtype, [0], np_dtype, quantization=ofm.quantization) + zero = create_const_tensor("zero", [1], ofm.dtype, [0], quantization=ofm.quantization) memcpy = create_add_nop(f"{ofm.name}_copy") memcpy.add_input_tensor(ofm_clone) memcpy.add_input_tensor(zero) |