diff options
| -rw-r--r-- | ethosu/vela/tensor.py | 43 | ||||
| -rw-r--r-- | ethosu/vela/tflite_reader.py | 14 |
2 files changed, 22 insertions, 35 deletions
diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py index 8786d362..49f93cd9 100644 --- a/ethosu/vela/tensor.py +++ b/ethosu/vela/tensor.py @@ -15,6 +15,7 @@ # limitations under the License. # Description: # Internal representation of a Neural Network Tensor. +import copy import enum import uuid from collections import defaultdict @@ -392,34 +393,25 @@ class Tensor: return self.dtype.size_in_bits() / 8 return self.element_size_bytes - def clone(self, suffix="_clone"): - res = Tensor(self.shape, self.dtype, self.name + suffix) - res.storage_shape = list(self.storage_shape) - res.bandwidth_shape = list(self.bandwidth_shape) + # Returns a copy, renamed to self.name + suffix + # The references to Operators will be empty when returned + # Depending on set_unique, the copy is shallow, or deep + # For set_unique==True, a new equivalence_id will be set + def clone(self, suffix="_clone", set_unique=False): + if set_unique: + res = copy.deepcopy(self) + res.equivalence_id = uuid.uuid4() + else: + res = copy.copy(self) + res.storage_shape = list(self.storage_shape) + res.bandwidth_shape = list(self.bandwidth_shape) + if self.quantization is not None: + res.quantization = self.quantization.clone() + res.name = res.name + suffix res.ops = [] res.consumer_list = [] - res.values = self.values - res.quant_values = self.quant_values - res.mem_area = self.mem_area - res.mem_type = self.mem_type - res.format = self.format - res.purpose = self.purpose - res.sub_purpose = self.sub_purpose - res.alignment = self.alignment - res.bandwidth_compression_scale = self.bandwidth_compression_scale - res.storage_rounding_quantum = self.storage_rounding_quantum - - if self.quantization is not None: - res.quantization = self.quantization.clone() - else: - res.quantization = None - - res.resampling_mode = self.resampling_mode - - res.copy_compressed_weight_info(self) - res.avoid_NHCWB16 = self.avoid_NHCWB16 return res def clone_into_fast_storage(self, arch): @@ -806,9 +798,6 @@ class Tensor: return True - def set_random_equivalence_id(self): - self.equivalence_id = uuid.uuid4() - def __str__(self): return "<nng.Tensor '%s' shape=%s dtype=%s>" % (self.name, self.shape, self.dtype) diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py index 82feddd9..24f9f874 100644 --- a/ethosu/vela/tflite_reader.py +++ b/ethosu/vela/tflite_reader.py @@ -41,9 +41,8 @@ def decode_str(s): return s.decode("utf-8") -def clone_and_reshape_tensor(src_tens, reorder): - - tens = src_tens.clone("_reshape") +def clone_and_reshape_tensor(src_tens, reorder, set_unique): + tens = src_tens.clone("_reshape", set_unique) tens.shape = [src_tens.shape[idx] for idx in reorder] tens.bandwidth_shape = tens.shape tens.storage_shape = tens.shape @@ -153,17 +152,16 @@ class TFLiteSubgraph: if op.type.is_depthwise_conv2d_op() or op.type.is_conv2d_op() or op.type == Op.FullyConnected: if inputs[1].values is not None: if op.type == Op.FullyConnected: - inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0)) + inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 0), False) else: - inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0)) + inputs[1] = clone_and_reshape_tensor(inputs[1], (1, 2, 3, 0), False) if op.type.needs_bias() and len(inputs) <= op_type.info.indices.biases[0]: # No Bias tensor inputs.append(None) if inputs[-1] and inputs[-1].values is not None: - inputs[-1] = clone_and_reshape_tensor(inputs[-1], (0,)) # Since bias tensor is used for both bias and scale, - # set different equivalence_id for all bias tensors - inputs[-1].set_random_equivalence_id() + # a clone with a unique equivalence_id is needed + inputs[-1] = clone_and_reshape_tensor(inputs[-1], (0,), True) if opt_serializer is not None: op.attrs = opt_serializer.deserialize(op_data) |