diff options
Diffstat (limited to 'ethosu/vela/weight_compressor.py')
| -rw-r--r-- | ethosu/vela/weight_compressor.py | 8 |
1 files changed, 7 insertions, 1 deletions
diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py index 9edde601..df2b0573 100644 --- a/ethosu/vela/weight_compressor.py +++ b/ethosu/vela/weight_compressor.py @@ -234,6 +234,10 @@ def compress_weights(arch, nng, tens, npu_block_type, ofm_block_depth, ofm_depth else: tens.block_traversal = TensorBlockTraversal.DepthFirst + if tens.consumer_list[0].type == "Conv2DBackpropInputSwitchedBias": + # Transpose Convoluion, reverse weights in H and W axes + weights = np.flip(weights, axis=(0,1)) + # Slice weight stream up depth-ways into bricks and compress full_ofm_depth = quant_buf.shape[-1] for idx in range(0, full_ofm_depth, ofm_depth_step): @@ -273,7 +277,9 @@ def calc_scales_and_pack_biases(tens, arch, oc_quantum, rescale_for_faf=False): # the connected operator should expect a bias input unless it is a FullyConnected assert "Bias" in tens.consumer_list[0].type or tens.consumer_list[0].type.startswith("FullyConnected") # the input bias tensor is the same as that connected to the operator - assert tens is tens.consumer_list[0].inputs[2] + _, _, bias_tens, _ = tens.consumer_list[0].get_ifm_weights_biases_ofm() + assert tens is bias_tens + # the operator should only have a single output assert len(tens.consumer_list[0].outputs) == 1 |