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finalized quantization option in export_rdovae_weights.py

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Jan Buethe 2023-10-20 14:14:31 +02:00
parent 88c8b30785
commit 1accd2472e
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1 changed files with 10 additions and 10 deletions
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dnn/torch/rdovae/export_rdovae_weights.py
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@ -121,9 +121,9 @@ f"""
('core_encoder.module.state_dense_2' , 'gdense2' , 'TANH', True) ('core_encoder.module.state_dense_2' , 'gdense2' , 'TANH', True)
] ]
for name, export_name, _, _ in encoder_dense_layers: for name, export_name, _, quantize in encoder_dense_layers:
layer = model.get_submodule(name) layer = model.get_submodule(name)
dump_torch_weights(enc_writer, layer, name=export_name, verbose=True) dump_torch_weights(enc_writer, layer, name=export_name, verbose=True, quantize=quantize, scale=None)
encoder_gru_layers = [ encoder_gru_layers = [
@ -134,8 +134,8 @@ f"""
('core_encoder.module.gru5' , 'enc_gru5', 'TANH', True), ('core_encoder.module.gru5' , 'enc_gru5', 'TANH', True),
] ]
enc_max_rnn_units = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=True) enc_max_rnn_units = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=quantize, scale=None, recurrent_scale=None)
for name, export_name, _, _ in encoder_gru_layers]) for name, export_name, _, quantize in encoder_gru_layers])
encoder_conv_layers = [ encoder_conv_layers = [
@ -146,7 +146,7 @@ f"""
('core_encoder.module.conv5.conv' , 'enc_conv5', 'TANH', True), ('core_encoder.module.conv5.conv' , 'enc_conv5', 'TANH', True),
] ]
enc_max_conv_inputs = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, quantize=False) for name, export_name, _, _ in encoder_conv_layers]) enc_max_conv_inputs = max([dump_torch_weights(enc_writer, model.get_submodule(name), export_name, verbose=True, quantize=quantize, scale=None) for name, export_name, _, quantize in encoder_conv_layers])
del enc_writer del enc_writer
@ -159,9 +159,9 @@ f"""
('core_decoder.module.gru_init' , 'dec_gru_init', 'TANH', True), ('core_decoder.module.gru_init' , 'dec_gru_init', 'TANH', True),
] ]
for name, export_name, _, _ in decoder_dense_layers: for name, export_name, _, quantize in decoder_dense_layers:
layer = model.get_submodule(name) layer = model.get_submodule(name)
dump_torch_weights(dec_writer, layer, name=export_name, verbose=True) dump_torch_weights(dec_writer, layer, name=export_name, verbose=True, quantize=quantize, scale=None)
decoder_gru_layers = [ decoder_gru_layers = [
@ -172,8 +172,8 @@ f"""
('core_decoder.module.gru5' , 'dec_gru5', 'TANH', True), ('core_decoder.module.gru5' , 'dec_gru5', 'TANH', True),
] ]
dec_max_rnn_units = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=True) dec_max_rnn_units = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, input_sparse=True, quantize=quantize, scale=None, recurrent_scale=None)
for name, export_name, _, _ in decoder_gru_layers]) for name, export_name, _, quantize in decoder_gru_layers])
decoder_conv_layers = [ decoder_conv_layers = [
('core_decoder.module.conv1.conv' , 'dec_conv1', 'TANH', True), ('core_decoder.module.conv1.conv' , 'dec_conv1', 'TANH', True),
@ -183,7 +183,7 @@ f"""
('core_decoder.module.conv5.conv' , 'dec_conv5', 'TANH', True), ('core_decoder.module.conv5.conv' , 'dec_conv5', 'TANH', True),
] ]
dec_max_conv_inputs = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, quantize=False) for name, export_name, _, _ in decoder_conv_layers]) dec_max_conv_inputs = max([dump_torch_weights(dec_writer, model.get_submodule(name), export_name, verbose=True, quantize=quantize, scale=None) for name, export_name, _, quantize in decoder_conv_layers])
del dec_writer del dec_writer