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| author | Jean-Marc Valin <jmvalin@amazon.com> | 2023-10-19 23:06:52 +0300 |
|---|---|---|
| committer | Jean-Marc Valin <jmvalin@amazon.com> | 2023-10-19 23:06:52 +0300 |
| commit | d720955d617768aea7271076f3993e6263e8b84f (patch) | |
| tree | c4913ab3d130c494f684197df7ae77d6a2abcacd | |
| parent | 60ac1c6c99153a8ee5ba3e6f9f8fdd1bd3f54dc6 (diff) | |
Marking RDOVAE layers to quantize
| -rw-r--r-- | dnn/torch/rdovae/export_rdovae_weights.py | 50 |
1 files changed, 25 insertions, 25 deletions
diff --git a/dnn/torch/rdovae/export_rdovae_weights.py b/dnn/torch/rdovae/export_rdovae_weights.py index fc31e41d..856ec93e 100644 --- a/dnn/torch/rdovae/export_rdovae_weights.py +++ b/dnn/torch/rdovae/export_rdovae_weights.py @@ -116,9 +116,9 @@ f""" # encoder encoder_dense_layers = [ ('core_encoder.module.dense_1' , 'enc_dense1', 'TANH', False,), - ('core_encoder.module.z_dense' , 'enc_zdense', 'LINEAR', False,), - ('core_encoder.module.state_dense_1' , 'gdense1' , 'TANH', False,), - ('core_encoder.module.state_dense_2' , 'gdense2' , 'TANH', False) + ('core_encoder.module.z_dense' , 'enc_zdense', 'LINEAR', True,), + ('core_encoder.module.state_dense_1' , 'gdense1' , 'TANH', True,), + ('core_encoder.module.state_dense_2' , 'gdense2' , 'TANH', True) ] for name, export_name, _, _ in encoder_dense_layers: @@ -127,11 +127,11 @@ f""" encoder_gru_layers = [ - ('core_encoder.module.gru1' , 'enc_gru1', 'TANH', False), - ('core_encoder.module.gru2' , 'enc_gru2', 'TANH', False), - ('core_encoder.module.gru3' , 'enc_gru3', 'TANH', False), - ('core_encoder.module.gru4' , 'enc_gru4', 'TANH', False), - ('core_encoder.module.gru5' , 'enc_gru5', 'TANH', False), + ('core_encoder.module.gru1' , 'enc_gru1', 'TANH', True), + ('core_encoder.module.gru2' , 'enc_gru2', 'TANH', True), + ('core_encoder.module.gru3' , 'enc_gru3', 'TANH', True), + ('core_encoder.module.gru4' , 'enc_gru4', '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) @@ -139,11 +139,11 @@ f""" encoder_conv_layers = [ - ('core_encoder.module.conv1.conv' , 'enc_conv1', 'TANH', False), - ('core_encoder.module.conv2.conv' , 'enc_conv2', 'TANH', False), - ('core_encoder.module.conv3.conv' , 'enc_conv3', 'TANH', False), - ('core_encoder.module.conv4.conv' , 'enc_conv4', 'TANH', False), - ('core_encoder.module.conv5.conv' , 'enc_conv5', 'TANH', False), + ('core_encoder.module.conv1.conv' , 'enc_conv1', 'TANH', True), + ('core_encoder.module.conv2.conv' , 'enc_conv2', 'TANH', True), + ('core_encoder.module.conv3.conv' , 'enc_conv3', 'TANH', True), + ('core_encoder.module.conv4.conv' , 'enc_conv4', '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]) @@ -154,9 +154,9 @@ f""" # decoder decoder_dense_layers = [ ('core_decoder.module.dense_1' , 'dec_dense1', 'TANH', False), - ('core_decoder.module.output' , 'dec_output', 'LINEAR', False), + ('core_decoder.module.output' , 'dec_output', 'LINEAR', True), ('core_decoder.module.hidden_init' , 'dec_hidden_init', 'TANH', False), - ('core_decoder.module.gru_init' , 'dec_gru_init', 'TANH', False), + ('core_decoder.module.gru_init' , 'dec_gru_init', 'TANH', True), ] for name, export_name, _, _ in decoder_dense_layers: @@ -165,22 +165,22 @@ f""" decoder_gru_layers = [ - ('core_decoder.module.gru1' , 'dec_gru1', 'TANH', False), - ('core_decoder.module.gru2' , 'dec_gru2', 'TANH', False), - ('core_decoder.module.gru3' , 'dec_gru3', 'TANH', False), - ('core_decoder.module.gru4' , 'dec_gru4', 'TANH', False), - ('core_decoder.module.gru5' , 'dec_gru5', 'TANH', False), + ('core_decoder.module.gru1' , 'dec_gru1', 'TANH', True), + ('core_decoder.module.gru2' , 'dec_gru2', 'TANH', True), + ('core_decoder.module.gru3' , 'dec_gru3', 'TANH', True), + ('core_decoder.module.gru4' , 'dec_gru4', '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) for name, export_name, _, _ in decoder_gru_layers]) decoder_conv_layers = [ - ('core_decoder.module.conv1.conv' , 'dec_conv1', 'TANH', False), - ('core_decoder.module.conv2.conv' , 'dec_conv2', 'TANH', False), - ('core_decoder.module.conv3.conv' , 'dec_conv3', 'TANH', False), - ('core_decoder.module.conv4.conv' , 'dec_conv4', 'TANH', False), - ('core_decoder.module.conv5.conv' , 'dec_conv5', 'TANH', False), + ('core_decoder.module.conv1.conv' , 'dec_conv1', 'TANH', True), + ('core_decoder.module.conv2.conv' , 'dec_conv2', 'TANH', True), + ('core_decoder.module.conv3.conv' , 'dec_conv3', 'TANH', True), + ('core_decoder.module.conv4.conv' , 'dec_conv4', '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]) |