shithub: opus

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finalized quantization option in export_rdovae_weights.py

--- a/dnn/torch/rdovae/export_rdovae_weights.py

+++ b/dnn/torch/rdovae/export_rdovae_weights.py

@@ -121,9 +121,9 @@

         ('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)

-        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 = [

@@ -134,8 +134,8 @@

         ('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)

-                             for name, export_name, _, _ in encoder_gru_layers])

+    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, _, quantize in encoder_gru_layers])

     encoder_conv_layers = [

@@ -146,7 +146,7 @@

         ('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

@@ -159,9 +159,9 @@

         ('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)

-        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 = [

@@ -172,8 +172,8 @@

         ('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])

+    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, _, quantize in decoder_gru_layers])

     decoder_conv_layers = [

         ('core_decoder.module.conv1.conv'       , 'dec_conv1',   'TANH', True),

@@ -183,7 +183,7 @@

         ('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

-- 

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