shithub: opus

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Update encoder/decoder

--- a/dnn/training_tf2/decode_rdovae.py

+++ b/dnn/training_tf2/decode_rdovae.py

@@ -72,17 +72,20 @@

 bits = np.memmap(bits_file + "-bits.s16", dtype='int16', mode='r')

-nb_sequences = len(bits)//(20*sequence_size)//batch_size*batch_size

-bits = bits[:nb_sequences*sequence_size*20]

+nb_sequences = len(bits)//(40*sequence_size)//batch_size*batch_size

+bits = bits[:nb_sequences*sequence_size*40]

-bits = np.reshape(bits, (nb_sequences, sequence_size//4, 20*4))

+bits = np.reshape(bits, (nb_sequences, sequence_size//2, 20*4))

+bits = bits[:,1::2,:]

 print(bits.shape)

 quant = np.memmap(bits_file + "-quant.f32", dtype='float32', mode='r')

 state = np.memmap(bits_file + "-state.f32", dtype='float32', mode='r')

-quant = np.reshape(quant, (nb_sequences, sequence_size//4, 6*20*4))

-state = np.reshape(state, (nb_sequences, sequence_size//2, 16))

+quant = np.reshape(quant, (nb_sequences, sequence_size//2, 6*20*4))

+quant = quant[:,1::2,:]

+

+state = np.reshape(state, (nb_sequences, sequence_size//2, 24))

 state = state[:,-1,:]

 print("shapes are:")

--- a/dnn/training_tf2/encode_rdovae.py

+++ b/dnn/training_tf2/encode_rdovae.py

@@ -109,6 +109,6 @@

 quant_embed_dec.astype('float32').tofile(args.output + "-quant.f32")

 gru_state_dec = gru_state_dec[:,-1,:]

-dec_out = decoder([bits, quant_embed_dec, gru_state_dec])

+dec_out = decoder([bits[:,1::2,:], quant_embed_dec[:,1::2,:], gru_state_dec])

 dec_out.numpy().astype('float32').tofile(args.output + "-dec_out.f32")

--- a/dnn/training_tf2/rdovae.py

+++ b/dnn/training_tf2/rdovae.py

@@ -195,7 +195,7 @@

 nb_state_dim = 24

-def new_rdovae_encoder(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256):

+def new_rdovae_encoder(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256, training=False):

     feat = Input(shape=(None, nb_used_features), batch_size=batch_size)

     quant_id = Input(shape=(None,), batch_size=batch_size)

@@ -205,12 +205,13 @@

     quant_scale = Activation('softplus')(Lambda(lambda x: x[:,:,:nb_bits], name='quant_scale_embed')(quant_embed))

+    gru = CuDNNGRU if training else GRU

     enc_dense1 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense1')

-    enc_dense2 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense2')

+    enc_dense2 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense2')

     enc_dense3 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense3')

-    enc_dense4 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense4')

+    enc_dense4 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense4')

     enc_dense5 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='enc_dense5')

-    enc_dense6 = CuDNNGRU(cond_size, return_sequences=True, return_state=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense6')

+    enc_dense6 = gru(cond_size, return_sequences=True, return_state=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='enc_dense6')

     enc_dense7 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='enc_dense7')

     enc_dense8 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='enc_dense8')

@@ -238,7 +239,7 @@

     encoder = Model([feat, quant_id, lambda_val], [bits, quant_embed, global_bits], name='encoder')

     return encoder

-def new_rdovae_decoder(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256):

+def new_rdovae_decoder(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256, training=False):

     bits_input = Input(shape=(None, nb_bits), batch_size=batch_size, name="dec_bits")

     quant_embed_input = Input(shape=(None, 6*nb_bits), batch_size=batch_size, name="dec_embed")

     gru_state_input = Input(shape=(nb_state_dim,), batch_size=batch_size, name="dec_state")

@@ -247,9 +248,10 @@

     dec_dense1 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense1')

     dec_dense2 = Dense(cond_size, activation='tanh', kernel_constraint=constraint, name='dec_dense2')

     dec_dense3 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense3')

-    dec_dense4 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense4')

-    dec_dense5 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense5')

-    dec_dense6 = CuDNNGRU(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense6')

+    gru = CuDNNGRU if training else GRU

+    dec_dense4 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense4')

+    dec_dense5 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense5')

+    dec_dense6 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense6')

     dec_dense7 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense7')

     dec_dense8 = Dense(cond_size2, activation='tanh', kernel_constraint=constraint, name='dec_dense8')

@@ -313,7 +315,7 @@

     return Concatenate(axis=0)(y)

-def new_rdovae_model(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256):

+def new_rdovae_model(nb_used_features=20, nb_bits=17, bunch=4, nb_quant=40, batch_size=128, cond_size=128, cond_size2=256, training=False):

     feat = Input(shape=(None, nb_used_features), batch_size=batch_size)

     quant_id = Input(shape=(None,), batch_size=batch_size)

@@ -320,10 +322,10 @@

     lambda_val = Input(shape=(None, 1), batch_size=batch_size)

     lambda_bunched = AveragePooling1D(pool_size=bunch//2, strides=bunch//2, padding="valid")(lambda_val)

-    encoder = new_rdovae_encoder(nb_used_features, nb_bits, bunch, nb_quant, batch_size, cond_size, cond_size2)

+    encoder = new_rdovae_encoder(nb_used_features, nb_bits, bunch, nb_quant, batch_size, cond_size, cond_size2, training=training)

     ze, quant_embed_dec, gru_state_dec = encoder([feat, quant_id, lambda_val])

-    decoder = new_rdovae_decoder(nb_used_features, nb_bits, bunch, nb_quant, batch_size, cond_size, cond_size2)

+    decoder = new_rdovae_decoder(nb_used_features, nb_bits, bunch, nb_quant, batch_size, cond_size, cond_size2, training=training)

     split_decoder = new_split_decoder(decoder)

     dead_zone = Activation('softplus')(Lambda(lambda x: x[:,:,nb_bits:2*nb_bits], name='dead_zone_embed')(quant_embed_dec))

-- 

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