shithub: opus

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Change decoder architecture to be like the encoder

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

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

@@ -82,8 +82,8 @@

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

 print(bits.shape)

-lambda_val = 0.0007 * np.ones((nb_sequences, sequence_size//2, 1))

-quant_id = np.round(10*np.log(lambda_val/.0002)).astype('int16')

+lambda_val = 0.001 * np.ones((nb_sequences, sequence_size//2, 1))

+quant_id = np.round(3.8*np.log(lambda_val/.0002)).astype('int16')

 quant_id = quant_id[:,:,0]

 quant_embed = qembedding(quant_id)

 quant_scale = tf.math.softplus(quant_embed[:,:,:nbits])

@@ -98,7 +98,7 @@

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

 state = state[:,-1,:]

-state = pvq_quantize(state, 30)

+state = pvq_quantize(state, 82)

 #state = state/(1e-15+tf.norm(state, axis=-1,keepdims=True))

 print("shapes are:")

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

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

@@ -105,7 +105,7 @@

 bits.astype('float32').tofile(args.output + "-syms.f32")

 lambda_val = 0.001 * np.ones((nb_sequences, sequence_size//2, 1))

-quant_id = np.round(10*np.log(lambda_val/.0002)).astype('int16')

+quant_id = np.round(3.8*np.log(lambda_val/.0002)).astype('int16')

 quant_id = quant_id[:,:,0]

 quant_embed = qembedding(quant_id)

 quant_scale = tf.math.softplus(quant_embed[:,:,:nbits])

@@ -115,7 +115,7 @@

 bits = np.round(apply_dead_zone([bits, dead_zone]).numpy())

 bits = bits/quant_scale

-gru_state_dec = pvq_quantize(gru_state_dec, 30)

+gru_state_dec = pvq_quantize(gru_state_dec, 82)

 #gru_state_dec = gru_state_dec/(1e-15+tf.norm(gru_state_dec, axis=-1,keepdims=True))

 gru_state_dec = gru_state_dec[:,-1,:]

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

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

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

@@ -238,15 +238,15 @@

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

+    gru = CuDNNGRU if training else GRU

     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_dense2 = gru(cond_size, return_sequences=True, kernel_constraint=constraint, recurrent_constraint=constraint, name='dec_dense2')

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

-    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_dense5 = Dense(cond_size2, activation='tanh', kernel_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')

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

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

     dec_final = Dense(bunch*nb_used_features, activation='linear', name='dec_final')

@@ -260,10 +260,10 @@

     gru_state3 = Dense(cond_size, name="state3", activation='tanh')(gru_state_input)

     dec1 = dec_dense1(time_reverse(bits_input))

-    dec2 = dec_dense2(dec1)

+    dec2 = dec_dense2(dec1, initial_state=gru_state1)

     dec3 = dec_dense3(dec2)

-    dec4 = dec_dense4(dec3, initial_state=gru_state1)

-    dec5 = dec_dense5(dec4, initial_state=gru_state2)

+    dec4 = dec_dense4(dec3, initial_state=gru_state2)

+    dec5 = dec_dense5(dec4)

     dec6 = dec_dense6(dec5, initial_state=gru_state3)

     dec7 = dec_dense7(dec6)

     dec8 = dec_dense8(dec7)

@@ -340,7 +340,7 @@

     ndze_unquant = div([ndze,quant_scale])

     mod_select = Lambda(lambda x: x[0][:,x[1]::bunch//2,:])

-    gru_state_dec = Lambda(lambda x: pvq_quantize(x, 30))(gru_state_dec)

+    gru_state_dec = Lambda(lambda x: pvq_quantize(x, 82))(gru_state_dec)

     combined_output = []

     unquantized_output = []

     cat = Concatenate(name="out_cat")

--- a/dnn/training_tf2/train_rdovae.py

+++ b/dnn/training_tf2/train_rdovae.py

@@ -124,8 +124,8 @@

 #lambda_val = np.repeat(np.random.uniform(.0007, .002, (features.shape[0], 1, 1)), features.shape[1]//2, axis=1)

 #quant_id = np.round(10*np.log(lambda_val/.0007)).astype('int16')

 #quant_id = quant_id[:,:,0]

-quant_id = np.repeat(np.random.randint(39, size=(features.shape[0], 1, 1), dtype='int16'), features.shape[1]//2, axis=1)

-lambda_val = .0002*np.exp(quant_id/10.)

+quant_id = np.repeat(np.random.randint(16, size=(features.shape[0], 1, 1), dtype='int16'), features.shape[1]//2, axis=1)

+lambda_val = .0002*np.exp(quant_id/3.8)

 quant_id = quant_id[:,:,0]

 # dump models to disk as we go

-- 

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