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Improved imports according to Google standards

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-02-08 22:37:29 +01:00
parent 422e01a917
commit 1e422d3fa7
2 changed files with 44 additions and 40 deletions
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76
src/twomartens/masterthesis/aae/train.py
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@ -41,18 +41,20 @@ import math
import os
import pickle
import time
from typing import Callable, Dict, Sequence, Tuple
from typing import Callable
from typing import Dict
from typing import Sequence
from typing import Tuple
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import summary_ops_v2
from .model import Decoder, Encoder, XDiscriminator, ZDiscriminator
from .util import prepare_image
from twomartens.masterthesis.aae import model
from twomartens.masterthesis.aae import util
# shortcuts for tensorflow sub packages and classes
k = tf.keras.backend
AdamOptimizer = tf.train.AdamOptimizer
K = tf.keras.backend
tfe = tf.contrib.eager
GRACE: int = 10
@ -163,9 +165,9 @@ def train(dataset: tf.data.Dataset, iteration: int,
"""
# non-preserved tensors
y_real = k.ones(batch_size)
y_fake = k.zeros(batch_size)
sample = k.expand_dims(k.expand_dims(k.random_normal((64, zsize)), axis=1), axis=1)
y_real = K.ones(batch_size)
y_fake = K.zeros(batch_size)
sample = K.expand_dims(K.expand_dims(K.random_normal((64, zsize)), axis=1), axis=1)
# z generator function
z_generator = functools.partial(_get_z_variable, batch_size=batch_size, zsize=zsize)
@ -180,28 +182,28 @@ def train(dataset: tf.data.Dataset, iteration: int,
# checkpointed tensors and variables
checkpointables = {
'learning_rate_var': k.variable(lr),
'learning_rate_var': K.variable(lr),
}
checkpointables.update({
# get models
'encoder': Encoder(zsize),
'decoder': Decoder(channels),
'z_discriminator': ZDiscriminator(),
'x_discriminator': XDiscriminator(),
'encoder': model.Encoder(zsize),
'decoder': model.Decoder(channels),
'z_discriminator': model.ZDiscriminator(),
'x_discriminator': model.XDiscriminator(),
# define optimizers
'decoder_optimizer': AdamOptimizer(learning_rate=checkpointables['learning_rate_var'], beta1=0.5, beta2=0.999),
'enc_dec_optimizer': AdamOptimizer(learning_rate=checkpointables['learning_rate_var'], beta1=0.5, beta2=0.999),
'z_discriminator_optimizer': AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
'decoder_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'], beta1=0.5, beta2=0.999),
'enc_dec_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'], beta1=0.5, beta2=0.999),
'z_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
beta1=0.5, beta2=0.999),
'x_discriminator_optimizer': AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
'x_discriminator_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var'],
beta1=0.5, beta2=0.999),
# global step counter
'epoch_var': k.variable(-1, dtype=tf.int64),
'epoch_var': K.variable(-1, dtype=tf.int64),
'global_step': tf.train.get_or_create_global_step(),
'global_step_decoder': k.variable(0, dtype=tf.int64),
'global_step_enc_dec': k.variable(0, dtype=tf.int64),
'global_step_xd': k.variable(0, dtype=tf.int64),
'global_step_zd': k.variable(0, dtype=tf.int64),
'global_step_decoder': K.variable(0, dtype=tf.int64),
'global_step_enc_dec': K.variable(0, dtype=tf.int64),
'global_step_xd': K.variable(0, dtype=tf.int64),
'global_step_zd': K.variable(0, dtype=tf.int64),
})
# checkpoint
@ -243,10 +245,10 @@ def train(dataset: tf.data.Dataset, iteration: int,
))
# save sample image summary
def _save_sample(decoder: Decoder, global_step: tf.Variable, **kwargs) -> None:
def _save_sample(decoder: model.Decoder, global_step: tf.Variable, **kwargs) -> None:
resultsample = decoder(sample).cpu()
grid = prepare_image(resultsample)
summary_ops_v2.image(name='sample', tensor=k.expand_dims(grid, axis=0),
grid = util.prepare_image(resultsample)
summary_ops_v2.image(name='sample', tensor=K.expand_dims(grid, axis=0),
max_images=1, step=global_step)
with summary_ops_v2.always_record_summaries():
@ -310,8 +312,8 @@ def _train_one_epoch(epoch: int, dataset: tf.data.Dataset, targets_real: tf.Tens
verbose: bool,
targets_fake: tf.Tensor, z_generator: Callable[[], tf.Variable],
learning_rate_var: tf.Variable,
decoder: Decoder, encoder: Encoder, x_discriminator: XDiscriminator,
z_discriminator: ZDiscriminator, decoder_optimizer: tf.train.Optimizer,
decoder: model.Decoder, encoder: model.Encoder, x_discriminator: model.XDiscriminator,
z_discriminator: model.ZDiscriminator, decoder_optimizer: tf.train.Optimizer,
x_discriminator_optimizer: tf.train.Optimizer,
z_discriminator_optimizer: tf.train.Optimizer,
enc_dec_optimizer: tf.train.Optimizer,
@ -390,10 +392,10 @@ def _train_one_epoch(epoch: int, dataset: tf.data.Dataset, targets_real: tf.Tens
encoder_loss_avg(encoder_loss)
if int(global_step % LOG_FREQUENCY) == 0:
comparison = k.concatenate([x[:64], x_decoded[:64]], axis=0)
grid = prepare_image(comparison.cpu(), nrow=64)
comparison = K.concatenate([x[:64], x_decoded[:64]], axis=0)
grid = util.prepare_image(comparison.cpu(), nrow=64)
summary_ops_v2.image(name='reconstruction',
tensor=k.expand_dims(grid, axis=0), max_images=1,
tensor=K.expand_dims(grid, axis=0), max_images=1,
step=global_step)
global_step.assign_add(1)
@ -413,7 +415,7 @@ def _train_one_epoch(epoch: int, dataset: tf.data.Dataset, targets_real: tf.Tens
return outputs
def _train_xdiscriminator_step(x_discriminator: XDiscriminator, decoder: Decoder,
def _train_xdiscriminator_step(x_discriminator: model.XDiscriminator, decoder: model.Decoder,
optimizer: tf.train.Optimizer,
inputs: tf.Tensor, targets_real: tf.Tensor,
targets_fake: tf.Tensor, global_step: tf.Variable,
@ -463,7 +465,7 @@ def _train_xdiscriminator_step(x_discriminator: XDiscriminator, decoder: Decoder
return _xd_train_loss
def _train_decoder_step(decoder: Decoder, x_discriminator: XDiscriminator,
def _train_decoder_step(decoder: model.Decoder, x_discriminator: model.XDiscriminator,
optimizer: tf.train.Optimizer,
targets: tf.Tensor, global_step: tf.Variable,
global_step_decoder: tf.Variable,
@ -502,7 +504,7 @@ def _train_decoder_step(decoder: Decoder, x_discriminator: XDiscriminator,
return _decoder_train_loss
def _train_zdiscriminator_step(z_discriminator: ZDiscriminator, encoder: Encoder,
def _train_zdiscriminator_step(z_discriminator: model.ZDiscriminator, encoder: model.Encoder,
optimizer: tf.train.Optimizer,
inputs: tf.Tensor, targets_real: tf.Tensor,
targets_fake: tf.Tensor, global_step: tf.Variable,
@ -553,7 +555,7 @@ def _train_zdiscriminator_step(z_discriminator: ZDiscriminator, encoder: Encoder
return _zd_train_loss
def _train_enc_dec_step(encoder: Encoder, decoder: Decoder, z_discriminator: ZDiscriminator,
def _train_enc_dec_step(encoder: model.Encoder, decoder: model.Decoder, z_discriminator: model.ZDiscriminator,
optimizer: tf.train.Optimizer, inputs: tf.Tensor,
targets: tf.Tensor, global_step: tf.Variable,
global_step_enc_dec: tf.Variable) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
@ -608,8 +610,8 @@ def _get_z_variable(batch_size: int, zsize: int) -> tf.Variable:
:param zsize: size of the z latent space
:return: created variable
"""
z = k.reshape(k.random_normal((batch_size, zsize)), (-1, 1, 1, zsize))
return k.variable(z)
z = K.reshape(K.random_normal((batch_size, zsize)), (-1, 1, 1, zsize))
return K.variable(z)
def _normalize(feature: tf.Tensor, label: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
@ -620,7 +622,7 @@ def _normalize(feature: tf.Tensor, label: tf.Tensor) -> Tuple[tf.Tensor, tf.Tens
:param label: label tensor
:return: normalized tensor
"""
return k.expand_dims(tf.divide(feature, 255.0)), label
return K.expand_dims(tf.divide(feature, 255.0)), label
if __name__ == "__main__":

4
src/twomartens/masterthesis/aae/util.py
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@ -22,7 +22,9 @@ Functions:
"""
import math
from typing import Sequence, Tuple, Union
from typing import Sequence
from typing import Tuple
from typing import Union
import numpy as np
import tensorflow as tf