Removed obsolete auto-encoder code
Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
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Masterthesis package.
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Subpackages:
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``aae``: provides an implementation of Adversarial Auto Encoders
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``ssd_keras``: provides an implementation of SSD
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Modules:
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# -*- coding: utf-8 -*-
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#
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# Copyright 2019 Jim Martens
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Provides an AAE implementation.
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Modules:
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``model``: provides the keras models of the AAE implementation
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``train``: provides functionality to train the AAE
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``util``: provides helper functionality for visualization
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Todos:
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- make the implementation compatible with the YCB Video dataset
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"""
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# -*- coding: utf-8 -*-
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#
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# Copyright 2019 Jim Martens
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Data functionality for my AAE implementation.
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This module provides a function to prepare the training data.
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Functions:
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prepare_training_data(...): prepares the mnist training data
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"""
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import pickle
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from typing import Sequence
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from typing import Tuple
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import numpy as np
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import tensorflow as tf
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K = tf.keras.backend
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def prepare_training_data(test_fold_id: int,
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inlier_classes: Sequence[int],
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total_classes: int,
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fold_prefix: str = 'data/data_fold_',
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batch_size: int = 128,
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folds: int = 5) -> Tuple[tf.data.Dataset, tf.data.Dataset]:
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"""
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Prepares the MNIST training data.
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Args:
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test_fold_id: id of test fold
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inlier_classes: list of class ids that are considered inliers
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total_classes: total number of classes
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fold_prefix: the prefix for the fold pickle files (default: 'data/data_fold_')
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batch_size: size of batch (default: 128)
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folds: number of folds (default: 5)
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Returns:
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A tuple (train dataset, valid dataset)
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"""
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# prepare data
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mnist_train = []
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mnist_valid = []
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for i in range(folds):
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if i != test_fold_id: # exclude testing fold, representing 20% of each class
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with open(f"{fold_prefix}{i:d}.pkl", 'rb') as pkl:
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fold = pickle.load(pkl)
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if len(mnist_valid) == 0: # single out one fold, comprising 20% of each class
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mnist_valid = fold
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else: # form train set from remaining folds, comprising 60% of each class
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mnist_train += fold
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outlier_classes = []
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for i in range(total_classes):
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if i not in inlier_classes:
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outlier_classes.append(i)
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# keep only train classes
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mnist_train = [x for x in mnist_train if x[0] in inlier_classes]
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def _list_of_pairs_to_numpy(list_of_pairs: Sequence[Tuple[int, np.ndarray]]) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Converts a list of pairs to a numpy array.
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Args:
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list_of_pairs: list of pairs
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Returns:
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tuple (feature array, label array)
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"""
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return np.asarray([x[1] for x in list_of_pairs], np.float32), np.asarray([x[0] for x in list_of_pairs], np.int)
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mnist_train_x, mnist_train_y = _list_of_pairs_to_numpy(mnist_train)
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mnist_valid_x, mnist_valid_y = _list_of_pairs_to_numpy(mnist_valid)
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# get dataset
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train_dataset = tf.data.Dataset.from_tensor_slices((mnist_train_x, mnist_train_y))
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train_dataset = train_dataset.shuffle(mnist_train_x.shape[0]).batch(batch_size,
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drop_remainder=True).map(_normalize)
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valid_dataset = tf.data.Dataset.from_tensor_slices((mnist_valid_x, mnist_valid_y))
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valid_dataset = valid_dataset.shuffle(mnist_valid_x.shape[0]).batch(batch_size,
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drop_remainder=True).map(_normalize)
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return train_dataset, valid_dataset
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def _normalize(feature: tf.Tensor, label: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
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"""
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Normalizes a tensor from a 0-255 range to a 0-1 range and adds one dimension.
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:param feature: tensor to be normalized
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:param label: label tensor
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:return: normalized tensor
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"""
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return K.expand_dims(tf.divide(feature, 255.0)), label
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# -*- coding: utf-8 -*-
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#
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# Copyright 2019 Jim Martens
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Provides the models of my AAE implementation.
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Classes:
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``Encoder``: encodes an image input to a latent space
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``Decoder``: decodes data from a latent space to resemble input data
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``XDiscriminator``: differentiates between real input data and decoded input data
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``ZDiscriminator``: differentiates between z values drawn from a normal distribution (real) and the encoded input
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(fake)
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"""
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import tensorflow as tf
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# shortcuts for tensorflow - quasi imports
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keras = tf.keras
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k = tf.keras.backend
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class Encoder(keras.Model):
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"""
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Encodes input to a latent space.
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Args:
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zsize: size of the latent space
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"""
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def __init__(self, zsize: int) -> None:
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super().__init__(name='encoder')
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weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
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self.conv1 = keras.layers.Conv2D(filters=zsize * 4, kernel_size=3, strides=2, name='conv1',
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padding='same', kernel_initializer=weight_init,
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activation=keras.activations.sigmoid)
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self.conv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=3, strides=2, name='conv2',
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padding='same', kernel_initializer=weight_init)
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self.conv2_a = keras.layers.ReLU()
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self.conv3 = keras.layers.Conv2D(filters=zsize, kernel_size=3, strides=2, name='conv3',
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padding='same', kernel_initializer=weight_init)
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self.conv3_a = keras.layers.ReLU()
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self.flatten = keras.layers.Flatten(name='flatten')
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self.latent = keras.layers.Dense(units=zsize * (2 ** 5), name='latent')
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def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
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"""See base class."""
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result = self.conv1(inputs)
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result = self.conv2(result)
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result = self.conv2_a(result)
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result = self.conv3(result)
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result = self.conv3_a(result)
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result = self.flatten(result)
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result = self.latent(result)
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return result
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class Decoder(keras.Model):
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"""
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Generates input data from latent space values.
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"""
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def __init__(self, channels: int, zsize: int, image_size: int) -> None:
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"""
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Initializes the Decoder class.
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Args:
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channels: number of channels in the input image
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zsize: size of the latent space
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image_size: size of height/width of input image
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"""
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super().__init__(name='decoder')
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weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
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# calculate dimension of last conv layer in encoder
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conv_image_size = image_size / (2 ** 3)
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dimensions = zsize * conv_image_size * conv_image_size
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self.conv_shape = (-1, conv_image_size, conv_image_size, zsize)
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self.transform = keras.layers.Dense(units=dimensions, name='input_transform')
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self.deconv1 = keras.layers.Conv2DTranspose(filters=zsize, kernel_size=3, strides=1, name='deconv1',
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padding='same', kernel_initializer=weight_init)
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self.deconv1_a = keras.layers.ReLU()
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self.deconv2 = keras.layers.Conv2DTranspose(filters=zsize * 2, kernel_size=3, strides=2, name='deconv2',
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padding='same', kernel_initializer=weight_init)
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self.deconv2_a = keras.layers.ReLU()
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self.deconv3 = keras.layers.Conv2DTranspose(filters=zsize * 4, kernel_size=3, strides=2, name='deconv3',
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padding='same', kernel_initializer=weight_init)
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self.deconv3_a = keras.layers.ReLU()
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self.deconv4 = keras.layers.Conv2DTranspose(filters=channels, kernel_size=3, strides=2, name='deconv4',
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padding='same', kernel_initializer=weight_init)
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def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
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"""See base class."""
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result = self.transform(inputs)
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result = tf.reshape(result, self.conv_shape)
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result = self.deconv1(result)
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result = self.deconv1_a(result)
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result = self.deconv2(result)
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result = self.deconv2_a(result)
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result = self.deconv3(result)
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result = self.deconv3_a(result)
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result = self.deconv4(result)
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result = k.sigmoid(result)
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return result
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class ZDiscriminator(keras.Model):
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"""
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Discriminates between encoded inputs and latent space distribution.
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The latent space value is drawn from a normal distribution with ``0`` mean
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and a variance of ``1``.
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"""
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def __init__(self) -> None:
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super().__init__(name='zdiscriminator')
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weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
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self.zd1 = keras.layers.Dense(units=128, name='zd1', kernel_initializer=weight_init)
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self.zd1_a = keras.layers.LeakyReLU(alpha=0.2)
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self.zd2 = keras.layers.Dense(units=128, name='zd2', kernel_initializer=weight_init)
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self.zd2_a = keras.layers.LeakyReLU(alpha=0.2)
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self.zd3 = keras.layers.Dense(units=1, name='zd3', activation='sigmoid',
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kernel_initializer=weight_init)
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def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
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"""See base class."""
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result = self.zd1(inputs)
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result = self.zd1_a(result)
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result = self.zd2(result)
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result = self.zd2_a(result)
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result = self.zd3(result)
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return result
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class XDiscriminator(keras.Model):
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"""
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Discriminates between generated inputs and the actual inputs.
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"""
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def __init__(self) -> None:
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super().__init__(name='xdiscriminator')
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weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
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self.x_padded = keras.layers.ZeroPadding2D(padding=1)
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self.xd1 = keras.layers.Conv2D(filters=64, kernel_size=4, strides=2, name='xd1',
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padding='valid', kernel_initializer=weight_init)
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self.xd1_a = keras.layers.LeakyReLU(alpha=0.2)
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self.xd1_a_padded = keras.layers.ZeroPadding2D(padding=1)
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self.xd2 = keras.layers.Conv2D(filters=256, kernel_size=4, strides=2, name='xd2',
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padding='valid', kernel_initializer=weight_init)
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self.xd2_bn = keras.layers.BatchNormalization()
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self.xd2_a = keras.layers.LeakyReLU(alpha=0.2)
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self.xd2_a_padded = keras.layers.ZeroPadding2D(padding=1)
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self.xd3 = keras.layers.Conv2D(filters=512, kernel_size=4, strides=2, name='xd3',
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padding='valid', kernel_initializer=weight_init)
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self.xd3_bn = keras.layers.BatchNormalization()
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self.xd3_a = keras.layers.LeakyReLU(alpha=0.2)
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self.xd4 = keras.layers.Conv2D(filters=1, kernel_size=4, strides=1, name='xd4',
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padding='valid', kernel_initializer=weight_init,
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activation='sigmoid')
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def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
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"""See base class."""
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result = self.x_padded(inputs)
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result = self.xd1(result)
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result = self.xd1_a(result)
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result = self.xd1_a_padded(result)
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result = self.xd2(result)
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result = self.xd2_bn(result)
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result = self.xd2_a(result)
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result = self.xd2_a_padded(result)
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result = self.xd3(result)
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result = self.xd3_bn(result)
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result = self.xd3_a(result)
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result = self.xd4(result)
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return result
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# -*- coding: utf-8 -*-
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#
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# Copyright 2019 Jim Martens
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Functionality to run my auto-encoder implementation.
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This module provides a function to run a trained simple auto-encoder.
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Functions:
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run_simple(...): runs a trained simple auto-encoder
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"""
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import os
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import time
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from typing import Dict, Tuple
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import tensorflow as tf
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from tensorflow.python.ops import summary_ops_v2
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# shortcuts for tensorflow sub packages and classes
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from twomartens.masterthesis.aae import model, train, util
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K = tf.keras.backend
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tfe = tf.contrib.eager
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def run_simple(dataset: tf.data.Dataset,
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iteration: int,
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weights_prefix: str,
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image_size: int,
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channels: int = 3,
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zsize: int = 64,
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batch_size: int = 16,
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verbose: bool = False) -> None:
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"""
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Runs the trained auto-encoder for given data set.
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This function runs the trained auto-encoder
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Args:
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dataset: run dataset
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iteration: identifier for the used training run
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weights_prefix: prefix for trained weights directory
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image_size: height/width of input image
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channels: number of channels in input image (default: 3)
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zsize: size of the intermediary z (default: 64)
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batch_size: size of each batch (default: 16)
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verbose: if True training progress is printed to console (default: False)
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"""
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# checkpointed tensors and variables
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checkpointables = {
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# get models
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'encoder': model.Encoder(zsize),
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'decoder': model.Decoder(channels, zsize, image_size),
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}
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global_step = tf.train.get_or_create_global_step()
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# checkpoint
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checkpoint_dir = os.path.join(weights_prefix, str(iteration) + '/')
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os.makedirs(checkpoint_dir, exist_ok=True)
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latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
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checkpoint = tf.train.Checkpoint(**checkpointables)
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checkpoint.restore(latest_checkpoint)
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outputs = _run_one_epoch_simple(dataset,
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batch_size=batch_size,
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global_step=global_step,
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**checkpointables)
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if verbose:
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print((
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f"run time: {outputs['time']:.2f}, "
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f"Encoder + Decoder loss: {outputs['enc_dec_loss']:.3f}"
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))
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def _run_one_epoch_simple(dataset: tf.data.Dataset,
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batch_size: int,
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encoder: model.Encoder,
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decoder: model.Decoder,
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global_step: tf.Variable) -> Dict[str, float]:
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with summary_ops_v2.always_record_summaries():
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start_time = time.time()
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enc_dec_loss_avg = tfe.metrics.Mean(name='encoder_decoder_loss',
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dtype=tf.float32)
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for x in dataset:
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reconstruction_loss, x_decoded = _run_enc_dec_step_simple(encoder=encoder,
|
||||
decoder=decoder,
|
||||
inputs=x,
|
||||
global_step=global_step)
|
||||
enc_dec_loss_avg(reconstruction_loss)
|
||||
|
||||
if int(global_step % train.LOG_FREQUENCY) == 0:
|
||||
comparison = K.concatenate([x[:int(batch_size / 2)], x_decoded[:int(batch_size / 2)]], axis=0)
|
||||
grid = util.prepare_image(comparison.cpu(), nrow=int(batch_size / 2))
|
||||
summary_ops_v2.image(name='reconstruction',
|
||||
tensor=K.expand_dims(grid, axis=0), max_images=1,
|
||||
step=global_step)
|
||||
global_step.assign_add(1)
|
||||
|
||||
end_time = time.time()
|
||||
run_time = end_time - start_time
|
||||
|
||||
# final losses of epoch
|
||||
outputs = {
|
||||
'enc_dec_loss': enc_dec_loss_avg.result(False),
|
||||
'run_time': run_time
|
||||
}
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
def _run_enc_dec_step_simple(encoder: model.Encoder, decoder: model.Decoder,
|
||||
inputs: tf.Tensor,
|
||||
global_step: tf.Variable) -> Tuple[tf.Tensor, tf.Tensor]:
|
||||
"""
|
||||
Runs the encoder and decoder jointly for one step (one batch).
|
||||
|
||||
Args:
|
||||
encoder: instance of encoder model
|
||||
decoder: instance of decoder model
|
||||
inputs: inputs from data set
|
||||
global_step: the global step variable
|
||||
|
||||
Returns:
|
||||
tuple of reconstruction loss, reconstructed input, latent space value
|
||||
"""
|
||||
z = encoder(inputs)
|
||||
x_decoded = decoder(z)
|
||||
|
||||
reconstruction_loss = tf.losses.log_loss(inputs, x_decoded)
|
||||
|
||||
if int(global_step % train.LOG_FREQUENCY) == 0:
|
||||
summary_ops_v2.scalar(name='reconstruction_loss', tensor=reconstruction_loss,
|
||||
step=global_step)
|
||||
|
||||
return reconstruction_loss, x_decoded
|
||||
|
|
@ -1,247 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Copyright 2019 Jim Martens
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Training functionality for my AAE implementation.
|
||||
|
||||
This module provides a function to train a simple auto-encoder.
|
||||
|
||||
Attributes:
|
||||
LOG_FREQUENCY: number of steps that must pass before logging happens
|
||||
|
||||
Functions:
|
||||
train_simple(...): trains a simple auto-encoder only with reconstruction loss
|
||||
|
||||
"""
|
||||
import os
|
||||
import time
|
||||
from typing import Dict
|
||||
from typing import Tuple
|
||||
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.ops import summary_ops_v2
|
||||
|
||||
from twomartens.masterthesis.aae import model
|
||||
from twomartens.masterthesis.aae import util
|
||||
|
||||
# shortcuts for tensorflow sub packages and classes
|
||||
K = tf.keras.backend
|
||||
tfe = tf.contrib.eager
|
||||
|
||||
LOG_FREQUENCY: int = 10
|
||||
|
||||
|
||||
def train_simple(dataset: tf.data.Dataset,
|
||||
iteration: int,
|
||||
weights_prefix: str,
|
||||
image_size: int,
|
||||
channels: int = 3,
|
||||
zsize: int = 64,
|
||||
lr: float = 0.0001,
|
||||
train_epoch: int = 1,
|
||||
batch_size: int = 16,
|
||||
verbose: bool = False) -> None:
|
||||
"""
|
||||
Trains auto-encoder for given data set.
|
||||
|
||||
This function creates checkpoints after every
|
||||
epoch as well as after finishing training (or stopping early). When starting
|
||||
this function with the same ``iteration`` then the training will try to
|
||||
continue where it ended last time by restoring a saved checkpoint.
|
||||
The loss values are provided as scalar summaries. Reconstruction images are
|
||||
provided as summary images.
|
||||
|
||||
Args:
|
||||
dataset: train dataset
|
||||
iteration: identifier for the current training run
|
||||
weights_prefix: prefix for weights directory
|
||||
image_size: height/width of input image
|
||||
channels: number of channels in input image (default: 3)
|
||||
zsize: size of the intermediary z (default: 64)
|
||||
lr: initial learning rate (default: 0.0001)
|
||||
train_epoch: number of epochs to train (default: 1)
|
||||
batch_size: size of each batch (default: 16)
|
||||
verbose: if True training progress is printed to console (default: False)
|
||||
"""
|
||||
|
||||
# checkpointed tensors and variables
|
||||
checkpointables = {
|
||||
'learning_rate_var': K.variable(lr),
|
||||
}
|
||||
checkpointables.update({
|
||||
# get models
|
||||
'encoder': model.Encoder(zsize),
|
||||
'decoder': model.Decoder(channels, zsize, image_size),
|
||||
# define optimizers
|
||||
'enc_dec_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var']),
|
||||
# global step counter
|
||||
'epoch_var': K.variable(-1, dtype=tf.int64),
|
||||
'global_step': tf.train.get_or_create_global_step(),
|
||||
'global_step_enc_dec': K.variable(0, dtype=tf.int64),
|
||||
})
|
||||
|
||||
# checkpoint
|
||||
checkpoint_dir = os.path.join(weights_prefix, str(iteration) + '/')
|
||||
os.makedirs(checkpoint_dir, exist_ok=True)
|
||||
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
|
||||
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
|
||||
checkpoint = tf.train.Checkpoint(**checkpointables)
|
||||
checkpoint.restore(latest_checkpoint)
|
||||
|
||||
def _get_last_epoch(epoch_var: tf.Variable, **kwargs) -> int:
|
||||
return int(epoch_var)
|
||||
|
||||
last_epoch = _get_last_epoch(**checkpointables)
|
||||
previous_epochs = 0
|
||||
if last_epoch != -1:
|
||||
previous_epochs = last_epoch + 1
|
||||
|
||||
with summary_ops_v2.always_record_summaries():
|
||||
summary_ops_v2.scalar(name='learning_rate', tensor=checkpointables['learning_rate_var'],
|
||||
step=checkpointables['global_step'])
|
||||
|
||||
for epoch in range(train_epoch - previous_epochs):
|
||||
_epoch = epoch + previous_epochs
|
||||
outputs = _train_one_epoch_simple(_epoch, dataset,
|
||||
verbose=verbose,
|
||||
batch_size=batch_size,
|
||||
**checkpointables)
|
||||
|
||||
if verbose:
|
||||
print((
|
||||
f"[{_epoch + 1:d}/{train_epoch:d}] - "
|
||||
f"train time: {outputs['per_epoch_time']:.2f}, "
|
||||
f"Encoder + Decoder loss: {outputs['enc_dec_loss']:.3f}"
|
||||
))
|
||||
|
||||
# save weights at end of epoch
|
||||
checkpoint.save(checkpoint_prefix)
|
||||
|
||||
if verbose:
|
||||
print("Training finish!... save model weights")
|
||||
|
||||
# save trained models
|
||||
checkpoint.save(checkpoint_prefix)
|
||||
|
||||
|
||||
def _train_one_epoch_simple(epoch: int,
|
||||
dataset: tf.data.Dataset,
|
||||
verbose: bool,
|
||||
batch_size: int,
|
||||
learning_rate_var: tf.Variable,
|
||||
decoder: model.Decoder,
|
||||
encoder: model.Encoder,
|
||||
enc_dec_optimizer: tf.train.Optimizer,
|
||||
global_step: tf.Variable,
|
||||
global_step_enc_dec: tf.Variable,
|
||||
epoch_var: tf.Variable) -> Dict[str, float]:
|
||||
with summary_ops_v2.always_record_summaries():
|
||||
epoch_var.assign(epoch)
|
||||
epoch_start_time = time.time()
|
||||
# define loss variables
|
||||
enc_dec_loss_avg = tfe.metrics.Mean(name='encoder_decoder_loss', dtype=tf.float32)
|
||||
|
||||
# update learning rate
|
||||
if (epoch + 1) % 30 == 0:
|
||||
learning_rate_var.assign(learning_rate_var.value() / 4)
|
||||
summary_ops_v2.scalar(name='learning_rate', tensor=learning_rate_var,
|
||||
step=global_step)
|
||||
if verbose:
|
||||
print("learning rate change!")
|
||||
|
||||
for x in dataset:
|
||||
reconstruction_loss, x_decoded = _train_enc_dec_step_simple(encoder=encoder,
|
||||
decoder=decoder,
|
||||
optimizer=enc_dec_optimizer,
|
||||
inputs=x,
|
||||
global_step_enc_dec=global_step_enc_dec,
|
||||
global_step=global_step)
|
||||
enc_dec_loss_avg(reconstruction_loss)
|
||||
|
||||
if int(global_step % LOG_FREQUENCY) == 0:
|
||||
comparison = K.concatenate([x[:int(batch_size / 2)], x_decoded[:int(batch_size / 2)]], axis=0)
|
||||
grid = util.prepare_image(comparison.cpu(), nrow=int(batch_size/2))
|
||||
summary_ops_v2.image(name='reconstruction',
|
||||
tensor=K.expand_dims(grid, axis=0), max_images=1,
|
||||
step=global_step)
|
||||
global_step.assign_add(1)
|
||||
|
||||
epoch_end_time = time.time()
|
||||
per_epoch_time = epoch_end_time - epoch_start_time
|
||||
|
||||
# final losses of epoch
|
||||
outputs = {
|
||||
'enc_dec_loss': enc_dec_loss_avg.result(False),
|
||||
'per_epoch_time': per_epoch_time,
|
||||
}
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
def _train_enc_dec_step_simple(encoder: model.Encoder, decoder: model.Decoder,
|
||||
optimizer: tf.train.Optimizer,
|
||||
inputs: tf.Tensor,
|
||||
global_step: tf.Variable,
|
||||
global_step_enc_dec: tf.Variable) -> Tuple[tf.Tensor, tf.Tensor]:
|
||||
"""
|
||||
Trains the encoder and decoder jointly for one step (one batch).
|
||||
|
||||
Args:
|
||||
encoder: instance of encoder model
|
||||
decoder: instance of decoder model
|
||||
optimizer: instance of chosen optimizer
|
||||
inputs: inputs from data set
|
||||
global_step: the global step variable
|
||||
global_step_enc_dec: global step variable for enc_dec
|
||||
|
||||
Returns:
|
||||
tuple of reconstruction loss, reconstructed input, z value
|
||||
"""
|
||||
with tf.GradientTape() as tape:
|
||||
z = encoder(inputs)
|
||||
x_decoded = decoder(z)
|
||||
|
||||
reconstruction_loss = tf.losses.log_loss(inputs, x_decoded)
|
||||
|
||||
enc_dec_grads = tape.gradient(reconstruction_loss,
|
||||
encoder.trainable_variables + decoder.trainable_variables)
|
||||
if int(global_step % LOG_FREQUENCY) == 0:
|
||||
summary_ops_v2.scalar(name='reconstruction_loss', tensor=reconstruction_loss,
|
||||
step=global_step)
|
||||
for grad, variable in zip(enc_dec_grads, encoder.trainable_variables + decoder.trainable_variables):
|
||||
summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
|
||||
step=global_step)
|
||||
summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
|
||||
step=global_step)
|
||||
optimizer.apply_gradients(zip(enc_dec_grads,
|
||||
encoder.trainable_variables + decoder.trainable_variables),
|
||||
global_step=global_step_enc_dec)
|
||||
|
||||
return reconstruction_loss, x_decoded
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from twomartens.masterthesis.aae.data import prepare_training_data
|
||||
tf.enable_eager_execution()
|
||||
inlier_classes = [8]
|
||||
iteration = 2
|
||||
train_dataset, _ = prepare_training_data(test_fold_id=0, inlier_classes=inlier_classes,
|
||||
total_classes=10)
|
||||
train_summary_writer = summary_ops_v2.create_file_writer(
|
||||
'./summaries/train/number-' + str(inlier_classes[0]) + '/' + str(iteration))
|
||||
with train_summary_writer.as_default():
|
||||
train_simple(dataset=train_dataset, iteration=iteration,
|
||||
weights_prefix='weights/' + str(inlier_classes[0]) + '/')
|
||||
|
|
@ -1,569 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Copyright 2019 Jim Martens
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Training functionality for my AAE implementation.
|
||||
|
||||
This module provides functions to train the Adversarial Auto Encoder.
|
||||
|
||||
Attributes:
|
||||
GRACE: specifies the number of epochs that the training loss can stagnate or worsen
|
||||
before the training is stopped early
|
||||
TOTAL_LOSS_GRACE_CAP: upper limit for total loss, grace countdown only enabled if total loss higher
|
||||
|
||||
Functions:
|
||||
prepare_training_data(...): prepares the mnist training data
|
||||
train(...): trains the AAE models
|
||||
|
||||
Todos:
|
||||
- fix early stopping
|
||||
- fix losses reaching exactly zero
|
||||
|
||||
"""
|
||||
import functools
|
||||
import os
|
||||
import time
|
||||
from typing import Callable
|
||||
from typing import Dict
|
||||
from typing import Tuple
|
||||
|
||||
import math
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.ops import summary_ops_v2
|
||||
|
||||
from twomartens.masterthesis.aae import model
|
||||
from twomartens.masterthesis.aae import util
|
||||
from twomartens.masterthesis.aae.train import LOG_FREQUENCY
|
||||
|
||||
# shortcuts for tensorflow sub packages and classes
|
||||
K = tf.keras.backend
|
||||
tfe = tf.contrib.eager
|
||||
|
||||
GRACE: int = 10
|
||||
TOTAL_LOSS_GRACE_CAP: int = 6
|
||||
|
||||
|
||||
def train(dataset: tf.data.Dataset,
|
||||
iteration: int,
|
||||
weights_prefix: str,
|
||||
channels: int = 1,
|
||||
zsize: int = 32,
|
||||
lr: float = 0.002,
|
||||
batch_size: int = 128,
|
||||
train_epoch: int = 80,
|
||||
verbose: bool = True,
|
||||
early_stopping: bool = False) -> None:
|
||||
"""
|
||||
Trains AAE for given data set.
|
||||
|
||||
This function provides early stopping and creates checkpoints after every
|
||||
epoch as well as after finishing training (or stopping early). When starting
|
||||
this function with the same ``iteration`` then the training will try to
|
||||
continue where it ended last time by restoring a saved checkpoint.
|
||||
The loss values are provided as scalar summaries. Reconstruction and sample
|
||||
images are provided as summary images.
|
||||
|
||||
Args:
|
||||
dataset: train dataset
|
||||
iteration: identifier for the current training run
|
||||
weights_prefix: prefix for weights directory
|
||||
channels: number of channels in input image (default: 1)
|
||||
zsize: size of the intermediary z (default: 32)
|
||||
lr: initial learning rate (default: 0.002)
|
||||
batch_size: the size of each batch (default: 128)
|
||||
train_epoch: number of epochs to train (default: 80)
|
||||
verbose: if True prints train progress info to console (default: True)
|
||||
early_stopping: if True the early stopping mechanic is enabled (default: False)
|
||||
|
||||
Notes:
|
||||
The training stops early if for ``GRACE`` number of epochs the loss is not
|
||||
decreasing. Specifically all individual losses are accounted for and any one
|
||||
of those not decreasing triggers a ``strike``. If the total loss, which is
|
||||
a sum of all individual losses, is also not decreasing and has a total
|
||||
value of more than ``TOTAL_LOSS_GRACE_CAP``, the counter for the remaining grace period is
|
||||
decreased. If in any epoch afterwards all losses are decreasing the grace
|
||||
period is reset to ``GRACE``. Lastly the training loop will be stopped early
|
||||
if the grace counter reaches ``0`` at the end of an epoch.
|
||||
"""
|
||||
|
||||
# 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)
|
||||
# z generator function
|
||||
z_generator = functools.partial(_get_z_variable, batch_size=batch_size, zsize=zsize)
|
||||
|
||||
# non-preserved python variables
|
||||
encoder_lowest_loss = math.inf
|
||||
decoder_lowest_loss = math.inf
|
||||
enc_dec_lowest_loss = math.inf
|
||||
zd_lowest_loss = math.inf
|
||||
xd_lowest_loss = math.inf
|
||||
total_lowest_loss = math.inf
|
||||
grace_period = GRACE
|
||||
|
||||
# checkpointed tensors and variables
|
||||
checkpointables = {
|
||||
'learning_rate_var': K.variable(lr),
|
||||
}
|
||||
checkpointables.update({
|
||||
# get models
|
||||
'encoder': model.Encoder(zsize),
|
||||
'decoder': model.Decoder(channels, zsize),
|
||||
'z_discriminator': model.ZDiscriminator(),
|
||||
'x_discriminator': model.XDiscriminator(),
|
||||
# define optimizers
|
||||
'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': 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),
|
||||
'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),
|
||||
})
|
||||
|
||||
# checkpoint
|
||||
checkpoint_dir = os.path.join(weights_prefix, str(iteration) + '/')
|
||||
os.makedirs(checkpoint_dir, exist_ok=True)
|
||||
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
|
||||
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
|
||||
checkpoint = tf.train.Checkpoint(**checkpointables)
|
||||
checkpoint.restore(latest_checkpoint)
|
||||
|
||||
def _get_last_epoch(epoch_var: tf.Variable, **kwargs) -> int:
|
||||
return int(epoch_var)
|
||||
|
||||
last_epoch = _get_last_epoch(**checkpointables)
|
||||
previous_epochs = 0
|
||||
if last_epoch != -1:
|
||||
previous_epochs = last_epoch + 1
|
||||
|
||||
with summary_ops_v2.always_record_summaries():
|
||||
summary_ops_v2.scalar(name='learning_rate', tensor=checkpointables['learning_rate_var'],
|
||||
step=checkpointables['global_step'])
|
||||
|
||||
for epoch in range(train_epoch - previous_epochs):
|
||||
_epoch = epoch + previous_epochs
|
||||
outputs = _train_one_epoch(_epoch, dataset, targets_real=y_real,
|
||||
targets_fake=y_fake, z_generator=z_generator,
|
||||
verbose=verbose, batch_size=batch_size,
|
||||
**checkpointables)
|
||||
|
||||
if verbose:
|
||||
print((
|
||||
f"[{_epoch + 1:d}/{train_epoch:d}] - "
|
||||
f"train time: {outputs['per_epoch_time']:.2f}, "
|
||||
f"Decoder loss: {outputs['decoder_loss']:.3f}, "
|
||||
f"X Discriminator loss: {outputs['xd_loss']:.3f}, "
|
||||
f"Z Discriminator loss: {outputs['zd_loss']:.3f}, "
|
||||
f"Encoder + Decoder loss: {outputs['enc_dec_loss']:.3f}, "
|
||||
f"Encoder loss: {outputs['encoder_loss']:.3f}"
|
||||
))
|
||||
|
||||
# save sample image summary
|
||||
def _save_sample(decoder: model.Decoder, global_step: tf.Variable, **kwargs) -> None:
|
||||
resultsample = decoder(sample).cpu()
|
||||
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():
|
||||
_save_sample(**checkpointables)
|
||||
|
||||
# save weights at end of epoch
|
||||
checkpoint.save(checkpoint_prefix)
|
||||
|
||||
# check for improvements in error reduction - otherwise early stopping
|
||||
if early_stopping:
|
||||
strike = False
|
||||
total_strike = False
|
||||
total_loss = outputs['encoder_loss'] + outputs['decoder_loss'] + outputs['enc_dec_loss'] + \
|
||||
outputs['xd_loss'] + outputs['zd_loss']
|
||||
if total_loss < total_lowest_loss:
|
||||
total_lowest_loss = total_loss
|
||||
elif total_loss > TOTAL_LOSS_GRACE_CAP:
|
||||
total_strike = True
|
||||
if outputs['encoder_loss'] < encoder_lowest_loss:
|
||||
encoder_lowest_loss = outputs['encoder_loss']
|
||||
else:
|
||||
strike = True
|
||||
if outputs['decoder_loss'] < decoder_lowest_loss:
|
||||
decoder_lowest_loss = outputs['decoder_loss']
|
||||
else:
|
||||
strike = True
|
||||
if outputs['enc_dec_loss'] < enc_dec_lowest_loss:
|
||||
enc_dec_lowest_loss = outputs['enc_dec_loss']
|
||||
else:
|
||||
strike = True
|
||||
if outputs['xd_loss'] < xd_lowest_loss:
|
||||
xd_lowest_loss = outputs['xd_loss']
|
||||
else:
|
||||
strike = True
|
||||
if outputs['zd_loss'] < zd_lowest_loss:
|
||||
zd_lowest_loss = outputs['zd_loss']
|
||||
else:
|
||||
strike = True
|
||||
|
||||
if strike and total_strike:
|
||||
grace_period -= 1
|
||||
elif strike:
|
||||
pass
|
||||
else:
|
||||
grace_period = GRACE
|
||||
|
||||
if grace_period == 0:
|
||||
break
|
||||
|
||||
if verbose:
|
||||
if grace_period > 0:
|
||||
print("Training finish!... save model weights")
|
||||
if grace_period == 0:
|
||||
print("Training stopped early!... save model weights")
|
||||
|
||||
# save trained models
|
||||
checkpoint.save(checkpoint_prefix)
|
||||
|
||||
|
||||
def _train_one_epoch(epoch: int,
|
||||
dataset: tf.data.Dataset,
|
||||
targets_real: tf.Tensor,
|
||||
verbose: bool,
|
||||
batch_size: int,
|
||||
targets_fake: tf.Tensor,
|
||||
z_generator: Callable[[], tf.Variable],
|
||||
learning_rate_var: tf.Variable,
|
||||
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,
|
||||
global_step: tf.Variable,
|
||||
global_step_xd: tf.Variable,
|
||||
global_step_zd: tf.Variable,
|
||||
global_step_decoder: tf.Variable,
|
||||
global_step_enc_dec: tf.Variable,
|
||||
epoch_var: tf.Variable) -> Dict[str, float]:
|
||||
with summary_ops_v2.always_record_summaries():
|
||||
epoch_var.assign(epoch)
|
||||
epoch_start_time = time.time()
|
||||
# define loss variables
|
||||
encoder_loss_avg = tfe.metrics.Mean(name='encoder_loss', dtype=tf.float32)
|
||||
decoder_loss_avg = tfe.metrics.Mean(name='decoder_loss', dtype=tf.float32)
|
||||
enc_dec_loss_avg = tfe.metrics.Mean(name='encoder_decoder_loss', dtype=tf.float32)
|
||||
zd_loss_avg = tfe.metrics.Mean(name='z_discriminator_loss', dtype=tf.float32)
|
||||
xd_loss_avg = tfe.metrics.Mean(name='x_discriminator_loss', dtype=tf.float32)
|
||||
|
||||
# update learning rate
|
||||
if (epoch + 1) % 30 == 0:
|
||||
learning_rate_var.assign(learning_rate_var.value() / 4)
|
||||
summary_ops_v2.scalar(name='learning_rate', tensor=learning_rate_var,
|
||||
step=global_step)
|
||||
if verbose:
|
||||
print("learning rate change!")
|
||||
|
||||
for x, _ in dataset:
|
||||
# x discriminator
|
||||
_xd_train_loss = _train_xdiscriminator_step(x_discriminator=x_discriminator,
|
||||
decoder=decoder,
|
||||
optimizer=x_discriminator_optimizer,
|
||||
inputs=x,
|
||||
targets_real=targets_real,
|
||||
targets_fake=targets_fake,
|
||||
global_step_xd=global_step_xd,
|
||||
global_step=global_step,
|
||||
z_generator=z_generator)
|
||||
xd_loss_avg(_xd_train_loss)
|
||||
|
||||
# --------
|
||||
# decoder
|
||||
_decoder_train_loss = _train_decoder_step(decoder=decoder,
|
||||
x_discriminator=x_discriminator,
|
||||
optimizer=decoder_optimizer,
|
||||
targets=targets_real,
|
||||
global_step_decoder=global_step_decoder,
|
||||
global_step=global_step,
|
||||
z_generator=z_generator)
|
||||
decoder_loss_avg(_decoder_train_loss)
|
||||
|
||||
# ---------
|
||||
# z discriminator
|
||||
_zd_train_loss = _train_zdiscriminator_step(z_discriminator=z_discriminator,
|
||||
encoder=encoder,
|
||||
optimizer=z_discriminator_optimizer,
|
||||
inputs=x,
|
||||
targets_real=targets_real,
|
||||
targets_fake=targets_fake,
|
||||
global_step_zd=global_step_zd,
|
||||
global_step=global_step,
|
||||
z_generator=z_generator)
|
||||
zd_loss_avg(_zd_train_loss)
|
||||
|
||||
# -----------
|
||||
# encoder + decoder
|
||||
encoder_loss, reconstruction_loss, x_decoded = _train_enc_dec_step(encoder=encoder,
|
||||
decoder=decoder,
|
||||
z_discriminator=z_discriminator,
|
||||
optimizer=enc_dec_optimizer,
|
||||
inputs=x,
|
||||
targets=targets_real,
|
||||
global_step_enc_dec=global_step_enc_dec,
|
||||
global_step=global_step)
|
||||
enc_dec_loss_avg(reconstruction_loss)
|
||||
encoder_loss_avg(encoder_loss)
|
||||
|
||||
if int(global_step % LOG_FREQUENCY) == 0:
|
||||
comparison = K.concatenate([x[:batch_size/2], x_decoded[:batch_size/2]], axis=0)
|
||||
grid = util.prepare_image(comparison.cpu(), nrow=int(batch_size/2))
|
||||
summary_ops_v2.image(name='reconstruction',
|
||||
tensor=K.expand_dims(grid, axis=0), max_images=1,
|
||||
step=global_step)
|
||||
global_step.assign_add(1)
|
||||
|
||||
epoch_end_time = time.time()
|
||||
per_epoch_time = epoch_end_time - epoch_start_time
|
||||
|
||||
# final losses of epoch
|
||||
outputs = {
|
||||
'decoder_loss': decoder_loss_avg.result(False),
|
||||
'encoder_loss': encoder_loss_avg.result(False),
|
||||
'enc_dec_loss': enc_dec_loss_avg.result(False),
|
||||
'xd_loss': xd_loss_avg.result(False),
|
||||
'zd_loss': zd_loss_avg.result(False),
|
||||
'per_epoch_time': per_epoch_time,
|
||||
}
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
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,
|
||||
global_step_xd: tf.Variable,
|
||||
z_generator: Callable[[], tf.Variable]) -> tf.Tensor:
|
||||
"""
|
||||
Trains the x discriminator model for one step (one batch).
|
||||
|
||||
:param x_discriminator: instance of x discriminator model
|
||||
:param decoder: instance of decoder model
|
||||
:param optimizer: instance of chosen optimizer
|
||||
:param inputs: inputs from dataset
|
||||
:param targets_real: target tensor for real loss calculation
|
||||
:param targets_fake: target tensor for fake loss calculation
|
||||
:param global_step: the global step variable
|
||||
:param global_step_xd: global step variable for xd
|
||||
:param z_generator: callable function that returns a z variable
|
||||
:return: the calculated loss
|
||||
"""
|
||||
with tf.GradientTape() as tape:
|
||||
xd_result_1 = tf.squeeze(x_discriminator(inputs))
|
||||
xd_real_loss = tf.losses.log_loss(targets_real, xd_result_1)
|
||||
|
||||
z = z_generator()
|
||||
x_fake = decoder(z)
|
||||
xd_result_2 = tf.squeeze(x_discriminator(x_fake))
|
||||
xd_fake_loss = tf.losses.log_loss(targets_fake, xd_result_2)
|
||||
|
||||
_xd_train_loss = xd_real_loss + xd_fake_loss
|
||||
|
||||
xd_grads = tape.gradient(_xd_train_loss, x_discriminator.trainable_variables)
|
||||
if int(global_step % LOG_FREQUENCY) == 0:
|
||||
summary_ops_v2.scalar(name='x_discriminator_real_loss', tensor=xd_real_loss,
|
||||
step=global_step)
|
||||
summary_ops_v2.scalar(name='x_discriminator_fake_loss', tensor=xd_fake_loss,
|
||||
step=global_step)
|
||||
summary_ops_v2.scalar(name='x_discriminator_loss', tensor=_xd_train_loss,
|
||||
step=global_step)
|
||||
for grad, variable in zip(xd_grads, x_discriminator.trainable_variables):
|
||||
summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
|
||||
step=global_step)
|
||||
summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
|
||||
step=global_step)
|
||||
optimizer.apply_gradients(zip(xd_grads, x_discriminator.trainable_variables),
|
||||
global_step=global_step_xd)
|
||||
|
||||
return _xd_train_loss
|
||||
|
||||
|
||||
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,
|
||||
z_generator: Callable[[], tf.Variable]) -> tf.Tensor:
|
||||
"""
|
||||
Trains the decoder model for one step (one batch).
|
||||
|
||||
:param decoder: instance of decoder model
|
||||
:param x_discriminator: instance of the x discriminator model
|
||||
:param optimizer: instance of chosen optimizer
|
||||
:param targets: target tensor for loss calculation
|
||||
:param global_step: the global step variable
|
||||
:param global_step_decoder: global step variable for decoder
|
||||
:param z_generator: callable function that returns a z variable
|
||||
:return: the calculated loss
|
||||
"""
|
||||
with tf.GradientTape() as tape:
|
||||
z = z_generator()
|
||||
|
||||
x_fake = decoder(z)
|
||||
xd_result = tf.squeeze(x_discriminator(x_fake))
|
||||
_decoder_train_loss = tf.losses.log_loss(targets, xd_result)
|
||||
|
||||
grads = tape.gradient(_decoder_train_loss, decoder.trainable_variables)
|
||||
if int(global_step % LOG_FREQUENCY) == 0:
|
||||
summary_ops_v2.scalar(name='decoder_loss', tensor=_decoder_train_loss,
|
||||
step=global_step)
|
||||
for grad, variable in zip(grads, decoder.trainable_variables):
|
||||
summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
|
||||
step=global_step)
|
||||
summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
|
||||
step=global_step)
|
||||
optimizer.apply_gradients(zip(grads, decoder.trainable_variables),
|
||||
global_step=global_step_decoder)
|
||||
|
||||
return _decoder_train_loss
|
||||
|
||||
|
||||
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,
|
||||
global_step_zd: tf.Variable,
|
||||
z_generator: Callable[[], tf.Variable]) -> tf.Tensor:
|
||||
"""
|
||||
Trains the z discriminator one step (one batch).
|
||||
|
||||
:param z_discriminator: instance of z discriminator model
|
||||
:param encoder: instance of encoder model
|
||||
:param optimizer: instance of chosen optimizer
|
||||
:param inputs: inputs from dataset
|
||||
:param targets_real: target tensor for real loss calculation
|
||||
:param targets_fake: target tensor for fake loss calculation
|
||||
:param global_step: the global step variable
|
||||
:param global_step_zd: global step variable for zd
|
||||
:param z_generator: callable function that returns a z variable
|
||||
:return: the calculated loss
|
||||
"""
|
||||
with tf.GradientTape() as tape:
|
||||
z = z_generator()
|
||||
|
||||
zd_result = tf.squeeze(z_discriminator(z))
|
||||
zd_real_loss = tf.losses.log_loss(targets_real, zd_result)
|
||||
|
||||
z = tf.squeeze(encoder(inputs))
|
||||
zd_result = tf.squeeze(z_discriminator(z))
|
||||
zd_fake_loss = tf.losses.log_loss(targets_fake, zd_result)
|
||||
|
||||
_zd_train_loss = zd_real_loss + zd_fake_loss
|
||||
|
||||
zd_grads = tape.gradient(_zd_train_loss, z_discriminator.trainable_variables)
|
||||
if int(global_step % LOG_FREQUENCY) == 0:
|
||||
summary_ops_v2.scalar(name='z_discriminator_real_loss', tensor=zd_real_loss,
|
||||
step=global_step)
|
||||
summary_ops_v2.scalar(name='z_discriminator_fake_loss', tensor=zd_fake_loss,
|
||||
step=global_step)
|
||||
summary_ops_v2.scalar(name='z_discriminator_loss', tensor=_zd_train_loss,
|
||||
step=global_step)
|
||||
for grad, variable in zip(zd_grads, z_discriminator.trainable_variables):
|
||||
summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
|
||||
step=global_step)
|
||||
summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
|
||||
step=global_step)
|
||||
optimizer.apply_gradients(zip(zd_grads, z_discriminator.trainable_variables),
|
||||
global_step=global_step_zd)
|
||||
|
||||
return _zd_train_loss
|
||||
|
||||
|
||||
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]:
|
||||
"""
|
||||
Trains the encoder and decoder jointly for one step (one batch).
|
||||
|
||||
:param encoder: instance of encoder model
|
||||
:param decoder: instance of decoder model
|
||||
:param z_discriminator: instance of z discriminator model
|
||||
:param optimizer: instance of chosen optimizer
|
||||
:param inputs: inputs from dataset
|
||||
:param targets: target tensor for loss calculation
|
||||
:param global_step: the global step variable
|
||||
:param global_step_enc_dec: global step variable for enc_dec
|
||||
:return: tuple of encoder loss, reconstruction loss, reconstructed input
|
||||
"""
|
||||
with tf.GradientTape() as tape:
|
||||
z = encoder(inputs)
|
||||
x_decoded = decoder(z)
|
||||
|
||||
zd_result = tf.squeeze(z_discriminator(tf.squeeze(z)))
|
||||
encoder_loss = tf.losses.log_loss(targets, zd_result) * 2.0
|
||||
reconstruction_loss = tf.losses.log_loss(inputs, x_decoded)
|
||||
_enc_dec_train_loss = encoder_loss + reconstruction_loss
|
||||
|
||||
enc_dec_grads = tape.gradient(_enc_dec_train_loss,
|
||||
encoder.trainable_variables + decoder.trainable_variables)
|
||||
if int(global_step % LOG_FREQUENCY) == 0:
|
||||
summary_ops_v2.scalar(name='encoder_loss', tensor=encoder_loss,
|
||||
step=global_step)
|
||||
summary_ops_v2.scalar(name='reconstruction_loss', tensor=reconstruction_loss,
|
||||
step=global_step)
|
||||
summary_ops_v2.scalar(name='encoder_decoder_loss', tensor=_enc_dec_train_loss,
|
||||
step=global_step)
|
||||
for grad, variable in zip(enc_dec_grads, encoder.trainable_variables + decoder.trainable_variables):
|
||||
summary_ops_v2.histogram(name='gradients/' + variable.name, tensor=tf.math.l2_normalize(grad),
|
||||
step=global_step)
|
||||
summary_ops_v2.histogram(name='variables/' + variable.name, tensor=tf.math.l2_normalize(variable),
|
||||
step=global_step)
|
||||
optimizer.apply_gradients(zip(enc_dec_grads,
|
||||
encoder.trainable_variables + decoder.trainable_variables),
|
||||
global_step=global_step_enc_dec)
|
||||
|
||||
return encoder_loss, reconstruction_loss, x_decoded
|
||||
|
||||
|
||||
def _get_z_variable(batch_size: int, zsize: int) -> tf.Variable:
|
||||
"""
|
||||
Creates and returns a z variable taken from a normal distribution.
|
||||
|
||||
:param batch_size: size of the batch
|
||||
: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)
|
||||
|
|
@ -1,172 +0,0 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Copyright 2019 Jim Martens
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
Utility functionality for visualizing predictions.
|
||||
|
||||
Functions:
|
||||
prepare_image(...): prepares a tensor to be visualized as an image
|
||||
|
||||
"""
|
||||
import math
|
||||
from typing import Sequence
|
||||
from typing import Tuple
|
||||
from typing import Union
|
||||
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
k = tf.keras.backend
|
||||
|
||||
|
||||
def prepare_image(tensor: Union[tf.Tensor, Sequence[tf.Tensor]], nrow: int = 8,
|
||||
padding: int = 2,
|
||||
normalize: bool = False, range_value: Tuple[float, float] = None,
|
||||
scale_each: bool = False, pad_value: float = 0.0) -> tf.Tensor:
|
||||
"""
|
||||
Prepares a tensor to be saved as image and returns it.
|
||||
|
||||
Args:
|
||||
tensor: Image to be saved.
|
||||
given a mini-batch tensor, saves the tensor as a grid of images by calling make_grid.
|
||||
nrow: Number of images displayed in each row of the grid.
|
||||
The Final grid size is (B / nrow, nrow). Default is 8.
|
||||
padding: amount of padding. Default is 2.
|
||||
normalize: If True, shift the image to the range (0, 1),
|
||||
by subtracting the minimum and dividing by the maximum pixel value.
|
||||
range_value: tuple (min, max) where min and max are numbers,
|
||||
then these numbers are used to normalize the image. By default, min and max
|
||||
are computed from the tensor.
|
||||
scale_each: If True, scale each image in the batch of
|
||||
images separately rather than the (min, max) over all images.
|
||||
pad_value: Value for the padded pixels.
|
||||
|
||||
Returns:
|
||||
the prepared tensor
|
||||
"""
|
||||
grid = _make_grid(tensor, nrow, padding, normalize, range_value,
|
||||
scale_each, pad_value)
|
||||
min_pixel_value = 0
|
||||
max_pixel_value = 255
|
||||
grid *= max_pixel_value
|
||||
grid = tf.clip_by_value(grid, min_pixel_value, max_pixel_value)
|
||||
grid = tf.cast(grid, tf.uint8)
|
||||
|
||||
return grid
|
||||
|
||||
|
||||
def _make_grid(tensor: Union[tf.Tensor, Sequence[tf.Tensor]], nrow: int = 8, padding: int = 2,
|
||||
normalize: bool = False, range_value: Tuple[float, float] = None,
|
||||
scale_each: bool = False, pad_value: float = 0.0) -> tf.Tensor:
|
||||
"""
|
||||
Make a grid of images.
|
||||
|
||||
Example:
|
||||
See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_
|
||||
|
||||
:param tensor: 4D mini-batch Tensor of shape (B x C x H x W)
|
||||
or a list of images all of the same size.
|
||||
:param nrow: Number of images displayed in each row of the grid.
|
||||
The Final grid size is (B / nrow, nrow). Default is 8.
|
||||
:param padding: amount of padding. Default is 2.
|
||||
:param normalize: If True, shift the image to the range (0, 1),
|
||||
by subtracting the minimum and dividing by the maximum pixel value.
|
||||
:param range_value: tuple (min, max) where min and max are numbers,
|
||||
then these numbers are used to normalize the image. By default, min and max
|
||||
are computed from the tensor.
|
||||
:param scale_each: If True, scale each image in the batch of
|
||||
images separately rather than the (min, max) over all images.
|
||||
:param pad_value: Value for the padded pixels.
|
||||
:return: tensor containing image grid
|
||||
"""
|
||||
if not (tf.contrib.framework.is_tensor(tensor) or
|
||||
(isinstance(tensor, list) and all(tf.contrib.framework.is_tensor(t) for t in tensor))):
|
||||
raise TypeError('tensor or list of tensors expected, got {}'.format(type(tensor)))
|
||||
|
||||
# if list of tensors, convert to a 4D mini-batch Tensor
|
||||
if isinstance(tensor, list):
|
||||
tensor = k.stack(tensor, axis=0)
|
||||
tensor_shape = tf.shape(tensor).numpy()
|
||||
tensor_rank = tf.rank(tensor).numpy()
|
||||
|
||||
if tensor_rank == 2: # single image H x W
|
||||
tensor = k.reshape(tensor, (tensor_shape[0], tensor_shape[1], 1))
|
||||
if tensor_rank == 3: # single image
|
||||
if tensor_shape[2] == 1: # if single-channel, convert to 3-channel
|
||||
tensor = k.concatenate((tensor, tensor, tensor), axis=2)
|
||||
tensor = k.reshape(tensor, (1, tensor_shape[0], tensor_shape[1], tensor_shape[2]))
|
||||
|
||||
if tensor_rank == 4 and tensor_shape[3] == 1: # single-channel images
|
||||
tensor = k.concatenate((tensor, tensor, tensor), axis=3)
|
||||
|
||||
if normalize is True:
|
||||
if range_value is not None:
|
||||
assert isinstance(range_value, tuple), \
|
||||
"range_value has to be a tuple (min, max) if specified. min and max are numbers"
|
||||
|
||||
def norm_ip(img: tf.Tensor, min_v: float, max_v: float) -> tf.Tensor:
|
||||
"""
|
||||
Internal function to clip given tensor to given min and max values.
|
||||
:param img: tensor to be clipped
|
||||
:param min_v: min value
|
||||
:param max_v: max value
|
||||
:return: clipped tensor
|
||||
"""
|
||||
img = tf.clip_by_value(img, min_v, max_v)
|
||||
img = tf.add(img, -min_v)
|
||||
return tf.divide(img, max_v - min_v + 1e-5)
|
||||
|
||||
def norm_range(t: tf.Tensor, range_v: Tuple[float, float] = None) -> tf.Tensor:
|
||||
"""
|
||||
Internal function to normalize a tensor to a given range.
|
||||
:param t: tensor to be normalized
|
||||
:param range_v: tuple with (min, max) range values
|
||||
:return: normalized tensor
|
||||
"""
|
||||
if range_v is not None:
|
||||
return norm_ip(t, range_v[0], range_v[1])
|
||||
else:
|
||||
return norm_ip(t, float(k.min(t)), float(k.max(t)))
|
||||
|
||||
if scale_each is True:
|
||||
updated_tensors = []
|
||||
for t in tensor: # loop over mini-batch dimension
|
||||
updated_tensors.append(norm_range(t, range_value))
|
||||
tensor = k.constant(np.array(updated_tensors))
|
||||
else:
|
||||
tensor = norm_range(tensor, range_value)
|
||||
|
||||
if tensor_shape[0] == 1:
|
||||
return tf.squeeze(tensor)
|
||||
|
||||
# make the mini-batch of images into a grid
|
||||
nmaps = tensor_shape[0]
|
||||
xmaps = min(nrow, nmaps)
|
||||
ymaps = int(math.ceil(float(nmaps) / xmaps))
|
||||
height, width = int(tensor_shape[1] + padding), int(tensor_shape[2] + padding)
|
||||
grid = tf.fill((height * ymaps + padding, width * xmaps + padding, 3), pad_value).numpy()
|
||||
tensor_numpy = tensor.numpy()
|
||||
i = 0
|
||||
for y in range(ymaps):
|
||||
for x in range(xmaps):
|
||||
if i >= nmaps:
|
||||
break
|
||||
start_height = y * height + padding
|
||||
start_width = x * width + padding
|
||||
np.copyto(grid[start_height: start_height + height - padding,
|
||||
start_width:start_width + width - padding], tensor_numpy[i, :, :, :])
|
||||
i = i + 1
|
||||
return k.constant(grid)
|
||||
|
|
@ -69,14 +69,12 @@ def prepare(args: argparse.Namespace) -> None:
|
|||
|
||||
|
||||
def train(args: argparse.Namespace) -> None:
|
||||
_train_execute_action(args, _ssd_train, _auto_encoder_train)
|
||||
_train_execute_action(args, _ssd_train)
|
||||
|
||||
|
||||
def test(args: argparse.Namespace) -> None:
|
||||
if args.network == "ssd" or args.network == "bayesian_ssd":
|
||||
_ssd_test(args)
|
||||
elif args.network == "auto_encoder":
|
||||
_auto_encoder_test(args)
|
||||
|
||||
|
||||
def evaluate(args: argparse.Namespace) -> None:
|
||||
|
|
@ -152,11 +150,9 @@ def _config_execute_action(args: argparse.Namespace, on_get: callable,
|
|||
on_list()
|
||||
|
||||
|
||||
def _train_execute_action(args: argparse.Namespace, on_ssd: callable, on_auto_encoder: callable) -> None:
|
||||
def _train_execute_action(args: argparse.Namespace, on_ssd: callable) -> None:
|
||||
if args.network == "ssd" or args.network == "bayesian_ssd":
|
||||
on_ssd(args)
|
||||
elif args.network == "auto_encoder":
|
||||
on_auto_encoder(args)
|
||||
|
||||
|
||||
def _ssd_train(args: argparse.Namespace) -> None:
|
||||
|
|
@ -1060,81 +1056,3 @@ def _visualise_ose_f1(open_set_error: np.ndarray, f1_scores: np.ndarray,
|
|||
|
||||
pyplot.savefig(f"{output_path}/ose-f1-{file_suffix}.png")
|
||||
pyplot.close(figure)
|
||||
|
||||
|
||||
def _auto_encoder_train(args: argparse.Namespace) -> None:
|
||||
import os
|
||||
|
||||
from tensorflow.python.ops import summary_ops_v2
|
||||
|
||||
from twomartens.masterthesis import data
|
||||
from twomartens.masterthesis.aae import train
|
||||
|
||||
tf.enable_eager_execution()
|
||||
coco_path = args.coco_path
|
||||
category = args.category
|
||||
batch_size = 16
|
||||
image_size = 256
|
||||
coco_data = data.load_coco_train(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size,
|
||||
resized_shape=(image_size, image_size))
|
||||
summary_path = conf.get_property("Paths.summary")
|
||||
summary_path = f"{summary_path}/{args.network}/train/category-{category}/{args.iteration}"
|
||||
train_summary_writer = summary_ops_v2.create_file_writer(
|
||||
summary_path
|
||||
)
|
||||
os.makedirs(summary_path, exist_ok=True)
|
||||
|
||||
weights_path = conf.get_property("Paths.weights")
|
||||
weights_path = f"{weights_path}/{args.network}/category-{category}"
|
||||
os.makedirs(weights_path, exist_ok=True)
|
||||
if args.debug:
|
||||
with train_summary_writer.as_default():
|
||||
train.train_simple(coco_data, iteration=args.iteration,
|
||||
weights_prefix=weights_path,
|
||||
zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size,
|
||||
channels=3, train_epoch=args.num_epochs, batch_size=batch_size)
|
||||
else:
|
||||
train.train_simple(coco_data, iteration=args.iteration,
|
||||
weights_prefix=weights_path,
|
||||
zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size,
|
||||
channels=3, train_epoch=args.num_epochs, batch_size=batch_size)
|
||||
|
||||
|
||||
def _auto_encoder_test(args: argparse.Namespace) -> None:
|
||||
import os
|
||||
|
||||
from tensorflow.python.ops import summary_ops_v2
|
||||
|
||||
from twomartens.masterthesis import data
|
||||
from twomartens.masterthesis.aae import run
|
||||
|
||||
tf.enable_eager_execution()
|
||||
coco_path = conf.get_property("Paths.coco")
|
||||
category = args.category
|
||||
category_trained = args.category_trained
|
||||
batch_size = 16
|
||||
image_size = 256
|
||||
coco_data = data.load_coco_val(coco_path, category, num_epochs=1,
|
||||
batch_size=batch_size, resized_shape=(image_size, image_size))
|
||||
|
||||
summary_path = conf.get_property("Paths.summary")
|
||||
summary_path = f"{summary_path}/{args.network}/val/category-{category}/{args.iteration}"
|
||||
os.makedirs(summary_path, exist_ok=True)
|
||||
use_summary_writer = summary_ops_v2.create_file_writer(
|
||||
summary_path
|
||||
)
|
||||
|
||||
weights_path = conf.get_property("Paths.weights")
|
||||
weights_path = f"{weights_path}/{args.network}/category-{category_trained}"
|
||||
os.makedirs(weights_path, exist_ok=True)
|
||||
if args.debug:
|
||||
with use_summary_writer.as_default():
|
||||
run.run_simple(coco_data, iteration=args.iteration_trained,
|
||||
weights_prefix=weights_path,
|
||||
zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
|
||||
image_size=image_size)
|
||||
else:
|
||||
run.run_simple(coco_data, iteration=args.iteration_trained,
|
||||
weights_prefix=weights_path,
|
||||
zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
|
||||
image_size=image_size)
|
||||
|
|
|
|||
|
|
@ -155,13 +155,9 @@ def _build_train(parser: argparse.ArgumentParser) -> None:
|
|||
sub_parsers.required = True
|
||||
|
||||
ssd_parser = sub_parsers.add_parser("ssd", help="SSD")
|
||||
# ssd_bayesian_parser = sub_parsers.add_parser("bayesian_ssd", help="SSD with dropout layers")
|
||||
auto_encoder_parser = sub_parsers.add_parser("auto_encoder", help="Auto-encoder network")
|
||||
|
||||
# build sub parsers
|
||||
_build_ssd_train(ssd_parser)
|
||||
# _build_bayesian_ssd(ssd_bayesian_parser)
|
||||
_build_auto_encoder_train(auto_encoder_parser)
|
||||
|
||||
|
||||
def _build_ssd_train(parser: argparse.ArgumentParser) -> None:
|
||||
|
|
@ -169,36 +165,21 @@ def _build_ssd_train(parser: argparse.ArgumentParser) -> None:
|
|||
parser.add_argument("iteration", type=int, help="the training iteration")
|
||||
|
||||
|
||||
def _build_auto_encoder_train(parser: argparse.ArgumentParser) -> None:
|
||||
parser.add_argument("category", type=int, help="the COCO category to use")
|
||||
parser.add_argument("num_epochs", type=int, help="the number of epochs to train", default=80)
|
||||
parser.add_argument("iteration", type=int, help="the training iteration")
|
||||
|
||||
|
||||
def _build_test(parser: argparse.ArgumentParser) -> None:
|
||||
sub_parsers = parser.add_subparsers(dest="network")
|
||||
sub_parsers.required = True
|
||||
|
||||
ssd_bayesian_parser = sub_parsers.add_parser("bayesian_ssd", help="SSD with dropout layers")
|
||||
ssd_parser = sub_parsers.add_parser("ssd", help="SSD")
|
||||
auto_encoder_parser = sub_parsers.add_parser("auto_encoder", help="Auto-encoder network")
|
||||
|
||||
# build sub parsers
|
||||
_build_ssd_test(ssd_bayesian_parser)
|
||||
_build_ssd_test(ssd_parser)
|
||||
_build_auto_encoder_test(auto_encoder_parser)
|
||||
|
||||
|
||||
def _build_ssd_test(parser: argparse.ArgumentParser) -> None:
|
||||
parser.add_argument("iteration", type=int, help="the validation iteration")
|
||||
parser.add_argument("train_iteration", type=int, help="the train iteration")
|
||||
|
||||
|
||||
def _build_auto_encoder_test(parser: argparse.ArgumentParser) -> None:
|
||||
parser.add_argument("category", type=int, help="the COCO category to validate")
|
||||
parser.add_argument("category_trained", type=int, help="the trained COCO category")
|
||||
parser.add_argument("iteration", type=int, help="the validation iteration")
|
||||
parser.add_argument("iteration_trained", type=int, help="the training iteration")
|
||||
|
||||
|
||||
def _build_evaluate(parser: argparse.ArgumentParser) -> None:
|
||||
|
|
@ -228,11 +209,9 @@ def _build_visualise_metrics(parser: argparse.ArgumentParser) -> None:
|
|||
|
||||
ssd_bayesian_parser = sub_parsers.add_parser("bayesian_ssd", help="SSD with dropout layers")
|
||||
ssd_parser = sub_parsers.add_parser("ssd", help="SSD")
|
||||
auto_encoder_parser = sub_parsers.add_parser("auto_encoder", help="Auto-encoder network")
|
||||
|
||||
ssd_bayesian_parser.add_argument("iteration", type=int, help="the validation iteration to use")
|
||||
ssd_parser.add_argument("iteration", type=int, help="the validation iteration to use")
|
||||
auto_encoder_parser.add_argument("iteration", type=int, help="the validation iteration to use")
|
||||
|
||||
|
||||
def _build_measure(parser: argparse.ArgumentParser) -> None:
|
||||
|
|
|
|||
Loading…
Reference in New Issue