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video prediction model code

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video_prediction/README.md 0 → 100644
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# Video Prediction with Neural Advection
*A TensorFlow implementation of the models described in [Finn et al. (2016)]
(http://arxiv.org/abs/1605.07157).*
This video prediction model, which is optionally conditioned on actions,
predictions future video by internally predicting how to transform the last
image (which may have been predicted) into the next image. As a result, it can
reuse apperance information from previous frames and can better generalize to
objects not seen in the training set. Some example predictions on novel objects
are shown below:
![Animation](https://storage.googleapis.com/push_gens/novelgengifs9/16_70.gif)
![Animation](https://storage.googleapis.com/push_gens/novelgengifs9/2_96.gif)
![Animation](https://storage.googleapis.com/push_gens/novelgengifs9/1_38.gif)
![Animation](https://storage.googleapis.com/push_gens/novelgengifs9/11_10.gif)
![Animation](https://storage.googleapis.com/push_gens/novelgengifs9/3_34.gif)
When the model is conditioned on actions, it changes it's predictions based on
the passed in action. Here we show the models predictions in response to varying
the magnitude of the passed in actions, from small to large:
![Animation](https://storage.googleapis.com/push_gens/webgifs/0xact_0.gif)
![Animation](https://storage.googleapis.com/push_gens/05xact_0.gif)
![Animation](https://storage.googleapis.com/push_gens/webgifs/1xact_0.gif)
![Animation](https://storage.googleapis.com/push_gens/webgifs/15xact_0.gif)
![Animation](https://storage.googleapis.com/push_gens/webgifs/0xact_17.gif)
![Animation](https://storage.googleapis.com/push_gens/webgifs/05xact_17.gif)
![Animation](https://storage.googleapis.com/push_gens/webgifs/1xact_17.gif)
![Animation](https://storage.googleapis.com/push_gens/webgifs/15xact_17.gif)
Because the model is trained with an l2 objective, it represents uncertainty as
blur.
## Requirements
* Tensorflow (see tensorflow.org for installation instructions)
* spatial_tranformer model in tensorflow/models, for the spatial tranformer
predictor (STP).
## Data
The data used to train this model is located
[here](https://sites.google.com/site/brainrobotdata/home/push-dataset).
To download the robot data, run the following.
```shell
./download_data.sh
```
## Training the model
To train the model, run the prediction_train.py file.
```shell
python prediction_train.py
```
There are several flags which can control the model that is trained, which are
exeplified below:
```shell
python prediction_train.py \
--data_dir=push/push_train \ # path to the training set.
--model=CDNA \ # the model type to use - DNA, CDNA, or STP
--output_dir=./checkpoints \ # where to save model checkpoints
--event_log_dir=./summaries \ # where to save training statistics
--num_iterations=100000 \ # number of training iterations
--pretrained_model=model \ # path to model to initialize from, random if emtpy
--sequence_length=10 \ # the number of total frames in a sequence
--context_frames=2 \ # the number of ground truth frames to pass in at start
--use_state=1 \ # whether or not to condition on actions and the initial state
--num_masks=10 \ # the number of transformations and corresponding masks
--schedsamp_k=900.0 \ # the constant used for scheduled sampling or -1
--train_val_split=0.95 \ # the percentage of training data for validation
--batch_size=32 \ # the training batch size
--learning_rate=0.001 \ # the initial learning rate for the Adam optimizer
```
If the dynamic neural advection (DNA) model is being used, the `--num_masks`
option should be set to one.
The `--context_frames` option defines both the number of initial ground truth
frames to pass in, as well as when to start penalizing the model's predictions.
The data directory `--data_dir` should contain tfrecord files with the format
used in the released push dataset. See
[here](https://sites.google.com/site/brainrobotdata/home/push-dataset) for
details. If the `--use_state` option is not set, then the data only needs to
contain image sequences, not states and actions.
## Contact
To ask questions or report issues please open an issue on the tensorflow/models
[issues tracker](https://github.com/tensorflow/models/issues).
Please assign issues to @cbfinn.
## Credits
This code was written by Chelsea Finn.
video_prediction/download_data.sh 0 → 100755
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#!/bin/bash
# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# 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.
# ==============================================================================
# Example:
#
# download_dataset.sh datafiles.txt ./tmp
#
# will download all of the files listed in the file, datafiles.txt, into
# a directory, "./tmp".
#
# Each line of the datafiles.txt file should contain the path from the
# bucket root to a file.
ARGC="$#"
LISTING_FILE=push_datafiles.txt
if [ "${ARGC}" -ge 1 ]; then
LISTING_FILE=$1
fi
OUTPUT_DIR="./"
if [ "${ARGC}" -ge 2 ]; then
OUTPUT_DIR=$2
fi
echo "OUTPUT_DIR=$OUTPUT_DIR"
mkdir "${OUTPUT_DIR}"
function download_file {
FILE=$1
BUCKET="https://storage.googleapis.com/brain-robotics-data"
URL="${BUCKET}/${FILE}"
OUTPUT_FILE="${OUTPUT_DIR}/${FILE}"
DIRECTORY=`dirname ${OUTPUT_FILE}`
echo DIRECTORY=$DIRECTORY
mkdir -p "${DIRECTORY}"
curl --output ${OUTPUT_FILE} ${URL}
}
while read filename; do
download_file $filename
done <${LISTING_FILE}
video_prediction/lstm_ops.py 0 → 100644
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# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# 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.
# ==============================================================================
"""Convolutional LSTM implementation."""
import tensorflow as tf
from tensorflow.contrib.slim import add_arg_scope
from tensorflow.contrib.slim import layers
def init_state(inputs,
state_shape,
state_initializer=tf.zeros_initializer,
dtype=tf.float32):
"""Helper function to create an initial state given inputs.
Args:
inputs: input Tensor, at least 2D, the first dimension being batch_size
state_shape: the shape of the state.
state_initializer: Initializer(shape, dtype) for state Tensor.
dtype: Optional dtype, needed when inputs is None.
Returns:
A tensors representing the initial state.
"""
if inputs is not None:
# Handle both the dynamic shape as well as the inferred shape.
inferred_batch_size = inputs.get_shape().with_rank_at_least(1)[0]
batch_size = tf.shape(inputs)[0]
dtype = inputs.dtype
else:
inferred_batch_size = 0
batch_size = 0
initial_state = state_initializer(
tf.pack([batch_size] + state_shape),
dtype=dtype)
initial_state.set_shape([inferred_batch_size] + state_shape)
return initial_state
@add_arg_scope
def basic_conv_lstm_cell(inputs,
state,
num_channels,
filter_size=5,
forget_bias=1.0,
scope=None,
reuse=None):
"""Basic LSTM recurrent network cell, with 2D convolution connctions.
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.
Args:
inputs: input Tensor, 4D, batch x height x width x channels.
state: state Tensor, 4D, batch x height x width x channels.
num_channels: the number of output channels in the layer.
filter_size: the shape of the each convolution filter.
forget_bias: the initial value of the forget biases.
scope: Optional scope for variable_scope.
reuse: whether or not the layer and the variables should be reused.
Returns:
a tuple of tensors representing output and the new state.
"""
spatial_size = inputs.get_shape()[1:3]
if state is None:
state = init_state(inputs, list(spatial_size) + [2 * num_channels])
with tf.variable_scope(scope,
'BasicConvLstmCell',
[inputs, state],
reuse=reuse):
inputs.get_shape().assert_has_rank(4)
state.get_shape().assert_has_rank(4)
c, h = tf.split(3, 2, state)
inputs_h = tf.concat(3, [inputs, h])
# Parameters of gates are concatenated into one conv for efficiency.
i_j_f_o = layers.conv2d(inputs_h,
4 * num_channels, [filter_size, filter_size],
stride=1,
activation_fn=None,
scope='Gates')
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = tf.split(3, 4, i_j_f_o)
new_c = c * tf.sigmoid(f + forget_bias) + tf.sigmoid(i) * tf.tanh(j)
new_h = tf.tanh(new_c) * tf.sigmoid(o)
return new_h, tf.concat(3, [new_c, new_h])
video_prediction/prediction_input.py 0 → 100644
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# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# 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.
# ==============================================================================
"""Code for building the input for the prediction model."""
import os
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import flags
from tensorflow.python.platform import gfile
FLAGS = flags.FLAGS
# Original image dimensions
ORIGINAL_WIDTH = 640
ORIGINAL_HEIGHT = 512
COLOR_CHAN = 3
# Default image dimensions.
IMG_WIDTH = 64
IMG_HEIGHT = 64
# Dimension of the state and action.
STATE_DIM = 5
def build_tfrecord_input(training=True):
"""Create input tfrecord tensors.
Args:
training: training or validation data.
Returns:
list of tensors corresponding to images, actions, and states. The images
tensor is 5D, batch x time x height x width x channels. The state and
action tensors are 3D, batch x time x dimension.
Raises:
RuntimeError: if no files found.
"""
filenames = gfile.Glob(os.path.join(FLAGS.data_dir, '*'))
if not filenames:
raise RuntimeError('No data files found.')
index = int(np.floor(FLAGS.train_val_split * len(filenames)))
if training:
filenames = filenames[:index]
else:
filenames = filenames[index:]
filename_queue = tf.train.string_input_producer(filenames, shuffle=True)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
image_seq, state_seq, action_seq = [], [], []
for i in range(FLAGS.sequence_length):
image_name = 'move/' + str(i) + '/image/encoded'
action_name = 'move/' + str(i) + '/commanded_pose/vec_pitch_yaw'
state_name = 'move/' + str(i) + '/endeffector/vec_pitch_yaw'
if FLAGS.use_state:
features = {image_name: tf.FixedLenFeature([1], tf.string),
action_name: tf.FixedLenFeature([STATE_DIM], tf.float32),
state_name: tf.FixedLenFeature([STATE_DIM], tf.float32)}
else:
features = {image_name: tf.FixedLenFeature([1], tf.string)}
features = tf.parse_single_example(serialized_example, features=features)
image_buffer = tf.reshape(features[image_name], shape=[])
image = tf.image.decode_jpeg(image_buffer, channels=COLOR_CHAN)
image.set_shape([ORIGINAL_HEIGHT, ORIGINAL_WIDTH, COLOR_CHAN])
if IMG_HEIGHT != IMG_WIDTH:
raise ValueError('Unequal height and width unsupported')
crop_size = min(ORIGINAL_HEIGHT, ORIGINAL_WIDTH)
image = tf.image.resize_image_with_crop_or_pad(image, crop_size, crop_size)
image = tf.reshape(image, [1, crop_size, crop_size, COLOR_CHAN])
image = tf.image.resize_bicubic(image, [IMG_HEIGHT, IMG_WIDTH])
image = tf.cast(image, tf.float32) / 255.0
image_seq.append(image)
if FLAGS.use_state:
state = tf.reshape(features[state_name], shape=[1, STATE_DIM])
state_seq.append(state)
action = tf.reshape(features[action_name], shape=[1, STATE_DIM])
action_seq.append(action)
image_seq = tf.concat(0, image_seq)
if FLAGS.use_state:
state_seq = tf.concat(0, state_seq)
action_seq = tf.concat(0, action_seq)
[image_batch, action_batch, state_batch] = tf.train.batch(
[image_seq, action_seq, state_seq],
FLAGS.batch_size,
num_threads=FLAGS.batch_size,
capacity=100 * FLAGS.batch_size)
return image_batch, action_batch, state_batch
else:
image_batch = tf.train.batch(
[image_seq],
FLAGS.batch_size,
num_threads=FLAGS.batch_size,
capacity=100 * FLAGS.batch_size)
zeros_batch = tf.zeros([FLAGS.batch_size, FLAGS.sequence_length, STATE_DIM])
return image_batch, zeros_batch, zeros_batch
video_prediction/prediction_model.py 0 → 100644
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# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# 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.
# ==============================================================================
"""Model architecture for predictive model, including CDNA, DNA, and STP."""
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.layers.python import layers as tf_layers
from lstm_ops import basic_conv_lstm_cell
# Amount to use when lower bounding tensors
RELU_SHIFT = 1e-12
# kernel size for DNA and CDNA.
DNA_KERN_SIZE = 5
def construct_model(images,
actions=None,
states=None,
iter_num=-1.0,
k=-1,
use_state=True,
num_masks=10,
stp=False,
cdna=True,
dna=False,
context_frames=2):
"""Build convolutional lstm video predictor using STP, CDNA, or DNA.
Args:
images: tensor of ground truth image sequences
actions: tensor of action sequences
states: tensor of ground truth state sequences
iter_num: tensor of the current training iteration (for sched. sampling)
k: constant used for scheduled sampling. -1 to feed in own prediction.
use_state: True to include state and action in prediction
num_masks: the number of different pixel motion predictions (and
the number of masks for each of those predictions)
stp: True to use Spatial Transformer Predictor (STP)
cdna: True to use Convoluational Dynamic Neural Advection (CDNA)
dna: True to use Dynamic Neural Advection (DNA)
context_frames: number of ground truth frames to pass in before
feeding in own predictions
Returns:
gen_images: predicted future image frames
gen_states: predicted future states
Raises:
ValueError: if more than one network option specified or more than 1 mask
specified for DNA model.
"""
if stp + cdna + dna != 1:
raise ValueError('More than one, or no network option specified.')
batch_size, img_height, img_width, color_channels = images[0].get_shape()[0:4]
lstm_func = basic_conv_lstm_cell
# Generated robot states and images.
gen_states, gen_images = [], []
current_state = states[0]
if k == -1:
feedself = True
else:
# Scheduled sampling:
# Calculate number of ground-truth frames to pass in.
num_ground_truth = tf.to_int32(
tf.round(tf.to_float(batch_size) * (k / (k + tf.exp(iter_num / k)))))
feedself = False
# LSTM state sizes and states.
lstm_size = np.int32(np.array([32, 32, 64, 64, 128, 64, 32]))
lstm_state1, lstm_state2, lstm_state3, lstm_state4 = None, None, None, None
lstm_state5, lstm_state6, lstm_state7 = None, None, None
for image, action in zip(images[:-1], actions[:-1]):
# Reuse variables after the first timestep.
reuse = bool(gen_images)
done_warm_start = len(gen_images) > context_frames - 1
with slim.arg_scope(
[lstm_func, slim.layers.conv2d, slim.layers.fully_connected,
tf_layers.layer_norm, slim.layers.conv2d_transpose],
reuse=reuse):
if feedself and done_warm_start:
# Feed in generated image.
prev_image = gen_images[-1]
elif done_warm_start:
# Scheduled sampling
prev_image = scheduled_sample(image, gen_images[-1], batch_size,
num_ground_truth)
else:
# Always feed in ground_truth
prev_image = image
# Predicted state is always fed back in
state_action = tf.concat(1, [action, current_state])
enc0 = slim.layers.conv2d(
prev_image,
32, [5, 5],
stride=2,
scope='scale1_conv1',
normalizer_fn=tf_layers.layer_norm,
normalizer_params={'scope': 'layer_norm1'})
hidden1, lstm_state1 = lstm_func(
enc0, lstm_state1, lstm_size[0], scope='state1')
hidden1 = tf_layers.layer_norm(hidden1, scope='layer_norm2')
hidden2, lstm_state2 = lstm_func(
hidden1, lstm_state2, lstm_size[1], scope='state2')
hidden2 = tf_layers.layer_norm(hidden2, scope='layer_norm3')
enc1 = slim.layers.conv2d(
hidden2, hidden2.get_shape()[3], [3, 3], stride=2, scope='conv2')
hidden3, lstm_state3 = lstm_func(
enc1, lstm_state3, lstm_size[2], scope='state3')
hidden3 = tf_layers.layer_norm(hidden3, scope='layer_norm4')
hidden4, lstm_state4 = lstm_func(
hidden3, lstm_state4, lstm_size[3], scope='state4')
hidden4 = tf_layers.layer_norm(hidden4, scope='layer_norm5')
enc2 = slim.layers.conv2d(
hidden4, hidden4.get_shape()[3], [3, 3], stride=2, scope='conv3')
# Pass in state and action.
smear = tf.reshape(
state_action,
[int(batch_size), 1, 1, int(state_action.get_shape()[1])])
smear = tf.tile(
smear, [1, int(enc2.get_shape()[1]), int(enc2.get_shape()[2]), 1])
if use_state:
enc2 = tf.concat(3, [enc2, smear])
enc3 = slim.layers.conv2d(
enc2, hidden4.get_shape()[3], [1, 1], stride=1, scope='conv4')
hidden5, lstm_state5 = lstm_func(
enc3, lstm_state5, lstm_size[4], scope='state5') # last 8x8
hidden5 = tf_layers.layer_norm(hidden5, scope='layer_norm6')
enc4 = slim.layers.conv2d_transpose(
hidden5, hidden5.get_shape()[3], 3, stride=2, scope='convt1')
hidden6, lstm_state6 = lstm_func(
enc4, lstm_state6, lstm_size[5], scope='state6') # 16x16
hidden6 = tf_layers.layer_norm(hidden6, scope='layer_norm7')
# Skip connection.
hidden6 = tf.concat(3, [hidden6, enc1]) # both 16x16
enc5 = slim.layers.conv2d_transpose(
hidden6, hidden6.get_shape()[3], 3, stride=2, scope='convt2')
hidden7, lstm_state7 = lstm_func(
enc5, lstm_state7, lstm_size[6], scope='state7') # 32x32
hidden7 = tf_layers.layer_norm(hidden7, scope='layer_norm8')
# Skip connection.
hidden7 = tf.concat(3, [hidden7, enc0]) # both 32x32
enc6 = slim.layers.conv2d_transpose(
hidden7,
hidden7.get_shape()[3], 3, stride=2, scope='convt3',
normalizer_fn=tf_layers.layer_norm,
normalizer_params={'scope': 'layer_norm9'})
if dna:
# Using largest hidden state for predicting untied conv kernels.
enc7 = slim.layers.conv2d_transpose(
enc6, DNA_KERN_SIZE**2, 1, stride=1, scope='convt4')
else:
# Using largest hidden state for predicting a new image layer.
enc7 = slim.layers.conv2d_transpose(
enc6, color_channels, 1, stride=1, scope='convt4')
# This allows the network to also generate one image from scratch,
# which is useful when regions of the image become unoccluded.
transformed = [tf.nn.sigmoid(enc7)]
if stp:
stp_input0 = tf.reshape(hidden5, [int(batch_size), -1])
stp_input1 = slim.layers.fully_connected(
stp_input0, 100, scope='fc_stp')
transformed += stp_transformation(prev_image, stp_input1, num_masks)
elif cdna:
cdna_input = tf.reshape(hidden5, [int(batch_size), -1])
transformed += cdna_transformation(prev_image, cdna_input, num_masks,
int(color_channels))
elif dna:
# Only one mask is supported (more should be unnecessary).
if num_masks != 1:
raise ValueError('Only one mask is supported for DNA model.')
transformed = [dna_transformation(prev_image, enc7)]
masks = slim.layers.conv2d_transpose(
enc6, num_masks + 1, 1, stride=1, scope='convt7')
masks = tf.reshape(
tf.nn.softmax(tf.reshape(masks, [-1, num_masks + 1])),
[int(batch_size), int(img_height), int(img_width), num_masks + 1])
mask_list = tf.split(3, num_masks + 1, masks)
output = mask_list[0] * prev_image
for layer, mask in zip(transformed, mask_list[1:]):
output += layer * mask
gen_images.append(output)
current_state = slim.layers.fully_connected(
state_action,
int(current_state.get_shape()[1]),
scope='state_pred',
activation_fn=None)
gen_states.append(current_state)
return gen_images, gen_states
## Utility functions
def stp_transformation(prev_image, stp_input, num_masks):
"""Apply spatial transformer predictor (STP) to previous image.
Args:
prev_image: previous image to be transformed.
stp_input: hidden layer to be used for computing STN parameters.
num_masks: number of masks and hence the number of STP transformations.
Returns:
List of images transformed by the predicted STP parameters.
"""
# Only import spatial transformer if needed.
from spatial_transformer import transformer
identity_params = tf.convert_to_tensor(
np.array([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], np.float32))
transformed = []
for i in range(num_masks - 1):
params = slim.layers.fully_connected(
stp_input, 6, scope='stp_params' + str(i),
activation_fn=None) + identity_params
transformed.append(transformer(prev_image, params))
return transformed
def cdna_transformation(prev_image, cdna_input, num_masks, color_channels):
"""Apply convolutional dynamic neural advection to previous image.
Args:
prev_image: previous image to be transformed.
cdna_input: hidden lyaer to be used for computing CDNA kernels.
num_masks: the number of masks and hence the number of CDNA transformations.
color_channels: the number of color channels in the images.
Returns:
List of images transformed by the predicted CDNA kernels.
"""
batch_size = int(cdna_input.get_shape()[0])
# Predict kernels using linear function of last hidden layer.
cdna_kerns = slim.layers.fully_connected(
cdna_input,
DNA_KERN_SIZE * DNA_KERN_SIZE * num_masks,
scope='cdna_params',
activation_fn=None)
# Reshape and normalize.
cdna_kerns = tf.reshape(
cdna_kerns, [batch_size, DNA_KERN_SIZE, DNA_KERN_SIZE, 1, num_masks])
cdna_kerns = tf.nn.relu(cdna_kerns - RELU_SHIFT) + RELU_SHIFT
norm_factor = tf.reduce_sum(cdna_kerns, [1, 2, 3], keep_dims=True)
cdna_kerns /= norm_factor
cdna_kerns = tf.tile(cdna_kerns, [1, 1, 1, color_channels, 1])
cdna_kerns = tf.split(0, batch_size, cdna_kerns)
prev_images = tf.split(0, batch_size, prev_image)
# Transform image.
transformed = []
for kernel, preimg in zip(cdna_kerns, prev_images):
kernel = tf.squeeze(kernel)
if len(kernel.get_shape()) == 3:
kernel = tf.expand_dims(kernel, -1)
transformed.append(
tf.nn.depthwise_conv2d(preimg, kernel, [1, 1, 1, 1], 'SAME'))
transformed = tf.concat(0, transformed)
transformed = tf.split(3, num_masks, transformed)
return transformed
def dna_transformation(prev_image, dna_input):
"""Apply dynamic neural advection to previous image.
Args:
prev_image: previous image to be transformed.
dna_input: hidden lyaer to be used for computing DNA transformation.
Returns:
List of images transformed by the predicted CDNA kernels.
"""
# Construct translated images.
prev_image_pad = tf.pad(prev_image, [[0, 0], [2, 2], [2, 2], [0, 0]])
image_height = int(prev_image.get_shape()[1])
image_width = int(prev_image.get_shape()[2])
inputs = []
for xkern in range(DNA_KERN_SIZE):
for ykern in range(DNA_KERN_SIZE):
inputs.append(
tf.expand_dims(
tf.slice(prev_image_pad, [0, xkern, ykern, 0],
[-1, image_height, image_width, -1]), [3]))
inputs = tf.concat(3, inputs)
# Normalize channels to 1.
kernel = tf.nn.relu(dna_input - RELU_SHIFT) + RELU_SHIFT
kernel = tf.expand_dims(
kernel / tf.reduce_sum(
kernel, [3], keep_dims=True), [4])
return tf.reduce_sum(kernel * inputs, [3], keep_dims=False)
def scheduled_sample(ground_truth_x, generated_x, batch_size, num_ground_truth):
"""Sample batch with specified mix of ground truth and generated data points.
Args:
ground_truth_x: tensor of ground-truth data points.
generated_x: tensor of generated data points.
batch_size: batch size
num_ground_truth: number of ground-truth examples to include in batch.
Returns:
New batch with num_ground_truth sampled from ground_truth_x and the rest
from generated_x.
"""
idx = tf.random_shuffle(tf.range(int(batch_size)))
ground_truth_idx = tf.gather(idx, tf.range(num_ground_truth))
generated_idx = tf.gather(idx, tf.range(num_ground_truth, int(batch_size)))
ground_truth_examps = tf.gather(ground_truth_x, ground_truth_idx)
generated_examps = tf.gather(generated_x, generated_idx)
return tf.dynamic_stitch([ground_truth_idx, generated_idx],
[ground_truth_examps, generated_examps])
video_prediction/prediction_train.py 0 → 100644
View file @ d67ea249
# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# 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.
# ==============================================================================
"""Code for training the prediction model."""
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import app
from tensorflow.python.platform import flags
from prediction_input import build_tfrecord_input
from prediction_model import construct_model
# How often to record tensorboard summaries.
SUMMARY_INTERVAL = 40
# How often to run a batch through the validation model.
VAL_INTERVAL = 200
# How often to save a model checkpoint
SAVE_INTERVAL = 2000
# tf record data location:
DATA_DIR = 'push/push_train'
# local output directory
OUT_DIR = '/tmp/data'
FLAGS = flags.FLAGS
flags.DEFINE_string('data_dir', DATA_DIR, 'directory containing data.')
flags.DEFINE_string('output_dir', OUT_DIR, 'directory for model checkpoints.')
flags.DEFINE_string('event_log_dir', OUT_DIR, 'directory for writing summary.')
flags.DEFINE_integer('num_iterations', 100000, 'number of training iterations.')
flags.DEFINE_string('pretrained_model', '',
'filepath of a pretrained model to initialize from.')
flags.DEFINE_integer('sequence_length', 10,
'sequence length, including context frames.')
flags.DEFINE_integer('context_frames', 2, '# of frames before predictions.')
flags.DEFINE_integer('use_state', 1,
'Whether or not to give the state+action to the model')
flags.DEFINE_string('model', 'CDNA',
'model architecture to use - CDNA, DNA, or STP')
flags.DEFINE_integer('num_masks', 10,
'number of masks, usually 1 for DNA, 10 for CDNA, STN.')
flags.DEFINE_float('schedsamp_k', 900.0,
'The k hyperparameter for scheduled sampling,'
'-1 for no scheduled sampling.')
flags.DEFINE_float('train_val_split', 0.95,
'The percentage of files to use for the training set,'
' vs. the validation set.')
flags.DEFINE_integer('batch_size', 32, 'batch size for training')
flags.DEFINE_float('learning_rate', 0.001,
'the base learning rate of the generator')
## Helper functions
def peak_signal_to_noise_ratio(true, pred):
"""Image quality metric based on maximal signal power vs. power of the noise.
Args:
true: the ground truth image.
pred: the predicted image.
Returns:
peak signal to noise ratio (PSNR)
"""
return 10.0 * tf.log(1.0 / mean_squared_error(true, pred)) / tf.log(10.0)
def mean_squared_error(true, pred):
"""L2 distance between tensors true and pred.
Args:
true: the ground truth image.
pred: the predicted image.
Returns:
mean squared error between ground truth and predicted image.
"""
return tf.reduce_sum(tf.square(true - pred)) / tf.to_float(tf.size(pred))
class Model(object):
def __init__(self,
images=None,
actions=None,
states=None,
sequence_length=None,
reuse_scope=None):
if sequence_length is None:
sequence_length = FLAGS.sequence_length
self.prefix = prefix = tf.placeholder(tf.string, [])
self.iter_num = tf.placeholder(tf.float32, [])
summaries = []
# Split into timesteps.
actions = tf.split(1, actions.get_shape()[1], actions)
actions = [tf.squeeze(act) for act in actions]
states = tf.split(1, states.get_shape()[1], states)
states = [tf.squeeze(st) for st in states]
images = tf.split(1, images.get_shape()[1], images)
images = [tf.squeeze(img) for img in images]
if reuse_scope is None:
gen_images, gen_states = construct_model(
images,
actions,
states,
iter_num=self.iter_num,
k=FLAGS.schedsamp_k,
use_state=FLAGS.use_state,
num_masks=FLAGS.num_masks,
cdna=FLAGS.model == 'CDNA',
dna=FLAGS.model == 'DNA',
stp=FLAGS.model == 'STP',
context_frames=FLAGS.context_frames)
else: # If it's a validation or test model.
with tf.variable_scope(reuse_scope, reuse=True):
gen_images, gen_states = construct_model(
images,
actions,
states,
iter_num=self.iter_num,
k=FLAGS.schedsamp_k,
use_state=FLAGS.use_state,
num_masks=FLAGS.num_masks,
cdna=FLAGS.model == 'CDNA',
dna=FLAGS.model == 'DNA',
stp=FLAGS.model == 'STP',
context_frames=FLAGS.context_frames)
# L2 loss, PSNR for eval.
loss, psnr_all = 0.0, 0.0
for i, x, gx in zip(
range(len(gen_images)), images[FLAGS.context_frames:],
gen_images[FLAGS.context_frames - 1:]):
recon_cost = mean_squared_error(x, gx)
psnr_i = peak_signal_to_noise_ratio(x, gx)
psnr_all += psnr_i
summaries.append(
tf.scalar_summary(prefix + '_recon_cost' + str(i), recon_cost))
summaries.append(tf.scalar_summary(prefix + '_psnr' + str(i), psnr_i))
loss += recon_cost
for i, state, gen_state in zip(
range(len(gen_states)), states[FLAGS.context_frames:],
gen_states[FLAGS.context_frames - 1:]):
state_cost = mean_squared_error(state, gen_state) * 1e-4
summaries.append(
tf.scalar_summary(prefix + '_state_cost' + str(i), state_cost))
loss += state_cost
summaries.append(tf.scalar_summary(prefix + '_psnr_all', psnr_all))
self.psnr_all = psnr_all
self.loss = loss = loss / np.float32(len(images) - FLAGS.context_frames)
summaries.append(tf.scalar_summary(prefix + '_loss', loss))
self.lr = tf.placeholder_with_default(FLAGS.learning_rate, ())
self.train_op = tf.train.AdamOptimizer(self.lr).minimize(loss)
self.summ_op = tf.merge_summary(summaries)
def main(unused_argv):
print 'Constructing models and inputs.'
with tf.variable_scope('model', reuse=None) as training_scope:
images, actions, states = build_tfrecord_input(training=True)
model = Model(images, actions, states, FLAGS.sequence_length)
with tf.variable_scope('val_model', reuse=None):
val_images, val_actions, val_states = build_tfrecord_input(training=False)
val_model = Model(val_images, val_actions, val_states,
FLAGS.sequence_length, training_scope)
print 'Constructing saver.'
# Make saver.
saver = tf.train.Saver(
tf.get_collection(tf.GraphKeys.VARIABLES), max_to_keep=0)
# Make training session.
sess = tf.InteractiveSession()
summary_writer = tf.train.SummaryWriter(
FLAGS.event_log_dir, graph=sess.graph, flush_secs=10)
if FLAGS.pretrained_model:
saver.restore(sess, FLAGS.pretrained_model)
tf.train.start_queue_runners(sess)
sess.run(tf.initialize_all_variables())
tf.logging.info('iteration number, cost')
# Run training.
for itr in range(FLAGS.num_iterations):
# Generate new batch of data.
feed_dict = {model.prefix: 'train',
model.iter_num: np.float32(itr),
model.lr: FLAGS.learning_rate}
cost, _, summary_str = sess.run([model.loss, model.train_op, model.summ_op],
feed_dict)
# Print info: iteration #, cost.
tf.logging.info(str(itr) + ' ' + str(cost))
if (itr) % VAL_INTERVAL == 2:
# Run through validation set.
feed_dict = {val_model.lr: 0.0,
val_model.prefix: 'val',
val_model.iter_num: np.float32(itr)}
_, val_summary_str = sess.run([val_model.train_op, val_model.summ_op],
feed_dict)
summary_writer.add_summary(val_summary_str, itr)
if (itr) % SAVE_INTERVAL == 2:
tf.logging.info('Saving model.')
saver.save(sess, FLAGS.output_dir + '/model' + str(itr))
if (itr) % SUMMARY_INTERVAL:
summary_writer.add_summary(summary_str, itr)
tf.logging.info('Saving model.')
saver.save(sess, FLAGS.output_dir + '/model')
tf.logging.info('Training complete')
tf.logging.flush()
if __name__ == '__main__':
app.run()
video_prediction/push_datafiles.txt 0 → 100644
View file @ d67ea249
push/push_testnovel/push_testnovel.tfrecord-00000-of-00005
push/push_testnovel/push_testnovel.tfrecord-00001-of-00005
push/push_testnovel/push_testnovel.tfrecord-00002-of-00005
push/push_testnovel/push_testnovel.tfrecord-00003-of-00005
push/push_testnovel/push_testnovel.tfrecord-00004-of-00005
push/push_testseen/push_testseen.tfrecord-00000-of-00005
push/push_testseen/push_testseen.tfrecord-00001-of-00005
push/push_testseen/push_testseen.tfrecord-00002-of-00005
push/push_testseen/push_testseen.tfrecord-00003-of-00005
push/push_testseen/push_testseen.tfrecord-00004-of-00005
push/push_train/push_train.tfrecord-00000-of-00264
push/push_train/push_train.tfrecord-00001-of-00264
push/push_train/push_train.tfrecord-00002-of-00264
push/push_train/push_train.tfrecord-00003-of-00264
push/push_train/push_train.tfrecord-00004-of-00264
push/push_train/push_train.tfrecord-00005-of-00264
push/push_train/push_train.tfrecord-00006-of-00264
push/push_train/push_train.tfrecord-00007-of-00264
push/push_train/push_train.tfrecord-00008-of-00264
push/push_train/push_train.tfrecord-00009-of-00264
push/push_train/push_train.tfrecord-00010-of-00264
push/push_train/push_train.tfrecord-00011-of-00264
push/push_train/push_train.tfrecord-00012-of-00264
push/push_train/push_train.tfrecord-00013-of-00264
push/push_train/push_train.tfrecord-00014-of-00264
push/push_train/push_train.tfrecord-00015-of-00264
push/push_train/push_train.tfrecord-00016-of-00264
push/push_train/push_train.tfrecord-00017-of-00264
push/push_train/push_train.tfrecord-00018-of-00264
push/push_train/push_train.tfrecord-00019-of-00264
push/push_train/push_train.tfrecord-00020-of-00264
push/push_train/push_train.tfrecord-00021-of-00264
push/push_train/push_train.tfrecord-00022-of-00264
push/push_train/push_train.tfrecord-00023-of-00264
push/push_train/push_train.tfrecord-00024-of-00264
push/push_train/push_train.tfrecord-00025-of-00264
push/push_train/push_train.tfrecord-00026-of-00264
push/push_train/push_train.tfrecord-00027-of-00264
push/push_train/push_train.tfrecord-00028-of-00264
push/push_train/push_train.tfrecord-00029-of-00264
push/push_train/push_train.tfrecord-00030-of-00264
push/push_train/push_train.tfrecord-00031-of-00264
push/push_train/push_train.tfrecord-00032-of-00264
push/push_train/push_train.tfrecord-00033-of-00264
push/push_train/push_train.tfrecord-00034-of-00264
push/push_train/push_train.tfrecord-00035-of-00264
push/push_train/push_train.tfrecord-00036-of-00264
push/push_train/push_train.tfrecord-00037-of-00264
push/push_train/push_train.tfrecord-00038-of-00264
push/push_train/push_train.tfrecord-00039-of-00264
push/push_train/push_train.tfrecord-00040-of-00264
push/push_train/push_train.tfrecord-00041-of-00264
push/push_train/push_train.tfrecord-00042-of-00264
push/push_train/push_train.tfrecord-00043-of-00264
push/push_train/push_train.tfrecord-00044-of-00264
push/push_train/push_train.tfrecord-00045-of-00264
push/push_train/push_train.tfrecord-00046-of-00264
push/push_train/push_train.tfrecord-00047-of-00264
push/push_train/push_train.tfrecord-00048-of-00264
push/push_train/push_train.tfrecord-00049-of-00264
push/push_train/push_train.tfrecord-00050-of-00264
push/push_train/push_train.tfrecord-00051-of-00264
push/push_train/push_train.tfrecord-00052-of-00264
push/push_train/push_train.tfrecord-00053-of-00264
push/push_train/push_train.tfrecord-00054-of-00264
push/push_train/push_train.tfrecord-00055-of-00264
push/push_train/push_train.tfrecord-00056-of-00264
push/push_train/push_train.tfrecord-00057-of-00264
push/push_train/push_train.tfrecord-00058-of-00264
push/push_train/push_train.tfrecord-00059-of-00264
push/push_train/push_train.tfrecord-00060-of-00264
push/push_train/push_train.tfrecord-00061-of-00264
push/push_train/push_train.tfrecord-00062-of-00264
push/push_train/push_train.tfrecord-00063-of-00264
push/push_train/push_train.tfrecord-00064-of-00264
push/push_train/push_train.tfrecord-00065-of-00264
push/push_train/push_train.tfrecord-00066-of-00264
push/push_train/push_train.tfrecord-00067-of-00264
push/push_train/push_train.tfrecord-00068-of-00264
push/push_train/push_train.tfrecord-00069-of-00264
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push/push_train/push_train.tfrecord-00071-of-00264
push/push_train/push_train.tfrecord-00072-of-00264
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push/push_train/push_train.tfrecord-00077-of-00264
push/push_train/push_train.tfrecord-00078-of-00264
push/push_train/push_train.tfrecord-00079-of-00264
push/push_train/push_train.tfrecord-00080-of-00264
push/push_train/push_train.tfrecord-00081-of-00264
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push/push_train/push_train.tfrecord-00083-of-00264
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push/push_train/push_train.tfrecord-00086-of-00264
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push/push_train/push_train.tfrecord-00109-of-00264
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push/push_train/push_train.tfrecord-00115-of-00264
push/push_train/push_train.tfrecord-00116-of-00264
push/push_train/push_train.tfrecord-00117-of-00264
push/push_train/push_train.tfrecord-00118-of-00264
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push/push_train/push_train.tfrecord-00121-of-00264
push/push_train/push_train.tfrecord-00122-of-00264
push/push_train/push_train.tfrecord-00123-of-00264
push/push_train/push_train.tfrecord-00124-of-00264
push/push_train/push_train.tfrecord-00125-of-00264
push/push_train/push_train.tfrecord-00126-of-00264
push/push_train/push_train.tfrecord-00127-of-00264
push/push_train/push_train.tfrecord-00128-of-00264
push/push_train/push_train.tfrecord-00129-of-00264
push/push_train/push_train.tfrecord-00130-of-00264
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push/push_train/push_train.tfrecord-00135-of-00264
push/push_train/push_train.tfrecord-00136-of-00264
push/push_train/push_train.tfrecord-00137-of-00264
push/push_train/push_train.tfrecord-00138-of-00264
push/push_train/push_train.tfrecord-00139-of-00264
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push/push_train/push_train.tfrecord-00141-of-00264
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push/push_train/push_train.tfrecord-00158-of-00264
push/push_train/push_train.tfrecord-00159-of-00264
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push/push_train/push_train.tfrecord-00161-of-00264
push/push_train/push_train.tfrecord-00162-of-00264
push/push_train/push_train.tfrecord-00163-of-00264
push/push_train/push_train.tfrecord-00164-of-00264
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push/push_train/push_train.tfrecord-00174-of-00264
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push/push_train/push_train.tfrecord-00177-of-00264
push/push_train/push_train.tfrecord-00178-of-00264
push/push_train/push_train.tfrecord-00179-of-00264
push/push_train/push_train.tfrecord-00180-of-00264
push/push_train/push_train.tfrecord-00181-of-00264
push/push_train/push_train.tfrecord-00182-of-00264
push/push_train/push_train.tfrecord-00183-of-00264
push/push_train/push_train.tfrecord-00184-of-00264
push/push_train/push_train.tfrecord-00185-of-00264
push/push_train/push_train.tfrecord-00186-of-00264
push/push_train/push_train.tfrecord-00187-of-00264
push/push_train/push_train.tfrecord-00188-of-00264
push/push_train/push_train.tfrecord-00189-of-00264
push/push_train/push_train.tfrecord-00190-of-00264
push/push_train/push_train.tfrecord-00191-of-00264
push/push_train/push_train.tfrecord-00192-of-00264
push/push_train/push_train.tfrecord-00193-of-00264
push/push_train/push_train.tfrecord-00194-of-00264
push/push_train/push_train.tfrecord-00195-of-00264
push/push_train/push_train.tfrecord-00196-of-00264
push/push_train/push_train.tfrecord-00197-of-00264
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