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# Copyright 2018 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.
# ==============================================================================
"""Utility functions for training."""
import collections
import six
import tensorflow as tf
from deeplab.core import preprocess_utils
from deeplab.utils import train_utils
from feelvos.utils import embedding_utils
from feelvos.utils import eval_utils
slim = tf.contrib.slim
add_softmax_cross_entropy_loss_for_each_scale = (
train_utils.add_softmax_cross_entropy_loss_for_each_scale)
get_model_gradient_multipliers = train_utils.get_model_gradient_multipliers
get_model_learning_rate = train_utils.get_model_learning_rate
resolve_shape = preprocess_utils.resolve_shape
def add_triplet_loss_for_each_scale(batch_size, num_frames_per_video,
embedding_dim, scales_to_embeddings,
labels, scope):
"""Adds triplet loss for logits of each scale.
Args:
batch_size: Int, the number of video chunks sampled per batch
num_frames_per_video: Int, the number of frames per video.
embedding_dim: Int, the dimension of the learned embedding
scales_to_embeddings: A map from embedding names for different scales to
embeddings. The embeddings have shape [batch, embeddings_height,
embeddings_width, embedding_dim].
labels: Groundtruth labels with shape [batch, image_height, image_width, 1].
scope: String, the scope for the loss.
Raises:
ValueError: labels is None.
"""
if labels is None:
raise ValueError('No label for triplet loss.')
for scale, embeddings in scales_to_embeddings.iteritems():
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
resolve_shape(embeddings, 4)[1:3],
align_corners=True)
# Reshape from [batch * num_frames, ...] to [batch, num_frames, ...].
h = tf.shape(embeddings)[1]
w = tf.shape(embeddings)[2]
new_labels_shape = tf.stack([batch_size, num_frames_per_video, h, w, 1])
reshaped_labels = tf.reshape(scaled_labels, new_labels_shape)
new_embeddings_shape = tf.stack([batch_size, num_frames_per_video, h, w,
-1])
reshaped_embeddings = tf.reshape(embeddings, new_embeddings_shape)
with tf.name_scope(loss_scope):
total_loss = tf.constant(0, dtype=tf.float32)
for n in range(batch_size):
embedding = reshaped_embeddings[n]
label = reshaped_labels[n]
n_pixels = h * w
n_anchors_used = 256
sampled_anchor_indices = tf.random_shuffle(tf.range(n_pixels))[
:n_anchors_used]
anchors_pool = tf.reshape(embedding[0], [-1, embedding_dim])
anchors_pool_classes = tf.reshape(label[0], [-1])
anchors = tf.gather(anchors_pool, sampled_anchor_indices)
anchor_classes = tf.gather(anchors_pool_classes, sampled_anchor_indices)
pos_neg_pool = tf.reshape(embedding[1:], [-1, embedding_dim])
pos_neg_pool_classes = tf.reshape(label[1:], [-1])
dists = embedding_utils.pairwise_distances(anchors, pos_neg_pool)
pos_mask = tf.equal(anchor_classes[:, tf.newaxis],
pos_neg_pool_classes[tf.newaxis, :])
neg_mask = tf.logical_not(pos_mask)
pos_mask_f = tf.cast(pos_mask, tf.float32)
neg_mask_f = tf.cast(neg_mask, tf.float32)
pos_dists = pos_mask_f * dists + 1e20 * neg_mask_f
neg_dists = neg_mask_f * dists + 1e20 * pos_mask_f
pos_dists_min = tf.reduce_min(pos_dists, axis=1)
neg_dists_min = tf.reduce_min(neg_dists, axis=1)
margin = 1.0
loss = tf.nn.relu(pos_dists_min - neg_dists_min + margin)
# Handle case that no positive is present (per anchor).
any_pos = tf.reduce_any(pos_mask, axis=1)
loss *= tf.cast(any_pos, tf.float32)
# Average over anchors
loss = tf.reduce_mean(loss, axis=0)
total_loss += loss
total_loss /= batch_size
# Scale the loss up a bit.
total_loss *= 3.0
tf.add_to_collection(tf.GraphKeys.LOSSES, total_loss)
def add_dynamic_softmax_cross_entropy_loss_for_each_scale(
scales_to_logits, labels, ignore_label, loss_weight=1.0,
upsample_logits=True, scope=None, top_k_percent_pixels=1.0,
hard_example_mining_step=100000):
"""Adds softmax cross entropy loss per scale for logits with varying classes.
Also adds summaries for mIoU.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits are a list of length batch_size of tensors of shape
[time, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch_size * time, image_height,
image_width, 1].
ignore_label: Integer, label to ignore.
loss_weight: Float, loss weight.
upsample_logits: Boolean, upsample logits or not.
scope: String, the scope for the loss.
top_k_percent_pixels: A float, the value lies in [0.0, 1.0]. When its
value < 1.0, only compute the loss for the top k percent pixels (e.g.,
the top 20% pixels). This is useful for hard pixel mining.
hard_example_mining_step: An integer, the training step in which the
hard exampling mining kicks off. Note that we gradually reduce the
mining percent to the top_k_percent_pixels. For example, if
hard_example_mining_step=100K and top_k_percent_pixels=0.25, then
mining percent will gradually reduce from 100% to 25% until 100K steps
after which we only mine top 25% pixels.
Raises:
ValueError: Label or logits is None.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
if top_k_percent_pixels < 0 or top_k_percent_pixels > 1:
raise ValueError('Unexpected value of top_k_percent_pixels.')
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
assert isinstance(logits, collections.Sequence)
logits = [tf.image.resize_bilinear(
x,
preprocess_utils.resolve_shape(labels, 4)[1:3],
align_corners=True) for x in logits]
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
assert isinstance(logits, collections.Sequence)
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits[0], 4)[1:3],
align_corners=True)
batch_size = len(logits)
num_time = preprocess_utils.resolve_shape(logits[0])[0]
reshaped_labels = tf.reshape(
scaled_labels, ([batch_size, num_time] +
preprocess_utils.resolve_shape(scaled_labels)[1:]))
for n, logits_n in enumerate(logits):
labels_n = reshaped_labels[n]
labels_n = tf.reshape(labels_n, shape=[-1])
not_ignore_mask = tf.to_float(tf.not_equal(labels_n,
ignore_label)) * loss_weight
num_classes_n = tf.shape(logits_n)[-1]
one_hot_labels = slim.one_hot_encoding(
labels_n, num_classes_n, on_value=1.0, off_value=0.0)
logits_n_flat = tf.reshape(logits_n, shape=[-1, num_classes_n])
if top_k_percent_pixels == 1.0:
tf.losses.softmax_cross_entropy(
one_hot_labels,
logits_n_flat,
weights=not_ignore_mask,
scope=loss_scope)
else:
# Only compute the loss for top k percent pixels.
# First, compute the loss for all pixels. Note we do not put the loss
# to loss_collection and set reduction = None to keep the shape.
num_pixels = tf.to_float(tf.shape(logits_n_flat)[0])
pixel_losses = tf.losses.softmax_cross_entropy(
one_hot_labels,
logits_n_flat,
weights=not_ignore_mask,
scope='pixel_losses',
loss_collection=None,
reduction=tf.losses.Reduction.NONE)
# Compute the top_k_percent pixels based on current training step.
if hard_example_mining_step == 0:
# Directly focus on the top_k pixels.
top_k_pixels = tf.to_int32(top_k_percent_pixels * num_pixels)
else:
# Gradually reduce the mining percent to top_k_percent_pixels.
global_step = tf.to_float(tf.train.get_or_create_global_step())
ratio = tf.minimum(1.0, global_step / hard_example_mining_step)
top_k_pixels = tf.to_int32(
(ratio * top_k_percent_pixels + (1.0 - ratio)) * num_pixels)
_, top_k_indices = tf.nn.top_k(pixel_losses,
k=top_k_pixels,
sorted=True,
name='top_k_percent_pixels')
# Compute the loss for the top k percent pixels.
tf.losses.softmax_cross_entropy(
tf.gather(one_hot_labels, top_k_indices),
tf.gather(logits_n_flat, top_k_indices),
weights=tf.gather(not_ignore_mask, top_k_indices),
scope=loss_scope)
pred_n = tf.argmax(logits_n, axis=-1, output_type=tf.int32)[
..., tf.newaxis]
labels_n = labels[n * num_time: (n + 1) * num_time]
miou = eval_utils.calculate_multi_object_miou_tf(pred_n, labels_n)
tf.summary.scalar('miou', miou)
def get_model_init_fn(train_logdir,
tf_initial_checkpoint,
initialize_last_layer,
last_layers,
ignore_missing_vars=False):
"""Gets the function initializing model variables from a checkpoint.
Args:
train_logdir: Log directory for training.
tf_initial_checkpoint: TensorFlow checkpoint for initialization.
initialize_last_layer: Initialize last layer or not.
last_layers: Last layers of the model.
ignore_missing_vars: Ignore missing variables in the checkpoint.
Returns:
Initialization function.
"""
if tf_initial_checkpoint is None:
tf.logging.info('Not initializing the model from a checkpoint.')
return None
if tf.train.latest_checkpoint(train_logdir):
tf.logging.info('Ignoring initialization; other checkpoint exists')
return None
tf.logging.info('Initializing model from path: %s', tf_initial_checkpoint)
# Variables that will not be restored.
exclude_list = ['global_step']
if not initialize_last_layer:
exclude_list.extend(last_layers)
variables_to_restore = slim.get_variables_to_restore(exclude=exclude_list)
if variables_to_restore:
return slim.assign_from_checkpoint_fn(
tf_initial_checkpoint,
variables_to_restore,
ignore_missing_vars=ignore_missing_vars)
return None
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