def test_create_sample_distance_fn_case_2(self):
# Shape = [1, 2, 2].
lhs = tf.constant([[[1.0, 2.0], [3.0, 4.0]]])
# Shape = [1, 3, 2].
rhs = tf.constant([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]])
distances = loss_utils.create_sample_distance_fn(
pair_type=common.DISTANCE_PAIR_TYPE_ALL_PAIRS,
distance_kernel=common.DISTANCE_KERNEL_SQUARED_L2,
pairwise_reduction=common.DISTANCE_REDUCTION_MEAN,
componentwise_reduction=tf.identity)(lhs, rhs)
self.assertAllClose(distances, [56.0 / 6.0])
def test_create_sample_distance_fn_case_1(self):
# Shape = [2, 3, 2].
lhs = tf.constant([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],
[[7.0, 8.0], [9.0, 10.0], [11.0, 12.0]]])
# Shape = [2, 4, 2].
rhs = tf.constant([[[16.0, 15.0], [14.0, 13.0], [12.0, 11.0], [10.0, 9.0]],
[[8.0, 7.0], [6.0, 5.0], [4.0, 3.0], [2.0, 1.0]]])
distances = loss_utils.create_sample_distance_fn(
pair_type=common.DISTANCE_PAIR_TYPE_ALL_PAIRS,
distance_kernel=common.DISTANCE_KERNEL_SQUARED_L2,
pairwise_reduction=functools.partial(tf.math.reduce_min, axis=[-2, -1]),
componentwise_reduction=tf.identity)(lhs, rhs)
self.assertAllClose(distances, [34.0, 2.0])
def test_create_sample_distance_fn_case_2(self):
# Shape = [1, 2, 2].
lhs = tf.constant([[[1.0, 2.0], [3.0, 4.0]]])
# Shape = [1, 3, 2].
rhs = tf.constant([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]])
distances = loss_utils.create_sample_distance_fn(
pair_type=common.DISTANCE_PAIR_TYPE_ALL_PAIRS,
distance_kernel=common.DISTANCE_KERNEL_SQUARED_L2,
pairwise_reduction=common.DISTANCE_REDUCTION_MEAN,
componentwise_reduction=tf.identity)(lhs, rhs)
with self.session() as sess:
sess.run(tf.global_variables_initializer())
distances_result = sess.run(distances)
self.assertAllClose(distances_result, [56.0 / 6.0])
def test_compute_keypoint_triplet_losses_with_sample_mining_embeddings(
self):
# Shape = [3, 3, 1, 2].
anchor_embeddings = tf.constant([
[[[1.0, 2.0]], [[1.0, 2.0]], [[1.0, 2.0]]],
[[[3.0, 4.0]], [[3.0, 4.0]], [[3.0, 4.0]]],
[[[5.0, 6.0]], [[5.0, 6.0]], [[5.0, 6.0]]],
])
# Shape = [3, 3, 1, 2].
positive_embeddings = tf.constant([
[[[2.0, 1.0]], [[2.0, 1.0]], [[2.0, 1.0]]],
[[[6.0, 5.0]], [[6.0, 5.0]], [[6.0, 5.0]]],
[[[7.0, 6.0]], [[7.0, 6.0]], [[7.0, 6.0]]],
])
# Shape = [4, 3, 2, 2].
match_embeddings = tf.constant([
[[[3.0, 2.0], [3.0, 2.0]], [[3.0, 2.0], [3.0, 2.0]],
[[3.0, 2.0], [3.0, 2.0]]],
[[[4.0, 3.0], [4.0, 3.0]], [[4.0, 3.0], [4.0, 3.0]],
[[4.0, 3.0], [4.0, 3.0]]],
[[[6.0, 5.0], [6.0, 5.0]], [[6.0, 5.0], [6.0, 5.0]],
[[6.0, 5.0], [6.0, 5.0]]],
[[[8.0, 7.0], [8.0, 7.0]], [[8.0, 7.0], [8.0, 7.0]],
[[8.0, 7.0], [8.0, 7.0]]],
])
# Shape = [3, 1].
anchor_keypoints = tf.constant([[1], [2], [3]])
# Shape = [4, 1].
match_keypoints = tf.constant([[1], [2], [3], [4]])
def mock_keypoint_distance_fn(unused_lhs, unused_rhs):
return tf.constant([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 0.0, 1.0],
[1.0, 0.0, 1.0, 0.0]])
# Shape = [3, 3, 1, 2].
anchor_mining_embeddings = tf.constant([
[[[1.0, 2.0]], [[1.0, 2.0]], [[1.0, 2.0]]],
[[[3.0, 4.0]], [[3.0, 4.0]], [[3.0, 4.0]]],
[[[5.0, 6.0]], [[5.0, 6.0]], [[5.0, 6.0]]],
])
# Shape = [3, 3, 1, 2].
positive_mining_embeddings = tf.constant([
[[[1.0, 2.0]], [[1.0, 2.0]], [[1.0, 2.0]]],
[[[5.0, 6.0]], [[5.0, 6.0]], [[5.0, 6.0]]],
[[[6.0, 7.0]], [[6.0, 7.0]], [[6.0, 7.0]]],
])
# Shape = [4, 3, 1, 2].
match_mining_embeddings = tf.constant([
[[[2.0, 3.0]], [[2.0, 3.0]], [[2.0, 3.0]]],
[[[3.0, 4.0]], [[3.0, 4.0]], [[3.0, 4.0]]],
[[[5.0, 6.0]], [[5.0, 6.0]], [[5.0, 6.0]]],
[[[7.0, 8.0]], [[7.0, 8.0]], [[7.0, 8.0]]],
])
loss, summaries = loss_utils.compute_keypoint_triplet_losses(
anchor_embeddings,
positive_embeddings,
match_embeddings,
anchor_keypoints,
match_keypoints,
margin=120.0,
min_negative_keypoint_distance=0.5,
use_semi_hard=True,
exclude_inactive_triplet_loss=True,
embedding_sample_distance_fn=loss_utils.create_sample_distance_fn(
pairwise_reduction=functools.partial(tf.math.reduce_sum,
axis=[-2, -1]),
componentwise_reduction=functools.partial(tf.math.reduce_sum,
axis=[-1])),
keypoint_distance_fn=mock_keypoint_distance_fn,
anchor_mining_embeddings=anchor_mining_embeddings,
positive_mining_embeddings=positive_mining_embeddings,
match_mining_embeddings=match_mining_embeddings)
with self.session() as sess:
loss_result, summaries_result = sess.run([loss, summaries])
self.assertAlmostEqual(loss_result, 57.0)
expected_summaries_result = {
'triplet_loss/Margin': 120.0,
'triplet_loss/Anchor/Positive/Distance/Mean': 48.0 / 3,
'triplet_loss/Anchor/Positive/Distance/Median': 12.0,
'triplet_loss/Anchor/HardNegative/Distance/Mean': 48.0 / 3,
'triplet_loss/Anchor/HardNegative/Distance/Median': 12.0,
'triplet_loss/Anchor/SemiHardNegative/Distance/Mean': 348.0 / 3,
'triplet_loss/Anchor/SemiHardNegative/Distance/Median': 120.0,
'triplet_loss/HardNegative/Loss/All': 360.0 / 3,
'triplet_loss/HardNegative/Loss/Active': 360.0 / 3,
'triplet_loss/HardNegative/ActiveTripletNum': 3,
'triplet_loss/HardNegative/ActiveTripletRatio': 1.0,
'triplet_loss/SemiHardNegative/Loss/All': 114.0 / 3,
'triplet_loss/SemiHardNegative/Loss/Active': 114.0 / 2,
'triplet_loss/SemiHardNegative/ActiveTripletNum': 2,
'triplet_loss/SemiHardNegative/ActiveTripletRatio': 2.0 / 3,
'triplet_mining/Anchor/Positive/Distance/Mean': 30.0 / 3,
'triplet_mining/Anchor/Positive/Distance/Median': 6.0,
'triplet_mining/Anchor/HardNegative/Distance/Mean': 6.0 / 3,
'triplet_mining/Anchor/HardNegative/Distance/Median': 0.0,
'triplet_mining/Anchor/SemiHardNegative/Distance/Mean': 156.0 / 3,
'triplet_mining/Anchor/SemiHardNegative/Distance/Median': 54.0,
'triplet_mining/HardNegative/Loss/All': 384.0 / 3,
'triplet_mining/HardNegative/Loss/Active': 384.0 / 3,
'triplet_mining/HardNegative/ActiveTripletNum': 3,
'triplet_mining/HardNegative/ActiveTripletRatio': 1.0,
'triplet_mining/SemiHardNegative/Loss/All': 234.0 / 3,
'triplet_mining/SemiHardNegative/Loss/Active': 234.0 / 3,
'triplet_mining/SemiHardNegative/ActiveTripletNum': 3,
'triplet_mining/SemiHardNegative/ActiveTripletRatio': 1.0,
}
self._assert_dict_equal_or_almost_equal(summaries_result,
expected_summaries_result)
def _validate_and_setup(common_module, keypoint_profiles_module, models_module,
keypoint_distance_config_override, embedder_fn_kwargs):
"""Validates and sets up training configurations."""
# Set default values for unspecified flags.
if FLAGS.use_normalized_embeddings_for_triplet_mining is None:
FLAGS.use_normalized_embeddings_for_triplet_mining = (
FLAGS.use_normalized_embeddings_for_triplet_loss)
if FLAGS.use_normalized_embeddings_for_positive_pairwise_loss is None:
FLAGS.use_normalized_embeddings_for_positive_pairwise_loss = (
FLAGS.use_normalized_embeddings_for_triplet_loss)
if FLAGS.positive_pairwise_distance_type is None:
FLAGS.positive_pairwise_distance_type = FLAGS.triplet_distance_type
if FLAGS.positive_pairwise_distance_kernel is None:
FLAGS.positive_pairwise_distance_kernel = FLAGS.triplet_distance_kernel
if FLAGS.positive_pairwise_pairwise_reduction is None:
FLAGS.positive_pairwise_pairwise_reduction = (
FLAGS.triplet_pairwise_reduction)
if FLAGS.positive_pairwise_componentwise_reduction is None:
FLAGS.positive_pairwise_componentwise_reduction = (
FLAGS.triplet_componentwise_reduction)
# Validate flags.
validate_flag = common_module.validate
validate_flag(FLAGS.model_input_keypoint_type,
common_module.SUPPORTED_TRAINING_MODEL_INPUT_KEYPOINT_TYPES)
validate_flag(FLAGS.embedding_type,
common_module.SUPPORTED_EMBEDDING_TYPES)
validate_flag(FLAGS.base_model_type,
common_module.SUPPORTED_BASE_MODEL_TYPES)
validate_flag(FLAGS.keypoint_distance_type,
common_module.SUPPORTED_KEYPOINT_DISTANCE_TYPES)
validate_flag(FLAGS.triplet_distance_type,
common_module.SUPPORTED_DISTANCE_TYPES)
validate_flag(FLAGS.triplet_distance_kernel,
common_module.SUPPORTED_DISTANCE_KERNELS)
validate_flag(FLAGS.triplet_pairwise_reduction,
common_module.SUPPORTED_PAIRWISE_DISTANCE_REDUCTIONS)
validate_flag(FLAGS.triplet_componentwise_reduction,
common_module.SUPPORTED_COMPONENTWISE_DISTANCE_REDUCTIONS)
validate_flag(FLAGS.positive_pairwise_distance_type,
common_module.SUPPORTED_DISTANCE_TYPES)
validate_flag(FLAGS.positive_pairwise_distance_kernel,
common_module.SUPPORTED_DISTANCE_KERNELS)
validate_flag(FLAGS.positive_pairwise_pairwise_reduction,
common_module.SUPPORTED_PAIRWISE_DISTANCE_REDUCTIONS)
validate_flag(FLAGS.positive_pairwise_componentwise_reduction,
common_module.SUPPORTED_COMPONENTWISE_DISTANCE_REDUCTIONS)
if FLAGS.embedding_type == common_module.EMBEDDING_TYPE_POINT:
if FLAGS.triplet_distance_type in [
common_module.DISTANCE_TYPE_SAMPLE,
common_module.DISTANCE_TYPE_CENTER_AND_SAMPLE
]:
raise ValueError(
'No support for triplet distance type `%s` for embedding type `%s`.'
% (FLAGS.triplet_distance_type, FLAGS.embedding_type))
if FLAGS.kl_regularization_loss_weight > 0.0:
raise ValueError(
'No support for KL regularization loss for embedding type `%s`.'
% FLAGS.embedding_type)
if ((FLAGS.triplet_distance_type in [
common_module.DISTANCE_TYPE_SAMPLE,
common_module.DISTANCE_TYPE_CENTER_AND_SAMPLE
] or FLAGS.positive_pairwise_distance_type in [
common_module.DISTANCE_TYPE_SAMPLE,
common_module.DISTANCE_TYPE_CENTER_AND_SAMPLE
]) and FLAGS.num_embedding_samples <= 0):
raise ValueError(
'Must specify positive `num_embedding_samples` to use `%s` '
'triplet/positive pairwise distance type.' %
FLAGS.triplet_distance_type)
if (((FLAGS.triplet_distance_kernel in [
common_module.DISTANCE_KERNEL_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_EXPECTED_LIKELIHOOD
]) != (FLAGS.triplet_pairwise_reduction in [
common_module.DISTANCE_REDUCTION_NEG_LOG_MEAN,
common_module.DISTANCE_REDUCTION_LOWER_HALF_NEG_LOG_MEAN,
common_module.DISTANCE_REDUCTION_ONE_MINUS_MEAN
])) or ((FLAGS.positive_pairwise_distance_kernel in [
common_module.DISTANCE_KERNEL_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_EXPECTED_LIKELIHOOD
]) != (FLAGS.positive_pairwise_pairwise_reduction in [
common_module.DISTANCE_REDUCTION_NEG_LOG_MEAN,
common_module.DISTANCE_REDUCTION_LOWER_HALF_NEG_LOG_MEAN,
common_module.DISTANCE_REDUCTION_ONE_MINUS_MEAN
]))):
raise ValueError(
'Must use `L2_SIGMOID_MATCHING_PROB` or `EXPECTED_LIKELIHOOD` distance '
'kernel and `NEG_LOG_MEAN` or `LOWER_HALF_NEG_LOG_MEAN` parwise reducer'
' in pairs.')
keypoint_profile_2d = keypoint_profiles_module.create_keypoint_profile_or_die(
FLAGS.input_keypoint_profile_name_2d)
# Set up configurations.
configs = {
'keypoint_profile_3d':
keypoint_profiles_module.create_keypoint_profile_or_die(
FLAGS.input_keypoint_profile_name_3d),
'keypoint_profile_2d':
keypoint_profile_2d,
'embedder_fn':
models_module.get_embedder(
base_model_type=FLAGS.base_model_type,
embedding_type=FLAGS.embedding_type,
num_embedding_components=FLAGS.num_embedding_components,
embedding_size=FLAGS.embedding_size,
num_embedding_samples=FLAGS.num_embedding_samples,
is_training=True,
num_fc_blocks=FLAGS.num_fc_blocks,
num_fcs_per_block=FLAGS.num_fcs_per_block,
num_hidden_nodes=FLAGS.num_hidden_nodes,
num_bottleneck_nodes=FLAGS.num_bottleneck_nodes,
weight_max_norm=FLAGS.weight_max_norm,
dropout_rate=FLAGS.dropout_rate,
**embedder_fn_kwargs),
'triplet_embedding_keys':
pipeline_utils.get_embedding_keys(FLAGS.triplet_distance_type,
common_module=common_module),
'triplet_mining_embedding_keys':
pipeline_utils.get_embedding_keys(FLAGS.triplet_distance_type,
replace_samples_with_means=True,
common_module=common_module),
'triplet_embedding_sample_distance_fn':
loss_utils.create_sample_distance_fn(
pair_type=common_module.DISTANCE_PAIR_TYPE_ALL_PAIRS,
distance_kernel=FLAGS.triplet_distance_kernel,
pairwise_reduction=FLAGS.triplet_pairwise_reduction,
componentwise_reduction=FLAGS.triplet_componentwise_reduction,
# We initialize the sigmoid parameters to avoid model being stuck
# in a `dead zone` at the beginning of training.
L2_SIGMOID_MATCHING_PROB_a_initializer=(
tf.initializers.constant(-0.65)),
L2_SIGMOID_MATCHING_PROB_b_initializer=(
tf.initializers.constant(-0.5)),
EXPECTED_LIKELIHOOD_min_stddev=0.1,
EXPECTED_LIKELIHOOD_max_squared_mahalanobis_distance=100.0),
'positive_pairwise_embedding_keys':
pipeline_utils.get_embedding_keys(
FLAGS.positive_pairwise_distance_type,
common_module=common_module),
'positive_pairwise_embedding_sample_distance_fn':
loss_utils.create_sample_distance_fn(
pair_type=common_module.DISTANCE_PAIR_TYPE_ALL_PAIRS,
distance_kernel=FLAGS.positive_pairwise_distance_kernel,
pairwise_reduction=FLAGS.positive_pairwise_pairwise_reduction,
componentwise_reduction=(
FLAGS.positive_pairwise_componentwise_reduction),
# We initialize the sigmoid parameters to avoid model being stuck
# in a `dead zone` at the beginning of training.
L2_SIGMOID_MATCHING_PROB_a_initializer=(
tf.initializers.constant(-0.65)),
L2_SIGMOID_MATCHING_PROB_b_initializer=(
tf.initializers.constant(-0.5)),
EXPECTED_LIKELIHOOD_min_stddev=0.1,
EXPECTED_LIKELIHOOD_max_squared_mahalanobis_distance=100.0),
'summarize_matching_sigmoid_vars':
FLAGS.triplet_distance_kernel
in [common_module.DISTANCE_KERNEL_L2_SIGMOID_MATCHING_PROB],
'random_projection_azimuth_range': [
float(x) / 180.0 * math.pi
for x in FLAGS.random_projection_azimuth_range
],
'random_projection_elevation_range': [
float(x) / 180.0 * math.pi
for x in FLAGS.random_projection_elevation_range
],
'random_projection_roll_range': [
float(x) / 180.0 * math.pi
for x in FLAGS.random_projection_roll_range
],
}
if FLAGS.keypoint_distance_type == common_module.KEYPOINT_DISTANCE_TYPE_MPJPE:
configs.update({
'keypoint_distance_fn':
keypoint_utils.compute_procrustes_aligned_mpjpes,
'min_negative_keypoint_distance':
FLAGS.min_negative_keypoint_mpjpe
})
# We use the following assignments to get around pytype check failures.
# TODO(liuti): Figure out a better workaround.
if 'keypoint_distance_fn' in keypoint_distance_config_override:
configs['keypoint_distance_fn'] = (
keypoint_distance_config_override['keypoint_distance_fn'])
if 'min_negative_keypoint_distance' in keypoint_distance_config_override:
configs['min_negative_keypoint_distance'] = (
keypoint_distance_config_override['min_negative_keypoint_distance']
)
if ('keypoint_distance_fn' not in configs
or 'min_negative_keypoint_distance' not in configs):
raise ValueError('Invalid keypoint distance config: %s.' %
str(configs))
if FLAGS.task == 0 and not FLAGS.profile_only:
# Save all key flags.
pipeline_utils.create_dir_and_save_flags(flags, FLAGS.train_log_dir,
'all_flags.train.json')
return configs
def _validate_and_setup(common_module, keypoint_profiles_module, models_module,
keypoint_distance_config_override,
create_model_input_fn_kwargs, embedder_fn_kwargs):
"""Validates and sets up training configurations."""
# Set default values for unspecified flags.
if FLAGS.use_normalized_embeddings_for_triplet_mining is None:
FLAGS.use_normalized_embeddings_for_triplet_mining = (
FLAGS.use_normalized_embeddings_for_triplet_loss)
if FLAGS.use_normalized_embeddings_for_positive_pairwise_loss is None:
FLAGS.use_normalized_embeddings_for_positive_pairwise_loss = (
FLAGS.use_normalized_embeddings_for_triplet_loss)
if FLAGS.positive_pairwise_distance_type is None:
FLAGS.positive_pairwise_distance_type = FLAGS.triplet_distance_type
if FLAGS.positive_pairwise_distance_kernel is None:
FLAGS.positive_pairwise_distance_kernel = FLAGS.triplet_distance_kernel
if FLAGS.positive_pairwise_pairwise_reduction is None:
FLAGS.positive_pairwise_pairwise_reduction = (
FLAGS.triplet_pairwise_reduction)
if FLAGS.positive_pairwise_componentwise_reduction is None:
FLAGS.positive_pairwise_componentwise_reduction = (
FLAGS.triplet_componentwise_reduction)
# Validate flags.
validate_flag = common_module.validate
validate_flag(FLAGS.model_input_keypoint_type,
common_module.SUPPORTED_TRAINING_MODEL_INPUT_KEYPOINT_TYPES)
validate_flag(FLAGS.model_input_keypoint_mask_type,
common_module.SUPPORTED_MODEL_INPUT_KEYPOINT_MASK_TYPES)
validate_flag(FLAGS.embedding_type,
common_module.SUPPORTED_EMBEDDING_TYPES)
validate_flag(FLAGS.base_model_type,
common_module.SUPPORTED_BASE_MODEL_TYPES)
validate_flag(FLAGS.keypoint_distance_type,
common_module.SUPPORTED_KEYPOINT_DISTANCE_TYPES)
validate_flag(FLAGS.triplet_distance_type,
common_module.SUPPORTED_DISTANCE_TYPES)
validate_flag(FLAGS.triplet_distance_kernel,
common_module.SUPPORTED_DISTANCE_KERNELS)
validate_flag(FLAGS.triplet_pairwise_reduction,
common_module.SUPPORTED_PAIRWISE_DISTANCE_REDUCTIONS)
validate_flag(FLAGS.triplet_componentwise_reduction,
common_module.SUPPORTED_COMPONENTWISE_DISTANCE_REDUCTIONS)
validate_flag(FLAGS.positive_pairwise_distance_type,
common_module.SUPPORTED_DISTANCE_TYPES)
validate_flag(FLAGS.positive_pairwise_distance_kernel,
common_module.SUPPORTED_DISTANCE_KERNELS)
validate_flag(FLAGS.positive_pairwise_pairwise_reduction,
common_module.SUPPORTED_PAIRWISE_DISTANCE_REDUCTIONS)
validate_flag(FLAGS.positive_pairwise_componentwise_reduction,
common_module.SUPPORTED_COMPONENTWISE_DISTANCE_REDUCTIONS)
if FLAGS.embedding_type == common_module.EMBEDDING_TYPE_POINT:
if FLAGS.triplet_distance_type != common_module.DISTANCE_TYPE_CENTER:
raise ValueError(
'No support for triplet distance type `%s` for embedding type `%s`.'
% (FLAGS.triplet_distance_type, FLAGS.embedding_type))
if FLAGS.kl_regularization_loss_weight > 0.0:
raise ValueError(
'No support for KL regularization loss for embedding type `%s`.'
% FLAGS.embedding_type)
if ((FLAGS.triplet_distance_type in [
common_module.DISTANCE_TYPE_SAMPLE,
] or FLAGS.positive_pairwise_distance_type in [
common_module.DISTANCE_TYPE_SAMPLE,
]) and FLAGS.num_embedding_samples <= 0):
raise ValueError(
'Must specify positive `num_embedding_samples` to use `%s` '
'triplet/positive pairwise distance type.' %
FLAGS.triplet_distance_type)
if (((FLAGS.triplet_distance_kernel in [
common_module.DISTANCE_KERNEL_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_SQUARED_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_EXPECTED_LIKELIHOOD,
]) != (FLAGS.triplet_pairwise_reduction in [
common_module.DISTANCE_REDUCTION_NEG_LOG_MEAN,
common_module.DISTANCE_REDUCTION_LOWER_HALF_NEG_LOG_MEAN,
common_module.DISTANCE_REDUCTION_ONE_MINUS_MEAN
])) or ((FLAGS.positive_pairwise_distance_kernel in [
common_module.DISTANCE_KERNEL_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_SQUARED_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_EXPECTED_LIKELIHOOD,
]) != (FLAGS.positive_pairwise_pairwise_reduction in [
common_module.DISTANCE_REDUCTION_NEG_LOG_MEAN,
common_module.DISTANCE_REDUCTION_LOWER_HALF_NEG_LOG_MEAN,
common_module.DISTANCE_REDUCTION_ONE_MINUS_MEAN
]))):
raise ValueError(
'Must use `L2_SIGMOID_MATCHING_PROB` or `EXPECTED_LIKELIHOOD` distance '
'kernel and `NEG_LOG_MEAN` or `LOWER_HALF_NEG_LOG_MEAN` parwise reducer'
' in pairs.')
keypoint_profile_2d = keypoint_profiles_module.create_keypoint_profile_or_die(
FLAGS.input_keypoint_profile_name_2d)
# Set up configurations.
configs = {
'keypoint_profile_3d':
keypoint_profiles_module.create_keypoint_profile_or_die(
FLAGS.input_keypoint_profile_name_3d),
'keypoint_profile_2d':
keypoint_profile_2d,
'create_model_input_fn':
functools.partial(
input_generator.create_model_input,
model_input_keypoint_mask_type=(
FLAGS.model_input_keypoint_mask_type),
uniform_keypoint_jittering_max_offset_2d=(
FLAGS.uniform_keypoint_jittering_max_offset_2d),
gaussian_keypoint_jittering_offset_stddev_2d=(
FLAGS.gaussian_keypoint_jittering_offset_stddev_2d),
keypoint_dropout_probs=[
float(x) for x in FLAGS.keypoint_dropout_probs
],
set_on_mask_for_non_anchors=FLAGS.set_on_mask_for_non_anchors,
mix_mask_sub_batches=FLAGS.mix_mask_sub_batches,
forced_mask_on_part_names=FLAGS.forced_mask_on_part_names,
forced_mask_off_part_names=FLAGS.forced_mask_off_part_names,
**create_model_input_fn_kwargs),
'embedder_fn':
models_module.get_embedder(
base_model_type=FLAGS.base_model_type,
embedding_type=FLAGS.embedding_type,
num_embedding_components=FLAGS.num_embedding_components,
embedding_size=FLAGS.embedding_size,
num_embedding_samples=FLAGS.num_embedding_samples,
is_training=True,
num_fc_blocks=FLAGS.num_fc_blocks,
num_fcs_per_block=FLAGS.num_fcs_per_block,
num_hidden_nodes=FLAGS.num_hidden_nodes,
num_bottleneck_nodes=FLAGS.num_bottleneck_nodes,
weight_max_norm=FLAGS.weight_max_norm,
dropout_rate=FLAGS.dropout_rate,
**embedder_fn_kwargs),
'triplet_embedding_keys':
pipeline_utils.get_embedding_keys(FLAGS.triplet_distance_type,
common_module=common_module),
'triplet_mining_embedding_keys':
pipeline_utils.get_embedding_keys(FLAGS.triplet_distance_type,
replace_samples_with_means=True,
common_module=common_module),
'positive_pairwise_embedding_keys':
pipeline_utils.get_embedding_keys(
FLAGS.positive_pairwise_distance_type,
common_module=common_module),
'summarize_matching_sigmoid_vars':
FLAGS.triplet_distance_kernel in [
common_module.DISTANCE_KERNEL_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_SQUARED_L2_SIGMOID_MATCHING_PROB
],
'random_projection_azimuth_range': [
float(x) / 180.0 * math.pi
for x in FLAGS.random_projection_azimuth_range
],
'random_projection_elevation_range': [
float(x) / 180.0 * math.pi
for x in FLAGS.random_projection_elevation_range
],
'random_projection_roll_range': [
float(x) / 180.0 * math.pi
for x in FLAGS.random_projection_roll_range
],
'random_projection_camera_depth_range':
[float(x) for x in FLAGS.random_projection_camera_depth_range],
}
embedding_sample_distance_fn_kwargs = {
'EXPECTED_LIKELIHOOD_min_stddev': 0.1,
'EXPECTED_LIKELIHOOD_max_squared_mahalanobis_distance': 100.0,
}
if FLAGS.triplet_distance_kernel in [
common_module.DISTANCE_KERNEL_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_SQUARED_L2_SIGMOID_MATCHING_PROB
] or FLAGS.positive_pairwise_distance_kernel in [
common_module.DISTANCE_KERNEL_L2_SIGMOID_MATCHING_PROB,
common_module.DISTANCE_KERNEL_SQUARED_L2_SIGMOID_MATCHING_PROB
]:
# We only need sigmoid parameters when a related distance kernel is used.
sigmoid_raw_a, sigmoid_a, sigmoid_b = pipeline_utils.get_sigmoid_parameters(
name='MatchingSigmoid',
raw_a_initial_value=FLAGS.sigmoid_raw_a_initial,
b_initial_value=FLAGS.sigmoid_b_initial,
a_range=(None, FLAGS.sigmoid_a_max))
embedding_sample_distance_fn_kwargs.update({
'L2_SIGMOID_MATCHING_PROB_a':
sigmoid_a,
'L2_SIGMOID_MATCHING_PROB_b':
sigmoid_b,
'SQUARED_L2_SIGMOID_MATCHING_PROB_a':
sigmoid_a,
'SQUARED_L2_SIGMOID_MATCHING_PROB_b':
sigmoid_b,
})
configs.update({
'sigmoid_raw_a': sigmoid_raw_a,
'sigmoid_a': sigmoid_a,
'sigmoid_b': sigmoid_b,
})
configs.update({
'triplet_embedding_sample_distance_fn':
loss_utils.create_sample_distance_fn(
pair_type=common_module.DISTANCE_PAIR_TYPE_ALL_PAIRS,
distance_kernel=FLAGS.triplet_distance_kernel,
pairwise_reduction=FLAGS.triplet_pairwise_reduction,
componentwise_reduction=FLAGS.triplet_componentwise_reduction,
**embedding_sample_distance_fn_kwargs),
'positive_pairwise_embedding_sample_distance_fn':
loss_utils.create_sample_distance_fn(
pair_type=common_module.DISTANCE_PAIR_TYPE_ALL_PAIRS,
distance_kernel=FLAGS.positive_pairwise_distance_kernel,
pairwise_reduction=FLAGS.positive_pairwise_pairwise_reduction,
componentwise_reduction=(
FLAGS.positive_pairwise_componentwise_reduction),
**embedding_sample_distance_fn_kwargs),
})
if FLAGS.keypoint_distance_type == common_module.KEYPOINT_DISTANCE_TYPE_MPJPE:
configs.update({
'keypoint_distance_fn':
keypoint_utils.compute_procrustes_aligned_mpjpes,
'min_negative_keypoint_distance':
FLAGS.min_negative_keypoint_mpjpe
})
# We use the following assignments to get around pytype check failures.
# TODO(liuti): Figure out a better workaround.
if 'keypoint_distance_fn' in keypoint_distance_config_override:
configs['keypoint_distance_fn'] = (
keypoint_distance_config_override['keypoint_distance_fn'])
if 'min_negative_keypoint_distance' in keypoint_distance_config_override:
configs['min_negative_keypoint_distance'] = (
keypoint_distance_config_override['min_negative_keypoint_distance']
)
if ('keypoint_distance_fn' not in configs
or 'min_negative_keypoint_distance' not in configs):
raise ValueError('Invalid keypoint distance config: %s.' %
str(configs))
if FLAGS.task == 0 and not FLAGS.profile_only:
# Save all key flags.
pipeline_utils.create_dir_and_save_flags(flags, FLAGS.train_log_dir,
'all_flags.train.json')
return configs
