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train_rooms.py
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train_rooms.py
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import os
import sys
import shutil
import tempfile
import numpy as np
from datetime import datetime
from collections import namedtuple, defaultdict
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_probability as tfp
import matplotlib.cm
import dirt
import dirt.matrices
import dirt.lighting
import dirt.projection
import utils
import sgvb_tf_utils
from eager_klqp import IntegratedEagerKlqp, GenerativeMode
output_base_path = './output'
class Hyperparams:
_task_id = os.getenv('SLURM_ARRAY_TASK_ID')
rng = np.random.default_rng(seed=int(_task_id)) if _task_id is not None else None
def __init__(self):
self._hyperparameter_to_value = {}
for arg in sys.argv[1:]:
name, value = arg.split('=')
if ',' in value:
values = value.split(',')
if self.rng is None:
raise RuntimeError('hyperparameter {} has {} values specified, but we are not running as a job array'.format(name, len(values)))
value = self.rng.choice(values)
print('hyper: randomly selected value {} for {}'.format(value, name))
self._hyperparameter_to_value[name] = value
self._requested_hyperparameters = set()
def __call__(self, default, name, converter=float):
# Retrieve the value of the given parameter specified on the command-line, or return default
if name in self._hyperparameter_to_value:
value = converter(self._hyperparameter_to_value[name])
use_default = False
else:
value = default
use_default = True
if name not in self._requested_hyperparameters:
print('hyper: {} = {}{}'.format(name, value, ' (default)' if use_default else ''))
self._requested_hyperparameters.add(name)
return value
def verify_args(self):
# Check that no hyperparameters were given on the command-line, that were not also requested through hyper(...)
missing = []
for name in self._hyperparameter_to_value:
if name not in self._requested_hyperparameters:
missing.append(name)
if len(missing) > 0:
raise RuntimeError('the following unrecognised hyperparameters were specified: ' + ', '.join(missing))
hyper = Hyperparams()
episodes_per_batch = hyper(8, 'eppb', int)
frames_per_episode = 3
frame_width, frame_height = 80, 80
random_seed = hyper(0, 'seed', int)
subbatch_count = hyper(8, 'subbatches', int)
final_eval = hyper(0, 'final-eval', int)
def get_unit_cube(vertices_only=False):
vertices = tf.constant([[x, y, z] for z in [-1, 1] for y in [-1, 1] for x in [-1, 1]], dtype=tf.float32)
if vertices_only:
return vertices
quads = [
[0, 1, 3, 2], [4, 5, 7, 6], # back, front
[1, 5, 4, 0], [2, 6, 7, 3], # bottom, top
[4, 6, 2, 0], [3, 7, 5, 1], # left, right
]
triangles = tf.constant(sum([[[a, b, c], [c, d, a]] for [a, b, c, d] in quads], []), dtype=tf.int32)
return vertices, triangles
def sample_texture(texture, uvs):
# texture is indexed by eib, v, u, channel
# uvs is indexed by eib, fie, y, x, u/v
uv_indices = uvs * [texture.get_shape()[2].value - 1, texture.get_shape()[1].value - 1]
vu_indices = uv_indices[..., ::-1]
vu_indices = tf.maximum(vu_indices, 0.)
vu_indices = tf.minimum(vu_indices, tf.cast(tf.shape(texture)[1:3], tf.float32) - 1.00001)
floor_vu_indices = tf.floor(vu_indices)
frac_vu_indices = vu_indices - floor_vu_indices
floor_vu_indices = tf.cast(floor_vu_indices, tf.int32)
floor_evu_indices = tf.concat([
tf.tile(
tf.range(episodes_per_batch)[:, None, None, None, None],
[1, floor_vu_indices.shape[1], frame_height, frame_width, 1]
),
floor_vu_indices
], axis=-1) # :: eib, fie, y, x, eib/y/x
neighbours = tf.gather_nd(
texture,
tf.stack([
floor_evu_indices,
floor_evu_indices + [0, 0, 1],
floor_evu_indices + [0, 1, 0],
floor_evu_indices + [0, 1, 1]
]),
) # :: neighbour. eib, fie, y, x, r/g/b
top_left, top_right, bottom_left, bottom_right = tf.unstack(neighbours)
texture_samples = \
top_left * (1. - frac_vu_indices[..., 1:]) * (1. - frac_vu_indices[..., :1]) + \
top_right * frac_vu_indices[..., 1:] * (1. - frac_vu_indices[..., :1]) + \
bottom_left * (1. - frac_vu_indices[..., 1:]) * frac_vu_indices[..., :1] + \
bottom_right * frac_vu_indices[..., 1:] * frac_vu_indices[..., :1]
return texture_samples
def sample_voxels(
episode_and_object_indices, # :: fg-pixel, eib/obj
locations, # :: fg-pixel, sample, x/y/z
voxels # :: eib, obj, z, y, x, channel
):
# result :: fg-pixel, sample, channel
if voxels.shape[2] == 1:
voxels = tf.tile(voxels, [1, 1, 2, 1, 1, 1])
if voxels.shape[3] == 1:
voxels = tf.tile(voxels, [1, 1, 1, 2, 1, 1])
if voxels.shape[4] == 1:
voxels = tf.tile(voxels, [1, 1, 1, 1, 2, 1])
dhw = tf.convert_to_tensor(voxels.shape[2:5], dtype=tf.int64)
dhw_f = tf.cast(dhw, tf.float32)
zyx_indices = (locations[:, :, ::-1] + 1.) / 2. * dhw_f
zyx_indices = tf.maximum(zyx_indices, 0.)
zyx_indices = tf.minimum(zyx_indices, dhw_f - 1.00001)
floor_zyx_indices = tf.floor(zyx_indices) # :: fg-pixel, sample, z/y/x
frac_zyx_indices = zyx_indices - floor_zyx_indices
floor_zyx_indices = tf.cast(floor_zyx_indices, tf.int64)
floor_zyx_indices = tf.concat([
tf.tile(episode_and_object_indices[:, None], [1, locations.get_shape()[1].value, 1]),
floor_zyx_indices
], axis=-1) # :: fg-pixel, sample, eib/obj/z/y/x
neighbours = tf.gather_nd(
voxels,
tf.stack([
floor_zyx_indices + [0, 0, 0, 0, 0], floor_zyx_indices + [0, 0, 0, 0, 1],
floor_zyx_indices + [0, 0, 0, 1, 0], floor_zyx_indices + [0, 0, 0, 1, 1],
floor_zyx_indices + [0, 0, 1, 0, 0], floor_zyx_indices + [0, 0, 1, 0, 1],
floor_zyx_indices + [0, 0, 1, 1, 0], floor_zyx_indices + [0, 0, 1, 1, 1]
]),
) # :: z-floor/-ceil * y-floor/-ceil * x-floor/-ceil, fg-pixel, sample, channel
neighbours = tf.reshape(neighbours, tf.concat([[2, 2, 2], tf.shape(neighbours)[1:4]], axis=0))
one_minus_frac_zyx_indices = 1. - frac_zyx_indices # :: fg-pixel, sample, z/y/x
neighbours *= \
tf.stack([one_minus_frac_zyx_indices[..., 0], frac_zyx_indices[..., 0]], axis=0)[:, None, None, :, :, None] * \
tf.stack([one_minus_frac_zyx_indices[..., 1], frac_zyx_indices[..., 1]], axis=0)[None, :, None, :, :, None] * \
tf.stack([one_minus_frac_zyx_indices[..., 2], frac_zyx_indices[..., 2]], axis=0)[None, None, :, :, :, None]
return tf.reduce_sum(neighbours, axis=[0, 1, 2])
def get_3d_object_depthmaps(object_positions_world, object_azimuths, object_sizes, world_to_view_matrices, projection_matrix, far_depth_value):
frame_count = object_positions_world.get_shape()[1].value
object_count = object_positions_world.get_shape()[2].value
cube_vertices_object, cube_faces = get_unit_cube()
object_rotation_matrices = dirt.matrices.rodrigues(object_azimuths[:, :, None] * [0., 1., 0.], three_by_three=True) # :: eib, obj, x/y/z (in), x/y/z (out)
cube_vertices_world = tf.matmul(cube_vertices_object[None, None, :, :] * object_sizes[:, :, None, :] / 2., object_rotation_matrices) # :: eib, obj, vertex, x/y/z
cube_vertices_world = cube_vertices_world[:, None, :, :, :] + object_positions_world[:, :, :, None, :] # :: eib, fie, obj, vertex, x/y/z
cube_vertices_world = tf.concat([cube_vertices_world, tf.ones_like(cube_vertices_world[..., :1])], axis=-1) # :: eib, fie, obj, vertex, x/y/z/w
# ** would be nice to share this with clip-to-voxel stuff in render_3d_objects_over_background
cube_vertices_view = tf.einsum('efovi,efij->efovj', cube_vertices_world, world_to_view_matrices)
cube_vertices_clip = tf.einsum('efovj,jk->efovk', cube_vertices_view, projection_matrix) # ditto
cube_vertices_clip_flat = tf.reshape(cube_vertices_clip, [-1] + cube_vertices_clip.get_shape()[3:].as_list())
object_depths = dirt.rasterise_batch(
background=far_depth_value * tf.ones([episodes_per_batch * frame_count * object_count, frame_height, frame_width, 1]),
vertices=cube_vertices_clip_flat,
vertex_colors=cube_vertices_clip_flat[..., 3:],
faces=tf.tile(cube_faces[None], [episodes_per_batch * frame_count * object_count, 1, 1])
) # :: eib * fie * obj, y, x, singleton
object_depths = tf.reshape(object_depths, [episodes_per_batch, frame_count, object_count, frame_height, frame_width])
return object_depths, object_rotation_matrices
def render_3d_objects_over_background(
object_voxel_colours, # :: eib, obj, z, y, x, r/g/b
object_voxel_alphas, # :: eib, obj, z, y, x
object_positions_world, # :: eib, fie, obj, x/y/z
object_azimuths, # :: eib, obj
object_sizes, # :: eib, obj, x/y/z
object_presences, # :: eib, obj
background_pixels, # :: eib, fie, y, x, r/g/b
background_depth, # :: eib, fie, y, x
world_to_view_matrices, # :: eib, fie, x/y/z/w (in), x/y/z/w (out)
projection_matrix, # :: x/y/z/w (in), x/y/z/w (out)
composed_output=True, # if set to false, we render each object into its own image
object_colour_transforms=None # :: eib, fie, obj, x/y/z/w (in), x/y/z/w (out)
):
# print(' before (MB):', tf.contrib.memory_stats.BytesInUse().numpy() // 1024 ** 2)
# print(' background_pixels', background_pixels.shape.num_elements() // 1024 ** 2, tf.contrib.memory_stats.BytesInUse().numpy() // 1024 ** 2)
object_count = object_positions_world.get_shape()[2].value
object_initial_distances = -tf.matmul(utils.to_homogeneous(object_positions_world[:, 0]), world_to_view_matrices[:, 0])[..., 2] # :: eib, obj
object_indices_farthest_to_nearest = tf.argsort(object_initial_distances, axis=-1, direction='DESCENDING') # :: eib, obj-by-depth
object_voxel_colours = tf.gather(object_voxel_colours, object_indices_farthest_to_nearest, batch_dims=1)
object_voxel_alphas = tf.gather(object_voxel_alphas, object_indices_farthest_to_nearest, batch_dims=1)
object_positions_world = tf.gather(object_positions_world, object_indices_farthest_to_nearest, axis=2, batch_dims=1)
object_azimuths = tf.gather(object_azimuths, object_indices_farthest_to_nearest, batch_dims=1)
object_sizes = tf.gather(object_sizes, object_indices_farthest_to_nearest, batch_dims=1)
object_presences = tf.gather(object_presences, object_indices_farthest_to_nearest, batch_dims=1)
if object_colour_transforms is not None:
object_colour_transforms = tf.gather(object_colour_transforms, object_indices_farthest_to_nearest, axis=2, batch_dims=1)
far_depth_value = 2.e1
object_depths, object_rotation_matrices = get_3d_object_depthmaps(
object_positions_world, object_azimuths, object_sizes,
world_to_view_matrices, projection_matrix,
far_depth_value
)
object_masks = tf.cast(tf.not_equal(object_depths, far_depth_value), tf.float32) # :: eib, fie, obj, y, x
object_masks = tf.reshape(
tf.nn.max_pool(tf.reshape(object_masks, [-1, frame_height, frame_width, 1]), [1, 5, 5, 1], strides=[1, 1, 1, 1], padding='SAME'),
[episodes_per_batch, frames_per_episode, object_count, frame_height, frame_width]
)
if final_eval:
downsample_factor_x, downsample_factor_y = 1, 1
else:
downsample_factor_x, downsample_factor_y = 2, 2
object_masks = object_masks[:, :, :, ::downsample_factor_y, ::downsample_factor_x]
fg_pixel_indices = tf.where(object_masks) # :: fg-pixel, eib/fie/obj/y/x
fg_pixel_indices = tf.gather(fg_pixel_indices, tf.argsort(fg_pixel_indices[:, 2])) # thus, now 'grouped' by object-index
fg_first_pixel_index_by_object = [tf.reduce_min(tf.where(tf.greater_equal(fg_pixel_indices[:, 2], object_index))) for object_index in range(object_count)]
clip_to_voxel_matrices = tf.linalg.inv(dirt.matrices.compose(
dirt.matrices.scale(object_sizes[:, None, :] * [1., -1., 1.] / 2.),
dirt.matrices.pad_3x3_to_4x4(object_rotation_matrices[:, None, :]),
dirt.matrices.translation(object_positions_world),
world_to_view_matrices[:, :, None],
projection_matrix[None, None, None]
))
clip_to_voxel_matrices_by_fg_pixel = tf.gather_nd(clip_to_voxel_matrices, fg_pixel_indices[:, :3])
# print(' clip_to_voxel_matrices_by_fg_pixel', clip_to_voxel_matrices_by_fg_pixel.shape.num_elements() // 1024 ** 2, tf.contrib.memory_stats.BytesInUse().numpy() // 1024 ** 2)
with tf.device('/cpu:0'): # ** necessary under CUDA 10.0 due to a bug in cublasGemmBatchedEx
ray_starts, ray_directions = dirt.projection.unproject_pixels_to_rays(
tf.cast(fg_pixel_indices[:, 3:][:, ::-1] * [downsample_factor_x, downsample_factor_y], tf.float32) + [(downsample_factor_x - 1) / 2, (downsample_factor_y - 1) / 2] + 0.5, # the + 0.5 accounts for the fact that OpenGL has pixel centres at half-integer coordinates
clip_to_voxel_matrices_by_fg_pixel,
[[frame_width, frame_height]]
) # each :: fg-pixel, x/y/z
# print(' ray_starts', ray_starts.shape.num_elements() // 1024 ** 2, tf.contrib.memory_stats.BytesInUse().numpy() // 1024 ** 2)
# See http://viclw17.github.io/2018/07/16/raytracing-ray-sphere-intersection/; we have the sphere centered
# at the origin, with radius sqrt(3) so it circumscribes the voxel cube
quadratic_as = tf.reduce_sum(tf.square(ray_directions), axis=-1)
quadratic_bs = tf.reduce_sum(ray_directions * ray_starts, axis=-1) * 2.
quadratic_cs = tf.reduce_sum(tf.square(ray_starts), axis=-1) - 3. # the 3. here is the square of the circumscribing radius
discriminants = tf.square(quadratic_bs) - 4. * quadratic_as * quadratic_cs
discriminants = tf.nn.relu(discriminants) + 1.e-12 # in theory, the relu is an identity, as we should always intersect the sphere
near_ts = (-quadratic_bs - tf.sqrt(discriminants)) / (2. * quadratic_as) # :: fg-pixel
far_ts = (-quadratic_bs + tf.sqrt(discriminants)) / (2. * quadratic_as)
# sample_count = max(object_voxel_colours.get_shape()[2:5].as_list()) * 5 // 4 # number of samples to take along each ray
sample_count = 32
# Note that we store samples in order farthest-to-nearest, which matches reduce_alpha
most_distant_sample_locations = ray_starts + far_ts[:, None] * ray_directions # :: fg-pixel, x/y/z
sample_spacings = (near_ts - far_ts)[:, None] * ray_directions / (sample_count - 1) # ditto
sample_locations = most_distant_sample_locations[:, None, :] + sample_spacings[:, None, :] * tf.range(sample_count, dtype=tf.float32)[None, :, None] # :: fg-pixel, sample, x/y/z
# print(' post-sample_locations (MB)', tf.contrib.memory_stats.BytesInUse().numpy() // 1024 ** 2)
# The padding (and corresponding adjustment to sample_locations) adds a one-voxel 'clamping' border to colour,
# and a zero border to alpha; this ensures correct values are sampled just outside the voxel cuboid, given that
# sample_voxels clamps the texel coordinates
original_voxel_dhw = object_voxel_colours.shape[2:5].as_list()
padded_voxel_dhw = [dim + 2 for dim in original_voxel_dhw]
padded_voxel_colours = tf.reshape(
tf.pad(tf.reshape(object_voxel_colours, [-1] + original_voxel_dhw + [3]), [[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]], mode='SYMMETRIC'),
[episodes_per_batch, object_count] + padded_voxel_dhw + [3]
)
padded_voxel_alphas = tf.pad(
object_voxel_alphas,
[[0, 0], [0, 0], [1, 1], [1, 1], [1, 1]]
)
padded_sample_locations = sample_locations * (tf.constant(original_voxel_dhw, dtype=tf.float32) / padded_voxel_dhw[::-1])[::-1]
sample_colours_and_alphas = sample_voxels(
fg_pixel_indices[:, 0:3:2], # :: fg-pixel, eib/obj
padded_sample_locations,
tf.concat([
padded_voxel_colours,
padded_voxel_alphas[..., None] * object_presences[..., None, None, None, None]
], axis=-1)
)
sample_colours = sample_colours_and_alphas[..., :3] # :: fg-pixel, sample, r/g/b
sample_alphas = sample_colours_and_alphas[..., 3] # :: fg-pixel, sample
if object_colour_transforms is not None:
# This is used when rendering depth-maps; then the 'colour' of each voxel should be its view-space
# coordinate, but this varies from frame to frame, whereas object_voxel_colours is constant. So, we
# allow a per-frame transform matrix to be applied after the sampling stage, which is equivalent but
# keeps memory usage tractable
# fg_pixels :: fg-pixel, eib/fie/obj/y/x; it is ordered by object
# object_colour_transforms :: eib, fie, obj, x/y/z/w (in), x/y/z/w (out)
colour_transforms_by_sample = tf.gather_nd(object_colour_transforms, fg_pixel_indices[:, :3], ) # :: fg-pixel, x/y/z/w (in), x/y/z/w (out)
sample_colours = tf.einsum('fsi,fij->fsj', utils.to_homogeneous(sample_colours), colour_transforms_by_sample)[:, :, :3]
# sample_precomposed_colours is the 'full' alpha composition for a given pixel except for the component involving the
# background, which may itself have been overlaid by a more-distant object; sample_bg_weights is the weighting that
# the background should receive
sample_precomposed_colours = utils.reduce_alpha(sample_colours[:, 1:], sample_alphas[:, 1:], sample_colours[:, 0] * sample_alphas[:, 0, None], axis=1) # :: fg-pixel, r/g/b
sample_bg_weights = tf.reduce_prod(1. - sample_alphas, axis=1) # :: fg-pixel
if composed_output:
final_pixels = background_pixels
else: # this is used for evaluating segmentation, when we require one mask per object
final_pixels_by_object = [background_pixels] * object_count
for object_index in range(object_count):
first_fg_pixel_index_for_object = fg_first_pixel_index_by_object[object_index]
last_fg_pixel_index_for_object = fg_first_pixel_index_by_object[object_index + 1] if object_index < object_count - 1 else fg_pixel_indices.shape[0]
fg_pixel_indices_for_object = fg_pixel_indices[first_fg_pixel_index_for_object : last_fg_pixel_index_for_object] # :: fg-pixel-in-obj, eib/fie/obj/y/x
sample_precomposed_colours_for_object = sample_precomposed_colours[first_fg_pixel_index_for_object : last_fg_pixel_index_for_object] # :: fg-pixel-in-obj, r/g/b
sample_bg_weights_for_object = sample_bg_weights[first_fg_pixel_index_for_object : last_fg_pixel_index_for_object] # :: fg-pixel-in-obj
if downsample_factor_y == 1 and downsample_factor_x == 1:
fg_pixel_indices_for_object = tf.concat([fg_pixel_indices_for_object[:, :2], fg_pixel_indices_for_object[:, 3:]], axis=-1) # :: fg-pixel-in-obj, eib/fie/y/x
elif downsample_factor_y == 2 and downsample_factor_x == 2:
fg_pixel_indices_for_object = tf.concat([
tf.tile(fg_pixel_indices_for_object[:, :2], [downsample_factor_y * downsample_factor_x, 1]),
tf.concat([
fg_pixel_indices_for_object[:, 3:] * [downsample_factor_y, downsample_factor_x],
fg_pixel_indices_for_object[:, 3:] * [downsample_factor_y, downsample_factor_x] + [0, 1],
fg_pixel_indices_for_object[:, 3:] * [downsample_factor_y, downsample_factor_x] + [1, 0],
fg_pixel_indices_for_object[:, 3:] * [downsample_factor_y, downsample_factor_x] + [1, 1]
], axis=0)
], axis=-1) # :: fg-pixel-in-obj, eib/fie/y/x
else:
assert False
sample_precomposed_colours_for_object = tf.tile(sample_precomposed_colours_for_object, [downsample_factor_y * downsample_factor_x, 1])
sample_bg_weights_for_object = tf.tile(sample_bg_weights_for_object, [downsample_factor_y * downsample_factor_x])
if not composed_output:
final_pixels = final_pixels_by_object[object_index]
pixel_backgrounds = tf.gather_nd(final_pixels, fg_pixel_indices_for_object) # :: fg-pixel-in-obj, r/g/b
composed_colours = pixel_backgrounds * sample_bg_weights_for_object[:, None] + sample_precomposed_colours_for_object # :: fg-pixel-in-obj, r/g/b
final_pixels = tf.tensor_scatter_nd_update(final_pixels, fg_pixel_indices_for_object, composed_colours)
if not composed_output:
final_pixels_by_object[object_index] = final_pixels
if composed_output:
return final_pixels
else:
return tf.stack(final_pixels_by_object, axis=0)
def render_meshes_over_background(
object_vertices, # eib, obj, vertex, x/y/z/w
object_textures, # eib, obj, v, u, r/g/b; may be the string 'depth' to render a depthmap
object_faces, # face, vertex-in-face
object_uvs, # vertex, u/v
object_positions_world, # eib, fie, obj, x/y/z
object_azimuths, # eib, obj
object_sizes, # eib, obj, x/y/z
object_presences, # eib, obj
background_pixels, # eib, fie, y, x, r/g/b
background_depth, # eib, fie, y, x
world_to_view_matrices, # eib, fie, x/y/z/w (in), x/y/z/w (out)
projection_matrix, # x/y/z/w (in), x/y/z/w (out)
composed_output=True # if set to false, we render each object into its own image
):
object_count = object_vertices.get_shape()[1].value
object_initial_distances = -tf.matmul(utils.to_homogeneous(object_positions_world[:, 0]), world_to_view_matrices[:, 0])[..., 2] # :: eib, obj
object_indices_farthest_to_nearest = tf.argsort(object_initial_distances, axis=-1, direction='DESCENDING') # :: eib, obj-by-depth
object_vertices = tf.gather(object_vertices, object_indices_farthest_to_nearest, batch_dims=1)
if object_textures != 'depth':
object_textures = tf.gather(object_textures, object_indices_farthest_to_nearest, batch_dims=1)
object_positions_world = tf.gather(object_positions_world, object_indices_farthest_to_nearest, axis=2, batch_dims=1)
object_azimuths = tf.gather(object_azimuths, object_indices_farthest_to_nearest, batch_dims=1)
object_sizes = tf.gather(object_sizes, object_indices_farthest_to_nearest, batch_dims=1)
object_presences = tf.gather(object_presences, object_indices_farthest_to_nearest, batch_dims=1)
object_to_clip_matrices = dirt.matrices.compose(
dirt.matrices.scale(object_sizes[:, None, :] * [1., -1., 1.] / 2.),
dirt.matrices.rodrigues(object_azimuths[:, None, :, None] * [0., 1., 0.]),
dirt.matrices.translation(object_positions_world),
world_to_view_matrices[:, :, None],
projection_matrix[None, None, None]
) # eib, fie, obj, x/y/z/w (in), x/y/z/w (out)
object_vertices_clip = tf.einsum('eovi,efoij->efovj', object_vertices, object_to_clip_matrices) # eib, fie, obj, vertex, x/y/z/w
def do_shading(mask_and_uvs_and_depth, object_presence, object_texture, background):
mask = mask_and_uvs_and_depth[..., :1] * object_presence[:, None, None, None]
uvs = mask_and_uvs_and_depth[..., 1:3]
depth = mask_and_uvs_and_depth[..., 3:]
if object_textures == 'depth':
texture_samples = depth
else:
uvs = tf.reshape(uvs, [episodes_per_batch, frames_per_episode, frame_height, frame_width, 2])
texture_samples = tf.reshape(
sample_texture(object_texture, uvs),
[episodes_per_batch * frames_per_episode, frame_height, frame_width, 3]
)
return texture_samples * mask + background * (1 - mask)
final_pixels_flat = tf.reshape(background_pixels, [episodes_per_batch * frames_per_episode, frame_height, frame_width, 3])
if not composed_output: # this is used for evaluating segmentation, when we require one mask per object
final_pixels_flat_by_object = []
for object_index in range(object_count): # note that objects are already (approximately) sorted farthest-to-nearest
vertices_for_object_clip_flat = tf.reshape(
object_vertices_clip[:, :, object_index],
[-1] + object_vertices_clip.shape[3:].as_list()
) # eib * fie, vertex, x/y/z/w
far_depth_value = 1.e2 # this is the 'background' value for the depth-map; typically all pixels are covered by a wall, so it rarely appears
final_pixels_flat = dirt.rasterise_batch_deferred(
background_attributes=tf.zeros([episodes_per_batch * frames_per_episode, frame_height, frame_width, 4]) + [0., 0., 0., far_depth_value],
vertices=vertices_for_object_clip_flat,
vertex_attributes=tf.concat([
tf.ones([episodes_per_batch * frames_per_episode, vertices_for_object_clip_flat.get_shape()[1], 1]),
tf.tile(object_uvs[None], [episodes_per_batch * frames_per_episode, 1, 1]),
vertices_for_object_clip_flat[..., 3:]
], axis=-1),
faces=tf.tile(object_faces[None], [episodes_per_batch * frames_per_episode, 1, 1]),
shader_fn=do_shading,
shader_additional_inputs=[
tf.reshape(
tf.tile(object_presences[:, None, object_index], [1, frames_per_episode]),
[episodes_per_batch * frames_per_episode]
),
object_textures[:, object_index] if object_textures != 'depth' else tf.zeros([]),
final_pixels_flat if composed_output else tf.zeros([episodes_per_batch * frames_per_episode, frame_height, frame_width, 3])
]
)
if not composed_output:
final_pixels_flat_by_object.append(final_pixels_flat)
if composed_output:
return tf.reshape(
final_pixels_flat,
[episodes_per_batch, frames_per_episode, frame_height, frame_width, 3]
)
else:
return tf.reshape(
tf.stack(final_pixels_flat_by_object, axis=0),
[object_count, episodes_per_batch, frames_per_episode, frame_height, frame_width, 3]
)
def get_voxel_bboxes_at_threshold(threshold, object_alphas, object_presences, voxel_to_view_matrices, voxel_coordinates):
# threshold is a python float, giving the presence * alpha threshold that should be used to
# determine which part of an object counts as 'solid'
# object_alphas :: eib, obj, z, y, x
# object_presences :: eib, obj
# voxel_to_view_matrices :: eib, fie, obj, x/y/z/w (in), x/y/z/w (out)
# voxel_coordinates :: z, y, x, x/y/z; this is a constant tensor passed to avoid repetition, that
# stores the voxel-space locations of the voxels for any object
# result :: eib, fie, obj, min-x/-y/-z / max-x/-y/-z / score, where score is presence * max(alpha)
# and is constant over frames
# Note that this function includes all objects regardless of visibility; the evaluation code
# filters to only those which reproject into the frame
# 1. Downsample alphas and coordinates, to reduce memory usage
downsample_factor = 3
object_alphas = tf.nn.max_pool3d(
tf.reshape(object_alphas, [-1] + object_alphas.get_shape()[2:].as_list() + [1]),
ksize=downsample_factor, strides=downsample_factor,
padding='VALID'
)
object_alphas = tf.reshape(object_alphas, object_presences.get_shape().as_list() + object_alphas.get_shape()[1:-1].as_list())
voxel_coordinates = tf.nn.avg_pool3d(
tf.reshape(voxel_coordinates, [1] + voxel_coordinates.get_shape().as_list()),
ksize=downsample_factor, strides=downsample_factor,
padding='VALID'
)[0]
# 2. For every (downsampled) point in every object, push it through the voxel-to-view
# transform and drop the w-coordinate
voxel_coordinates_view = tf.einsum('zyxi,efoij->efozyxj', utils.to_homogeneous(voxel_coordinates), voxel_to_view_matrices)[..., :3]
# 3. For each object, find which points have alpha*presence exceeding the threshold,
# and calculate the bounding-box of such points
object_alpha_times_presence = object_alphas * object_presences[:, :, None, None, None]
masks = tf.greater(object_alpha_times_presence, threshold) # :: eib, obj, z, y, x
masks_f = tf.cast(masks, tf.float32)[:, None, :, :, :, :, None] # :: eib, 1, obj, z, y, x, 1
large_coord = 1.e6 # this should be larger than any actually-occurring coordinate!
bbox_min_corners = tf.reduce_min(
voxel_coordinates_view * masks_f + tf.ones_like(voxel_coordinates_view) * large_coord * (1. - masks_f),
axis=[3, 4, 5]
) # :: eib, fie, obj, x/y/z
bbox_max_corners = tf.reduce_max(
voxel_coordinates_view * masks_f + tf.ones_like(voxel_coordinates_view) * -large_coord * (1. - masks_f),
axis=[3, 4, 5]
) # :: eib, fie, obj, x/y/z
# 4. Calculate the object scores, ensuring that any objects that had no greater-than-threshold
# voxels are assigned exactly zero score (so the evaluation can ignore them)
scores = tf.reduce_max(object_alpha_times_presence, axis=[2, 3, 4]) # :: eib, obj
object_partly_visible = tf.reduce_any(masks, axis=[2, 3, 4]) # :: eib, obj
scores = tf.where(object_partly_visible, scores, tf.zeros_like(scores))
return tf.concat([
bbox_min_corners, bbox_max_corners,
tf.tile(scores[:, None, :, None], [1, frames_per_episode, 1, 1])
], axis=-1)
def get_mesh_bboxes(object_vertices, object_positions_world, object_azimuths, object_sizes, object_presences, world_to_view_matrices):
# ** this duplicates render_meshes_over_background!
object_to_view_matrices = dirt.matrices.compose(
dirt.matrices.scale(object_sizes[:, None, :] * [1., -1., 1.] / 2.),
dirt.matrices.rodrigues(object_azimuths[:, None, :, None] * [0., 1., 0.]),
dirt.matrices.translation(object_positions_world),
world_to_view_matrices[:, :, None],
) # eib, fie, obj, x/y/z/w (in), x/y/z/w (out)
object_vertices_view = tf.einsum('eovi,efoij->efovj', object_vertices, object_to_view_matrices)[..., :3] # eib, fie, obj, vertex, x/y/z/w
bbox_min_corners = tf.reduce_min(object_vertices_view, axis=3)
bbox_max_corners = tf.reduce_max(object_vertices_view, axis=3)
return tf.concat([
bbox_min_corners, bbox_max_corners,
tf.tile(object_presences[:, None, :, None], [1, frames_per_episode, 1, 1])
], axis=-1)
def binarise_and_modalise_masks(masks_by_depth, thresholds):
# masks_by_depth :: obj eib, fie, y, x
# thresholds :: *
# result :: *, obj, eib, fie, y, x
binarised_masks_by_threshold = tf.cast(tf.greater(masks_by_depth, thresholds[..., None, None, None, None, None]), tf.float32) # threshold, obj, eib, fie, y, x
nearer_object_exists = tf.minimum(tf.cumsum(binarised_masks_by_threshold, axis=-5, reverse=True, exclusive=True), 1.)
return binarised_masks_by_threshold * (1. - nearer_object_exists)
class TfRecordsGroundTruth:
min_object_count = 1
max_object_count = 4
subfolder = '{}-{}-obj_static_shadows_scale-1'.format(min_object_count, max_object_count)
raw_frames_per_episode = 6
data_string = 'gqn_{}e-{}f_{}'.format(episodes_per_batch, frames_per_episode, subfolder)
base_scale_xz = 8.
base_scale_y = 1.5
def __init__(self, path):
def decode_serialised_examples(serialised_examples):
features = tf.io.parse_single_example(
serialised_examples,
features={
'frames': tf.io.FixedLenFeature([self.raw_frames_per_episode], tf.string),
'masks': tf.io.FixedLenFeature([self.raw_frames_per_episode], tf.string),
'depths': tf.io.FixedLenFeature([self.raw_frames_per_episode, frame_height, frame_width], tf.float32),
'normals': tf.io.FixedLenFeature([self.raw_frames_per_episode, frame_height, frame_width, 3], tf.float32),
'bboxes': tf.io.FixedLenFeature([self.raw_frames_per_episode, self.max_object_count, 2, 3], tf.float32),
'camera_positions': tf.io.FixedLenFeature([self.raw_frames_per_episode, 3], tf.float32),
'camera_yaws': tf.io.FixedLenFeature([self.raw_frames_per_episode], tf.float32),
'camera_pitches': tf.io.FixedLenFeature([self.raw_frames_per_episode], tf.float32),
'camera_matrices': tf.io.FixedLenFeature([self.raw_frames_per_episode, 4, 4], tf.float32),
})
first_frame_index = tf.random.uniform([], maxval=self.raw_frames_per_episode - frames_per_episode, dtype=tf.int32)
frames = features['frames'][first_frame_index : first_frame_index + frames_per_episode]
frames = tf.map_fn(tf.image.decode_jpeg, frames, tf.uint8)
frames.set_shape([frames_per_episode, frame_height, frame_width, 3])
frames = tf.cast(frames, tf.float32) / 255.
camera_matrices = features['camera_matrices'][first_frame_index : first_frame_index + frames_per_episode]
camera_matrices = tf.transpose(camera_matrices, [0, 2, 1])
masks = features['masks'][first_frame_index : first_frame_index + frames_per_episode]
masks = tf.map_fn(tf.image.decode_png, masks, tf.uint8)
masks.set_shape([frames_per_episode, frame_height, frame_width, 3])
depths = features['depths'][first_frame_index : first_frame_index + frames_per_episode]
normals = features['normals'][first_frame_index : first_frame_index + frames_per_episode]
bboxes = features['bboxes'][first_frame_index : first_frame_index + frames_per_episode]
return frames, camera_matrices, depths, normals, masks, bboxes
def get_dataset_for_split(split):
filenames = tf.data.Dataset.list_files(path + '/' + self.subfolder + '/' + split + '/*')
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=4) \
.map(decode_serialised_examples)
if split == 'train': # i.e. val/test datasets are iterated only once, and in deterministic order
dataset = dataset \
.repeat() \
.shuffle(episodes_per_batch * 10)
dataset = dataset.batch(episodes_per_batch, drop_remainder=True)
if split == 'train': # i.e. val/test datasets are not subbatched
dataset = dataset.batch(subbatch_count, drop_remainder=True)
return dataset.prefetch(10)
self._split_to_dataset = {
split: get_dataset_for_split(split)
for split in ['train', 'val', 'test']
}
def get_projection_matrix(self):
near_plane_distance = 0.05
fov_angle = np.pi / 4
near_plane_right = near_plane_distance * tf.tan(fov_angle / 2.)
return dirt.matrices.perspective_projection(
near_plane_distance,
far=100.,
right=near_plane_right,
aspect=frame_height / frame_width
) # :: x/y/x/z (in), x/y/z/w (out)
def batches(self, split):
for batch in self._split_to_dataset[split]:
yield batch
class RoomVae(tf.keras.Model):
beta = hyper(5.e-1, 'beta')
beta_anneal_start = hyper(0, 'beta-anneal-start', int)
beta_anneal_duration = hyper(0, 'beta-anneal-duration', int)
initial_beta = hyper(beta, 'initial-beta')
room_circumference_segments = 64
room_vertical_segments = 24
scene_embedding_channels = hyper(32, 'sec', int)
bg_embedding_channels = hyper(12, 'bgec', int)
bg_shape_bottleneck_channels = 4
bg_texture_bottleneck_channels = 64
grid_cells = [6, 1, 7] # along z, y, x
total_grid_cells = grid_cells[0] * grid_cells[1] * grid_cells[2]
object_embedding_channels = 16
object_representation = hyper('voxels', 'obj-repr', str)
assert object_representation in {'voxels', 'mesh'}
if object_representation == 'mesh':
object_circumference_segments = 16
object_vertical_segments = 8
param_string = 'se-{}'.format(scene_embedding_channels)
param_string += '_bg[det-e-{}_b-{}-{}_sphere_{}c-{}l]'.format(bg_embedding_channels, bg_shape_bottleneck_channels, bg_texture_bottleneck_channels, room_circumference_segments, room_vertical_segments)
param_string += '_objects[{}det-e-{}]'.format('mesh_' if object_representation == 'mesh' else '', object_embedding_channels)
param_string += '_beta-{}_4-pyr'.format(beta)
def build_mesh(self, phi_max, theta_max, vertical_segments, circumference_segments):
vertices = []
faces = []
uvs = []
adjacencies = np.zeros([vertical_segments * circumference_segments, vertical_segments * circumference_segments], dtype=np.float32)
for phi_index, phi in enumerate(np.linspace(-phi_max, phi_max, vertical_segments)):
for theta_index, theta in enumerate(np.linspace(-theta_max, theta_max, circumference_segments)):
vertices.append([
np.sin(theta) * np.cos(phi),
np.sin(phi),
-np.cos(theta) * np.cos(phi)
])
uvs.append([
theta_index / (circumference_segments - 1),
phi_index / (vertical_segments - 1)
])
if phi_index > 0 and theta_index > 0:
bottom_right = phi_index * circumference_segments + theta_index
assert bottom_right == len(vertices) - 1
bottom_left = bottom_right - 1
top_right = bottom_right - circumference_segments
top_left = top_right - 1
faces.extend([
[top_left, top_right, bottom_right],
[top_left, bottom_right, bottom_left]
])
adjacencies[top_left, top_right] = adjacencies[top_right, top_left] = 1.
adjacencies[top_right, bottom_right] = adjacencies[bottom_right, top_right] = 1.
adjacencies[bottom_right, bottom_left] = adjacencies[bottom_left, bottom_right] = 1.
adjacencies[bottom_left, top_left] = adjacencies[top_left, bottom_left] = 1.
adjacencies[top_left, bottom_right] = adjacencies[bottom_right, top_left] = 1.
return vertices, faces, uvs, adjacencies
def __init__(self, gt):
super(RoomVae, self).__init__()
vertices, faces, uvs, adjacency_matrix = self.build_mesh(phi_max=1.2, theta_max=np.pi / 2 if frames_per_episode < 16 else np.pi, vertical_segments=self.room_vertical_segments, circumference_segments=self.room_circumference_segments)
print('room: {} vertices, {} faces'.format(len(vertices), len(faces)))
self.canonical_vertices = tf.constant(vertices, dtype=tf.float32)
self.canonical_faces = tf.constant(faces, dtype=tf.int32)
self.canonical_uvs = tf.constant(uvs, dtype=tf.float32)
self.canonical_vertices *= [gt.base_scale_xz, gt.base_scale_y, gt.base_scale_xz]
self.adjacency_matrix = tf.constant(adjacency_matrix, dtype=tf.float32)
self.laplacian = tf.eye(int(self.adjacency_matrix.shape[0])) - self.adjacency_matrix / tf.reduce_sum(self.adjacency_matrix, axis=1, keepdims=True)
self.creases = tf.constant(self.get_creases(faces), dtype=tf.int32) # ** we could replace get_creases, and just construct the crease data in build_cylinder, alongside the adjacency matrix
if self.object_representation == 'mesh':
object_vertices, object_faces, object_uvs, object_adjacency_matrix = self.build_mesh(phi_max=np.pi / 2, theta_max=np.pi, vertical_segments=self.object_vertical_segments, circumference_segments=self.object_circumference_segments)
print('objects: {} vertices, {} faces'.format(len(object_vertices), len(object_faces)))
self.object_canonical_vertices = tf.constant(object_vertices, dtype=tf.float32) * [1., 1., -1.] # flip along z so seam is initially away from camera
self.object_faces = tf.constant(object_faces, dtype=tf.int32)
self.object_uvs = tf.constant(object_uvs, dtype=tf.float32)
self.object_adjacency_matrix = tf.constant(object_adjacency_matrix, dtype=tf.float32)
self.object_laplacian = tf.eye(int(self.object_adjacency_matrix.shape[0])) - self.object_adjacency_matrix / tf.reduce_sum(self.object_adjacency_matrix, axis=1, keepdims=True)
self.object_creases = tf.constant(self.get_creases(object_faces), dtype=tf.int32) # ** we could replace get_creases, and just construct the crease data in build_cylinder, alongside the adjacency matrix
self.projection_matrix = gt.get_projection_matrix()
self.base_scale_xz = gt.base_scale_xz
self.base_scale_y = gt.base_scale_y
self.grid_centres = (tf.stack(tf.meshgrid(
(tf.range(self.grid_cells[0], dtype=tf.float32) + 0.5) / self.grid_cells[0],
(tf.range(self.grid_cells[1], dtype=tf.float32) + 0.5) / self.grid_cells[1],
(tf.range(self.grid_cells[2], dtype=tf.float32) + 0.5) / self.grid_cells[2],
indexing='ij'
), axis=-1)[..., ::-1] - 0.5) # indexed by grid-z, grid-y, grid-x, x/y/z
self.grid_centres = self.grid_centres * [gt.base_scale_xz * 0.75, gt.base_scale_y, gt.base_scale_xz] - [0., 0.5, gt.base_scale_xz / 2]
self.mask_visualisation_threshold = 0.25 if self.object_representation == 'voxels' else 0.5 # this is adjusted following evaluation
if not hyper(1, 'large-enc', int):
self.temporal_encoder = tf.keras.Sequential(name='temporal_encoder', layers=[
tf.keras.layers.Conv3D(32, kernel_size=[1, 7, 7], strides=[1, 2, 2], activation=tf.nn.relu),
utils.GroupNormalization(groups=4, reduction_axes=[-4, -3, -2]),
tf.keras.layers.Conv3D(48, kernel_size=[1, 3, 3], activation=tf.nn.relu),
tf.keras.layers.Conv3D(64, kernel_size=[2, 1, 1], activation=tf.nn.relu),
tf.keras.layers.MaxPool3D([1, 2, 2]),
utils.GroupNormalization(groups=4, reduction_axes=[-4, -3, -2]),
tf.keras.layers.Conv3D(48, kernel_size=[1, 3, 3], activation=tf.nn.relu),
tf.keras.layers.Conv3D(64, kernel_size=[2 if frames_per_episode > 2 else 1, 1, 1], activation=tf.nn.relu),
tf.keras.layers.MaxPool3D([1, 2, 2]),
utils.GroupNormalization(groups=4, reduction_axes=[-4, -3, -2]),
tf.keras.layers.Conv3D(128, kernel_size=[1, 3, 3], activation=tf.nn.relu),
tf.keras.layers.Flatten(),
utils.LayerNormalization(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
utils.ResDense(activation=tf.nn.relu),
])
else:
two_if_long_episodes = 2 if frames_per_episode > 14 else 1
self.temporal_encoder = tf.keras.Sequential(name='temporal_encoder', layers=[
tf.keras.layers.Conv3D(32, kernel_size=[two_if_long_episodes, 7, 7], strides=[two_if_long_episodes, 2, 2], activation=tf.nn.relu),
utils.GroupNormalization(groups=4, reduction_axes=[-4, -3, -2]),
tf.keras.layers.Conv3D(64, kernel_size=[1, 3, 3], strides=[1, 2, 2], activation=tf.nn.relu),
utils.Residual(tf.keras.layers.Conv3D(64, kernel_size=[1, 3, 3], activation=tf.nn.relu, padding='SAME')),
utils.GroupNormalization(groups=4, reduction_axes=[-4, -3, -2]),
tf.keras.layers.Conv3D(96, kernel_size=[2, 1, 1], activation=tf.nn.relu),
utils.GroupNormalization(groups=6, reduction_axes=[-4, -3, -2]),
tf.keras.layers.Conv3D(128, kernel_size=[two_if_long_episodes, 3, 3], strides=[two_if_long_episodes, 2, 2], activation=tf.nn.relu),
utils.Residual(tf.keras.layers.Conv3D(128, kernel_size=[1, 3, 3], activation=tf.nn.relu, padding='SAME')),
utils.GroupNormalization(groups=4, reduction_axes=[-4, -3, -2]),
tf.keras.layers.Conv3D(192, kernel_size=[2 if frames_per_episode > 2 else 1, 1, 1], activation=tf.nn.relu),
utils.GroupNormalization(groups=6, reduction_axes=[-4, -3, -2]),
tf.keras.layers.Conv3D(256, kernel_size=[1, 3, 3], activation=tf.nn.relu),
tf.keras.layers.Flatten(),
utils.LayerNormalization(),
tf.keras.layers.Dense(1024, activation=tf.nn.relu),
utils.ResDense(activation=tf.nn.relu),
])
self.camera_motion_encoder = tf.keras.Sequential(name='camera_motion_encoder', layers=[
tf.keras.layers.Dense(128, activation=tf.nn.elu),
utils.LayerNormalization(),
utils.ResDense(activation=tf.nn.elu),
utils.LayerNormalization(),
utils.ResDense(activation=tf.nn.elu),
utils.LayerNormalization(),
])
self.encoding_to_scene_embedding = tf.keras.layers.Dense(self.scene_embedding_channels * 2)
self.scene_embedding_to_bg_embedding = tf.keras.Sequential(name='scene_embedding_to_bg_embedding', layers=[
tf.keras.layers.Dense(128, activation=tf.nn.elu),
utils.ResDense(tf.nn.elu),
tf.keras.layers.Dense(self.bg_embedding_channels)
])
self.scene_embedding_to_object_params = tf.keras.Sequential(name='scene_embedding_to_object_params', layers=[
tf.keras.layers.Dense(128, activation=tf.nn.elu),
utils.ResDense(tf.nn.elu),
tf.keras.layers.Dense(self.total_grid_cells * (self.object_embedding_channels + 3 + (frames_per_episode - 1) * 3 + 1 + 1)) # appearance-embedding mean/sigma, xyz offset/velocities, presence, azimuth
])
self.bg_embedding_to_bottlenecks = tf.keras.layers.Dense(self.bg_shape_bottleneck_channels + self.bg_texture_bottleneck_channels)
# ** Note this is tied to the values of room_circumference_segments and room_vertical_segments
self.bg_bottleneck_to_offsets = tf.keras.Sequential(name='bg_embedding_to_offsets', layers=[
tf.keras.layers.Dense(480, activation=tf.nn.elu),
tf.keras.layers.Reshape([3, 8, -1]),
tf.keras.layers.Conv2D(96, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=96, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(64, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=64, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(48, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=48, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(32, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=32, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.Conv2D(4, kernel_size=[3, 3], padding='same'),
])
self.bg_bottleneck_to_texture = tf.keras.Sequential(name='bg_embedding_to_texture', layers=[
tf.keras.layers.Dense(720, activation=tf.nn.elu),
tf.keras.layers.Reshape([6, 12, -1]),
tf.keras.layers.Conv2D(128, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=128, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(96, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=96, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(64, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=64, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(48, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=48, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(32, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=32, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(24, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=24, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.Conv2D(3, kernel_size=[3, 3])
])
if self.object_representation == 'voxels':
if hyper(1, 'large-voxels', int):
voxel_dec_initial_size = 3
else:
voxel_dec_initial_size = 2
voxel_dec_final_size = voxel_dec_initial_size * 8
self.object_embedding_to_appearance = tf.keras.Sequential(name='object_embedding_to_appearance', layers=[
tf.keras.layers.Dense(voxel_dec_initial_size * voxel_dec_initial_size * 64, activation=tf.nn.elu),
tf.keras.layers.Reshape([voxel_dec_initial_size, voxel_dec_initial_size, 64]),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(64, kernel_size=3, padding='SAME', activation=tf.nn.elu),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(48, kernel_size=3, padding='SAME', activation=tf.nn.elu),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(32, kernel_size=3, padding='SAME', activation=tf.nn.elu),
tf.keras.layers.Conv2D(voxel_dec_final_size * 4, kernel_size=4, padding='SAME', activation=None),
tf.keras.layers.Reshape([voxel_dec_final_size, voxel_dec_final_size, voxel_dec_final_size, 4]),
tf.keras.layers.Permute([3, 1, 2, 4])
])
elif self.object_representation == 'mesh':
# ** Note this is tied to the values of object_circumference_segments and object_vertical_segments
self.object_embedding_to_offsets = tf.keras.Sequential(name='object_embedding_to_offsets', layers=[
tf.keras.layers.Dense(128, activation=tf.nn.elu),
tf.keras.layers.Reshape([2, 4, -1]),
tf.keras.layers.Conv2D(96, kernel_size=[2, 2], activation=tf.nn.elu, padding='same'),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(64, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=64, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(48, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
tf.keras.layers.Conv2D(4, kernel_size=1, kernel_initializer='zeros')
])
self.object_embedding_to_texture = tf.keras.Sequential(name='object_embedding_to_texture', layers=[
tf.keras.layers.Dense(180, activation=tf.nn.elu),
tf.keras.layers.Reshape([3, 6, -1]),
tf.keras.layers.Conv2D(96, kernel_size=[2, 2], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=96, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(),
tf.keras.layers.Conv2D(64, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
utils.Residual(tf.keras.layers.Conv2D(filters=64, kernel_size=1, activation=tf.nn.elu)),
# tf.keras.layers.UpSampling2D(),
# tf.keras.layers.Conv2D(32, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
# utils.Residual(tf.keras.layers.Conv2D(filters=32, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.UpSampling2D(size=4, interpolation='bilinear'),
tf.keras.layers.Conv2D(24, kernel_size=[3, 3], activation=tf.nn.elu, padding='same'),
# utils.Residual(tf.keras.layers.Conv2D(filters=24, kernel_size=1, activation=tf.nn.elu)),
tf.keras.layers.Conv2D(3, kernel_size=1)
])
if self.beta == self.initial_beta:
beta = self.beta
else:
def beta():
assert self.beta > self.initial_beta and self.beta_anneal_duration > 0
iteration = tf.cast(tf.train.get_global_step(), tf.float32)
annealed_beta = self.initial_beta + (self.beta - self.initial_beta) * (iteration - self.beta_anneal_start) / self.beta_anneal_duration
return tf.clip_by_value(annealed_beta, self.initial_beta, self.beta)
self.inferencer = IntegratedEagerKlqp(
self.generative,
self.variational,
integrated_name_to_values={},
conditioning_names=['maybe_camera_matrices'],
beta=beta,
verbose=True
)
self.optimiser = tf.train.AdamOptimizer(1.e-4)
tf.train.create_global_step()
self.checkpoint = tf.train.Checkpoint(optimizer=self.optimiser, model=self)
restore_suffix_and_iteration = hyper('', 'restore', str) # path@iteration, path is either the part after param-string but before 'checkpoints', i.e. seed and timestamp, or the whole identifier
if restore_suffix_and_iteration != '':
restore_suffix, restore_iteration = restore_suffix_and_iteration.split('@')
if restore_suffix.count('/') > 1:
self.restored_job_name = os.path.join(output_base_path, restore_suffix)
else:
self.restored_job_name = os.path.join(output_base_path, gt.data_string, self.param_string, restore_suffix)
checkpoint_path = os.path.join(self.restored_job_name, 'checkpoints')
self.temporary_path_for_deletion = tempfile.mkdtemp() # this is necesssary as our original paths are often too long for tensorflow to handle
shortened_path = os.path.join(self.temporary_path_for_deletion, 'ckpt')
os.symlink(checkpoint_path, shortened_path)
shortened_path = os.path.join(shortened_path, 'ckpt-{}'.format(restore_iteration))
print('restoring checkpoint ' + checkpoint_path + ' via symlink ' + shortened_path)
self.checkpoint.restore(shortened_path)
self.first_iteration = int(restore_iteration) + 1 # add one as we save the checkpoint after the gradient update for that iteration, so we start with the next one
else:
self.temporary_path_for_deletion = None
self.restored_job_name = None
self.first_iteration = 0
def get_room_vertices(self, vertex_offsets, transform_clip_and_hinge):
vertex_offsets = tf.reshape(vertex_offsets, [episodes_per_batch, self.room_vertical_segments * self.room_circumference_segments, 4])
room_vertices = self.canonical_vertices * tf.exp(transform_clip_and_hinge(vertex_offsets[..., :1], -2., 2.5))
room_vertices += tf.tanh(vertex_offsets[..., 1:]) * 1.
room_vertices = tf.concat([room_vertices, tf.ones_like(room_vertices[..., :1])], axis=-1) # :: eib, vertex, x/y/z/w
return room_vertices
def get_object_vertices(self, vertex_offsets, transform_clip_and_hinge):
vertex_offsets = tf.reshape(
vertex_offsets,
[episodes_per_batch, self.total_grid_cells, self.object_vertical_segments * self.object_circumference_segments, 4]
)
object_vertices = self.object_canonical_vertices * tf.exp(transform_clip_and_hinge(vertex_offsets[..., :1], -0.7, 0.7))
object_vertices += tf.tanh(vertex_offsets[..., 1:]) * 0.2
object_vertices = tf.concat([object_vertices, tf.ones_like(object_vertices[..., :1])], axis=-1) # :: eib, obj, vertex, x/y/z/w
return object_vertices
def get_l2_laplacian_loss(self, vertices, laplacian):
delta_coordinates = tf.matmul(tf.tile(laplacian[None, :, :], [int(vertices.shape[0]), 1, 1]), vertices[:, :, :-1]) # indexed by eib, vertex, x/y/z
delta_norms = tf.norm(delta_coordinates, axis=2)
return tf.reduce_mean(delta_norms, axis=1)
def get_creases(self, faces):
# Result is indexed by crease-index, 1st endpoint / 2nd endpoint / 'left' other-vertex / 'right' other-vertex
# There is one entry per edge in the mesh; pairs of endpoints correspond to the actual edges; 'left'/'right' vertices are the
# other vertices that belong to the two face that including the relevant edge
# Note that this assumes 'simple' topology, with exactly one or two faces touching each edge; edges with only one face
# touching are not included in the result (as they do not constitute a crease)