def grid_from_latents(z,
dmodel,
rows,
cols,
anchor_images,
tight,
shoulders,
save_path,
args=None,
batch_size=24,
template_dict={},
emb_l=None):
use_embedded = True
if emb_l is None:
emb_l = [None] * len(z)
use_embedded = False
z_queue = z[:]
e_queue = emb_l[:]
samples = None
# print("========> DECODING {} at a time".format(batch_size))
while (len(z_queue) > 0):
cur_z = z_queue[:batch_size]
cur_e = e_queue[:batch_size]
z_queue = z_queue[batch_size:]
e_queue = e_queue[batch_size:]
if use_embedded:
decoded = dmodel.decode_embedded(cur_z, cur_e)
else:
decoded = dmodel.sample_at(cur_z)
if samples is None:
samples = decoded
else:
samples = np.concatenate((samples, decoded), axis=0)
# samples = dmodel.sample_at(z)
if shoulders:
samples, rows, cols = add_shoulders(samples, anchor_images, rows, cols)
try:
one_sample = next(item for item in samples if item is not None)
except StopIteration:
print("No samples found to save")
return
# each sample is 3xsizexsize
template_dict["SIZE"] = one_sample.shape[1]
final_save_path = emit_filename(save_path, template_dict, args)
after_last_slash = final_save_path.rfind("/") + 1
outfile_temp = final_save_path[:after_last_slash] + '_' + final_save_path[
after_last_slash:]
print("Saving image file {}".format(final_save_path))
dirname = os.path.dirname(final_save_path)
if dirname != '' and not os.path.exists(dirname):
os.makedirs(dirname)
img = grid2img(samples, rows, cols, not tight)
img.save(outfile_temp)
os.rename(outfile_temp, final_save_path)
os.system("touch {}".format(final_save_path))
def grid_from_latents(z,
dmodel,
rows,
cols,
anchor_images,
tight,
shoulders,
save_path,
batch_size=24):
z_queue = z[:]
samples = None
# print("========> DECODING {} at a time".format(batch_size))
while (len(z_queue) > 0):
cur_z = z_queue[:batch_size]
z_queue = z_queue[batch_size:]
decoded = dmodel.sample_at(cur_z)
if samples is None:
samples = decoded
else:
samples = np.concatenate((samples, decoded), axis=0)
# samples = dmodel.sample_at(z)
if shoulders:
samples, rows, cols = add_shoulders(samples, anchor_images, rows, cols)
print('Preparing image grid...')
img = grid2img(samples, rows, cols, not tight)
img.save(save_path)
def grid_from_latents(z, dmodel, rows, cols, anchor_images, tight, shoulders, save_path, batch_size=24):
z_queue = z[:]
samples = None
# print("========> DECODING {} at a time".format(batch_size))
while(len(z_queue) > 0):
cur_z = z_queue[:batch_size]
z_queue = z_queue[batch_size:]
decoded = dmodel.sample_at(cur_z)
if samples is None:
samples = decoded
else:
samples = np.concatenate((samples, decoded), axis=0)
# samples = dmodel.sample_at(z)
if shoulders:
samples, rows, cols = add_shoulders(samples, anchor_images, rows, cols)
print('Preparing image grid...')
img = grid2img(samples, rows, cols, not tight)
img.save(save_path)
def run_with_args(args, dmodel, cur_anchor_image, cur_save_path, cur_z_step):
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
anchor_images = None
if args.anchors:
_, get_anchor_images = lazy_init_fuel_dependencies()
allowed = None
prohibited = None
include_targets = False
if(args.allowed):
include_targets = True
allowed = map(int, args.allowed.split(","))
if(args.prohibited):
include_targets = True
prohibited = map(int, args.prohibited.split(","))
anchor_images = get_anchor_images(args.dataset, args.split, args.offset, args.stepsize, args.numanchors, allowed, prohibited, args.image_size, args.color_convert, include_targets=include_targets)
if cur_anchor_image is not None:
_, _, anchor_images = anchors_from_image(cur_anchor_image, image_size=(args.image_size, args.image_size))
if args.offset > 0:
anchor_images = anchor_images[args.offset:]
# untested
if args.numanchors is not None:
anchor_images = anchor_images[:args.numanchors]
if args.passthrough:
print('Preparing image grid...')
img = grid2img(anchor_images, args.rows, args.cols, not args.tight)
img.save(cur_save_path)
sys.exit(0)
if dmodel is None:
model_class_parts = args.model_class.split(".")
model_class_name = model_class_parts[-1]
model_module_name = ".".join(model_class_parts[:-1])
print("Loading {} interface from {}".format(model_class_name, model_module_name))
ModelClass = getattr(importlib.import_module(model_module_name), model_class_name)
print("Loading model from {}".format(args.model))
dmodel = ModelClass(filename=args.model)
if anchor_images is not None:
x_queue = anchor_images[:]
anchors = None
# print("========> ENCODING {} at a time".format(args.batch_size))
while(len(x_queue) > 0):
cur_x = x_queue[:args.batch_size]
x_queue = x_queue[args.batch_size:]
encoded = dmodel.encode_images(cur_x)
if anchors is None:
anchors = encoded
else:
anchors = np.concatenate((anchors, encoded), axis=0)
# anchors = dmodel.encode_images(anchor_images)
elif args.anchor_vectors is not None:
anchors = get_json_vectors(args.anchor_vectors)
else:
anchors = None
if args.invert_anchors:
anchors = -1 * anchors
if args.encoder:
if anchors is not None:
output_vectors(anchors)
else:
stream_output_vectors(dmodel, args.dataset, args.split, batch_size=args.batch_size)
sys.exit(0)
global_offset = None
if args.anchor_offset is not None:
# compute anchors as offsets from existing anchor
offsets = get_json_vectors(args.anchor_offset)
if args.anchor_noise:
anchors = anchors_noise_offsets(anchors, offsets, args.rows, args.cols, args.spacing,
cur_z_step, args.anchor_offset_x, args.anchor_offset_y,
args.anchor_offset_x_minscale, args.anchor_offset_y_minscale, args.anchor_offset_x_maxscale, args.anchor_offset_y_maxscale)
else:
anchors = anchors_from_offsets(anchors[0], offsets, args.anchor_offset_x, args.anchor_offset_y,
args.anchor_offset_x_minscale, args.anchor_offset_y_minscale, args.anchor_offset_x_maxscale, args.anchor_offset_y_maxscale)
if args.global_offset is not None:
offsets = get_json_vectors(args.global_offset)
if args.global_ramp:
offsets = cur_z_step * offsets
global_offset = get_global_offset(offsets, args.global_indices, args.global_scale)
z_dim = dmodel.get_zdim()
# I don't remember what partway/encircle do so they are not handling the chain layout
# this handles the case (at least) of mines with random anchors
if (args.partway is not None) or args.encircle or (args.mine and anchors is None):
srows=((args.rows // args.spacing) + 1)
scols=((args.cols // args.spacing) + 1)
rand_anchors = generate_latent_grid(z_dim, rows=srows, cols=scols, fan=False, gradient=False,
spherical=False, gaussian=False, anchors=None, anchor_images=None, mine=False, chain=False,
spacing=args.spacing, analogy=False, rand_uniform=args.uniform)
if args.partway is not None:
l = len(rand_anchors)
clipped_anchors = anchors[:l]
anchors = (1.0 - args.partway) * rand_anchors + args.partway * clipped_anchors
elif args.encircle:
anchors = surround_anchors(srows, scols, anchors, rand_anchors)
else:
anchors = rand_anchors
z = generate_latent_grid(z_dim, args.rows, args.cols, args.fan, args.gradient, not args.linear, args.gaussian,
anchors, anchor_images, args.mine, args.chain, args.spacing, args.analogy)
if global_offset is not None:
z = z + global_offset
grid_from_latents(z, dmodel, args.rows, args.cols, anchor_images, args.tight, args.shoulders, cur_save_path, args.batch_size)
return dmodel
def run_with_args(args,
dmodel,
cur_anchor_image,
cur_save_path,
cur_z_step,
cur_basename="basename",
range_data=None,
template_dict={}):
anchor_images = None
anchor_labels = None
if args.anchors:
allowed = None
prohibited = None
include_targets = False
if (args.allowed):
include_targets = True
allowed = map(int, args.allowed.split(","))
if (args.prohibited):
include_targets = True
prohibited = map(int, args.prohibited.split(","))
anchor_images = get_anchor_images(args.dataset,
args.split,
args.offset,
args.stepsize,
args.numanchors,
allowed,
prohibited,
args.image_size,
args.color_convert,
include_targets=include_targets)
if args.with_labels:
anchor_labels = get_anchor_labels(args.dataset, args.split,
args.offset, args.stepsize,
args.numanchors)
if args.anchor_glob is not None:
files = plat.sampling.real_glob(args.anchor_glob)
if args.offset > 0:
files = files[args.offset:]
if args.stepsize > 1:
files = files[::args.stepsize]
if args.numanchors is not None:
files = files[:args.numanchors]
anchor_images = anchors_from_filelist(files)
print("Read {} images from {} files".format(len(anchor_images),
len(files)))
if len(anchor_images) == 0:
print("No images, cannot contine")
sys.exit(0)
if cur_anchor_image is not None:
_, _, anchor_images = anchors_from_image(cur_anchor_image,
image_size=(args.image_size,
args.image_size))
if args.offset > 0:
anchor_images = anchor_images[args.offset:]
if args.stepsize > 0:
anchor_images = anchor_images[::args.stepsize]
if args.numanchors is not None:
anchor_images = anchor_images[:args.numanchors]
# at this point we can make a dummy anchor_labels if we need
if anchor_images is not None and anchor_labels is None:
anchor_labels = [None] * len(anchor_images)
if args.passthrough:
# determine final filename string
image_size = anchor_images[0].shape[1]
save_path = plat.sampling.emit_filename(cur_save_path, {}, args)
print("Preparing image file {}".format(save_path))
img = grid2img(anchor_images, args.rows, args.cols, not args.tight)
img.save(save_path)
sys.exit(0)
if dmodel is None:
dmodel = zoo.load_model(args.model, args.model_file, args.model_type,
args.model_interface)
embedded = None
if anchor_images is not None:
x_queue = anchor_images[:]
c_queue = anchor_labels[:]
anchors = None
# print("========> ENCODING {} at a time".format(args.batch_size))
while (len(x_queue) > 0):
cur_x = x_queue[:args.batch_size]
cur_c = c_queue[:args.batch_size]
x_queue = x_queue[args.batch_size:]
c_queue = c_queue[args.batch_size:]
encoded = dmodel.encode_images(cur_x, cur_c)
try:
emb_l = dmodel.embed_labels(cur_c)
except AttributeError:
emb_l = [None] * args.batch_size
if anchors is None:
anchors = encoded
embedded = emb_l
else:
anchors = np.concatenate((anchors, encoded), axis=0)
embedded = np.concatenate((embedded, emb_l), axis=0)
# anchors = dmodel.encode_images(anchor_images)
elif args.anchor_vectors is not None:
anchors = get_json_vectors(args.anchor_vectors)
else:
anchors = None
if args.invert_anchors:
anchors = -1 * anchors
if args.encoder:
if anchors is not None:
plat.sampling.output_vectors(anchors, args.save_path)
else:
plat.sampling.stream_output_vectors(dmodel,
args.dataset,
args.split,
args.save_path,
batch_size=args.batch_size)
sys.exit(0)
global_offset = None
if args.anchor_offset is not None:
# compute anchors as offsets from existing anchor
offsets = get_json_vectors(args.anchor_offset)
if args.anchor_wave:
anchors = plat.sampling.anchors_wave_offsets(
anchors, offsets, args.rows, args.cols, args.spacing,
args.radial_wave, args.clip_wave, cur_z_step,
args.anchor_offset_x, args.anchor_offset_x_minscale,
args.anchor_offset_x_maxscale)
elif args.anchor_noise:
anchors = plat.sampling.anchors_noise_offsets(
anchors, offsets, args.rows, args.cols, args.spacing,
cur_z_step, args.anchor_offset_x, args.anchor_offset_y,
args.anchor_offset_x_minscale, args.anchor_offset_y_minscale,
args.anchor_offset_x_maxscale, args.anchor_offset_y_maxscale)
elif range_data is not None:
anchors = plat.sampling.anchors_json_offsets(
anchors, offsets, args.rows, args.cols, args.spacing,
cur_z_step, args.anchor_offset_x, args.anchor_offset_y,
args.anchor_offset_x_minscale, args.anchor_offset_y_minscale,
args.anchor_offset_x_maxscale, args.anchor_offset_y_maxscale,
range_data)
else:
anchors = plat.sampling.anchors_from_offsets(
anchors[0], offsets, args.anchor_offset_x,
args.anchor_offset_y, args.anchor_offset_x_minscale,
args.anchor_offset_y_minscale, args.anchor_offset_x_maxscale,
args.anchor_offset_y_maxscale)
if args.global_offset is not None:
offsets = get_json_vectors(args.global_offset)
if args.global_ramp:
offsets = cur_z_step * offsets
global_offset = plat.sampling.get_global_offset(
offsets, args.global_indices, args.global_scale)
z_dim = dmodel.get_zdim()
# I don't remember what partway/encircle do so they are not handling the chain layout
# this handles the case (at least) of mines with random anchors
if (args.partway is not None) or args.encircle or (anchors is None):
srows = ((args.rows // args.spacing) + 1)
scols = ((args.cols // args.spacing) + 1)
rand_anchors = plat.sampling.generate_latent_grid(
z_dim,
rows=srows,
cols=scols,
fan=False,
gradient=False,
spherical=False,
gaussian=False,
anchors=None,
anchor_images=None,
mine=False,
chain=False,
spacing=args.spacing,
analogy=False,
rand_uniform=args.uniform)
if args.partway is not None:
l = len(rand_anchors)
clipped_anchors = anchors[:l]
anchors = (1.0 - args.partway
) * rand_anchors + args.partway * clipped_anchors
elif args.encircle:
anchors = surround_anchors(srows, scols, anchors, rand_anchors)
else:
anchors = rand_anchors
z = plat.sampling.generate_latent_grid(z_dim, args.rows, args.cols,
args.fan, args.gradient,
not args.linear, args.gaussian,
anchors, anchor_images, True,
args.chain, args.spacing,
args.analogy)
if global_offset is not None:
z = z + global_offset
template_dict["BASENAME"] = cur_basename
# emb_l = None
# emb_l = [None] * len(z)
if args.clone_label is not None:
emb_l = np.tile(embedded[args.clone_label], [len(z), 1])
else:
emb_l = plat.sampling.generate_latent_grid(
z_dim, args.rows, args.cols, args.fan, args.gradient,
not args.linear, args.gaussian, embedded, anchor_images, True,
args.chain, args.spacing, args.analogy)
#TODO - maybe not best way to check if labels are valid
# if anchor_labels is None or anchor_labels[0] is None:
# emb_l = [None] * len(z)
plat.sampling.grid_from_latents(z,
dmodel,
args.rows,
args.cols,
anchor_images,
args.tight,
args.shoulders,
cur_save_path,
args,
args.batch_size,
template_dict=template_dict,
emb_l=emb_l)
return dmodel
def run_with_args(args, dmodel, cur_anchor_image, cur_save_path, cur_z_step, cur_basename="basename", range_data=None, template_dict={}):
anchor_images = None
anchor_labels = None
if args.anchors:
allowed = None
prohibited = None
include_targets = False
if(args.allowed):
include_targets = True
allowed = map(int, args.allowed.split(","))
if(args.prohibited):
include_targets = True
prohibited = map(int, args.prohibited.split(","))
anchor_images = get_anchor_images(args.dataset, args.split, args.offset, args.stepsize, args.numanchors, allowed, prohibited, args.image_size, args.color_convert, include_targets=include_targets)
if args.with_labels:
anchor_labels = get_anchor_labels(args.dataset, args.split, args.offset, args.stepsize, args.numanchors)
if args.anchor_glob is not None:
files = plat.sampling.real_glob(args.anchor_glob)
if args.offset > 0:
files = files[args.offset:]
if args.stepsize > 1:
files = files[::args.stepsize]
if args.numanchors is not None:
files = files[:args.numanchors]
anchor_images = anchors_from_filelist(files, args.channels)
print("Read {} images from {} files".format(len(anchor_images), len(files)))
print("First 5 files: ", files[:5])
if len(anchor_images) == 0:
print("No images, cannot contine")
sys.exit(0)
if cur_anchor_image is not None:
# _, _, anchor_images = anchors_from_image(cur_anchor_image, channels=args.channels, image_size=(args.image_size, args.image_size))
anchor_images = anchors_from_filelist([cur_anchor_image], channels=args.channels)
if args.offset > 0:
anchor_images = anchor_images[args.offset:]
if args.stepsize > 0:
anchor_images = anchor_images[::args.stepsize]
if args.numanchors is not None:
anchor_images = anchor_images[:args.numanchors]
# at this point we can make a dummy anchor_labels if we need
if anchor_images is not None and anchor_labels is None:
anchor_labels = [None] * len(anchor_images)
if args.passthrough:
# determine final filename string
image_size = anchor_images[0].shape[1]
save_path = plat.sampling.emit_filename(cur_save_path, {}, args);
print("Preparing image file {}".format(save_path))
img = grid2img(anchor_images, args.rows, args.cols, not args.tight)
img.save(save_path)
sys.exit(0)
if dmodel is None:
dmodel = zoo.load_model(args.model, args.model_file, args.model_type, args.model_interface)
if args.seed is not None:
print("Setting random seed to ", args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
else:
np.random.seed(None)
random.seed(None)
embedded = None
if anchor_images is not None:
x_queue = anchor_images[:]
c_queue = anchor_labels[:]
anchors = None
# print("========> ENCODING {} at a time".format(args.batch_size))
while(len(x_queue) > 0):
cur_x = x_queue[:args.batch_size]
cur_c = c_queue[:args.batch_size]
x_queue = x_queue[args.batch_size:]
c_queue = c_queue[args.batch_size:]
# TODO: remove vestiges of conditional encode/decode
# encoded = dmodel.encode_images(cur_x, cur_c)
encoded = dmodel.encode_images(cur_x)
try:
emb_l = dmodel.embed_labels(cur_c)
except AttributeError:
emb_l = [None] * args.batch_size
if anchors is None:
anchors = encoded
embedded = emb_l
else:
anchors = np.concatenate((anchors, encoded), axis=0)
embedded = np.concatenate((embedded, emb_l), axis=0)
# anchors = dmodel.encode_images(anchor_images)
elif args.anchor_vectors is not None:
anchors = get_json_vectors(args.anchor_vectors)
# print("Read vectors: ", anchors.shape)
vsize = anchors.shape[-1]
anchors = anchors.reshape([-1, vsize])
print("Read vectors: ", anchors.shape)
else:
anchors = None
if args.invert_anchors:
anchors = -1 * anchors
if args.encoder:
if anchors is not None:
plat.sampling.output_vectors(anchors, args.save_path)
else:
plat.sampling.stream_output_vectors(dmodel, args.dataset, args.split, args.save_path, batch_size=args.batch_size)
sys.exit(0)
global_offset = None
if args.anchor_offset is not None:
# compute anchors as offsets from existing anchor
offsets = get_json_vectors_list(args.anchor_offset)
if args.anchor_wave:
anchors = plat.sampling.anchors_wave_offsets(anchors, offsets, args.rows, args.cols, args.spacing,
args.radial_wave, args.clip_wave, cur_z_step, args.anchor_offset_x,
args.anchor_offset_x_minscale, args.anchor_offset_x_maxscale)
elif args.anchor_noise:
anchors = plat.sampling.anchors_noise_offsets(anchors, offsets, args.rows, args.cols, args.spacing,
cur_z_step, args.anchor_offset_x, args.anchor_offset_y,
args.anchor_offset_x_minscale, args.anchor_offset_y_minscale, args.anchor_offset_x_maxscale, args.anchor_offset_y_maxscale)
elif range_data is not None:
anchors = plat.sampling.anchors_json_offsets(anchors, offsets, args.rows, args.cols, args.spacing,
cur_z_step, args.anchor_offset_x, args.anchor_offset_y,
args.anchor_offset_x_minscale, args.anchor_offset_y_minscale, args.anchor_offset_x_maxscale, args.anchor_offset_y_maxscale,
range_data)
else:
anchors = plat.sampling.anchors_from_offsets(anchors[0], offsets, args.anchor_offset_x, args.anchor_offset_y,
args.anchor_offset_x_minscale, args.anchor_offset_y_minscale, args.anchor_offset_x_maxscale, args.anchor_offset_y_maxscale)
if args.global_offset is not None:
offsets = get_json_vectors(args.global_offset)
if args.global_ramp:
offsets = cur_z_step * offsets
global_offset = plat.sampling.get_global_offset(offsets, args.global_indices, args.global_scale)
z_dim = dmodel.get_zdim()
# I don't remember what partway/encircle do so they are not handling the chain layout
# this handles the case (at least) of mines with random anchors
if (args.partway is not None) or args.encircle or (anchors is None):
srows=((args.rows // args.spacing) + 1)
scols=((args.cols // args.spacing) + 1)
rand_anchors = plat.sampling.generate_latent_grid(z_dim, rows=srows, cols=scols, fan=False, gradient=False,
spherical=False, gaussian=False, anchors=None, anchor_images=None, mine=False, chain=False,
spacing=args.spacing, analogy=False, rand_uniform=args.uniform)
if args.partway is not None:
l = len(rand_anchors)
clipped_anchors = anchors[:l]
anchors = (1.0 - args.partway) * rand_anchors + args.partway * clipped_anchors
elif args.encircle:
anchors = surround_anchors(srows, scols, anchors, rand_anchors)
else:
anchors = rand_anchors
z = plat.sampling.generate_latent_grid(z_dim, args.rows, args.cols, args.fan, args.gradient, not args.linear, args.gaussian,
anchors, anchor_images, True, args.chain, args.spacing, args.analogy)
if args.write_anchors:
plat.sampling.output_vectors(anchors, "anchors.json")
if global_offset is not None:
z = z + global_offset
template_dict["BASENAME"] = cur_basename
# emb_l = None
# emb_l = [None] * len(z)
embedded_labels = None
# TODO: this could be more elegant
if embedded is not None and embedded[0] is not None:
if args.clone_label is not None:
embedded_labels = np.tile(embedded[args.clone_label], [len(z), 1])
else:
embedded_labels = plat.sampling.generate_latent_grid(z_dim, args.rows, args.cols, args.fan, args.gradient, not args.linear, args.gaussian,
embedded, anchor_images, True, args.chain, args.spacing, args.analogy)
#TODO - maybe not best way to check if labels are valid
# if anchor_labels is None or anchor_labels[0] is None:
# emb_l = [None] * len(z)
plat.sampling.grid_from_latents(z, dmodel, args.rows, args.cols, anchor_images, args.tight, args.shoulders, cur_save_path, args, args.batch_size, template_dict=template_dict, emb_l=embedded_labels)
return dmodel
def run_with_args(args, dmodel, cur_anchor_image, cur_save_path, cur_z_step):
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
anchor_images = None
if args.anchors:
_, get_anchor_images = lazy_init_fuel_dependencies()
allowed = None
prohibited = None
include_targets = False
if (args.allowed):
include_targets = True
allowed = map(int, args.allowed.split(","))
if (args.prohibited):
include_targets = True
prohibited = map(int, args.prohibited.split(","))
anchor_images = get_anchor_images(args.dataset,
args.split,
args.offset,
args.stepsize,
args.numanchors,
allowed,
prohibited,
args.image_size,
args.color_convert,
include_targets=include_targets)
if cur_anchor_image is not None:
_, _, anchor_images = anchors_from_image(cur_anchor_image,
image_size=(args.image_size,
args.image_size))
if args.offset > 0:
anchor_images = anchor_images[args.offset:]
# untested
if args.numanchors is not None:
anchor_images = anchor_images[:args.numanchors]
if args.passthrough:
print('Preparing image grid...')
img = grid2img(anchor_images, args.rows, args.cols, not args.tight)
img.save(cur_save_path)
sys.exit(0)
if dmodel is None:
model_class_parts = args.model_class.split(".")
model_class_name = model_class_parts[-1]
model_module_name = ".".join(model_class_parts[:-1])
print("Loading {} interface from {}".format(model_class_name,
model_module_name))
ModelClass = getattr(importlib.import_module(model_module_name),
model_class_name)
print("Loading model from {}".format(args.model))
dmodel = ModelClass(filename=args.model)
if anchor_images is not None:
x_queue = anchor_images[:]
anchors = None
# print("========> ENCODING {} at a time".format(args.batch_size))
while (len(x_queue) > 0):
cur_x = x_queue[:args.batch_size]
x_queue = x_queue[args.batch_size:]
encoded = dmodel.encode_images(cur_x)
if anchors is None:
anchors = encoded
else:
anchors = np.concatenate((anchors, encoded), axis=0)
# anchors = dmodel.encode_images(anchor_images)
elif args.anchor_vectors is not None:
anchors = get_json_vectors(args.anchor_vectors)
else:
anchors = None
if args.invert_anchors:
anchors = -1 * anchors
if args.encoder:
if anchors is not None:
output_vectors(anchors)
else:
stream_output_vectors(dmodel,
args.dataset,
args.split,
batch_size=args.batch_size)
sys.exit(0)
global_offset = None
if args.anchor_offset is not None:
# compute anchors as offsets from existing anchor
offsets = get_json_vectors(args.anchor_offset)
if args.anchor_noise:
anchors = anchors_noise_offsets(
anchors, offsets, args.rows, args.cols, args.spacing,
cur_z_step, args.anchor_offset_x, args.anchor_offset_y,
args.anchor_offset_x_minscale, args.anchor_offset_y_minscale,
args.anchor_offset_x_maxscale, args.anchor_offset_y_maxscale)
else:
anchors = anchors_from_offsets(
anchors[0], offsets, args.anchor_offset_x,
args.anchor_offset_y, args.anchor_offset_x_minscale,
args.anchor_offset_y_minscale, args.anchor_offset_x_maxscale,
args.anchor_offset_y_maxscale)
if args.global_offset is not None:
offsets = get_json_vectors(args.global_offset)
if args.global_ramp:
offsets = cur_z_step * offsets
global_offset = get_global_offset(offsets, args.global_indices,
args.global_scale)
z_dim = dmodel.get_zdim()
# I don't remember what partway/encircle do so they are not handling the chain layout
# this handles the case (at least) of mines with random anchors
if (args.partway is not None) or args.encircle or (args.mine
and anchors is None):
srows = ((args.rows // args.spacing) + 1)
scols = ((args.cols // args.spacing) + 1)
rand_anchors = generate_latent_grid(z_dim,
rows=srows,
cols=scols,
fan=False,
gradient=False,
spherical=False,
gaussian=False,
anchors=None,
anchor_images=None,
mine=False,
chain=False,
spacing=args.spacing,
analogy=False,
rand_uniform=args.uniform)
if args.partway is not None:
l = len(rand_anchors)
clipped_anchors = anchors[:l]
anchors = (1.0 - args.partway
) * rand_anchors + args.partway * clipped_anchors
elif args.encircle:
anchors = surround_anchors(srows, scols, anchors, rand_anchors)
else:
anchors = rand_anchors
z = generate_latent_grid(z_dim, args.rows, args.cols, args.fan,
args.gradient, not args.linear, args.gaussian,
anchors, anchor_images, args.mine, args.chain,
args.spacing, args.analogy)
if global_offset is not None:
z = z + global_offset
grid_from_latents(z, dmodel, args.rows, args.cols, anchor_images,
args.tight, args.shoulders, cur_save_path,
args.batch_size)
return dmodel