def __init__(self, interface, z_dim, image_size, channels, dataset, split, save_subdir, **kwargs):
super(SampleCheckpoint, self).__init__(path=None, **kwargs)
self.interface = interface
self.image_size = image_size
self.channels = channels
self.save_subdir = save_subdir
self.iteration = 0
self.epoch_src = "{0}/sample.png".format(save_subdir)
self.rows=7
self.cols=10
self.spacing = 3
self.z_dim = z_dim
numanchors = 10 + 10
#putting this import here allows serialized models to be loaded without chips
from chips.fuel_helper import get_anchor_images
self.anchor_images = get_anchor_images(dataset, split, numanchors=numanchors, image_size=image_size, include_targets=False)
if not os.path.exists(self.save_subdir):
os.makedirs(self.save_subdir)
def __init__(self, interface, z_dim, image_size, channels, dataset, split,
save_subdir, **kwargs):
super(SampleCheckpoint, self).__init__(path=None, **kwargs)
self.interface = interface
self.image_size = image_size
self.channels = channels
self.save_subdir = save_subdir
self.iteration = 0
self.epoch_src = "{0}/sample.png".format(save_subdir)
self.rows = 7
self.cols = 10
self.spacing = 3
self.z_dim = z_dim
numanchors = 10 + 10
#putting this import here allows serialized models to be loaded without chips
from chips.fuel_helper import get_anchor_images
self.anchor_images = get_anchor_images(dataset,
split,
numanchors=numanchors,
image_size=image_size,
include_targets=False)
if not os.path.exists(self.save_subdir):
os.makedirs(self.save_subdir)
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):
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
