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Latent space playground work

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This commit is contained in:
James Betker 2020-11-27 12:03:16 -07:00
parent 4ab49b0d69
commit 11d2b70bdd
1 changed files with 54 additions and 35 deletions
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89
codes/scripts/srflow_latent_space_playground.py
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@ -20,14 +20,16 @@ from models.ExtensibleTrainer import ExtensibleTrainer
from utils import util
def image_2_tensor(impath, desired_size):
def image_2_tensor(impath, max_size=None):
img = Image.open(impath)
if desired_size is not None:
factor = max(desired_size[0] / img.width, desired_size[1] / img.height)
if max_size is not None:
factor = min(max_size / img.width, max_size / img.height)
new_size = (int(math.ceil(img.width * factor)), int(math.ceil(img.height * factor)))
img = img.resize(new_size, Image.BICUBIC)
img = img.resize(new_size, Image.LANCZOS)
'''
# Useful for setting an image to an exact size.
h_gap = img.height - desired_size[1]
w_gap = img.width - desired_size[0]
assert h_gap >= 0 and w_gap >= 0
@ -35,18 +37,19 @@ def image_2_tensor(impath, desired_size):
hb = desired_size[1] + ht
wl = w_gap // 2
wr = desired_size[1] + wl
'''
timg = torchvision.transforms.ToTensor()(img).unsqueeze(0)
if desired_size is not None:
timg = timg[:, :3, ht:hb, wl:wr]
assert timg.shape[2] == desired_size[1] and timg.shape[3] == desired_size[0]
else:
# Enforce that the input must have a input dimension that is a factor of 16.
b, c, h, w = timg.shape
h = (h // 16) * 16
w = (w // 16) * 16
timg = timg[:, :3, :h, :w]
#if desired_size is not None:
# timg = timg[:, :3, ht:hb, wl:wr]
# assert timg.shape[2] == desired_size[1] and timg.shape[3] == desired_size[0]
#else:
# Enforce that the input must have a input dimension that is a factor of 16.
b, c, h, w = timg.shape
h = (h // 16) * 16
w = (w // 16) * 16
timg = timg[:, :3, :h, :w]
return timg
@ -88,34 +91,49 @@ def fetch_spatial_metrics_for_latents(latents):
return dt_scales, dt_biases
def spatial_norm(latents):
def spatial_norm(latents, exclusion_list=[]):
nlatents = []
for i in range(len(latents)):
latent = latents[i]
b, c, h, w = latent.shape
s = latent.std(dim=[2, 3]).view(1,c,1,1)
b = latent.mean(dim=[2, 3]).view(1,c,1,1)
nlatents.append((latents[i] - b) / s)
if i in exclusion_list:
nlatents.append(latent)
else:
b, c, h, w = latent.shape
s = latent.std(dim=[2, 3]).view(1,c,1,1)
b = latent.mean(dim=[2, 3]).view(1,c,1,1)
nlatents.append((latents[i] - b) / s)
return nlatents
def local_norm(latents):
def local_norm(latents, exclusion_list=[]):
nlatents = []
for i in range(len(latents)):
latent = latents[i]
b, c, h, w = latent.shape
s = latent.std(dim=[1]).view(1,1,h,w)
b = latent.mean(dim=[1]).view(1,1,h,w)
nlatents.append((latents[i] - b) / s)
if i in exclusion_list:
nlatents.append(latent)
else:
b, c, h, w = latent.shape
s = latent.std(dim=[1]).view(1,1,h,w)
b = latent.mean(dim=[1]).view(1,1,h,w)
nlatents.append((latents[i] - b) / s)
return nlatents
# Extracts a rectangle of the same shape as <lat> from <ref> and returns it. This is taken from the center of <ref>
def extract_center_latent(ref, lat):
_, _, h, w = lat.shape
_, _, rh, rw = ref.shape
dw = (rw - w) / 2
dh = (rh - h) / 2
return ref[:, :, math.floor(dh):-math.ceil(dh), math.floor(dw):-math.ceil(dw)]
if __name__ == "__main__":
#### options
torch.backends.cudnn.benchmark = True
srg_analyze = False
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../options/train_exd_imgsetext_srflow8x.yml')
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../experiments/train_exd_imgset_srflow/train_exd_imgset_srflow.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)
utils.util.loaded_options = opt
@ -132,19 +150,19 @@ if __name__ == "__main__":
gen = model.networks['generator']
gen.eval()
mode = "restore" # restore | latent_transfer | feed_through
mode = "feed_through" # restore | latent_transfer | feed_through
#imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\val2\\lr\\*"
imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\pure_adrianna_full\\images\\*"
desired_size = None # (640,640) # <- Required when doing style transfer.
scale = 8
resample_factor = 1 # When != 1, the HR image is upsampled by this factor using a bicubic to get the local latents.
temperature = 1
scale = 2
resample_factor = 2 # When != 1, the HR image is upsampled by this factor using a bicubic to get the local latents.
temperature = .3
output_path = "E:\\4k6k\\mmsr\\results\\latent_playground"
# Data types <- used to perform latent transfer.
data_path = "F:\\4k6k\\datasets\\ns_images\\imagesets\\images-half"
#data_type_filters = ["*alexa*", "*lanette*", "*80755*", "*x-art-1912*", "*joli_high*", "*stacy-cruz*"]
data_type_filters = ["*lanette*"]
data_type_filters = ["*alexa*", "*lanette*", "*80755*", "*x-art-1912*", "*joli_high*", "*stacy-cruz*"]
#data_type_filters = ["*lanette*"]
max_size = 1100 # Should be set to 2x the largest single dimension of the input space, otherwise an error will occur.
max_ref_datatypes = 30 # Only picks this many images from the above data types to sample from.
interpolation_steps = 30
@ -153,7 +171,7 @@ if __name__ == "__main__":
if mode == "latent_transfer":
# Just get the **one** result for each pattern and use that latent.
dt_imgs = [glob(os.path.join(data_path, p))[-5] for p in data_type_filters]
dt_transfers = [image_2_tensor(i, desired_size) for i in dt_imgs]
dt_transfers = [image_2_tensor(i, max_size) for i in dt_imgs]
# Downsample the images because they are often just too big to feed through the network (probably needs to be parameterized)
for j in range(len(dt_transfers)):
if min(dt_transfers[j].shape[2], dt_transfers[j].shape[3]) > 1600:
@ -173,7 +191,7 @@ if __name__ == "__main__":
img_files = glob(imgs_to_resample_pattern)
random.shuffle(img_files)
for im_it, img_file in enumerate(tqdm(img_files)):
t = image_2_tensor(img_file, desired_size).to(model.env['device'])
t = image_2_tensor(img_file).to(model.env['device'])
if resample_factor != 1:
t = F.interpolate(t, scale_factor=resample_factor, mode="bicubic")
resample_img = t
@ -184,6 +202,7 @@ if __name__ == "__main__":
multiple_latents = False
if mode == "restore":
latents = local_norm(spatial_norm(latents))
#latents = spatial_norm(latents)
latents = [l * temperature for l in latents]
elif mode == "feed_through":
latents = [torch.randn_like(l) * temperature for l in latents]
@ -192,7 +211,7 @@ if __name__ == "__main__":
for slat in dt_latents:
assert slat[0].shape[2] >= latents[0].shape[2]
assert slat[0].shape[3] >= latents[0].shape[3]
dts.append([sl[:,:,:l.shape[2],:l.shape[3]] * temperature for l, sl in zip(latents, slat)])
dts.append([extract_center_latent(sl, l) * temperature for l, sl in zip(latents, slat)])
latents = dts
multiple_latents = True
@ -201,7 +220,6 @@ if __name__ == "__main__":
lats = [latents]
else:
lats = latents
torchvision.utils.save_image(resample_img, os.path.join(output_path, "%i_orig.jpg" %(im_it)))
for j in range(len(lats)):
hr, _ = gen(lr=F.interpolate(resample_img, scale_factor=1/scale, mode="area"),
z=lats[j][0],
@ -211,4 +229,5 @@ if __name__ == "__main__":
if torch.isnan(torch.max(hr)):
continue
os.makedirs(os.path.join(output_path), exist_ok=True)
torchvision.utils.save_image(resample_img, os.path.join(output_path, "%i_orig.jpg" %(im_it)))
torchvision.utils.save_image(hr, os.path.join(output_path, "%i_%i.jpg" % (im_it,j)))