Update to srflow_latent_space_playground
This commit is contained in:
James Betker
parent
fd356580c0
commit
5f5420ff4a
|
|
@ -66,7 +66,7 @@ def fetch_latents_for_image(gen, img, scale, lr_infer=interpolate_lr):
|
|||
|
||||
def fetch_latents_for_images(gen, imgs, scale, lr_infer=interpolate_lr):
|
||||
latents = []
|
||||
for img in tqdm(imgs):
|
||||
for img in imgs:
|
||||
z, _, _ = gen(gt=img,
|
||||
lr=lr_infer(img, scale),
|
||||
epses=[],
|
||||
|
|
@ -115,7 +115,7 @@ if __name__ == "__main__":
|
|||
torch.backends.cudnn.benchmark = True
|
||||
srg_analyze = False
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../experiments/train_exd_imgset_srflow/train_exd_imgset_srflow.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../options/train_exd_imgsetext_srflow8x.yml')
|
||||
opt = option.parse(parser.parse_args().opt, is_train=False)
|
||||
opt = option.dict_to_nonedict(opt)
|
||||
utils.util.loaded_options = opt
|
||||
|
|
@ -132,85 +132,83 @@ if __name__ == "__main__":
|
|||
gen = model.networks['generator']
|
||||
gen.eval()
|
||||
|
||||
mode = "latent_transfer"
|
||||
imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\val2\\lr\\*"
|
||||
mode = "restore" # 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 = 2
|
||||
resample_factor = 2 # When != 1, the HR image is upsampled by this factor using a bicubic to get the local latents.
|
||||
temperature = .65
|
||||
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
|
||||
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_ref_datatypes = 30 # Only picks this many images from the above data types to sample from.
|
||||
interpolation_steps = 30
|
||||
|
||||
with torch.no_grad():
|
||||
# Fetch the images to resample.
|
||||
resample_imgs = []
|
||||
img_files = glob(imgs_to_resample_pattern)
|
||||
for i, img_file in enumerate(img_files):
|
||||
if i > 5:
|
||||
break
|
||||
t = image_2_tensor(img_file, desired_size).to(model.env['device'])
|
||||
if resample_factor != 1:
|
||||
t = F.interpolate(t, scale_factor=resample_factor, mode="bicubic")
|
||||
resample_imgs.append(t)
|
||||
|
||||
# Fetch the latent metrics & latents for each image we are resampling.
|
||||
latents = fetch_latents_for_images(gen, resample_imgs, scale)
|
||||
|
||||
multiple_latents = False
|
||||
if mode == "restore":
|
||||
for i, latent_set in enumerate(latents):
|
||||
latents[i] = local_norm(spatial_norm(latent_set))
|
||||
latents[i] = [l * temperature for l in latents[i]]
|
||||
elif mode == "feed_through":
|
||||
latents = [torch.randn_like(l) * temperature for l in latents[i]]
|
||||
elif mode == "latent_transfer":
|
||||
# Compute latent variables for the reference images.
|
||||
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]
|
||||
# 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:
|
||||
dt_transfers[j] = F.interpolate(dt_transfers[j], scale_factor=1/2, mode='area')
|
||||
corruptor = ImageCorruptor({'fixed_corruptions':['jpeg-low', 'gaussian_blur_5']})
|
||||
|
||||
dt_transfers[j] = F.interpolate(dt_transfers[j], scale_factor=1 / 2, mode='area')
|
||||
corruptor = ImageCorruptor({'fixed_corruptions': ['jpeg-medium', 'gaussian_blur_3']})
|
||||
def corrupt_and_downsample(img, scale):
|
||||
img = F.interpolate(img, scale_factor=1/scale, mode="area")
|
||||
img = F.interpolate(img, scale_factor=1 / scale, mode="area")
|
||||
from data.util import torch2cv, cv2torch
|
||||
cvimg = torch2cv(img)
|
||||
cvimg = corruptor.corrupt_images([cvimg])[0]
|
||||
img = cv2torch(cvimg)
|
||||
torchvision.utils.save_image(img, "corrupted_lq_%i.png" % (random.randint(0,100),))
|
||||
torchvision.utils.save_image(img, "corrupted_lq_%i.png" % (random.randint(0, 100),))
|
||||
return img
|
||||
|
||||
dt_latents = [fetch_latents_for_image(gen, i, scale, corrupt_and_downsample) for i in dt_transfers]
|
||||
tlatents = []
|
||||
for lat in latents:
|
||||
|
||||
# Fetch the images to resample.
|
||||
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'])
|
||||
if resample_factor != 1:
|
||||
t = F.interpolate(t, scale_factor=resample_factor, mode="bicubic")
|
||||
resample_img = t
|
||||
|
||||
# Fetch the latent metrics & latents for each image we are resampling.
|
||||
latents = fetch_latents_for_images(gen, [resample_img], scale)[0]
|
||||
|
||||
multiple_latents = False
|
||||
if mode == "restore":
|
||||
latents = local_norm(spatial_norm(latents))
|
||||
latents = [l * temperature for l in latents]
|
||||
elif mode == "feed_through":
|
||||
latents = [torch.randn_like(l) * temperature for l in latents]
|
||||
elif mode == "latent_transfer":
|
||||
dts = []
|
||||
for slat in dt_latents:
|
||||
assert slat[0].shape[2] >= lat[0].shape[2]
|
||||
assert slat[0].shape[3] >= lat[0].shape[3]
|
||||
dts.append([sl[:,:,:l.shape[2],:l.shape[3]] * temperature for l, sl in zip(lat, slat)])
|
||||
tlatents.append(dts)
|
||||
latents = tlatents
|
||||
multiple_latents = True
|
||||
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)])
|
||||
latents = dts
|
||||
multiple_latents = True
|
||||
|
||||
# Re-compute each image with the new metrics
|
||||
for i, img in enumerate(resample_imgs):
|
||||
# Re-compute each image with the new metrics
|
||||
if not multiple_latents:
|
||||
lats = [latents[i]]
|
||||
lats = [latents]
|
||||
else:
|
||||
lats = latents[i]
|
||||
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(img, scale_factor=1/scale, mode="area"),
|
||||
hr, _ = gen(lr=F.interpolate(resample_img, scale_factor=1/scale, mode="area"),
|
||||
z=lats[j][0],
|
||||
reverse=True,
|
||||
epses=lats[j],
|
||||
add_gt_noise=False)
|
||||
if torch.isnan(torch.max(hr)):
|
||||
continue
|
||||
os.makedirs(os.path.join(output_path), exist_ok=True)
|
||||
torchvision.utils.save_image(hr, os.path.join(output_path, "%i_%i.png" % (i,j)))
|
||||
torchvision.utils.save_image(hr, os.path.join(output_path, "%i_%i.jpg" % (im_it,j)))
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user