def generate_batch_translation_dataset(self, batch_size, truncation=0.4):
# generate noise -> BxN(2)x128
noise_A = truncated_noise_sample(batch_size=batch_size,
truncation=truncation)
noise_B = truncated_noise_sample(batch_size=batch_size,
truncation=truncation)
n_A = torch.from_numpy(noise_A).unsqueeze(0)
n_B = torch.from_numpy(noise_B).unsqueeze(0)
n_AB = torch.cat([n_A, n_B], dim=0).to(self.device)
n_AB = n_AB.view((N // 2) * batch_size, -1) # (B*2)x128
# generate class -> BxN(2)x1000
idx_list = range(len(self.class_list))
idx_ab = [random.sample(idx_list, 2)
for i in range(batch_size)] # 2 pair idx
class_ab = [[
self.class_list[idx_ab[i][0]], self.class_list[idx_ab[i][1]]
] for i in range(len(idx_ab))]
class_ab = np.array(class_ab).transpose()
class_A = one_hot_from_int(class_ab[0],
batch_size=batch_size) # Bx1000 numpy array
class_B = one_hot_from_int(class_ab[1], batch_size=batch_size)
c_A = torch.from_numpy(class_A).unsqueeze(0)
c_B = torch.from_numpy(class_B).unsqueeze(0)
c_AB = torch.cat([c_A, c_B], dim=0).to(self.device)
c_AB = c_AB.view((N // 2) * batch_size, -1) # (B*2)x1000
with torch.no_grad():
output = self.big_gan(n_AB[A + B + A + B], c_AB[A + B + B + A],
truncation)
# generate class idx
idx_ab = torch.from_numpy(np.array(idx_ab).transpose()) # 2xB
return output.view(N, batch_size, 3, output.size(2),
output.size(3)), idx_ab.long()
def generate_batch_morphing_dataset(self,
batch_size,
interp_noise=True,
interp_class=True,
truncation=0.4):
# generate noise -> BxN(3)x128
noise_A = truncated_noise_sample(batch_size=batch_size,
truncation=truncation)
noise_B = truncated_noise_sample(
batch_size=batch_size,
truncation=truncation) if interp_noise else 0
n_A = torch.from_numpy(noise_A).expand(N, batch_size, -1)
n_B = torch.from_numpy(noise_B).expand(
N, batch_size, -1) if interp_noise else n_A.clone()
# generate class -> BxN(3)x1000
idx_list = range(len(self.class_list))
idx_ab = [random.sample(idx_list, 2)
for i in range(batch_size)] # 2 pair idx
class_ab = [[
self.class_list[idx_ab[i][0]], self.class_list[idx_ab[i][1]]
] for i in range(len(idx_ab))]
class_ab = np.array(class_ab).transpose()
class_A = one_hot_from_int(class_ab[0],
batch_size=batch_size) # Bx1000 numpy array
class_B = one_hot_from_int(class_ab[1], batch_size=batch_size)
c_A = torch.from_numpy(class_A).expand(N, batch_size, -1)
c_B = torch.from_numpy(class_B).expand(
N, batch_size, -1) if interp_class else c_A.clone()
# # sample index for intermediate image
# idx_0 = random.randint(0, n_frames-i_frames)
# idx_1 = idx_0 + i_frames - 1
# idx_i = random.randint(idx_0, idx_1) # sample index for intermediate image, including end points
# interpolation -> BxN(3)xZ
# t = torch.linspace(start=0, end=1.0, steps=n_frames)
# t = t[[idx_0, idx_i, idx_1]].unsqueeze(1).unsqueeze(1).expand(3, batch_size, 1)
a = torch.cat([
torch.zeros(1, batch_size, 1),
torch.rand(1, batch_size, 1),
torch.ones(1, batch_size, 1)
])
n = self.interp_func(n_A, n_B, a).to(self.device)
c = self.interp_func(c_A, c_B, a).to(self.device)
n = n.view(N * batch_size, -1) # (B*N(3))xZ
c = c.view(N * batch_size, -1)
with torch.no_grad():
output = self.big_gan(n, c, truncation)
# generate class idx
idx_ab = torch.from_numpy(np.array(idx_ab).transpose()) # 2xB
return output.view(N, batch_size, 3, output.size(2),
output.size(3)), a[1].squeeze(dim=1), idx_ab.long()
def recover_latent(trial):
trueZ = truncated_noise_sample(truncation=truncation, batch_size=batches)
noise = truncated_noise_sample(truncation=maxNoise, batch_size=batches)
class_vec = one_hot_from_names(['fountain'], batch_size=batches)
z = torch.from_numpy(trueZ + noise)
print(z)
##print('diff:\n', z-trueZ)
opt = optim.Adam([z.requires_grad_()])
with torch.no_grad():
trueZImg = model(torch.from_numpy(trueZ), torch.from_numpy(class_vec),
truncation).requires_grad_()
zImg = model(z, torch.from_numpy(class_vec), truncation).requires_grad_()
zImg0 = zImg.clone()
i = 0
while (i < 5):
lf = nn.MSELoss()
loss = lf(zImg, trueZImg)
loss.backward()
opt.step()
opt.zero_grad()
i += 1
print(i, ': ImageMSE: ', mse_loss(zImg, trueZImg), '\sVecMSE: ',
mse_loss(z, torch.from_numpy(trueZ)))
zImg = model(z, torch.from_numpy(class_vec),
truncation).requires_grad_()
##with torch.no_grad():
##zImg = model(torch.from_numpy(z, class_vec, truncation)
trial = 1
#Save Images
saveOriginal = 'output/' + str(trial) + '_original'
saveNoisy = 'output/' + str(trial) + '_noisy'
saveFixed = 'output/' + str(trial) + '_fixed'
ensure_dir(saveOriginal)
save_as_images(trueZImg, saveOriginal)
ensure_dir(saveNoisy)
save_as_images(zImg0, saveNoisy)
ensure_dir(saveFixed)
save_as_images(zImg, saveFixed)
#Save vectors
saveOriginal = 'output/' + str(trial) + 'originalVec_.pt'
saveNoisy = 'output/' + str(trial) + '_noisyVec.pt'
saveFixed = 'output/' + str(trial) + '_fixedVec.pt'
ensure_dir(saveOriginal)
torch.save(trueZImg, saveOriginal)
ensure_dir(saveNoisy)
torch.save(zImg0, saveNoisy)
ensure_dir(saveFixed)
torch.save(zImg, saveFixed)
def translate_sentiment(self, audio_sentiment, noise=0.1):
# Step 1: Find the closest word match to the audio sentiment
dists, idx = self.database.kneighbors(audio_sentiment)
song_words = [self.words[cur_index[0]] for cur_index in idx]
song_words = [x.split(',')[0].lower() for x in song_words]
word_ids = [self.class_ids[cur_index] for cur_index in idx]
class_vectors = []
noise_vectors = []
# Calculate the weighted nearest class neighbors
for i in range(len(word_ids)):
cur_class_vector = one_hot_from_int(word_ids[i],
batch_size=len(word_ids[i]))
# If k = 1
if len(word_ids[i]) == 1:
cur_class_vector = cur_class_vector[0]
else:
similarities = np.exp(-dists[i] / self.temperature)
similarities = similarities / np.sum(similarities)
similarities = similarities.reshape((-1, 1))
cur_class_vector = np.sum(cur_class_vector * similarities,
axis=0)
class_vectors.append(cur_class_vector)
noise_vectors.append(
truncated_noise_sample(truncation=noise, batch_size=1)[0])
class_vectors = np.float32(class_vectors)
noise_vectors = np.float32(noise_vectors)
return class_vectors, noise_vectors, song_words
def generate_random_morph_sequence(count, silent=False):
noise_vector = truncated_noise_sample(truncation=0.5, batch_size=1)
nums = []
for i in range(count):
all = list(range(1, 1001))
num = random.choice(all)
all.remove(num)
nums.append(num)
nums.append(nums[0])
path = "animations/class_%s" % "_".join([str(a) for a in nums])
if not os.path.exists(path):
os.makedirs(path)
frames = []
for i in range(len(nums) - 1):
num1 = nums[i]
num2 = nums[i + 1]
frames += make_frames(list(range(total_frames + 1)), total_frames,
num1, num2, noise_vector, path, i * total_frames)
if not silent:
open_file(path)
convert_to_video(frames)
def Hess_all_BigGAN_optim(param):
lr = 10 ** param[0, 0]
wd = 10 ** param[0, 1]
beta1 = 1 - 10 ** param[0, 2] # param[2] = log10(1 - beta1)
beta2 = 1 - 10 ** param[0, 3] # param[3] = log10(1 - beta2)
noise_init = torch.from_numpy(truncated_noise_sample(1, 128)).cuda()
class_init = 0.06 * torch.randn(1, 128).cuda()
latent_coef = (torch.cat((noise_init, class_init), dim=1) @ evc_all).detach().clone().requires_grad_(True)
optim = Adam([latent_coef], lr=lr, weight_decay=wd, betas=(beta1, beta2))
# torch.optim.lr_scheduler
scores_all = []
for step in range(300):
optim.zero_grad()
latent_code = latent_coef @ evc_all.T
noise_vec = latent_code[:, :128]
class_vec = latent_code[:, 128:]
fitimg = BGAN.generator(latent_code, 0.7)
fitimg = torch.clamp((1.0 + fitimg) / 2.0, 0, 1)
dsim = alpha * ImDist(fitimg, target_tsr) + L1loss(fitimg, target_tsr) #
dsim.backward()
optim.step()
scores_all.append(dsim.item())
if (step + 1) % 10 == 0:
print("step%d loss %.2f norm: cls: %.2f nois: %.1f" % (step, dsim.item(), class_vec.norm(), noise_vec.norm()))
imcmp = ToPILImage()(make_grid(torch.cat((fitimg, target_tsr)).cpu()))
# imcmp.show()
imcmp.save(join(savedir, "Hall%06d_%.3f.jpg" % (np.random.randint(1000000), dsim.item())))
plt.figure()
plt.plot(scores_all)
plt.title("lr %.E wd %.E beta1 %.3f beta2 %.3f"%(lr,wd,beta1,beta2))
plt.savefig(join(savedir, "traj_Hall%06d_%.3f.jpg" % (np.random.randint(1000000), dsim.item())))
return dsim.item() if not torch.isnan(dsim) else 1E6
def Hess_sep_BigGAN_optim(param):
lr1 = 10 ** param[0, 0]
wd1 = 10 ** param[0, 1]
lr2 = 10 ** param[0, 2]
wd2 = 10 ** param[0, 3]
noise_init = torch.from_numpy(truncated_noise_sample(1, 128)).cuda()
class_init = 0.06 * torch.randn(1, 128).cuda()
noise_coef = (noise_init @ evc_nois).detach().clone().requires_grad_(True)
class_coef = (class_init @ evc_clas).detach().clone().requires_grad_(True)
optim1 = Adam([noise_coef], lr=lr1, weight_decay=wd1, betas=(0.9, 0.999))
optim2 = Adam([class_coef], lr=lr2, weight_decay=wd2, betas=(0.9, 0.999))
# torch.optim.lr_scheduler
for step in range(300):
optim1.zero_grad()
optim2.zero_grad()
class_vec = class_coef @ evc_clas.T
noise_vec = noise_coef @ evc_nois.T
fitimg = BGAN.generator(torch.cat((noise_vec, class_vec), dim=1), 0.7)
fitimg = torch.clamp((1.0 + fitimg) / 2.0, 0, 1)
dsim = alpha * ImDist(fitimg, target_tsr) + L1loss(fitimg, target_tsr) #
dsim.backward()
optim1.step()
optim2.step()
if (step + 1) % 10 == 0:
print("step%d loss %.2f norm: cls: %.2f nois: %.1f" % (step, dsim.item(), class_vec.norm(), noise_vec.norm()))
imcmp = ToPILImage()(make_grid(torch.cat((fitimg, target_tsr)).cpu()))
imcmp.show()
imcmp.save(join(savedir, "Hsep%06d_%.3f.jpg" % (np.random.randint(1000000), dsim.item())))
return dsim.item() if not torch.isnan(dsim) else 1E6
def generate_images(num_images, class_index, model, truncation: float = 1.):
print("[info] initializing input variables...")
batch_size = min(10, num_images)
class_vec = one_hot_from_int(class_index, batch_size=num_images) * .97
noise_vec = truncated_noise_sample(truncation=truncation,
batch_size=num_images)
noise_tensor = torch.tensor(noise_vec, dtype=torch.float)
class_tensor = torch.tensor(class_vec, dtype=torch.float)
print('[info] preparing model inference...')
noise_tensor = noise_tensor.to(device)
class_tensor = class_tensor.to(device)
print(
f'[info] generating {num_images} images of class index {class_index}...'
)
images = []
for i in range(0, num_images, batch_size):
torch.cuda.empty_cache()
noise_feed = noise_tensor[i:i + batch_size]
class_feed = class_tensor[i:i + batch_size]
with torch.no_grad():
output = model(noise_feed, class_feed, truncation)
output_cpu = output.cpu().data.numpy()
for out in output_cpu:
image = np.array(toimage(out))
images.append(image)
print('[info] done.\n')
torch.cuda.empty_cache()
return images
def generate(model):
# Prepare a input
truncation = 0.4
batch_size = 10
class_vector = one_hot_from_names(['vase'], batch_size=batch_size)
noise_vector = truncated_noise_sample(truncation=truncation, batch_size=batch_size)
# All in tensors
noise_vector = torch.from_numpy(noise_vector)
class_vector = torch.from_numpy(class_vector)
# If you have a GPU, put everything on cuda
noise_vector = noise_vector.to('cuda')
class_vector = class_vector.to('cuda')
model.to('cuda')
# Generate an image
with torch.no_grad():
output = model(noise_vector, class_vector, truncation)
# If you have a GPU put back on CPU
output = output.to('cpu')
# If you have a sixtel compatible terminal you can display the images in the terminal
# (see https://github.com/saitoha/libsixel for details)
# display_in_terminal(output)
# Save results as png images
save_as_images(output, file_name='output/output')
def biggan(inp: List[str], metadata):
truncation = 0.4
try:
class_vector = one_hot_from_names(inp, batch_size=len(inp))
noise_vector = truncated_noise_sample(truncation=truncation, batch_size=len(inp))
noise_vector = torch.from_numpy(noise_vector)
class_vector = torch.from_numpy(class_vector)
with torch.no_grad():
output = modelBG(noise_vector, class_vector, truncation)
except:
inp = ['cat']
class_vector = torch.from_numpy(one_hot_from_names(inp, batch_size=len(inp)))
noise_vector = torch.from_numpy(truncated_noise_sample(truncation=truncation, batch_size=len(inp)))
with torch.no_grad():
output = modelBG(noise_vector, class_vector, truncation)
return convert_to_images(output)[0]
def __init__(self, config):
super(DeepMindBigGANLatentSpace, self).__init__()
self.config = config
self.z = torch.nn.Parameter(
torch.tensor(truncated_noise_sample(self.config.batch_size)).to(
self.config.device))
self.class_labels = torch.nn.Parameter(
torch.rand(self.config.batch_size,
self.config.num_classes).to(self.config.device))
def sample_latent(self, batch_size: int, seed: int = None) -> torch.Tensor:
"""Samples random codes from the latent space"""
if seed is None:
seed = np.random.randint(np.iinfo(
np.int32).max) # use (reproducible) global rand state
noise_vector = truncated_noise_sample(truncation=self.truncation,
batch_size=batch_size,
seed=seed)
noise = torch.from_numpy(noise_vector)
return noise.to(self.device)
def create_noise_vector(
batch_size: int = 1,
dim_z: int = 128,
truncation: float = 1.0,
device: str = "cpu",
seed: Any = None,
):
# 643 - weird faces
noise_vector = truncated_noise_sample(batch_size, dim_z, truncation, seed)
noise_vector = torch.from_numpy(noise_vector)
return noise_vector.to(device)
def get_sample_image(self):
n = truncated_noise_sample(batch_size=self.batch_size,
truncation=self.truncation)
c_idx = np.random.choice(self.class_list, size=1)
c = one_hot_from_int(c_idx, batch_size=self.batch_size)
# np to torch
n = torch.from_numpy(n).to(self.device)
c = torch.from_numpy(c).to(self.device)
with torch.no_grad():
output = self.big_gan(n, c, self.truncation)
return output
def generate_class(num):
truncation = 0.5
batch_size = 1
for variation in range(1, 6):
path = "images/class_%d" % num
filename = "%s/%d.png" % (path, variation)
if not os.path.exists(filename):
noise_vector = truncated_noise_sample(truncation=truncation,
batch_size=batch_size)
class_vector = get_classvector(num)
if not os.path.exists(path):
os.makedirs(path)
get_image(class_vector, noise_vector, truncation).save(filename)
def generate_image(dense_class_vector=None,
name=None,
noise_seed_vector=None,
truncation=0.4,
gan_model=None,
pretrained_gan_model_name='biggan-deep-128'):
""" Utility function to generate an image (numpy uint8 array) from either:
- a name (string): converted in an associated ImageNet class and then
a dense class embedding using BigGAN's internal ImageNet class embeddings.
- a dense_class_vector (torch.Tensor with 128 elements): used as a replacement of BigGAN internal
ImageNet class embeddings.
Other args:
- noise_seed_vector: a vector used to control the seed (seed set to the sum of the vector elements)
- truncation: a float between 0 and 1 to control image quality/diversity tradeoff (see BigGAN paper)
- gan_model: a BigGAN model from pytorch_pretrained_biggan library.
If None a model is instanciated from a pretrained model name given by `pretrained_gan_model_name`
List of possible names: https://github.com/huggingface/pytorch-pretrained-BigGAN#models
- pretrained_gan_model_name: shortcut name of the GAN model to instantiate if no gan_model is provided. Default to 'biggan-deep-128'
"""
seed = int(noise_seed_vector.sum().item()
) if noise_seed_vector is not None else None
noise_vector = truncated_noise_sample(truncation=truncation,
batch_size=1,
seed=seed)
noise_vector = torch.from_numpy(noise_vector)
if gan_model is None:
gan_model = BigGAN.from_pretrained(pretrained_gan_model_name)
if name is not None:
class_vector = one_hot_from_names([name], batch_size=1)
class_vector = torch.from_numpy(class_vector)
dense_class_vector = gan_model.embeddings(class_vector)
# input_vector = torch.cat([noise_vector, gan_class_vect.unsqueeze(0)], dim=1)
# dense_class_vector = torch.matmul(class_vector, gan.embeddings.weight.t())
else:
dense_class_vector = dense_class_vector.view(1, 128)
input_vector = torch.cat([noise_vector, dense_class_vector], dim=1)
# Generate an image
with torch.no_grad():
output = gan_model.generator(input_vector, truncation)
output = output.cpu().numpy()
output = output.transpose((0, 2, 3, 1))
output = ((output + 1.0) / 2.0) * 256
output.clip(0, 255, out=output)
output = np.asarray(np.uint8(output[0]), dtype=np.uint8)
return output
def translate_sentiment(self, audio_sentiment, noise):
song_words = []
audio_sentiment = torch.tensor(audio_sentiment).to(config['device'])
class_vectors, noise_vectors = self.net(audio_sentiment)
class_vectors = torch.softmax(class_vectors / self.temperature, dim=1)
cur_noise = torch.tensor(
truncated_noise_sample(batch_size=len(noise_vectors),
truncation=noise)).to(config['device'])
noise_vectors = noise_vectors * self.noise_scaler + cur_noise
# Get the names of the imagenet classes
class_ids = class_vectors.argmax(1).cpu().detach().numpy()
for i in class_ids:
song_words.append(self.imagenet_classes[i])
class_vectors, noise_vectors = class_vectors.cpu().detach().numpy(
), noise_vectors.cpu().detach().numpy()
return class_vectors, noise_vectors, song_words
def generate_ps(self, inp, N):
truncation = 0.1
rvs = []
st = 0
while st < N:
ed = min(st+self.batch_size, N)
class_v = np.random.dirichlet([1]*1000, size=ed-st)
noise_v = truncated_noise_sample(truncation=truncation, batch_size=ed-st)
class_v = torch.FloatTensor(class_v)
noise_v = torch.FloatTensor(noise_v)
with torch.no_grad():
output = self.model(noise_v, class_v, truncation)
output = (output.cpu().numpy() + 1) / 2
output = output[:, :, 16:-16, 16:-16] #Using only the 224*224 in the middle
rvs.append(output)
st = ed
rvs = np.concatenate(rvs, axis=0)
return rvs
def generate_random_morph():
all = list(range(1, 1001))
num1 = random.choice(all)
all.remove(num1)
num2 = random.choice(all)
noise_vector = truncated_noise_sample(truncation=0.5, batch_size=1)
path = "animations/class_%d_%d" % (num1, num2)
if not os.path.exists(path):
os.makedirs(path)
make_frames(
list(range(total_frames + 1)),
total_frames,
num1,
num2,
noise_vector,
path,
)
open_file(path)
def generate_data(random_state, batch_size, num_images_per_classes, device,
output_path):
generator_model = BigGAN.from_pretrained("biggan-deep-128",
cache_dir=os.path.join(
"./data/checkpoint",
"cached_model"))
generator_model = generator_model.to(device)
# prepare a input
truncation = 0.4
op_paths.build_dirs(f"{output_path}")
for class_idx in range(1000):
_id = 0
num_batches = int(num_images_per_classes / batch_size)
op_paths.build_dirs(f"{output_path}/{class_idx}")
for _ in range(num_batches):
class_vector = one_hot_from_int(class_idx, batch_size=batch_size)
noise_vector = truncated_noise_sample(truncation=truncation,
batch_size=batch_size)
noise_vector = torch.from_numpy(noise_vector).to(device)
class_vector = torch.from_numpy(class_vector).to(device)
# generate images
with torch.no_grad():
generated_images = generator_model(noise_vector, class_vector,
truncation).clamp(min=-1,
max=1)
for image in generated_images:
torchvision.utils.save_image(
image,
fp=f"{output_path}/{class_idx}/{_id}",
format="JPEG",
scale_each=True,
normalize=True,
)
_id += 1
print(f"finished {class_idx + 1}/1000.")
def generate_image(thing="mushroom",
model_name="biggan-deep-512",
truncation=0.4):
"Generate an image of *thing* from the model, save it and return the path"
if model_name in ["waifu", "celeb"]:
return generate_waifu(model_name, truncation)
global img_i
model = get_model(model_name)
# Prepare a input
class_vector = one_hot_from_names([thing], batch_size=1)
noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1)
# All in tensors
noise_vector = torch.from_numpy(noise_vector)
class_vector = torch.from_numpy(class_vector)
# If you have a GPU, put everything on cuda
noise_vector = noise_vector.to('cuda')
class_vector = class_vector.to('cuda')
model.to('cuda')
# Generate an image
with torch.no_grad():
output = model(noise_vector, class_vector, truncation)
# If you have a GPU put back on CPU
output = output.to('cpu')
img = convert_to_images(output)
out = img[0]
file_name = f"images/{img_i}.png"
img_i += 1
os.system("mkdir -p images/")
out.save(file_name, 'png')
print(
f"Generated an image of {thing} in file {file_name} with model {model_name}"
)
return file_name
def main():
args = parser.parse_args()
import logging
logging.basicConfig(level=logging.INFO)
# Load pre-trained model tokenizer (vocabulary)
global model
model = BigGAN.from_pretrained(args.model_dir).to('cuda')
label_str = args.labels.strip()
labels = [l.replace('_', ' ') for l in label_str.split(',') if len(l)>0]
class_base_vecs = one_hot_from_names(labels)
label_alt_str = args.labels_alt.strip()
labels_alt = [l.replace('_', ' ') for l in label_alt_str.split(',') if len(l)>0]
print(labels, labels_alt)
if len(labels_alt) > 0:
assert len(labels_alt) == len(labels)
c1 = one_hot_from_names(labels_alt)
class_base_vecs = args.mixture_prop * class_base_vecs + (1-args.mixture_prop) * c1
outs = []
labels = []
for _ in trange(0, args.n_samples // args.batch_size):
# Prepare a input
cls = np.random.randint(0, class_base_vecs.shape[0], size=(args.batch_size,))
class_vector = class_base_vecs[cls]
noise_vector = truncated_noise_sample(
truncation=args.truncation, batch_size=args.batch_size)
outs.append(gen_image(
noise_vector, class_vector, args.crop_ratio, args.truncation))
labels.append(cls)
outs = np.concatenate(outs)
labels = np.concatenate(labels)
np.savez(args.dump_dir+'.npz', args=vars(args), samples=outs, labels=labels)
Image.fromarray(tile_images(outs[:81])).save(args.dump_dir+'.samples-81.png')
Image.fromarray(tile_images(outs[:16])).save(args.dump_dir+'.samples-16.png')
def optim_BigGAN(param):
lr1 = 10**param[0,0]
lr2 = 10**param[0,1]
wd1 = 10**param[0,2]
wd2 = 10**param[0,3]
mom1 = param[0,4]
mom2 = param[0,5]
noise_init = torch.from_numpy(truncated_noise_sample(1, 128)).cuda()
class_init = 0.06 * torch.randn(1, 128).cuda()
alpha = 5
class_vec = class_init.detach().clone().cuda().requires_grad_(True)
noise_vec = noise_init.detach().clone().cuda().requires_grad_(True)
optim1 = SGD([noise_vec], lr=lr1, weight_decay=wd1, momentum=mom1)
optim2 = SGD([class_vec], lr=lr2, weight_decay=wd2, momentum=mom2)
for step in range(300):
optim1.zero_grad()
optim2.zero_grad()
fitimg = BGAN.generator(torch.cat((noise_vec, class_vec), dim=1), 0.7)
fitimg = torch.clamp((1.0 + fitimg) / 2.0, 0, 1)
dsim = alpha * ImDist(fitimg, target_tsr) + L1loss(fitimg, target_tsr) #
dsim.backward()
optim1.step()
optim2.step()
classnorm = class_vec.norm()
noisenorm = noise_vec.norm()
if classnorm > 1.25:
class_vec = (class_vec / classnorm * 0.7).detach().clone()
optim2 = SGD([class_vec], lr=lr2, weight_decay=wd2, momentum=mom2)
print("Class space renormalize")
if noisenorm > 13:
noise_vec = (noise_vec / noisenorm * 10).detach().clone()
optim1 = SGD([noise_vec], lr=lr1, weight_decay=wd1, momentum=mom1)
print("Noise space renormalize")
if (step + 1) % 10 == 0:
print("step%d loss %.2f norm: cls: %.2f nois: %.1f" % (step, dsim.item(), classnorm, noisenorm))
imcmp = ToPILImage()(make_grid(torch.cat((fitimg, target_tsr)).cpu()))
imcmp.save(join(savedir, "%06d_%.3f.jpg" % (np.random.randint(1000000), dsim.item())))
return dsim.item() if not torch.isnan(dsim) else 1E6
def __getitem__(self, idx):
if idx > self.n_iters:
raise StopIteration
# Sample the space
idx = np.random.randint(0, 1000, self.in_batch)
class_vectors = one_hot_from_int(idx, batch_size=self.in_batch)
noise_vectors = truncated_noise_sample(truncation=self.noise,
batch_size=self.in_batch)
class_vectors = torch.tensor(class_vectors).to(config['device'])
noise_vectors = torch.tensor(noise_vectors).to(config['device'])
with torch.no_grad():
output = self.gan_model(noise_vectors, class_vectors, self.noise)
# Convert to PIL Image
output = output.detach().cpu().numpy()
output = np.uint8(np.clip(((output + 1) / 2.0) * 256, 0, 255))
output = output.transpose((0, 2, 3, 1))
images = []
# Pre-process each image to feed them into image sentiment analysis model
for i in range(output.shape[0]):
cur_img = Image.fromarray(output[i])
cur_img = self.transform_image(cur_img)
images.append(cur_img)
images = torch.stack(images).to(config['device'])
# Feed-forward image sentiment analysis
sentiment = self.sentiment_model(images)
class_vectors = class_vectors.cpu().detach().numpy()
noise_vectors = noise_vectors.cpu().detach().numpy()
sentiment = sentiment.cpu().detach().numpy()
return class_vectors, noise_vectors, sentiment
classes=[classes[s] for s in np.argsort(chromasort[:num_classes])]
#initialize first class vector
cv1=np.zeros(1000)
for pi,p in enumerate(chromasort[:num_classes]):
if num_classes < 12:
cv1[classes[pi]] = chroma[p][np.min([np.where(chrow>0)[0][0] for chrow in chroma])]
else:
cv1[classes[p]] = chroma[p][np.min([np.where(chrow>0)[0][0] for chrow in chroma])]
#initialize first noise vector
nv1 = truncated_noise_sample(truncation=truncation)[0]
#initialize list of class and noise vectors
class_vectors=[cv1]
noise_vectors=[nv1]
#initialize previous vectors (will be used to track the previous frame)
cvlast=cv1
nvlast=nv1
#initialize the direction of noise vector unit updates
update_dir=np.zeros(128)
for ni,n in enumerate(nv1):
if n<0:
update_dir[ni] = 1
import torch
from pytorch_pretrained_biggan import (BigGAN, one_hot_from_names, truncated_noise_sample,
save_as_images, display_in_terminal)
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
import logging
logging.basicConfig(level=logging.INFO)
# Load pre-trained model tokenizer (vocabulary)
model = BigGAN.from_pretrained('biggan-deep-512')
# Prepare a input
truncation = 0.4
class_vector = one_hot_from_names(['soap bubble', 'coffee', 'mushroom'], batch_size=3)
noise_vector = truncated_noise_sample(truncation=truncation, batch_size=3)
# All in tensors
noise_vector = torch.from_numpy(noise_vector)
class_vector = torch.from_numpy(class_vector)
# # If you have a GPU, put everything on cuda
# noise_vector = noise_vector.to('cuda')
# class_vector = class_vector.to('cuda')
# model.to('cuda')
# Generate an image
with torch.no_grad():
output = model(noise_vector, class_vector, truncation)
# If you have a GPU put back on CPU
# output = output.to('cpu')
evc_clas = torch.from_numpy(data['eigvects_clas_avg']).cuda()
evc_nois = torch.from_numpy(data['eigvects_nois_avg']).cuda()
evc_all = torch.from_numpy(data['eigvects_avg']).cuda()
#%%
from imageio import imread
target = imread("block042_thread000_gen_gen041_001030.bmp")
target_tsr = torch.from_numpy(target / 255.0).permute([2, 0, 1]).unsqueeze(0)
target_tsr = target_tsr.float().cuda()
#%%
"""
Provide a bunch of metric,
"""
#%% Official CMAES algorithm
import cma
optimizer = cma.CMAEvolutionStrategy(128 * [0.0], 0.06)
fixnoise = truncated_noise_sample(1,128)
noise_vec = torch.from_numpy(fixnoise)
#%%
import tqdm
for i in tqdm.trange(50):
codes = optimizer.ask()
# boundary handling
# evaluate and passing values
codes_tsr = torch.from_numpy(np.array(codes)).float()
latent_code = torch.cat((noise_vec.repeat(18, 1), codes_tsr), dim=1).cuda()
with torch.no_grad():
imgs = BGAN.generator(latent_code, 0.7)
imgs = (imgs + 1.0) / 2.0
dsims = ImDist(imgs, target_tsr).squeeze()
L1dsim = (imgs - target_tsr).abs().mean([1,2,3])
evc_ctrl,
figdir=figdir,
nsamp=5,
titstr="%s" % modelnm,
savelabel=modelnm)
#%%
"""Compute layer-wise Hessian for real BigGANs"""
from pytorch_pretrained_biggan import truncated_noise_sample
BGAN = loadBigGAN()
G = BigGAN_wrapper(BGAN)
triali = 0
savedir = r"E:\OneDrive - Washington University in St. Louis\HessNetArchit\BigGAN\real_Hessians"
for triali in tqdm(range(50)):
noisevec = torch.from_numpy(truncated_noise_sample(1, 128, 0.6)).cuda()
feat = torch.cat(
(noisevec, BGAN.embeddings.weight[:, triali:triali + 1].clone().T),
dim=1)
eigvals, eigvects, H = hessian_compute(
G,
feat,
ImDist,
hessian_method="BP",
)
np.savez(join(savedir, "eig_full_trial%d.npz" % (triali)),
H=H,
eva=eigvals,
evc=eigvects,
feat=feat.cpu().detach().numpy())
feat.requires_grad_(True)
def BigGAN_invert(target_tsr,
param,
basis="all",
maxstep=600,
init_code=None,
ckpt_steps=(50, 100, 200, 300, 400, 500),
savedir=savedir,
namestr="",
RND=None):
lr = 10**param[0, 0]
beta1 = 1 - 10**param[0, 1] # param[2] = log10(1 - beta1)
beta2 = 1 - 10**param[0, 2] # param[3] = log10(1 - beta2)
reg_w1 = 10**param[0, 3] # param[2] = log10(1 - beta1)
reg_w2 = 10**param[0, 4] # param[3] = log10(1 - beta2)
sched_gamma = param[0, 5]
if init_code is None:
noise_init = torch.from_numpy(truncated_noise_sample(1, 128)).cuda()
class_init = 0.06 * torch.randn(1, 128).cuda()
elif isinstance(init_code, np.ndarray):
code_init = torch.from_numpy(init_code)
noise_init = code_init[:, :128]
class_init = code_init[:, 128:]
elif isinstance(init_code, torch.Tensor):
noise_init = init_code[:, :128]
class_init = init_code[:, 128:]
else:
raise
if basis == "all":
latent_coef = (torch.cat((noise_init, class_init), dim=1)
@ evc_all).detach().clone().requires_grad_(True)
elif basis == "sep":
latent_coef = (torch.cat(
(noise_init @ evc_nois, class_init @ evc_clas),
dim=1)).detach().clone().requires_grad_(True)
else:
latent_coef = (torch.cat((noise_init, class_init),
dim=1)).detach().clone().requires_grad_(True)
optim = Adam([latent_coef], lr=lr, weight_decay=0, betas=(beta1, beta2))
scheduler = torch.optim.lr_scheduler.StepLR(optim,
step_size=200,
gamma=sched_gamma)
RNDid = np.random.randint(1000000) if RND is None else RND
scores_all = []
nos_norm = []
cls_norm = []
for step in range(maxstep):
optim.zero_grad()
if basis == "all":
latent_code = latent_coef @ evc_all.T
elif basis == "sep":
latent_code = torch.cat((latent_coef[:, :128] @ evc_nois.T,
latent_coef[:, 128:] @ evc_clas.T),
dim=1)
else:
latent_code = latent_coef
noise_vec = latent_code[:, :128]
class_vec = latent_code[:, 128:]
fitimg = BGAN.generator(latent_code, 0.7)
fitimg = torch.clamp((1.0 + fitimg) / 2.0, 0, 1)
dsim = alpha * ImDist(fitimg, target_tsr) + L1loss(fitimg,
target_tsr) #
loss = dsim + reg_w1 * noise_vec.pow(2).sum() + reg_w2 * class_vec.pow(
2).sum()
loss.backward()
optim.step()
scheduler.step()
scores_all.append(dsim.item())
nos_norm.append(noise_vec.norm().item())
cls_norm.append(class_vec.norm().item())
if (step + 1) % 10 == 0:
print("step%d loss %.2f norm: cls: %.2f nois: %.1f" %
(step, scores_all[-1], cls_norm[-1], nos_norm[-1]))
if (step + 1) in ckpt_steps:
imcmp = ToPILImage()(make_grid(
torch.cat((fitimg, target_tsr)).cpu()))
imcmp.save(
join(
savedir, "%s_H%sreg%06d_%.3f_s%d.jpg" %
(namestr, basis, RNDid, dsim.item(), step + 1)))
imcmp = ToPILImage()(make_grid(torch.cat((fitimg, target_tsr)).cpu()))
imcmp.save(
join(
savedir, "%s_H%sreg%06d_%.3f_final.jpg" %
(namestr, basis, RNDid, dsim.item())))
# imcmp.show()
fig, ax = plt.subplots()
ax.plot(scores_all, label="Loss")
ax.set_ylabel("Image Dissimilarity", color="blue", fontsize=14)
plt.legend()
ax2 = ax.twinx()
ax2.plot(nos_norm, color="orange", label="noise")
ax2.plot(cls_norm, color="magenta", label="class")
ax2.set_ylabel("L2 Norm", color="red", fontsize=14)
plt.legend()
plt.title("lr %.E beta1 %.3f beta2 %.3f wd_nos %.E wd_cls %.E gamma %.1f" %
(lr, beta1, beta2, reg_w1, reg_w2, sched_gamma))
plt.savefig(
join(
savedir, "%s_traj_H%sreg%06d_%.3f.jpg" %
(namestr, basis, RNDid, dsim.item())))
np.savez(join(savedir, "%s_code_H%sreg%06d.jpg" % (
namestr,
basis,
RNDid,
)),
dsim=dsim.item(),
scores_all=np.array(scores_all),
nos_norm=np.array(nos_norm),
cls_norm=np.array(cls_norm),
code=latent_code.detach().cpu().numpy())
return dsim.item()
exprecord = []
csr_min, csr_max = 550, 600
if len(sys.argv) > 1:
csr_min = int(sys.argv[1])
csr_max = int(sys.argv[2])
for imgid in tqdm(range(csr_min, csr_max)):
print("Processing image %d" % imgid)
imgnm = "val_crop_%08d" % imgid
img = imread(join(imgfolder, "val_crop_%08d.JPEG" % imgid))
target_tsr = torch.from_numpy(img / 255.0).permute([2, 0, 1]).unsqueeze(0)
target_tsr = target_tsr.float().cuda()
#%%
for triali in range(5):
RNDid = np.random.randint(1000000)
noise_init = torch.from_numpy(truncated_noise_sample(1, 128)).cuda()
class_init = 0.06 * torch.randn(1, 128).cuda()
init_code = torch.cat((noise_init, class_init), 1)
dsim_all = BigGAN_invert(
target_tsr,
np.array([[-1.0, -0.5, -2.44, -4.66, -3.2552, 0.4563]]),
init_code=init_code,
basis="all",
maxstep=600,
ckpt_steps=(50, 100, 200, 300, 400, 500),
savedir=savedir,
namestr=imgnm,
RND=RNDid)
dsim_sep = BigGAN_invert(target_tsr,
np.array([[-1, -0.5, -2.24, -5, -3.5, 0.59]]),
init_code=init_code,
