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invert.py
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invert.py
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import argparse
import os
import clip
import kornia.augmentation as kaugs
import torch
import torch.nn as nn
import torchvision
from helpers.augmentations import ColorJitter, RepeatBatch, Jitter, TotalVariation
from helpers.utils import Normalization, Scale, freeze_module
from torch.nn.utils import clip_grad_norm_
torch.autograd.set_detect_anomaly(True)
parser = argparse.ArgumentParser(description='inverting clip!')
parser.add_argument('--num_iters', default=3400, type=int)
parser.add_argument('--save_every', default=100, type=int)
parser.add_argument('--print_every', default=50, type=int)
parser.add_argument('--batch_size', default=13, type=int)
parser.add_argument('-p', '--prompt', action='append', type=str, default=[])
parser.add_argument('-e', '--extra_prompts', action='append', type=str, default=[])
parser.add_argument('--lr', default=0.1, type=float)
parser.add_argument('--tv', default=0.005, type=float)
parser.add_argument('--jitter', action='store_true')
parser.add_argument('--color', action='store_true')
parser.add_argument('--img_size', default=64, type=int)
parser.add_argument('--eps', default=2 / 255)
parser.add_argument('--optimizer', default='adam')
parser.add_argument('--bri', type=float, default=0.4)
parser.add_argument('--con', type=float, default=0.4)
parser.add_argument('--sat', type=float, default=0.4)
parser.add_argument('--l1', type=float, default=0.)
parser.add_argument('--trial', type=int, default=1)
parser.add_argument('--cg_std', type=float, default=0.)
parser.add_argument('--cg_mean', type=float, default=0.)
parser.add_argument('--model_name', default='ViT-B/16')
parser.add_argument('--prompt_id', type=int, default=0)
args = parser.parse_args()
args.prompt = ' '.join(args.prompt)
print(f'prompt: <{args.prompt}>')
print(f'extra prompts are: {args.extra_prompts}')
device = "cuda" if torch.cuda.is_available() else "cpu"
# ['RN50', 'RN101', 'RN50x4', 'RN50x16', 'ViT-B/32', 'ViT-B/16']
model_names = [args.model_name]
models = []
for model_name in model_names:
model, preprocess = clip.load(model_name, device)
model = model.float()
model = model.cuda()
models.append(model)
normalizer = Normalization([0.48145466, 0.4578275, 0.40821073], [0.26862954, 0.26130258, 0.27577711]).cuda()
scale = Scale(224)
prompts = [args.prompt]
text_inputs = torch.cat([clip.tokenize(f"{c}") for c in prompts]).to(device)
corrects, total = 0, 0
for model in models:
freeze_module(model)
image = torch.rand((1, 3, args.img_size, args.img_size)).cuda()
image.requires_grad_()
def get_optimizer(image):
if args.optimizer == 'adam':
optimizer = torch.optim.Adam([image], lr=args.lr)
else:
optimizer = torch.optim.LBFGS([image], lr=args.lr)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=2000)
return optimizer, scheduler
optimizer, scheduler = get_optimizer(image)
criterion = nn.CrossEntropyLoss()
text_features_map = {}
for model in models:
text_feature = model.encode_text(text_inputs)
text_feature = text_feature / text_feature.norm(dim=-1, keepdim=True)
text_features_map[model] = text_feature
save_path = f'images/{args.prompt}/{args.trial}/{args.lr}_{args.tv}_{args.cg_std}_{args.cg_mean}'
os.makedirs(save_path, exist_ok=True)
seq = []
if args.jitter:
jitter = Jitter()
seq.append(jitter)
seq.append(RepeatBatch(args.batch_size))
pre_aug = nn.Sequential(*seq)
aug = kaugs.AugmentationSequential(
kaugs.RandomAffine(30, [0.1, 0.1], [0.7, 1.2], p=.5, padding_mode='border'),
same_on_batch=False,
)
tv_module = TotalVariation()
color_jitter = ColorJitter(args.batch_size, True, mean=args.cg_mean, std=args.cg_std)
targets = torch.tensor([0] * args.batch_size).cuda()
def forward(image, model):
image_input = pre_aug(image)
image_input = aug(image_input)
scale = Scale(model.visual.input_resolution)
image_input = scale(image_input)
image_input = color_jitter(image_input)
image_input = normalizer(image_input)
image_features = model.encode_image(image_input)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
l2_loss = torch.norm(image_features - text_features_map[model], dim=1)
loss = torch.mean(l2_loss)
return loss, l2_loss
change_scale_schedule = [900, 1800]
softmax = nn.Softmax(dim=1)
for i in range(args.num_iters):
max_grad_norm = 1.
if i in change_scale_schedule:
new_res = image.shape[2] * 2
if args.jitter:
jitter.lim = jitter.lim * 2
if new_res >= 224:
new_res = 224
up_sample = Scale(new_res)
image = up_sample(image.detach())
image.requires_grad_(True)
optimizer, scheduler = get_optimizer(image)
def closure():
optimizer.zero_grad()
other_loss = tv_module(image)
loss = args.tv * other_loss
image_input = image
l1_loss = torch.norm(image_input, p=1)
loss = loss + args.l1 * l1_loss
for model in models:
xent_loss, scores = forward(image_input, model)
loss = loss + xent_loss * (1 / len(models))
loss.backward()
clip_grad_norm_([image], max_grad_norm)
image.data = torch.clip(image.data, 0, 1)
if i % args.print_every == 0:
print(f'{i:04d}: loss is {loss:.4f}, xent: {xent_loss:.4f}, tv: {other_loss:.4f}, l1: {l1_loss:.4f}')
if i % args.save_every == 0:
path = os.path.join(save_path, f'{i}.png')
torchvision.utils.save_image(image, path, normalize=True, scale_each=True)
return loss
optimizer.step(closure)
if i >= 3400:
scheduler.step()
path = os.path.join(save_path, 'final.png')
torchvision.utils.save_image(image, path, normalize=True, scale_each=True)