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- import argparse
- import math
- import os
- from torchvision.utils import save_image
- import torch
- import numpy as np
- from PIL import Image
- from tqdm import tqdm
- import numpy as np
- import skvideo.io
- import curriculums
- from torchvision import transforms
- def tensor_to_PIL(img):
- img = img.squeeze() * 0.5 + 0.5
- return Image.fromarray(img.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy())
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- parser = argparse.ArgumentParser()
- parser.add_argument('generator_path', type=str)
- parser.add_argument('image_path', type=str)
- parser.add_argument('--seed', type=int, default=None)
- parser.add_argument('--image_size', type=int, default=128)
- parser.add_argument('--num_frames', type=int, default=64)
- parser.add_argument('--max_batch_size', type=int, default=2400000)
- opt = parser.parse_args()
- generator = torch.load(opt.generator_path, map_location=torch.device(device))
- ema_file = opt.generator_path.split('generator')[0] + 'ema.pth'
- ema = torch.load(ema_file, map_location=device)
- ema.copy_to(generator.parameters())
- generator.set_device(device)
- generator.eval()
- if opt.seed is not None:
- torch.manual_seed(opt.seed)
- gt_image = Image.open(opt.image_path).convert('RGB')
- transform = transforms.Compose(
- [transforms.Resize(256), transforms.CenterCrop(256), transforms.Resize((opt.image_size, opt.image_size), interpolation=0), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
- gt_image = transform(gt_image).to(device)
-
- options = {
- 'img_size': opt.image_size,
- 'fov': 12,
- 'ray_start': 0.88,
- 'ray_end': 1.12,
- 'num_steps': 24,
- 'h_stddev': 0,
- 'v_stddev': 0,
- 'h_mean': torch.tensor(math.pi/2).to(device),
- 'v_mean': torch.tensor(math.pi/2).to(device),
- 'hierarchical_sample': False,
- 'sample_dist': None,
- 'clamp_mode': 'relu',
- 'nerf_noise': 0,
- }
- render_options = {
- 'img_size': 256,
- 'fov': 12,
- 'ray_start': 0.88,
- 'ray_end': 1.12,
- 'num_steps': 48,
- 'h_stddev': 0,
- 'v_stddev': 0,
- 'v_mean': math.pi/2,
- 'hierarchical_sample': True,
- 'sample_dist': None,
- 'clamp_mode': 'relu',
- 'nerf_noise': 0,
- 'last_back': True,
- }
- z = torch.randn((10000, 256), device=device)
- with torch.no_grad():
- frequencies, phase_shifts = generator.siren.mapping_network(z)
- w_frequencies = frequencies.mean(0, keepdim=True)
- w_phase_shifts = phase_shifts.mean(0, keepdim=True)
- w_frequency_offsets = torch.zeros_like(w_frequencies)
- w_phase_shift_offsets = torch.zeros_like(w_phase_shifts)
- w_frequency_offsets.requires_grad_()
- w_phase_shift_offsets.requires_grad_()
- frames = []
- n_iterations_pose = 0
- n_iterations = 700
- os.makedirs('debug', exist_ok=True)
- save_image(gt_image, "debug/gt.jpg", normalize=True)
-
- optimizer = torch.optim.Adam([w_frequency_offsets, w_phase_shift_offsets], lr=1e-2, weight_decay = 1e-4)
- scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 100, gamma=0.75)
- for i in range(n_iterations):
- noise_w_frequencies = 0.03 * torch.randn_like(w_frequencies) * (n_iterations - i)/n_iterations
- noise_w_phase_shifts = 0.03 * torch.randn_like(w_phase_shifts) * (n_iterations - i)/n_iterations
- frame, _ = generator.forward_with_frequencies(w_frequencies + noise_w_frequencies + w_frequency_offsets, w_phase_shifts + noise_w_phase_shifts + w_phase_shift_offsets, **options)
- loss = torch.nn.MSELoss()(frame, gt_image)
- loss = loss.mean()
-
- loss.backward()
- optimizer.step()
- optimizer.zero_grad()
-
-
- scheduler.step()
- if i % 100 == 0:
- save_image(frame, f"debug/{i}.jpg", normalize=True)
- with torch.no_grad():
- for angle in [-0.7, -0.5, -0.3, 0, 0.3, 0.5, 0.7]:
- img, _ = generator.staged_forward_with_frequencies(w_frequencies + w_frequency_offsets, w_phase_shifts + w_phase_shift_offsets, h_mean=(math.pi/2 + angle), max_batch_size=opt.max_batch_size, lock_view_dependence=True, **render_options)
- save_image(img, f"debug/{i}_{angle}.jpg", normalize=True)
- trajectory = []
- for t in np.linspace(0, 1, 24):
- pitch = 0.2 * t
- yaw = 0
- trajectory.append((pitch, yaw))
- for t in np.linspace(0, 1, opt.num_frames):
- pitch = 0.2 * np.cos(t * 2 * math.pi)
- yaw = 0.4 * np.sin(t * 2 * math.pi)
- trajectory.append((pitch, yaw))
-
- output_name = 'reconstructed.mp4'
- writer = skvideo.io.FFmpegWriter(os.path.join('debug', output_name), outputdict={'-pix_fmt': 'yuv420p', '-crf': '21'})
- frames = []
- depths = []
- with torch.no_grad():
- for pitch, yaw in tqdm(trajectory):
- render_options['h_mean'] = yaw + 3.14/2
- render_options['v_mean'] = pitch + 3.14/2
- frame, depth_map = generator.staged_forward_with_frequencies(w_frequencies + w_frequency_offsets, w_phase_shifts + w_phase_shift_offsets, max_batch_size=opt.max_batch_size, lock_view_dependence=True, **render_options)
- frames.append(tensor_to_PIL(frame))
-
- depths.append(depth_map.unsqueeze(0).expand(-1, 3, -1, -1).squeeze().permute(1, 2, 0).cpu().numpy())
- for frame in frames:
- writer.writeFrame(np.array(frame))
- writer.close()
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