gen_videos_interp.py

''' Generate videos using pretrained network pickle.
Code adapted from following paper
"Efficient Geometry-aware 3D Generative Adversarial Networks."
See LICENSES/LICENSE_EG3D for original license.
'''

import os
import re
from typing import List, Optional, Tuple, Union

import click
import dnnlib
import imageio
import numpy as np
import scipy.interpolate
import torch
from tqdm import tqdm
import mrcfile

import legacy

from camera_utils import LookAtPoseSampler
from torch_utils import misc
from training.triplane import TriPlaneGenerator
#----------------------------------------------------------------------------

def layout_grid(img, grid_w=None, grid_h=1, float_to_uint8=True, chw_to_hwc=True, to_numpy=True):
    batch_size, channels, img_h, img_w = img.shape
    if grid_w is None:
        grid_w = batch_size // grid_h
    assert batch_size == grid_w * grid_h
    if float_to_uint8:
        img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
    img = img.reshape(grid_h, grid_w, channels, img_h, img_w)
    img = img.permute(2, 0, 3, 1, 4)
    img = img.reshape(channels, grid_h * img_h, grid_w * img_w)
    if chw_to_hwc:
        img = img.permute(1, 2, 0)
    if to_numpy:
        img = img.cpu().numpy()
    return img

def create_samples(N=256, voxel_origin=[0, 0, 0], cube_length=2.0):
    # NOTE: the voxel_origin is actually the (bottom, left, down) corner, not the middle
    voxel_origin = np.array(voxel_origin) - cube_length/2
    voxel_size = cube_length / (N - 1)

    overall_index = torch.arange(0, N ** 3, 1, out=torch.LongTensor())
    samples = torch.zeros(N ** 3, 3)

    # transform first 3 columns
    # to be the x, y, z index
    samples[:, 2] = overall_index % N
    samples[:, 1] = (overall_index.float() / N) % N
    samples[:, 0] = ((overall_index.float() / N) / N) % N

    # transform first 3 columns
    # to be the x, y, z coordinate
    samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]
    samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
    samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]

    num_samples = N ** 3

    return samples.unsqueeze(0), voxel_origin, voxel_size

#----------------------------------------------------------------------------

def gen_interp_video(G, mp4: str, seeds, pose_cond, shuffle_seed=None, w_frames=60*4, kind='cubic', grid_dims=(1,1), num_keyframes=None, wraps=2, psi=1, truncation_cutoff=14, cfg='FFHQ', image_mode='image', gen_shapes=False, device=torch.device('cuda'), **video_kwargs):
    grid_w = grid_dims[0]
    grid_h = grid_dims[1]

    if num_keyframes is None:
        if len(seeds) % (grid_w*grid_h) != 0:
            raise ValueError('Number of input seeds must be divisible by grid W*H')
        num_keyframes = len(seeds) // (grid_w*grid_h)

    all_seeds = np.zeros(num_keyframes*grid_h*grid_w, dtype=np.int64)
    for idx in range(num_keyframes*grid_h*grid_w):
        all_seeds[idx] = seeds[idx % len(seeds)]

    if shuffle_seed is not None:
        rng = np.random.RandomState(seed=shuffle_seed)
        rng.shuffle(all_seeds)

    camera_lookat_point = torch.tensor([0, 0, 0.2], device=device) if cfg == 'FFHQ' else torch.tensor([0, 0, 0], device=device)

    zs = torch.from_numpy(np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds])).to(device)
    pose_cond_rad = pose_cond/180*np.pi
    cam2world_pose = LookAtPoseSampler.sample(pose_cond_rad, 3.14/2, camera_lookat_point, radius=2.7, device=device)
    intrinsics = torch.tensor([[4.2647, 0, 0.5], [0, 4.2647, 0.5], [0, 0, 1]], device=device)
    c = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
    c = c.repeat(len(zs), 1)
    ws = G.mapping(z=zs, c=c, truncation_psi=psi, truncation_cutoff=truncation_cutoff)
    _ = G.synthesis(ws[:1], c[:1]) # warm up
    ws = ws.reshape(grid_h, grid_w, num_keyframes, *ws.shape[1:])

    # Interpolation.
    grid = []
    for yi in range(grid_h):
        row = []
        for xi in range(grid_w):
            x = np.arange(-num_keyframes * wraps, num_keyframes * (wraps + 1))
            y = np.tile(ws[yi][xi].cpu().numpy(), [wraps * 2 + 1, 1, 1])
            interp = scipy.interpolate.interp1d(x, y, kind=kind, axis=0)
            row.append(interp)
        grid.append(row)

    # Render video.
    max_batch = 10000000
    voxel_resolution = 512
    video_out = imageio.get_writer(mp4, mode='I', fps=60, codec='libx264', **video_kwargs)

    if gen_shapes:
        outdir = 'interpolation_{}_{}/'.format(all_seeds[0], all_seeds[1])
        os.makedirs(outdir, exist_ok=True)
    all_poses = []
    for frame_idx in tqdm(range(num_keyframes * w_frames)):
        imgs = []
        for yi in range(grid_h):
            for xi in range(grid_w):
                if cfg == "Head":
                    pitch_range = 0.5
                    cam2world_pose = LookAtPoseSampler.sample(3.14/2 + 2 * 3.14 * frame_idx / (num_keyframes * w_frames),  3.14/2 -0.05 + pitch_range * np.sin(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
                                                            camera_lookat_point, radius=2.7, device=device)
                else:
                    pitch_range = 0.25
                    yaw_range = 1.5 # 0.35
                    cam2world_pose = LookAtPoseSampler.sample(3.14/2 + yaw_range * np.sin(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
                                                            3.14/2 -0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
                                                            camera_lookat_point, radius=2.7, device=device)
                all_poses.append(cam2world_pose.squeeze().cpu().numpy())
                intrinsics = torch.tensor([[4.2647, 0, 0.5], [0, 4.2647, 0.5], [0, 0, 1]], device=device)
                c = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)

                interp = grid[yi][xi]
                # change 0->1->0 interp to 0->1
                w = torch.from_numpy(interp(frame_idx / w_frames/2)).to(device)
                
                entangle = 'camera'
                if entangle == 'conditioning':
                    c_forward = torch.cat([LookAtPoseSampler.sample(3.14/2,
                                                                    3.14/2,
                                                                    camera_lookat_point,
                                                                    radius=2.7, device=device).reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
                    w_c = G.mapping(z=zs[0:1], c=c[0:1], truncation_psi=psi, truncation_cutoff=truncation_cutoff)
                    img = G.synthesis(ws=w_c, c=c_forward, noise_mode='const')[image_mode][0]
                elif entangle == 'camera':
                    # if 'delta_c_temp' in locals():
                    #     delta_c = delta_c_temp
                    # else:
                    #     delta_c = G.t_mapping(zs[yi*grid_w+xi:yi*grid_w+xi+1], c[:1], truncation_psi=1.0, truncation_cutoff=None, update_emas=False)
                    #     delta_c = torch.squeeze(delta_c, 1)
                    #     delta_c_temp= delta_c
                    # delta_c = G.t_mapping(zs[yi*grid_w+xi:yi*grid_w+xi+1], c[:1], truncation_psi=1.0, truncation_cutoff=None, update_emas=False)
                    # delta_c = torch.squeeze(delta_c, 1)
                    # c[:,3] += delta_c[:,0]
                    # c[:,7] += delta_c[:,1]
                    # c[:,11] += delta_c[:,2]
                    img = G.synthesis(ws=w.unsqueeze(0), c=c[0:1], noise_mode='const')[image_mode][0]
                elif entangle == 'both':
                    w_c = G.mapping(z=zs[0:1], c=c[0:1], truncation_psi=psi, truncation_cutoff=truncation_cutoff)
                    if 'delta_c_temp' in locals():
                        # print("a")
                        delta_c = delta_c_temp
                    else:
                        delta_c = G.t_mapping(zs[yi*grid_w+xi:yi*grid_w+xi+1], c[:1], truncation_psi=1.0, truncation_cutoff=None, update_emas=False)
                        delta_c = torch.squeeze(delta_c, 1)
                        delta_c_temp = delta_c
                    c[:,3] += delta_c[:,0]
                    c[:,7] += delta_c[:,1]
                    c[:,11] += delta_c[:,2]
                    img = G.synthesis(ws=w_c, c=c[0:1], noise_mode='const')[image_mode][0]

                if image_mode == 'image_depth':
                    img = -img
                    img = (img - img.min()) / (img.max() - img.min()) * 2 - 1
                else:
                    img = img.clamp(min=-1,max=1)

                imgs.append(img)

                if gen_shapes:
                    # generate shapes
                    print('Generating shape for frame %d / %d ...' % (frame_idx, num_keyframes * w_frames))
                    
                    samples, voxel_origin, voxel_size = create_samples(N=voxel_resolution, voxel_origin=[0, 0, 0], cube_length=G.rendering_kwargs['box_warp'])
                    samples = samples.to(device)
                    sigmas = torch.zeros((samples.shape[0], samples.shape[1], 1), device=device)
                    transformed_ray_directions_expanded = torch.zeros((samples.shape[0], max_batch, 3), device=device)
                    transformed_ray_directions_expanded[..., -1] = -1

                    head = 0
                    with tqdm(total = samples.shape[1]) as pbar:
                        with torch.no_grad():
                            while head < samples.shape[1]:
                                torch.manual_seed(0)
                                sigma = G.sample_mixed(samples[:, head:head+max_batch], transformed_ray_directions_expanded[:, :samples.shape[1]-head], w.unsqueeze(0), truncation_psi=psi, noise_mode='const')['sigma']
                                sigmas[:, head:head+max_batch] = sigma
                                head += max_batch
                                pbar.update(max_batch)

                    sigmas = sigmas.reshape((voxel_resolution, voxel_resolution, voxel_resolution)).cpu().numpy()
                    sigmas = np.flip(sigmas, 0)
                    
                    pad = int(30 * voxel_resolution / 256)
                    pad_top = int(38 * voxel_resolution / 256)
                    sigmas[:pad] = 0
                    sigmas[-pad:] = 0
                    sigmas[:, :pad] = 0
                    sigmas[:, -pad_top:] = 0
                    sigmas[:, :, :pad] = 0
                    sigmas[:, :, -pad:] = 0

                    output_ply = True
                    if output_ply:
                        from shape_utils import convert_sdf_samples_to_ply
                        convert_sdf_samples_to_ply(np.transpose(sigmas, (2, 1, 0)), [0, 0, 0], 1, os.path.join(outdir, f'{frame_idx:04d}_shape.ply'), level=10)
                    else: # output mrc
                        with mrcfile.new_mmap(outdir + f'{frame_idx:04d}_shape.mrc', overwrite=True, shape=sigmas.shape, mrc_mode=2) as mrc:
                            mrc.data[:] = sigmas

        video_out.append_data(layout_grid(torch.stack(imgs), grid_w=grid_w, grid_h=grid_h))
    video_out.close()
    all_poses = np.stack(all_poses)

    if gen_shapes:
        print(all_poses.shape)
        with open(mp4.replace('.mp4', '_trajectory.npy'), 'wb') as f:
            np.save(f, all_poses)

#----------------------------------------------------------------------------

def parse_range(s: Union[str, List[int]]) -> List[int]:
    '''Parse a comma separated list of numbers or ranges and return a list of ints.

    Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
    '''
    if isinstance(s, list): return s
    ranges = []
    range_re = re.compile(r'^(\d+)-(\d+)$')
    for p in s.split(','):
        m = range_re.match(p)
        if m:
            ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
        else:
            ranges.append(int(p))
    return ranges

#----------------------------------------------------------------------------

def parse_tuple(s: Union[str, Tuple[int,int]]) -> Tuple[int, int]:
    '''Parse a 'M,N' or 'MxN' integer tuple.

    Example:
        '4x2' returns (4,2)
        '0,1' returns (0,1)
    '''
    if isinstance(s, tuple): return s
    m = re.match(r'^(\d+)[x,](\d+)$', s)
    if m:
        return (int(m.group(1)), int(m.group(2)))
    raise ValueError(f'cannot parse tuple {s}')

#----------------------------------------------------------------------------

@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--seeds', type=parse_range, help='List of random seeds', required=True)
@click.option('--output', help='Output path', type=str, required=False)
@click.option('--shuffle-seed', type=int, help='Random seed to use for shuffling seed order', default=None)
@click.option('--grid', type=parse_tuple, help='Grid width/height, e.g. \'4x3\' (default: 1x1)', default=(1,1))
@click.option('--num-keyframes', type=int, help='Number of seeds to interpolate through.  If not specified, determine based on the length of the seeds array given by --seeds.', default=None)
@click.option('--w-frames', type=int, help='Number of frames to interpolate between latents', default=240)
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
@click.option('--trunc-cutoff', 'truncation_cutoff', type=int, help='Truncation cutoff', default=14, show_default=True)
@click.option('--reload_modules', help='Overload persistent modules?', type=bool, required=False, metavar='BOOL', default=False, show_default=True)
@click.option('--cfg', help='Config', type=click.Choice(['FFHQ', 'Cats', 'Head']), required=False, metavar='STR', default='FFHQ', show_default=True)
@click.option('--image_mode', help='Image mode', type=click.Choice(['image', 'image_depth', 'image_raw']), required=False, metavar='STR', default='image', show_default=True)
@click.option('--sample_mult', 'sampling_multiplier', type=float, help='Multiplier for depth sampling in volume rendering', default=1, show_default=True)
@click.option('--nrr', type=int, help='Neural rendering resolution override', default=None, show_default=True)
@click.option('--shapes', type=bool, help='Gen shapes for shape interpolation', default=False, show_default=True)
@click.option('--interpolate', type=bool, help='Interpolate between seeds', default=True, show_default=True)
@click.option('--pose_cond', type=int, help='pose_cond angle', default=90, show_default=True)

def generate_images(
    network_pkl: str,
    seeds: List[int],
    output: str,
    shuffle_seed: Optional[int],
    truncation_psi: float,
    truncation_cutoff: int,
    grid: Tuple[int,int],
    num_keyframes: Optional[int],
    w_frames: int,
    reload_modules: bool,
    cfg: str,
    image_mode: str,
    sampling_multiplier: float,
    nrr: Optional[int],
    shapes: bool,
    interpolate: bool,
    pose_cond: int,
):
    """Render a latent vector interpolation video.

    Examples:

    \b
    # Render a 4x2 grid of interpolations for seeds 0 through 31.
    python gen_video.py --output=lerp.mp4 --trunc=1 --seeds=0-31 --grid=4x2 \\
        --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl

    Animation length and seed keyframes:

    The animation length is either determined based on the --seeds value or explicitly
    specified using the --num-keyframes option.

    When num keyframes is specified with --num-keyframes, the output video length
    will be 'num_keyframes*w_frames' frames.

    If --num-keyframes is not specified, the number of seeds given with
    --seeds must be divisible by grid size W*H (--grid).  In this case the
    output video length will be '# seeds/(w*h)*w_frames' frames.
    """

    print('Loading networks from "%s"...' % network_pkl)
    device = torch.device('cuda:0')
    with dnnlib.util.open_url(network_pkl) as f:
        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore

    G.rendering_kwargs['depth_resolution'] = int(G.rendering_kwargs['depth_resolution'] * sampling_multiplier)
    G.rendering_kwargs['depth_resolution_importance'] = int(G.rendering_kwargs['depth_resolution_importance'] * sampling_multiplier)
    if nrr is not None: G.neural_rendering_resolution = nrr
    # Specify reload_modules=True if you want code modifications to take effect; otherwise uses pickled code
    # if reload_modules:
    # print("Reloading Modules!")
    G_new = TriPlaneGenerator(*G.init_args, **G.init_kwargs).eval().requires_grad_(False).to(device)
    misc.copy_params_and_buffers(G, G_new, require_all=True)
    G_new.neural_rendering_resolution = G.neural_rendering_resolution
    G_new.rendering_kwargs = G.rendering_kwargs
    G = G_new

    if truncation_cutoff == 0:
        truncation_psi = 1.0 # truncation cutoff of 0 means no truncation anyways
    if truncation_psi == 1.0:
        truncation_cutoff = 14 # no truncation so doesn't matter where we cutoff
    network_pkl = os.path.basename(network_pkl)
    output = os.path.splitext(network_pkl)[0] + '_' + str(pose_cond) + '.mp4'
    if interpolate:
        gen_interp_video(G=G, mp4=output, pose_cond = pose_cond, bitrate='100M', grid_dims=grid, num_keyframes=num_keyframes, w_frames=w_frames, seeds=seeds, shuffle_seed=shuffle_seed, psi=truncation_psi, truncation_cutoff=truncation_cutoff, cfg=cfg, image_mode=image_mode, gen_shapes=shapes, device=device)
    else:
        os.makedirs(output)
        for seed in seeds:
            output = os.path.join(output, f'{seed}.mp4')
            seeds_ = [seed]
            gen_interp_video(G=G, mp4=output, pose_cond = pose_cond, bitrate='30M', grid_dims=grid, num_keyframes=num_keyframes, w_frames=w_frames, seeds=seeds_, shuffle_seed=shuffle_seed, psi=truncation_psi, truncation_cutoff=truncation_cutoff, cfg=cfg, image_mode=image_mode, device=device)

#----------------------------------------------------------------------------

if __name__ == "__main__":
    generate_images() # pylint: disable=no-value-for-parameter

#----------------------------------------------------------------------------