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renderer.py
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renderer.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Wrap the generator to render a sequence of images"""
import torch
import torch.nn.functional as F
import numpy as np
from torch import random
import tqdm
import copy
import trimesh
class Renderer(object):
def __init__(self, generator, discriminator=None, program=None):
self.generator = generator
self.discriminator = discriminator
self.sample_tmp = 0.65
self.program = program
self.seed = 0
if (program is not None) and (len(program.split(':')) == 2):
from training.dataset import ImageFolderDataset
self.image_data = ImageFolderDataset(program.split(':')[1])
self.program = program.split(':')[0]
else:
self.image_data = None
def set_random_seed(self, seed):
self.seed = seed
torch.manual_seed(seed)
np.random.seed(seed)
def __call__(self, *args, **kwargs):
self.generator.eval() # eval mode...
if self.program is None:
if hasattr(self.generator, 'get_final_output'):
return self.generator.get_final_output(*args, **kwargs)
return self.generator(*args, **kwargs)
if self.image_data is not None:
batch_size = 1
indices = (np.random.rand(batch_size) * len(self.image_data)).tolist()
rimages = np.stack([self.image_data._load_raw_image(int(i)) for i in indices], 0)
rimages = torch.from_numpy(rimages).float().to(kwargs['z'].device) / 127.5 - 1
kwargs['img'] = rimages
outputs = getattr(self, f"render_{self.program}")(*args, **kwargs)
if self.image_data is not None:
imgs = outputs if not isinstance(outputs, tuple) else outputs[0]
size = imgs[0].size(-1)
rimg = F.interpolate(rimages, (size, size), mode='bicubic', align_corners=False)
imgs = [torch.cat([img, rimg], 0) for img in imgs]
outputs = imgs if not isinstance(outputs, tuple) else (imgs, outputs[1])
return outputs
def get_additional_params(self, ws, t=0):
gen = self.generator.synthesis
batch_size = ws.size(0)
kwargs = {}
if not hasattr(gen, 'get_latent_codes'):
return kwargs
s_val, t_val, r_val = [[0, 0, 0]], [[0.5, 0.5, 0.5]], [0.]
# kwargs["transformations"] = gen.get_transformations(batch_size=batch_size, mode=[s_val, t_val, r_val], device=ws.device)
# kwargs["bg_rotation"] = gen.get_bg_rotation(batch_size, device=ws.device)
# kwargs["light_dir"] = gen.get_light_dir(batch_size, device=ws.device)
kwargs["latent_codes"] = gen.get_latent_codes(batch_size, tmp=self.sample_tmp, device=ws.device)
kwargs["camera_matrices"] = self.get_camera_traj(t, ws.size(0), device=ws.device)
return kwargs
def get_camera_traj(self, t, batch_size=1, traj_type='pigan', device='cpu'):
gen = self.generator.synthesis
if traj_type == 'pigan':
range_u, range_v = gen.C.range_u, gen.C.range_v
pitch = 0.2 * np.cos(t * 2 * np.pi) + np.pi/2
yaw = 0.4 * np.sin(t * 2 * np.pi)
u = (yaw - range_u[0]) / (range_u[1] - range_u[0])
v = (pitch - range_v[0]) / (range_v[1] - range_v[0])
cam = gen.get_camera(batch_size=batch_size, mode=[u, v, 0.5], device=device)
else:
raise NotImplementedError
return cam
def render_rotation_camera(self, *args, **kwargs):
batch_size, n_steps = 2, kwargs["n_steps"]
gen = self.generator.synthesis
if 'img' not in kwargs:
ws = self.generator.mapping(*args, **kwargs)
else:
ws, _ = self.generator.encoder(kwargs['img'])
# ws = ws.repeat(batch_size, 1, 1)
# kwargs["not_render_background"] = True
if hasattr(gen, 'get_latent_codes'):
kwargs["latent_codes"] = gen.get_latent_codes(batch_size, tmp=self.sample_tmp, device=ws.device)
kwargs.pop('img', None)
out = []
cameras = []
relatve_range_u = kwargs['relative_range_u']
u_samples = np.linspace(relatve_range_u[0], relatve_range_u[1], n_steps)
for step in tqdm.tqdm(range(n_steps)):
# Set Camera
u = u_samples[step]
kwargs["camera_matrices"] = gen.get_camera(batch_size=batch_size, mode=[u, 0.5, 0.5], device=ws.device)
cameras.append(gen.get_camera(batch_size=batch_size, mode=[u, 0.5, 0.5], device=ws.device))
with torch.no_grad():
out_i = gen(ws, **kwargs)
if isinstance(out_i, dict):
out_i = out_i['img']
out.append(out_i)
if 'return_cameras' in kwargs and kwargs["return_cameras"]:
return out, cameras
else:
return out
def render_rotation_camera3(self, styles=None, *args, **kwargs):
gen = self.generator.synthesis
n_steps = 36 # 120
if styles is None:
batch_size = 2
if 'img' not in kwargs:
ws = self.generator.mapping(*args, **kwargs)
else:
ws = self.generator.encoder(kwargs['img'])['ws']
# ws = ws.repeat(batch_size, 1, 1)
else:
ws = styles
batch_size = ws.size(0)
# kwargs["not_render_background"] = True
# Get Random codes and bg rotation
self.sample_tmp = 0.72
if hasattr(gen, 'get_latent_codes'):
kwargs["latent_codes"] = gen.get_latent_codes(batch_size, tmp=self.sample_tmp, device=ws.device)
kwargs.pop('img', None)
# if getattr(gen, "use_noise", False):
# from dnnlib.geometry import extract_geometry
# kwargs['meshes'] = {}
# low_res, high_res = gen.resolution_vol, gen.img_resolution
# res = low_res * 2
# while res <= high_res:
# kwargs['meshes'][res] = [trimesh.Trimesh(*extract_geometry(gen, ws, resolution=res, threshold=30.))]
# kwargs['meshes'][res] += [
# torch.randn(len(kwargs['meshes'][res][0].vertices),
# 2, device=ws.device)[kwargs['meshes'][res][0].faces]]
# res = res * 2
# if getattr(gen, "use_noise", False):
# kwargs['voxel_noise'] = gen.get_voxel_field(styles=ws, n_vols=2048, return_noise=True, sphere_noise=True)
# if getattr(gen, "use_voxel_noise", False):
# kwargs['voxel_noise'] = gen.get_voxel_field(styles=ws, n_vols=128, return_noise=True)
kwargs['noise_mode'] = 'const'
out = []
tspace = np.linspace(0, 1, n_steps)
range_u, range_v = gen.C.range_u, gen.C.range_v
for step in tqdm.tqdm(range(n_steps)):
t = tspace[step]
pitch = 0.2 * np.cos(t * 2 * np.pi) + np.pi/2
yaw = 0.4 * np.sin(t * 2 * np.pi)
u = (yaw - range_u[0]) / (range_u[1] - range_u[0])
v = (pitch - range_v[0]) / (range_v[1] - range_v[0])
kwargs["camera_matrices"] = gen.get_camera(
batch_size=batch_size, mode=[u, v, t], device=ws.device)
with torch.no_grad():
out_i = gen(ws, **kwargs)
if isinstance(out_i, dict):
out_i = out_i['img']
out.append(out_i)
return out
def render_rotation_both(self, *args, **kwargs):
gen = self.generator.synthesis
batch_size, n_steps = 1, 36
if 'img' not in kwargs:
ws = self.generator.mapping(*args, **kwargs)
else:
ws, _ = self.generator.encoder(kwargs['img'])
ws = ws.repeat(batch_size, 1, 1)
# kwargs["not_render_background"] = True
# Get Random codes and bg rotation
kwargs["latent_codes"] = gen.get_latent_codes(batch_size, tmp=self.sample_tmp, device=ws.device)
kwargs.pop('img', None)
out = []
tspace = np.linspace(0, 1, n_steps)
range_u, range_v = gen.C.range_u, gen.C.range_v
for step in tqdm.tqdm(range(n_steps)):
t = tspace[step]
pitch = 0.2 * np.cos(t * 2 * np.pi) + np.pi/2
yaw = 0.4 * np.sin(t * 2 * np.pi)
u = (yaw - range_u[0]) / (range_u[1] - range_u[0])
v = (pitch - range_v[0]) / (range_v[1] - range_v[0])
kwargs["camera_matrices"] = gen.get_camera(
batch_size=batch_size, mode=[u, v, 0.5], device=ws.device)
with torch.no_grad():
out_i = gen(ws, **kwargs)
if isinstance(out_i, dict):
out_i = out_i['img']
kwargs_n = copy.deepcopy(kwargs)
kwargs_n.update({'render_option': 'early,no_background,up64,depth,normal'})
out_n = gen(ws, **kwargs_n)
out_n = F.interpolate(out_n,
size=(out_i.size(-1), out_i.size(-1)),
mode='bicubic', align_corners=True)
out_i = torch.cat([out_i, out_n], 0)
out.append(out_i)
return out
def render_rotation_grid(self, styles=None, return_cameras=False, *args, **kwargs):
gen = self.generator.synthesis
if styles is None:
batch_size = 1
ws = self.generator.mapping(*args, **kwargs)
ws = ws.repeat(batch_size, 1, 1)
else:
ws = styles
batch_size = ws.size(0)
kwargs["latent_codes"] = gen.get_latent_codes(batch_size, tmp=self.sample_tmp, device=ws.device)
kwargs.pop('img', None)
if getattr(gen, "use_voxel_noise", False):
kwargs['voxel_noise'] = gen.get_voxel_field(styles=ws, n_vols=128, return_noise=True)
out = []
cameras = []
range_u, range_v = gen.C.range_u, gen.C.range_v
a_steps, b_steps = 6, 3
aspace = np.linspace(-0.4, 0.4, a_steps)
bspace = np.linspace(-0.2, 0.2, b_steps) * -1
for b in tqdm.tqdm(range(b_steps)):
for a in range(a_steps):
t_a = aspace[a]
t_b = bspace[b]
camera_mat = gen.camera_matrix.repeat(batch_size, 1, 1).to(ws.device)
loc_x = np.cos(t_b) * np.cos(t_a)
loc_y = np.cos(t_b) * np.sin(t_a)
loc_z = np.sin(t_b)
loc = torch.tensor([[loc_x, loc_y, loc_z]], dtype=torch.float32).to(ws.device)
from dnnlib.camera import look_at
R = look_at(loc)
RT = torch.eye(4).reshape(1, 4, 4).repeat(batch_size, 1, 1)
RT[:, :3, :3] = R
RT[:, :3, -1] = loc
world_mat = RT.to(ws.device)
#kwargs["camera_matrices"] = gen.get_camera(
# batch_size=batch_size, mode=[u, v, 0.5], device=ws.device)
kwargs["camera_matrices"] = (camera_mat, world_mat, "random", None)
with torch.no_grad():
out_i = gen(ws, **kwargs)
if isinstance(out_i, dict):
out_i = out_i['img']
# kwargs_n = copy.deepcopy(kwargs)
# kwargs_n.update({'render_option': 'early,no_background,up64,depth,normal'})
# out_n = gen(ws, **kwargs_n)
# out_n = F.interpolate(out_n,
# size=(out_i.size(-1), out_i.size(-1)),
# mode='bicubic', align_corners=True)
# out_i = torch.cat([out_i, out_n], 0)
out.append(out_i)
if return_cameras:
return out, cameras
else:
return out
def render_rotation_camera_grid(self, *args, **kwargs):
batch_size, n_steps = 1, 60
gen = self.generator.synthesis
bbox_generator = self.generator.synthesis.boundingbox_generator
ws = self.generator.mapping(*args, **kwargs)
ws = ws.repeat(batch_size, 1, 1)
# Get Random codes and bg rotation
kwargs["latent_codes"] = gen.get_latent_codes(batch_size, tmp=self.sample_tmp, device=ws.device)
del kwargs['render_option']
out = []
for v in [0.15, 0.5, 1.05]:
for step in tqdm.tqdm(range(n_steps)):
# Set Camera
u = step * 1.0 / (n_steps - 1) - 1.0
kwargs["camera_matrices"] = gen.get_camera(batch_size=batch_size, mode=[u, v, 0.5], device=ws.device)
with torch.no_grad():
out_i = gen(ws, render_option=None, **kwargs)
if isinstance(out_i, dict):
out_i = out_i['img']
# option_n = 'early,no_background,up64,depth,direct_depth'
# option_n = 'early,up128,no_background,depth,normal'
# out_n = gen(ws, render_option=option_n, **kwargs)
# out_n = F.interpolate(out_n,
# size=(out_i.size(-1), out_i.size(-1)),
# mode='bicubic', align_corners=True)
# out_i = torch.cat([out_i, out_n], 0)
out.append(out_i)
# out += out[::-1]
return out