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# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import os
import numpy as np
import torch
import nvdiffrast.torch as dr
from src.models.geometry.rep_3d import util
######################################################################################
# Smooth pooling / mip computation with linear gradient upscaling
######################################################################################
class texture2d_mip(torch.autograd.Function):
@staticmethod
def forward(ctx, texture):
return util.avg_pool_nhwc(texture, (2,2))
@staticmethod
def backward(ctx, dout):
gy, gx = torch.meshgrid(torch.linspace(0.0 + 0.25 / dout.shape[1], 1.0 - 0.25 / dout.shape[1], dout.shape[1]*2, device="cuda"),
torch.linspace(0.0 + 0.25 / dout.shape[2], 1.0 - 0.25 / dout.shape[2], dout.shape[2]*2, device="cuda"),
indexing='ij')
uv = torch.stack((gx, gy), dim=-1)
return dr.texture(dout * 0.25, uv[None, ...].contiguous(), filter_mode='linear', boundary_mode='clamp')
########################################################################################################
# Simple texture class. A texture can be either
# - A 3D tensor (using auto mipmaps)
# - A list of 3D tensors (full custom mip hierarchy)
########################################################################################################
class Texture2D(torch.nn.Module):
# Initializes a texture from image data.
# Input can be constant value (1D array) or texture (3D array) or mip hierarchy (list of 3d arrays)
def __init__(self, init, min_max=None):
super(Texture2D, self).__init__()
if isinstance(init, np.ndarray):
init = torch.tensor(init, dtype=torch.float32, device='cuda')
elif isinstance(init, list) and len(init) == 1:
init = init[0]
if isinstance(init, list):
self.data = list(torch.nn.Parameter(mip.clone().detach(), requires_grad=True) for mip in init)
elif len(init.shape) == 4:
self.data = torch.nn.Parameter(init.clone().detach(), requires_grad=True)
elif len(init.shape) == 3:
self.data = torch.nn.Parameter(init[None, ...].clone().detach(), requires_grad=True)
elif len(init.shape) == 2:
self.data = torch.nn.Parameter(init[None, :, :, None].repeat(1,1,1,3).clone().detach(), requires_grad=True)
# breakpoint()
elif len(init.shape) == 1:
self.data = torch.nn.Parameter(init[None, None, None, :].clone().detach(), requires_grad=True) # Convert constant to 1x1 tensor
else:
assert False, "Invalid texture object"
self.min_max = min_max
# Filtered (trilinear) sample texture at a given location
def sample(self, texc, texc_deriv, filter_mode='linear-mipmap-linear'):
if isinstance(self.data, list):
out = dr.texture(self.data[0], texc, texc_deriv, mip=self.data[1:], filter_mode=filter_mode)
else:
if self.data.shape[1] > 1 and self.data.shape[2] > 1:
mips = [self.data]
while mips[-1].shape[1] > 1 and mips[-1].shape[2] > 1:
mips += [texture2d_mip.apply(mips[-1])]
out = dr.texture(mips[0], texc, texc_deriv, mip=mips[1:], filter_mode=filter_mode)
else:
out = dr.texture(self.data, texc, texc_deriv, filter_mode=filter_mode)
return out
def getRes(self):
return self.getMips()[0].shape[1:3]
def getChannels(self):
return self.getMips()[0].shape[3]
def getMips(self):
if isinstance(self.data, list):
return self.data
else:
return [self.data]
# In-place clamp with no derivative to make sure values are in valid range after training
def clamp_(self):
if self.min_max is not None:
for mip in self.getMips():
for i in range(mip.shape[-1]):
mip[..., i].clamp_(min=self.min_max[0][i], max=self.min_max[1][i])
# In-place clamp with no derivative to make sure values are in valid range after training
def normalize_(self):
with torch.no_grad():
for mip in self.getMips():
mip = util.safe_normalize(mip)
########################################################################################################
# Helper function to create a trainable texture from a regular texture. The trainable weights are
# initialized with texture data as an initial guess
########################################################################################################
def create_trainable(init, res=None, auto_mipmaps=True, min_max=None):
with torch.no_grad():
if isinstance(init, Texture2D):
assert isinstance(init.data, torch.Tensor)
min_max = init.min_max if min_max is None else min_max
init = init.data
elif isinstance(init, np.ndarray):
init = torch.tensor(init, dtype=torch.float32, device='cuda')
# Pad to NHWC if needed
if len(init.shape) == 1: # Extend constant to NHWC tensor
init = init[None, None, None, :]
elif len(init.shape) == 3:
init = init[None, ...]
# Scale input to desired resolution.
if res is not None:
init = util.scale_img_nhwc(init, res)
# Genreate custom mipchain
if not auto_mipmaps:
mip_chain = [init.clone().detach().requires_grad_(True)]
while mip_chain[-1].shape[1] > 1 or mip_chain[-1].shape[2] > 1:
new_size = [max(mip_chain[-1].shape[1] // 2, 1), max(mip_chain[-1].shape[2] // 2, 1)]
mip_chain += [util.scale_img_nhwc(mip_chain[-1], new_size)]
return Texture2D(mip_chain, min_max=min_max)
else:
return Texture2D(init, min_max=min_max)
########################################################################################################
# Convert texture to and from SRGB
########################################################################################################
def srgb_to_rgb(texture):
return Texture2D(list(util.srgb_to_rgb(mip) for mip in texture.getMips()))
def rgb_to_srgb(texture):
return Texture2D(list(util.rgb_to_srgb(mip) for mip in texture.getMips()))
########################################################################################################
# Utility functions for loading / storing a texture
########################################################################################################
def _load_mip2D(fn, lambda_fn=None, channels=None):
imgdata = torch.tensor(util.load_image(fn), dtype=torch.float32, device='cuda')
if channels is not None:
imgdata = imgdata[..., 0:channels]
if lambda_fn is not None:
imgdata = lambda_fn(imgdata)
return imgdata.detach().clone()
def load_texture2D(fn, lambda_fn=None, channels=None):
base, ext = os.path.splitext(fn)
if os.path.exists(base + "_0" + ext):
mips = []
while os.path.exists(base + ("_%d" % len(mips)) + ext):
mips += [_load_mip2D(base + ("_%d" % len(mips)) + ext, lambda_fn, channels)]
return Texture2D(mips)
else:
return Texture2D(_load_mip2D(fn, lambda_fn, channels))
def _save_mip2D(fn, mip, mipidx, lambda_fn):
if lambda_fn is not None:
data = lambda_fn(mip).detach().cpu().numpy()
else:
data = mip.detach().cpu().numpy()
if mipidx is None:
util.save_image(fn, data)
else:
base, ext = os.path.splitext(fn)
util.save_image(base + ("_%d" % mipidx) + ext, data)
def save_texture2D(fn, tex, lambda_fn=None):
if isinstance(tex.data, list):
for i, mip in enumerate(tex.data):
_save_mip2D(fn, mip[0,...], i, lambda_fn)
else:
_save_mip2D(fn, tex.data[0,...], None, lambda_fn)
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