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import torch
import torch.nn as nn
from torch import autograd
from torch.optim import Adam
import torch.nn.functional as F
import sys
try:
import tinycudann as tcnn
except ImportError:
print("This script requires the tiny-cuda-nn extension for PyTorch.")
print("You can install it by running:")
print("============================================================")
print("tiny-cuda-nn$ cd bindings/torch")
print("tiny-cuda-nn/bindings/torch$ python setup.py install")
print("============================================================")
sys.exit()
class SDF(nn.Module):
def __init__(self, hash=True, n_levels=12, log2_hashmap_size=15, base_resolution=16, smoothstep=False) -> None:
super().__init__()
self.encoder = tcnn.Encoding(3, {
"otype": "HashGrid" if hash else "DenseGrid",
"n_levels": n_levels,
"n_features_per_level": 2,
"log2_hashmap_size": log2_hashmap_size,
"base_resolution": base_resolution,
"per_level_scale": 1.5,
"interpolation": "Smoothstep" if smoothstep else "Linear"
})
self.decoder = nn.Sequential(
nn.Linear(self.encoder.n_output_dims, 64),
nn.ReLU(True),
nn.Linear(64, 1)
)
def forward(self, x):
encoded = self.encoder(x).to(dtype=torch.float)
sdf = self.decoder(encoded)
return sdf
def forward_with_nablas(self, x):
with torch.enable_grad():
x = x.requires_grad_(True)
sdf = self.forward(x)
nablas = autograd.grad(
sdf,
x,
torch.ones_like(sdf, device=x.device),
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
return sdf, nablas
if __name__ == '__main__':
"""
NOTE: Jianfei: I provide three testing tools for backward_backward functionality.
Play around as you want :)
1. test_train(): train a toy SDF model with eikonal term.
2. grad_check(): check backward_backward numerical correctness via torch.autograd.gradcheck.
3. vis_graph(): visualize torch compute graph
"""
def test_():
device = torch.device("cuda")
model = SDF(True, n_levels=1, log2_hashmap_size=15, base_resolution=4, smoothstep=False).to(device)
x = (torch.tensor([[0.3, 0.4, 0.5]], dtype=torch.float, device=device)).requires_grad_(True)
sdf, nablas = model.forward_with_nablas(x)
autograd.grad(
nablas,
x,
torch.ones_like(nablas, device=x.device),
create_graph=False,
retain_graph=False,
only_inputs=True)[0]
def test_train():
"""
train a toy SDF model with eikonal term.
"""
from tqdm import tqdm
device = torch.device("cuda")
model = SDF(True, 4, base_resolution=12).to(device)
# model = SDF(False, 4, base_resolution=12).to(device)
optimizer = Adam(model.parameters(), 2.0e-3)
with tqdm(range(10000)) as pbar:
for _ in pbar:
x = torch.rand([51200,3], dtype=torch.float, device=device)
sdf, nablas = model.forward_with_nablas(x)
nablas_norm: torch.Tensor = nablas.norm(dim=-1)
# eikonal term
loss = F.mse_loss(nablas_norm, nablas_norm.new_ones(nablas_norm.shape), reduction='mean')
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.set_postfix(loss=loss.item())
def grad_check():
"""
check backward_backward numerical correctness via torch.autograd.gradcheck
"""
import numpy as np
from types import SimpleNamespace
from tinycudann.modules import _module_function_backward, _module_function, _torch_precision, _C
dtype = _torch_precision(_C.preferred_precision())
device = torch.device("cuda")
# NOTE: need a smaller net when gradcheck, otherwise will OOM
model = SDF(True, n_levels=4, log2_hashmap_size=19, base_resolution=4, smoothstep=True).to(device)
# model = SDF(True, n_levels=1, log2_hashmap_size=15, base_resolution=8, smoothstep=False).to(device)
def apply_on_x(x):
params = model.encoder.params.to(_torch_precision(model.encoder.native_tcnn_module.param_precision())).contiguous()
return _module_function.apply(
model.encoder.native_tcnn_module, x, params, 128.0
)
# ✓ y w.r.t. x i.e. dy_dx (passed)
autograd.gradcheck(
apply_on_x,
# (torch.rand([1,3], dtype=torch.float, device=device)).requires_grad_(True),
(torch.tensor([[0.17, 0.55, 0.79]], dtype=torch.float, device=device)).requires_grad_(True),
eps=1.0e-3)
# ✓ dL_dx w.r.t. x i.e. ddLdx_dx (passed)
# ✓ dL_dx w.r.t. dL_dy i.e. ddLdx_ddLdy (passed)
autograd.gradgradcheck(
apply_on_x,
# (torch.rand([1,3], dtype=torch.float, device=device)).requires_grad_(True),
(torch.tensor([[0.17, 0.55, 0.79]], dtype=torch.float, device=device)).requires_grad_(True),
eps=1.0e-3,
nondet_tol=0.001 # due to non-determinism of atomicAdd
)
def backward_apply_on_x(x):
dL_dy = torch.ones([*x.shape[:-1], model.encoder.n_output_dims], dtype=dtype, device=device)
params = model.encoder.params.to(_torch_precision(model.encoder.native_tcnn_module.param_precision())).contiguous()
native_ctx, y = model.encoder.native_tcnn_module.fwd(x, params)
dummy_ctx_fwd = SimpleNamespace(
native_tcnn_module=model.encoder.native_tcnn_module,
loss_scale=model.encoder.loss_scale,
native_ctx=native_ctx)
return _module_function_backward.apply(dummy_ctx_fwd, dL_dy, x, params, y)
def backward_apply_on_params(params):
x = (torch.tensor([[0.17, 0.55, 0.79]], dtype=torch.float, device=device)).requires_grad_(True)
dL_dy = torch.ones([*x.shape[:-1], model.encoder.n_output_dims], dtype=dtype, device=device)
params = params.to(_torch_precision(model.encoder.native_tcnn_module.param_precision())).contiguous()
native_ctx, y = model.encoder.native_tcnn_module.fwd(x, params)
dummy_ctx_fwd = SimpleNamespace(
native_tcnn_module=model.encoder.native_tcnn_module,
loss_scale=model.encoder.loss_scale,
native_ctx=native_ctx)
return _module_function_backward.apply(dummy_ctx_fwd, dL_dy, x, params, y)
def backward_apply_on_dLdy(dL_dy):
x = (torch.tensor([[0.17, 0.55, 0.79]], dtype=torch.float, device=device)).requires_grad_(True)
# params = model.encoder.params.data.to(_torch_precision(model.encoder.native_tcnn_module.param_precision())).contiguous()
params = model.encoder.params.to(_torch_precision(model.encoder.native_tcnn_module.param_precision())).contiguous()
native_ctx, y = model.encoder.native_tcnn_module.fwd(x, params)
dummy_ctx_fwd = SimpleNamespace(
native_tcnn_module=model.encoder.native_tcnn_module,
loss_scale=model.encoder.loss_scale,
native_ctx=native_ctx)
return _module_function_backward.apply(dummy_ctx_fwd, dL_dy, x, params, y)
# NOTE: partial passed (Jacobian mismatch for output 1 with respect to input 0, which is ddLdgrid_dx)
# ✓ dL_dx w.r.t. x i.e. ddLdx_dx (passed)
# ✓ dL_dgrid w.r.t. x i.e. ddLdgrid_dx (currently do not support second order gradients from grid's gradient.)
# autograd.gradcheck(
# backward_apply_on_x,
# # (torch.rand([1,3], dtype=torch.float, device=device)).requires_grad_(True),
# (torch.tensor([[0.17, 0.55, 0.79]], dtype=torch.float, device=device)).requires_grad_(True),
# eps=1.0e-4
# )
# NOTE: passed
# ✓ dL_dx w.r.t. grid i.e. ddLdx_dgrid (passed)
# ✓ dL_dgrid w.r.t. grid i.e. ddLdgrid_dgrid (all zero)
autograd.gradcheck(
backward_apply_on_params,
model.encoder.params,
eps=1.0e-3
)
# NOTE: partial passed (Jacobian mismatch for output 1 with respect to input 0, which is ddLdgrid_ddLdy)
# ✓ dL_dx w.r.t. dL_dy i.e. ddLdx_ddLdy (passed)
# x dL_dgrid w.r.t. dL_dy i.e. ddLdgrid_ddLdy (currently do not support second order gradients from grid's gradient.)
autograd.gradcheck(
backward_apply_on_dLdy,
torch.randn([1,model.encoder.n_output_dims], dtype=dtype, device=device).requires_grad_(True),
eps=1.0e-3, atol=0.01, rtol=0.001
)
def vis_graph():
"""
visualize torch compute graphs
"""
import torchviz
device = torch.device("cuda")
# NOTE: need a smaller net when gradcheck, otherwise will OOM
model = SDF(True, n_levels=4, log2_hashmap_size=15, base_resolution=4).to(device)
x = torch.tensor([[0.17, 0.55, 0.79]], dtype=torch.float, device=device)
sdf, nablas = model.forward_with_nablas(x)
torchviz.make_dot(
(nablas, sdf, x, model.encoder.params, *list(model.decoder.parameters())),
{'nablas': nablas, 'sdf': sdf, 'x': x, 'grid_param': model.encoder.params,
**{n:p for n, p in model.decoder.named_parameters(prefix='decoder')}
}).render("attached", format="png")
def check_throw():
network = tcnn.Network(3, 1, network_config={
"otype": "FullyFusedMLP", # Component type.
"activation": 'ReLU', # Activation of hidden layers.
"output_activation": 'None', # Activation of the output layer.
"n_neurons": 64, # Neurons in each hidden layer. # May only be 16, 32, 64, or 128.
"n_hidden_layers": 5, # Number of hidden layers.
}, seed=42)
if __name__ == "__main__":
# test_()
test_train()
# grad_check()
# vis_graph()
# check_throw()
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