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nerf_helpers.py
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nerf_helpers.py
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import torch
import os
torch.autograd.set_detect_anomaly(True)
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def load_nerf(args, device):
"""Instantiate NeRF's MLP model.
"""
embed_fn, input_ch = get_embedder(args.multires, args.i_embed)
embeddirs_fn, input_ch_views = get_embedder(args.multires_views, args.i_embed)
output_ch = 4
skips = [4]
model = NeRF(D=args.netdepth, W=args.netwidth,
input_ch=input_ch, output_ch=output_ch, skips=skips,
input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs).to(device)
model_fine = NeRF(D=args.netdepth_fine, W=args.netwidth_fine,
input_ch=input_ch, output_ch=output_ch, skips=skips,
input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs).to(device)
network_query_fn = lambda inputs, viewdirs, network_fn: run_network(inputs, viewdirs, network_fn,
embed_fn=embed_fn,
embeddirs_fn=embeddirs_fn,
netchunk=args.netchunk)
# Load checkpoint
ckpt_dir = args.ckpt_dir
ckpt_name = args.model_name
ckpt_path = os.path.join(ckpt_dir + '/' + ckpt_name, ckpt_name+'.tar')
print('Found ckpts', ckpt_path)
print('Reloading from', ckpt_path)
ckpt = torch.load(ckpt_path)
# Load model
model.load_state_dict(ckpt['network_fn_state_dict'])
model_fine.load_state_dict(ckpt['network_fine_state_dict'])
render_kwargs = {
'network_query_fn': network_query_fn,
'perturb': args.perturb,
'N_importance': args.N_importance,
'network_fine': model_fine,
'N_samples': args.N_samples,
'network_fn': model,
'use_viewdirs': args.use_viewdirs,
'white_bkgd': args.white_bkgd,
'raw_noise_std': args.raw_noise_std
}
# NDC only good for LLFF-style forward facing data
if args.dataset_type != 'llff' or args.no_ndc:
print('Not ndc!')
render_kwargs['ndc'] = False
render_kwargs['lindisp'] = args.lindisp
# Disable updating of the weights
for param in model.parameters():
param.requires_grad = False
for param in model_fine.parameters():
param.requires_grad = False
return render_kwargs
# Positional encoding (section 5.1)
class Embedder:
def __init__(self, **kwargs):
self.kwargs = kwargs
self.create_embedding_fn()
def create_embedding_fn(self):
embed_fns = []
d = self.kwargs['input_dims']
out_dim = 0
if self.kwargs['include_input']:
embed_fns.append(lambda x: x)
out_dim += d
max_freq = self.kwargs['max_freq_log2']
N_freqs = self.kwargs['num_freqs']
if self.kwargs['log_sampling']:
freq_bands = 2. ** torch.linspace(0., max_freq, steps=N_freqs)
else:
freq_bands = torch.linspace(2. ** 0., 2. ** max_freq, steps=N_freqs)
for freq in freq_bands:
for p_fn in self.kwargs['periodic_fns']:
embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
out_dim += d
self.embed_fns = embed_fns
self.out_dim = out_dim
def embed(self, inputs):
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
def get_embedder(multires, i=0):
if i == -1:
return nn.Identity(), 3
embed_kwargs = {
'include_input': True,
'input_dims': 3,
'max_freq_log2': multires - 1,
'num_freqs': multires,
'log_sampling': True,
'periodic_fns': [torch.sin, torch.cos],
}
embedder_obj = Embedder(**embed_kwargs)
embed = lambda x, eo=embedder_obj: eo.embed(x)
return embed, embedder_obj.out_dim
# Model
class NeRF(nn.Module):
def __init__(self, D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False):
"""
"""
super(NeRF, self).__init__()
self.D = D
self.W = W
self.input_ch = input_ch
self.input_ch_views = input_ch_views
self.skips = skips
self.use_viewdirs = use_viewdirs
self.pts_linears = nn.ModuleList(
[nn.Linear(input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + input_ch, W) for i in
range(D - 1)])
### Implementation according to the official code release (https://github.com/bmild/nerf/blob/master/run_nerf_helpers.py#L104-L105)
self.views_linears = nn.ModuleList([nn.Linear(input_ch_views + W, W // 2)])
### Implementation according to the paper
# self.views_linears = nn.ModuleList(
# [nn.Linear(input_ch_views + W, W//2)] + [nn.Linear(W//2, W//2) for i in range(D//2)])
if use_viewdirs:
self.feature_linear = nn.Linear(W, W)
self.alpha_linear = nn.Linear(W, 1)
self.rgb_linear = nn.Linear(W // 2, 3)
else:
self.output_linear = nn.Linear(W, output_ch)
def forward(self, x):
input_pts, input_views = torch.split(x, [self.input_ch, self.input_ch_views], dim=-1)
h = input_pts
for i, l in enumerate(self.pts_linears):
h = self.pts_linears[i](h)
h = F.relu(h)
if i in self.skips:
h = torch.cat([input_pts, h], -1)
if self.use_viewdirs:
alpha = self.alpha_linear(h)
feature = self.feature_linear(h)
h = torch.cat([feature, input_views], -1)
for i, l in enumerate(self.views_linears):
h = self.views_linears[i](h)
h = F.relu(h)
rgb = self.rgb_linear(h)
outputs = torch.cat([rgb, alpha], -1)
else:
outputs = self.output_linear(h)
return outputs
def load_weights_from_keras(self, weights):
assert self.use_viewdirs, "Not implemented if use_viewdirs=False"
# Load pts_linears
for i in range(self.D):
idx_pts_linears = 2 * i
self.pts_linears[i].weight.data = torch.from_numpy(np.transpose(weights[idx_pts_linears]))
self.pts_linears[i].bias.data = torch.from_numpy(np.transpose(weights[idx_pts_linears + 1]))
# Load feature_linear
idx_feature_linear = 2 * self.D
self.feature_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_feature_linear]))
self.feature_linear.bias.data = torch.from_numpy(np.transpose(weights[idx_feature_linear + 1]))
# Load views_linears
idx_views_linears = 2 * self.D + 2
self.views_linears[0].weight.data = torch.from_numpy(np.transpose(weights[idx_views_linears]))
self.views_linears[0].bias.data = torch.from_numpy(np.transpose(weights[idx_views_linears + 1]))
# Load rgb_linear
idx_rbg_linear = 2 * self.D + 4
self.rgb_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_rbg_linear]))
self.rgb_linear.bias.data = torch.from_numpy(np.transpose(weights[idx_rbg_linear + 1]))
# Load alpha_linear
idx_alpha_linear = 2 * self.D + 6
self.alpha_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_alpha_linear]))
self.alpha_linear.bias.data = torch.from_numpy(np.transpose(weights[idx_alpha_linear + 1]))
def batchify(fn, chunk):
"""Constructs a version of 'fn' that applies to smaller batches.
"""
if chunk is None:
return fn
def ret(inputs):
return torch.cat([fn(inputs[i:i+chunk]) for i in range(0, inputs.shape[0], chunk)], 0)
return ret
def run_network(inputs, viewdirs, fn, embed_fn, embeddirs_fn, netchunk=1024*64):
"""Prepares inputs and applies network 'fn'.
"""
inputs_flat = torch.reshape(inputs, [-1, inputs.shape[-1]])
embedded = embed_fn(inputs_flat)
if viewdirs is not None:
input_dirs = viewdirs[:,None].expand(inputs.shape)
input_dirs_flat = torch.reshape(input_dirs, [-1, input_dirs.shape[-1]])
embedded_dirs = embeddirs_fn(input_dirs_flat)
embedded = torch.cat([embedded, embedded_dirs], -1)
outputs_flat = batchify(fn, netchunk)(embedded)
outputs = torch.reshape(outputs_flat, list(inputs.shape[:-1]) + [outputs_flat.shape[-1]])
return outputs