/
custom_layers.py
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/
custom_layers.py
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import geometry
import torchvision
import util
from pytorch_prototyping import pytorch_prototyping
import torch
from torch import nn
def init_recurrent_weights(self):
for m in self.modules():
if type(m) in [nn.GRU, nn.LSTM, nn.RNN]:
for name, param in m.named_parameters():
if 'weight_ih' in name:
nn.init.kaiming_normal_(param.data)
elif 'weight_hh' in name:
nn.init.orthogonal_(param.data)
elif 'bias' in name:
param.data.fill_(0)
def lstm_forget_gate_init(lstm_layer):
for name, parameter in lstm_layer.named_parameters():
if not "bias" in name: continue
n = parameter.size(0)
start, end = n // 4, n // 2
parameter.data[start:end].fill_(1.)
def clip_grad_norm_hook(x, max_norm=10):
total_norm = x.norm()
total_norm = total_norm ** (1 / 2.)
clip_coef = max_norm / (total_norm + 1e-6)
if clip_coef < 1:
return x * clip_coef
class DepthSampler(nn.Module):
def __init__(self):
super().__init__()
def forward(self,
xy,
depth,
cam2world,
intersection_net,
intrinsics):
self.logs = list()
batch_size, _, _ = cam2world.shape
intersections = geometry.world_from_xy_depth(xy=xy, depth=depth, cam2world=cam2world, intrinsics=intrinsics)
depth = geometry.depth_from_world(intersections, cam2world)
if self.training:
print(depth.min(), depth.max())
return intersections, depth
class Raymarcher(nn.Module):
def __init__(self,
num_feature_channels,
raymarch_steps):
super().__init__()
self.n_feature_channels = num_feature_channels
self.steps = raymarch_steps
hidden_size = 16
self.lstm = nn.LSTMCell(input_size=self.n_feature_channels,
hidden_size=hidden_size)
self.lstm.apply(init_recurrent_weights)
lstm_forget_gate_init(self.lstm)
self.out_layer = nn.Linear(hidden_size, 1)
self.counter = 0
def forward(self,
cam2world,
phi,
uv,
intrinsics):
batch_size, num_samples, _ = uv.shape
log = list()
ray_dirs = geometry.get_ray_directions(uv,
cam2world=cam2world,
intrinsics=intrinsics)
initial_depth = torch.zeros((batch_size, num_samples, 1)).normal_(mean=0.05, std=5e-4).cuda()
init_world_coords = geometry.world_from_xy_depth(uv,
initial_depth,
intrinsics=intrinsics,
cam2world=cam2world)
world_coords = [init_world_coords]
depths = [initial_depth]
states = [None]
for step in range(self.steps):
v = phi(world_coords[-1])
state = self.lstm(v.view(-1, self.n_feature_channels), states[-1])
if state[0].requires_grad:
state[0].register_hook(lambda x: x.clamp(min=-10, max=10))
signed_distance = self.out_layer(state[0]).view(batch_size, num_samples, 1)
new_world_coords = world_coords[-1] + ray_dirs * signed_distance
states.append(state)
world_coords.append(new_world_coords)
depth = geometry.depth_from_world(world_coords[-1], cam2world)
if self.training:
print("Raymarch step %d: Min depth %0.6f, max depth %0.6f" %
(step, depths[-1].min().detach().cpu().numpy(), depths[-1].max().detach().cpu().numpy()))
depths.append(depth)
if not self.counter % 100:
# Write tensorboard summary for each step of ray-marcher.
drawing_depths = torch.stack(depths, dim=0)[:, 0, :, :]
drawing_depths = util.lin2img(drawing_depths).repeat(1, 3, 1, 1)
log.append(('image', 'raycast_progress',
torch.clamp(torchvision.utils.make_grid(drawing_depths, scale_each=False, normalize=True), 0.0,
5),
100))
# Visualize residual step distance (i.e., the size of the final step)
fig = util.show_images([util.lin2img(signed_distance)[i, :, :, :].detach().cpu().numpy().squeeze()
for i in range(batch_size)])
log.append(('figure', 'stopping_distances', fig, 100))
self.counter += 1
return world_coords[-1], depths[-1], log
class DeepvoxelsRenderer(nn.Module):
def __init__(self,
nf0,
in_channels,
input_resolution,
img_sidelength):
super().__init__()
self.nf0 = nf0
self.in_channels = in_channels
self.input_resolution = input_resolution
self.img_sidelength = img_sidelength
self.num_down_unet = util.num_divisible_by_2(input_resolution)
self.num_upsampling = util.num_divisible_by_2(img_sidelength) - self.num_down_unet
self.build_net()
def build_net(self):
self.net = [
pytorch_prototyping.Unet(in_channels=self.in_channels,
out_channels=3 if self.num_upsampling <= 0 else 4 * self.nf0,
outermost_linear=True if self.num_upsampling <= 0 else False,
use_dropout=True,
dropout_prob=0.1,
nf0=self.nf0 * (2 ** self.num_upsampling),
norm=nn.BatchNorm2d,
max_channels=8 * self.nf0,
num_down=self.num_down_unet)
]
if self.num_upsampling > 0:
self.net += [
pytorch_prototyping.UpsamplingNet(per_layer_out_ch=self.num_upsampling * [self.nf0],
in_channels=4 * self.nf0,
upsampling_mode='transpose',
use_dropout=True,
dropout_prob=0.1),
pytorch_prototyping.Conv2dSame(self.nf0, out_channels=self.nf0 // 2, kernel_size=3, bias=False),
nn.BatchNorm2d(self.nf0 // 2),
nn.ReLU(True),
pytorch_prototyping.Conv2dSame(self.nf0 // 2, 3, kernel_size=3)
]
self.net += [nn.Tanh()]
self.net = nn.Sequential(*self.net)
def forward(self, input):
batch_size, _, ch = input.shape
input = input.permute(0, 2, 1).view(batch_size, ch, self.img_sidelength, self.img_sidelength)
out = self.net(input)
return out.view(batch_size, 3, -1).permute(0, 2, 1)