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import abc
import os
from typing import Sequence
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.optim.lr_scheduler
from torch import nn
def compute_plane_tv(t):
batch_size, c, h, w = t.shape
count_h = batch_size * c * (h - 1) * w
count_w = batch_size * c * h * (w - 1)
h_tv = torch.square(t[..., 1:, :] - t[..., :h-1, :]).sum()
w_tv = torch.square(t[..., :, 1:] - t[..., :, :w-1]).sum()
return 2 * (h_tv / count_h + w_tv / count_w) # This is summing over batch and c instead of avg
def compute_plane_smoothness(t):
batch_size, c, h, w = t.shape
# Convolve with a second derivative filter, in the time dimension which is dimension 2
first_difference = t[..., 1:, :] - t[..., :h-1, :] # [batch, c, h-1, w]
second_difference = first_difference[..., 1:, :] - first_difference[..., :h-2, :] # [batch, c, h-2, w]
# Take the L2 norm of the result
return torch.square(second_difference).mean()
class Regularizer():
def __init__(self, reg_type, initialization):
self.reg_type = reg_type
self.initialization = initialization
self.weight = float(self.initialization)
self.last_reg = None
def step(self, global_step):
pass
def report(self, d):
if self.last_reg is not None:
d[self.reg_type].update(self.last_reg.item())
def regularize(self, *args, **kwargs) -> torch.Tensor:
out = self._regularize(*args, **kwargs) * self.weight
self.last_reg = out.detach()
return out
@abc.abstractmethod
def _regularize(self, *args, **kwargs) -> torch.Tensor:
raise NotImplementedError()
def __str__(self):
return f"Regularizer({self.reg_type}, weight={self.weight})"
class PlaneTV(Regularizer):
def __init__(self, initial_value, what: str = 'field'):
if what not in {'field', 'proposal_network'}:
raise ValueError(f'what must be one of "field" or "proposal_network" '
f'but {what} was passed.')
name = f'planeTV-{what[:2]}'
super().__init__(name, initial_value)
self.what = what
def step(self, global_step):
pass
def _regularize(self, model, **kwargs):
multi_res_grids: Sequence[nn.ParameterList]
if self.what == 'field':
multi_res_grids = model.field.grids
elif self.what == 'proposal_network':
multi_res_grids = [p.grids for p in model.proposal_networks]
else:
raise NotImplementedError(self.what)
total = 0
# Note: input to compute_plane_tv should be of shape [batch_size, c, h, w]
for grids in multi_res_grids:
if len(grids) == 3:
spatial_grids = [0, 1, 2]
else:
spatial_grids = [0, 1, 3] # These are the spatial grids; the others are spatiotemporal
for grid_id in spatial_grids:
total += compute_plane_tv(grids[grid_id])
for grid in grids:
# grid: [1, c, h, w]
total += compute_plane_tv(grid)
return total
class TimeSmoothness(Regularizer):
def __init__(self, initial_value, what: str = 'field'):
if what not in {'field', 'proposal_network'}:
raise ValueError(f'what must be one of "field" or "proposal_network" '
f'but {what} was passed.')
name = f'time-smooth-{what[:2]}'
super().__init__(name, initial_value)
self.what = what
def _regularize(self, model, **kwargs) -> torch.Tensor:
multi_res_grids: Sequence[nn.ParameterList]
if self.what == 'field':
multi_res_grids = model.field.grids
elif self.what == 'proposal_network':
multi_res_grids = [p.grids for p in model.proposal_networks]
else:
raise NotImplementedError(self.what)
total = 0
# model.grids is 6 x [1, rank * F_dim, reso, reso]
for grids in multi_res_grids:
if len(grids) == 3:
time_grids = []
else:
time_grids = [2, 4, 5]
for grid_id in time_grids:
total += compute_plane_smoothness(grids[grid_id])
return torch.as_tensor(total)
class L1ProposalNetwork(Regularizer):
def __init__(self, initial_value):
super().__init__('l1-proposal-network', initial_value)
def _regularize(self, model, **kwargs) -> torch.Tensor:
grids = [p.grids for p in model.proposal_networks]
total = 0.0
for pn_grids in grids:
for grid in pn_grids:
total += torch.abs(grid).mean()
return torch.as_tensor(total)
class DepthTV(Regularizer):
def __init__(self, initial_value):
super().__init__('tv-depth', initial_value)
def _regularize(self, model, model_out, **kwargs) -> torch.Tensor:
depth = model_out['depth']
tv = compute_plane_tv(
depth.reshape(64, 64)[None, None, :, :]
)
return tv
class L1TimePlanes(Regularizer):
def __init__(self, initial_value, what='field'):
if what not in {'field', 'proposal_network'}:
raise ValueError(f'what must be one of "field" or "proposal_network" '
f'but {what} was passed.')
super().__init__(f'l1-time-{what[:2]}', initial_value)
self.what = what
def _regularize(self, model, **kwargs) -> torch.Tensor:
# model.grids is 6 x [1, rank * F_dim, reso, reso]
multi_res_grids: Sequence[nn.ParameterList]
if self.what == 'field':
multi_res_grids = model.field.grids
elif self.what == 'proposal_network':
multi_res_grids = [p.grids for p in model.proposal_networks]
else:
raise NotImplementedError(self.what)
total = 0.0
for grids in multi_res_grids:
if len(grids) == 3:
continue
else:
# These are the spatiotemporal grids
spatiotemporal_grids = [2, 4, 5]
for grid_id in spatiotemporal_grids:
total += torch.abs(1 - grids[grid_id]).mean()
return torch.as_tensor(total)
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