# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# SOFTWARE.
# File author: Shariq Farooq Bhat
import torch
import torch.nn as nn
class SeedBinRegressor(nn.Module):
def __init__(self, in_features, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10):
"""Bin center regressor network. Bin centers are bounded on (min_depth, max_depth) interval.
Args:
in_features (int): input channels
n_bins (int, optional): Number of bin centers. Defaults to 16.
mlp_dim (int, optional): Hidden dimension. Defaults to 256.
min_depth (float, optional): Min depth value. Defaults to 1e-3.
max_depth (float, optional): Max depth value. Defaults to 10.
"""
super().__init__()
self.version = "1_1"
self.min_depth = min_depth
self.max_depth = max_depth
self._net = nn.Sequential(
nn.Conv2d(in_features, mlp_dim, 1, 1, 0),
nn.ReLU(inplace=True),
nn.Conv2d(mlp_dim, n_bins, 1, 1, 0),
nn.ReLU(inplace=True)
)
def forward(self, x):
"""
Returns tensor of bin_width vectors (centers). One vector b for every pixel
"""
B = self._net(x)
eps = 1e-3
B = B + eps
B_widths_normed = B / B.sum(dim=1, keepdim=True)
B_widths = (self.max_depth - self.min_depth) * \
B_widths_normed # .shape NCHW
# pad has the form (left, right, top, bottom, front, back)
B_widths = nn.functional.pad(
B_widths, (0, 0, 0, 0, 1, 0), mode='constant', value=self.min_depth)
B_edges = torch.cumsum(B_widths, dim=1) # .shape NCHW
B_centers = 0.5 * (B_edges[:, :-1, ...] + B_edges[:, 1:, ...])
return B_widths_normed, B_centers
class SeedBinRegressorUnnormed(nn.Module):
def __init__(self, in_features, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10):
"""Bin center regressor network. Bin centers are unbounded
Args:
in_features (int): input channels
n_bins (int, optional): Number of bin centers. Defaults to 16.
mlp_dim (int, optional): Hidden dimension. Defaults to 256.
min_depth (float, optional): Not used. (for compatibility with SeedBinRegressor)
max_depth (float, optional): Not used. (for compatibility with SeedBinRegressor)
"""
super().__init__()
self.version = "1_1"
self._net = nn.Sequential(
nn.Conv2d(in_features, mlp_dim, 1, 1, 0),
nn.ReLU(inplace=True),
nn.Conv2d(mlp_dim, n_bins, 1, 1, 0),
nn.Softplus()
)
def forward(self, x):
"""
Returns tensor of bin_width vectors (centers). One vector b for every pixel
"""
B_centers = self._net(x)
return B_centers, B_centers
class Projector(nn.Module):
def __init__(self, in_features, out_features, mlp_dim=128):
"""Projector MLP
Args:
in_features (int): input channels
out_features (int): output channels
mlp_dim (int, optional): hidden dimension. Defaults to 128.
"""
super().__init__()
self._net = nn.Sequential(
nn.Conv2d(in_features, mlp_dim, 1, 1, 0),
nn.ReLU(inplace=True),
nn.Conv2d(mlp_dim, out_features, 1, 1, 0),
)
def forward(self, x):
return self._net(x)
class LinearSplitter(nn.Module):
def __init__(self, in_features, prev_nbins, split_factor=2, mlp_dim=128, min_depth=1e-3, max_depth=10):
super().__init__()
self.prev_nbins = prev_nbins
self.split_factor = split_factor
self.min_depth = min_depth
self.max_depth = max_depth
self._net = nn.Sequential(
nn.Conv2d(in_features, mlp_dim, 1, 1, 0),
nn.GELU(),
nn.Conv2d(mlp_dim, prev_nbins * split_factor, 1, 1, 0),
nn.ReLU()
)
def forward(self, x, b_prev, prev_b_embedding=None, interpolate=True, is_for_query=False):
"""
x : feature block; shape - n, c, h, w
b_prev : previous bin widths normed; shape - n, prev_nbins, h, w
"""
if prev_b_embedding is not None:
if interpolate:
prev_b_embedding = nn.functional.interpolate(prev_b_embedding, x.shape[-2:], mode='bilinear', align_corners=True)
x = x + prev_b_embedding
S = self._net(x)
eps = 1e-3
S = S + eps
n, c, h, w = S.shape
S = S.view(n, self.prev_nbins, self.split_factor, h, w)
S_normed = S / S.sum(dim=2, keepdim=True) # fractional splits
b_prev = nn.functional.interpolate(b_prev, (h,w), mode='bilinear', align_corners=True)
b_prev = b_prev / b_prev.sum(dim=1, keepdim=True) # renormalize for gurantees
# print(b_prev.shape, S_normed.shape)
# if is_for_query:(1).expand(-1, b_prev.size(0)//n, -1, -1, -1, -1).flatten(0,1) # TODO ? can replace all this with a single torch.repeat?
b = b_prev.unsqueeze(2) * S_normed
b = b.flatten(1,2) # .shape n, prev_nbins * split_factor, h, w
# calculate bin centers for loss calculation
B_widths = (self.max_depth - self.min_depth) * b # .shape N, nprev * splitfactor, H, W
# pad has the form (left, right, top, bottom, front, back)
B_widths = nn.functional.pad(B_widths, (0,0,0,0,1,0), mode='constant', value=self.min_depth)
B_edges = torch.cumsum(B_widths, dim=1) # .shape NCHW
B_centers = 0.5 * (B_edges[:, :-1, ...] + B_edges[:,1:,...])
return b, B_centers