# MIT License # Copyright (c) 2022 Intelligent Systems Lab Org # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # 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 # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # 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