# Part of the implementation is borrowed and modified from Mask DINO, publicly available at # https://github.com/IDEA-Research/MaskDINO import copy import math import torch import torch.nn.functional as F from torch import Tensor, nn class Conv2d(torch.nn.Conv2d): def __init__(self, *args, **kwargs): norm = kwargs.pop('norm', None) activation = kwargs.pop('activation', None) super().__init__(*args, **kwargs) self.norm = norm self.activation = activation def forward(self, x): x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) if self.norm is not None: x = self.norm(x) if self.activation is not None: x = self.activation(x) return x class MLP(nn.Module): """ Very simple multi-layer perceptron (also called FFN)""" def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super().__init__() self.num_layers = num_layers h = [hidden_dim] * (num_layers - 1) self.layers = nn.ModuleList( nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) def forward(self, x): for i, layer in enumerate(self.layers): x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) return x def get_bounding_boxes(mask_tensor): boxes = torch.zeros( mask_tensor.shape[0], 4, dtype=torch.float32, device=mask_tensor.device) x_any = torch.any(mask_tensor, dim=1) y_any = torch.any(mask_tensor, dim=2) for idx in range(mask_tensor.shape[0]): x = torch.where(x_any[idx, :])[0] y = torch.where(y_any[idx, :])[0] if len(x) > 0 and len(y) > 0: boxes[idx, :] = torch.as_tensor([x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32, device=mask_tensor.device) return boxes def box_xyxy_to_cxcywh(x): x0, y0, x1, y1 = x.unbind(-1) b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] return torch.stack(b, dim=-1) def inverse_sigmoid(x, eps=1e-5): x = x.clamp(min=0, max=1) x1 = x.clamp(min=eps) x2 = (1 - x).clamp(min=eps) return torch.log(x1 / x2) def gen_encoder_output_proposals(memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor): """ Args: memory: bs, h_0*w_0+...+h_{L-1}*w_{L-1}, d_model memory_padding_mask: bs, h_0*w_0+...+h_{L-1}*w_{L-1} spatial_shapes: nlevel, 2 Returns: output_memory: bs, h_0*w_0+...+h_{L-1}*w_{L-1}, d_model output_proposals: bs, h_0*w_0+...+h_{L-1}*w_{L-1}, 4 """ N_, S_, C_ = memory.shape proposals = [] _cur = 0 for lvl, (H_, W_) in enumerate(spatial_shapes): mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H_ * W_)].view( N_, H_, W_, 1) valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) grid_y, grid_x = torch.meshgrid( torch.linspace( 0, H_ - 1, H_, dtype=torch.float32, device=memory.device), torch.linspace( 0, W_ - 1, W_, dtype=torch.float32, device=memory.device)) grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2) grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) proposal = torch.cat((grid, wh), -1).view(N_, -1, 4) proposals.append(proposal) _cur += (H_ * W_) output_proposals = torch.cat(proposals, 1) output_proposals_valid = (output_proposals > 0.01) & ( output_proposals < 0.99) output_proposals_valid = output_proposals_valid.all(-1, keepdim=True) output_proposals = torch.log(output_proposals / (1 - output_proposals)) output_proposals = output_proposals.masked_fill( memory_padding_mask.unsqueeze(-1), float('inf')) output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf')) output_memory = memory output_memory = output_memory.masked_fill( memory_padding_mask.unsqueeze(-1), float(0)) output_memory = output_memory.masked_fill(~output_proposals_valid, float(0)) return output_memory, output_proposals def gen_sineembed_for_position(pos_tensor): # n_query, bs, _ = pos_tensor.size() # sineembed_tensor = torch.zeros(n_query, bs, 256) scale = 2 * math.pi dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device) dim_t = 10000**(2 * (dim_t // 2) / 128) x_embed = pos_tensor[:, :, 0] * scale y_embed = pos_tensor[:, :, 1] * scale pos_x = x_embed[:, :, None] / dim_t pos_y = y_embed[:, :, None] / dim_t pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2) if pos_tensor.size(-1) == 2: pos = torch.cat((pos_y, pos_x), dim=2) elif pos_tensor.size(-1) == 4: w_embed = pos_tensor[:, :, 2] * scale pos_w = w_embed[:, :, None] / dim_t pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2) h_embed = pos_tensor[:, :, 3] * scale pos_h = h_embed[:, :, None] / dim_t pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2) pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2) else: raise ValueError('Unknown pos_tensor shape(-1):{}'.format( pos_tensor.size(-1))) return pos def _get_activation_fn(activation): """Return an activation function given a string""" if activation == 'relu': return F.relu if activation == 'gelu': return F.gelu if activation == 'glu': return F.glu if activation == 'prelu': return nn.PReLU() if activation == 'selu': return F.selu raise RuntimeError(F'activation should be relu/gelu, not {activation}.') def _get_clones(module, N, layer_share=False): if layer_share: return nn.ModuleList([module for i in range(N)]) else: return nn.ModuleList([copy.deepcopy(module) for i in range(N)])