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| # ------------------------------------------------------------------------ | |
| # Grounding DINO | |
| # url: https://github.com/IDEA-Research/GroundingDINO | |
| # Copyright (c) 2023 IDEA. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
| # ------------------------------------------------------------------------ | |
| # DINO | |
| # Copyright (c) 2022 IDEA. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
| # ------------------------------------------------------------------------ | |
| # Conditional DETR | |
| # Copyright (c) 2021 Microsoft. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
| # ------------------------------------------------------------------------ | |
| # Copied from DETR (https://github.com/facebookresearch/detr) | |
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
| # ------------------------------------------------------------------------ | |
| """ | |
| Various positional encodings for the transformer. | |
| """ | |
| import math | |
| import torch | |
| from torch import nn | |
| from groundingdino.util.misc import NestedTensor | |
| class PositionEmbeddingSine(nn.Module): | |
| """ | |
| This is a more standard version of the position embedding, very similar to the one | |
| used by the Attention is all you need paper, generalized to work on images. | |
| """ | |
| def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): | |
| super().__init__() | |
| self.num_pos_feats = num_pos_feats | |
| self.temperature = temperature | |
| self.normalize = normalize | |
| if scale is not None and normalize is False: | |
| raise ValueError("normalize should be True if scale is passed") | |
| if scale is None: | |
| scale = 2 * math.pi | |
| self.scale = scale | |
| def forward(self, tensor_list: NestedTensor): | |
| x = tensor_list.tensors | |
| mask = tensor_list.mask | |
| assert mask is not None | |
| not_mask = ~mask | |
| y_embed = not_mask.cumsum(1, dtype=torch.float32) | |
| x_embed = not_mask.cumsum(2, dtype=torch.float32) | |
| if self.normalize: | |
| eps = 1e-6 | |
| # if os.environ.get("SHILONG_AMP", None) == '1': | |
| # eps = 1e-4 | |
| # else: | |
| # eps = 1e-6 | |
| y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale | |
| x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale | |
| dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) | |
| dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) | |
| 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=4 | |
| ).flatten(3) | |
| pos_y = torch.stack( | |
| (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 | |
| ).flatten(3) | |
| pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) | |
| return pos | |
| class PositionEmbeddingSineHW(nn.Module): | |
| """ | |
| This is a more standard version of the position embedding, very similar to the one | |
| used by the Attention is all you need paper, generalized to work on images. | |
| """ | |
| def __init__( | |
| self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None | |
| ): | |
| super().__init__() | |
| self.num_pos_feats = num_pos_feats | |
| self.temperatureH = temperatureH | |
| self.temperatureW = temperatureW | |
| self.normalize = normalize | |
| if scale is not None and normalize is False: | |
| raise ValueError("normalize should be True if scale is passed") | |
| if scale is None: | |
| scale = 2 * math.pi | |
| self.scale = scale | |
| def forward(self, tensor_list: NestedTensor): | |
| x = tensor_list.tensors | |
| mask = tensor_list.mask | |
| assert mask is not None | |
| not_mask = ~mask | |
| y_embed = not_mask.cumsum(1, dtype=torch.float32) | |
| x_embed = not_mask.cumsum(2, dtype=torch.float32) | |
| # import ipdb; ipdb.set_trace() | |
| if self.normalize: | |
| eps = 1e-6 | |
| y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale | |
| x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale | |
| dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) | |
| dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats) | |
| pos_x = x_embed[:, :, :, None] / dim_tx | |
| dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) | |
| dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats) | |
| pos_y = y_embed[:, :, :, None] / dim_ty | |
| pos_x = torch.stack( | |
| (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 | |
| ).flatten(3) | |
| pos_y = torch.stack( | |
| (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 | |
| ).flatten(3) | |
| pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) | |
| # import ipdb; ipdb.set_trace() | |
| return pos | |
| class PositionEmbeddingLearned(nn.Module): | |
| """ | |
| Absolute pos embedding, learned. | |
| """ | |
| def __init__(self, num_pos_feats=256): | |
| super().__init__() | |
| self.row_embed = nn.Embedding(50, num_pos_feats) | |
| self.col_embed = nn.Embedding(50, num_pos_feats) | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| nn.init.uniform_(self.row_embed.weight) | |
| nn.init.uniform_(self.col_embed.weight) | |
| def forward(self, tensor_list: NestedTensor): | |
| x = tensor_list.tensors | |
| h, w = x.shape[-2:] | |
| i = torch.arange(w, device=x.device) | |
| j = torch.arange(h, device=x.device) | |
| x_emb = self.col_embed(i) | |
| y_emb = self.row_embed(j) | |
| pos = ( | |
| torch.cat( | |
| [ | |
| x_emb.unsqueeze(0).repeat(h, 1, 1), | |
| y_emb.unsqueeze(1).repeat(1, w, 1), | |
| ], | |
| dim=-1, | |
| ) | |
| .permute(2, 0, 1) | |
| .unsqueeze(0) | |
| .repeat(x.shape[0], 1, 1, 1) | |
| ) | |
| return pos | |
| def build_position_encoding(args): | |
| N_steps = args.hidden_dim // 2 | |
| if args.position_embedding in ("v2", "sine"): | |
| # TODO find a better way of exposing other arguments | |
| position_embedding = PositionEmbeddingSineHW( | |
| N_steps, | |
| temperatureH=args.pe_temperatureH, | |
| temperatureW=args.pe_temperatureW, | |
| normalize=True, | |
| ) | |
| elif args.position_embedding in ("v3", "learned"): | |
| position_embedding = PositionEmbeddingLearned(N_steps) | |
| else: | |
| raise ValueError(f"not supported {args.position_embedding}") | |
| return position_embedding | |