support gsam2 image predictor model
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rentainhe
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# ------------------------------------------------------------------------
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# Grounding DINO
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# url: https://github.com/IDEA-Research/GroundingDINO
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# Copyright (c) 2023 IDEA. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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# DINO
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# Copyright (c) 2022 IDEA. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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# Conditional DETR
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# Copyright (c) 2021 Microsoft. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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# Copied from DETR (https://github.com/facebookresearch/detr)
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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# ------------------------------------------------------------------------
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"""
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Various positional encodings for the transformer.
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"""
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import math
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import torch
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from torch import nn
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from grounding_dino.groundingdino.util.misc import NestedTensor
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class PositionEmbeddingSine(nn.Module):
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"""
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This is a more standard version of the position embedding, very similar to the one
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used by the Attention is all you need paper, generalized to work on images.
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"""
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def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
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super().__init__()
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self.num_pos_feats = num_pos_feats
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self.temperature = temperature
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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mask = tensor_list.mask
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assert mask is not None
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not_mask = ~mask
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y_embed = not_mask.cumsum(1, dtype=torch.float32)
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x_embed = not_mask.cumsum(2, dtype=torch.float32)
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if self.normalize:
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eps = 1e-6
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# if os.environ.get("SHILONG_AMP", None) == '1':
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# eps = 1e-4
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# else:
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# eps = 1e-6
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
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pos_x = x_embed[:, :, :, None] / dim_t
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pos_y = y_embed[:, :, :, None] / dim_t
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pos_x = torch.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos_y = torch.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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return pos
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class PositionEmbeddingSineHW(nn.Module):
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"""
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This is a more standard version of the position embedding, very similar to the one
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used by the Attention is all you need paper, generalized to work on images.
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"""
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def __init__(
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self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
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):
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super().__init__()
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self.num_pos_feats = num_pos_feats
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self.temperatureH = temperatureH
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self.temperatureW = temperatureW
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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mask = tensor_list.mask
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assert mask is not None
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not_mask = ~mask
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y_embed = not_mask.cumsum(1, dtype=torch.float32)
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x_embed = not_mask.cumsum(2, dtype=torch.float32)
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# import ipdb; ipdb.set_trace()
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if self.normalize:
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eps = 1e-6
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
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pos_x = x_embed[:, :, :, None] / dim_tx
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dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
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pos_y = y_embed[:, :, :, None] / dim_ty
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pos_x = torch.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos_y = torch.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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# import ipdb; ipdb.set_trace()
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return pos
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class PositionEmbeddingLearned(nn.Module):
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"""
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Absolute pos embedding, learned.
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"""
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def __init__(self, num_pos_feats=256):
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super().__init__()
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self.row_embed = nn.Embedding(50, num_pos_feats)
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self.col_embed = nn.Embedding(50, num_pos_feats)
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self.reset_parameters()
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def reset_parameters(self):
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nn.init.uniform_(self.row_embed.weight)
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nn.init.uniform_(self.col_embed.weight)
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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h, w = x.shape[-2:]
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i = torch.arange(w, device=x.device)
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j = torch.arange(h, device=x.device)
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x_emb = self.col_embed(i)
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y_emb = self.row_embed(j)
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pos = (
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torch.cat(
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[
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x_emb.unsqueeze(0).repeat(h, 1, 1),
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y_emb.unsqueeze(1).repeat(1, w, 1),
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],
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dim=-1,
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)
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.permute(2, 0, 1)
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.unsqueeze(0)
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.repeat(x.shape[0], 1, 1, 1)
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)
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return pos
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def build_position_encoding(args):
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N_steps = args.hidden_dim // 2
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if args.position_embedding in ("v2", "sine"):
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# TODO find a better way of exposing other arguments
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position_embedding = PositionEmbeddingSineHW(
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N_steps,
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temperatureH=args.pe_temperatureH,
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temperatureW=args.pe_temperatureW,
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normalize=True,
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)
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elif args.position_embedding in ("v3", "learned"):
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position_embedding = PositionEmbeddingLearned(N_steps)
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else:
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raise ValueError(f"not supported {args.position_embedding}")
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return position_embedding
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