support gsam2 image predictor model
This commit is contained in:
rentainhe
268
grounding_dino/groundingdino/models/GroundingDINO/utils.py
Normal file
268
grounding_dino/groundingdino/models/GroundingDINO/utils.py
Normal file
@@ -0,0 +1,268 @@
|
||||
# ------------------------------------------------------------------------
|
||||
# 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]
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
import copy
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import Tensor, nn
|
||||
|
||||
|
||||
def _get_clones(module, N, layer_share=False):
|
||||
# import ipdb; ipdb.set_trace()
|
||||
if layer_share:
|
||||
return nn.ModuleList([module for i in range(N)])
|
||||
else:
|
||||
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
|
||||
|
||||
|
||||
def get_sine_pos_embed(
|
||||
pos_tensor: torch.Tensor,
|
||||
num_pos_feats: int = 128,
|
||||
temperature: int = 10000,
|
||||
exchange_xy: bool = True,
|
||||
):
|
||||
"""generate sine position embedding from a position tensor
|
||||
Args:
|
||||
pos_tensor (torch.Tensor): shape: [..., n].
|
||||
num_pos_feats (int): projected shape for each float in the tensor.
|
||||
temperature (int): temperature in the sine/cosine function.
|
||||
exchange_xy (bool, optional): exchange pos x and pos y. \
|
||||
For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
|
||||
Returns:
|
||||
pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
|
||||
"""
|
||||
scale = 2 * math.pi
|
||||
dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
|
||||
dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
|
||||
|
||||
def sine_func(x: torch.Tensor):
|
||||
sin_x = x * scale / dim_t
|
||||
sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
|
||||
return sin_x
|
||||
|
||||
pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
|
||||
if exchange_xy:
|
||||
pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
|
||||
pos_res = torch.cat(pos_res, dim=-1)
|
||||
return pos_res
|
||||
|
||||
|
||||
def gen_encoder_output_proposals(
|
||||
memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
|
||||
):
|
||||
"""
|
||||
Input:
|
||||
- memory: bs, \sum{hw}, d_model
|
||||
- memory_padding_mask: bs, \sum{hw}
|
||||
- spatial_shapes: nlevel, 2
|
||||
- learnedwh: 2
|
||||
Output:
|
||||
- output_memory: bs, \sum{hw}, d_model
|
||||
- output_proposals: bs, \sum{hw}, 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)
|
||||
|
||||
# import ipdb; ipdb.set_trace()
|
||||
|
||||
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) # H_, W_, 2
|
||||
|
||||
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
|
||||
|
||||
if learnedwh is not None:
|
||||
# import ipdb; ipdb.set_trace()
|
||||
wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
|
||||
else:
|
||||
wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
|
||||
|
||||
# scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
|
||||
# grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
|
||||
# wh = torch.ones_like(grid) / scale
|
||||
proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
|
||||
proposals.append(proposal)
|
||||
_cur += H_ * W_
|
||||
# import ipdb; ipdb.set_trace()
|
||||
output_proposals = torch.cat(proposals, 1)
|
||||
output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
|
||||
-1, keepdim=True
|
||||
)
|
||||
output_proposals = torch.log(output_proposals / (1 - output_proposals)) # unsigmoid
|
||||
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))
|
||||
|
||||
# output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
|
||||
# output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
|
||||
|
||||
return output_memory, output_proposals
|
||||
|
||||
|
||||
class RandomBoxPerturber:
|
||||
def __init__(
|
||||
self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
|
||||
) -> None:
|
||||
self.noise_scale = torch.Tensor(
|
||||
[x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
|
||||
)
|
||||
|
||||
def __call__(self, refanchors: Tensor) -> Tensor:
|
||||
nq, bs, query_dim = refanchors.shape
|
||||
device = refanchors.device
|
||||
|
||||
noise_raw = torch.rand_like(refanchors)
|
||||
noise_scale = self.noise_scale.to(device)[:query_dim]
|
||||
|
||||
new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
|
||||
return new_refanchors.clamp_(0, 1)
|
||||
|
||||
|
||||
def sigmoid_focal_loss(
|
||||
inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
|
||||
):
|
||||
"""
|
||||
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
|
||||
Args:
|
||||
inputs: A float tensor of arbitrary shape.
|
||||
The predictions for each example.
|
||||
targets: A float tensor with the same shape as inputs. Stores the binary
|
||||
classification label for each element in inputs
|
||||
(0 for the negative class and 1 for the positive class).
|
||||
alpha: (optional) Weighting factor in range (0,1) to balance
|
||||
positive vs negative examples. Default = -1 (no weighting).
|
||||
gamma: Exponent of the modulating factor (1 - p_t) to
|
||||
balance easy vs hard examples.
|
||||
Returns:
|
||||
Loss tensor
|
||||
"""
|
||||
prob = inputs.sigmoid()
|
||||
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
|
||||
p_t = prob * targets + (1 - prob) * (1 - targets)
|
||||
loss = ce_loss * ((1 - p_t) ** gamma)
|
||||
|
||||
if alpha >= 0:
|
||||
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
|
||||
loss = alpha_t * loss
|
||||
|
||||
if no_reduction:
|
||||
return loss
|
||||
|
||||
return loss.mean(1).sum() / num_boxes
|
||||
|
||||
|
||||
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_activation_fn(activation, d_model=256, batch_dim=0):
|
||||
"""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 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 * (torch.div(dim_t, 2, rounding_mode='floor')) / 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
|
||||
|
||||
|
||||
class ContrastiveEmbed(nn.Module):
|
||||
def __init__(self, max_text_len=256):
|
||||
"""
|
||||
Args:
|
||||
max_text_len: max length of text.
|
||||
"""
|
||||
super().__init__()
|
||||
self.max_text_len = max_text_len
|
||||
|
||||
def forward(self, x, text_dict):
|
||||
"""_summary_
|
||||
|
||||
Args:
|
||||
x (_type_): _description_
|
||||
text_dict (_type_): _description_
|
||||
{
|
||||
'encoded_text': encoded_text, # bs, 195, d_model
|
||||
'text_token_mask': text_token_mask, # bs, 195
|
||||
# True for used tokens. False for padding tokens
|
||||
}
|
||||
Returns:
|
||||
_type_: _description_
|
||||
"""
|
||||
assert isinstance(text_dict, dict)
|
||||
|
||||
y = text_dict["encoded_text"]
|
||||
text_token_mask = text_dict["text_token_mask"]
|
||||
|
||||
res = x @ y.transpose(-1, -2)
|
||||
res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
|
||||
|
||||
# padding to max_text_len
|
||||
new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
|
||||
new_res[..., : res.shape[-1]] = res
|
||||
|
||||
return new_res
|
||||
Reference in New Issue
Block a user