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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] | |
# ------------------------------------------------------------------------ | |
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 | |