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import torch.nn as nn
import numpy as np
from mmdet.core import bbox_xyxy_to_cxcywh
from mmdet.models.utils.transformer import inverse_sigmoid
def memory_refresh(memory, prev_exist):
memory_shape = memory.shape
view_shape = [1 for _ in range(len(memory_shape))]
prev_exist = prev_exist.view(-1, *view_shape[1:])
return memory * prev_exist
def topk_gather(feat, topk_indexes):
if topk_indexes is not None:
feat_shape = feat.shape
topk_shape = topk_indexes.shape
view_shape = [1 for _ in range(len(feat_shape))]
view_shape[:2] = topk_shape[:2]
topk_indexes = topk_indexes.view(*view_shape)
feat = torch.gather(feat, 1, topk_indexes.repeat(1, 1, *feat_shape[2:]))
return feat
def apply_ltrb(locations, pred_ltrb):
"""
:param locations: (1, H, W, 2)
:param pred_ltrb: (N, H, W, 4)
"""
pred_boxes = torch.zeros_like(pred_ltrb)
pred_boxes[..., 0] = (locations[..., 0] - pred_ltrb[..., 0])# x1
pred_boxes[..., 1] = (locations[..., 1] - pred_ltrb[..., 1])# y1
pred_boxes[..., 2] = (locations[..., 0] + pred_ltrb[..., 2])# x2
pred_boxes[..., 3] = (locations[..., 1] + pred_ltrb[..., 3])# y2
min_xy = pred_boxes[..., 0].new_tensor(0)
max_xy = pred_boxes[..., 0].new_tensor(1)
pred_boxes = torch.where(pred_boxes < min_xy, min_xy, pred_boxes)
pred_boxes = torch.where(pred_boxes > max_xy, max_xy, pred_boxes)
pred_boxes = bbox_xyxy_to_cxcywh(pred_boxes)
return pred_boxes
def apply_center_offset(locations, center_offset):
"""
:param locations: (1, H, W, 2)
:param pred_ltrb: (N, H, W, 4)
"""
centers_2d = torch.zeros_like(center_offset)
locations = inverse_sigmoid(locations)
centers_2d[..., 0] = locations[..., 0] + center_offset[..., 0] # x1
centers_2d[..., 1] = locations[..., 1] + center_offset[..., 1] # y1
centers_2d = centers_2d.sigmoid()
return centers_2d
@torch.no_grad()
def locations(features, stride, pad_h, pad_w):
"""
Arguments:
features: (N, C, H, W)
Return:
locations: (H, W, 2)
"""
h, w = features.size()[-2:]
device = features.device
shifts_x = (torch.arange(
0, stride*w, step=stride,
dtype=torch.float32, device=device
) + stride // 2 ) / pad_w
shifts_y = (torch.arange(
0, h * stride, step=stride,
dtype=torch.float32, device=device
) + stride // 2) / pad_h
shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
shift_x = shift_x.reshape(-1)
shift_y = shift_y.reshape(-1)
locations = torch.stack((shift_x, shift_y), dim=1)
locations = locations.reshape(h, w, 2)
return locations
def gaussian_2d(shape, sigma=1.0):
"""Generate gaussian map.
Args:
shape (list[int]): Shape of the map.
sigma (float, optional): Sigma to generate gaussian map.
Defaults to 1.
Returns:
np.ndarray: Generated gaussian map.
"""
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m + 1, -n:n + 1]
h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h
def draw_heatmap_gaussian(heatmap, center, radius, k=1):
"""Get gaussian masked heatmap.
Args:
heatmap (torch.Tensor): Heatmap to be masked.
center (torch.Tensor): Center coord of the heatmap.
radius (int): Radius of gaussian.
K (int, optional): Multiple of masked_gaussian. Defaults to 1.
Returns:
torch.Tensor: Masked heatmap.
"""
diameter = 2 * radius + 1
gaussian = gaussian_2d((diameter, diameter), sigma=diameter / 6)
x, y = int(center[0]), int(center[1])
height, width = heatmap.shape[0:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = torch.from_numpy(
gaussian[radius - top:radius + bottom,
radius - left:radius + right]).to(heatmap.device,
torch.float32)
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
torch.max(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
return heatmap
class SELayer_Linear(nn.Module):
def __init__(self, channels, act_layer=nn.ReLU, gate_layer=nn.Sigmoid):
super().__init__()
self.conv_reduce = nn.Linear(channels, channels)
self.act1 = act_layer()
self.conv_expand = nn.Linear(channels, channels)
self.gate = gate_layer()
def forward(self, x, x_se):
x_se = self.conv_reduce(x_se)
x_se = self.act1(x_se)
x_se = self.conv_expand(x_se)
return x * self.gate(x_se)
class MLN(nn.Module):
'''
Args:
c_dim (int): dimension of latent code c
f_dim (int): feature dimension
'''
def __init__(self, c_dim, f_dim=256):
super().__init__()
self.c_dim = c_dim
self.f_dim = f_dim
self.reduce = nn.Sequential(
nn.Linear(c_dim, f_dim),
nn.ReLU(),
)
self.gamma = nn.Linear(f_dim, f_dim)
self.beta = nn.Linear(f_dim, f_dim)
self.ln = nn.LayerNorm(f_dim, elementwise_affine=False)
self.reset_parameters()
def reset_parameters(self):
nn.init.zeros_(self.gamma.weight)
nn.init.zeros_(self.beta.weight)
nn.init.ones_(self.gamma.bias)
nn.init.zeros_(self.beta.bias)
def forward(self, x, c):
x = self.ln(x)
c = self.reduce(c)
gamma = self.gamma(c)
beta = self.beta(c)
out = gamma * x + beta
return out
def transform_reference_points(reference_points, egopose, reverse=False, translation=True):
reference_points = torch.cat([reference_points, torch.ones_like(reference_points[..., 0:1])], dim=-1)
if reverse:
matrix = egopose.inverse()
else:
matrix = egopose
if not translation:
matrix[..., :3, 3] = 0.0
reference_points = (matrix.unsqueeze(1) @ reference_points.unsqueeze(-1)).squeeze(-1)[..., :3]
return reference_points
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