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from typing import List, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ripe import utils
from ripe.utils.utils import gridify
log = utils.get_pylogger(__name__)
class KeypointSampler(nn.Module):
"""
Sample keypoints according to a Heatmap
Adapted from: https://github.com/verlab/DALF_CVPR_2023/blob/main/modules/models/DALF.py
"""
def __init__(self, window_size=8):
super().__init__()
self.window_size = window_size
self.idx_cells = None # Cache for meshgrid indices
def sample(self, grid):
"""
Sample keypoints given a grid where each cell has logits stacked in last dimension
Input
grid: [B, C, H//w, W//w, w*w]
Returns
log_probs: [B, C, H//w, W//w ] - logprobs of selected samples
choices: [B, C, H//w, W//w] indices of choices
accept_mask: [B, C, H//w, W//w] mask of accepted keypoints
"""
chooser = torch.distributions.Categorical(logits=grid)
choices = chooser.sample()
logits_selected = torch.gather(grid, -1, choices.unsqueeze(-1)).squeeze(-1)
flipper = torch.distributions.Bernoulli(logits=logits_selected)
accepted_choices = flipper.sample()
# Sum log-probabilities is equivalent to multiplying the probabilities
log_probs = chooser.log_prob(choices) + flipper.log_prob(accepted_choices)
accept_mask = accepted_choices.gt(0)
return (
log_probs.squeeze(1),
choices,
accept_mask.squeeze(1),
logits_selected.squeeze(1),
)
def precompute_idx_cells(self, H, W, device):
idx_cells = gridify(
torch.dstack(
torch.meshgrid(
torch.arange(H, dtype=torch.float32, device=device),
torch.arange(W, dtype=torch.float32, device=device),
)
)
.permute(2, 0, 1)
.unsqueeze(0)
.expand(1, -1, -1, -1),
window_size=self.window_size,
)
return idx_cells
def forward(self, x, mask_padding=None):
"""
Sample keypoints from a heatmap
Input
x: [B, C, H, W] Heatmap
mask_padding: [B, 1, H, W] Mask for padding (optional)
Returns
keypoints: [B, H//w, W//w, 2] Keypoints in (x, y) format
log_probs: [B, H//w, W//w] Log probabilities of selected keypoints
mask: [B, H//w, W//w] Mask of accepted keypoints
mask_padding: [B, 1, H//w, W//w] Mask of padding (optional)
logits_selected: [B, H//w, W//w] Logits of selected keypoints
"""
B, C, H, W = x.shape
keypoint_cells = gridify(x, self.window_size)
mask_padding = (
(torch.min(gridify(mask_padding, self.window_size), dim=4).values) if mask_padding is not None else None
)
if self.idx_cells is None or self.idx_cells.shape[2:4] != (
H // self.window_size,
W // self.window_size,
):
self.idx_cells = self.precompute_idx_cells(H, W, x.device)
log_probs, idx, mask, logits_selected = self.sample(keypoint_cells)
keypoints = (
torch.gather(
self.idx_cells.expand(B, -1, -1, -1, -1),
-1,
idx.repeat(1, 2, 1, 1).unsqueeze(-1),
)
.squeeze(-1)
.permute(0, 2, 3, 1)
)
# flip keypoints to (x, y) format
return keypoints.flip(-1), log_probs, mask, mask_padding, logits_selected
class RIPE(nn.Module):
"""
Base class for extracting keypoints and descriptors
Input
x: [B, C, H, W] Images
Returns
kpts:
list of size [B] with detected keypoints
descs:
list of size [B] with descriptors
"""
def __init__(
self,
net,
upsampler,
window_size: int = 8,
non_linearity_dect=None,
desc_shares: Optional[List[int]] = None,
descriptor_dim: int = 256,
device=None,
):
super().__init__()
self.net = net
self.detector = KeypointSampler(window_size)
self.upsampler = upsampler
self.sampler = None
self.window_size = window_size
self.non_linearity_dect = non_linearity_dect if non_linearity_dect is not None else nn.Identity()
log.info(f"Training with window size {window_size}.")
log.info(f"Use {non_linearity_dect} as final non-linearity before the detection heatmap.")
dim_coarse_desc = self.get_dim_raw_desc()
if desc_shares is not None:
assert upsampler.name == "HyperColumnFeatures", (
"Individual descriptor convolutions are only supported with HyperColumnFeatures"
)
assert len(desc_shares) == 4, "desc_shares should have 4 elements"
assert sum(desc_shares) == descriptor_dim, f"sum of desc_shares should be {descriptor_dim}"
self.conv_dim_reduction_coarse_desc = nn.ModuleList()
for dim_in, dim_out in zip(dim_coarse_desc, desc_shares):
log.info(f"Training dim reduction descriptor with {dim_in} -> {dim_out} 1x1 conv")
self.conv_dim_reduction_coarse_desc.append(
nn.Conv1d(dim_in, dim_out, kernel_size=1, stride=1, padding=0)
)
else:
if descriptor_dim is not None:
log.info(f"Training dim reduction descriptor with {sum(dim_coarse_desc)} -> {descriptor_dim} 1x1 conv")
self.conv_dim_reduction_coarse_desc = nn.Conv1d(
sum(dim_coarse_desc),
descriptor_dim,
kernel_size=1,
stride=1,
padding=0,
)
else:
log.warning(
f"No descriptor dimension specified, no 1x1 conv will be applied! Direct usage of {sum(dim_coarse_desc)}-dimensional raw descriptor"
)
self.conv_dim_reduction_coarse_desc = nn.Identity()
def get_dim_raw_desc(self):
layers_dims_encoder = self.net.get_dim_layers_encoder()
if self.upsampler.name == "InterpolateSparse2d":
return [layers_dims_encoder[-1]]
elif self.upsampler.name == "HyperColumnFeatures":
return layers_dims_encoder
else:
raise ValueError(f"Unknown interpolator {self.upsampler.name}")
@torch.inference_mode()
def detectAndCompute(self, img, threshold=0.5, top_k=2048, output_aux=False):
self.train(False)
if img.dim() == 3:
img = img.unsqueeze(0)
out = self(img, training=False)
B, K, H, W = out["heatmap"].shape
assert B == 1, "Batch size should be 1"
kpts = [{"xy": self.NMS(out["heatmap"][b], threshold)} for b in range(B)]
if top_k is not None:
for b in range(B):
scores = out["heatmap"][b].squeeze(0)[kpts[b]["xy"][:, 1].long(), kpts[b]["xy"][:, 0].long()]
sorted_idx = torch.argsort(-scores)
kpts[b]["xy"] = kpts[b]["xy"][sorted_idx[:top_k]]
if "logprobs" in kpts[b]:
kpts[b]["logprobs"] = kpts[b]["xy"][sorted_idx[:top_k]]
if kpts[0]["xy"].shape[0] == 0:
raise RuntimeError("No keypoints detected")
# the following works for batch size 1 only
descs = self.get_descs(out["coarse_descs"], img, kpts[0]["xy"].unsqueeze(0), H, W)
descs = descs.squeeze(0)
score_map = out["heatmap"][0].squeeze(0)
kpts = kpts[0]["xy"]
scores = score_map[kpts[:, 1], kpts[:, 0]]
scores /= score_map.max()
sort_idx = torch.argsort(-scores)
kpts, descs, scores = kpts[sort_idx], descs[sort_idx], scores[sort_idx]
if output_aux:
return (
kpts.float(),
descs,
scores,
{
"heatmap": out["heatmap"],
"descs": out["coarse_descs"],
"conv": self.conv_dim_reduction_coarse_desc,
},
)
return kpts.float(), descs, scores
def NMS(self, x, threshold=3.0, kernel_size=3):
pad = kernel_size // 2
local_max = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=pad)(x)
pos = (x == local_max) & (x > threshold)
return pos.nonzero()[..., 1:].flip(-1)
def get_descs(self, feature_map, guidance, kpts, H, W):
descs = self.upsampler(feature_map, kpts, H, W)
if isinstance(self.conv_dim_reduction_coarse_desc, nn.ModuleList):
# individual descriptor convolutions for each layer
desc_conv = []
for desc, conv in zip(descs, self.conv_dim_reduction_coarse_desc):
desc_conv.append(conv(desc.permute(0, 2, 1)).permute(0, 2, 1))
desc = torch.cat(desc_conv, dim=-1)
else:
desc = torch.cat(descs, dim=-1)
desc = self.conv_dim_reduction_coarse_desc(desc.permute(0, 2, 1)).permute(0, 2, 1)
desc = F.normalize(desc, dim=2)
return desc
def forward(self, x, mask_padding=None, training=False):
B, C, H, W = x.shape
out = self.net(x)
out["heatmap"] = self.non_linearity_dect(out["heatmap"])
# print(out['map'].shape, out['descr'].shape)
if training:
kpts, log_probs, mask, mask_padding, logits_selected = self.detector(out["heatmap"], mask_padding)
filter_A = kpts[:, :, :, 0] >= 16
filter_B = kpts[:, :, :, 1] >= 16
filter_C = kpts[:, :, :, 0] < W - 16
filter_D = kpts[:, :, :, 1] < H - 16
filter_all = filter_A * filter_B * filter_C * filter_D
mask = mask * filter_all
return (
kpts.view(B, -1, 2),
log_probs.view(B, -1),
mask.view(B, -1),
mask_padding.view(B, -1),
logits_selected.view(B, -1),
out,
)
else:
return out
def output_number_trainable_params(model):
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
nb_params = sum([np.prod(p.size()) for p in model_parameters])
print(f"Number of trainable parameters: {nb_params:d}")
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