# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------
# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Modified by Xueyan Zou (xueyan@cs.wisc.edu)
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/matcher.py
"""
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import warnings
import torch
import torch.nn.functional as F
import numpy as np
from scipy.optimize import linear_sum_assignment
from torch import nn
from torch.cuda.amp import autocast
from .point_features import point_sample
from ..language.loss import vl_similarity
def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):
"""
Compute the DICE loss, similar to generalized IOU for masks
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).
"""
inputs = inputs.sigmoid()
inputs = inputs.flatten(1)
numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets)
denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]
loss = 1 - (numerator + 1) / (denominator + 1)
return loss
batch_dice_loss_jit = torch.jit.script(
batch_dice_loss
) # type: torch.jit.ScriptModule
def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):
"""
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).
Returns:
Loss tensor
"""
hw = inputs.shape[1]
pos = F.binary_cross_entropy_with_logits(
inputs, torch.ones_like(inputs), reduction="none"
)
neg = F.binary_cross_entropy_with_logits(
inputs, torch.zeros_like(inputs), reduction="none"
)
loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum(
"nc,mc->nm", neg, (1 - targets)
)
return loss / hw
batch_sigmoid_ce_loss_jit = torch.jit.script(
batch_sigmoid_ce_loss
) # type: torch.jit.ScriptModule
class HungarianMatcher(nn.Module):
"""This class computes an assignment between the targets and the predictions of the network
For efficiency reasons, the targets don't include the no_object. Because of this, in general,
there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
while the others are un-matched (and thus treated as non-objects).
"""
def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_dice: float = 1, num_points: int = 0, spatial_cost = None):
"""Creates the matcher
Params:
cost_class: This is the relative weight of the classification error in the matching cost
cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost
cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost
"""
super().__init__()
self.cost_class = cost_class
self.cost_mask = cost_mask
self.cost_dice = cost_dice
self.num_points = num_points
self.spatial_cost_class = cost_class
self.spatial_cost_mask = cost_mask
self.spatial_cost_dice = cost_dice
assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0"
@torch.no_grad()
def memory_efficient_forward(self, outputs, targets):
"""More memory-friendly matching"""
bs, num_queries = outputs["pred_logits"].shape[:2]
if bs == 0 or len(targets) == 0:
return None
indices = []
# Iterate through batch size
for b in range(bs):
out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes]
tgt_ids = targets[b]["labels"]
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
cost_class = -out_prob[:, tgt_ids]
out_mask = outputs["pred_masks"][b] # [num_queries, H_pred, W_pred]
# gt masks are already padded when preparing target
tgt_mask = targets[b]["masks"].to(out_mask)
out_mask = out_mask[:, None]
tgt_mask = tgt_mask[:, None]
# all masks share the same set of points for efficient matching!
point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device, dtype=tgt_mask.dtype)
# get gt labels
tgt_mask = point_sample(
tgt_mask,
point_coords.repeat(tgt_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
out_mask = point_sample(
out_mask,
point_coords.repeat(out_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
with autocast(enabled=False):
out_mask = out_mask.float()
tgt_mask = tgt_mask.float()
# Compute the focal loss between masks
cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)
# Compute the dice loss betwen masks
cost_dice = batch_dice_loss_jit(out_mask, tgt_mask)
# Final cost matrix
C = (
self.cost_mask * cost_mask
+ self.cost_class * cost_class
+ self.cost_dice * cost_dice
)
C = C.reshape(num_queries, -1).cpu()
if C.isnan().any():
C[C.isnan()] = 1e6 ### temporary fix
warnings.warn("NAN in Cost Matrix!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
raise
indices.append(linear_sum_assignment(C))
return [
(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
for i, j in indices
]
@torch.no_grad()
def openimage_forward(self, outputs, targets, extra):
"""More memory-friendly matching"""
bs, num_queries = outputs["pred_captions"].shape[:2]
if bs == 0 or len(targets) == 0:
return None
neg_class_emb = extra['neg_class_emb']
neg_hash = extra['neg_hash']
_, unique_indices = np.unique(neg_hash.cpu().numpy(), return_index=True)
neg_class_emb = neg_class_emb[unique_indices]
neg_hash = neg_hash[unique_indices]
indices = []
pred_logits = []
# Iterate through batch size
for b in range(bs):
_pos_class_emb = targets[b]['pos_class_emb']
_pos_hash = targets[b]['pos_hash']
_neg_overlap_pos = ~(neg_hash[..., None] == _pos_hash).any(-1)
_neg_class_emb = neg_class_emb[_neg_overlap_pos]
t_emb = torch.cat((_pos_class_emb, _neg_class_emb))
v_emb = outputs["pred_captions"][b]
del _pos_class_emb
del _neg_class_emb
t_emb = t_emb / (t_emb.norm(dim=-1, keepdim=True) + 1e-7)
v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7)
out_prob = vl_similarity(v_emb, t_emb, temperature=extra['lang_logit'])
pred_logits += [out_prob]
out_prob = out_prob.softmax(-1)
tgt_ids = targets[b]["labels"]
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
cost_class = -out_prob[:, tgt_ids]
out_mask = outputs["pred_masks"][b] # [num_queries, H_pred, W_pred]
# gt masks are already padded when preparing target
tgt_mask = targets[b]["masks"].to(out_mask)
out_mask = out_mask[:, None]
tgt_mask = tgt_mask[:, None]
# all masks share the same set of points for efficient matching!
point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device, dtype=tgt_mask.dtype)
# get gt labels
tgt_mask = point_sample(
tgt_mask,
point_coords.repeat(tgt_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
out_mask = point_sample(
out_mask,
point_coords.repeat(out_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
with autocast(enabled=False):
out_mask = out_mask.float()
tgt_mask = tgt_mask.float()
# Compute the focal loss between masks
cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)
# Compute the dice loss betwen masks
cost_dice = batch_dice_loss_jit(out_mask, tgt_mask)
# Final cost matrix
C = (
self.cost_mask * cost_mask
+ self.cost_class * cost_class
+ self.cost_dice * cost_dice
)
C = C.reshape(num_queries, -1).cpu()
if C.isnan().any():
C[C.isnan()] = 1e6 ### temporary fix
warnings.warn("NAN in Cost Matrix!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
raise
indices.append(linear_sum_assignment(C))
return [
(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
for i, j in indices
], pred_logits
@torch.no_grad()
def grounding_forward(self, outputs, targets, extra):
"""More memory-friendly matching"""
bs, num_queries = outputs["pred_gmasks"].shape[:2]
if bs == 0 or len(targets) == 0:
return None
indices = []
# Iterate through batch size
for b in range(bs):
out_prob = outputs["pred_logits"][b]
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
cost_class = -out_prob.softmax(dim=0)
out_mask = outputs["pred_gmasks"][b] # [num_queries, H_pred, W_pred]
# gt masks are already padded when preparing target
tgt_mask = targets[b]["grounding_masks"].to(out_mask)
out_mask = out_mask[:, None]
tgt_mask = tgt_mask[:, None]
# all masks share the same set of points for efficient matching!
point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device, dtype=tgt_mask.dtype)
# get gt labels
tgt_mask = point_sample(
tgt_mask,
point_coords.repeat(tgt_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
out_mask = point_sample(
out_mask,
point_coords.repeat(out_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
with autocast(enabled=False):
out_mask = out_mask.float()
tgt_mask = tgt_mask.float()
# Compute the focal loss between masks
cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)
# Compute the dice loss betwen masks
cost_dice = batch_dice_loss_jit(out_mask, tgt_mask)
# Final cost matrix
C = (
self.cost_mask * cost_mask
+ self.cost_class * cost_class
+ self.cost_dice * cost_dice
)
C = C.reshape(num_queries, -1).cpu()
if C.isnan().any():
C[C.isnan()] = 1e6 ### temporary fix
warnings.warn("NAN in Cost Matrix!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
raise
indices.append(linear_sum_assignment(C))
return [
(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
for i, j in indices
]
@torch.no_grad()
def spatial_forward(self, outputs, targets, extra):
"""More memory-friendly matching"""
bs, num_queries = outputs["pred_smasks"].shape[:2]
if bs == 0 or len(targets) == 0:
return None
indices = []
# Iterate through batch size
for b in range(bs):
out_mask = outputs["pred_smasks"][b] # [num_queries, H_pred, W_pred]
# gt masks are already padded when preparing target
tgt_mask = targets[b]["gt_spatial_masks"].to(out_mask)
nd,ns = outputs["pred_pos_logits"][b].shape
index_masking = 1-torch.eye(ns, device=out_mask.device, dtype=tgt_mask.dtype).repeat_interleave(nd//ns,dim=0)
neg_masking = torch.zeros((nd,ns), device=out_mask.device, dtype=tgt_mask.dtype)
neg_masking.masked_fill_(index_masking.bool(), -float('inf'))
pos_masking = torch.zeros((nd,ns), device=out_mask.device, dtype=tgt_mask.dtype)
pos_masking.masked_fill_(index_masking.bool(), float('inf'))
out_prob = (outputs["pred_pos_logits"][b]+neg_masking)[:,:len(tgt_mask)] # remove redundant predictions for padding
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
cost_class = -out_prob.softmax(dim=0)
out_mask = out_mask[:, None]
tgt_mask = tgt_mask[:, None]
# all masks share the same set of points for efficient matching!
point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device, dtype=tgt_mask.dtype)
# get gt labels
tgt_mask = point_sample(
tgt_mask,
point_coords.repeat(tgt_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
out_mask = point_sample(
out_mask,
point_coords.repeat(out_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
with autocast(enabled=False):
out_mask = out_mask.float()
tgt_mask = tgt_mask.float()
# Compute the focal loss between masks
cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask) + pos_masking[:,:len(tgt_mask)]
# Compute the dice loss betwen masks
cost_dice = batch_dice_loss_jit(out_mask, tgt_mask) + pos_masking[:,:len(tgt_mask)]
# Final cost matrix
C = (
self.spatial_cost_mask * cost_mask
+ self.spatial_cost_class * cost_class
+ self.spatial_cost_dice * cost_dice
)
C = C.reshape(num_queries, -1).cpu()
if C.isnan().any():
C[C.isnan()] = 1e6 ### temporary fix
warnings.warn("NAN in Cost Matrix!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
raise
indices.append(linear_sum_assignment(C))
return [
(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
for i, j in indices
]
@torch.no_grad()
def spatial_forward_pn(self, outputs, targets, extra):
"""More memory-friendly matching"""
bs, num_queries = outputs["pred_smasks"].shape[:2]
if bs == 0 or len(targets) == 0:
return None
fp_mask = extra['false_positive_mask']
gt_mask = torch.stack([targets[b]["gt_spatial_masks"] for b in range(bs)])
indices = []
# Iterate through batch size
for b in range(bs):
out_prob = outputs["pred_neg_logits"][b]
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
cost_class = -out_prob.softmax(dim=0)
out_mask = outputs["pred_smasks"][b] # [num_queries, H_pred, W_pred]
tgt_mask = fp_mask[b].to(out_mask)
ign_mask = (gt_mask[b] | fp_mask[b]).to(out_mask)
out_mask = out_mask[:, None]
tgt_mask = tgt_mask[:, None]
ign_mask = ign_mask[:, None]
# all masks share the same set of points for efficient matching!
point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device, dtype=tgt_mask.dtype)
# get gt labels
tgt_mask = point_sample(
tgt_mask,
point_coords.repeat(tgt_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
out_mask = point_sample(
out_mask,
point_coords.repeat(out_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
ign_mask = point_sample(
ign_mask,
point_coords.repeat(ign_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
with autocast(enabled=False):
out_mask = out_mask.float()
tgt_mask = tgt_mask.float()
ign_mask = ign_mask.float()
# Compute the focal loss between masks
cost_mask = batch_sigmoid_ce_loss_jit(out_mask*ign_mask, tgt_mask*ign_mask)
# Compute the dice loss betwen masks
cost_dice = batch_dice_loss_jit(out_mask*ign_mask, tgt_mask*ign_mask)
# Final cost matrix
C = (
self.spatial_cost_mask * cost_mask
+ self.spatial_cost_class * cost_class
+ self.spatial_cost_dice * cost_dice
)
C = C.reshape(num_queries, -1).cpu()
if C.isnan().any():
C[C.isnan()] = 1e6 ### temporary fix
warnings.warn("NAN in Cost Matrix!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
raise
indices.append(linear_sum_assignment(C))
return [
(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
for i, j in indices
]
@torch.no_grad()
def caption_forward_womask(self, outputs, targets, extra):
"""More memory-friendly matching"""
bs, _ = outputs["pred_logits"].shape[:2]
if bs == 0 or len(targets) == 0:
return None
indices = []
t_emb = torch.cat([t['captions'] for t in targets])
v_emb = outputs['unmatched_pred_captions']
caption_target_count = np.cumsum([0] + [len(t['captions']) for t in targets])
# Iterate through batch size
for b in range(bs):
v_emb[b] = v_emb[b] / (v_emb[b].norm(dim=-1, keepdim=True) + 1e-7)
num_queries = len(v_emb[b])
out_prob = vl_similarity(v_emb[b][None,], t_emb, temperature=extra['temperature']).softmax(-1)[0]
tgt_ids = [idx for idx in range(caption_target_count[b], caption_target_count[b+1])]
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
cost_class = -out_prob[:, tgt_ids]
# Final cost matrix
C = (self.cost_class * cost_class)
C = C.reshape(num_queries, -1).cpu()
if C.isnan().any():
C[C.isnan()] = 1e6 ### temporary fix
warnings.warn("NAN in Cost Matrix!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
raise
indices.append(linear_sum_assignment(C))
return [
(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
for i, j in indices
]
@torch.no_grad()
def caption_forward_wmask(self, outputs, targets, extra):
"""More memory-friendly matching"""
bs, _ = outputs["pred_logits"].shape[:2]
if bs == 0 or len(targets) == 0:
return None
indices = []
t_emb = torch.cat([t['captions'] for t in targets])
v_emb = outputs['unmatched_pred_captions']
caption_target_count = np.cumsum([0] + [len(t['captions']) for t in targets])
# Iterate through batch size
for b in range(bs):
v_emb[b] = v_emb[b] / (v_emb[b].norm(dim=-1, keepdim=True) + 1e-7)
num_queries = len(v_emb[b])
out_prob = vl_similarity(v_emb[b][None,], t_emb, temperature=extra['temperature']).softmax(-1)[0]
tgt_ids = [idx for idx in range(caption_target_count[b], caption_target_count[b+1])]
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
cost_class = -out_prob[:, tgt_ids]
out_mask = outputs["pred_masks"][b] # [num_queries, H_pred, W_pred]
# gt masks are already padded when preparing target
tgt_mask = targets[b]["masks"].to(out_mask)
out_mask = out_mask[:, None]
tgt_mask = tgt_mask[:, None]
# all masks share the same set of points for efficient matching!
point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device, dtype=tgt_mask.dtype)
# get gt labels
tgt_mask = point_sample(
tgt_mask,
point_coords.repeat(tgt_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
out_mask = point_sample(
out_mask,
point_coords.repeat(out_mask.shape[0], 1, 1),
align_corners=False,
).squeeze(1)
with autocast(enabled=False):
out_mask = out_mask.float()
tgt_mask = tgt_mask.float()
# Compute the focal loss between masks
cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)
# Compute the dice loss betwen masks
cost_dice = batch_dice_loss_jit(out_mask, tgt_mask)
# Final cost matrix
C = (
self.cost_mask * cost_mask
+ self.cost_class * cost_class
+ self.cost_dice * cost_dice
)
C = C.reshape(num_queries, -1).cpu()
if C.isnan().any():
C[C.isnan()] = 1e6 ### temporary fix
warnings.warn("NAN in Cost Matrix!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
raise
indices.append(linear_sum_assignment(C))
return [
(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
for i, j in indices
]
@torch.no_grad()
def forward(self, outputs, targets, mode='default', extra={}):
"""Performs the matching
Params:
outputs: This is a dict that contains at least these entries:
"pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
"pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks
targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
"labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
objects in the target) containing the class labels
"masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks
Returns:
A list of size batch_size, containing tuples of (index_i, index_j) where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected targets (in order)
For each batch element, it holds:
len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
"""
if mode == 'default':
return self.memory_efficient_forward(outputs, targets)
elif mode == 'grounding':
return self.grounding_forward(outputs, targets, extra)
elif mode == 'spatial':
return self.spatial_forward(outputs, targets, extra)
elif mode == 'spatial_pn':
return self.spatial_forward_pn(outputs, targets, extra)
elif mode == 'caption_womask':
return self.caption_forward_womask(outputs, targets, extra)
elif mode == 'caption_wmask':
return self.caption_forward_wmask(outputs, targets, extra)
else:
assert False, "Mode {} is not supported.".format(mode)
def __repr__(self, _repr_indent=4):
head = "Matcher " + self.__class__.__name__
body = [
"cost_class: {}".format(self.cost_class),
"cost_mask: {}".format(self.cost_mask),
"cost_dice: {}".format(self.cost_dice),
]
lines = [head] + [" " * _repr_indent + line for line in body]
return "\n".join(lines)