# 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/detr.py
"""
MaskFormer criterion.
"""
import logging
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.utils.comm import get_world_size
from timm.loss import SoftTargetCrossEntropy
from .point_features import (
get_uncertain_point_coords_with_randomness,
point_sample,
)
from ..language.loss import ql_multi_contrastive_loss, image_text_contrastive_loss_queue, vl_similarity, all_gather_grad
from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list, _max_by_axis
from ..utils import box_ops
# from image2html.visualizer import VL
def dice_loss(
inputs: torch.Tensor,
targets: torch.Tensor,
num_masks: float,
):
"""
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 * (inputs * targets).sum(-1)
denominator = inputs.sum(-1) + targets.sum(-1)
loss = 1 - (numerator + 1) / (denominator + 1)
return loss.sum() / num_masks
dice_loss_jit = torch.jit.script(
dice_loss
) # type: torch.jit.ScriptModule
def sigmoid_ce_loss(
inputs: torch.Tensor,
targets: torch.Tensor,
num_masks: float,
):
"""
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
"""
loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
return loss.mean(1).sum() / num_masks
sigmoid_ce_loss_jit = torch.jit.script(
sigmoid_ce_loss
) # type: torch.jit.ScriptModule
def calculate_uncertainty(logits):
"""
We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the
foreground class in `classes`.
Args:
logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or
class-agnostic, where R is the total number of predicted masks in all images and C is
the number of foreground classes. The values are logits.
Returns:
scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
the most uncertain locations having the highest uncertainty score.
"""
assert logits.shape[1] == 1
gt_class_logits = logits.clone()
return -(torch.abs(gt_class_logits))
class SetCriterion(nn.Module):
"""This class computes the loss for DETR.
The process happens in two steps:
1) we compute hungarian assignment between ground truth boxes and the outputs of the model
2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
"""
def __init__(self, num_classes, matcher, weight_dict, eos_coef, top_x_layers, losses,
num_points, oversample_ratio, importance_sample_ratio, grounding_weight):
"""Create the criterion.
Parameters:
num_classes: number of object categories, omitting the special no-object category
matcher: module able to compute a matching between targets and proposals
weight_dict: dict containing as key the names of the losses and as values their relative weight.
eos_coef: relative classification weight applied to the no-object category
losses: list of all the losses to be applied. See get_loss for list of available losses.
"""
super().__init__()
self.num_classes = num_classes
self.matcher = matcher
self.weight_dict = weight_dict
self.eos_coef = eos_coef
self.top_x_layers = top_x_layers
self.losses = losses
empty_weight = torch.ones(self.num_classes + 1)
empty_weight[-1] = self.eos_coef
self.register_buffer("empty_weight", empty_weight)
# pointwise mask loss parameters
self.num_points = num_points
self.oversample_ratio = oversample_ratio
self.importance_sample_ratio = importance_sample_ratio
# grounding
self.grounding_weight = grounding_weight
def loss_labels(self, outputs, targets, indices, num_masks, layer_id, extra):
"""Classification loss (NLL)
targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
"""
if layer_id > self.top_x_layers['mask']:
return {"loss_mask_ce_0": 0}
if indices is None or len(targets) == 0:
loss_ce = outputs['pred_logits'].sum() * 0.0
losses = {"loss_mask_ce_0": loss_ce}
return losses
assert "pred_logits" in outputs
src_logits = outputs["pred_logits"].type(self.empty_weight.dtype)
idx = self._get_src_permutation_idx(indices)
target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
target_classes = torch.full(
src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device
)
target_classes[idx] = target_classes_o
if src_logits.shape[2] == self.num_classes+1:
empty_weight = torch.ones(self.num_classes + 1).to(src_logits.device).type(self.empty_weight.dtype)
empty_weight[-1] = self.eos_coef
else:
empty_weight = torch.ones(self.num_classes + 1000 + 1).to(src_logits.device).type(self.empty_weight.dtype)
empty_weight[self.num_classes] = self.eos_coef
loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes)
losses = {"loss_mask_ce_0": loss_ce}
return losses
def loss_labels_openimage(self, outputs, targets, indices, num_masks, layer_id, extra):
"""Classification loss (NLL)
targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
"""
if layer_id > self.top_x_layers['mask']:
return {"loss_openimage_ce_0": 0}
assert "pred_captions" in outputs
if indices is None or len(targets) == 0 or (len(targets) > 0 and len(targets[0]['labels']) == 0):
loss_ce = outputs['pred_captions'].sum() * 0.0
losses = {"loss_openimage_ce_0": loss_ce}
return losses
# compute i2t loss
loss_openimage_ce = 0
losses = {}
for b in range(len(indices)):
pred_logit = outputs["pred_logits"][b][indices[b][0]]
gt_logit = torch.zeros_like(pred_logit)
select_idx = torch.stack((torch.arange(len(indices[b][1])), indices[b][1])).tolist()
gt_logit[select_idx] = 1
loss_openimage_ce += torch.sum(-gt_logit * F.log_softmax(pred_logit, dim=-1), dim=-1).mean()
loss_openimage_ce = loss_openimage_ce / len(indices)
losses.update({"loss_openimage_ce_0": loss_openimage_ce})
return losses
def loss_itc(self, outputs, targets, indices, num_masks, layer_id, extra):
if layer_id >= self.top_x_layers['retrieval']:
return {"loss_retrieval_decoder_0": 0}
t_emb = torch.cat([x['caption_proj'] for x in targets], dim=0)
v_emb = outputs['pred_captions'][:,-1]
loss_contrast = image_text_contrastive_loss_queue(v_emb, t_emb, extra['lang_encoder'], extra['training'])
# compute query-token contrastive loss
ttk_emb = torch.cat([x['caption_tokens'] for x in targets], dim=0)
ttk_mask = torch.cat([x['caption_mask'] for x in targets], dim=0).float()
ttk_mask = ttk_mask * torch.cumsum(ttk_mask, dim=1)
vtk_emb = outputs['pred_captions'][:,:-1]
keep = torch.cat([x['caption_mask'] for x in targets], dim=0).bool()
ttk_emb = ttk_emb / (ttk_emb.norm(dim=-1, keepdim=True) + 1e-7)
vtk_emb = vtk_emb / (vtk_emb.norm(dim=-1, keepdim=True) + 1e-7)
logit_scale = extra['lang_encoder'].logit_scale.exp().clamp(max=100)
# prepare gt
gt = (torch.eye(vtk_emb.shape[0]).type_as(ttk_mask).unsqueeze(-1) * ttk_mask.unsqueeze(0).repeat(vtk_emb.shape[0], 1, 1))[:,keep].flatten(1)
gt = gt / (gt.sum(1, keepdim=True) + 1e-7)
# compute i2t loss
logits = logit_scale * (vtk_emb @ ttk_emb[keep].transpose(0, 1)).mean(1)
loss_contrast_fine_vt = SoftTargetCrossEntropy()(logits, gt)
# loss_contrast_fine = loss_contrast_fine_vt # i2t only
# compute t2i loss
bs, nq, _ = vtk_emb.shape
logits = logit_scale * (ttk_emb @ vtk_emb.flatten(0,1).transpose(0, 1)).reshape(bs,-1,bs,nq).mean(dim=-1)[keep,:]
loss_contrast_fine_tv = SoftTargetCrossEntropy()(logits, gt.t())
# compute loss
loss_contrast_fine = (loss_contrast_fine_vt * 0.7 + loss_contrast_fine_tv * 0.3)
losses = {"loss_retrieval_decoder_0": loss_contrast + loss_contrast_fine * 0.5}
return losses
def loss_captionings(self, outputs, targets, indices, num_masks, layer_id, extra):
if layer_id >= self.top_x_layers['captioning']:
return {"loss_captioning_0": 0}
pred_captions_gen = outputs['pred_captionings'][:, :-1]
token_embs = extra['token_embedding'].weight
# token_embs = (token_embs / token_embs.norm(dim=-1, keepdim=True) + 1e-7)
# pred_captions_gen = (pred_captions_gen / pred_captions_gen.norm(dim=-1, keepdim=True) + 1e-7)
pred_captions_gen = pred_captions_gen @ token_embs.t()
# temperature = extra['lang_encoder'].logit_scale
# logit_scale = temperature.exp().clamp(max=100)
target_captions_gen = torch.cat([target['caption_tokenids'] for target in targets], 0)[:, 1:]
target_captions_gen_mask = torch.cat([target['caption_mask'] for target in targets], 0)[:, 1:]
# loss_caption = F.cross_entropy(pred_captions_gen.transpose(1,2) * logit_scale, target_captions_gen, reduction='none')
loss_caption = F.cross_entropy(pred_captions_gen.transpose(1,2), target_captions_gen, reduction='none')
loss_caption = (loss_caption * target_captions_gen_mask).sum() / (target_captions_gen_mask.sum() + 1)
losses = {"loss_captioning_0": loss_caption}
return losses
def loss_captions(self, outputs, targets, indices, num_masks, layer_id, extra):
if layer_id >= self.top_x_layers['caption']:
return {"loss_caption_0": 0}
matched_tokens = [m[0] for m in indices]
t_emb_class = torch.cat([extra['class_embeddings'][targets[bs]['labels'][m[1]]] for bs, m in enumerate(indices)])
t_hash_class = torch.cat([torch.tensor(targets[bs]['labels_hash'])[m[1]] for bs, m in enumerate(indices)])
# pred_captions denotes all unmatched object queries.
unmatched_pred_captions = []
matched_pred_captions = []
for idx, m in enumerate(matched_tokens):
unmatched_masks = torch.ones(outputs['pred_captions'].shape[1:-1]).bool()
matched_masks = torch.zeros(outputs['pred_captions'].shape[1:-1]).bool()
unmatched_masks[m] = False
matched_masks[m] = True
unmatched_pred_captions.append(outputs['pred_captions'][idx][unmatched_masks])
matched_pred_captions.append(outputs['pred_captions'][idx][matched_masks])
outputs['unmatched_pred_captions'] = unmatched_pred_captions
v_emb_class = torch.cat(matched_pred_captions)
v_emb_class = v_emb_class / (v_emb_class.norm(dim=-1, keepdim=True) + 1e-7)
indices = self.matcher(outputs, targets, mode="caption_womask", extra={'temperature':extra['lang_logit']})
src_idx = self._get_src_permutation_idx(indices)
t_emb = torch.cat([t['captions'][indices[bs][1]] for bs,t in enumerate(targets)])
t_hash = torch.cat([torch.tensor(t['captions_hash'])[indices[bs][1]] for bs,t in enumerate(targets)])
unmatched_pred_captions, _ = nested_tensor_from_tensor_list(unmatched_pred_captions).decompose()
v_emb = unmatched_pred_captions[src_idx]
v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7)
loss_contrast = ql_multi_contrastive_loss(torch.cat((v_emb, v_emb_class)), torch.cat((t_emb, t_emb_class)), torch.cat((t_hash, t_hash_class)), temperature=extra['lang_logit'])
losses = {"loss_caption_0": loss_contrast}
return losses
def loss_masks(self, outputs, targets, indices, num_masks, layer_id, extra):
"""Compute the losses related to the masks: the focal loss and the dice loss.
targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
"""
if layer_id >= self.top_x_layers['mask']:
return {"loss_mask_bce_0": 0, "loss_mask_dice_0": 0}
assert "pred_masks" in outputs
if indices is None or len(targets) == 0:
loss = outputs['pred_masks'].sum() * 0.0
losses = {"loss_mask_bce_0": loss, "loss_mask_dice_0": loss}
return losses
src_idx = self._get_src_permutation_idx(indices)
tgt_idx = self._get_tgt_permutation_idx(indices)
src_masks = outputs["pred_masks"]
src_masks = src_masks[src_idx]
masks = [t["masks"] for t in targets]
# TODO use valid to mask invalid areas due to padding in loss
target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
target_masks = target_masks.to(src_masks)
target_masks = target_masks[tgt_idx]
# No need to upsample predictions as we are using normalized coordinates :)
# N x 1 x H x W
src_masks = src_masks[:, None]
target_masks = target_masks[:, None]
with torch.no_grad():
# sample point_coords
point_coords = get_uncertain_point_coords_with_randomness(
src_masks,
lambda logits: calculate_uncertainty(logits),
self.num_points,
self.oversample_ratio,
self.importance_sample_ratio,
).type(src_masks.dtype)
# get gt labels
point_labels = point_sample(
target_masks,
point_coords,
align_corners=False,
).squeeze(1)
point_logits = point_sample(
src_masks,
point_coords,
align_corners=False,
).squeeze(1)
losses = {
"loss_mask_bce_0": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),
"loss_mask_dice_0": dice_loss_jit(point_logits, point_labels, num_masks),
}
del src_masks
del target_masks
return losses
def loss_groundings(self, outputs, targets, indices, num_masks, layer_id, extra):
"""Compute the losses related to the masks: the focal loss and the dice loss.
targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
"""
assert "pred_gmasks" in outputs
assert "pred_gtexts" in outputs
if layer_id >= self.top_x_layers['grounding']:
return {"loss_grounding_bce_0": 0, "loss_grounding_dice_0": 0, "loss_grounding_ce_0": 0}
masks = [t["grounding_masks"] for t in targets]
if indices is None or None in masks:
loss = outputs['pred_gmasks'].sum() * 0.0
return {"loss_grounding_bce_0": loss, "loss_grounding_dice_0": loss, "loss_grounding_ce_0": loss}
pred_logits = []
for b in range(len(indices)):
t_emb = targets[b]['grounding_class_embs']
v_emb = outputs["pred_gtexts"][b]
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]
outputs['pred_logits'] = pred_logits
indices = self.matcher(outputs, targets, mode='grounding', extra={'temperature':extra['lang_logit']})
src_idx = self._get_src_permutation_idx(indices)
tgt_idx = self._get_tgt_permutation_idx(indices)
src_masks = outputs["pred_gmasks"]
src_masks = src_masks[src_idx]
# TODO use valid to mask invalid areas due to padding in loss
target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
target_masks = target_masks.to(src_masks)
target_masks = target_masks[tgt_idx]
# No need to upsample predictions as we are using normalized coordinates :)
# N x 1 x H x W
src_masks = src_masks[:, None]
target_masks = target_masks[:, None]
with torch.no_grad():
# sample point_coords
point_coords = get_uncertain_point_coords_with_randomness(
src_masks,
lambda logits: calculate_uncertainty(logits),
self.num_points,
self.oversample_ratio,
self.importance_sample_ratio,
).type(src_masks.dtype)
# get gt labels
point_labels = point_sample(
target_masks,
point_coords,
align_corners=False,
).squeeze(1)
point_logits = point_sample(
src_masks,
point_coords,
align_corners=False,
).squeeze(1)
losses = {
"loss_grounding_bce_0": sigmoid_ce_loss_jit(point_logits, point_labels, len(src_masks)),
"loss_grounding_dice_0": dice_loss_jit(point_logits, point_labels, len(src_masks)),
}
# compute query-token contrastive loss
# ttk_emb = torch.cat([x['caption_tokens'] for x in targets], dim=0)
# ttk_mask = torch.cat([x['caption_mask'] for x in targets], dim=0).float()
# ttk_mask = ttk_mask * torch.cumsum(ttk_mask, dim=1)
# vtk_emb = outputs['pred_captions'][:,:-1]
# keep = torch.cat([x['caption_mask'] for x in targets], dim=0).bool()
# ttk_emb = ttk_emb / (ttk_emb.norm(dim=-1, keepdim=True) + 1e-7)
# vtk_emb = vtk_emb / (vtk_emb.norm(dim=-1, keepdim=True) + 1e-7)
# logit_scale = extra['lang_encoder'].logit_scale.exp().clamp(max=100)
# # prepare gt
# gt = (torch.eye(vtk_emb.shape[0]).type_as(ttk_mask).unsqueeze(-1) * ttk_mask.unsqueeze(0).repeat(vtk_emb.shape[0], 1, 1))[:,keep].flatten(1)
# gt = gt / (gt.sum(1, keepdim=True) + 1e-7)
# # compute i2t loss
# logits = logit_scale * (vtk_emb @ ttk_emb[keep].transpose(0, 1)).mean(1)
# loss_contrast_fine_vt = SoftTargetCrossEntropy()(logits, gt)
# # loss_contrast_fine = loss_contrast_fine_vt # i2t only
# # compute t2i loss
# bs, nq, _ = vtk_emb.shape
# logits = logit_scale * (ttk_emb @ vtk_emb.flatten(0,1).transpose(0, 1)).reshape(bs,-1,bs,nq).mean(dim=-1)[keep,:]
# loss_contrast_fine_tv = SoftTargetCrossEntropy()(logits, gt.t())
# # compute loss
# loss_contrast_fine = (loss_contrast_fine_vt * 0.7 + loss_contrast_fine_tv * 0.3)
# compute t2i loss
loss_grd_ce = 0
for b in range(len(indices)):
task = targets[b]['grounding_task']
pred_logit = outputs["pred_logits"][b]
gt_logit = torch.zeros_like(pred_logit)
select_idx = torch.stack((indices[b][0], indices[b][1])).tolist()
gt_logit[select_idx] = 1
t_hash = torch.tensor(targets[b]['grounding_hash'], device=gt_logit.device)
hash_table = torch.zeros((len(t_hash), len(t_hash)), device=gt_logit.device)
for idx in range(0, len(hash_table)):
hash_table[idx][t_hash==t_hash[idx]] = 1
hash_table = hash_table / hash_table.sum(-1, keepdim=True)
gt_logit = gt_logit @ hash_table
loss_grd_ce += self.grounding_weight[task]*torch.sum(-gt_logit.t() * F.log_softmax(pred_logit.t(), dim=-1), dim=-1).mean()
loss_grd_ce = loss_grd_ce / len(indices)
losses.update({"loss_grounding_ce_0": loss_grd_ce})
del src_masks
del target_masks
return losses
def loss_spatials(self, outputs, targets, indices, num_masks, layer_id, extra):
"""Compute the losses related to the masks: the focal loss and the dice loss.
targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
"""
assert "pred_smasks" in outputs
assert "pred_smaskembs" in outputs
if layer_id >= self.top_x_layers['spatial']:
loss = outputs['pred_smasks'].sum() * 0.0
loss_grd_ce = outputs["pred_smasks"].sum() * 0.0
return {"loss_spatial_bce_0": loss, "loss_spatial_dice_0": loss, "loss_spatial_ce_0": loss_grd_ce}
gt_masks = [x['gt_spatial_masks'] for x in targets]
# compute a keep index with batch size to avoid empty gt_masks
stack_gt_mask = torch.cat(gt_masks)
bs,_,_ = stack_gt_mask.shape
stack_gt_mask = stack_gt_mask.view(bs,-1).sum(dim=-1)
keep = stack_gt_mask > 0 # only keep sample contain positive mask
if keep.sum() == 0:
loss = outputs['pred_smasks'].sum() * 0.0
loss_grd_ce = outputs["pred_smasks"].sum() * 0.0
return {"loss_spatial_bce_0": loss, "loss_spatial_dice_0": loss, "loss_spatial_ce_0": loss_grd_ce}
# mask embedding logits
v_emb = outputs["pred_smaskembs"] # [bs, nq, 512]
# pos mask
s_emb = outputs["pred_pspatials"] # [bs, ns, 512]
pred_logits = v_emb @ s_emb.transpose(1,2)
outputs['pred_pos_logits'] = pred_logits # [bs, nq, 1]
indices = self.matcher(outputs, targets, mode='spatial', extra={})
src_idx = self._get_src_permutation_idx(indices)
tgt_idx = self._get_tgt_permutation_idx(indices)
# pos class loss
pred_logit = torch.cat([o[:len(t['gt_spatial_masks'])] for o,t in zip(outputs["pred_pos_logits"].transpose(1,2), targets)])
gt_logit = torch.zeros_like(pred_logit)
gt_logit = gt_logit[keep]
_src_idx = [torch.arange(keep.sum(), device=src_idx[0].device), src_idx[1][keep.cpu()]]
gt_logit[_src_idx] = 1
pred_logit = pred_logit[keep]
loss_spa_ce_pos = torch.sum(-gt_logit * F.log_softmax(pred_logit, dim=-1), dim=-1).mean()
# neg mask
# s_emb = outputs["pred_nspatials"] # [bs, ns, 512]
# neg_mask = (s_emb.sum(dim=list(range(1, len(s_emb.shape)))) != 0).float()[keep]
# pred_logits = v_emb @ s_emb.transpose(1,2)
# outputs['pred_neg_logits'] = pred_logits # [bs, nq, 1]
# indices = self.matcher(outputs, targets, mode='spatial_pn', extra=extra)
# src_idx = self._get_src_permutation_idx(indices)
# tgt_idx = self._get_tgt_permutation_idx(indices)
# src_masks_neg = outputs["pred_smasks"][src_idx][keep]
# src_masks_neg = src_masks_neg*(neg_mask[:,None,None])
# src_masks_neg = src_masks_neg.clip(0) * (-1)
# neg class loss
# pred_logit = outputs["pred_neg_logits"]
# gt_logit = torch.zeros_like(pred_logit)
# gt_logit[src_idx] = 1
# bs,_,ns = pred_logit[keep].shape
# pred_logit = pred_logit[keep].transpose(1,2).view(bs*ns,-1)
# gt_logit = gt_logit[keep].transpose(1,2).view(bs*ns,-1)
# loss_spa_ce_neg = (torch.sum(-gt_logit * F.log_softmax(pred_logit, dim=-1), dim=-1)*neg_mask).sum() / (neg_mask.sum()+1e-6)
# recompute a keep index with matched tgt
stack_gt_mask = nn.utils.rnn.pad_sequence(gt_masks, padding_value=-1).transpose(0,1)[tgt_idx]
bs,_,_ = stack_gt_mask.shape
target_masks = stack_gt_mask
stack_gt_mask = stack_gt_mask.view(bs,-1).sum(dim=-1)
keep = stack_gt_mask > 0 # only keep sample contain positive mask
src_masks_pos = outputs["pred_smasks"][src_idx][keep]
# TODO use valid to mask invalid areas due to padding in loss
target_masks = target_masks.to(src_masks_pos)
target_masks = target_masks[keep]
# mul = extra['spatial_query_mode'][keep]
# src_masks_cur = src_masks_cur.clip(0) * mul[:,None,None]
# src_masks_cur = src_masks_cur
# if neg_mask[0] == 1:
# import cv2
# print(src_masks_pos.shape)
# print(src_masks_neg.shape)
# print(target_masks.shape)
# # import pdb; pdb.set_trace()
# v_pos_mask = (src_masks_pos[0].sigmoid() > 0.5).float().cpu().detach().numpy() * 255
# v_neg_mask = (_src_masks_neg[0].sigmoid() > 0.5).float().cpu().detach().numpy() * 255
# v_sum = ((src_masks_pos[0]-_src_masks_neg[0].clip(0)).sigmoid() > 0.5).float().cpu().detach().numpy() * 255
# v_gt = target_masks[0].float().cpu().detach().numpy() * 255
# cv2.imwrite('v_pos_mask.png', v_pos_mask)
# cv2.imwrite('v_neg_mask.png', v_neg_mask)
# cv2.imwrite('v_sum.png', v_sum)
# cv2.imwrite('v_gt.png', v_gt)
# import pdb; pdb.set_trace()
# src_masks = (src_masks_pos + src_masks_neg)[:, None]
src_masks = src_masks_pos[:, None]
target_masks = target_masks[:, None]
# debug visualization
# with torch.no_grad():
# import cv2
# import numpy as np
# v_src_masks = (F.interpolate(src_masks, size=target_masks.shape[-2:], mode='bilinear', align_corners=False).sigmoid() > 0.5).float().cpu().numpy()[:,0] * 255
# v_target_masks = target_masks.float().cpu().numpy()[:,0] * 255
# v_masks = np.concatenate([v_src_masks, v_target_masks], axis=2)
# for i in range(len(src_masks)):
# v1 = v_src_masks[i]
# v2 = v_target_masks[i]
# v = np.concatenate([v1,v2], axis=1)
# cv2.imwrite('v{}.png'.format(i), v)
# import pdb; pdb.set_trace()
# visualization
# VL.step()
# v_img = batched_inputs[0]['image'].permute(1,2,0).cpu().numpy()
# VL.add_image(v_img[:,:,::-1])
# candidate_masks = batched_inputs[0]['spatial_query']['rand_shape'].float().cpu().numpy()
# gt_masks = batched_inputs[0]['spatial_query']['gt_masks'].float().cpu().numpy()
# texts = ['cmask' for i in range(len(candidate_masks))]
# VL.overlay_obj_mask_to_image(v_img[:,:,::-1], candidate_masks, texts)
# texts = ['gmask' for i in range(len(candidate_masks))]
# VL.overlay_obj_mask_to_image(v_img[:,:,::-1], gt_masks, texts)
# import cv2
# for i in range(len(src_masks)):
# visual_src_mask_cur = (src_masks_cur[i].sigmoid()>0.5).detach().float().cpu().numpy() * 255
# visual_src_mask_mem = (src_masks_mem[i].sigmoid()>0.5).detach().float().cpu().numpy() * 255
# visual_src_mask = (src_masks[i,0].sigmoid()>0.5).detach().float().cpu().numpy() * 255
# visual_target_mask = (target_masks[i,0].sigmoid()>0.5).detach().float().cpu().numpy() * 255
# cv2.imwrite('visual_src_mask_cur_{}_{}.png'.format(i, mul[i].item()), visual_src_mask_cur)
# cv2.imwrite('visual_src_mask_mem_{}_{}.png'.format(i, mul[i].item()), visual_src_mask_mem)
# cv2.imwrite('visual_src_mask_{}_{}.png'.format(i, mul[i].item()), visual_src_mask)
# cv2.imwrite('visual_target_mask_{}_{}.png'.format(i, mul[i].item()), visual_target_mask)
# import pdb; pdb.set_trace()
with torch.no_grad():
# sample point_coords
point_coords = get_uncertain_point_coords_with_randomness(
src_masks,
lambda logits: calculate_uncertainty(logits),
self.num_points,
self.oversample_ratio,
self.importance_sample_ratio,
).type(src_masks.dtype)
# get gt labels
point_labels = point_sample(
target_masks,
point_coords,
align_corners=False,
).squeeze(1)
point_logits = point_sample(
src_masks,
point_coords,
align_corners=False,
).squeeze(1)
num_masks = len(src_masks)
losses = {
"loss_spatial_bce_0": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),
"loss_spatial_dice_0": dice_loss_jit(point_logits, point_labels, num_masks),
}
# losses.update({"loss_spatial_ce_0": loss_spa_ce_pos + loss_spa_ce_neg})
losses.update({"loss_spatial_ce_0": loss_spa_ce_pos})
del src_masks
del target_masks
return losses
def loss_boxes(self, outputs, targets, indices, num_boxes, layer_id, extra):
"""Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss
targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]
The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size.
"""
if layer_id >= self.top_x_layers['box']:
return {"loss_bbox_0": 0, "loss_giou_0": 0}
assert 'pred_boxes' in outputs
if indices is None or len(targets) == 0:
loss = outputs['pred_boxes'].sum() * 0.0
losses = {"loss_bbox_0": loss, "loss_giou_0": loss}
return losses
src_idx = self._get_src_permutation_idx(indices)
tgt_idx = self._get_tgt_permutation_idx(indices)
src_boxes = outputs["pred_boxes"]
src_boxes = src_boxes[src_idx].sigmoid()
target_boxes = [t['boxes'] for t in targets]
max_size = _max_by_axis([list(box.shape) for box in target_boxes])
max_size = [len(target_boxes)] + max_size
empty_boxes = torch.zeros(max_size).to(src_boxes.device)
for idx, tar_box in enumerate(target_boxes):
empty_boxes[idx,:tar_box.shape[0],:] = tar_box
target_boxes = empty_boxes[tgt_idx]
# target_isthings = [t['is_things'] for t in targets]
# max_size = _max_by_axis([list(lab.shape) for lab in target_isthings])
# max_size = [len(target_isthings)] + max_size
# empty_lab = torch.zeros(max_size).to(src_boxes.device)
# for idx, tar_thing in enumerate(target_isthings):
# empty_lab[idx,:tar_thing.shape[0]] = tar_thing
# target_isthings = empty_lab[tgt_idx]
loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction='none')
losses = {}
losses['loss_bbox_0'] = loss_bbox.sum() / num_boxes
loss_giou = 1 - torch.diag(box_ops.generalized_box_iou(
box_ops.box_cxcywh_to_xyxy(src_boxes),
box_ops.box_cxcywh_to_xyxy(target_boxes)))
losses['loss_giou_0'] = loss_giou.sum() / num_boxes
return losses
def _get_src_permutation_idx(self, indices):
# permute predictions following indices
batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
src_idx = torch.cat([src for (src, _) in indices])
return batch_idx, src_idx
def _get_tgt_permutation_idx(self, indices):
# permute targets following indices
batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
tgt_idx = torch.cat([tgt for (_, tgt) in indices])
return batch_idx, tgt_idx
def get_loss(self, loss, outputs, targets, indices, num_masks, layer_id, extra):
loss_map = {
'labels': self.loss_labels,
'masks': self.loss_masks,
'boxes': self.loss_boxes,
'captions': self.loss_captions,
'retrievals': self.loss_itc,
'captionings': self.loss_captionings,
'groundings': self.loss_groundings,
'labels_openimage': self.loss_labels_openimage,
'spatials': self.loss_spatials,
}
assert loss in loss_map, f"do you really want to compute {loss} loss?"
return loss_map[loss](outputs, targets, indices, num_masks, layer_id, extra)
def forward(self, outputs, targets, extra=None):
"""This performs the loss computation.
Parameters:
outputs: dict of tensors, see the output specification of the model for the format
targets: list of dicts, such that len(targets) == batch_size.
The expected keys in each dict depends on the losses applied, see each loss' doc
"""
outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"}
# Retrieve the matching between the outputs of the last layer and the targets
indices = self.matcher(outputs_without_aux, targets)
# Compute the average number of target boxes accross all nodes, for normalization purposes
num_masks = sum(len(t["labels"]) for t in targets)
num_masks = torch.as_tensor(
[num_masks], dtype=torch.float, device=next(iter(outputs_without_aux.values())).device
)
if is_dist_avail_and_initialized():
torch.distributed.all_reduce(num_masks)
num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()
# Compute all the requested losses
losses = {}
for loss in self.losses:
losses.update(self.get_loss(loss, outputs, targets, indices, num_masks, 0, extra))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if "aux_outputs" in outputs:
# NOTE: we reverse the aux_outputs so that the first is the second last layer
for i, aux_outputs in enumerate(outputs["aux_outputs"][::-1]):
indices = self.matcher(aux_outputs, targets)
for loss in self.losses:
l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks, (i+1), extra)
l_dict = {k.replace('_0', f"_{i+1}"): v for k, v in l_dict.items()}
losses.update(l_dict)
return losses
def forward_vlp(self, outputs, targets, extra=None):
"""This performs the loss computation.
Parameters:
outputs: dict of tensors, see the output specification of the model for the format
targets: list of dicts, such that len(targets) == batch_size.
The expected keys in each dict depends on the losses applied, see each loss' doc
"""
# Compute all the requested losses
losses = {}
num_masks = indices = None
for loss in self.losses:
losses.update(self.get_loss(loss, outputs, targets, indices, num_masks, 0, extra))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if "aux_outputs" in outputs:
# NOTE: we reverse the aux_outputs so that the first is the second last layer
for i, aux_outputs in enumerate(outputs["aux_outputs"][::-1]):
for loss in self.losses:
l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks, (i+1), extra)
l_dict = {k.replace('_0', f"_{i+1}"): v for k, v in l_dict.items()}
losses.update(l_dict)
return losses
def forward_grounding(self, outputs, targets, extra=None):
"""This performs the loss computation.
Parameters:
outputs: dict of tensors, see the output specification of the model for the format
targets: list of dicts, such that len(targets) == batch_size.
The expected keys in each dict depends on the losses applied, see each loss' doc
"""
# Compute all the requested losses
losses = {}
indices = [[] for i in range(len(targets))]
# Compute the average number of target boxes accross all nodes, for normalization purposes
num_masks = sum(len(t["grounding_masks"]) for t in targets) + 1e-7
num_masks = torch.as_tensor(
[num_masks], dtype=torch.float, device=next(iter(outputs.values())).device
)
if is_dist_avail_and_initialized():
torch.distributed.all_reduce(num_masks)
num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()
for loss in self.losses:
losses.update(self.get_loss(loss, outputs, targets, indices, num_masks, 0, extra))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if "aux_outputs" in outputs:
# NOTE: we reverse the aux_outputs so that the first is the second last layer
for i, aux_outputs in enumerate(outputs["aux_outputs"][::-1]):
for loss in self.losses:
l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks, (i+1), extra)
l_dict = {k.replace('_0', f"_{i+1}"): v for k, v in l_dict.items()}
losses.update(l_dict)
return losses
def forward_openimage(self, outputs, targets, extra=None):
"""This performs the loss computation.
Parameters:
outputs: dict of tensors, see the output specification of the model for the format
targets: list of dicts, such that len(targets) == batch_size.
The expected keys in each dict depends on the losses applied, see each loss' doc
"""
neg_class_emb = all_gather_grad(torch.cat([x['neg_class_emb'] for x in targets]))
neg_hash = all_gather_grad(torch.cat([x['neg_hash'] for x in targets]))
extra['neg_class_emb'] = neg_class_emb
extra['neg_hash'] = neg_hash
outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"}
# Retrieve the matching between the outputs of the last layer and the targets
indices, pred_logits = self.matcher.openimage_forward(outputs_without_aux, targets, extra=extra)
outputs['pred_logits'] = pred_logits
# Compute the average number of target boxes accross all nodes, for normalization purposes
num_masks = sum(len(t["labels"]) for t in targets)
num_masks = torch.as_tensor(
[num_masks], dtype=torch.float, device=neg_class_emb.device
)
if is_dist_avail_and_initialized():
torch.distributed.all_reduce(num_masks)
num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()
# Compute all the requested losses
losses = {}
for loss in self.losses:
losses.update(self.get_loss(loss, outputs, targets, indices, num_masks, 0, extra))
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if "aux_outputs" in outputs:
# NOTE: we reverse the aux_outputs so that the first is the second last layer
for i, aux_outputs in enumerate(outputs["aux_outputs"][::-1]):
indices, pred_logits = self.matcher.openimage_forward(aux_outputs, targets, extra=extra)
aux_outputs['pred_logits'] = pred_logits
for loss in self.losses:
l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks, (i+1), extra)
l_dict = {k.replace('_0', f"_{i+1}"): v for k, v in l_dict.items()}
losses.update(l_dict)
return losses
def __repr__(self):
head = "Criterion " + self.__class__.__name__
body = [
"matcher: {}".format(self.matcher.__repr__(_repr_indent=8)),
"losses: {}".format(self.losses),
"weight_dict: {}".format(self.weight_dict),
"num_classes: {}".format(self.num_classes),
"eos_coef: {}".format(self.eos_coef),
"num_points: {}".format(self.num_points),
"oversample_ratio: {}".format(self.oversample_ratio),
"importance_sample_ratio: {}".format(self.importance_sample_ratio),
]
_repr_indent = 4
lines = [head] + [" " * _repr_indent + line for line in body]
return "\n".join(lines)