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# 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 ([email protected]) | |
# -------------------------------------------------------- | |
# 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) | |