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# Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
from typing import Dict, List, Optional, Tuple
import torch
from torch import nn
import json
from detectron2.utils.events import get_event_storage
from detectron2.config import configurable
from detectron2.structures import ImageList, Instances, Boxes
import detectron2.utils.comm as comm
from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
from detectron2.modeling.meta_arch.rcnn import GeneralizedRCNN
from detectron2.modeling.postprocessing import detector_postprocess
from detectron2.utils.visualizer import Visualizer, _create_text_labels
from detectron2.data.detection_utils import convert_image_to_rgb
from torch.cuda.amp import autocast
from ..text.text_encoder import build_text_encoder
from ..utils import load_class_freq, get_fed_loss_inds
@META_ARCH_REGISTRY.register()
class CustomRCNN(GeneralizedRCNN):
'''
Add image labels
'''
@configurable
def __init__(
self,
with_image_labels = False,
dataset_loss_weight = [],
fp16 = False,
sync_caption_batch = False,
roi_head_name = '',
cap_batch_ratio = 4,
with_caption = False,
dynamic_classifier = False,
**kwargs):
"""
"""
self.with_image_labels = with_image_labels
self.dataset_loss_weight = dataset_loss_weight
self.fp16 = fp16
self.with_caption = with_caption
self.sync_caption_batch = sync_caption_batch
self.roi_head_name = roi_head_name
self.cap_batch_ratio = cap_batch_ratio
self.dynamic_classifier = dynamic_classifier
self.return_proposal = False
if self.dynamic_classifier:
self.freq_weight = kwargs.pop('freq_weight')
self.num_classes = kwargs.pop('num_classes')
self.num_sample_cats = kwargs.pop('num_sample_cats')
super().__init__(**kwargs)
assert self.proposal_generator is not None
if self.with_caption:
assert not self.dynamic_classifier
self.text_encoder = build_text_encoder(pretrain=True)
for v in self.text_encoder.parameters():
v.requires_grad = False
@classmethod
def from_config(cls, cfg):
ret = super().from_config(cfg)
ret.update({
'with_image_labels': cfg.WITH_IMAGE_LABELS,
'dataset_loss_weight': cfg.MODEL.DATASET_LOSS_WEIGHT,
'fp16': cfg.FP16,
'with_caption': cfg.MODEL.WITH_CAPTION,
'sync_caption_batch': cfg.MODEL.SYNC_CAPTION_BATCH,
'dynamic_classifier': cfg.MODEL.DYNAMIC_CLASSIFIER,
'roi_head_name': cfg.MODEL.ROI_HEADS.NAME,
'cap_batch_ratio': cfg.MODEL.CAP_BATCH_RATIO,
})
if ret['dynamic_classifier']:
ret['freq_weight'] = load_class_freq(
cfg.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH,
cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT)
ret['num_classes'] = cfg.MODEL.ROI_HEADS.NUM_CLASSES
ret['num_sample_cats'] = cfg.MODEL.NUM_SAMPLE_CATS
return ret
def inference(
self,
batched_inputs: Tuple[Dict[str, torch.Tensor]],
detected_instances: Optional[List[Instances]] = None,
do_postprocess: bool = True,
):
assert not self.training
assert detected_instances is None
images = self.preprocess_image(batched_inputs)
features = self.backbone(images.tensor)
proposals, _ = self.proposal_generator(images, features, None)
results, _ = self.roi_heads(images, features, proposals)
if do_postprocess:
assert not torch.jit.is_scripting(), \
"Scripting is not supported for postprocess."
return CustomRCNN._postprocess(
results, batched_inputs, images.image_sizes)
else:
return results
def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]):
"""
Add ann_type
Ignore proposal loss when training with image labels
"""
if not self.training:
return self.inference(batched_inputs)
images = self.preprocess_image(batched_inputs)
ann_type = 'box'
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
if self.with_image_labels:
for inst, x in zip(gt_instances, batched_inputs):
inst._ann_type = x['ann_type']
inst._pos_category_ids = x['pos_category_ids']
ann_types = [x['ann_type'] for x in batched_inputs]
assert len(set(ann_types)) == 1
ann_type = ann_types[0]
if ann_type in ['prop', 'proptag']:
for t in gt_instances:
t.gt_classes *= 0
if self.fp16: # TODO (zhouxy): improve
with autocast():
features = self.backbone(images.tensor.half())
features = {k: v.float() for k, v in features.items()}
else:
features = self.backbone(images.tensor)
cls_features, cls_inds, caption_features = None, None, None
if self.with_caption and 'caption' in ann_type:
inds = [torch.randint(len(x['captions']), (1,))[0].item() \
for x in batched_inputs]
caps = [x['captions'][ind] for ind, x in zip(inds, batched_inputs)]
caption_features = self.text_encoder(caps).float()
if self.sync_caption_batch:
caption_features = self._sync_caption_features(
caption_features, ann_type, len(batched_inputs))
if self.dynamic_classifier and ann_type != 'caption':
cls_inds = self._sample_cls_inds(gt_instances, ann_type) # inds, inv_inds
ind_with_bg = cls_inds[0].tolist() + [-1]
cls_features = self.roi_heads.box_predictor[
0].cls_score.zs_weight[:, ind_with_bg].permute(1, 0).contiguous()
classifier_info = cls_features, cls_inds, caption_features
proposals, proposal_losses = self.proposal_generator(
images, features, gt_instances)
if self.roi_head_name in ['StandardROIHeads', 'CascadeROIHeads']:
proposals, detector_losses = self.roi_heads(
images, features, proposals, gt_instances)
else:
proposals, detector_losses = self.roi_heads(
images, features, proposals, gt_instances,
ann_type=ann_type, classifier_info=classifier_info)
if self.vis_period > 0:
storage = get_event_storage()
if storage.iter % self.vis_period == 0:
self.visualize_training(batched_inputs, proposals)
losses = {}
losses.update(detector_losses)
if self.with_image_labels:
if ann_type in ['box', 'prop', 'proptag']:
losses.update(proposal_losses)
else: # ignore proposal loss for non-bbox data
losses.update({k: v * 0 for k, v in proposal_losses.items()})
else:
losses.update(proposal_losses)
if len(self.dataset_loss_weight) > 0:
dataset_sources = [x['dataset_source'] for x in batched_inputs]
assert len(set(dataset_sources)) == 1
dataset_source = dataset_sources[0]
for k in losses:
losses[k] *= self.dataset_loss_weight[dataset_source]
if self.return_proposal:
return proposals, losses
else:
return losses
def _sync_caption_features(self, caption_features, ann_type, BS):
has_caption_feature = (caption_features is not None)
BS = (BS * self.cap_batch_ratio) if (ann_type == 'box') else BS
rank = torch.full(
(BS, 1), comm.get_rank(), dtype=torch.float32,
device=self.device)
if not has_caption_feature:
caption_features = rank.new_zeros((BS, 512))
caption_features = torch.cat([caption_features, rank], dim=1)
global_caption_features = comm.all_gather(caption_features)
caption_features = torch.cat(
[x.to(self.device) for x in global_caption_features], dim=0) \
if has_caption_feature else None # (NB) x (D + 1)
return caption_features
def _sample_cls_inds(self, gt_instances, ann_type='box'):
if ann_type == 'box':
gt_classes = torch.cat(
[x.gt_classes for x in gt_instances])
C = len(self.freq_weight)
freq_weight = self.freq_weight
else:
gt_classes = torch.cat(
[torch.tensor(
x._pos_category_ids,
dtype=torch.long, device=x.gt_classes.device) \
for x in gt_instances])
C = self.num_classes
freq_weight = None
assert gt_classes.max() < C, '{} {}'.format(gt_classes.max(), C)
inds = get_fed_loss_inds(
gt_classes, self.num_sample_cats, C,
weight=freq_weight)
cls_id_map = gt_classes.new_full(
(self.num_classes + 1,), len(inds))
cls_id_map[inds] = torch.arange(len(inds), device=cls_id_map.device)
return inds, cls_id_map |