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detectron2_given_box_maxnms.py
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# coding=utf-8
# Copyleft 2019 Project LXRT
import argparse
import json
import h5py
from tqdm import tqdm
import os
# import some common libraries
import cv2
import numpy as np
import torch
import torch.nn as nn
from pathlib import Path
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import detectron2
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.modeling.postprocessing import detector_postprocess
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers, FastRCNNOutputs
# fast_rcnn_inference_single_image
from detectron2.structures.boxes import Boxes
from detectron2.structures.instances import Instances
from detectron2.data import MetadataCatalog
from torchvision.ops import nms
from detectron2.structures import Boxes, Instances
D2_ROOT = os.path.dirname(os.path.dirname(
detectron2.__file__)) # Root of detectron2
# DATA_ROOT = os.getenv('COCO_IMG_ROOT', '/ssd-playpen/data/mscoco/images/')
# MIN_BOXES = 36
# MAX_BOXES = 36
# NUM_OBJECTS = 36
# min_n_regions = 20
# min_caption_len = 2
DIM = 2048
# Load VG Classes
data_path = 'demo/data/genome/1600-400-20'
vg_classes = []
with open(os.path.join(D2_ROOT, data_path, 'objects_vocab.txt')) as f:
for object in f.readlines():
vg_classes.append(object.split(',')[0].lower().strip())
vg_attrs = []
with open(os.path.join(D2_ROOT, data_path, 'attributes_vocab.txt')) as f:
for object in f.readlines():
vg_attrs.append(object.split(',')[0].lower().strip())
MetadataCatalog.get("vg").thing_classes = vg_classes
MetadataCatalog.get("vg").attr_classes = vg_attrs
def doit(raw_image, raw_boxes, predictor):
# Process Boxes
raw_boxes = Boxes(torch.from_numpy(raw_boxes).cuda())
with torch.no_grad():
raw_height, raw_width = raw_image.shape[:2]
# print("Original image size: ", (raw_height, raw_width))
# Preprocessing
image = predictor.transform_gen.get_transform(raw_image).apply_image(raw_image)
# print("Transformed image size: ", image.shape[:2])
# Scale the box
new_height, new_width = image.shape[:2]
scale_x = 1. * new_width / raw_width
scale_y = 1. * new_height / raw_height
#print(scale_x, scale_y)
boxes = raw_boxes.clone()
boxes.scale(scale_x=scale_x, scale_y=scale_y)
# ----
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
inputs = [{"image": image, "height": raw_height, "width": raw_width}]
images = predictor.model.preprocess_image(inputs)
# Run Backbone Res1-Res4
features = predictor.model.backbone(images.tensor)
# Run RoI head for each proposal (RoI Pooling + Res5)
proposal_boxes = [boxes]
features = [features[f] for f in predictor.model.roi_heads.in_features]
box_features = predictor.model.roi_heads._shared_roi_transform(
features, proposal_boxes
)
feature_pooled = box_features.mean(dim=[2, 3]) # pooled to 1x1
# print('Pooled features size:', feature_pooled.shape)
# Predict classes pred_class_logits, pred_proposal_deltas = predictor.model.roi_heads.box_predictor(feature_pooled) and boxes for each proposal.
pred_class_logits, pred_attr_logits, pred_proposal_deltas = predictor.model.roi_heads.box_predictor(
feature_pooled)
pred_class_prob = nn.functional.softmax(pred_class_logits, -1)
pred_scores, pred_classes = pred_class_prob[..., :-1].max(-1)
attr_prob = pred_attr_logits[..., :-1].softmax(-1)
max_attr_prob, max_attr_label = attr_prob.max(-1)
# Detectron2 Formatting (for visualization only)
roi_features = feature_pooled
instances = Instances(
image_size=(raw_height, raw_width),
pred_boxes=raw_boxes,
scores=pred_scores,
pred_classes=pred_classes,
attr_scores=max_attr_prob,
attr_classes=max_attr_label
)
return instances, roi_features
def build_model():
cfg = get_cfg() # Renew the cfg file
cfg.merge_from_file(os.path.join(
D2_ROOT, "configs/VG-Detection/faster_rcnn_R_101_C4_attr_caffemaxpool.yaml"))
cfg.MODEL.RPN.POST_NMS_TOPK_TEST = 300
cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.6
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.2
# cfg.INPUT.MIN_SIZE_TEST = 600
# cfg.INPUT.MAX_SIZE_TEST = 1000
# cfg.MODEL.RPN.NMS_THRESH = 0.7
# Find a model from detectron2's model zoo. You can either use the https://dl.fbaipublicfiles.... url, or use the following shorthand
# cfg.MODEL.WEIGHTS = "http://nlp.cs.unc.edu/models/faster_rcnn_from_caffe.pkl"
cfg.MODEL.WEIGHTS = "http://nlp.cs.unc.edu/models/faster_rcnn_from_caffe_attr.pkl"
detector = DefaultPredictor(cfg)
return detector
def extract(output_fname, dataloader, desc):
detector = build_model()
with h5py.File(output_fname, 'w') as f:
with torch.no_grad():
for i, batch in tqdm(enumerate(dataloader),
desc=desc,
ncols=150,
total=len(dataloader)):
img_ids = batch['img_ids']
# feat_list, info_list = feature_extractor.get_detectron_features(batch)
boxes = batch['boxes']
imgs = batch['imgs']
assert len(imgs) == 1
img = imgs[0]
img_id = img_ids[0]
boxes = boxes[0]
instances, features = doit(img, boxes, detector)
instances = instances.to('cpu')
features = features.to('cpu')
num_objects = len(instances)
# assert num_objects == NUM_OBJECTS
# assert features.shape == (NUM_OBJECTS, dim)
grp = f.create_group(img_id)
grp['features'] = features.numpy() # [num_features, 2048]
grp['obj_id'] = instances.pred_classes.numpy()
grp['obj_conf'] = instances.scores.numpy()
grp['attr_id'] = instances.attr_classes.numpy()
grp['attr_conf'] = instances.attr_scores.numpy()
grp['boxes'] = boxes
grp['img_w'] = img.shape[1]
grp['img_h'] = img.shape[0]
grp['num_objects'] = num_objects
if 'captions' in batch:
captions = batch['captions']
grp['captions'] = np.array(captions, dtype=h5py.string_dtype(encoding='utf-8'))