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[update] vcoco official evaluation
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Masato Tamura committed Mar 11, 2021
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10 changes: 9 additions & 1 deletion README.md
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Expand Up @@ -74,7 +74,7 @@ Note that only Python2 can be used for this conversion because `vsrl_utils.py` i
V-COCO annotations with the HOIA format, `corre_vcoco.npy`, `test_vcoco.json`, and `trainval_vcoco.json` will be generated to `annotations` directory.

### Pre-trained parameters
Our QPIC have to be pre-trained with the COCO object detection dataset. For the HICO-DET training, this pre-training can be omitted by using the parameters of DETR. The parameters can be downloaded from here for [ResNet50](https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth), and for [ResNet101](https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth). For the V-COCO training, this pre-training has to be carried out because some images of the V-COCO evaluation set are contained in the training set of DETR. We excluded the images and pre-trained QPIC for the V-COCO evaluation.
Our QPIC have to be pre-trained with the COCO object detection dataset. For the HICO-DET training, this pre-training can be omitted by using the parameters of DETR. The parameters can be downloaded from here for [ResNet50](https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth), and for [ResNet101](https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth). For the V-COCO training, this pre-training has to be carried out because some images of the V-COCO evaluation set are contained in the training set of DETR. We excluded the images and pre-trained QPIC for the V-COCO evaluation.

After downloading or pre-training, move the pre-trained parameters to the `params` directory and convert the parameters with the following command (e.g. downloaded ResNet50 parameters).
```
Expand Down Expand Up @@ -147,6 +147,14 @@ The evaluation is conducted at the end of each epoch during the training. The re
```
`test_mAP`, `test_mAP rare`, and `test_mAP non-rare` are the results of the default full, rare, and non-rare setting, respectively.

For the official evaluation of V-COCO, a pickle file of detection results have to be generated. You can generate the file as follows.
```
python generate_vcoco_official.py \
--param_path logs/checkpoint.pth
--save_path vcoco.pickle
--hoi_path data/v-coco
```

## Results
HICO-DET.
|| Full (D) | Rare (D) | Non-rare (D) | Full(KO) | Rare (KO) | Non-rare (KO) |
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290 changes: 290 additions & 0 deletions generate_vcoco_official.py
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# ------------------------------------------------------------------------
# Copyright (c) Hitachi, Ltd. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
import argparse
from pathlib import Path
import numpy as np
import copy
import pickle

import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader

from datasets.vcoco import build as build_dataset
from models.backbone import build_backbone
from models.transformer import build_transformer
import util.misc as utils
from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_size, interpolate,
is_dist_avail_and_initialized)


class DETRHOI(nn.Module):

def __init__(self, backbone, transformer, num_obj_classes, num_verb_classes, num_queries, aux_loss=False):
super().__init__()
self.num_queries = num_queries
self.transformer = transformer
hidden_dim = transformer.d_model
self.query_embed = nn.Embedding(num_queries, hidden_dim)
self.obj_class_embed = nn.Linear(hidden_dim, num_obj_classes + 1)
self.verb_class_embed = nn.Linear(hidden_dim, num_verb_classes)
self.sub_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
self.obj_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
self.input_proj = nn.Conv2d(backbone.num_channels, hidden_dim, kernel_size=1)
self.backbone = backbone
self.aux_loss = aux_loss

def forward(self, samples: NestedTensor):
if not isinstance(samples, NestedTensor):
samples = nested_tensor_from_tensor_list(samples)
features, pos = self.backbone(samples)

src, mask = features[-1].decompose()
assert mask is not None
hs = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos[-1])[0]

outputs_obj_class = self.obj_class_embed(hs)
outputs_verb_class = self.verb_class_embed(hs)
outputs_sub_coord = self.sub_bbox_embed(hs).sigmoid()
outputs_obj_coord = self.obj_bbox_embed(hs).sigmoid()
out = {'pred_obj_logits': outputs_obj_class[-1], 'pred_verb_logits': outputs_verb_class[-1],
'pred_sub_boxes': outputs_sub_coord[-1], 'pred_obj_boxes': outputs_obj_coord[-1]}
return out


class MLP(nn.Module):
""" Very simple multi-layer perceptron (also called FFN)"""

def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))

def forward(self, x):
for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
return x


class PostProcessHOI(nn.Module):

def __init__(self, num_queries, subject_category_id, correct_mat):
super().__init__()
self.max_hois = 100

self.num_queries = num_queries
self.subject_category_id = subject_category_id

correct_mat = np.concatenate((correct_mat, np.ones((correct_mat.shape[0], 1))), axis=1)
self.register_buffer('correct_mat', torch.from_numpy(correct_mat))

@torch.no_grad()
def forward(self, outputs, target_sizes):
out_obj_logits, out_verb_logits, out_sub_boxes, out_obj_boxes = outputs['pred_obj_logits'], \
outputs['pred_verb_logits'], \
outputs['pred_sub_boxes'], \
outputs['pred_obj_boxes']

assert len(out_obj_logits) == len(target_sizes)
assert target_sizes.shape[1] == 2

obj_prob = F.softmax(out_obj_logits, -1)
obj_scores, obj_labels = obj_prob[..., :-1].max(-1)

verb_scores = out_verb_logits.sigmoid()

img_h, img_w = target_sizes.unbind(1)
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(verb_scores.device)
sub_boxes = box_cxcywh_to_xyxy(out_sub_boxes)
sub_boxes = sub_boxes * scale_fct[:, None, :]
obj_boxes = box_cxcywh_to_xyxy(out_obj_boxes)
obj_boxes = obj_boxes * scale_fct[:, None, :]

results = []
for os, ol, vs, sb, ob in zip(obj_scores, obj_labels, verb_scores, sub_boxes, obj_boxes):
sl = torch.full_like(ol, self.subject_category_id)
l = torch.cat((sl, ol))
b = torch.cat((sb, ob))
bboxes = [{'bbox': bbox, 'category_id': label} for bbox, label in zip(b.to('cpu').numpy(), l.to('cpu').numpy())]

hoi_scores = vs * os.unsqueeze(1)

verb_labels = torch.arange(hoi_scores.shape[1], device=self.correct_mat.device).view(1, -1).expand(
hoi_scores.shape[0], -1)
object_labels = ol.view(-1, 1).expand(-1, hoi_scores.shape[1])
masks = self.correct_mat[verb_labels.reshape(-1), object_labels.reshape(-1)].view(hoi_scores.shape)
hoi_scores *= masks

ids = torch.arange(b.shape[0])

hois = [{'subject_id': subject_id, 'object_id': object_id, 'category_id': category_id, 'score': score} for
subject_id, object_id, category_id, score in zip(ids[:ids.shape[0] // 2].to('cpu').numpy(),
ids[ids.shape[0] // 2:].to('cpu').numpy(),
verb_labels.to('cpu').numpy(), hoi_scores.to('cpu').numpy())]

results.append({
'predictions': bboxes,
'hoi_prediction': hois
})

return results


def get_args_parser():
parser = argparse.ArgumentParser('Set transformer detector', add_help=False)
parser.add_argument('--batch_size', default=2, type=int)

# * Backbone
parser.add_argument('--backbone', default='resnet50', type=str,
help="Name of the convolutional backbone to use")
parser.add_argument('--dilation', action='store_true',
help="If true, we replace stride with dilation in the last convolutional block (DC5)")
parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
help="Type of positional embedding to use on top of the image features")

# * Transformer
parser.add_argument('--enc_layers', default=6, type=int,
help="Number of encoding layers in the transformer")
parser.add_argument('--dec_layers', default=6, type=int,
help="Number of decoding layers in the transformer")
parser.add_argument('--dim_feedforward', default=2048, type=int,
help="Intermediate size of the feedforward layers in the transformer blocks")
parser.add_argument('--hidden_dim', default=256, type=int,
help="Size of the embeddings (dimension of the transformer)")
parser.add_argument('--dropout', default=0.1, type=float,
help="Dropout applied in the transformer")
parser.add_argument('--nheads', default=8, type=int,
help="Number of attention heads inside the transformer's attentions")
parser.add_argument('--num_queries', default=100, type=int,
help="Number of query slots")
parser.add_argument('--num_verb_queries', default=100, type=int,
help="Number of query slots")
parser.add_argument('--pre_norm', action='store_true')

# * HOI
parser.add_argument('--subject_category_id', default=0, type=int)
parser.add_argument('--missing_category_id', default=80, type=int)

parser.add_argument('--hoi_path', type=str)
parser.add_argument('--param_path', type=str, required=True)
parser.add_argument('--save_path', type=str, required=True)

parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--num_workers', default=2, type=int)

return parser


def main(args):
print("git:\n {}\n".format(utils.get_sha()))

print(args)

valid_obj_ids = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 27, 28, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 46, 47,
48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
58, 59, 60, 61, 62, 63, 64, 65, 67, 70,
72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
82, 84, 85, 86, 87, 88, 89, 90)

verb_classes = ['hold_obj', 'stand', 'sit_instr', 'ride_instr', 'walk', 'look_obj', 'hit_instr', 'hit_obj',
'eat_obj', 'eat_instr', 'jump_instr', 'lay_instr', 'talk_on_phone_instr', 'carry_obj',
'throw_obj', 'catch_obj', 'cut_instr', 'cut_obj', 'run', 'work_on_computer_instr',
'ski_instr', 'surf_instr', 'skateboard_instr', 'smile', 'drink_instr', 'kick_obj',
'point_instr', 'read_obj', 'snowboard_instr']

device = torch.device(args.device)

dataset_val = build_dataset(image_set='val', args=args)

sampler_val = torch.utils.data.SequentialSampler(dataset_val)

data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val,
drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers)

args.lr_backbone = 0
args.masks = False
backbone = build_backbone(args)
transformer = build_transformer(args)
model = DETRHOI(backbone, transformer, len(valid_obj_ids) + 1, len(verb_classes),
args.num_queries, args.num_verb_queries)
post_processor = PostProcessHOI(args.num_queries, args.subject_category_id, dataset_val.correct_mat)
model.to(device)
post_processor.to(device)

checkpoint = torch.load(args.param_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])

detections = generate(model, post_processor, data_loader_val, device, verb_classes, args.missing_category_id)

with open(args.save_path, 'wb') as f:
pickle.dump(detections, f, protocol=2)


@torch.no_grad()
def generate(model, post_processor, data_loader, device, verb_classes, missing_category_id):
model.eval()

metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Generate:'

detections = []
for samples, targets in metric_logger.log_every(data_loader, 10, header):
samples = samples.to(device)

outputs = model(samples)
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
results = post_processor(outputs, orig_target_sizes)

for img_results, img_targets in zip(results, targets):
for hoi in img_results['hoi_prediction']:
detection = {
'image_id': img_targets['img_id'],
'person_box': img_results['predictions'][hoi['subject_id']]['bbox'].tolist()
}
if img_results['predictions'][hoi['object_id']]['category_id'] == missing_category_id:
object_box = [np.nan, np.nan, np.nan, np.nan]
else:
object_box = img_results['predictions'][hoi['object_id']]['bbox'].tolist()
cut_agent = 0
hit_agent = 0
eat_agent = 0
for idx, score in zip(hoi['category_id'], hoi['score']):
verb_class = verb_classes[idx]
score = score.item()
if len(verb_class.split('_')) == 1:
detection['{}_agent'.format(verb_class)] = score
elif 'cut_' in verb_class:
detection[verb_class] = object_box + [score]
cut_agent = score if score > cut_agent else cut_agent
elif 'hit_' in verb_class:
detection[verb_class] = object_box + [score]
hit_agent = score if score > hit_agent else hit_agent
elif 'eat_' in verb_class:
detection[verb_class] = object_box + [score]
eat_agent = score if score > eat_agent else eat_agent
else:
detection[verb_class] = object_box + [score]
detection['{}_agent'.format(
verb_class.replace('_obj', '').replace('_instr', ''))] = score
detection['cut_agent'] = cut_agent
detection['hit_agent'] = hit_agent
detection['eat_agent'] = eat_agent
detections.append(detection)

return detections


if __name__ == '__main__':
parser = argparse.ArgumentParser(parents=[get_args_parser()])
args = parser.parse_args()
main(args)

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