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inference.py
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inference.py
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from __future__ import print_function
import sys
import os
import numpy as np
import argparse
import ipdb
from tqdm import tqdm
import torch
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import matplotlib
from PIL import Image, ImageDraw, ImageFont
from model.vsgats.model import AGRNN as vsgat_hico
from model.pgception import PGception
from datasets.hico_constants import HicoConstants
from datasets.hico_dataset import HicoDataset, collate_fn
from datasets import metadata
from utils.vis_tool import vis_img, vis_img_vcoco
from datasets.vcoco_constants import VcocoConstants
from datasets.vcoco_dataset import VcocoDataset
from datasets.vcoco_dataset import collate_fn as vcoco_collate_fn
from model.vsgats.vcoco_model import AGRNN as vsgat_vcoco
matplotlib.use('TKAgg')
def main(args):
# Load checkpoint and set up model
try:
# use GPU if available else revert to CPU
device = torch.device('cuda:0' if torch.cuda.is_available() and args.gpu else 'cpu')
print("Testing on", device)
# set up model and initialize it with uploaded checkpoint
if args.dataset == 'hico':
# load checkpoint
checkpoint = torch.load(args.main_pretrained_hico, map_location=device)
print('vsgats Checkpoint loaded!')
pg_checkpoint = torch.load(args.pretrained_hico, map_location=device)
data_const = HicoConstants(feat_type=checkpoint['feat_type'])
vs_gats = vsgat_hico(feat_type=checkpoint['feat_type'], bias=checkpoint['bias'], bn=checkpoint['bn'], dropout=checkpoint['dropout'], multi_attn=checkpoint['multi_head'], layer=checkpoint['layers'], diff_edge=checkpoint['diff_edge']) #2 )
if args.dataset == 'vcoco':
# load checkpoint
checkpoint = torch.load(args.main_pretrained_vcoco, map_location=device)
print('vsgats Checkpoint loaded!')
pg_checkpoint = torch.load(args.pretrained_vcoco, map_location=device)
data_const = VcocoConstants()
vs_gats = vsgat_vcoco(feat_type=checkpoint['feat_type'], bias=checkpoint['bias'], bn=checkpoint['bn'], dropout=checkpoint['dropout'], multi_attn=checkpoint['multi_head'], layer=checkpoint['layers'], diff_edge=checkpoint['diff_edge']) #2 )
vs_gats.load_state_dict(checkpoint['state_dict'])
vs_gats.to(device)
vs_gats.eval()
print(pg_checkpoint['o_c_l'], pg_checkpoint['b_l'], pg_checkpoint['attn'], pg_checkpoint['lr'], pg_checkpoint['dropout'])
# pgception = PGception(action_num=24, classifier_mod='cat', o_c_l=[64,64,128,128], last_h_c=256, bias=pg_checkpoint['bias'], drop=pg_checkpoint['dropout'], bn=pg_checkpoint['bn'])
pgception = PGception(action_num=pg_checkpoint['a_n'], layers=1, classifier_mod=pg_checkpoint['classifier_mod'], o_c_l=pg_checkpoint['o_c_l'], last_h_c=pg_checkpoint['last_h_c'], bias=pg_checkpoint['bias'], drop=pg_checkpoint['dropout'], bn=pg_checkpoint['bn'], agg_first=pg_checkpoint['agg_first'], attn=pg_checkpoint['attn'], b_l=pg_checkpoint['b_l'])
# pgception = PGception(action_num=pg_checkpoint['a_n'], drop=pg_checkpoint['dropout'])
pgception.load_state_dict(pg_checkpoint['state_dict'])
pgception.to(device)
pgception.eval()
print('Constructed model successfully!')
except Exception as e:
print('Failed to load checkpoint or construct model!', e)
sys.exit(1)
# prepare for data
if args.dataset == 'hico':
original_imgs_dir = os.path.join(data_const.infer_dir, 'original_imgs/hico')
# original_imgs_dir = './datasets/hico/images/test2015'
save_path = os.path.join(data_const.infer_dir,'processed_imgs/hico')
test_dataset = HicoDataset(data_const=data_const, subset='test')
dataloader = sorted(os.listdir(original_imgs_dir))
# dataloader = ['HICO_test2015_00000128.jpg']
else:
original_imgs_dir = os.path.join(data_const.infer_dir, 'original_imgs/vcoco')
# original_imgs_dir = './datasets/vcoco/coco/images/val2014'
save_path = os.path.join(data_const.infer_dir,'processed_imgs/vcoco')
test_dataset = VcocoDataset(data_const=data_const, subset='vcoco_test', pg_only=False)
# dataloader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False, collate_fn=vcoco_collate_fn)
dataloader = sorted(os.listdir(original_imgs_dir))
dataloader = ['COCO_val2014_000000150361.jpg']
if not os.path.exists(original_imgs_dir):
os.makedirs(original_imgs_dir)
if not os.path.exists(save_path):
os.mkdir(save_path)
print('result images will be kept here{}'.format(save_path))
# ipdb.set_trace()
for data in tqdm(dataloader):
# load corresponding data
# print("Testing on image named {}".format(img))
if args.dataset == 'hico':
img = data
global_id = data.split('.')[0]
test_data = test_dataset.sample_date(global_id)
test_data = collate_fn([test_data])
det_boxes = test_data['det_boxes'][0]
roi_scores = test_data['roi_scores'][0]
roi_labels = test_data['roi_labels'][0]
keypoints = test_data['keypoints'][0]
edge_labels = test_data['edge_labels']
node_num = test_data['node_num']
features = test_data['features']
spatial_feat = test_data['spatial_feat']
word2vec = test_data['word2vec']
pose_normalized = test_data["pose_to_human"]
pose_to_obj_offset = test_data["pose_to_obj_offset"]
else:
# global_id = data['global_id'][0]
img = data
global_id = str(int((data.split('.')[0].split('_')[-1])))
test_data = test_dataset.sample_date(global_id)
test_data = vcoco_collate_fn([test_data])
# img = data['img_name'][0][:].astype(np.uint8).tostring().decode('ascii').split("/")[-1]
# test_data = data
det_boxes = test_data['det_boxes'][0]
roi_scores = test_data['roi_scores'][0]
roi_labels = test_data['roi_labels'][0]
edge_labels = test_data['edge_labels']
node_num = test_data['node_num']
features = test_data['features']
spatial_feat = test_data['spatial_feat']
word2vec = test_data['word2vec']
pose_normalized = test_data["pose_to_human"]
pose_to_obj_offset = test_data["pose_to_obj_offset"]
# inference
pose_to_obj_offset, pose_normalized, features, spatial_feat, word2vec = pose_to_obj_offset.to(device), pose_normalized.to(device), features.to(device), spatial_feat.to(device), word2vec.to(device)
outputs, attn, attn_lang = vs_gats(node_num, features, spatial_feat, word2vec, [roi_labels]) # !NOTE: it is important to set [roi_labels]
pg_outputs = pgception(pose_normalized, pose_to_obj_offset)
# action_score = nn.Sigmoid()(outputs+pg_outputs)
# action_score = action_score.cpu().detach().numpy()
det_outputs = nn.Sigmoid()(outputs+pg_outputs)
det_outputs = det_outputs.cpu().detach().numpy()
# show result
# import ipdb; ipdb.set_trace()
if args.dataset == 'hico':
image = Image.open(os.path.join('datasets/hico/images/test2015', img)).convert('RGB')
image_temp = image.copy()
gt_img = vis_img(image, det_boxes, roi_labels, roi_scores, edge_labels.cpu().numpy(), score_thresh=0.5)
det_img = vis_img(image_temp, det_boxes, roi_labels, roi_scores, det_outputs, score_thresh=0.5)
if args.dataset == 'vcoco':
image = Image.open(os.path.join(data_const.original_image_dir, 'val2014', img)).convert('RGB')
image_temp = image.copy()
gt_img = vis_img_vcoco(image, det_boxes, roi_labels, roi_scores, edge_labels.cpu().numpy(), score_thresh=0.1)
det_img = vis_img_vcoco(image_temp, det_boxes, roi_labels, roi_scores, det_outputs, score_thresh=0.5)
# det_img.save('/home/birl/ml_dl_projects/bigjun/hoi/VS_GATs/inference_imgs/original_imgs'+'/'+img)
det_img.save(save_path+'/'+img.split("/")[-1])
# fig = plt.figure(figsize=(100,100))
# fig.suptitle(img, fontsize=16)
# ax1 = plt.subplot(1,2,1)
# ax1.set_title('ground_truth')
# plt.imshow(np.array(gt_img))
# plt.axis('off')
# ax2 = plt.subplot(1,2,2)
# ax2.set_title('action_detection')
# plt.imshow(np.array(det_img))
# plt.axis('off')
# plt.ion()
# plt.pause(10)
# plt.close()
# ipdb.set_trace()
def str2bool(arg):
arg = arg.lower()
if arg in ['yes', 'true', '1']:
return True
elif arg in ['no', 'false', '0']:
return False
else:
# raise argparse.ArgumentTypeError('Boolean value expected!')
pass
if __name__ == "__main__":
# set some arguments
parser = argparse.ArgumentParser(description='inference of the model')
parser.add_argument('--dataset', type=str, default='hico', choices=['hico', 'vcoco'],
help='which datasets you choose: [hico, vcoco]')
parser.add_argument('--pretrained_hico', '-p_h', type=str, default='./checkpoints/hico/640064_mid128_offset_posetoh_trainval/checkpoint_160_epoch.pth',
help='Location of the checkpoint file: ./checkpoints/hico/640064_mid128_offset_posetoh_trainval/checkpoint_160_epoch.pth')
parser.add_argument('--pretrained_vcoco', '-p_v', type=str, default='./checkpoints/vcoco/v4_640064_mid128_offset_posetohuman_trainval/checkpoint_400_epoch.pth',
help='Location of the checkpoint file: ./checkpoints/vcoco/v4_640064_mid128_offset_posetohuman_trainval/checkpoint_400_epoch.pth')
parser.add_argument('--main_pretrained_hico', '--m_p_h', type=str, default='/home/birl/ml_dl_projects/bigjun/hoi/PGception/checkpoints/hico_vsgats/hico_checkpoint.pth',
help='Location of the checkpoint file of exciting method: /home/birl/ml_dl_projects/bigjun/hoi/PGception/checkpoints/hico_vsgats/hico_checkpoint.pth')
parser.add_argument('--main_pretrained_vcoco', '--m_p_v', type=str, default='/home/birl/ml_dl_projects/bigjun/hoi/PGception/checkpoints/vcoco_vsgats/checkpoint_600_epoch.pth',
help='Location of the checkpoint file of exciting method: /home/birl/ml_dl_projects/bigjun/hoi/PGception/checkpoints/hico_vsgats/checkpoint_600_epoch.pth')
parser.add_argument('--gpu', type=str2bool, default='true',
help='use GPU or not: true')
parser.add_argument('--random_data', type=str2bool, default='false',
help='select data randomly from the test dataset: true')
args = parser.parse_args()
# inferencing
main(args)