-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy pathsingle_image_inference.py
162 lines (140 loc) · 8.28 KB
/
single_image_inference.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
import sys
sys.path.insert(0,'/proj/digbose92/VQA/VisualQuestion_VQA/Visual_All')
from torch.utils.data import Dataset, DataLoader
from model_combined import *
from vqa_dataset_attention import *
from dataset_vqa import Dictionary, VQAFeatureDataset
import torch
from collections import OrderedDict
import argparse
import torch.nn.parallel
import torchvision.transforms as transforms
from torch.utils.data.dataloader import DataLoader
from torchvision import models
import torch.nn as nn
import json
from PIL import Image
import pickle
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import h5py
def load_model(args):
torch.cuda.manual_seed_all(args.seed)
model_checkpoint=torch.load(args.model_path)
new_state_dict = OrderedDict()
for k, v in model_checkpoint.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
#new_state_dict["classifier.main.2.bias"]=new_state_dict.pop("classifier.main.3.bias")
#new_state_dict["classifier.main.2.weight_g"]=new_state_dict.pop("classifier.main.3.weight_g")
#new_state_dict["classifier.main.2.weight_v"]=new_state_dict.pop("classifier.main.3.weight_v")
print('Model checkpoint loaded')
dictionary=Dictionary.load_from_file(args.pickle_path)
train_dataset=Dataset_VQA(img_root_dir=args.image_root_dir,feats_data_path=args.feats_data_path,dictionary=dictionary,choice='train',dataroot=args.data_root,arch_choice=args.arch_choice,layer_option=args.layer_option)
#train_dataset=Dataset_VQA(img_root_dir=args.image_root_dir,feats_data_path=args.feats_data_path,dictionary=dictionary,choice='train',dataroot=args.data_root,arch_choice=args.arch_choice,layer_option=args.layer_option)
print('Loading the attention model')
attention_model = attention_baseline(train_dataset, num_hid=args.num_hid, dropout= args.dropout, norm=args.norm,\
activation=args.activation, drop_L=args.dropout_L, drop_G=args.dropout_G,\
drop_W=args.dropout_W, drop_C=args.dropout_C)
#attention_model=attention_mfh(train_dataset, num_hid=args.num_hid, dropout= args.dropout, norm=args.norm,\
#activation=args.activation, drop_L=args.dropout_L, drop_G=args.dropout_G,\
#drop_W=args.dropout_W, drop_C=args.dropout_C,mfb_out_dim=args.mfb_out_dim)
attention_model.load_state_dict(new_state_dict)
attention_model.train(False)
#torch.cuda.manual_seed(args.seed)
torch.cuda.set_device(args.device)
attention_model.to(args.device)
return(attention_model)
def preproc_question(str,max_length,dictionary):
tokens = dictionary.tokenize(str, False)
tokens = tokens[:max_length]
if len(tokens) < max_length:
padding = [dictionary.padding_idx] * (max_length - len(tokens))
tokens = padding + tokens
return(tokens)
if __name__ == "__main__":
#parser.add_argument('--epochs', type=int, default=40)
parser = argparse.ArgumentParser()
parser.add_argument('--image_root_dir', type=str, default="/data/digbose92/VQA/COCO")
parser.add_argument('--pickle_path', type=str, default="../Visual_All/data/dictionary.pkl")
parser.add_argument('--feats_data_path', type=str, default="/data/digbose92/VQA/COCO/train_hdf5_COCO/")
parser.add_argument('--data_root', type=str, default="/proj/digbose92/VQA/VisualQuestion_VQA/common_resources")
parser.add_argument('--npy_file', type=str, default="../../VisualQuestion_VQA/Visual_All/data/glove6b_init_300d.npy")
parser.add_argument('--model_path', type=str, default="results_GRU_uni/results_resnet_152_hid_512_YES_NO_ADAM/model.pth")
parser.add_argument('--image_model', type=str, default=None)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--num_hid', type=int, default=512) # they used 1024
parser.add_argument('--dropout', type=float, default=0.3)
parser.add_argument('--dropout_L', type=float, default=0.1)
parser.add_argument('--dropout_G', type=float, default=0.2)
parser.add_argument('--dropout_W', type=float, default=0.4)
parser.add_argument('--dropout_C', type=float, default=0.5)
parser.add_argument('--activation', type=str, default='LeakyReLU', help='PReLU, ReLU, LeakyReLU, Tanh, Hardtanh, Sigmoid, RReLU, ELU, SELU')
parser.add_argument('--norm', type=str, default='weight', help='weight, batch, layer, none')
parser.add_argument('--choice', type=str, default='val', help='choice of the split')
parser.add_argument('--seed', type=int, default=9731, help='random seed')
parser.add_argument('--arch_choice', type=str, default='resnet152', help='choice of the network')
parser.add_argument('--layer_option', type=str, default='pool', help='choice of the layer')
parser.add_argument('--num_workers', type=int, default=4, help='number of the workers')
parser.add_argument('--device', type=int, default=0, help='GPU device id')
parser.add_argument('--class_metadata_file', type=str, default='/proj/digbose92/VQA/VisualQuestion_VQA/Visual_All/data/Train_Class_Distribution.csv', help='Path of class metadata file')
parser.add_argument('--rcnn_path',type=str,default="/proj/digbose92/VQA/VisualQuestion_VQA/Visual_All/data/val36_imgid2idx.pkl",help="Path of the rcnn features file")
parser.add_argument('--bert_option',type=bool,default=False,help="Whether to use bert or not")
parser.add_argument('--mfb_out_dim', type=int, default=1000, help='mfb output dimension')
args = parser.parse_args()
class_meta_data=pd.read_csv('/proj/digbose92/VQA/VisualQuestion_VQA/Visual_All/data/Train_Class_Distribution.csv')
#class_meta_data={}
class_label_map=class_meta_data['Label_names'].tolist()
#class_label_map=['no','yes']
valid_rcnn_pickle_file="/proj/digbose92/VQA/VisualQuestion_VQA/Visual_All/data/val36_imgid2idx.pkl"
pkl_path=pickle.load(open(valid_rcnn_pickle_file,'rb'))
model=load_model(args)
#model.eval()
print('Load the validation json file')
valid_questions=json.load(open('/proj/digbose92/VQA/VisualQuestion_VQA/common_resources/v2_OpenEnded_mscoco_val2014_yes_no_questions.json'))['questions']
valid_entry=valid_questions[91]
print(valid_entry)
dictionary=Dictionary.load_from_file('../Visual_All/data/dictionary.pkl')
print(valid_entry['question'])
tokens=preproc_question(valid_entry['question'],14,dictionary)
pkl_data=pickle.load(open('/proj/digbose92/VQA/VisualQuestion_VQA/common_resources/val_target_yes_no_ans.pkl','rb'))
question_ids=[pkl_data[i]['question_id'] for i, question in enumerate(pkl_data)]
id=question_ids.index(valid_entry['question_id'])
print(pkl_data[id])
print(id)
########################################## RCNN features extraction here ###############################
#hdfeatures="/data/digbose92/VQA/COCO/train_hdf5_COCO/train_rcnn_36.hdf5"
#h5_features=h5py.File(hdfeatures)['image_features']
############################################ resnet152 feature extraction here ##########################
print("================== LOADING HDF5 resnet152 features==================")
hdfeatures="/data/digbose92/VQA/COCO/train_hdf5_COCO/val_feats_resnet152_pool.hdf5"
h5_features=h5py.File(hdfeatures)['feats']
file_path="/data/digbose92/VQA/COCO/train_hdf5_COCO/val_filenames_resnet152.txt"
fl_p=open(file_path)
file_list=list(fl_p.readlines())
file_list=[filename.split("\n")[0] for filename in file_list]
choice='val'
image_id=pkl_data[id]['image_id']
image_path='COCO_'+choice+'2014_'+str(image_id).zfill(12)+'.jpg'
folder="/data/digbose92/VQA/COCO/val2014"
file_path=os.path.join(folder,image_path)
idx=file_list.index(file_path)
#idx=pkl_path[image_id]
feat=torch.from_numpy(h5_features[idx])
feat=feat.view(feat.size(0),feat.size(1)*feat.size(2))
feat=feat.transpose(1,0)
feat=feat.unsqueeze(0)
tokens=torch.from_numpy(np.array(tokens))
tokens=tokens.unsqueeze(0)
feat=feat.to(0)
tokens=tokens.to(0)
print(feat.size())
print(tokens.size())
pred=model(feat,tokens)
logits = torch.max(pred, 1)[1].data
print(logits)
print(pkl_data[id]['Class_Label'])
print('Actual label:',class_label_map[pkl_data[id]['Class_Label']])
print('Predicted label:',class_label_map[logits.cpu().numpy()[0]])