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dataloader_server.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import h5py
import os
import numpy as np
import random
import torch
# import skimage
# import skimage.io
# import scipy.misc
import pdb
class DataLoaderRaw():
def __init__(self, opt):
self.opt = opt
self.json_path = getattr(opt, 'coco_json', '')
self.batch_size = getattr(opt, 'batch_size', 10)
self.seq_per_img = 1
self.vocab_size = getattr(opt, 'vocab_size', 0)
self.coco_json = json.load(open(os.path.join(self.json_path)))
self.cocotlk = json.load(open(os.path.join(opt.input_json)))
self.ix_to_word = self.cocotlk['ix_to_word']
#opts for feats
self.use_att = getattr(opt, 'use_att', True)
self.use_box = getattr(opt, 'use_box', 0)
self.norm_att_feat = getattr(opt, 'norm_att_feat', 0)
self.norm_box_feat = getattr(opt, 'norm_box_feat', 0)
# self.cnn_model = opt.get('cnn_model', 'resnet101')
# self.my_resnet = getattr(misc.resnet, self.cnn_model)()
# self.my_resnet.load_state_dict(torch.load('./data/imagenet_weights/'+self.cnn_model+'.pth'))
# self.my_resnet = myResnet(self.my_resnet)
self.input_fc_dir = self.opt.input_fc_dir
self.input_att_dir = self.opt.input_att_dir
self.input_box_dir = self.opt.input_box_dir
self.files = []
self.ids = []
# print(len(self.coco_json))
#loading image details from coco test file
if len(self.coco_json) > 0:
print('reading from ' + opt.coco_json)
for k,v in enumerate(self.coco_json['images']):
fullpath = os.path.join(v['file_name'])
self.files.append(fullpath)
self.ids.append(v['id'])
else:
# read in all the filenames from the folder
print('listing all images in directory ' + self.folder_path)
def isImage(f):
supportedExt = ['.jpg','.JPG','.jpeg','.JPEG','.png','.PNG','.ppm','.PPM']
for ext in supportedExt:
start_idx = f.rfind(ext)
if start_idx >= 0 and start_idx + len(ext) == len(f):
return True
return False
#Getting all the files (sequence doesn't matter)
n = 1
for root, dirs, files in os.walk(self.folder_path, topdown=False):
for file in files:
fullpath = os.path.join(self.folder_path, file)
if isImage(fullpath):
self.files.append(fullpath)
self.ids.append(str(n)) # just order them sequentially wrt json not img
n = n + 1
self.N = len(self.files)
print('DataLoaderRaw found ', self.N, ' images')
self.iterator = 0
#init ends
#get feats one by one for batch
def get_feats(self, index):
"""This function returns features corresponding to given image indexes
"""
ix = index
# fc_feat = np.load(os.path.join(self.input_fc_dir, str(ix) + '.npy'))
if self.use_att:
att_feat = np.load(os.path.join(self.input_att_dir, str(ix) + '.npz'))['feat']
# Reshape to K x C
att_feat = att_feat.reshape(-1, att_feat.shape[-1])
if self.norm_att_feat:
att_feat = att_feat / np.linalg.norm(att_feat, 2, 1, keepdims=True)
if self.use_box:
box_feat = np.load(os.path.join(self.input_box_dir, str(ix) + '.npy'))
# divided by image width and height
x1,y1,x2,y2 = np.hsplit(box_feat, 4)
h,w = self.coco_json[str(ix)]['height'], self.coco_json[str(ix)]['width']
box_feat = np.hstack((x1/w, y1/h, x2/w, y2/h, (x2-x1)*(y2-y1)/(w*h)))
if self.norm_box_feat:
box_feat = box_feat / np.linalg.norm(box_feat, 2, 1, keepdims=True)
att_feat = np.hstack([att_feat, box_feat])
# sort the features by the size of boxes
att_feat = np.stack(sorted(att_feat, key=lambda x:x[-1], reverse=True))
else:
att_feat = np.zeros((1,1,1))
return (np.load(os.path.join(self.input_fc_dir, str(ix) + '.npy')), att_feat)
def get_batch(self, batch_size=None):
batch_size = batch_size or self.batch_size
fc_batch = []
att_batch = []
max_index = self.N
wrapped = False
infos = []
seq_per_img =1
# pdb.set_trace()
for i in range(batch_size):
ri = self.iterator
ri_next = ri + 1
if ri_next >= max_index:
ri_next = 0
wrapped = True
# wrap back around
self.iterator = ri_next
# pdb.set_trace()
img = self.ids[ri]
info_struct = {}
info_struct['id'] = self.coco_json['images'][ri]['id']
infos.append(info_struct)
tmp_fc, tmp_att = self.get_feats(img)
fc_batch.append(tmp_fc)
att_batch.append(tmp_att)
data = {}
data['fc_feats'] = np.stack(reduce(lambda x,y:x+y, [[_]*seq_per_img for _ in fc_batch]))
# merge att_feats
max_att_len = max([_.shape[0] for _ in att_batch])
data['att_feats'] = np.zeros([len(att_batch)*seq_per_img, max_att_len, att_batch[0].shape[1]], dtype = 'float32')
for i in range(len(att_batch)):
data['att_feats'][i*seq_per_img:(i+1)*seq_per_img, :att_batch[i].shape[0]] = att_batch[i]
data['att_masks'] = np.zeros(data['att_feats'].shape[:2], dtype='float32')
for i in range(len(att_batch)):
data['att_masks'][i*seq_per_img:(i+1)*seq_per_img, :att_batch[i].shape[0]] = 1
# set att_masks to None if attention features have same length
if data['att_masks'].sum() == data['att_masks'].size:
data['att_masks'] = None
data['bounds'] = {'it_pos_now': self.iterator, 'it_max': self.N, 'wrapped': wrapped}
data['infos'] = infos
return data
def reset_iterator(self, split):
self.iterator = 0
def get_vocab_size(self):
return len(self.ix_to_word)
def get_vocab(self):
return self.ix_to_word