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charades_fine.py
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charades_fine.py
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import torch
import torch.utils.data as data_utl
from torch.utils.data.dataloader import default_collate
import numpy as np
import json
import csv
import h5py
import random
import os
import os.path
import functools
import torchvision
#import torch.utils.data as data
from PIL import Image
import cv2
random.seed(0)
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def accimage_loader(path):
try:
import accimage
return accimage.Image(path)
except IOError:
# Potentially a decoding problem, fall back to PIL.Image
return pil_loader(path)
def get_default_image_loader():
torchvision.set_image_backend('accimage')
from torchvision import get_image_backend
if get_image_backend() == 'accimage':
return accimage_loader
else:
return pil_loader
def video_loader(video_dir_path, vid, frame_indices, image_loader):
video = []
for i in frame_indices:
image_path = os.path.join(video_dir_path, vid, vid+'-'+str(i).zfill(6)+'.jpg')
#image_path = os.path.join(video_dir_path, 'frame_{:05d}.jpg'.format(i))
if os.path.exists(image_path):
video.append(image_loader(image_path))
else:
return video
return video
def get_default_video_loader():
image_loader = get_default_image_loader()
return functools.partial(video_loader, image_loader=image_loader)
def video_to_tensor(pic):
"""Convert a ``numpy.ndarray`` to tensor.
Converts a numpy.ndarray (T x H x W x C)
to a torch.FloatTensor of shape (C x T x H x W)
Args:
pic (numpy.ndarray): Video to be converted to tensor.
Returns:
Tensor: Converted video.
"""
return torch.from_numpy(pic.transpose([3,0,1,2]))
def load_rgb_frames(image_dir, vid, start, num, stride, video_loader):
#frames = []
frame_indices = list(range(start, start+num, stride))
frames = video_loader(image_dir, vid, frame_indices)
return frames
def make_dataset(split_file, split, root, num_classes=157):
dataset = []
with open(split_file, 'r') as f:
data = json.load(f)
pre_data_file = split_file[:-5]+'_'+split+'labeldata_160.npy' #labeldata_160
if os.path.exists(pre_data_file):
print('{} exists'.format(pre_data_file))
dataset = np.load(pre_data_file, allow_pickle=True)
else:
print('{} does not exist'.format(pre_data_file))
i = 0
for vid in data.keys():
if data[vid]['subset'] != split:
continue
if not os.path.exists(os.path.join(root, vid)):
continue
num_frames = len(os.listdir(os.path.join(root, vid)))
if num_frames < (2*80+2):
continue
label = np.zeros((num_classes,num_frames), np.float32)
fps = num_frames/data[vid]['duration']
for ann in data[vid]['actions']:
for fr in range(0,num_frames,1):
if fr/fps > ann[1] and fr/fps < ann[2]:
label[ann[0], fr] = 1 # binary classification
dataset.append((vid, label, data[vid]['duration'], num_frames))
i += 1
print(i, vid)
np.save(pre_data_file, dataset)
print('dataset size:%d'%len(dataset))
return dataset
class Charades(data_utl.Dataset):
def __init__(self, split_file, split, root, spatial_transform=None, task='class', frames=80, gamma_tau=5, crops=1, extract_feat=False):
self.data = make_dataset(split_file, split, root)
self.split_file = split_file
self.root = root
self.frames = frames * 2
self.gamma_tau = gamma_tau * 2 #2
self.loader = get_default_video_loader()
self.spatial_transform = spatial_transform
self.crops = crops
self.split = 'testing' if extract_feat else split
self.task = task
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is class_index of the target class.
"""
vid, label, dur, nf = self.data[index]
if self.split == 'testing':
frames = nf
start_f = 1
else:
frames = min(self.frames, nf)
start_f = random.randint(1, max(self.gamma_tau, nf-frames))
stride_f = self.gamma_tau
if self.split == 'testing' and self.task == 'loc':
stride_f = stride_f//self.crops
imgs = load_rgb_frames(self.root, vid, start_f, frames, stride_f, self.loader) #stride_f
label = label[:, start_f-1:start_f-1+frames:1] #stride_f
label = torch.from_numpy(label)
if self.task == 'class':
label = torch.max(label, dim=1)[0] # C T --> C
if self.spatial_transform is not None:
self.spatial_transform.randomize_parameters(224)
imgs_l = [self.spatial_transform(img) for img in imgs]
imgs_l = torch.stack(imgs_l, 0).permute(1, 0, 2, 3) # T C H W --> C T H W
step = 1
if self.split == 'testing': #self.crops > 1:
if self.task == 'class':
step = int((imgs_l.shape[1] - 1 - self.frames//self.gamma_tau)//(self.crops-1))
if step == 0:
clips = [imgs_l[:,:self.frames//self.gamma_tau,...] for i in range(self.crops)]
clips = torch.stack(clips, 0)
else:
clips = [imgs_l[:,i:i+self.frames//self.gamma_tau,...] for i in range(0, step*self.crops, step)]
clips = torch.stack(clips, 0)
if self.task == 'loc': #self.crops > 1:
clips = [imgs_l[:,i::self.crops,...][:,:frames//self.gamma_tau,...] for i in range(0, self.crops)]
clips = torch.stack(clips, 0) # N C T H W
label = label[:,:(frames//self.gamma_tau)*self.gamma_tau]
else:
clips = imgs_l.unsqueeze(0) # 1 C T H W
meta = torch.from_numpy(np.array([start_f//self.gamma_tau, frames//self.gamma_tau,
nf//self.gamma_tau, stride_f//self.gamma_tau]))
return clips, label, vid
def __len__(self):
return len(self.data)
def mt_collate_fn(batch):
"Pads data and puts it into a tensor of same dimensions"
max_len_clips = 0
max_len_labels = 0
for b in batch:
if b[0].shape[2] > max_len_clips:
max_len_clips = b[0].shape[2]
if b[1].shape[1] > max_len_labels:
max_len_labels = b[1].shape[1]
new_batch = []
for b in batch:
clips = np.zeros((b[0].shape[0], b[0].shape[1], max_len_clips, b[0].shape[3], b[0].shape[4]), np.float32)
label = np.zeros((b[1].shape[0], max_len_labels), np.float32)
mask = np.zeros((max_len_labels), np.float32)
clips[:,:,:b[0].shape[2],:,:] = b[0] #[:,:,:min(cap_clip,b[0].shape[2]),:,:]
label[:,:b[1].shape[1]] = b[1] #[:,:min(cap_label,b[1].shape[1])]
mask[:b[1].shape[1]] = 1
new_batch.append([torch.from_numpy(clips), torch.from_numpy(label), torch.from_numpy(mask), b[2]])
return default_collate(new_batch)