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‎LICENSE.txt

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Apache License
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‎README.md

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# I3D models trained on Kinetics
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## Overview
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This repository contains trained models reported in the paper "[Quo Vadis,
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Action Recognition? A New Model and the Kinetics
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Dataset](https://arxiv.org/abs/1705.07750)" by Joao Carreira and Andrew
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Zisserman.
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This code is based on Deepmind's [Kinetics-I3D](https://github.com/deepmind/kinetics-i3d). Including PyTorch versions of their models.
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# Fine-tuning and Feature Extraction
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We provide code to extract I3D features and fine-tune I3D for charades. Our fine-tuned models on charades are also available in the models director (in addition to Deepmind's trained models).

‎charades_dataset.py

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import torch
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import torch.utils.data as data_utl
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from torch.utils.data.dataloader import default_collate
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import numpy as np
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import json
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import csv
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import h5py
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import random
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import os
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import os.path
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import cv2
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def video_to_tensor(pic):
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"""Convert a ``numpy.ndarray`` to tensor.
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Converts a numpy.ndarray (T x H x W x C)
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to a torch.FloatTensor of shape (C x T x H x W)
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Args:
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pic (numpy.ndarray): Video to be converted to tensor.
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Returns:
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Tensor: Converted video.
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"""
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return torch.from_numpy(pic.transpose([3,0,1,2]))
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def load_rgb_frames(image_dir, vid, start, num):
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frames = []
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for i in range(start, start+num):
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img = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'.jpg'))[:, :, [2, 1, 0]]
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w,h,c = img.shape
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if w < 226 or h < 226:
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d = 226.-min(w,h)
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sc = 1+d/min(w,h)
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img = cv2.resize(img,dsize=(0,0),fx=sc,fy=sc)
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img = (img/255.)*2 - 1
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frames.append(img)
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return np.asarray(frames, dtype=np.float32)
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def load_flow_frames(image_dir, vid, start, num):
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frames = []
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for i in range(start, start+num):
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imgx = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'x.jpg'), cv2.IMREAD_GRAYSCALE)
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imgy = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'y.jpg'), cv2.IMREAD_GRAYSCALE)
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w,h = imgx.shape
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if w < 224 or h < 224:
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d = 224.-min(w,h)
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sc = 1+d/min(w,h)
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imgx = cv2.resize(imgx,dsize=(0,0),fx=sc,fy=sc)
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imgy = cv2.resize(imgy,dsize=(0,0),fx=sc,fy=sc)
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imgx = (imgx/255.)*2 - 1
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imgy = (imgy/255.)*2 - 1
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img = np.asarray([imgx, imgy]).transpose([1,2,0])
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frames.append(img)
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return np.asarray(frames, dtype=np.float32)
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def make_dataset(split_file, split, root, mode, num_classes=157):
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dataset = []
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with open(split_file, 'r') as f:
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data = json.load(f)
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i = 0
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for vid in data.keys():
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if data[vid]['subset'] != split:
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continue
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if not os.path.exists(os.path.join(root, vid)):
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continue
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num_frames = len(os.listdir(os.path.join(root, vid)))
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if mode == 'flow':
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num_frames = num_frames//2
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if num_frames < 66:
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continue
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label = np.zeros((num_classes,num_frames), np.float32)
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fps = num_frames/data[vid]['duration']
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for ann in data[vid]['actions']:
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for fr in range(0,num_frames,1):
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if fr/fps > ann[1] and fr/fps < ann[2]:
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label[ann[0], fr] = 1 # binary classification
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dataset.append((vid, label, data[vid]['duration'], num_frames))
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i += 1
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return dataset
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class Charades(data_utl.Dataset):
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def __init__(self, split_file, split, root, mode, transforms=None):
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self.data = make_dataset(split_file, split, root, mode)
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self.split_file = split_file
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self.transforms = transforms
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self.mode = mode
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self.root = root
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def __getitem__(self, index):
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (image, target) where target is class_index of the target class.
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"""
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vid, label, dur, nf = self.data[index]
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start_f = random.randint(1,nf-65)
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if self.mode == 'rgb':
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imgs = load_rgb_frames(self.root, vid, start_f, 64)
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else:
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imgs = load_flow_frames(self.root, vid, start_f, 64)
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label = label[:, start_f:start_f+64]
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imgs = self.transforms(imgs)
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return video_to_tensor(imgs), torch.from_numpy(label)
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def __len__(self):
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return len(self.data)

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