Full Code of Finspire13/pytorch-i3d-feature-extraction for AI

Repository: Finspire13/pytorch-i3d-feature-extraction
Branch: master
Commit: d7f54526eba5
Files: 13
Total size: 192.1 MB

Directory structure:
gitextract_00tbcfn6/

├── .gitignore
├── LICENSE.txt
├── README.md
├── charades_dataset.py
├── charades_dataset_full.py
├── extract_features.py
├── models/
│   ├── flow_charades.pt
│   ├── flow_imagenet.pt
│   ├── rgb_charades.pt
│   └── rgb_imagenet.pt
├── pytorch_i3d.py
├── train_i3d.py
└── videotransforms.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
features/*
__pycache__/*


================================================
FILE: LICENSE.txt
================================================

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================================================
FILE: README.md
================================================
# I3D Feature Extraction

## Usage
* Format the videos to 25 FPS.
* Convert the videos into frame images and optical flows.
* `python3 extract_features.py ...`

## Parameters
<pre>
--mode:              rgb or flow
--load_model:        path of the I3D model
--input_dir:         folder of converted videos
--output_dir:        folder of extracted features
--batch_size:        batch size for snippets
--sample_mode:       oversample, center_crop or resize
--frequency:         how many frames between adjacent snippet
--usezip/no-usezip:  whether the frame images are zipped
</pre>

## Important: Use PyTorch 0.3

## Input Folder Structure
<pre>
InputFolder
├── video1
│   ├── flow_x.zip
│   ├── flow_y.zip
│   └── img.zip
└── video2
    ├── flow_x.zip
    ├── flow_y.zip
    └── img.zip
</pre>
Frame images and flows can also be unzipped.



# I3D models trained on Kinetics (Old Readme)

## Overview

This repository contains trained models reported in the paper "[Quo Vadis,
Action Recognition? A New Model and the Kinetics
Dataset](https://arxiv.org/abs/1705.07750)" by Joao Carreira and Andrew
Zisserman.

This code is based on Deepmind's [Kinetics-I3D](https://github.com/deepmind/kinetics-i3d). Including PyTorch versions of their models.

## Note
This code was written for PyTorch 0.3. Version 0.4 and newer may cause issues.


# Fine-tuning and Feature Extraction
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). The deepmind pre-trained models were converted to PyTorch and give identical results (flow_imagenet.pt and rgb_imagenet.pt). These models were pretrained on imagenet and kinetics (see [Kinetics-I3D](https://github.com/deepmind/kinetics-i3d) for details). 

## Fine-tuning I3D
[train_i3d.py](train_i3d.py) contains the code to fine-tune I3D based on the details in the paper and obtained from the authors. Specifically, this version follows the settings to fine-tune on the [Charades](allenai.org/plato/charades/) dataset based on the author's implementation that won the Charades 2017 challenge. Our fine-tuned RGB and Flow I3D models are available in the model directory (rgb_charades.pt and flow_charades.pt).

This relied on having the optical flow and RGB frames extracted and saved as images on dist. [charades_dataset.py](charades_dataset.py) contains our code to load video segments for training.

## Feature Extraction
[extract_features.py](extract_features.py) contains the code to load a pre-trained I3D model and extract the features and save the features as numpy arrays. The [charades_dataset_full.py](charades_dataset_full.py) script loads an entire video to extract per-segment features.


================================================
FILE: charades_dataset.py
================================================
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 cv2

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):
  frames = []
  for i in range(start, start+num):
    img = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'.jpg'))[:, :, [2, 1, 0]]
    w,h,c = img.shape
    if w < 226 or h < 226:
        d = 226.-min(w,h)
        sc = 1+d/min(w,h)
        img = cv2.resize(img,dsize=(0,0),fx=sc,fy=sc)
    img = (img/255.)*2 - 1
    frames.append(img)
  return np.asarray(frames, dtype=np.float32)

def load_flow_frames(image_dir, vid, start, num):
  frames = []
  for i in range(start, start+num):
    imgx = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'x.jpg'), cv2.IMREAD_GRAYSCALE)
    imgy = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'y.jpg'), cv2.IMREAD_GRAYSCALE)
    
    w,h = imgx.shape
    if w < 224 or h < 224:
        d = 224.-min(w,h)
        sc = 1+d/min(w,h)
        imgx = cv2.resize(imgx,dsize=(0,0),fx=sc,fy=sc)
        imgy = cv2.resize(imgy,dsize=(0,0),fx=sc,fy=sc)
        
    imgx = (imgx/255.)*2 - 1
    imgy = (imgy/255.)*2 - 1
    img = np.asarray([imgx, imgy]).transpose([1,2,0])
    frames.append(img)
  return np.asarray(frames, dtype=np.float32)


def make_dataset(split_file, split, root, mode, num_classes=157):
    dataset = []
    with open(split_file, 'r') as f:
        data = json.load(f)

    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 mode == 'flow':
            num_frames = num_frames//2
            
        if num_frames < 66:
            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
    
    return dataset


class Charades(data_utl.Dataset):

    def __init__(self, split_file, split, root, mode, transforms=None):
        
        self.data = make_dataset(split_file, split, root, mode)
        self.split_file = split_file
        self.transforms = transforms
        self.mode = mode
        self.root = root

    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]
        start_f = random.randint(1,nf-65)

        if self.mode == 'rgb':
            imgs = load_rgb_frames(self.root, vid, start_f, 64)
        else:
            imgs = load_flow_frames(self.root, vid, start_f, 64)
        label = label[:, start_f:start_f+64]

        imgs = self.transforms(imgs)

        return video_to_tensor(imgs), torch.from_numpy(label)

    def __len__(self):
        return len(self.data)


================================================
FILE: charades_dataset_full.py
================================================
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 os
import os.path

import cv2

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):
  frames = []
  for i in range(start, start+num):
    img = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'.jpg'))[:, :, [2, 1, 0]]
    w,h,c = img.shape
    if w < 226 or h < 226:
        d = 226.-min(w,h)
        sc = 1+d/min(w,h)
        img = cv2.resize(img,dsize=(0,0),fx=sc,fy=sc)
    img = (img/255.)*2 - 1
    frames.append(img)
  return np.asarray(frames, dtype=np.float32)

def load_flow_frames(image_dir, vid, start, num):
  frames = []
  for i in range(start, start+num):
    imgx = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'x.jpg'), cv2.IMREAD_GRAYSCALE)
    imgy = cv2.imread(os.path.join(image_dir, vid, vid+'-'+str(i).zfill(6)+'y.jpg'), cv2.IMREAD_GRAYSCALE)
    
    w,h = imgx.shape
    if w < 224 or h < 224:
        d = 224.-min(w,h)
        sc = 1+d/min(w,h)
        imgx = cv2.resize(imgx,dsize=(0,0),fx=sc,fy=sc)
        imgy = cv2.resize(imgy,dsize=(0,0),fx=sc,fy=sc)
        
    imgx = (imgx/255.)*2 - 1
    imgy = (imgy/255.)*2 - 1
    img = np.asarray([imgx, imgy]).transpose([1,2,0])
    frames.append(img)
  return np.asarray(frames, dtype=np.float32)


def make_dataset(split_file, split, root, mode, num_classes=157):
    dataset = []
    with open(split_file, 'r') as f:
        data = json.load(f)

    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 mode == 'flow':
            num_frames = num_frames//2
            
        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
    
    return dataset


class Charades(data_utl.Dataset):

    def __init__(self, split_file, split, root, mode, transforms=None, save_dir='', num=0):
        
        self.data = make_dataset(split_file, split, root, mode)
        self.split_file = split_file
        self.transforms = transforms
        self.mode = mode
        self.root = root
        self.save_dir = save_dir

    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 os.path.exists(os.path.join(self.save_dir, vid+'.npy')):
            return 0, 0, vid

        if self.mode == 'rgb':
            imgs = load_rgb_frames(self.root, vid, 1, nf)
        else:
            imgs = load_flow_frames(self.root, vid, 1, nf)

        imgs = self.transforms(imgs)

        return video_to_tensor(imgs), torch.from_numpy(label), vid

    def __len__(self):
        return len(self.data)


================================================
FILE: extract_features.py
================================================
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"   
import sys
import io
import zipfile
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import argparse
import torchvision
from PIL import Image

import numpy as np

from pytorch_i3d import InceptionI3d

import pdb


def load_frame(frame_file, resize=False):

    data = Image.open(frame_file)

    assert(data.size[1] == 256)
    assert(data.size[0] == 340)

    if resize:
        data = data.resize((224, 224), Image.ANTIALIAS)

    data = np.array(data)
    data = data.astype(float)
    data = (data * 2 / 255) - 1

    assert(data.max()<=1.0)
    assert(data.min()>=-1.0)

    return data


def load_zipframe(zipdata, name, resize=False):

    stream = zipdata.read(name)
    data = Image.open(io.BytesIO(stream))

    assert(data.size[1] == 256)
    assert(data.size[0] == 340)

    if resize:
        data = data.resize((224, 224), Image.ANTIALIAS)

    data = np.array(data)
    data = data.astype(float)
    data = (data * 2 / 255) - 1

    assert(data.max()<=1.0)
    assert(data.min()>=-1.0)

    return data




def oversample_data(data): # (39, 16, 224, 224, 2)  # Check twice

    data_flip = np.array(data[:,:,:,::-1,:])

    data_1 = np.array(data[:, :, :224, :224, :])
    data_2 = np.array(data[:, :, :224, -224:, :])
    data_3 = np.array(data[:, :, 16:240, 58:282, :])   # ,:,16:240,58:282,:
    data_4 = np.array(data[:, :, -224:, :224, :])
    data_5 = np.array(data[:, :, -224:, -224:, :])

    data_f_1 = np.array(data_flip[:, :, :224, :224, :])
    data_f_2 = np.array(data_flip[:, :, :224, -224:, :])
    data_f_3 = np.array(data_flip[:, :, 16:240, 58:282, :])
    data_f_4 = np.array(data_flip[:, :, -224:, :224, :])
    data_f_5 = np.array(data_flip[:, :, -224:, -224:, :])

    return [data_1, data_2, data_3, data_4, data_5,
        data_f_1, data_f_2, data_f_3, data_f_4, data_f_5]




def load_rgb_batch(frames_dir, rgb_files, 
                   frame_indices, resize=False):

    if resize:
        batch_data = np.zeros(frame_indices.shape + (224,224,3))
    else:
        batch_data = np.zeros(frame_indices.shape + (256,340,3))

    for i in range(frame_indices.shape[0]):
        for j in range(frame_indices.shape[1]):

            batch_data[i,j,:,:,:] = load_frame(os.path.join(frames_dir, 
                rgb_files[frame_indices[i][j]]), resize)

    return batch_data


def load_ziprgb_batch(rgb_zipdata, rgb_files, 
                   frame_indices, resize=False):

    if resize:
        batch_data = np.zeros(frame_indices.shape + (224,224,3))
    else:
        batch_data = np.zeros(frame_indices.shape + (256,340,3))

    for i in range(frame_indices.shape[0]):
        for j in range(frame_indices.shape[1]):

            batch_data[i,j,:,:,:] = load_zipframe(rgb_zipdata, 
                rgb_files[frame_indices[i][j]], resize)

    return batch_data


def load_flow_batch(frames_dir, flow_x_files, flow_y_files, 
                    frame_indices, resize=False):

    if resize:
        batch_data = np.zeros(frame_indices.shape + (224,224,2))
    else:
        batch_data = np.zeros(frame_indices.shape + (256,340,2))

    for i in range(frame_indices.shape[0]):
        for j in range(frame_indices.shape[1]):

            batch_data[i,j,:,:,0] = load_frame(os.path.join(frames_dir, 
                flow_x_files[frame_indices[i][j]]), resize)

            batch_data[i,j,:,:,1] = load_frame(os.path.join(frames_dir, 
                flow_y_files[frame_indices[i][j]]), resize)

    return batch_data


def load_zipflow_batch(flow_x_zipdata, flow_y_zipdata, 
                    flow_x_files, flow_y_files, 
                    frame_indices, resize=False):

    if resize:
        batch_data = np.zeros(frame_indices.shape + (224,224,2))
    else:
        batch_data = np.zeros(frame_indices.shape + (256,340,2))

    for i in range(frame_indices.shape[0]):
        for j in range(frame_indices.shape[1]):

            batch_data[i,j,:,:,0] = load_zipframe(flow_x_zipdata, 
                flow_x_files[frame_indices[i][j]], resize)

            batch_data[i,j,:,:,1] = load_zipframe(flow_y_zipdata, 
                flow_y_files[frame_indices[i][j]], resize)

    return batch_data



def run(mode='rgb', load_model='', sample_mode='oversample', frequency=16,
    input_dir='', output_dir='', batch_size=40, usezip=False):

    chunk_size = 16

    assert(mode in ['rgb', 'flow'])
    assert(sample_mode in ['oversample', 'center_crop', 'resize'])
    
    # setup the model
    if mode == 'flow':
        i3d = InceptionI3d(400, in_channels=2)
    else:
        i3d = InceptionI3d(400, in_channels=3)
    
    #i3d.replace_logits(157)
    i3d.load_state_dict(torch.load(load_model))
    i3d.cuda()

    i3d.train(False)  # Set model to evaluate mode

    def forward_batch(b_data):
        b_data = b_data.transpose([0, 4, 1, 2, 3])
        b_data = torch.from_numpy(b_data)   # b,c,t,h,w  # 40x3x16x224x224

        b_data = Variable(b_data.cuda(), volatile=True).float()
        b_features = i3d.extract_features(b_data)
        
        b_features = b_features.data.cpu().numpy()[:,:,0,0,0]
        return b_features


    video_names = [i for i in os.listdir(input_dir) if i[0] == 'v']

    for video_name in video_names:

        save_file = '{}-{}.npz'.format(video_name, mode)
        if save_file in os.listdir(output_dir):
            continue

        frames_dir = os.path.join(input_dir, video_name)


        if mode == 'rgb':
            if usezip:
                rgb_zipdata = zipfile.ZipFile(os.path.join(frames_dir, 'img.zip'), 'r')
                rgb_files = [i for i in rgb_zipdata.namelist() if i.startswith('img')]
            else:
                rgb_files = [i for i in os.listdir(frames_dir) if i.startswith('img')]

            rgb_files.sort()
            frame_cnt = len(rgb_files)

        else:
            if usezip:
                flow_x_zipdata = zipfile.ZipFile(os.path.join(frames_dir, 'flow_x.zip'), 'r')
                flow_x_files = [i for i in flow_x_zipdata.namelist() if i.startswith('x_')]

                flow_y_zipdata = zipfile.ZipFile(os.path.join(frames_dir, 'flow_y.zip'), 'r')
                flow_y_files = [i for i in flow_y_zipdata.namelist() if i.startswith('y_')]
            else:
                flow_x_files = [i for i in os.listdir(frames_dir) if i.startswith('flow_x')]
                flow_y_files = [i for i in os.listdir(frames_dir) if i.startswith('flow_y')]

            flow_x_files.sort()
            flow_y_files.sort()
            assert(len(flow_y_files) == len(flow_x_files))
            frame_cnt = len(flow_y_files)



        # clipped_length = (frame_cnt // chunk_size) * chunk_size   # Cut frames

        # Cut frames
        assert(frame_cnt > chunk_size)
        clipped_length = frame_cnt - chunk_size
        clipped_length = (clipped_length // frequency) * frequency  # The start of last chunk

        frame_indices = [] # Frames to chunks
        for i in range(clipped_length // frequency + 1):
            frame_indices.append(
                [j for j in range(i * frequency, i * frequency + chunk_size)])

        frame_indices = np.array(frame_indices)

        #frame_indices = np.reshape(frame_indices, (-1, 16)) # Frames to chunks
        chunk_num = frame_indices.shape[0]

        batch_num = int(np.ceil(chunk_num / batch_size))    # Chunks to batches
        frame_indices = np.array_split(frame_indices, batch_num, axis=0)

        if sample_mode == 'oversample':
            full_features = [[] for i in range(10)]
        else:
            full_features = [[]]

        for batch_id in range(batch_num):
            
            require_resize = sample_mode == 'resize'

            if mode == 'rgb':
                if usezip:
                    batch_data = load_ziprgb_batch(rgb_zipdata, rgb_files, 
                        frame_indices[batch_id], require_resize)
                else:                
                    batch_data = load_rgb_batch(frames_dir, rgb_files, 
                        frame_indices[batch_id], require_resize)
            else:
                if usezip:
                    batch_data = load_zipflow_batch(
                        flow_x_zipdata, flow_y_zipdata,
                        flow_x_files, flow_y_files, 
                        frame_indices[batch_id], require_resize)
                else:
                    batch_data = load_flow_batch(frames_dir, 
                        flow_x_files, flow_y_files, 
                        frame_indices[batch_id], require_resize)

            if sample_mode == 'oversample':
                batch_data_ten_crop = oversample_data(batch_data)

                for i in range(10):
                    pdb.set_trace()
                    assert(batch_data_ten_crop[i].shape[-2]==224)
                    assert(batch_data_ten_crop[i].shape[-3]==224)
                    full_features[i].append(forward_batch(batch_data_ten_crop[i]))

            else:
                if sample_mode == 'center_crop':
                    batch_data = batch_data[:,:,16:240,58:282,:] # Centrer Crop  (39, 16, 224, 224, 2)
                
                assert(batch_data.shape[-2]==224)
                assert(batch_data.shape[-3]==224)
                full_features[0].append(forward_batch(batch_data))



        full_features = [np.concatenate(i, axis=0) for i in full_features]
        full_features = [np.expand_dims(i, axis=0) for i in full_features]
        full_features = np.concatenate(full_features, axis=0)

        np.savez(os.path.join(output_dir, save_file), 
            feature=full_features,
            frame_cnt=frame_cnt,
            video_name=video_name)

        print('{} done: {} / {}, {}'.format(
            video_name, frame_cnt, clipped_length, full_features.shape))



if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('--mode', type=str)
    parser.add_argument('--load_model', type=str)
    parser.add_argument('--input_dir', type=str)
    parser.add_argument('--output_dir', type=str)
    parser.add_argument('--batch_size', type=int, default=40)
    parser.add_argument('--sample_mode', type=str)
    parser.add_argument('--frequency', type=int, default=16)

    parser.add_argument('--usezip', dest='usezip', action='store_true')
    parser.add_argument('--no-usezip', dest='usezip', action='store_false')
    parser.set_defaults(usezip=True)

    args = parser.parse_args()

    run(mode=args.mode, 
        load_model=args.load_model,
        sample_mode=args.sample_mode,
        input_dir=args.input_dir, 
        output_dir=args.output_dir,
        batch_size=args.batch_size,
        frequency=args.frequency,
        usezip=args.usezip)


================================================
FILE: models/flow_charades.pt
================================================
[File too large to display: 47.5 MB]

================================================
FILE: models/flow_imagenet.pt
================================================
[File too large to display: 48.4 MB]

================================================
FILE: models/rgb_charades.pt
================================================
[File too large to display: 47.6 MB]

================================================
FILE: models/rgb_imagenet.pt
================================================
[File too large to display: 48.5 MB]

================================================
FILE: pytorch_i3d.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

import numpy as np

import os
import sys
from collections import OrderedDict


class MaxPool3dSamePadding(nn.MaxPool3d):
    
    def compute_pad(self, dim, s):
        if s % self.stride[dim] == 0:
            return max(self.kernel_size[dim] - self.stride[dim], 0)
        else:
            return max(self.kernel_size[dim] - (s % self.stride[dim]), 0)

    def forward(self, x):
        # compute 'same' padding
        (batch, channel, t, h, w) = x.size()
        #print t,h,w
        out_t = np.ceil(float(t) / float(self.stride[0]))
        out_h = np.ceil(float(h) / float(self.stride[1]))
        out_w = np.ceil(float(w) / float(self.stride[2]))
        #print out_t, out_h, out_w
        pad_t = self.compute_pad(0, t)
        pad_h = self.compute_pad(1, h)
        pad_w = self.compute_pad(2, w)
        #print pad_t, pad_h, pad_w

        pad_t_f = pad_t // 2
        pad_t_b = pad_t - pad_t_f
        pad_h_f = pad_h // 2
        pad_h_b = pad_h - pad_h_f
        pad_w_f = pad_w // 2
        pad_w_b = pad_w - pad_w_f

        pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b)
        #print x.size()
        #print pad
        x = F.pad(x, pad)
        return super(MaxPool3dSamePadding, self).forward(x)
    

class Unit3D(nn.Module):

    def __init__(self, in_channels,
                 output_channels,
                 kernel_shape=(1, 1, 1),
                 stride=(1, 1, 1),
                 padding=0,
                 activation_fn=F.relu,
                 use_batch_norm=True,
                 use_bias=False,
                 name='unit_3d'):
        
        """Initializes Unit3D module."""
        super(Unit3D, self).__init__()
        
        self._output_channels = output_channels
        self._kernel_shape = kernel_shape
        self._stride = stride
        self._use_batch_norm = use_batch_norm
        self._activation_fn = activation_fn
        self._use_bias = use_bias
        self.name = name
        self.padding = padding
        
        self.conv3d = nn.Conv3d(in_channels=in_channels,
                                out_channels=self._output_channels,
                                kernel_size=self._kernel_shape,
                                stride=self._stride,
                                padding=0, # we always want padding to be 0 here. We will dynamically pad based on input size in forward function
                                bias=self._use_bias)
        
        if self._use_batch_norm:
            self.bn = nn.BatchNorm3d(self._output_channels, eps=0.001, momentum=0.01)

    def compute_pad(self, dim, s):
        if s % self._stride[dim] == 0:
            return max(self._kernel_shape[dim] - self._stride[dim], 0)
        else:
            return max(self._kernel_shape[dim] - (s % self._stride[dim]), 0)

            
    def forward(self, x):
        # compute 'same' padding
        (batch, channel, t, h, w) = x.size()
        #print t,h,w
        out_t = np.ceil(float(t) / float(self._stride[0]))
        out_h = np.ceil(float(h) / float(self._stride[1]))
        out_w = np.ceil(float(w) / float(self._stride[2]))
        #print out_t, out_h, out_w
        pad_t = self.compute_pad(0, t)
        pad_h = self.compute_pad(1, h)
        pad_w = self.compute_pad(2, w)
        #print pad_t, pad_h, pad_w

        pad_t_f = pad_t // 2
        pad_t_b = pad_t - pad_t_f
        pad_h_f = pad_h // 2
        pad_h_b = pad_h - pad_h_f
        pad_w_f = pad_w // 2
        pad_w_b = pad_w - pad_w_f

        pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b)
        #print x.size()
        #print pad
        x = F.pad(x, pad)
        #print x.size()        

        x = self.conv3d(x)
        if self._use_batch_norm:
            x = self.bn(x)
        if self._activation_fn is not None:
            x = self._activation_fn(x)
        return x



class InceptionModule(nn.Module):
    def __init__(self, in_channels, out_channels, name):
        super(InceptionModule, self).__init__()

        self.b0 = Unit3D(in_channels=in_channels, output_channels=out_channels[0], kernel_shape=[1, 1, 1], padding=0,
                         name=name+'/Branch_0/Conv3d_0a_1x1')
        self.b1a = Unit3D(in_channels=in_channels, output_channels=out_channels[1], kernel_shape=[1, 1, 1], padding=0,
                          name=name+'/Branch_1/Conv3d_0a_1x1')
        self.b1b = Unit3D(in_channels=out_channels[1], output_channels=out_channels[2], kernel_shape=[3, 3, 3],
                          name=name+'/Branch_1/Conv3d_0b_3x3')
        self.b2a = Unit3D(in_channels=in_channels, output_channels=out_channels[3], kernel_shape=[1, 1, 1], padding=0,
                          name=name+'/Branch_2/Conv3d_0a_1x1')
        self.b2b = Unit3D(in_channels=out_channels[3], output_channels=out_channels[4], kernel_shape=[3, 3, 3],
                          name=name+'/Branch_2/Conv3d_0b_3x3')
        self.b3a = MaxPool3dSamePadding(kernel_size=[3, 3, 3],
                                stride=(1, 1, 1), padding=0)
        self.b3b = Unit3D(in_channels=in_channels, output_channels=out_channels[5], kernel_shape=[1, 1, 1], padding=0,
                          name=name+'/Branch_3/Conv3d_0b_1x1')
        self.name = name

    def forward(self, x):    
        b0 = self.b0(x)
        b1 = self.b1b(self.b1a(x))
        b2 = self.b2b(self.b2a(x))
        b3 = self.b3b(self.b3a(x))
        return torch.cat([b0,b1,b2,b3], dim=1)


class InceptionI3d(nn.Module):
    """Inception-v1 I3D architecture.
    The model is introduced in:
        Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
        Joao Carreira, Andrew Zisserman
        https://arxiv.org/pdf/1705.07750v1.pdf.
    See also the Inception architecture, introduced in:
        Going deeper with convolutions
        Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
        Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
        http://arxiv.org/pdf/1409.4842v1.pdf.
    """

    # Endpoints of the model in order. During construction, all the endpoints up
    # to a designated `final_endpoint` are returned in a dictionary as the
    # second return value.
    VALID_ENDPOINTS = (
        'Conv3d_1a_7x7',
        'MaxPool3d_2a_3x3',
        'Conv3d_2b_1x1',
        'Conv3d_2c_3x3',
        'MaxPool3d_3a_3x3',
        'Mixed_3b',
        'Mixed_3c',
        'MaxPool3d_4a_3x3',
        'Mixed_4b',
        'Mixed_4c',
        'Mixed_4d',
        'Mixed_4e',
        'Mixed_4f',
        'MaxPool3d_5a_2x2',
        'Mixed_5b',
        'Mixed_5c',
        'Logits',
        'Predictions',
    )

    def __init__(self, num_classes=400, spatial_squeeze=True,
                 final_endpoint='Logits', name='inception_i3d', in_channels=3, dropout_keep_prob=0.5):
        """Initializes I3D model instance.
        Args:
          num_classes: The number of outputs in the logit layer (default 400, which
              matches the Kinetics dataset).
          spatial_squeeze: Whether to squeeze the spatial dimensions for the logits
              before returning (default True).
          final_endpoint: The model contains many possible endpoints.
              `final_endpoint` specifies the last endpoint for the model to be built
              up to. In addition to the output at `final_endpoint`, all the outputs
              at endpoints up to `final_endpoint` will also be returned, in a
              dictionary. `final_endpoint` must be one of
              InceptionI3d.VALID_ENDPOINTS (default 'Logits').
          name: A string (optional). The name of this module.
        Raises:
          ValueError: if `final_endpoint` is not recognized.
        """

        if final_endpoint not in self.VALID_ENDPOINTS:
            raise ValueError('Unknown final endpoint %s' % final_endpoint)

        super(InceptionI3d, self).__init__()
        self._num_classes = num_classes
        self._spatial_squeeze = spatial_squeeze
        self._final_endpoint = final_endpoint
        self.logits = None

        if self._final_endpoint not in self.VALID_ENDPOINTS:
            raise ValueError('Unknown final endpoint %s' % self._final_endpoint)

        self.end_points = {}
        end_point = 'Conv3d_1a_7x7'
        self.end_points[end_point] = Unit3D(in_channels=in_channels, output_channels=64, kernel_shape=[7, 7, 7],
                                            stride=(2, 2, 2), padding=(3,3,3),  name=name+end_point)
        if self._final_endpoint == end_point: return
        
        end_point = 'MaxPool3d_2a_3x3'
        self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[1, 3, 3], stride=(1, 2, 2),
                                                             padding=0)
        if self._final_endpoint == end_point: return
        
        end_point = 'Conv3d_2b_1x1'
        self.end_points[end_point] = Unit3D(in_channels=64, output_channels=64, kernel_shape=[1, 1, 1], padding=0,
                                       name=name+end_point)
        if self._final_endpoint == end_point: return
        
        end_point = 'Conv3d_2c_3x3'
        self.end_points[end_point] = Unit3D(in_channels=64, output_channels=192, kernel_shape=[3, 3, 3], padding=1,
                                       name=name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'MaxPool3d_3a_3x3'
        self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[1, 3, 3], stride=(1, 2, 2),
                                                             padding=0)
        if self._final_endpoint == end_point: return
        
        end_point = 'Mixed_3b'
        self.end_points[end_point] = InceptionModule(192, [64,96,128,16,32,32], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'Mixed_3c'
        self.end_points[end_point] = InceptionModule(256, [128,128,192,32,96,64], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'MaxPool3d_4a_3x3'
        self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[3, 3, 3], stride=(2, 2, 2),
                                                             padding=0)
        if self._final_endpoint == end_point: return

        end_point = 'Mixed_4b'
        self.end_points[end_point] = InceptionModule(128+192+96+64, [192,96,208,16,48,64], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'Mixed_4c'
        self.end_points[end_point] = InceptionModule(192+208+48+64, [160,112,224,24,64,64], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'Mixed_4d'
        self.end_points[end_point] = InceptionModule(160+224+64+64, [128,128,256,24,64,64], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'Mixed_4e'
        self.end_points[end_point] = InceptionModule(128+256+64+64, [112,144,288,32,64,64], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'Mixed_4f'
        self.end_points[end_point] = InceptionModule(112+288+64+64, [256,160,320,32,128,128], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'MaxPool3d_5a_2x2'
        self.end_points[end_point] = MaxPool3dSamePadding(kernel_size=[2, 2, 2], stride=(2, 2, 2),
                                                             padding=0)
        if self._final_endpoint == end_point: return

        end_point = 'Mixed_5b'
        self.end_points[end_point] = InceptionModule(256+320+128+128, [256,160,320,32,128,128], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'Mixed_5c'
        self.end_points[end_point] = InceptionModule(256+320+128+128, [384,192,384,48,128,128], name+end_point)
        if self._final_endpoint == end_point: return

        end_point = 'Logits'
        self.avg_pool = nn.AvgPool3d(kernel_size=[2, 7, 7],
                                     stride=(1, 1, 1))
        self.dropout = nn.Dropout(dropout_keep_prob)
        self.logits = Unit3D(in_channels=384+384+128+128, output_channels=self._num_classes,
                             kernel_shape=[1, 1, 1],
                             padding=0,
                             activation_fn=None,
                             use_batch_norm=False,
                             use_bias=True,
                             name='logits')

        self.build()


    def replace_logits(self, num_classes):
        self._num_classes = num_classes
        self.logits = Unit3D(in_channels=384+384+128+128, output_channels=self._num_classes,
                             kernel_shape=[1, 1, 1],
                             padding=0,
                             activation_fn=None,
                             use_batch_norm=False,
                             use_bias=True,
                             name='logits')
        
    
    def build(self):
        for k in self.end_points.keys():
            self.add_module(k, self.end_points[k])
        
    def forward(self, x):
        for end_point in self.VALID_ENDPOINTS:
            if end_point in self.end_points:
                x = self._modules[end_point](x) # use _modules to work with dataparallel

        x = self.logits(self.dropout(self.avg_pool(x)))
        if self._spatial_squeeze:
            logits = x.squeeze(3).squeeze(3)
        # logits is batch X time X classes, which is what we want to work with
        return logits
        

    def extract_features(self, x):
        for end_point in self.VALID_ENDPOINTS:
            if end_point in self.end_points:
                x = self._modules[end_point](x)
        return self.avg_pool(x)


================================================
FILE: train_i3d.py
================================================
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"   
#os.environ["CUDA_VISIBLE_DEVICES"]='0,1,2,3'
import sys
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('-mode', type=str, help='rgb or flow')
parser.add_argument('-save_model', type=str)
parser.add_argument('-root', type=str)

args = parser.parse_args()


import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable

import torchvision
from torchvision import datasets, transforms
import videotransforms


import numpy as np

from pytorch_i3d import InceptionI3d

from charades_dataset import Charades as Dataset


def run(init_lr=0.1, max_steps=64e3, mode='rgb', root='/ssd/Charades_v1_rgb', train_split='charades/charades.json', batch_size=8*5, save_model=''):
    # setup dataset
    train_transforms = transforms.Compose([videotransforms.RandomCrop(224),
                                           videotransforms.RandomHorizontalFlip(),
    ])
    test_transforms = transforms.Compose([videotransforms.CenterCrop(224)])

    dataset = Dataset(train_split, 'training', root, mode, train_transforms)
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=36, pin_memory=True)

    val_dataset = Dataset(train_split, 'testing', root, mode, test_transforms)
    val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=36, pin_memory=True)    

    dataloaders = {'train': dataloader, 'val': val_dataloader}
    datasets = {'train': dataset, 'val': val_dataset}

    
    # setup the model
    if mode == 'flow':
        i3d = InceptionI3d(400, in_channels=2)
        i3d.load_state_dict(torch.load('models/flow_imagenet.pt'))
    else:
        i3d = InceptionI3d(400, in_channels=3)
        i3d.load_state_dict(torch.load('models/rgb_imagenet.pt'))
    i3d.replace_logits(157)
    #i3d.load_state_dict(torch.load('/ssd/models/000920.pt'))
    i3d.cuda()
    i3d = nn.DataParallel(i3d)

    lr = init_lr
    optimizer = optim.SGD(i3d.parameters(), lr=lr, momentum=0.9, weight_decay=0.0000001)
    lr_sched = optim.lr_scheduler.MultiStepLR(optimizer, [300, 1000])


    num_steps_per_update = 4 # accum gradient
    steps = 0
    # train it
    while steps < max_steps:#for epoch in range(num_epochs):
        print 'Step {}/{}'.format(steps, max_steps)
        print '-' * 10

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                i3d.train(True)
            else:
                i3d.train(False)  # Set model to evaluate mode
                
            tot_loss = 0.0
            tot_loc_loss = 0.0
            tot_cls_loss = 0.0
            num_iter = 0
            optimizer.zero_grad()
            
            # Iterate over data.
            for data in dataloaders[phase]:
                num_iter += 1
                # get the inputs
                inputs, labels = data

                # wrap them in Variable
                inputs = Variable(inputs.cuda())
                t = inputs.size(2)
                labels = Variable(labels.cuda())

                per_frame_logits = i3d(inputs)
                # upsample to input size
                per_frame_logits = F.upsample(per_frame_logits, t, mode='linear')

                # compute localization loss
                loc_loss = F.binary_cross_entropy_with_logits(per_frame_logits, labels)
                tot_loc_loss += loc_loss.data[0]

                # compute classification loss (with max-pooling along time B x C x T)
                cls_loss = F.binary_cross_entropy_with_logits(torch.max(per_frame_logits, dim=2)[0], torch.max(labels, dim=2)[0])
                tot_cls_loss += cls_loss.data[0]

                loss = (0.5*loc_loss + 0.5*cls_loss)/num_steps_per_update
                tot_loss += loss.data[0]
                loss.backward()

                if num_iter == num_steps_per_update and phase == 'train':
                    steps += 1
                    num_iter = 0
                    optimizer.step()
                    optimizer.zero_grad()
                    lr_sched.step()
                    if steps % 10 == 0:
                        print '{} Loc Loss: {:.4f} Cls Loss: {:.4f} Tot Loss: {:.4f}'.format(phase, tot_loc_loss/(10*num_steps_per_update), tot_cls_loss/(10*num_steps_per_update), tot_loss/10)
                        # save model
                        torch.save(i3d.module.state_dict(), save_model+str(steps).zfill(6)+'.pt')
                        tot_loss = tot_loc_loss = tot_cls_loss = 0.
            if phase == 'val':
                print '{} Loc Loss: {:.4f} Cls Loss: {:.4f} Tot Loss: {:.4f}'.format(phase, tot_loc_loss/num_iter, tot_cls_loss/num_iter, (tot_loss*num_steps_per_update)/num_iter) 
    


if __name__ == '__main__':
    # need to add argparse
    run(mode=args.mode, root=args.root, save_model=args.save_model)


================================================
FILE: videotransforms.py
================================================
import numpy as np
import numbers
import random

class RandomCrop(object):
    """Crop the given video sequences (t x h x w) at a random location.
    Args:
        size (sequence or int): Desired output size of the crop. If size is an
            int instead of sequence like (h, w), a square crop (size, size) is
            made.
    """

    def __init__(self, size):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size

    @staticmethod
    def get_params(img, output_size):
        """Get parameters for ``crop`` for a random crop.
        Args:
            img (PIL Image): Image to be cropped.
            output_size (tuple): Expected output size of the crop.
        Returns:
            tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
        """
        t, h, w, c = img.shape
        th, tw = output_size
        if w == tw and h == th:
            return 0, 0, h, w

        i = random.randint(0, h - th) if h!=th else 0
        j = random.randint(0, w - tw) if w!=tw else 0
        return i, j, th, tw

    def __call__(self, imgs):
        
        i, j, h, w = self.get_params(imgs, self.size)

        imgs = imgs[:, i:i+h, j:j+w, :]
        return imgs

    def __repr__(self):
        return self.__class__.__name__ + '(size={0})'.format(self.size)

class CenterCrop(object):
    """Crops the given seq Images at the center.
    Args:
        size (sequence or int): Desired output size of the crop. If size is an
            int instead of sequence like (h, w), a square crop (size, size) is
            made.
    """

    def __init__(self, size):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size

    def __call__(self, imgs):
        """
        Args:
            img (PIL Image): Image to be cropped.
        Returns:
            PIL Image: Cropped image.
        """
        t, h, w, c = imgs.shape
        th, tw = self.size
        i = int(np.round((h - th) / 2.))
        j = int(np.round((w - tw) / 2.))

        return imgs[:, i:i+th, j:j+tw, :]


    def __repr__(self):
        return self.__class__.__name__ + '(size={0})'.format(self.size)


class RandomHorizontalFlip(object):
    """Horizontally flip the given seq Images randomly with a given probability.
    Args:
        p (float): probability of the image being flipped. Default value is 0.5
    """

    def __init__(self, p=0.5):
        self.p = p

    def __call__(self, imgs):
        """
        Args:
            img (seq Images): seq Images to be flipped.
        Returns:
            seq Images: Randomly flipped seq images.
        """
        if random.random() < self.p:
            # t x h x w
            return np.flip(imgs, axis=2).copy()
        return imgs

    def __repr__(self):
        return self.__class__.__name__ + '(p={})'.format(self.p)