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
================================================
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
================================================
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
--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
## Important: Use PyTorch 0.3
## Input Folder Structure
InputFolder
├── video1
│ ├── flow_x.zip
│ ├── flow_y.zip
│ └── img.zip
└── video2
├── flow_x.zip
├── flow_y.zip
└── img.zip
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)