Repository: shaohua0116/Group-Normalization-Tensorflow
Branch: master
Commit: 551511c939fc
Files: 19
Total size: 82.5 KB
Directory structure:
gitextract_w213pial/
├── .gitignore
├── LICENSE
├── README.md
├── __init__.py
├── datasets/
│ ├── __init__.py
│ ├── cifar10.py
│ ├── fashion_mnist.py
│ ├── imagenet/
│ │ ├── __init__.py
│ │ └── map.py
│ ├── imagenet.py
│ ├── mnist.py
│ ├── svhn.py
│ └── tiny_imagenet.py
├── download.py
├── input_ops.py
├── model.py
├── ops.py
├── trainer.py
└── util.py
================================================
FILE CONTENTS
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================================================
FILE: .gitignore
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train_dir/
datasets/mnist
datasets/fashion_mnist
datasets/svhn
datasets/cifar10
.ropeproject/
*.py[cod]
*.sw[op]
*.hy
*.txt
*.gz
================================================
FILE: LICENSE
================================================
The MIT License (MIT)
Copyright (c) 2018 Shao-Hua Sun
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
================================================
FILE: README.md
================================================
# Group Normalization
As part of the implementation series of [Joseph Lim's group at USC](http://csail.mit.edu/~lim), our motivation is to accelerate (or sometimes delay) research in the AI community by promoting open-source projects. To this end, we implement state-of-the-art research papers, and publicly share them with concise reports. Please visit our [group github site](https://github.com/gitlimlab) for other projects.
This project is implemented by [Shao-Hua Sun](http://shaohua0116.github.io) and the codes have been reviewed by [Te-Lin Wu](https://github.com/telin0411) before being published.
## Descriptions
This project includes a Tensorflow implementation of Group Normalizations proposed in the paper [Group Normalization](https://arxiv.org/abs/1803.08494) by Wu et al. [Batch Normalization](https://arxiv.org/abs/1502.03167) (BN) has been widely employed in the trainings of deep neural networks to alleviate the internal covariate shift [1].Specifically, BN aims to transform the inputs of each layer in such a way that they have a mean output activation of zero and standard deviation of one. While BN demonstrates it effectiveness in a variety of fields including computer vision, natural language processing, speech processing, robotics, etc., BN's performance substantially decrease when the training batch size become smaller, which limits the gain of utilizing BN in a task requiring small batches constrained by memory consumption.
Motivated by this phenomenon, the Group Normalization (GN) technique is proposed. Instead of normalizing along the batch dimension, GN divides the channels into groups and computes within each group the mean and variance. Therefore, GN’s computation is independent of batch sizes, and so does its accuracy. The experiment section of the paper demonstrates the effectiveness of GN in a wide range of visual tasks, which include image classification (ImageNet), object detection and segmentation (COCO), and video classification (Kinect). This repository is simply a toy repository for those who want to quickly test GN and compare it against BN.
<img src="figure/gn.png" height="250"/>
The illustration from the original GN paper. Each cube represent a 4D tensor of feature maps. Note that the spatial dimension are combined as a single dimension for visualization. N denotes the batch axis, C denotes the channel axis, and H, W denote the spatial axes. The values in blue are normalized by the same mean and variance, computed by aggregating the values of these pixels.
Based on the implementation of this repository, GN is around 20% slower than BN on datasets such as CIFAR-10 and SVHN, which is probably because of the extra reshape and transpose operations. However, when the network goes deeper and the number of channels increase, GN gets even slower due to a larger group size. The model uses GN is around 4 times slower than the one uses BN when being trained ImageNet. This is not reported in the original GN paper.
\*This code is still being developed and subject to change.
## Prerequisites
- Python 2.7
- [Tensorflow 1.3.0](https://github.com/tensorflow/tensorflow/tree/r1.0)
- [SciPy](http://www.scipy.org/install.html)
- [NumPy](http://www.numpy.org/)
## Usage
### Datasets
Train models on MNIST, Fashion MNIST, SVHN, CIFAR-10 datasets:
- Download datasets with:
```bash
$ python download.py --dataset MNIST Fashion SVHN CIFAR10
```
Train models on [Tiny ImageNet](https://tiny-imagenet.herokuapp.com/)
- Download the dataset from the [webpage](https://tiny-imagenet.herokuapp.com/).
- Move the downloaded file (named ) to `datasets/tiny_imagenet` and unzip it.
Train models on [ImageNet](http://image-net.org/download-images)
- The ImageNet dataset is located in the Downloads section of the [website](http://image-net.org/download-images). Please specify the path to the downloaded dataset by changing the variable `__IMAGENET_IMG_PATH__` in `datasets/ImageNet.py`. Also, please provide a list of file names for trainings in the directory `__IMAGENET_LIST_PATH__` with the file name `train_list.txt`. By default, the `train_list.txt` includes all the training images in ImageNet dataset.
### Train models with downloaded datasets:
Specify the type of normalization you want to use by `--norm_type batch` or `--norm_type group`
and specify the batch size with `--batch_size BATCH_SIZE`.
```bash
$ python trainer.py --dataset MNIST --learning_rate 1e-3
$ python trainer.py --dataset Fashion --prefix test
$ python trainer.py --dataset SVHN --batch_size 128
$ python trainer.py --dataset CIFAR10
```
### Train and test your own datasets:
* Create a directory
```bash
$ mkdir datasets/YOUR_DATASET
```
* Store your data as an h5py file datasets/YOUR_DATASET/data.hy and each data point contains
* 'image': has shape [h, w, c], where c is the number of channels (grayscale images: 1, color images: 3)
* 'label': represented as an one-hot vector
* Maintain a list datasets/YOUR_DATASET/id.txt listing ids of all data points
* Modify trainer.py including args, data_info, etc.
* Finally, train and test models:
```bash
$ python trainer.py --dataset YOUR_DATASET
$ python evaler.py --dataset YOUR_DATASET
```
## Results
### CIFAR-10
| Color | Batch Size |
| :------- | ---------- |
| Orange | 1 |
| Blue | 2 |
| Sky blue | 4 |
| Red | 8 |
| Green | 16 |
| Pink | 32 |
- Loss
<img src="figure/cifar_group_loss.png" height="250"/>
- Accuracy
<img src="figure/cifar_group_acc.png" height="250"/>
### SVHN
| Color | Batch Size |
| :------- | ---------- |
| Pink | 1 |
| Blue | 2 |
| Sky blue | 4 |
| Green | 8 |
| Red | 16 |
| Orange | 32 |
- Loss
<img src="figure/svhn_group_loss.png" height="250"/>
- Accuracy
<img src="figure/svhn_group_acc.png" height="250"/>
### ImageNet
The trainings are ongoing...
| Color | Norm Type |
| :----- | ------------------- |
| Orange | Group Normalization |
| Blue | Batch Normalization |
<img src="figure/imagenet_ongoing.png" height="250"/>
### Conclusion
The Group Normalization divides the channels into groups and computes within each group the mean and variance, and therefore its performance independent of training batch sizes, which is verified with this implementation. However, the performance of Batch Normalization does not vary a lot with different batch sizes on smaller image datasets including CIFAR-10, SVHN, etc. The ImageNet experiments are ongoing and the results will be updated later.
## Related works
* [Group Normalization](https://arxiv.org/abs/1803.08494)
* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
* [Layer Normalization](https://arxiv.org/abs/1607.06450)
* [Instance Normalization: The Missing Ingredient for Fast Stylization](https://arxiv.org/abs/1607.08022)
## Author
Shao-Hua Sun / [@shaohua0116](https://shaohua0116.github.io/) @ [Joseph Lim's research lab](https://github.com/gitlimlab) @ USC
================================================
FILE: __init__.py
================================================
================================================
FILE: datasets/__init__.py
================================================
================================================
FILE: datasets/cifar10.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import numpy as np
import h5py
from util import log
__PATH__ = './datasets/cifar10'
rs = np.random.RandomState(123)
class Dataset(object):
def __init__(self, ids, name='default',
max_examples=None, is_train=True):
self._ids = list(ids)
self.name = name
self.is_train = is_train
if max_examples is not None:
self._ids = self._ids[:max_examples]
filename = 'data.hdf5'
file = os.path.join(__PATH__, filename)
log.info("Reading %s ...", file)
try:
self.data = h5py.File(file, 'r')
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
log.info("Reading Done: %s", file)
def get_data(self, id):
# preprocessing and data augmentation
m = self.data[id]['image'].value/255.
l = self.data[id]['label'].value.astype(np.float32)
return m, l
@property
def ids(self):
return self._ids
def __len__(self):
return len(self.ids)
def __repr__(self):
return 'Dataset (%s, %d examples)' % (
self.name,
len(self)
)
def create_default_splits(is_train=True):
id_train, id_test = all_ids(50000)
dataset_train = Dataset(id_train, name='train', is_train=False)
dataset_test = Dataset(id_test, name='test', is_train=False)
return dataset_train, dataset_test
def all_ids(num_trains):
id_filename = 'id.txt'
id_txt = os.path.join(__PATH__, id_filename)
try:
with open(id_txt, 'r') as fp:
_ids = [s.strip() for s in fp.readlines() if s]
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
id_train, id_test = _ids[:num_trains], _ids[num_trains:]
rs.shuffle(id_train)
rs.shuffle(id_test)
return id_train, id_test
================================================
FILE: datasets/fashion_mnist.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import numpy as np
import h5py
from util import log
__PATH__ = './datasets/fashion_mnist'
rs = np.random.RandomState(123)
class Dataset(object):
def __init__(self, ids, name='default',
max_examples=None, is_train=True):
self._ids = list(ids)
self.name = name
self.is_train = is_train
if max_examples is not None:
self._ids = self._ids[:max_examples]
filename = 'data.hdf5'
file = os.path.join(__PATH__, filename)
log.info("Reading %s ...", file)
try:
self.data = h5py.File(file, 'r')
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
log.info("Reading Done: %s", file)
def get_data(self, id):
# preprocessing and data augmentation
m = self.data[id]['image'].value/255.
l = self.data[id]['label'].value.astype(np.float32)
return m, l
@property
def ids(self):
return self._ids
def __len__(self):
return len(self.ids)
def __repr__(self):
return 'Dataset (%s, %d examples)' % (
self.name,
len(self)
)
def create_default_splits(is_train=True):
id_train, id_test = all_ids(60000)
dataset_train = Dataset(id_train, name='train', is_train=False)
dataset_test = Dataset(id_test, name='test', is_train=False)
return dataset_train, dataset_test
def all_ids(num_trains):
id_filename = 'id.txt'
id_txt = os.path.join(__PATH__, id_filename)
try:
with open(id_txt, 'r') as fp:
_ids = [s.strip() for s in fp.readlines() if s]
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
id_train, id_test = _ids[:num_trains], _ids[num_trains:]
rs.shuffle(id_train)
rs.shuffle(id_test)
return id_train, id_test
================================================
FILE: datasets/imagenet/__init__.py
================================================
================================================
FILE: datasets/imagenet/map.py
================================================
class2num = {
'n02119789': 1, # kit_fox
'n02100735': 2, # English_setter
'n02110185': 3, # Siberian_husky
'n02096294': 4, # Australian_terrier
'n02102040': 5, # English_springer
'n02066245': 6, # grey_whale
'n02509815': 7, # lesser_panda
'n02124075': 8, # Egyptian_cat
'n02417914': 9, # ibex
'n02123394': 10, # Persian_cat
'n02125311': 11, # cougar
'n02423022': 12, # gazelle
'n02346627': 13, # porcupine
'n02077923': 14, # sea_lion
'n02110063': 15, # malamute
'n02447366': 16, # badger
'n02109047': 17, # Great_Dane
'n02089867': 18, # Walker_hound
'n02102177': 19, # Welsh_springer_spaniel
'n02091134': 20, # whippet
'n02092002': 21, # Scottish_deerhound
'n02071294': 22, # killer_whale
'n02442845': 23, # mink
'n02504458': 24, # African_elephant
'n02092339': 25, # Weimaraner
'n02098105': 26, # soft-coated_wheaten_terrier
'n02096437': 27, # Dandie_Dinmont
'n02114712': 28, # red_wolf
'n02105641': 29, # Old_English_sheepdog
'n02128925': 30, # jaguar
'n02091635': 31, # otterhound
'n02088466': 32, # bloodhound
'n02096051': 33, # Airedale
'n02117135': 34, # hyena
'n02138441': 35, # meerkat
'n02097130': 36, # giant_schnauzer
'n02493509': 37, # titi
'n02457408': 38, # three-toed_sloth
'n02389026': 39, # sorrel
'n02443484': 40, # black-footed_ferret
'n02110341': 41, # dalmatian
'n02089078': 42, # black-and-tan_coonhound
'n02086910': 43, # papillon
'n02445715': 44, # skunk
'n02093256': 45, # Staffordshire_bullterrier
'n02113978': 46, # Mexican_hairless
'n02106382': 47, # Bouvier_des_Flandres
'n02441942': 48, # weasel
'n02113712': 49, # miniature_poodle
'n02113186': 50, # Cardigan
'n02105162': 51, # malinois
'n02415577': 52, # bighorn
'n02356798': 53, # fox_squirrel
'n02488702': 54, # colobus
'n02123159': 55, # tiger_cat
'n02098413': 56, # Lhasa
'n02422699': 57, # impala
'n02114855': 58, # coyote
'n02094433': 59, # Yorkshire_terrier
'n02111277': 60, # Newfoundland
'n02132136': 61, # brown_bear
'n02119022': 62, # red_fox
'n02091467': 63, # Norwegian_elkhound
'n02106550': 64, # Rottweiler
'n02422106': 65, # hartebeest
'n02091831': 66, # Saluki
'n02120505': 67, # grey_fox
'n02104365': 68, # schipperke
'n02086079': 69, # Pekinese
'n02112706': 70, # Brabancon_griffon
'n02098286': 71, # West_Highland_white_terrier
'n02095889': 72, # Sealyham_terrier
'n02484975': 73, # guenon
'n02137549': 74, # mongoose
'n02500267': 75, # indri
'n02129604': 76, # tiger
'n02090721': 77, # Irish_wolfhound
'n02396427': 78, # wild_boar
'n02108000': 79, # EntleBucher
'n02391049': 80, # zebra
'n02412080': 81, # ram
'n02108915': 82, # French_bulldog
'n02480495': 83, # orangutan
'n02110806': 84, # basenji
'n02128385': 85, # leopard
'n02107683': 86, # Bernese_mountain_dog
'n02085936': 87, # Maltese_dog
'n02094114': 88, # Norfolk_terrier
'n02087046': 89, # toy_terrier
'n02100583': 90, # vizsla
'n02096177': 91, # cairn
'n02494079': 92, # squirrel_monkey
'n02105056': 93, # groenendael
'n02101556': 94, # clumber
'n02123597': 95, # Siamese_cat
'n02481823': 96, # chimpanzee
'n02105505': 97, # komondor
'n02088094': 98, # Afghan_hound
'n02085782': 99, # Japanese_spaniel
'n02489166': 100, # proboscis_monkey
'n02364673': 101, # guinea_pig
'n02114548': 102, # white_wolf
'n02134084': 103, # ice_bear
'n02480855': 104, # gorilla
'n02090622': 105, # borzoi
'n02113624': 106, # toy_poodle
'n02093859': 107, # Kerry_blue_terrier
'n02403003': 108, # ox
'n02097298': 109, # Scotch_terrier
'n02108551': 110, # Tibetan_mastiff
'n02493793': 111, # spider_monkey
'n02107142': 112, # Doberman
'n02096585': 113, # Boston_bull
'n02107574': 114, # Greater_Swiss_Mountain_dog
'n02107908': 115, # Appenzeller
'n02086240': 116, # Shih-Tzu
'n02102973': 117, # Irish_water_spaniel
'n02112018': 118, # Pomeranian
'n02093647': 119, # Bedlington_terrier
'n02397096': 120, # warthog
'n02437312': 121, # Arabian_camel
'n02483708': 122, # siamang
'n02097047': 123, # miniature_schnauzer
'n02106030': 124, # collie
'n02099601': 125, # golden_retriever
'n02093991': 126, # Irish_terrier
'n02110627': 127, # affenpinscher
'n02106166': 128, # Border_collie
'n02326432': 129, # hare
'n02108089': 130, # boxer
'n02097658': 131, # silky_terrier
'n02088364': 132, # beagle
'n02111129': 133, # Leonberg
'n02100236': 134, # German_short-haired_pointer
'n02486261': 135, # patas
'n02115913': 136, # dhole
'n02486410': 137, # baboon
'n02487347': 138, # macaque
'n02099849': 139, # Chesapeake_Bay_retriever
'n02108422': 140, # bull_mastiff
'n02104029': 141, # kuvasz
'n02492035': 142, # capuchin
'n02110958': 143, # pug
'n02099429': 144, # curly-coated_retriever
'n02094258': 145, # Norwich_terrier
'n02099267': 146, # flat-coated_retriever
'n02395406': 147, # hog
'n02112350': 148, # keeshond
'n02109961': 149, # Eskimo_dog
'n02101388': 150, # Brittany_spaniel
'n02113799': 151, # standard_poodle
'n02095570': 152, # Lakeland_terrier
'n02128757': 153, # snow_leopard
'n02101006': 154, # Gordon_setter
'n02115641': 155, # dingo
'n02097209': 156, # standard_schnauzer
'n02342885': 157, # hamster
'n02097474': 158, # Tibetan_terrier
'n02120079': 159, # Arctic_fox
'n02095314': 160, # wire-haired_fox_terrier
'n02088238': 161, # basset
'n02408429': 162, # water_buffalo
'n02133161': 163, # American_black_bear
'n02328150': 164, # Angora
'n02410509': 165, # bison
'n02492660': 166, # howler_monkey
'n02398521': 167, # hippopotamus
'n02112137': 168, # chow
'n02510455': 169, # giant_panda
'n02093428': 170, # American_Staffordshire_terrier
'n02105855': 171, # Shetland_sheepdog
'n02111500': 172, # Great_Pyrenees
'n02085620': 173, # Chihuahua
'n02123045': 174, # tabby
'n02490219': 175, # marmoset
'n02099712': 176, # Labrador_retriever
'n02109525': 177, # Saint_Bernard
'n02454379': 178, # armadillo
'n02111889': 179, # Samoyed
'n02088632': 180, # bluetick
'n02090379': 181, # redbone
'n02443114': 182, # polecat
'n02361337': 183, # marmot
'n02105412': 184, # kelpie
'n02483362': 185, # gibbon
'n02437616': 186, # llama
'n02107312': 187, # miniature_pinscher
'n02325366': 188, # wood_rabbit
'n02091032': 189, # Italian_greyhound
'n02129165': 190, # lion
'n02102318': 191, # cocker_spaniel
'n02100877': 192, # Irish_setter
'n02074367': 193, # dugong
'n02504013': 194, # Indian_elephant
'n02363005': 195, # beaver
'n02102480': 196, # Sussex_spaniel
'n02113023': 197, # Pembroke
'n02086646': 198, # Blenheim_spaniel
'n02497673': 199, # Madagascar_cat
'n02087394': 200, # Rhodesian_ridgeback
'n02127052': 201, # lynx
'n02116738': 202, # African_hunting_dog
'n02488291': 203, # langur
'n02091244': 204, # Ibizan_hound
'n02114367': 205, # timber_wolf
'n02130308': 206, # cheetah
'n02089973': 207, # English_foxhound
'n02105251': 208, # briard
'n02134418': 209, # sloth_bear
'n02093754': 210, # Border_terrier
'n02106662': 211, # German_shepherd
'n02444819': 212, # otter
'n01882714': 213, # koala
'n01871265': 214, # tusker
'n01872401': 215, # echidna
'n01877812': 216, # wallaby
'n01873310': 217, # platypus
'n01883070': 218, # wombat
'n04086273': 219, # revolver
'n04507155': 220, # umbrella
'n04147183': 221, # schooner
'n04254680': 222, # soccer_ball
'n02672831': 223, # accordion
'n02219486': 224, # ant
'n02317335': 225, # starfish
'n01968897': 226, # chambered_nautilus
'n03452741': 227, # grand_piano
'n03642806': 228, # laptop
'n07745940': 229, # strawberry
'n02690373': 230, # airliner
'n04552348': 231, # warplane
'n02692877': 232, # airship
'n02782093': 233, # balloon
'n04266014': 234, # space_shuttle
'n03344393': 235, # fireboat
'n03447447': 236, # gondola
'n04273569': 237, # speedboat
'n03662601': 238, # lifeboat
'n02951358': 239, # canoe
'n04612504': 240, # yawl
'n02981792': 241, # catamaran
'n04483307': 242, # trimaran
'n03095699': 243, # container_ship
'n03673027': 244, # liner
'n03947888': 245, # pirate
'n02687172': 246, # aircraft_carrier
'n04347754': 247, # submarine
'n04606251': 248, # wreck
'n03478589': 249, # half_track
'n04389033': 250, # tank
'n03773504': 251, # missile
'n02860847': 252, # bobsled
'n03218198': 253, # dogsled
'n02835271': 254, # bicycle-built-for-two
'n03792782': 255, # mountain_bike
'n03393912': 256, # freight_car
'n03895866': 257, # passenger_car
'n02797295': 258, # barrow
'n04204347': 259, # shopping_cart
'n03791053': 260, # motor_scooter
'n03384352': 261, # forklift
'n03272562': 262, # electric_locomotive
'n04310018': 263, # steam_locomotive
'n02704792': 264, # amphibian
'n02701002': 265, # ambulance
'n02814533': 266, # beach_wagon
'n02930766': 267, # cab
'n03100240': 268, # convertible
'n03594945': 269, # jeep
'n03670208': 270, # limousine
'n03770679': 271, # minivan
'n03777568': 272, # Model_T
'n04037443': 273, # racer
'n04285008': 274, # sports_car
'n03444034': 275, # go-kart
'n03445924': 276, # golfcart
'n03785016': 277, # moped
'n04252225': 278, # snowplow
'n03345487': 279, # fire_engine
'n03417042': 280, # garbage_truck
'n03930630': 281, # pickup
'n04461696': 282, # tow_truck
'n04467665': 283, # trailer_truck
'n03796401': 284, # moving_van
'n03977966': 285, # police_van
'n04065272': 286, # recreational_vehicle
'n04335435': 287, # streetcar
'n04252077': 288, # snowmobile
'n04465501': 289, # tractor
'n03776460': 290, # mobile_home
'n04482393': 291, # tricycle
'n04509417': 292, # unicycle
'n03538406': 293, # horse_cart
'n03599486': 294, # jinrikisha
'n03868242': 295, # oxcart
'n02804414': 296, # bassinet
'n03125729': 297, # cradle
'n03131574': 298, # crib
'n03388549': 299, # four-poster
'n02870880': 300, # bookcase
'n03018349': 301, # china_cabinet
'n03742115': 302, # medicine_chest
'n03016953': 303, # chiffonier
'n04380533': 304, # table_lamp
'n03337140': 305, # file
'n03891251': 306, # park_bench
'n02791124': 307, # barber_chair
'n04429376': 308, # throne
'n03376595': 309, # folding_chair
'n04099969': 310, # rocking_chair
'n04344873': 311, # studio_couch
'n04447861': 312, # toilet_seat
'n03179701': 313, # desk
'n03982430': 314, # pool_table
'n03201208': 315, # dining_table
'n03290653': 316, # entertainment_center
'n04550184': 317, # wardrobe
'n07742313': 318, # Granny_Smith
'n07747607': 319, # orange
'n07749582': 320, # lemon
'n07753113': 321, # fig
'n07753275': 322, # pineapple
'n07753592': 323, # banana
'n07754684': 324, # jackfruit
'n07760859': 325, # custard_apple
'n07768694': 326, # pomegranate
'n12267677': 327, # acorn
'n12620546': 328, # hip
'n13133613': 329, # ear
'n11879895': 330, # rapeseed
'n12144580': 331, # corn
'n12768682': 332, # buckeye
'n03854065': 333, # organ
'n04515003': 334, # upright
'n03017168': 335, # chime
'n03249569': 336, # drum
'n03447721': 337, # gong
'n03720891': 338, # maraca
'n03721384': 339, # marimba
'n04311174': 340, # steel_drum
'n02787622': 341, # banjo
'n02992211': 342, # cello
'n04536866': 343, # violin
'n03495258': 344, # harp
'n02676566': 345, # acoustic_guitar
'n03272010': 346, # electric_guitar
'n03110669': 347, # cornet
'n03394916': 348, # French_horn
'n04487394': 349, # trombone
'n03494278': 350, # harmonica
'n03840681': 351, # ocarina
'n03884397': 352, # panpipe
'n02804610': 353, # bassoon
'n03838899': 354, # oboe
'n04141076': 355, # sax
'n03372029': 356, # flute
'n11939491': 357, # daisy
'n12057211': 358, # yellow_lady's_slipper
'n09246464': 359, # cliff
'n09468604': 360, # valley
'n09193705': 361, # alp
'n09472597': 362, # volcano
'n09399592': 363, # promontory
'n09421951': 364, # sandbar
'n09256479': 365, # coral_reef
'n09332890': 366, # lakeside
'n09428293': 367, # seashore
'n09288635': 368, # geyser
'n03498962': 369, # hatchet
'n03041632': 370, # cleaver
'n03658185': 371, # letter_opener
'n03954731': 372, # plane
'n03995372': 373, # power_drill
'n03649909': 374, # lawn_mower
'n03481172': 375, # hammer
'n03109150': 376, # corkscrew
'n02951585': 377, # can_opener
'n03970156': 378, # plunger
'n04154565': 379, # screwdriver
'n04208210': 380, # shovel
'n03967562': 381, # plow
'n03000684': 382, # chain_saw
'n01514668': 383, # cock
'n01514859': 384, # hen
'n01518878': 385, # ostrich
'n01530575': 386, # brambling
'n01531178': 387, # goldfinch
'n01532829': 388, # house_finch
'n01534433': 389, # junco
'n01537544': 390, # indigo_bunting
'n01558993': 391, # robin
'n01560419': 392, # bulbul
'n01580077': 393, # jay
'n01582220': 394, # magpie
'n01592084': 395, # chickadee
'n01601694': 396, # water_ouzel
'n01608432': 397, # kite
'n01614925': 398, # bald_eagle
'n01616318': 399, # vulture
'n01622779': 400, # great_grey_owl
'n01795545': 401, # black_grouse
'n01796340': 402, # ptarmigan
'n01797886': 403, # ruffed_grouse
'n01798484': 404, # prairie_chicken
'n01806143': 405, # peacock
'n01806567': 406, # quail
'n01807496': 407, # partridge
'n01817953': 408, # African_grey
'n01818515': 409, # macaw
'n01819313': 410, # sulphur-crested_cockatoo
'n01820546': 411, # lorikeet
'n01824575': 412, # coucal
'n01828970': 413, # bee_eater
'n01829413': 414, # hornbill
'n01833805': 415, # hummingbird
'n01843065': 416, # jacamar
'n01843383': 417, # toucan
'n01847000': 418, # drake
'n01855032': 419, # red-breasted_merganser
'n01855672': 420, # goose
'n01860187': 421, # black_swan
'n02002556': 422, # white_stork
'n02002724': 423, # black_stork
'n02006656': 424, # spoonbill
'n02007558': 425, # flamingo
'n02009912': 426, # American_egret
'n02009229': 427, # little_blue_heron
'n02011460': 428, # bittern
'n02012849': 429, # crane
'n02013706': 430, # limpkin
'n02018207': 431, # American_coot
'n02018795': 432, # bustard
'n02025239': 433, # ruddy_turnstone
'n02027492': 434, # red-backed_sandpiper
'n02028035': 435, # redshank
'n02033041': 436, # dowitcher
'n02037110': 437, # oystercatcher
'n02017213': 438, # European_gallinule
'n02051845': 439, # pelican
'n02056570': 440, # king_penguin
'n02058221': 441, # albatross
'n01484850': 442, # great_white_shark
'n01491361': 443, # tiger_shark
'n01494475': 444, # hammerhead
'n01496331': 445, # electric_ray
'n01498041': 446, # stingray
'n02514041': 447, # barracouta
'n02536864': 448, # coho
'n01440764': 449, # tench
'n01443537': 450, # goldfish
'n02526121': 451, # eel
'n02606052': 452, # rock_beauty
'n02607072': 453, # anemone_fish
'n02643566': 454, # lionfish
'n02655020': 455, # puffer
'n02640242': 456, # sturgeon
'n02641379': 457, # gar
'n01664065': 458, # loggerhead
'n01665541': 459, # leatherback_turtle
'n01667114': 460, # mud_turtle
'n01667778': 461, # terrapin
'n01669191': 462, # box_turtle
'n01675722': 463, # banded_gecko
'n01677366': 464, # common_iguana
'n01682714': 465, # American_chameleon
'n01685808': 466, # whiptail
'n01687978': 467, # agama
'n01688243': 468, # frilled_lizard
'n01689811': 469, # alligator_lizard
'n01692333': 470, # Gila_monster
'n01693334': 471, # green_lizard
'n01694178': 472, # African_chameleon
'n01695060': 473, # Komodo_dragon
'n01704323': 474, # triceratops
'n01697457': 475, # African_crocodile
'n01698640': 476, # American_alligator
'n01728572': 477, # thunder_snake
'n01728920': 478, # ringneck_snake
'n01729322': 479, # hognose_snake
'n01729977': 480, # green_snake
'n01734418': 481, # king_snake
'n01735189': 482, # garter_snake
'n01737021': 483, # water_snake
'n01739381': 484, # vine_snake
'n01740131': 485, # night_snake
'n01742172': 486, # boa_constrictor
'n01744401': 487, # rock_python
'n01748264': 488, # Indian_cobra
'n01749939': 489, # green_mamba
'n01751748': 490, # sea_snake
'n01753488': 491, # horned_viper
'n01755581': 492, # diamondback
'n01756291': 493, # sidewinder
'n01629819': 494, # European_fire_salamander
'n01630670': 495, # common_newt
'n01631663': 496, # eft
'n01632458': 497, # spotted_salamander
'n01632777': 498, # axolotl
'n01641577': 499, # bullfrog
'n01644373': 500, # tree_frog
'n01644900': 501, # tailed_frog
'n04579432': 502, # whistle
'n04592741': 503, # wing
'n03876231': 504, # paintbrush
'n03483316': 505, # hand_blower
'n03868863': 506, # oxygen_mask
'n04251144': 507, # snorkel
'n03691459': 508, # loudspeaker
'n03759954': 509, # microphone
'n04152593': 510, # screen
'n03793489': 511, # mouse
'n03271574': 512, # electric_fan
'n03843555': 513, # oil_filter
'n04332243': 514, # strainer
'n04265275': 515, # space_heater
'n04330267': 516, # stove
'n03467068': 517, # guillotine
'n02794156': 518, # barometer
'n04118776': 519, # rule
'n03841143': 520, # odometer
'n04141975': 521, # scale
'n02708093': 522, # analog_clock
'n03196217': 523, # digital_clock
'n04548280': 524, # wall_clock
'n03544143': 525, # hourglass
'n04355338': 526, # sundial
'n03891332': 527, # parking_meter
'n04328186': 528, # stopwatch
'n03197337': 529, # digital_watch
'n04317175': 530, # stethoscope
'n04376876': 531, # syringe
'n03706229': 532, # magnetic_compass
'n02841315': 533, # binoculars
'n04009552': 534, # projector
'n04356056': 535, # sunglasses
'n03692522': 536, # loupe
'n04044716': 537, # radio_telescope
'n02879718': 538, # bow
'n02950826': 539, # cannon
'n02749479': 540, # assault_rifle
'n04090263': 541, # rifle
'n04008634': 542, # projectile
'n03085013': 543, # computer_keyboard
'n04505470': 544, # typewriter_keyboard
'n03126707': 545, # crane
'n03666591': 546, # lighter
'n02666196': 547, # abacus
'n02977058': 548, # cash_machine
'n04238763': 549, # slide_rule
'n03180011': 550, # desktop_computer
'n03485407': 551, # hand-held_computer
'n03832673': 552, # notebook
'n06359193': 553, # web_site
'n03496892': 554, # harvester
'n04428191': 555, # thresher
'n04004767': 556, # printer
'n04243546': 557, # slot
'n04525305': 558, # vending_machine
'n04179913': 559, # sewing_machine
'n03602883': 560, # joystick
'n04372370': 561, # switch
'n03532672': 562, # hook
'n02974003': 563, # car_wheel
'n03874293': 564, # paddlewheel
'n03944341': 565, # pinwheel
'n03992509': 566, # potter's_wheel
'n03425413': 567, # gas_pump
'n02966193': 568, # carousel
'n04371774': 569, # swing
'n04067472': 570, # reel
'n04040759': 571, # radiator
'n04019541': 572, # puck
'n03492542': 573, # hard_disc
'n04355933': 574, # sunglass
'n03929660': 575, # pick
'n02965783': 576, # car_mirror
'n04258138': 577, # solar_dish
'n04074963': 578, # remote_control
'n03208938': 579, # disk_brake
'n02910353': 580, # buckle
'n03476684': 581, # hair_slide
'n03627232': 582, # knot
'n03075370': 583, # combination_lock
'n03874599': 584, # padlock
'n03804744': 585, # nail
'n04127249': 586, # safety_pin
'n04153751': 587, # screw
'n03803284': 588, # muzzle
'n04162706': 589, # seat_belt
'n04228054': 590, # ski
'n02948072': 591, # candle
'n03590841': 592, # jack-o'-lantern
'n04286575': 593, # spotlight
'n04456115': 594, # torch
'n03814639': 595, # neck_brace
'n03933933': 596, # pier
'n04485082': 597, # tripod
'n03733131': 598, # maypole
'n03794056': 599, # mousetrap
'n04275548': 600, # spider_web
'n01768244': 601, # trilobite
'n01770081': 602, # harvestman
'n01770393': 603, # scorpion
'n01773157': 604, # black_and_gold_garden_spider
'n01773549': 605, # barn_spider
'n01773797': 606, # garden_spider
'n01774384': 607, # black_widow
'n01774750': 608, # tarantula
'n01775062': 609, # wolf_spider
'n01776313': 610, # tick
'n01784675': 611, # centipede
'n01990800': 612, # isopod
'n01978287': 613, # Dungeness_crab
'n01978455': 614, # rock_crab
'n01980166': 615, # fiddler_crab
'n01981276': 616, # king_crab
'n01983481': 617, # American_lobster
'n01984695': 618, # spiny_lobster
'n01985128': 619, # crayfish
'n01986214': 620, # hermit_crab
'n02165105': 621, # tiger_beetle
'n02165456': 622, # ladybug
'n02167151': 623, # ground_beetle
'n02168699': 624, # long-horned_beetle
'n02169497': 625, # leaf_beetle
'n02172182': 626, # dung_beetle
'n02174001': 627, # rhinoceros_beetle
'n02177972': 628, # weevil
'n02190166': 629, # fly
'n02206856': 630, # bee
'n02226429': 631, # grasshopper
'n02229544': 632, # cricket
'n02231487': 633, # walking_stick
'n02233338': 634, # cockroach
'n02236044': 635, # mantis
'n02256656': 636, # cicada
'n02259212': 637, # leafhopper
'n02264363': 638, # lacewing
'n02268443': 639, # dragonfly
'n02268853': 640, # damselfly
'n02276258': 641, # admiral
'n02277742': 642, # ringlet
'n02279972': 643, # monarch
'n02280649': 644, # cabbage_butterfly
'n02281406': 645, # sulphur_butterfly
'n02281787': 646, # lycaenid
'n01910747': 647, # jellyfish
'n01914609': 648, # sea_anemone
'n01917289': 649, # brain_coral
'n01924916': 650, # flatworm
'n01930112': 651, # nematode
'n01943899': 652, # conch
'n01944390': 653, # snail
'n01945685': 654, # slug
'n01950731': 655, # sea_slug
'n01955084': 656, # chiton
'n02319095': 657, # sea_urchin
'n02321529': 658, # sea_cucumber
'n03584829': 659, # iron
'n03297495': 660, # espresso_maker
'n03761084': 661, # microwave
'n03259280': 662, # Dutch_oven
'n04111531': 663, # rotisserie
'n04442312': 664, # toaster
'n04542943': 665, # waffle_iron
'n04517823': 666, # vacuum
'n03207941': 667, # dishwasher
'n04070727': 668, # refrigerator
'n04554684': 669, # washer
'n03133878': 670, # Crock_Pot
'n03400231': 671, # frying_pan
'n04596742': 672, # wok
'n02939185': 673, # caldron
'n03063689': 674, # coffeepot
'n04398044': 675, # teapot
'n04270147': 676, # spatula
'n02699494': 677, # altar
'n04486054': 678, # triumphal_arch
'n03899768': 679, # patio
'n04311004': 680, # steel_arch_bridge
'n04366367': 681, # suspension_bridge
'n04532670': 682, # viaduct
'n02793495': 683, # barn
'n03457902': 684, # greenhouse
'n03877845': 685, # palace
'n03781244': 686, # monastery
'n03661043': 687, # library
'n02727426': 688, # apiary
'n02859443': 689, # boathouse
'n03028079': 690, # church
'n03788195': 691, # mosque
'n04346328': 692, # stupa
'n03956157': 693, # planetarium
'n04081281': 694, # restaurant
'n03032252': 695, # cinema
'n03529860': 696, # home_theater
'n03697007': 697, # lumbermill
'n03065424': 698, # coil
'n03837869': 699, # obelisk
'n04458633': 700, # totem_pole
'n02980441': 701, # castle
'n04005630': 702, # prison
'n03461385': 703, # grocery_store
'n02776631': 704, # bakery
'n02791270': 705, # barbershop
'n02871525': 706, # bookshop
'n02927161': 707, # butcher_shop
'n03089624': 708, # confectionery
'n04200800': 709, # shoe_shop
'n04443257': 710, # tobacco_shop
'n04462240': 711, # toyshop
'n03388043': 712, # fountain
'n03042490': 713, # cliff_dwelling
'n04613696': 714, # yurt
'n03216828': 715, # dock
'n02892201': 716, # brass
'n03743016': 717, # megalith
'n02788148': 718, # bannister
'n02894605': 719, # breakwater
'n03160309': 720, # dam
'n03000134': 721, # chainlink_fence
'n03930313': 722, # picket_fence
'n04604644': 723, # worm_fence
'n04326547': 724, # stone_wall
'n03459775': 725, # grille
'n04239074': 726, # sliding_door
'n04501370': 727, # turnstile
'n03792972': 728, # mountain_tent
'n04149813': 729, # scoreboard
'n03530642': 730, # honeycomb
'n03961711': 731, # plate_rack
'n03903868': 732, # pedestal
'n02814860': 733, # beacon
'n07711569': 734, # mashed_potato
'n07720875': 735, # bell_pepper
'n07714571': 736, # head_cabbage
'n07714990': 737, # broccoli
'n07715103': 738, # cauliflower
'n07716358': 739, # zucchini
'n07716906': 740, # spaghetti_squash
'n07717410': 741, # acorn_squash
'n07717556': 742, # butternut_squash
'n07718472': 743, # cucumber
'n07718747': 744, # artichoke
'n07730033': 745, # cardoon
'n07734744': 746, # mushroom
'n04209239': 747, # shower_curtain
'n03594734': 748, # jean
'n02971356': 749, # carton
'n03485794': 750, # handkerchief
'n04133789': 751, # sandal
'n02747177': 752, # ashcan
'n04125021': 753, # safe
'n07579787': 754, # plate
'n03814906': 755, # necklace
'n03134739': 756, # croquet_ball
'n03404251': 757, # fur_coat
'n04423845': 758, # thimble
'n03877472': 759, # pajama
'n04120489': 760, # running_shoe
'n03062245': 761, # cocktail_shaker
'n03014705': 762, # chest
'n03717622': 763, # manhole_cover
'n03777754': 764, # modem
'n04493381': 765, # tub
'n04476259': 766, # tray
'n02777292': 767, # balance_beam
'n07693725': 768, # bagel
'n03998194': 769, # prayer_rug
'n03617480': 770, # kimono
'n07590611': 771, # hot_pot
'n04579145': 772, # whiskey_jug
'n03623198': 773, # knee_pad
'n07248320': 774, # book_jacket
'n04277352': 775, # spindle
'n04229816': 776, # ski_mask
'n02823428': 777, # beer_bottle
'n03127747': 778, # crash_helmet
'n02877765': 779, # bottlecap
'n04435653': 780, # tile_roof
'n03724870': 781, # mask
'n03710637': 782, # maillot
'n03920288': 783, # Petri_dish
'n03379051': 784, # football_helmet
'n02807133': 785, # bathing_cap
'n04399382': 786, # teddy
'n03527444': 787, # holster
'n03983396': 788, # pop_bottle
'n03924679': 789, # photocopier
'n04532106': 790, # vestment
'n06785654': 791, # crossword_puzzle
'n03445777': 792, # golf_ball
'n07613480': 793, # trifle
'n04350905': 794, # suit
'n04562935': 795, # water_tower
'n03325584': 796, # feather_boa
'n03045698': 797, # cloak
'n07892512': 798, # red_wine
'n03250847': 799, # drumstick
'n04192698': 800, # shield
'n03026506': 801, # Christmas_stocking
'n03534580': 802, # hoopskirt
'n07565083': 803, # menu
'n04296562': 804, # stage
'n02869837': 805, # bonnet
'n07871810': 806, # meat_loaf
'n02799071': 807, # baseball
'n03314780': 808, # face_powder
'n04141327': 809, # scabbard
'n04357314': 810, # sunscreen
'n02823750': 811, # beer_glass
'n13052670': 812, # hen-of-the-woods
'n07583066': 813, # guacamole
'n03637318': 814, # lampshade
'n04599235': 815, # wool
'n07802026': 816, # hay
'n02883205': 817, # bow_tie
'n03709823': 818, # mailbag
'n04560804': 819, # water_jug
'n02909870': 820, # bucket
'n03207743': 821, # dishrag
'n04263257': 822, # soup_bowl
'n07932039': 823, # eggnog
'n03786901': 824, # mortar
'n04479046': 825, # trench_coat
'n03873416': 826, # paddle
'n02999410': 827, # chain
'n04367480': 828, # swab
'n03775546': 829, # mixing_bowl
'n07875152': 830, # potpie
'n04591713': 831, # wine_bottle
'n04201297': 832, # shoji
'n02916936': 833, # bulletproof_vest
'n03240683': 834, # drilling_platform
'n02840245': 835, # binder
'n02963159': 836, # cardigan
'n04370456': 837, # sweatshirt
'n03991062': 838, # pot
'n02843684': 839, # birdhouse
'n03482405': 840, # hamper
'n03942813': 841, # ping-pong_ball
'n03908618': 842, # pencil_box
'n03902125': 843, # pay-phone
'n07584110': 844, # consomme
'n02730930': 845, # apron
'n04023962': 846, # punching_bag
'n02769748': 847, # backpack
'n10148035': 848, # groom
'n02817516': 849, # bearskin
'n03908714': 850, # pencil_sharpener
'n02906734': 851, # broom
'n03788365': 852, # mosquito_net
'n02667093': 853, # abaya
'n03787032': 854, # mortarboard
'n03980874': 855, # poncho
'n03141823': 856, # crutch
'n03976467': 857, # Polaroid_camera
'n04264628': 858, # space_bar
'n07930864': 859, # cup
'n04039381': 860, # racket
'n06874185': 861, # traffic_light
'n04033901': 862, # quill
'n04041544': 863, # radio
'n07860988': 864, # dough
'n03146219': 865, # cuirass
'n03763968': 866, # military_uniform
'n03676483': 867, # lipstick
'n04209133': 868, # shower_cap
'n03782006': 869, # monitor
'n03857828': 870, # oscilloscope
'n03775071': 871, # mitten
'n02892767': 872, # brassiere
'n07684084': 873, # French_loaf
'n04522168': 874, # vase
'n03764736': 875, # milk_can
'n04118538': 876, # rugby_ball
'n03887697': 877, # paper_towel
'n13044778': 878, # earthstar
'n03291819': 879, # envelope
'n03770439': 880, # miniskirt
'n03124170': 881, # cowboy_hat
'n04487081': 882, # trolleybus
'n03916031': 883, # perfume
'n02808440': 884, # bathtub
'n07697537': 885, # hotdog
'n12985857': 886, # coral_fungus
'n02917067': 887, # bullet_train
'n03938244': 888, # pillow
'n15075141': 889, # toilet_tissue
'n02978881': 890, # cassette
'n02966687': 891, # carpenter's_kit
'n03633091': 892, # ladle
'n13040303': 893, # stinkhorn
'n03690938': 894, # lotion
'n03476991': 895, # hair_spray
'n02669723': 896, # academic_gown
'n03220513': 897, # dome
'n03127925': 898, # crate
'n04584207': 899, # wig
'n07880968': 900, # burrito
'n03937543': 901, # pill_bottle
'n03000247': 902, # chain_mail
'n04418357': 903, # theater_curtain
'n04590129': 904, # window_shade
'n02795169': 905, # barrel
'n04553703': 906, # washbasin
'n02783161': 907, # ballpoint
'n02802426': 908, # basketball
'n02808304': 909, # bath_towel
'n03124043': 910, # cowboy_boot
'n03450230': 911, # gown
'n04589890': 912, # window_screen
'n12998815': 913, # agaric
'n02992529': 914, # cellular_telephone
'n03825788': 915, # nipple
'n02790996': 916, # barbell
'n03710193': 917, # mailbox
'n03630383': 918, # lab_coat
'n03347037': 919, # fire_screen
'n03769881': 920, # minibus
'n03871628': 921, # packet
'n03733281': 922, # maze
'n03976657': 923, # pole
'n03535780': 924, # horizontal_bar
'n04259630': 925, # sombrero
'n03929855': 926, # pickelhaube
'n04049303': 927, # rain_barrel
'n04548362': 928, # wallet
'n02979186': 929, # cassette_player
'n06596364': 930, # comic_book
'n03935335': 931, # piggy_bank
'n06794110': 932, # street_sign
'n02825657': 933, # bell_cote
'n03388183': 934, # fountain_pen
'n04591157': 935, # Windsor_tie
'n04540053': 936, # volleyball
'n03866082': 937, # overskirt
'n04136333': 938, # sarong
'n04026417': 939, # purse
'n02865351': 940, # bolo_tie
'n02834397': 941, # bib
'n03888257': 942, # parachute
'n04235860': 943, # sleeping_bag
'n04404412': 944, # television
'n04371430': 945, # swimming_trunks
'n03733805': 946, # measuring_cup
'n07920052': 947, # espresso
'n07873807': 948, # pizza
'n02895154': 949, # breastplate
'n04204238': 950, # shopping_basket
'n04597913': 951, # wooden_spoon
'n04131690': 952, # saltshaker
'n07836838': 953, # chocolate_sauce
'n09835506': 954, # ballplayer
'n03443371': 955, # goblet
'n13037406': 956, # gyromitra
'n04336792': 957, # stretcher
'n04557648': 958, # water_bottle
'n03187595': 959, # dial_telephone
'n04254120': 960, # soap_dispenser
'n03595614': 961, # jersey
'n04146614': 962, # school_bus
'n03598930': 963, # jigsaw_puzzle
'n03958227': 964, # plastic_bag
'n04069434': 965, # reflex_camera
'n03188531': 966, # diaper
'n02786058': 967, # Band_Aid
'n07615774': 968, # ice_lolly
'n04525038': 969, # velvet
'n04409515': 970, # tennis_ball
'n03424325': 971, # gasmask
'n03223299': 972, # doormat
'n03680355': 973, # Loafer
'n07614500': 974, # ice_cream
'n07695742': 975, # pretzel
'n04033995': 976, # quilt
'n03710721': 977, # maillot
'n04392985': 978, # tape_player
'n03047690': 979, # clog
'n03584254': 980, # iPod
'n13054560': 981, # bolete
'n10565667': 982, # scuba_diver
'n03950228': 983, # pitcher
'n03729826': 984, # matchstick
'n02837789': 985, # bikini
'n04254777': 986, # sock
'n02988304': 987, # CD_player
'n03657121': 988, # lens_cap
'n04417672': 989, # thatch
'n04523525': 990, # vault
'n02815834': 991, # beaker
'n09229709': 992, # bubble
'n07697313': 993, # cheeseburger
'n03888605': 994, # parallel_bars
'n03355925': 995, # flagpole
'n03063599': 996, # coffee_mug
'n04116512': 997, # rubber_eraser
'n04325704': 998, # stole
'n07831146': 999, # carbonara
'n03255030': 1000, # dumbbell
}
================================================
FILE: datasets/imagenet.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import numpy as np
from skimage.io import imread
from scipy.misc import imresize
from datasets.imagenet.map import class2num
from util import log
__IMAGENET_IMG_PATH__ = '/YOUR_IMAGENET_PATH/ILSVRC/Data/CLS-LOC'
__IMAGENET_LIST_PATH__ = './datasets/tiny_imagenet'
rs = np.random.RandomState(123)
class Dataset(object):
def __init__(self, ids, name='default',
max_examples=None, is_train=True):
self._ids = list(ids)
self.name = name
self.is_train = is_train
if max_examples is not None:
self._ids = self._ids[:max_examples]
file = os.path.join(__IMAGENET_IMG_PATH__, self._ids[0])
try:
imread(file)
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
log.info("Reading Done: %s", file)
def load_image(self, id):
img = imread(
os.path.join(__IMAGENET_IMG_PATH__, id)) / 255.
img = imresize(img, [256, 256])
y = np.random.randint(img.shape[0]-224)
x = np.random.randint(img.shape[1]-224)
img = img[y:y+224, x:x+224, :3]
l = np.zeros(1000)
l[class2num[id.split('/')[-2]]] = 1
return img, l
def get_data(self, id):
# preprocessing and data augmentation
m, l = self.load_image(id)
return m, l
@property
def ids(self):
return self._ids
def __len__(self):
return len(self.ids)
def __size__(self):
return 114, 114
def __repr__(self):
return 'Dataset (%s, %d examples)' % (
self.name,
len(self)
)
def create_default_splits(is_train=True, ratio=0.8):
ids = all_ids()
num_trains = int(len(ids) * ratio)
dataset_train = Dataset(ids[:num_trains], name='train', is_train=False)
dataset_test = Dataset(ids[num_trains:], name='test', is_train=False)
return dataset_train, dataset_test
def all_ids():
id_filename = 'train_list.txt'
id_txt = os.path.join(__IMAGENET_LIST_PATH__, id_filename)
try:
with open(id_txt, 'r') as fp:
_ids = [s.strip() for s in fp.readlines() if s]
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
rs.shuffle(_ids)
return _ids
================================================
FILE: datasets/mnist.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import numpy as np
import h5py
from util import log
__PATH__ = './datasets/mnist'
rs = np.random.RandomState(123)
class Dataset(object):
def __init__(self, ids, name='default',
max_examples=None, is_train=True):
self._ids = list(ids)
self.name = name
self.is_train = is_train
if max_examples is not None:
self._ids = self._ids[:max_examples]
filename = 'data.hdf5'
file = os.path.join(__PATH__, filename)
log.info("Reading %s ...", file)
try:
self.data = h5py.File(file, 'r')
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
log.info("Reading Done: %s", file)
def get_data(self, id):
# preprocessing and data augmentation
m = self.data[id]['image'].value/255.
l = self.data[id]['label'].value.astype(np.float32)
return m, l
@property
def ids(self):
return self._ids
def __len__(self):
return len(self.ids)
def __repr__(self):
return 'Dataset (%s, %d examples)' % (
self.name,
len(self)
)
def create_default_splits(is_train=True):
id_train, id_test = all_ids(60000)
dataset_train = Dataset(id_train, name='train', is_train=False)
dataset_test = Dataset(id_test, name='test', is_train=False)
return dataset_train, dataset_test
def all_ids(num_trains):
id_filename = 'id.txt'
id_txt = os.path.join(__PATH__, id_filename)
try:
with open(id_txt, 'r') as fp:
_ids = [s.strip() for s in fp.readlines() if s]
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
id_train, id_test = _ids[:num_trains], _ids[num_trains:]
rs.shuffle(id_train)
rs.shuffle(id_test)
return id_train, id_test
================================================
FILE: datasets/svhn.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import numpy as np
import h5py
from util import log
__PATH__ = './datasets/svhn'
rs = np.random.RandomState(123)
class Dataset(object):
def __init__(self, ids, name='default',
max_examples=None, is_train=True):
self._ids = list(ids)
self.name = name
self.is_train = is_train
if max_examples is not None:
self._ids = self._ids[:max_examples]
filename = 'data.hdf5'
file = os.path.join(__PATH__, filename)
log.info("Reading %s ...", file)
try:
self.data = h5py.File(file, 'r')
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
log.info("Reading Done: %s", file)
def get_data(self, id):
# preprocessing and data augmentation
m = self.data[id]['image'].value/255.
l = self.data[id]['label'].value.astype(np.float32)
return m, l
@property
def ids(self):
return self._ids
def __len__(self):
return len(self.ids)
def __repr__(self):
return 'Dataset (%s, %d examples)' % (
self.name,
len(self)
)
def create_default_splits(is_train=True):
id_train, id_test = all_ids(60000)
dataset_train = Dataset(id_train, name='train', is_train=False)
dataset_test = Dataset(id_test, name='test', is_train=False)
return dataset_train, dataset_test
def all_ids(num_trains):
id_filename = 'id.txt'
id_txt = os.path.join(__PATH__, id_filename)
try:
with open(id_txt, 'r') as fp:
_ids = [s.strip() for s in fp.readlines() if s]
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
id_train, id_test = _ids[:num_trains], _ids[num_trains:]
rs.shuffle(id_train)
rs.shuffle(id_test)
return id_train, id_test
================================================
FILE: datasets/tiny_imagenet.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import numpy as np
from skimage.io import imread
from scipy.misc import imresize
from util import log
__IMAGENET_IMG_PATH__ = './datasets/tiny_imagenet/tiny-imagenet-200/'
__IMAGENET_LIST_PATH__ = './datasets/tiny_imagenet'
rs = np.random.RandomState(123)
class Dataset(object):
def __init__(self, ids, name='default',
max_examples=None, is_train=True):
self._ids = list(ids)
self.name = name
self.is_train = is_train
if max_examples is not None:
self._ids = self._ids[:max_examples]
file = os.path.join(__IMAGENET_IMG_PATH__, self._ids[0])
with open(os.path.join(__IMAGENET_IMG_PATH__, 'wnids.txt')) as f:
self.label_list = f.readlines()
self.label_list = [label.strip() for label in self.label_list]
with open(os.path.join(__IMAGENET_IMG_PATH__, 'val/val_annotations.txt')) as f:
self.val_label_list = f.readlines()
self.val_label_list = [label.split('\t')[1] for label in self.val_label_list]
try:
imread(file)
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
log.info("Reading Done: %s", file)
def load_image(self, id):
img = imread(
os.path.join(__IMAGENET_IMG_PATH__, id))/255.
img = imresize(img, [72, 72])
if len(img.shape) == 2:
img = np.stack([img, img, img], axis=-1)
y = np.random.randint(img.shape[0]-64)
x = np.random.randint(img.shape[1]-64)
img = img[y:y+64, x:x+64, :3]
l = np.zeros(200)
if id.split('/')[1] == 'train':
l[self.label_list.index(id.split('/')[-3])] = 1
elif id.split('/')[1] == 'val':
img_idx = int(id.split('/')[-1].split('_')[-1].split('.')[0])
l[self.label_list.index(self.val_label_list[img_idx])] = 1
return img, l
def get_data(self, id):
# preprocessing and data augmentation
m, l = self.load_image(id)
return m, l
@property
def ids(self):
return self._ids
def __len__(self):
return len(self.ids)
def __size__(self):
return 64, 64
def __repr__(self):
return 'Dataset (%s, %d examples)' % (
self.name,
len(self)
)
def create_default_splits(is_train=True, ratio=0.8):
id_train, id_test = all_ids()
dataset_train = Dataset(id_train, name='train', is_train=False)
dataset_test = Dataset(id_test, name='test', is_train=False)
return dataset_train, dataset_test
def all_ids():
id_train_path = os.path.join(__IMAGENET_LIST_PATH__, 'train_list.txt')
id_val_path = os.path.join(__IMAGENET_LIST_PATH__, 'val_list.txt')
try:
with open(id_train_path, 'r') as fp:
id_train = [s.strip() for s in fp.readlines() if s]
with open(id_val_path, 'r') as fp:
id_val = [s.strip() for s in fp.readlines() if s]
except:
raise IOError('Dataset not found. Please make sure the dataset was downloaded.')
rs.shuffle(id_train)
rs.shuffle(id_val)
return id_train, id_val
================================================
FILE: download.py
================================================
from __future__ import print_function
import os
import tarfile
import subprocess
import argparse
import h5py
import numpy as np
parser = argparse.ArgumentParser(description='Download datasets.')
parser.add_argument('--datasets', metavar='N', type=str, nargs='+',
choices=['MNIST', 'Fashion', 'SVHN', 'CIFAR10'])
def prepare_h5py(train_image, train_label, test_image,
test_label, data_dir, num_class=10, shape=None):
image = np.concatenate((train_image, test_image), axis=0).astype(np.uint8)
label = np.concatenate((train_label, test_label), axis=0).astype(np.uint8)
print('Preprocessing data...')
import progressbar
bar = progressbar.ProgressBar(
maxval=100, widgets=[progressbar.Bar('=', '[', ']'),
' ', progressbar.Percentage()]
)
bar.start()
f = h5py.File(os.path.join(data_dir, 'data.hdf5'), 'w')
with open(os.path.join(data_dir, 'id.txt'), 'w') as data_id:
for i in range(image.shape[0]):
if i % (image.shape[0] / 100) == 0:
bar.update(i / (image.shape[0] / 100))
grp = f.create_group(str(i))
data_id.write('{}\n'.format(i))
if shape:
grp['image'] = np.reshape(image[i], shape, order='F')
else:
grp['image'] = image[i]
label_vec = np.zeros(num_class)
label_vec[label[i] % num_class] = 1
grp['label'] = label_vec.astype(np.bool)
bar.finish()
f.close()
return
def check_file(data_dir):
if os.path.exists(data_dir):
if os.path.isfile(os.path.join(data_dir, 'data.hdf5')) and\
os.path.isfile(os.path.join(data_dir, 'id.txt')):
return True
else:
os.mkdir(data_dir)
return False
def download_mnist(download_path, fashion_mnist=False):
if not fashion_mnist:
data_url = 'http://yann.lecun.com/exdb/mnist/'
data_dir = os.path.join(download_path, 'mnist')
else:
data_url = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'
data_dir = os.path.join(download_path, 'fashion_mnist')
if check_file(data_dir):
if not fashion_mnist:
print('MNIST was downloaded.')
else:
print('Fashion MNIST was downloaded.')
return
keys = ['train-images-idx3-ubyte.gz', 'train-labels-idx1-ubyte.gz',
't10k-images-idx3-ubyte.gz', 't10k-labels-idx1-ubyte.gz']
for k in keys:
url = (data_url+k).format(**locals())
target_path = os.path.join(data_dir, k)
cmd = ['curl', url, '-o', target_path]
print('Downloading ', k)
subprocess.call(cmd)
cmd = ['gzip', '-d', target_path]
print('Unzip ', k)
subprocess.call(cmd)
num_mnist_train = 60000
num_mnist_test = 10000
fd = open(os.path.join(data_dir, 'train-images-idx3-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
train_image = loaded[16:].reshape((num_mnist_train, 28, 28, 1)).astype(np.float)
fd = open(os.path.join(data_dir, 'train-labels-idx1-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
train_label = np.asarray(loaded[8:].reshape((num_mnist_train)).astype(np.float))
fd = open(os.path.join(data_dir, 't10k-images-idx3-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
test_image = loaded[16:].reshape((num_mnist_test, 28, 28, 1)).astype(np.float)
fd = open(os.path.join(data_dir, 't10k-labels-idx1-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
test_label = np.asarray(loaded[8:].reshape((num_mnist_test)).astype(np.float))
prepare_h5py(train_image, train_label, test_image, test_label, data_dir)
for k in keys:
cmd = ['rm', '-f', os.path.join(data_dir, k[:-3])]
subprocess.call(cmd)
def download_svhn(download_path):
data_dir = os.path.join(download_path, 'svhn')
import scipy.io as sio
# svhn file loader
def svhn_loader(url, path):
cmd = ['curl', url, '-o', path]
subprocess.call(cmd)
m = sio.loadmat(path)
return m['X'], m['y']
if check_file(data_dir):
print('SVHN was downloaded.')
return
data_url = 'http://ufldl.stanford.edu/housenumbers/train_32x32.mat'
train_image, train_label = svhn_loader(data_url, os.path.join(data_dir, 'train_32x32.mat'))
data_url = 'http://ufldl.stanford.edu/housenumbers/test_32x32.mat'
test_image, test_label = svhn_loader(data_url, os.path.join(data_dir, 'test_32x32.mat'))
prepare_h5py(np.transpose(train_image, (3, 0, 1, 2)), train_label,
np.transpose(test_image, (3, 0, 1, 2)), test_label, data_dir)
cmd = ['rm', '-f', os.path.join(data_dir, '*.mat')]
subprocess.call(cmd)
def download_cifar10(download_path):
data_dir = os.path.join(download_path, 'cifar10')
# cifar file loader
def unpickle(file):
import cPickle
with open(file, 'rb') as fo:
dict = cPickle.load(fo)
return dict
if check_file(data_dir):
print('CIFAR was downloaded.')
return
data_url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
k = 'cifar-10-python.tar.gz'
target_path = os.path.join(data_dir, k)
print(target_path)
cmd = ['curl', data_url, '-o', target_path]
print('Downloading CIFAR10')
subprocess.call(cmd)
tarfile.open(target_path, 'r:gz').extractall(data_dir)
num_cifar_train = 50000
num_cifar_test = 10000
target_path = os.path.join(data_dir, 'cifar-10-batches-py')
train_image = []
train_label = []
for i in range(5):
fd = os.path.join(target_path, 'data_batch_{}'.format(i+1))
dict = unpickle(fd)
train_image.append(dict['data'])
train_label.append(dict['labels'])
train_image = np.reshape(np.stack(train_image, axis=0), [num_cifar_train, 32*32*3])
train_label = np.reshape(np.array(np.stack(train_label, axis=0)), [num_cifar_train])
fd = os.path.join(target_path, 'test_batch')
dict = unpickle(fd)
test_image = np.reshape(dict['data'], [num_cifar_test, 32*32*3])
test_label = np.reshape(dict['labels'], [num_cifar_test])
prepare_h5py(train_image, train_label, test_image, test_label, data_dir, shape=[32, 32, 3])
cmd = ['rm', '-f', os.path.join(data_dir, 'cifar-10-python.tar.gz')]
subprocess.call(cmd)
cmd = ['rm', '-rf', os.path.join(data_dir, 'cifar-10-batches-py')]
subprocess.call(cmd)
if __name__ == '__main__':
args = parser.parse_args()
path = './datasets'
if not os.path.exists(path):
os.mkdir(path)
if args.datasets is None:
raise ValueError('Please at least specify one dataset to be downloaded.')
if 'MNIST' in args.datasets:
download_mnist('./datasets')
if 'Fashion' in args.datasets:
download_mnist('./datasets', fashion_mnist=True)
if 'SVHN' in args.datasets:
download_svhn('./datasets')
if 'CIFAR10' in args.datasets:
download_cifar10('./datasets')
================================================
FILE: input_ops.py
================================================
import numpy as np
import tensorflow as tf
from util import log
def check_data_id(dataset, data_id):
if not data_id:
return
wrong = []
for id in data_id:
if id in dataset.data:
pass
else:
wrong.append(id)
if len(wrong) > 0:
raise RuntimeError("There are %d invalid ids, including %s" % (
len(wrong), wrong[:5]
))
def create_input_ops(dataset,
batch_size,
num_threads=1, # for creating batches
is_training=False,
data_id=None,
scope='inputs',
shuffle=True,
):
'''
Return a batched tensor for the inputs from the dataset.
'''
input_ops = {}
if data_id is None:
data_id = dataset.ids
log.info("input_ops [%s]: Using %d IDs from dataset", scope, len(data_id))
else:
log.info("input_ops [%s]: Using specified %d IDs", scope, len(data_id))
# single operations
with tf.device("/cpu:0"), tf.name_scope(scope):
input_ops['id'] = tf.train.string_input_producer(
tf.convert_to_tensor(data_id),
capacity=128
).dequeue(name='input_ids_dequeue')
m, label = dataset.get_data(data_id[0])
def load_fn(id):
# image [n, n], label: [m]
image, label = dataset.get_data(id)
return (id,
image.astype(np.float32),
label.astype(np.float32))
input_ops['id'], input_ops['image'], input_ops['label'] = tf.py_func(
load_fn, inp=[input_ops['id']],
Tout=[tf.string, tf.float32, tf.float32],
name='func_hp'
)
input_ops['id'].set_shape([])
input_ops['image'].set_shape(list(m.shape))
input_ops['label'].set_shape(list(label.shape))
# batchify
capacity = 2 * batch_size * num_threads
min_capacity = min(int(capacity * 0.75), 1024)
if shuffle:
batch_ops = tf.train.shuffle_batch(
input_ops,
batch_size=batch_size,
num_threads=num_threads,
capacity=capacity,
min_after_dequeue=min_capacity,
)
else:
batch_ops = tf.train.batch(
input_ops,
batch_size=batch_size,
num_threads=num_threads,
capacity=capacity,
)
return input_ops, batch_ops
================================================
FILE: model.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow.contrib.slim as slim
from ops import conv2d, fc, residual_block
from util import log, train_test_summary
class Model(object):
def __init__(self, config, debug_information=False, is_train=True):
self.debug = debug_information
self.config = config
self.batch_size = self.config.batch_size
self.input_height = self.config.data_info[0]
self.input_width = self.config.data_info[1]
self.c_dim = self.config.data_info[2]
self.num_class = self.config.data_info[3]
self.norm_type = self.config.norm_type
# create placeholders for the input
self.image = tf.placeholder(
name='image', dtype=tf.float32,
shape=[self.batch_size, self.input_height, self.input_width, self.c_dim],
)
self.label = tf.placeholder(
name='label', dtype=tf.float32, shape=[self.batch_size, self.num_class],
)
self.is_training = tf.placeholder_with_default(bool(is_train), [], name='is_training')
self.build(is_train=is_train)
def get_feed_dict(self, batch_chunk, step=None, is_training=None):
fd = {
self.image: batch_chunk['image'], # [B, h, w, c]
self.label: batch_chunk['label'], # [B, n]
}
if is_training is not None:
fd[self.is_training] = is_training
return fd
def build(self, is_train=True):
n = self.num_class
# build loss and accuracy {{{
def build_loss(logits, labels):
# Cross-entropy loss
loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)
# Classification accuracy
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(self.label, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
return tf.reduce_mean(loss), accuracy
# }}}
# Classifier: takes images as input and tries to output class label [B, n]
def C(img, scope='Classifier', reuse=False):
with tf.variable_scope(scope) as scope:
log.warn(scope.name)
_ = img
# MNIST, Fashion MNIST, SVHN, CIFAR
if not self.config.dataset == 'ImageNet':
# conv layers
num_channels = [64, 128, 256, 512]
for i in range(len(num_channels)):
_ = conv2d(_, num_channels[i], is_train, norm_type=self.norm_type,
info=not reuse, name='conv_{}'.format(i))
_ = slim.dropout(_, keep_prob=0.5, is_training=is_train)
# fc layers
_ = tf.reshape(_, [self.batch_size, -1])
num_fc_channels = [512, 128, 32, n]
for i in range(len(num_fc_channels)):
_ = fc(_, num_fc_channels[i], is_train, norm_type='none',
info=not reuse, name='fc_{}'.format(i))
# ImageNet
else:
# conv layers
num_channels = [64, 128, 256, 512, 1024]
num_residual_block = [0, 2, 3, 5, 2]
for i in range(len(num_channels)):
_ = conv2d(_, num_channels[i], is_train, norm_type=self.norm_type,
info=not reuse, name='conv_{}'.format(i))
for j in range(num_residual_block[i]):
_ = residual_block(_, num_channels[i], is_train,
norm_type=self.norm_type, info=not reuse,
name='residual_{}_{}'.format(i, j))
_ = tf.layers.average_pooling2d(_, [7, 7], [7, 7])
log.info('{} {}'.format(_.name, _.get_shape().as_list()))
# fc layers
_ = tf.reshape(_, [self.batch_size, -1])
num_fc_channels = [n]
for i in range(len(num_fc_channels)):
_ = fc(_, num_fc_channels[i], is_train, norm_type='none',
info=not reuse, name='fc_{}'.format(i))
return _
logits = C(self.image)
self.entropy, self.accuracy = build_loss(logits, self.label)
self.loss = self.entropy
train_test_summary("loss/accuracy", self.accuracy)
train_test_summary("loss/loss", self.loss)
train_test_summary("img/image", self.image, summary_type='image')
log.warn('Successfully loaded the model.')
================================================
FILE: ops.py
================================================
import tensorflow as tf
import tensorflow.contrib.slim as slim
from util import log
def norm(x, norm_type, is_train, G=32, esp=1e-5):
with tf.variable_scope('{}_norm'.format(norm_type)):
if norm_type == 'none':
output = x
elif norm_type == 'batch':
output = tf.contrib.layers.batch_norm(
x, center=True, scale=True, decay=0.999,
is_training=is_train, updates_collections=None
)
elif norm_type == 'group':
# normalize
# tranpose: [bs, h, w, c] to [bs, c, h, w] following the paper
x = tf.transpose(x, [0, 3, 1, 2])
N, C, H, W = x.get_shape().as_list()
G = min(G, C)
x = tf.reshape(x, [-1, G, C // G, H, W])
mean, var = tf.nn.moments(x, [2, 3, 4], keep_dims=True)
x = (x - mean) / tf.sqrt(var + esp)
# per channel gamma and beta
gamma = tf.Variable(tf.constant(1.0, shape=[C]), dtype=tf.float32, name='gamma')
beta = tf.Variable(tf.constant(0.0, shape=[C]), dtype=tf.float32, name='beta')
gamma = tf.reshape(gamma, [1, C, 1, 1])
beta = tf.reshape(beta, [1, C, 1, 1])
output = tf.reshape(x, [-1, C, H, W]) * gamma + beta
# tranpose: [bs, c, h, w, c] to [bs, h, w, c] following the paper
output = tf.transpose(output, [0, 2, 3, 1])
else:
raise NotImplementedError
return output
def lrelu(x, leak=0.2, name="lrelu"):
with tf.variable_scope(name):
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * x + f2 * abs(x)
def selu(x):
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
return scale * tf.where(x > 0.0, x, alpha * tf.exp(x) - alpha)
def huber_loss(labels, predictions, delta=1.0):
residual = tf.abs(predictions - labels)
condition = tf.less(residual, delta)
small_res = 0.5 * tf.square(residual)
large_res = delta * residual - 0.5 * tf.square(delta)
return tf.where(condition, small_res, large_res)
def conv2d(input, output_shape, is_train, info=False,
activation_fn=lrelu, norm_type='batch',
k=4, s=2, stddev=0.02, name="conv2d"):
with tf.variable_scope(name):
w = tf.get_variable('w', [k, k, input.get_shape()[-1], output_shape],
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv2d(input, w, strides=[1, s, s, 1], padding='SAME')
biases = tf.get_variable('biases', [output_shape],
initializer=tf.constant_initializer(0.0))
if activation_fn is not None:
activation = activation_fn(conv + biases)
else:
activation = conv + biases
output = norm(activation, norm_type, is_train)
if info: log.info('{} {}'.format(name, output))
return output
def fc(input, output_shape, is_train, info=False,
norm_type='batch', activation_fn=lrelu, name="fc"):
activation = slim.fully_connected(input, output_shape, activation_fn=activation_fn)
output = norm(activation, norm_type, is_train)
if info: log.info('{} {}'.format(name, output))
return output
def residual_block(input, output_shape, is_train, info=False, k=3, s=1,
name="residual", activation_fn=lrelu, norm_type='batch'):
with tf.variable_scope(name):
with tf.variable_scope('res1'):
_ = conv2d(input, output_shape, is_train, k=k, s=s,
activation_fn=None, norm_type=norm_type)
_ = norm(_, norm_type, is_train)
_ = activation_fn(_)
with tf.variable_scope('res2'):
_ = conv2d(_, output_shape, is_train, k=k, s=s,
activation_fn=None, norm_type=norm_type)
_ = norm(_, norm_type, is_train)
_ = activation_fn(_ + input)
if info: log.info('{} {}'.format(name, _.get_shape().as_list()))
return _
================================================
FILE: trainer.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
from util import log
from pprint import pprint
from model import Model
from input_ops import create_input_ops
import os
import time
import numpy as np
import tensorflow.contrib.slim as slim
import tensorflow as tf
class Trainer(object):
def __init__(self,
config,
dataset,
dataset_test):
self.config = config
hyper_parameter_str = '{}_lr_{}_bs_{}_norm_type_{}'.format(
config.dataset, config.learning_rate,
config.batch_size, config.norm_type
)
self.train_dir = './train_dir/%s-%s-%s' % (
config.prefix,
hyper_parameter_str,
time.strftime("%Y%m%d-%H%M%S")
)
if not os.path.exists(self.train_dir): os.makedirs(self.train_dir)
log.infov("Train Dir: %s", self.train_dir)
# --- input ops ---
self.batch_size = config.batch_size
_, self.batch_train = create_input_ops(dataset, self.batch_size,
is_training=True)
_, self.batch_test = create_input_ops(dataset_test, self.batch_size,
is_training=False)
# --- create model ---
self.model = Model(config)
# --- optimizer ---
self.global_step = tf.contrib.framework.get_or_create_global_step(graph=None)
self.learning_rate = config.learning_rate
self.check_op = tf.no_op()
all_vars = tf.trainable_variables()
slim.model_analyzer.analyze_vars(all_vars, print_info=True)
if not config.no_adjust_learning_rate:
config.learning_rate = config.learning_rate * config.batch_size
if not config.dataset == 'ImageNet':
self.optimizer = tf.contrib.layers.optimize_loss(
loss=self.model.loss,
global_step=self.global_step,
learning_rate=self.learning_rate,
optimizer=tf.train.AdamOptimizer,
clip_gradients=20.0,
name='optimizer_loss'
)
self.optimizer_dummy = tf.contrib.layers.optimize_loss(
loss=self.model.loss,
global_step=self.global_step,
learning_rate=self.learning_rate,
optimizer=tf.train.AdamOptimizer,
clip_gradients=20.0,
increment_global_step=False,
name='optimizer_loss_dummy'
)
else:
config.learning_rate = config.learning_rate * 1e2
self.optimizer = tf.contrib.layers.optimize_loss(
loss=self.model.loss,
global_step=self.global_step,
learning_rate=self.learning_rate,
optimizer=tf.train.MomentumOptimizer(self.learning_rate, momentum=0.9),
clip_gradients=20.0,
name='optimizer_loss'
)
self.optimizer_dummy = tf.contrib.layers.optimize_loss(
loss=self.model.loss,
global_step=self.global_step,
learning_rate=self.learning_rate,
optimizer=tf.train.MomentumOptimizer(self.learning_rate, momentum=0.9),
clip_gradients=20.0,
increment_global_step=False,
name='optimizer_loss_dummy'
)
self.train_summary_op = tf.summary.merge_all(key='train')
self.test_summary_op = tf.summary.merge_all(key='test')
self.saver = tf.train.Saver(max_to_keep=100)
self.pretrain_saver = tf.train.Saver(var_list=tf.trainable_variables(),
max_to_keep=100)
self.summary_writer = tf.summary.FileWriter(self.train_dir)
self.log_step = self.config.log_step
self.test_sample_step = self.config.test_sample_step
self.write_summary_step = self.config.write_summary_step
self.checkpoint_secs = 600 # 10 min
self.supervisor = tf.train.Supervisor(
logdir=self.train_dir,
is_chief=True,
saver=None,
summary_op=None,
summary_writer=self.summary_writer,
save_summaries_secs=300,
save_model_secs=self.checkpoint_secs,
global_step=self.global_step,
)
session_config = tf.ConfigProto(
allow_soft_placement=True,
gpu_options=tf.GPUOptions(allow_growth=True),
device_count={'GPU': 1},
)
self.session = self.supervisor.prepare_or_wait_for_session(config=session_config)
self.ckpt_path = config.checkpoint
if self.ckpt_path is not None:
log.info("Checkpoint path: %s", self.ckpt_path)
self.pretrain_saver.restore(self.session, self.ckpt_path)
log.info("Loaded the pretrain parameters from the provided checkpoint path")
def train(self):
log.infov("Training Starts!")
pprint(self.batch_train)
ckpt_save_step = self.config.ckpt_save_step
log_step = self.log_step
test_sample_step = self.test_sample_step
write_summary_step = self.write_summary_step
step = 0
for s in xrange(self.config.max_training_step):
# periodic inference
if s % test_sample_step == 0:
accuracy, test_summary, loss, step_time = \
self.run_test(self.batch_test, is_train=False)
self.log_step_message(step, accuracy, loss, step_time, is_train=False)
self.summary_writer.add_summary(test_summary, global_step=step)
step, accuracy, train_summary, loss, step_time = \
self.run_single_step(self.batch_train, s, is_train=True)
if not self.config.no_adjust_learning_rate:
for i in range(int(self.config.max_batch_size/self.config.batch_size-1)):
_, accuracy, train_summary, loss, step_time = \
self.run_single_step(self.batch_train, s, is_train=True,
update_global_step=False)
if s % log_step == 0:
self.log_step_message(step, accuracy, loss, step_time)
if s % write_summary_step == 0:
self.summary_writer.add_summary(train_summary, global_step=step)
if s % ckpt_save_step == 0 and s > 0:
log.infov("Saved checkpoint at %d", s)
self.saver.save(self.session,
os.path.join(self.train_dir, 'model'),
global_step=step)
def run_single_step(self, batch, step, is_train=True, update_global_step=True):
_start_time = time.time()
batch_chunk = self.session.run(batch)
fetch = [self.global_step, self.model.accuracy, self.train_summary_op,
self.model.loss, self.check_op,
self.optimizer if update_global_step else self.optimizer_dummy]
fetch_values = self.session.run(
fetch,
feed_dict=self.model.get_feed_dict(batch_chunk, step=step)
)
[step, accuracy, summary, loss] = fetch_values[:4]
_end_time = time.time()
return step, accuracy, summary, loss, (_end_time - _start_time)
def run_test(self, batch, is_train=False, repeat_times=8):
_start_time = time.time()
batch_chunk = self.session.run(batch)
accuracy, summary, loss = self.session.run(
[self.model.accuracy,
self.test_summary_op, self.model.loss],
feed_dict=self.model.get_feed_dict(batch_chunk, is_training=False)
)
_end_time = time.time()
return accuracy, summary, loss, (_end_time - _start_time)
def log_step_message(self, step, accuracy, loss, step_time, is_train=True):
if step_time == 0: step_time = 0.001
log_fn = (is_train and log.info or log.infov)
log_fn((" [{split_mode:5s} step {step:4d}] " +
"Loss: {loss:.5f} " +
"Accuracy: {accuracy:.2f}% "
"({sec_per_batch:.3f} sec/batch, {instance_per_sec:.3f} instances/sec) "
).format(split_mode=(is_train and 'train' or 'val'),
step=step,
loss=loss,
accuracy=accuracy*100,
sec_per_batch=step_time,
instance_per_sec=self.batch_size / step_time
)
)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--max_batch_size', type=int, default=64)
parser.add_argument('--prefix', type=str, default='default',
help='the nickname of this training job')
parser.add_argument('--checkpoint', type=str, default=None)
parser.add_argument('--dataset', type=str, default='MNIST',
choices=['MNIST', 'Fashion', 'SVHN',
'CIFAR10', 'ImageNet', 'TinyImageNet'])
parser.add_argument('--norm_type', type=str, default='batch',
choices=['batch', 'group'])
# Log
parser.add_argument('--max_training_step', type=int, default=100000)
parser.add_argument('--log_step', type=int, default=10)
parser.add_argument('--test_sample_step', type=int, default=10)
parser.add_argument('--write_summary_step', type=int, default=10)
parser.add_argument('--ckpt_save_step', type=int, default=1000)
# Learning
parser.add_argument('--learning_rate', type=float, default=1e-5)
parser.add_argument('--no_adjust_learning_rate', action='store_true', default=False)
config = parser.parse_args()
if config.dataset == 'MNIST':
import datasets.mnist as dataset
elif config.dataset == 'Fashion':
import datasets.fashion_mnist as dataset
elif config.dataset == 'SVHN':
import datasets.svhn as dataset
elif config.dataset == 'CIFAR10':
import datasets.cifar10 as dataset
elif config.dataset == 'TinyImageNet':
import datasets.tiny_imagenet as dataset
elif config.dataset == 'ImageNet':
import datasets.imagenet as dataset
else:
raise ValueError(config.dataset)
dataset_train, dataset_test = dataset.create_default_splits()
image, label = dataset_train.get_data(dataset_train.ids[0])
config.data_info = np.concatenate([np.asarray(image.shape), np.asarray(label.shape)])
trainer = Trainer(config,
dataset_train, dataset_test)
log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate)
trainer.train()
if __name__ == '__main__':
main()
================================================
FILE: util.py
================================================
""" Utilities """
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Logging
# =======
import logging
from colorlog import ColoredFormatter
import tensorflow as tf
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
formatter = ColoredFormatter(
"%(log_color)s[%(asctime)s] %(message)s",
datefmt=None,
reset=True,
log_colors={
'DEBUG': 'cyan',
'INFO': 'white,bold',
'INFOV': 'cyan,bold',
'WARNING': 'yellow',
'ERROR': 'red,bold',
'CRITICAL': 'red,bg_white',
},
secondary_log_colors={},
style='%'
)
ch.setFormatter(formatter)
log = logging.getLogger('Log')
log.setLevel(logging.DEBUG)
log.handlers = [] # No duplicated handlers
log.propagate = False # workaround for duplicated logs in ipython
log.addHandler(ch)
logging.addLevelName(logging.INFO + 1, 'INFOV')
def _infov(self, msg, *args, **kwargs):
self.log(logging.INFO + 1, msg, *args, **kwargs)
logging.Logger.infov = _infov
def train_test_summary(name, value, max_outputs=4, summary_type='scalar'):
if summary_type == 'scalar':
tf.summary.scalar(name, value, collections=['train'])
tf.summary.scalar("test_{}".format(name), value, collections=['test'])
elif summary_type == 'image':
tf.summary.image(name, value, max_outputs=max_outputs, collections=['train'])
tf.summary.image("test_{}".format(name), value,
max_outputs=max_outputs, collections=['test'])
gitextract_w213pial/ ├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── datasets/ │ ├── __init__.py │ ├── cifar10.py │ ├── fashion_mnist.py │ ├── imagenet/ │ │ ├── __init__.py │ │ └── map.py │ ├── imagenet.py │ ├── mnist.py │ ├── svhn.py │ └── tiny_imagenet.py ├── download.py ├── input_ops.py ├── model.py ├── ops.py ├── trainer.py └── util.py
SYMBOL INDEX (79 symbols across 12 files)
FILE: datasets/cifar10.py
class Dataset (line 16) | class Dataset(object):
method __init__ (line 18) | def __init__(self, ids, name='default',
method get_data (line 38) | def get_data(self, id):
method ids (line 45) | def ids(self):
method __len__ (line 48) | def __len__(self):
method __repr__ (line 51) | def __repr__(self):
function create_default_splits (line 58) | def create_default_splits(is_train=True):
function all_ids (line 66) | def all_ids(num_trains):
FILE: datasets/fashion_mnist.py
class Dataset (line 16) | class Dataset(object):
method __init__ (line 18) | def __init__(self, ids, name='default',
method get_data (line 38) | def get_data(self, id):
method ids (line 45) | def ids(self):
method __len__ (line 48) | def __len__(self):
method __repr__ (line 51) | def __repr__(self):
function create_default_splits (line 58) | def create_default_splits(is_train=True):
function all_ids (line 66) | def all_ids(num_trains):
FILE: datasets/imagenet.py
class Dataset (line 19) | class Dataset(object):
method __init__ (line 21) | def __init__(self, ids, name='default',
method load_image (line 38) | def load_image(self, id):
method get_data (line 51) | def get_data(self, id):
method ids (line 57) | def ids(self):
method __len__ (line 60) | def __len__(self):
method __size__ (line 63) | def __size__(self):
method __repr__ (line 66) | def __repr__(self):
function create_default_splits (line 73) | def create_default_splits(is_train=True, ratio=0.8):
function all_ids (line 83) | def all_ids():
FILE: datasets/mnist.py
class Dataset (line 16) | class Dataset(object):
method __init__ (line 18) | def __init__(self, ids, name='default',
method get_data (line 38) | def get_data(self, id):
method ids (line 45) | def ids(self):
method __len__ (line 48) | def __len__(self):
method __repr__ (line 51) | def __repr__(self):
function create_default_splits (line 58) | def create_default_splits(is_train=True):
function all_ids (line 66) | def all_ids(num_trains):
FILE: datasets/svhn.py
class Dataset (line 16) | class Dataset(object):
method __init__ (line 18) | def __init__(self, ids, name='default',
method get_data (line 38) | def get_data(self, id):
method ids (line 45) | def ids(self):
method __len__ (line 48) | def __len__(self):
method __repr__ (line 51) | def __repr__(self):
function create_default_splits (line 58) | def create_default_splits(is_train=True):
function all_ids (line 66) | def all_ids(num_trains):
FILE: datasets/tiny_imagenet.py
class Dataset (line 18) | class Dataset(object):
method __init__ (line 20) | def __init__(self, ids, name='default',
method load_image (line 44) | def load_image(self, id):
method get_data (line 63) | def get_data(self, id):
method ids (line 69) | def ids(self):
method __len__ (line 72) | def __len__(self):
method __size__ (line 75) | def __size__(self):
method __repr__ (line 78) | def __repr__(self):
function create_default_splits (line 85) | def create_default_splits(is_train=True, ratio=0.8):
function all_ids (line 93) | def all_ids():
FILE: download.py
function prepare_h5py (line 15) | def prepare_h5py(train_image, train_label, test_image,
function check_file (line 51) | def check_file(data_dir):
function download_mnist (line 61) | def download_mnist(download_path, fashion_mnist=False):
function download_svhn (line 115) | def download_svhn(download_path):
function download_cifar10 (line 144) | def download_cifar10(download_path):
FILE: input_ops.py
function check_data_id (line 6) | def check_data_id(dataset, data_id):
function create_input_ops (line 23) | def create_input_ops(dataset,
FILE: model.py
class Model (line 12) | class Model(object):
method __init__ (line 14) | def __init__(self, config, debug_information=False, is_train=True):
method get_feed_dict (line 38) | def get_feed_dict(self, batch_chunk, step=None, is_training=None):
method build (line 48) | def build(self, is_train=True):
FILE: ops.py
function norm (line 6) | def norm(x, norm_type, is_train, G=32, esp=1e-5):
function lrelu (line 38) | def lrelu(x, leak=0.2, name="lrelu"):
function selu (line 45) | def selu(x):
function huber_loss (line 51) | def huber_loss(labels, predictions, delta=1.0):
function conv2d (line 59) | def conv2d(input, output_shape, is_train, info=False,
function fc (line 77) | def fc(input, output_shape, is_train, info=False,
function residual_block (line 85) | def residual_block(input, output_shape, is_train, info=False, k=3, s=1,
FILE: trainer.py
class Trainer (line 20) | class Trainer(object):
method __init__ (line 21) | def __init__(self,
method train (line 139) | def train(self):
method run_single_step (line 177) | def run_single_step(self, batch, step, is_train=True, update_global_st...
method run_test (line 197) | def run_test(self, batch, is_train=False, repeat_times=8):
method log_step_message (line 212) | def log_step_message(self, step, accuracy, loss, step_time, is_train=T...
function main (line 229) | def main():
FILE: util.py
function _infov (line 45) | def _infov(self, msg, *args, **kwargs):
function train_test_summary (line 51) | def train_test_summary(name, value, max_outputs=4, summary_type='scalar'):
Condensed preview — 19 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (88K chars).
[
{
"path": ".gitignore",
"chars": 129,
"preview": "train_dir/\ndatasets/mnist\ndatasets/fashion_mnist\ndatasets/svhn\ndatasets/cifar10\n.ropeproject/\n*.py[cod]\n*.sw[op]\n*.hy\n*."
},
{
"path": "LICENSE",
"chars": 1079,
"preview": "The MIT License (MIT)\n\nCopyright (c) 2018 Shao-Hua Sun\n\nPermission is hereby granted, free of charge, to any person obta"
},
{
"path": "README.md",
"chars": 7126,
"preview": "# Group Normalization\n\nAs part of the implementation series of [Joseph Lim's group at USC](http://csail.mit.edu/~lim), o"
},
{
"path": "__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "datasets/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "datasets/cifar10.py",
"chars": 2023,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os."
},
{
"path": "datasets/fashion_mnist.py",
"chars": 2029,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os."
},
{
"path": "datasets/imagenet/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "datasets/imagenet/map.py",
"chars": 31382,
"preview": "class2num = {\n'n02119789': 1, # kit_fox\n'n02100735': 2, # English_setter\n'n02110185': 3, # Siberian_husky\n'n02096294'"
},
{
"path": "datasets/imagenet.py",
"chars": 2446,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os."
},
{
"path": "datasets/mnist.py",
"chars": 2021,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os."
},
{
"path": "datasets/svhn.py",
"chars": 2020,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os."
},
{
"path": "datasets/tiny_imagenet.py",
"chars": 3292,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os."
},
{
"path": "download.py",
"chars": 7089,
"preview": "from __future__ import print_function\nimport os\nimport tarfile\nimport subprocess\nimport argparse\nimport h5py\nimport nump"
},
{
"path": "input_ops.py",
"chars": 2490,
"preview": "import numpy as np\nimport tensorflow as tf\n\nfrom util import log\n\ndef check_data_id(dataset, data_id):\n if not data_i"
},
{
"path": "model.py",
"chars": 4805,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport ten"
},
{
"path": "ops.py",
"chars": 4041,
"preview": "import tensorflow as tf\nimport tensorflow.contrib.slim as slim\nfrom util import log\n\n\ndef norm(x, norm_type, is_train, G"
},
{
"path": "trainer.py",
"chars": 10953,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom six.m"
},
{
"path": "util.py",
"chars": 1556,
"preview": "\"\"\" Utilities \"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_f"
}
]
About this extraction
This page contains the full source code of the shaohua0116/Group-Normalization-Tensorflow GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 19 files (82.5 KB), approximately 26.5k tokens, and a symbol index with 79 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.