Release Overview
In theory this version is a stable working version of Alias-Free GAN supporting rosinality's unofficial implementation.
Changes from the Rosinality Repository
- Converted to run using pytorch lighting - Supports GPU and TPU training with many built in options. For more information see: https://pytorch-lightning.readthedocs.io/en/1.4.2/
- Added CPU op library for TPU support
- prepare_data.py renamed to convert_dataset.py and moved to
scripts/convert_dataset.py
- generate.py adapted and put in a script under
scripts/rosinality_generate.py
Scripts Added
Trainer
Creates an Alias-Free GAN instance and trains the model saving checkpoints based on kimgs (thousands of image).
Using Alias-Free GAN version: 1.0.0
usage: trainer.py [-h] --dataset_path DATASET_PATH [--resume_from RESUME_FROM]
--size SIZE [--batch BATCH] [--lr_g LR_G] [--lr_d LR_D]
[--r1 R1] [--augment AUGMENT] [--augment_p AUGMENT_P]
[--ada_target ADA_TARGET] [--ada_length ADA_LENGTH]
[--ada_every ADA_EVERY]
[--stylegan2_discriminator STYLEGAN2_DISCRIMINATOR]
[--save_sample_every_kimgs SAVE_SAMPLE_EVERY_KIMGS]
[--save_checkpoint_every_kimgs SAVE_CHECKPOINT_EVERY_KIMGS]
[--start_kimg_count START_KIMG_COUNT]
[--stop_training_at_kimgs STOP_TRAINING_AT_KIMGS]
[--sample_grid SAMPLE_GRID] [--logger [LOGGER]]
[--checkpoint_callback [CHECKPOINT_CALLBACK]]
[--default_root_dir DEFAULT_ROOT_DIR]
[--gradient_clip_val GRADIENT_CLIP_VAL]
[--gradient_clip_algorithm GRADIENT_CLIP_ALGORITHM]
[--process_position PROCESS_POSITION]
[--num_nodes NUM_NODES] [--num_processes NUM_PROCESSES]
[--devices DEVICES] [--gpus GPUS]
[--auto_select_gpus [AUTO_SELECT_GPUS]]
[--tpu_cores TPU_CORES] [--ipus IPUS]
[--log_gpu_memory LOG_GPU_MEMORY]
[--progress_bar_refresh_rate PROGRESS_BAR_REFRESH_RATE]
[--overfit_batches OVERFIT_BATCHES]
[--track_grad_norm TRACK_GRAD_NORM]
[--check_val_every_n_epoch CHECK_VAL_EVERY_N_EPOCH]
[--fast_dev_run [FAST_DEV_RUN]]
[--accumulate_grad_batches ACCUMULATE_GRAD_BATCHES]
[--max_epochs MAX_EPOCHS] [--min_epochs MIN_EPOCHS]
[--max_steps MAX_STEPS] [--min_steps MIN_STEPS]
[--max_time MAX_TIME]
[--limit_train_batches LIMIT_TRAIN_BATCHES]
[--limit_val_batches LIMIT_VAL_BATCHES]
[--limit_test_batches LIMIT_TEST_BATCHES]
[--limit_predict_batches LIMIT_PREDICT_BATCHES]
[--val_check_interval VAL_CHECK_INTERVAL]
[--flush_logs_every_n_steps FLUSH_LOGS_EVERY_N_STEPS]
[--log_every_n_steps LOG_EVERY_N_STEPS]
[--accelerator ACCELERATOR]
[--sync_batchnorm [SYNC_BATCHNORM]] [--precision PRECISION]
[--weights_summary WEIGHTS_SUMMARY]
[--weights_save_path WEIGHTS_SAVE_PATH]
[--num_sanity_val_steps NUM_SANITY_VAL_STEPS]
[--truncated_bptt_steps TRUNCATED_BPTT_STEPS]
[--resume_from_checkpoint RESUME_FROM_CHECKPOINT]
[--profiler PROFILER] [--benchmark [BENCHMARK]]
[--deterministic [DETERMINISTIC]]
[--reload_dataloaders_every_n_epochs RELOAD_DATALOADERS_EVERY_N_EPOCHS]
[--reload_dataloaders_every_epoch [RELOAD_DATALOADERS_EVERY_EPOCH]]
[--auto_lr_find [AUTO_LR_FIND]]
[--replace_sampler_ddp [REPLACE_SAMPLER_DDP]]
[--terminate_on_nan [TERMINATE_ON_NAN]]
[--auto_scale_batch_size [AUTO_SCALE_BATCH_SIZE]]
[--prepare_data_per_node [PREPARE_DATA_PER_NODE]]
[--plugins PLUGINS] [--amp_backend AMP_BACKEND]
[--amp_level AMP_LEVEL]
[--distributed_backend DISTRIBUTED_BACKEND]
[--move_metrics_to_cpu [MOVE_METRICS_TO_CPU]]
[--multiple_trainloader_mode MULTIPLE_TRAINLOADER_MODE]
[--stochastic_weight_avg [STOCHASTIC_WEIGHT_AVG]]
optional arguments:
-h, --help show this help message and exit
Trainer Script:
--dataset_path DATASET_PATH
Path to dataset. Required!
--resume_from RESUME_FROM
Resume from checkpoint or transfer learn off
pretrained model. Leave blank to train from scratch.
AliasFreeGAN Model:
--size SIZE Pixel dimension of model. Must be 256, 512, or 1024.
Required!
--batch BATCH Batch size. Will be overridden if
--auto_scale_batch_size is used. (default: 16)
--lr_g LR_G Generator learning rate. (default: 0.002)
--lr_d LR_D Discriminator learning rate. (default: 0.002)
--r1 R1 R1 regularization weights. (default: 10.0)
--augment AUGMENT Use augmentations. (default: False)
--augment_p AUGMENT_P
Augment probability, the probability that augmentation
is applied. 0.0 is 0 percent and 1.0 is 100. If set to
0.0 and augment is enabled AdaptiveAugmentation will
be used. (default: 0.0)
--ada_target ADA_TARGET
Target for AdaptiveAugmentation. (default: 0.6)
--ada_length ADA_LENGTH
(default: 500000)
--ada_every ADA_EVERY
How often to update augmentation probabilities when
using AdaptiveAugmentation. (default: 8)
--stylegan2_discriminator STYLEGAN2_DISCRIMINATOR
Provide path to a rosinality stylegan2 checkpoint to
load the discriminator from it. Will load second so if
you load another model first it will override that
discriminator.
kimg Saver Callback:
--save_sample_every_kimgs SAVE_SAMPLE_EVERY_KIMGS
Sets the frequency of saving samples in kimgs
(thousands of image). (default: 1)
--save_checkpoint_every_kimgs SAVE_CHECKPOINT_EVERY_KIMGS
Sets the frequency of saving model checkpoints in
kimgs (thousands of image). (default: 4)
--start_kimg_count START_KIMG_COUNT
Manually override the start count for kimgs. If not
set the count will be inferred from checkpoint name.
If count can not be inferred it will default to 0.
--stop_training_at_kimgs STOP_TRAINING_AT_KIMGS
Automatically stop training at this number of kimgs.
(default: 12800)
--sample_grid SAMPLE_GRID
Sample grid to use for samples. Saved under
assets/sample_grids. (default:
default_5x3_sample_grid)
pl.Trainer:
--logger [LOGGER] Logger (or iterable collection of loggers) for
experiment tracking. A ``True`` value uses the default
``TensorBoardLogger``. ``False`` will disable logging.
If multiple loggers are provided and the `save_dir`
property of that logger is not set, local files
(checkpoints, profiler traces, etc.) are saved in
``default_root_dir`` rather than in the ``log_dir`` of
any of the individual loggers.
--checkpoint_callback [CHECKPOINT_CALLBACK]
If ``True``, enable checkpointing. It will configure a
default ModelCheckpoint callback if there is no user-
defined ModelCheckpoint in :paramref:`~pytorch_lightni
ng.trainer.trainer.Trainer.callbacks`.
--default_root_dir DEFAULT_ROOT_DIR
Default path for logs and weights when no
logger/ckpt_callback passed. Default: ``os.getcwd()``.
Can be remote file paths such as `s3://mybucket/path`
or 'hdfs://path/'
--gradient_clip_val GRADIENT_CLIP_VAL
0 means don't clip.
--gradient_clip_algorithm GRADIENT_CLIP_ALGORITHM
'value' means clip_by_value, 'norm' means
clip_by_norm. Default: 'norm'
--process_position PROCESS_POSITION
orders the progress bar when running multiple models
on same machine.
--num_nodes NUM_NODES
number of GPU nodes for distributed training.
--num_processes NUM_PROCESSES
number of processes for distributed training with
distributed_backend=\"ddp_cpu\"
--devices DEVICES Will be mapped to either `gpus`, `tpu_cores`,
`num_processes` or `ipus`, based on the accelerator
type.
--gpus GPUS number of gpus to train on (int) or which GPUs to
train on (list or str) applied per node
--auto_select_gpus [AUTO_SELECT_GPUS]
If enabled and `gpus` is an integer, pick available
gpus automatically. This is especially useful when
GPUs are configured to be in \"exclusive mode\", such
that only one process at a time can access them.
--tpu_cores TPU_CORES
How many TPU cores to train on (1 or 8) / Single TPU
to train on [1]
--ipus IPUS How many IPUs to train on.
--log_gpu_memory LOG_GPU_MEMORY
None, 'min_max', 'all'. Might slow performance
--progress_bar_refresh_rate PROGRESS_BAR_REFRESH_RATE
How often to refresh progress bar (in steps). Value
``0`` disables progress bar. Ignored when a custom
progress bar is passed to
:paramref:`~Trainer.callbacks`. Default: None, means a
suitable value will be chosen based on the environment
(terminal, Google COLAB, etc.).
--overfit_batches OVERFIT_BATCHES
Overfit a fraction of training data (float) or a set
number of batches (int).
--track_grad_norm TRACK_GRAD_NORM
-1 no tracking. Otherwise tracks that p-norm. May be
set to 'inf' infinity-norm.
--check_val_every_n_epoch CHECK_VAL_EVERY_N_EPOCH
Check val every n train epochs.
--fast_dev_run [FAST_DEV_RUN]
runs n if set to ``n`` (int) else 1 if set to ``True``
batch(es) of train, val and test to find any bugs (ie:
a sort of unit test).
--accumulate_grad_batches ACCUMULATE_GRAD_BATCHES
Accumulates grads every k batches or as set up in the
dict.
--max_epochs MAX_EPOCHS
Stop training once this number of epochs is reached.
Disabled by default (None). If both max_epochs and
max_steps are not specified, defaults to
``max_epochs`` = 1000.
--min_epochs MIN_EPOCHS
Force training for at least these many epochs.
Disabled by default (None). If both min_epochs and
min_steps are not specified, defaults to
``min_epochs`` = 1.
--max_steps MAX_STEPS
Stop training after this number of steps. Disabled by
default (None).
--min_steps MIN_STEPS
Force training for at least these number of steps.
Disabled by default (None).
--max_time MAX_TIME Stop training after this amount of time has passed.
Disabled by default (None). The time duration can be
specified in the format DD:HH:MM:SS (days, hours,
minutes seconds), as a :class:`datetime.timedelta`, or
a dictionary with keys that will be passed to
:class:`datetime.timedelta`.
--limit_train_batches LIMIT_TRAIN_BATCHES
How much of training dataset to check (float =
fraction, int = num_batches)
--limit_val_batches LIMIT_VAL_BATCHES
How much of validation dataset to check (float =
fraction, int = num_batches)
--limit_test_batches LIMIT_TEST_BATCHES
How much of test dataset to check (float = fraction,
int = num_batches)
--limit_predict_batches LIMIT_PREDICT_BATCHES
How much of prediction dataset to check (float =
fraction, int = num_batches)
--val_check_interval VAL_CHECK_INTERVAL
How often to check the validation set. Use float to
check within a training epoch, use int to check every
n steps (batches).
--flush_logs_every_n_steps FLUSH_LOGS_EVERY_N_STEPS
How often to flush logs to disk (defaults to every 100
steps).
--log_every_n_steps LOG_EVERY_N_STEPS
How often to log within steps (defaults to every 50
steps).
--accelerator ACCELERATOR
Previously known as distributed_backend (dp, ddp,
ddp2, etc...). Can also take in an accelerator object
for custom hardware.
--sync_batchnorm [SYNC_BATCHNORM]
Synchronize batch norm layers between process
groups/whole world.
--precision PRECISION
Double precision (64), full precision (32) or half
precision (16). Can be used on CPU, GPU or TPUs.
--weights_summary WEIGHTS_SUMMARY
Prints a summary of the weights when training begins.
--weights_save_path WEIGHTS_SAVE_PATH
Where to save weights if specified. Will override
default_root_dir for checkpoints only. Use this if for
whatever reason you need the checkpoints stored in a
different place than the logs written in
`default_root_dir`. Can be remote file paths such as
`s3://mybucket/path` or 'hdfs://path/' Defaults to
`default_root_dir`.
--num_sanity_val_steps NUM_SANITY_VAL_STEPS
Sanity check runs n validation batches before starting
the training routine. Set it to `-1` to run all
batches in all validation dataloaders.
--truncated_bptt_steps TRUNCATED_BPTT_STEPS
Deprecated in v1.3 to be removed in 1.5. Please use :p
aramref:`~pytorch_lightning.core.lightning.LightningMo
dule.truncated_bptt_steps` instead.
--resume_from_checkpoint RESUME_FROM_CHECKPOINT
Path/URL of the checkpoint from which training is
resumed. If there is no checkpoint file at the path,
start from scratch. If resuming from mid-epoch
checkpoint, training will start from the beginning of
the next epoch.
--profiler PROFILER To profile individual steps during training and assist
in identifying bottlenecks.
--benchmark [BENCHMARK]
If true enables cudnn.benchmark.
--deterministic [DETERMINISTIC]
If true enables cudnn.deterministic.
--reload_dataloaders_every_n_epochs RELOAD_DATALOADERS_EVERY_N_EPOCHS
Set to a non-negative integer to reload dataloaders
every n epochs. Default: 0
--reload_dataloaders_every_epoch [RELOAD_DATALOADERS_EVERY_EPOCH]
Set to True to reload dataloaders every epoch. ..
deprecated:: v1.4 ``reload_dataloaders_every_epoch``
has been deprecated in v1.4 and will be removed in
v1.6. Please use
``reload_dataloaders_every_n_epochs``.
--auto_lr_find [AUTO_LR_FIND]
If set to True, will make trainer.tune() run a
learning rate finder, trying to optimize initial
learning for faster convergence. trainer.tune() method
will set the suggested learning rate in self.lr or
self.learning_rate in the LightningModule. To use a
different key set a string instead of True with the
key name.
--replace_sampler_ddp [REPLACE_SAMPLER_DDP]
Explicitly enables or disables sampler replacement. If
not specified this will toggled automatically when DDP
is used. By default it will add ``shuffle=True`` for
train sampler and ``shuffle=False`` for val/test
sampler. If you want to customize it, you can set
``replace_sampler_ddp=False`` and add your own
distributed sampler.
--terminate_on_nan [TERMINATE_ON_NAN]
If set to True, will terminate training (by raising a
`ValueError`) at the end of each training batch, if
any of the parameters or the loss are NaN or +/-inf.
--auto_scale_batch_size [AUTO_SCALE_BATCH_SIZE]
If set to True, will `initially` run a batch size
finder trying to find the largest batch size that fits
into memory. The result will be stored in
self.batch_size in the LightningModule. Additionally,
can be set to either `power` that estimates the batch
size through a power search or `binsearch` that
estimates the batch size through a binary search.
--prepare_data_per_node [PREPARE_DATA_PER_NODE]
If True, each LOCAL_RANK=0 will call prepare data.
Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare
data
--plugins PLUGINS Plugins allow modification of core behavior like ddp
and amp, and enable custom lightning plugins.
--amp_backend AMP_BACKEND
The mixed precision backend to use (\"native\" or
\"apex\")
--amp_level AMP_LEVEL
The optimization level to use (O1, O2, etc...).
--distributed_backend DISTRIBUTED_BACKEND
deprecated. Please use 'accelerator'
--move_metrics_to_cpu [MOVE_METRICS_TO_CPU]
Whether to force internal logged metrics to be moved
to cpu. This can save some gpu memory, but can make
training slower. Use with attention.
--multiple_trainloader_mode MULTIPLE_TRAINLOADER_MODE
How to loop over the datasets when there are multiple
train loaders. In 'max_size_cycle' mode, the trainer
ends one epoch when the largest dataset is traversed,
and smaller datasets reload when running out of their
data. In 'min_size' mode, all the datasets reload when
reaching the minimum length of datasets.
--stochastic_weight_avg [STOCHASTIC_WEIGHT_AVG]
Whether to use `Stochastic Weight Averaging (SWA)
<https://pytorch.org/blog/pytorch-1.6-now-includes-
stochastic-weight-averaging/>_`\n"
Generate Images
Using Alias-Free GAN version: 1.0.0
usage: generate_images.py [-h] --load_model LOAD_MODEL --outdir OUTDIR
[--model_arch MODEL_ARCH] [--seed_start SEED_START]
[--seed_stop SEED_STOP] [--trunc TRUNC]
[--batch BATCH] --size SIZE
optional arguments:
-h, --help show this help message and exit
Generate Script:
--load_model LOAD_MODEL
Load a model checkpoint to use for generating content.
--outdir OUTDIR Where to save the output images
--model_arch MODEL_ARCH
The model architecture of the model to be loaded.
(default: alias-free-rosinality-v1)
--seed_start SEED_START
Start range for seed values. (default: 0)
--seed_stop SEED_STOP
Stop range for seed values. Is inclusive. (default:
99)
--trunc TRUNC Truncation psi (default: 0.75)
--batch BATCH Number of images to generate each batch. default: 8)
AliasFreeGenerator:
--size SIZE Pixel dimension of model. Must be 256, 512, or 1024.
Required!
Generate Interpolation
Using Alias-Free GAN version: 1.0.0
usage: generate_interpolation.py [-h] --size SIZE --load_model LOAD_MODEL
--outdir OUTDIR [--model_arch MODEL_ARCH]
[--trunc TRUNC] [--batch BATCH]
[--save_z_vectors SAVE_Z_VECTORS]
[--log_args LOG_ARGS] [--method METHOD]
[--path_to_z_vectors PATH_TO_Z_VECTORS]
[--frames FRAMES] [--seeds SEEDS [SEEDS ...]]
[--easing EASING]
[--diameter DIAMETER [DIAMETER ...]]
optional arguments:
-h, --help show this help message and exit
AliasFreeGenerator:
--size SIZE Pixel dimension of model. Must be 256, 512, or 1024.
Required!
Generate Script:
--load_model LOAD_MODEL
Load a model checkpoint to use for generating content.
--outdir OUTDIR Where to save the output images
--model_arch MODEL_ARCH
The model architecture of the model to be loaded.
(default: alias-free-rosinality-v1)
--trunc TRUNC Truncation psi (default: 0.75)
--batch BATCH Number of images to generate each batch. default: 8)
--save_z_vectors SAVE_Z_VECTORS
Save the z vectors used to interpolate. default: True
--log_args LOG_ARGS Saves the arguments to a text file for later
reference. default: True
--method METHOD Select a method for interpolation. Options:
['circular', 'interpolate', 'load_z_vectors',
'simplex_noise'] default: interpolate
--path_to_z_vectors PATH_TO_Z_VECTORS
Path to saved z vectors to load. For method:
'load_z_vectors'
--frames FRAMES Total number of frames to generate. For methods:
'interpolate', 'circular', 'simplex_noise'
--seeds SEEDS [SEEDS ...]
Add a seed value to a interpolation walk. First seed
value will be used as the seed for a circular or noise
walk. If none are provided random ones will be
generated. For methods: 'interpolate', 'circular',
'simplex_noise'
--easing EASING How to ease between seeds. For method: 'interpolate'
Options: ['easeInBack', 'easeInBounce', 'easeInCirc',
'easeInCubic', 'easeInElastic', 'easeInExpo',
'easeInOutBack', 'easeInOutBounce', 'easeInOutCirc',
'easeInOutCubic', 'easeInOutElastic', 'easeInOutExpo',
'easeInOutQuad', 'easeInOutQuart', 'easeInOutQuint',
'easeInOutSine', 'easeInQuad', 'easeInQuart',
'easeInQuint', 'easeInSine', 'easeOutBack',
'easeOutBounce', 'easeOutCirc', 'easeOutCubic',
'easeOutElastic', 'easeOutExpo', 'easeOutQuad',
'easeOutQuart', 'easeOutQuint', 'easeOutSine',
'linear'] default: linear
--diameter DIAMETER [DIAMETER ...]
Defines the diameter of the circular or noise path. If
two arguments are passed they will be used as a min
and max a range for random diameters. For method:
'circular', 'simplex_noise'
Create Sample Grid Vectors
Creates a pytorch file with vectors to be used in trainer script to generate sample grid.
usage: create_sample_grid_vectors.py [-h] [--rows ROWS] [--cols COLS]
[--seed SEED] [--style_dim STYLE_DIM]
[--include_zero_point_five_vec INCLUDE_ZERO_POINT_FIVE_VEC]
--save_location SAVE_LOCATION
optional arguments:
-h, --help show this help message and exit
Create Sample Grid Vectors Script:
--rows ROWS Number of rows in sample grid (default: 3)
--cols COLS Number of columns in sample grid (default: 5)
--seed SEED Random seed to use (default: 0)
--style_dim STYLE_DIM
Style dimension size. (Not the same as model
resolution, you'll proably know if you have to change
this.) (default: 512)
--include_zero_point_five_vec INCLUDE_ZERO_POINT_FIVE_VEC
Include vector with 0.5 for every dimension. Will be
put in 0, 0 spot on the grid. (default: True)
--save_location SAVE_LOCATION
Where the sample grid vectors will be saved.
TPU Setup
A bash script to setup TPU IP addresses on colab.
Use following code to run in .ipynb notebook.
import os
with open('./scripts/tpu_setup.sh') as f:
os.environ.update(
line.replace('export ', '', 1).strip().split('=', 1) for line in f
if 'export' in line
)
pip freeze on Colab
You don't need all these packages but if there are package conflicts in the future this is a working setup for gpu training and inference.
absl-py==0.12.0
aiohttp==3.7.4.post0
alabaster==0.7.12
albumentations==0.1.12
altair==4.1.0
appdirs==1.4.4
argcomplete==1.12.3
argon2-cffi==20.1.0
arviz==0.11.2
astor==0.8.1
astropy==4.3.1
astunparse==1.6.3
async-timeout==3.0.1
atari-py==0.2.9
atomicwrites==1.4.0
attrs==21.2.0
audioread==2.1.9
autograd==1.3
Babel==2.9.1
backcall==0.2.0
beautifulsoup4==4.6.3
bleach==4.0.0
blis==0.4.1
bokeh==2.3.3
Bottleneck==1.3.2
branca==0.4.2
bs4==0.0.1
CacheControl==0.12.6
cached-property==1.5.2
cachetools==4.2.2
catalogue==1.0.0
certifi==2021.5.30
cffi==1.14.6
cftime==1.5.0
chardet==3.0.4
charset-normalizer==2.0.4
clang==5.0
click==7.1.2
cloudpickle==1.3.0
cmake==3.12.0
cmdstanpy==0.9.5
colorcet==2.0.6
colorlover==0.3.0
community==1.0.0b1
configparser==5.0.2
contextlib2==0.5.5
convertdate==2.3.2
coverage==3.7.1
coveralls==0.5
crcmod==1.7
cufflinks==0.17.3
cupy-cuda101==9.1.0
cvxopt==1.2.6
cvxpy==1.0.31
cycler==0.10.0
cymem==2.0.5
Cython==0.29.24
daft==0.0.4
dask==2.12.0
datascience==0.10.6
debugpy==1.0.0
decorator==4.4.2
defusedxml==0.7.1
descartes==1.1.0
dill==0.3.4
distributed==1.25.3
dlib @ file:///dlib-19.18.0-cp37-cp37m-linux_x86_64.whl
dm-tree==0.1.6
docker-pycreds==0.4.0
docopt==0.6.2
docutils==0.17.1
dopamine-rl==1.0.5
earthengine-api==0.1.278
easydict==1.9
ecos==2.0.7.post1
editdistance==0.5.3
en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz
entrypoints==0.3
ephem==4.0.0.2
et-xmlfile==1.1.0
fa2==0.3.5
fastai==1.0.61
fastdtw==0.3.4
fastprogress==1.0.0
fastrlock==0.6
fbprophet==0.7.1
feather-format==0.4.1
filelock==3.0.12
firebase-admin==4.4.0
fix-yahoo-finance==0.0.22
Flask==1.1.4
flatbuffers==1.12
folium==0.8.3
fsspec==2021.7.0
future==0.18.2
gast==0.4.0
GDAL==2.2.2
gdown==3.6.4
gensim==3.6.0
geographiclib==1.52
geopy==1.17.0
gin-config==0.4.0
gitdb==4.0.7
GitPython==3.1.18
glob2==0.7
google==2.0.3
google-api-core==1.26.3
google-api-python-client==1.12.8
google-auth==1.34.0
google-auth-httplib2==0.0.4
google-auth-oauthlib==0.4.5
google-cloud-bigquery==1.21.0
google-cloud-bigquery-storage==1.1.0
google-cloud-core==1.0.3
google-cloud-datastore==1.8.0
google-cloud-firestore==1.7.0
google-cloud-language==1.2.0
google-cloud-storage==1.18.1
google-cloud-translate==1.5.0
google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz
google-pasta==0.2.0
google-resumable-media==0.4.1
googleapis-common-protos==1.53.0
googledrivedownloader==0.4
graphviz==0.10.1
greenlet==1.1.1
grpcio==1.39.0
gspread==3.0.1
gspread-dataframe==3.0.8
gym==0.17.3
h5py==3.1.0
HeapDict==1.0.1
hijri-converter==2.1.3
holidays==0.10.5.2
holoviews==1.14.5
html5lib==1.0.1
httpimport==0.5.18
httplib2==0.17.4
httplib2shim==0.0.3
humanize==0.5.1
hyperopt==0.1.2
ideep4py==2.0.0.post3
idna==2.10
imageio==2.4.1
imagesize==1.2.0
imbalanced-learn==0.4.3
imblearn==0.0
imgaug==0.2.9
importlib-metadata==4.6.4
importlib-resources==5.2.2
imutils==0.5.4
inflect==2.1.0
iniconfig==1.1.1
intel-openmp==2021.3.0
intervaltree==2.1.0
ipykernel==4.10.1
ipython==5.5.0
ipython-genutils==0.2.0
ipython-sql==0.3.9
ipywidgets==7.6.3
itsdangerous==1.1.0
jax==0.2.19
jaxlib @ https://storage.googleapis.com/jax-releases/cuda110/jaxlib-0.1.70+cuda110-cp37-none-manylinux2010_x86_64.whl
jdcal==1.4.1
jedi==0.18.0
jieba==0.42.1
Jinja2==2.11.3
joblib==1.0.1
jpeg4py==0.1.4
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.3.5
jupyter-console==5.2.0
jupyter-core==4.7.1
jupyterlab-pygments==0.1.2
jupyterlab-widgets==1.0.0
kaggle==1.5.12
kapre==0.3.5
keras==2.6.0
Keras-Preprocessing==1.1.2
keras-vis==0.4.1
kiwisolver==1.3.1
korean-lunar-calendar==0.2.1
librosa==0.8.1
lightgbm==2.2.3
llvmlite==0.34.0
lmdb==0.99
LunarCalendar==0.0.9
lxml==4.2.6
Markdown==3.3.4
MarkupSafe==2.0.1
matplotlib==3.2.2
matplotlib-inline==0.1.2
matplotlib-venn==0.11.6
missingno==0.5.0
mistune==0.8.4
mizani==0.6.0
mkl==2019.0
mlxtend==0.14.0
more-itertools==8.8.0
moviepy==0.2.3.5
mpmath==1.2.1
msgpack==1.0.2
multidict==5.1.0
multiprocess==0.70.12.2
multitasking==0.0.9
murmurhash==1.0.5
music21==5.5.0
natsort==5.5.0
nbclient==0.5.4
nbconvert==5.6.1
nbformat==5.1.3
nest-asyncio==1.5.1
netCDF4==1.5.7
networkx==2.6.2
nibabel==3.0.2
ninja==1.10.2
nltk==3.2.5
notebook==5.3.1
numba==0.51.2
numexpr==2.7.3
numpy==1.19.5
nvidia-ml-py3==7.352.0
oauth2client==4.1.3
oauthlib==3.1.1
okgrade==0.4.3
opencv-contrib-python==4.1.2.30
opencv-python==4.1.2.30
opencv-python-headless==4.5.3.56
openpyxl==2.5.9
opensimplex==0.3
opt-einsum==3.3.0
osqp==0.6.2.post0
packaging==21.0
palettable==3.3.0
pandas==1.1.5
pandas-datareader==0.9.0
pandas-gbq==0.13.3
pandas-profiling==1.4.1
pandocfilters==1.4.3
panel==0.12.1
param==1.11.1
parso==0.8.2
pathlib==1.0.1
pathtools==0.1.2
patsy==0.5.1
pep517==0.11.0
pexpect==4.8.0
pickleshare==0.7.5
Pillow==7.1.2
pip-tools==6.2.0
plac==1.1.3
plotly==4.4.1
plotnine==0.6.0
pluggy==0.7.1
pooch==1.4.0
portpicker==1.3.9
prefetch-generator==1.0.1
preshed==3.0.5
prettytable==2.1.0
progressbar2==3.38.0
prometheus-client==0.11.0
promise==2.3
prompt-toolkit==1.0.18
protobuf==3.17.3
psutil==5.4.8
psycopg2==2.7.6.1
ptyprocess==0.7.0
py==1.10.0
pyarrow==3.0.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycocotools==2.0.2
pycparser==2.20
pyct==0.4.8
pydantic==1.8.2
pydata-google-auth==1.2.0
pyDeprecate==0.3.1
pydot==1.3.0
pydot-ng==2.0.0
pydotplus==2.0.2
PyDrive==1.3.1
pyemd==0.5.1
pyerfa==2.0.0
pyglet==1.5.0
Pygments==2.6.1
pygobject==3.26.1
pyhocon==0.3.58
pymc3==3.11.2
PyMeeus==0.5.11
pymongo==3.12.0
pymystem3==0.2.0
PyOpenGL==3.1.5
pyparsing==2.4.7
pyrsistent==0.18.0
pysndfile==1.3.8
PySocks==1.7.1
pystan==2.19.1.1
pytest==3.6.4
python-apt==0.0.0
python-chess==0.23.11
python-dateutil==2.8.2
python-louvain==0.15
python-slugify==5.0.2
python-utils==2.5.6
pytorch-lightning==1.4.2
pytorch-lightning-bolts==0.3.2
pytz==2018.9
pyviz-comms==2.1.0
PyWavelets==1.1.1
PyYAML==5.4.1
pyzmq==22.2.1
qdldl==0.1.5.post0
qtconsole==5.1.1
QtPy==1.10.0
regex==2019.12.20
requests==2.23.0
requests-oauthlib==1.3.0
resampy==0.2.2
retrying==1.3.3
rpy2==3.4.5
rsa==4.7.2
scikit-image==0.16.2
scikit-learn==0.22.2.post1
scipy==1.4.1
screen-resolution-extra==0.0.0
scs==2.1.4
seaborn==0.11.1
semver==2.13.0
Send2Trash==1.8.0
sentry-sdk==1.3.1
setuptools-git==1.2
Shapely==1.7.1
shortuuid==1.0.1
simplegeneric==0.8.1
six==1.15.0
sklearn==0.0
sklearn-pandas==1.8.0
smart-open==5.1.0
smmap==4.0.0
snowballstemmer==2.1.0
sortedcontainers==2.4.0
SoundFile==0.10.3.post1
spacy==2.2.4
Sphinx==1.8.5
sphinxcontrib-serializinghtml==1.1.5
sphinxcontrib-websupport==1.2.4
SQLAlchemy==1.4.22
sqlparse==0.4.1
srsly==1.0.5
statsmodels==0.10.2
subprocess32==3.5.4
sympy==1.7.1
tables==3.4.4
tabulate==0.8.9
tblib==1.7.0
tensorboard==2.6.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tensorflow @ file:///tensorflow-2.6.0-cp37-cp37m-linux_x86_64.whl
tensorflow-datasets==4.0.1
tensorflow-estimator==2.6.0
tensorflow-gcs-config==2.6.0
tensorflow-hub==0.12.0
tensorflow-metadata==1.2.0
tensorflow-probability==0.13.0
termcolor==1.1.0
terminado==0.11.0
testpath==0.5.0
text-unidecode==1.3
textblob==0.15.3
Theano-PyMC==1.1.2
thinc==7.4.0
tifffile==2021.8.8
toml==0.10.2
tomli==1.2.1
toolz==0.11.1
torch @ https://download.pytorch.org/whl/cu102/torch-1.9.0%2Bcu102-cp37-cp37m-linux_x86_64.whl
torchmetrics==0.5.0
torchsummary==1.5.1
torchtext==0.10.0
torchvision @ https://download.pytorch.org/whl/cu102/torchvision-0.10.0%2Bcu102-cp37-cp37m-linux_x86_64.whl
tornado==5.1.1
tqdm==4.62.0
traitlets==5.0.5
tweepy==3.10.0
typeguard==2.7.1
typing-extensions==3.7.4.3
tzlocal==1.5.1
uritemplate==3.0.1
urllib3==1.24.3
vega-datasets==0.9.0
wandb==0.12.0
wasabi==0.8.2
wcwidth==0.2.5
webencodings==0.5.1
Werkzeug==1.0.1
widgetsnbextension==3.5.1
wordcloud==1.5.0
wrapt==1.12.1
xarray==0.18.2
xgboost==0.90
xkit==0.0.0
xlrd==1.1.0
xlwt==1.3.0
yarl==1.6.3
yellowbrick==0.9.1
zict==2.0.0
zipp==3.5.0
Source code(tar.gz)
Source code(zip)
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