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)
64 Dec 22, 2022
502 Jan 3, 2023
12 Mar 17, 2022
4.8k Jan 9, 2023
134 Dec 30, 2022
27 Dec 16, 2022
113 Dec 23, 2022
77 Oct 5, 2022
18 Dec 23, 2022
82 Nov 29, 2022
157 Dec 11, 2022
184 Dec 12, 2022
406 Dec 23, 2022
68 Dec 30, 2022
8 Oct 3, 2022
3 Feb 8, 2022
12 Dec 5, 2022
148 Dec 21, 2022
906 Jan 3, 2023