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albumentations-team / autoalbument

AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
https://albumentations.ai/docs/autoalbument/
MIT License
198 stars 20 forks source link

FAA Model str error #14

Closed haritsahm closed 2 years ago

haritsahm commented 3 years ago

Hey guys! I got this error pop up after run !autoalbument-search --config-dir . with my custom model and dataset When i tried the CIFAR10 example, everything went well.

Can you guys show me what's wrong? Thank you

densenet.py

# Similar to DenseNet from PyTorch but with a little modification
class DenseNetClassificationModel(BaseDiscriminator):
    def __init__(self, *args, **kwargs):
        super().__init__()
        self.base_model = densenet121(num_classes=12)
        num_features = self.base_model.classifier.in_features
        self.discriminator = nn.Sequential(
            nn.Linear(num_features, num_features), nn.SiLU(), nn.Linear(num_features, 1)
        )

    def forward(self, input):
        x = self.base_model.forward_features(input)
        return self.base_model.forward_classifier(x), self.discriminator(x).view(-1)

dataset.py

import os
import cv2
import pandas as pd
import torch.utils.data

class SearchDataset(torch.utils.data.Dataset):

    def __init__(self, csv_path, image_dir, transform=None):
        df = pd.read_csv(csv_path)
        self.image_id = df['image'].values
        self.labels = df['labels'].values
        self.transform = transform
        self.image_dir = image_dir

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

    def __getitem__(self, idx):
        image_id = self.image_id[idx]
        label = self.labels[idx]

        image_path = os.path.join(self.image_dir, image_id)
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        augmented = self.transform(image=image)
        image = augmented['image']
        return {'image':image, 'target': label}

Error output

_version: 2
task: classification
policy_model:
  task_factor: 0.1
  gp_factor: 10
  temperature: 0.05
  num_sub_policies: 40
  num_chunks: 4
  operation_count: 4
  operations:
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_r: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_g: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_b: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.RandomBrightness
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.RandomContrast
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Solarize
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.HorizontalFlip
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.VerticalFlip
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Rotate
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftX
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftY
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Scale
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.CutoutFixedNumberOfHoles
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.CutoutFixedSize
classification_model:
  _target_: models.DenseNetClassificationModel
  num_classes: _MISSING_
  architecture: resnet18
  pretrained: false
data:
  dataset:
    _target_: dataset.SearchDataset
    csv_path: /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/train_encoded.csv
    image_dir: /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/train_images/img_sz_512/
  input_dtype: uint8
  preprocessing:
  - Resize:
      height: 512
      width: 512
  normalization:
    mean:
    - 0.485
    - 0.456
    - 0.406
    std:
    - 0.229
    - 0.224
    - 0.225
  dataloader:
    _target_: torch.utils.data.DataLoader
    batch_size: 16
    shuffle: true
    num_workers: 8
    pin_memory: true
    drop_last: true
searcher:
  _target_: autoalbument.faster_autoaugment.search.FasterAutoAugmentSearcher
trainer:
  _target_: pytorch_lightning.Trainer
  gpus: 1
  benchmark: true
  max_epochs: 20
  resume_from_checkpoint: null
optim:
  main:
    _target_: torch.optim.Adam
    lr: 0.001
    betas:
    - 0
    - 0.999
  policy:
    _target_: torch.optim.Adam
    lr: 0.001
    betas:
    - 0
    - 0.999
callbacks:
- _target_: autoalbument.callbacks.MonitorAverageParameterChange
- _target_: autoalbument.callbacks.SavePolicy
- _target_: pytorch_lightning.callbacks.ModelCheckpoint
  save_last: true
  dirpath: checkpoints
logger:
  _target_: pytorch_lightning.loggers.TensorBoardLogger
  save_dir: /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/outputs/2021-03-28/12-14-12/tensorboard_logs
seed: 42

Working directory: /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/outputs/2021-03-28/12-14-12
[2021-03-28 12:14:15,595][pytorch_lightning.utilities.seed][INFO] - Global seed set to 42
[2021-03-28 12:14:16,803][autoalbument.faster_autoaugment.datamodule][INFO] - Preprocessing transform:
Compose([
  Resize(always_apply=False, p=1, height=512, width=512, interpolation=1),
  Normalize(always_apply=False, p=1.0, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255),
  ToTensorV2(always_apply=True, p=1.0, transpose_mask=True),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:52: UserWarning: ModelCheckpoint(save_last=True, monitor=None) is a redundant configuration. You can save the last checkpoint with ModelCheckpoint(save_top_k=None, monitor=None).
  warnings.warn(*args, **kwargs)
[2021-03-28 12:14:17,028][pytorch_lightning.utilities.distributed][INFO] - GPU available: True, used: True
[2021-03-28 12:14:17,029][pytorch_lightning.utilities.distributed][INFO] - TPU available: None, using: 0 TPU cores
CSV Path /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/train_encoded.csv
Image dir /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/train_images/img_sz_512/
CSV Path /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/train_encoded.csv
Image dir /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/train_images/img_sz_512/
[2021-03-28 12:14:17,370][pytorch_lightning.accelerators.gpu][INFO] - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
2021-03-28 12:14:20.942561: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
[2021-03-28 12:14:22,133][pytorch_lightning.core.lightning][INFO] - 
  | Name         | Type                        | Params
-------------------------------------------------------------
0 | main_model   | DenseNetClassificationModel | 15.0 M
1 | policy_model | Policy                      | 6.4 K 
-------------------------------------------------------------
15.0 M    Trainable params
0         Non-trainable params
15.0 M    Total params
59.841    Total estimated model params size (MB)
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
  cpuset_checked))
Epoch 0:   0% 0/1164 [00:03<?, ?it/s]
Traceback (most recent call last):
  File "/usr/local/bin/autoalbument-search", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.7/dist-packages/hydra/main.py", line 37, in decorated_main
    strict=strict,
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 347, in _run_hydra
    lambda: hydra.run(
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 201, in run_and_report
    raise ex
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 198, in run_and_report
    return func()
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 350, in <lambda>
    overrides=args.overrides,
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/hydra.py", line 112, in run
    configure_logging=with_log_configuration,
  File "/usr/local/lib/python3.7/dist-packages/hydra/core/utils.py", line 127, in run_job
    ret.return_value = task_function(task_cfg)
  File "/usr/local/lib/python3.7/dist-packages/autoalbument/cli/search.py", line 55, in main
    searcher.search()
  File "/usr/local/lib/python3.7/dist-packages/autoalbument/faster_autoaugment/search.py", line 65, in search
    self.trainer.fit(self.model, datamodule=self.datamodule)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 499, in fit
    self.dispatch()
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 546, in dispatch
    self.accelerator.start_training(self)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py", line 73, in start_training
    self.training_type_plugin.start_training(trainer)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 114, in start_training
    self._results = trainer.run_train()
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 637, in run_train
    self.train_loop.run_training_epoch()
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py", line 493, in run_training_epoch
    batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py", line 659, in run_training_batch
    split_batch, batch_idx, opt_idx, self.trainer.hiddens
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py", line 293, in training_step
    training_step_output = self.trainer.accelerator.training_step(args)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py", line 156, in training_step
    return self.training_type_plugin.training_step(*args)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 125, in training_step
    return self.lightning_module.training_step(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/autoalbument/faster_autoaugment/models/faa_model.py", line 71, in training_step
    b = input.size(0) // 2
AttributeError: 'str' object has no attribute 'size'
haritsahm commented 3 years ago

It turned out the return data from dataset.py was wrong. dataset.py

import os
import cv2
import pandas as pd
import torch.utils.data

class SearchDataset(torch.utils.data.Dataset):

    def __init__(self, csv_path, image_dir, transform=None):
        df = pd.read_csv(csv_path)
        self.image_id = df['image'].values
        self.labels = df['labels'].values
        self.transform = transform
        self.image_dir = image_dir

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

    def __getitem__(self, idx):
        image_id = self.image_id[idx]
        label = self.labels[idx]

        image_path = os.path.join(self.image_dir, image_id)
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        if self.transform is not None:
            transformed = self.transform(image=image)
            image = transformed["image"]

        return image, label

I fixed it but another error popped up

env: HYDRA_FULL_ERROR=1
_version: 2
task: classification
policy_model:
  task_factor: 0.1
  gp_factor: 10
  temperature: 0.05
  num_sub_policies: 40
  num_chunks: 4
  operation_count: 4
  operations:
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_r: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_g: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_b: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.RandomBrightness
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.RandomContrast
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Solarize
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.HorizontalFlip
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.VerticalFlip
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Rotate
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftX
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftY
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Scale
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.CutoutFixedNumberOfHoles
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.CutoutFixedSize
classification_model:
  _target_: models.DenseNetClassificationModel
  num_classes: _MISSING_
  architecture: resnet18
  pretrained: false
data:
  dataset:
    _target_: dataset.SearchDataset
    csv_path: /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/train_encoded.csv
    image_dir: /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/train_images/img_sz_512/
  input_dtype: uint8
  preprocessing:
  - Resize:
      height: 512
      width: 512
  normalization:
    mean:
    - 0.485
    - 0.456
    - 0.406
    std:
    - 0.229
    - 0.224
    - 0.225
  dataloader:
    _target_: torch.utils.data.DataLoader
    batch_size: 16
    shuffle: true
    num_workers: 8
    pin_memory: true
    drop_last: true
searcher:
  _target_: autoalbument.faster_autoaugment.search.FasterAutoAugmentSearcher
trainer:
  _target_: pytorch_lightning.Trainer
  gpus: 1
  benchmark: false
  max_epochs: 20
  resume_from_checkpoint: null
optim:
  main:
    _target_: torch.optim.Adam
    lr: 0.001
    betas:
    - 0
    - 0.999
  policy:
    _target_: torch.optim.Adam
    lr: 0.001
    betas:
    - 0
    - 0.999
callbacks:
- _target_: autoalbument.callbacks.MonitorAverageParameterChange
- _target_: autoalbument.callbacks.SavePolicy
- _target_: pytorch_lightning.callbacks.ModelCheckpoint
  save_last: true
  dirpath: checkpoints
logger:
  _target_: pytorch_lightning.loggers.TensorBoardLogger
  save_dir: /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/outputs/2021-03-28/12-37-33/tensorboard_logs
seed: 42

Working directory: /content/Kaggle-Plant-Pathology-2021/Kaggle-Plant-Pathology-2021/outputs/2021-03-28/12-37-33
[2021-03-28 12:37:36,242][pytorch_lightning.utilities.seed][INFO] - Global seed set to 42
[2021-03-28 12:37:37,456][autoalbument.faster_autoaugment.datamodule][INFO] - Preprocessing transform:
Compose([
  Resize(always_apply=False, p=1, height=512, width=512, interpolation=1),
  Normalize(always_apply=False, p=1.0, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255),
  ToTensorV2(always_apply=True, p=1.0, transpose_mask=True),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:52: UserWarning: ModelCheckpoint(save_last=True, monitor=None) is a redundant configuration. You can save the last checkpoint with ModelCheckpoint(save_top_k=None, monitor=None).
  warnings.warn(*args, **kwargs)
[2021-03-28 12:37:37,678][pytorch_lightning.utilities.distributed][INFO] - GPU available: True, used: True
[2021-03-28 12:37:37,679][pytorch_lightning.utilities.distributed][INFO] - TPU available: None, using: 0 TPU cores
[2021-03-28 12:37:38,028][pytorch_lightning.accelerators.gpu][INFO] - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
2021-03-28 12:37:41.636444: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
[2021-03-28 12:37:42,830][pytorch_lightning.core.lightning][INFO] - 
  | Name         | Type                        | Params
-------------------------------------------------------------
0 | main_model   | DenseNetClassificationModel | 15.0 M
1 | policy_model | Policy                      | 6.4 K 
-------------------------------------------------------------
15.0 M    Trainable params
0         Non-trainable params
15.0 M    Total params
59.841    Total estimated model params size (MB)
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
  cpuset_checked))
Epoch 0:   0% 0/1164 [00:04<?, ?it/s]
Traceback (most recent call last):
  File "/usr/local/bin/autoalbument-search", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.7/dist-packages/hydra/main.py", line 37, in decorated_main
    strict=strict,
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 347, in _run_hydra
    lambda: hydra.run(
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 201, in run_and_report
    raise ex
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 198, in run_and_report
    return func()
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/utils.py", line 350, in <lambda>
    overrides=args.overrides,
  File "/usr/local/lib/python3.7/dist-packages/hydra/_internal/hydra.py", line 112, in run
    configure_logging=with_log_configuration,
  File "/usr/local/lib/python3.7/dist-packages/hydra/core/utils.py", line 127, in run_job
    ret.return_value = task_function(task_cfg)
  File "/usr/local/lib/python3.7/dist-packages/autoalbument/cli/search.py", line 55, in main
    searcher.search()
  File "/usr/local/lib/python3.7/dist-packages/autoalbument/faster_autoaugment/search.py", line 65, in search
    self.trainer.fit(self.model, datamodule=self.datamodule)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 499, in fit
    self.dispatch()
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 546, in dispatch
    self.accelerator.start_training(self)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py", line 73, in start_training
    self.training_type_plugin.start_training(trainer)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 114, in start_training
    self._results = trainer.run_train()
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py", line 637, in run_train
    self.train_loop.run_training_epoch()
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py", line 493, in run_training_epoch
    batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py", line 659, in run_training_batch
    split_batch, batch_idx, opt_idx, self.trainer.hiddens
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/training_loop.py", line 293, in training_step
    training_step_output = self.trainer.accelerator.training_step(args)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py", line 156, in training_step
    return self.training_type_plugin.training_step(*args)
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 125, in training_step
    return self.lightning_module.training_step(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/autoalbument/faster_autoaugment/models/faa_model.py", line 86, in training_step
    self.manual_backward(d_n_loss.unsqueeze(0), main_optimizer, -ones.unsqueeze(0))
  File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/lightning.py", line 1225, in manual_backward
    self.trainer.train_loop.backward(loss, optimizer=None, opt_idx=None, *args, **kwargs)
TypeError: backward() got multiple values for argument 'optimizer'
creafz commented 3 years ago

Hey @haritsahm

Could you please provide the output of the pip freeze command in your Python environment?

haritsahm commented 3 years ago

Hey @creafz, Here is the output of pip freeze. Oh i forgot to mention that I use this in Google Colab

/content
/content/Kaggle-Plant-Pathology-2021
absl-py==0.12.0
aiohttp==3.7.4.post0
alabaster==0.7.12
albumentations==0.5.2
altair==4.1.0
antlr4-python3-runtime==4.8
appdirs==1.4.4
argon2-cffi==20.1.0
astor==0.8.1
astropy==4.2
astunparse==1.6.3
async-generator==1.10
async-timeout==3.0.1
atari-py==0.2.6
atomicwrites==1.4.0
attrs==20.3.0
audioread==2.1.9
autoalbument==0.4.0
autograd==1.3
Babel==2.9.0
backcall==0.2.0
beautifulsoup4==4.6.3
bleach==3.3.0
blis==0.4.1
bokeh==2.3.0
Bottleneck==1.3.2
branca==0.4.2
bs4==0.0.1
CacheControl==0.12.6
cachetools==4.2.1
catalogue==1.0.0
certifi==2020.12.5
cffi==1.14.5
chainer==7.4.0
chardet==3.0.4
click==7.1.2
cloudpickle==1.3.0
cmake==3.12.0
cmdstanpy==0.9.5
colorama==0.4.4
colorcet==2.0.6
colorlover==0.3.0
community==1.0.0b1
contextlib2==0.5.5
convertdate==2.3.2
coverage==3.7.1
coveralls==0.5
crcmod==1.7
cufflinks==0.17.3
cupy-cuda101==7.4.0
cvxopt==1.2.6
cvxpy==1.0.31
cycler==0.10.0
cymem==2.0.5
Cython==0.29.22
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==0.3.3
distributed==1.25.3
dlib==19.18.0
dm-tree==0.1.5
docopt==0.6.2
docutils==0.16
dopamine-rl==1.0.5
earthengine-api==0.1.258
easydict==1.9
ecos==2.0.7.post1
editdistance==0.5.3
efficientnet-pytorch==0.6.3
en-core-web-sm==2.2.5
entrypoints==0.3
ephem==3.7.7.1
et-xmlfile==1.0.1
fa2==0.3.5
fancyimpute==0.4.3
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==1.1.2
flatbuffers==1.12
folium==0.8.3
fsspec==0.8.7
future==0.18.2
gast==0.3.3
GDAL==2.2.2
gdown==3.6.4
gensim==3.6.0
geographiclib==1.50
geopy==1.17.0
gin-config==0.4.0
glob2==0.7
google==2.0.3
google-api-core==1.26.2
google-api-python-client==1.12.8
google-auth==1.28.0
google-auth-httplib2==0.0.4
google-auth-oauthlib==0.4.3
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==1.0.0
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==1.0.0
grpcio==1.32.0
gspread==3.0.1
gspread-dataframe==3.0.8
gym==0.17.3
h5py==2.10.0
HeapDict==1.0.1
hijri-converter==2.1.1
holidays==0.10.5.2
holoviews==1.14.2
html5lib==1.0.1
httpimport==0.5.18
httplib2==0.17.4
httplib2shim==0.0.3
humanize==0.5.1
hydra-core==1.0.6
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==0.4.0
importlib-metadata==3.8.1
importlib-resources==5.1.2
imutils==0.5.4
inflect==2.1.0
iniconfig==1.1.1
intel-openmp==2021.2.0
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==0.2.11
jaxlib==0.1.64+cuda110
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==0.1.3.1
Keras==2.4.3
Keras-Preprocessing==1.1.2
keras-vis==0.4.1
kiwisolver==1.3.1
knnimpute==0.1.0
korean-lunar-calendar==0.2.1
librosa==0.8.0
lightgbm==2.2.3
llvmlite==0.34.0
lmdb==0.99
LunarCalendar==0.0.9
lxml==4.2.6
Markdown==3.3.4
MarkupSafe==1.1.1
matplotlib==3.2.2
matplotlib-venn==0.11.6
missingno==0.4.2
mistune==0.8.4
mizani==0.6.0
mkl==2019.0
mlxtend==0.14.0
more-itertools==8.7.0
moviepy==0.2.3.5
mpmath==1.2.1
msgpack==1.0.2
multidict==5.1.0
multiprocess==0.70.11.1
multitasking==0.0.9
munch==2.5.0
murmurhash==1.0.5
music21==5.5.0
natsort==5.5.0
nbclient==0.5.3
nbconvert==5.6.1
nbformat==5.1.2
nest-asyncio==1.5.1
networkx==2.5
nibabel==3.0.2
nltk==3.2.5
notebook==5.3.1
np-utils==0.5.12.1
numba==0.51.2
numexpr==2.7.3
numpy==1.19.5
nvidia-ml-py3==7.352.0
oauth2client==4.1.3
oauthlib==3.1.0
okgrade==0.4.3
omegaconf==2.0.6
opencv-contrib-python==4.1.2.30
opencv-python==4.1.2.30
opencv-python-headless==4.5.1.48
openpyxl==2.5.9
opt-einsum==3.3.0
osqp==0.6.2.post0
packaging==20.9
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==0.11.1
param==1.10.1
parso==0.8.1
pathlib==1.0.1
patsy==0.5.1
pexpect==4.8.0
pickleshare==0.7.5
Pillow==7.1.2
pip-tools==4.5.1
plac==1.1.3
plotly==4.4.1
plotnine==0.6.0
pluggy==0.7.1
pooch==1.3.0
portpicker==1.3.1
prefetch-generator==1.0.1
preshed==3.0.5
pretrainedmodels==0.7.4
prettytable==2.1.0
progressbar2==3.38.0
prometheus-client==0.9.0
promise==2.3
prompt-toolkit==1.0.18
protobuf==3.12.4
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
pydata-google-auth==1.1.0
pydot==1.3.0
pydot-ng==2.0.0
pydotplus==2.0.2
PyDrive==1.3.1
pyemd==0.5.1
pyerfa==1.7.2
pyglet==1.5.0
Pygments==2.6.1
pygobject==3.26.1
pymc3==3.7
PyMeeus==0.5.11
pymongo==3.11.3
pymystem3==0.2.0
PyOpenGL==3.1.5
pyparsing==2.4.7
pyrsistent==0.17.3
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==2.8.1
python-louvain==0.15
python-slugify==4.0.1
python-utils==2.5.6
pytorch-lightning==1.2.6
pytz==2018.9
pyviz-comms==2.0.1
PyWavelets==1.1.1
PyYAML==5.1.2
pyzmq==22.0.3
qdldl==0.1.5.post0
qtconsole==5.0.3
QtPy==1.9.0
regex==2019.12.20
requests==2.23.0
requests-oauthlib==1.3.0
resampy==0.2.2
retrying==1.3.3
rpy2==3.4.3
rsa==4.7.2
ruamel.yaml==0.17.2
ruamel.yaml.clib==0.2.2
scikit-image==0.16.2
scikit-learn==0.22.2.post1
scipy==1.4.1
screen-resolution-extra==0.0.0
scs==2.1.2
seaborn==0.11.1
segmentation-models-pytorch==0.1.3
Send2Trash==1.5.0
setuptools-git==1.2
Shapely==1.7.1
simplegeneric==0.8.1
six==1.15.0
sklearn==0.0
sklearn-pandas==1.8.0
smart-open==4.2.0
snowballstemmer==2.1.0
sortedcontainers==2.3.0
SoundFile==0.10.3.post1
spacy==2.2.4
Sphinx==1.8.5
sphinxcontrib-serializinghtml==1.1.4
sphinxcontrib-websupport==1.2.4
SQLAlchemy==1.4.3
sqlparse==0.4.1
srsly==1.0.5
statsmodels==0.10.2
sympy==1.7.1
tables==3.4.4
tabulate==0.8.9
tblib==1.7.0
tensorboard==2.4.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.4.1
tensorflow-datasets==4.0.1
tensorflow-estimator==2.4.0
tensorflow-gcs-config==2.4.0
tensorflow-hub==0.11.0
tensorflow-metadata==0.29.0
tensorflow-probability==0.12.1
termcolor==1.1.0
terminado==0.9.3
testpath==0.4.4
text-unidecode==1.3
textblob==0.15.3
textgenrnn==1.4.1
Theano==1.0.5
thinc==7.4.0
tifffile==2021.3.17
timm==0.3.2
toml==0.10.2
toolz==0.11.1
torch==1.8.1+cu101
torch-summary==1.4.5
torchmetrics==0.2.0
torchsummary==1.5.1
torchtext==0.9.1
torchvision==0.9.1+cu101
tornado==5.1.1
tqdm==4.41.1
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
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==0.15.1
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==3.4.1
creafz commented 3 years ago

Could you please try to downgrade PyTorch-Lightning to version 1.1.8 and rerun the code? The current version of AutoAlbument requires pytorch-lightning>=1.1.8,<1.2.

haritsahm commented 3 years ago

Okay so I was able to run the autoalbument-search but then the execution is finished without training or other processes. No errors, no warnings, just stop at the end CUDA Device.

env: HYDRA_FULL_ERROR=1
_version: 2
task: classification
policy_model:
  task_factor: 0.1
  gp_factor: 10
  temperature: 0.05
  num_sub_policies: 40
  num_chunks: 8
  operation_count: 6
  operations:
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_r: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_g: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftRGB
    shift_b: true
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.RandomBrightness
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.RandomContrast
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Solarize
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.HorizontalFlip
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.VerticalFlip
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Rotate
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftX
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.ShiftY
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.Scale
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.CutoutFixedNumberOfHoles
  - _target_: autoalbument.faster_autoaugment.models.policy_operations.CutoutFixedSize
classification_model:
  _target_: DenseNet.DenseNetClassificationModel
  num_classes: _MISSING_
  architecture: resnet18
  pretrained: false
data:
  dataset:
    _target_: dataset.SearchDataset
    csv_path: /content/Kaggle-Plant-Pathology-2021/train_simplified.csv
    image_dir: /content/train_images/img_sz_256
  input_dtype: uint8
  preprocessing:
  - Resize:
      height: 224
      width: 224
  normalization:
    mean:
    - 0.485
    - 0.456
    - 0.406
    std:
    - 0.229
    - 0.224
    - 0.225
  dataloader:
    _target_: torch.utils.data.DataLoader
    batch_size: 24
    shuffle: true
    num_workers: 4
    pin_memory: true
    drop_last: true
searcher:
  _target_: autoalbument.faster_autoaugment.search.FasterAutoAugmentSearcher
trainer:
  _target_: pytorch_lightning.Trainer
  gpus: 1
  benchmark: true
  max_epochs: 40
  resume_from_checkpoint: null
optim:
  main:
    _target_: torch.optim.Adam
    lr: 0.001
    betas:
    - 0
    - 0.999
  policy:
    _target_: torch.optim.Adam
    lr: 0.001
    betas:
    - 0
    - 0.999
callbacks:
- _target_: autoalbument.callbacks.MonitorAverageParameterChange
- _target_: autoalbument.callbacks.SavePolicy
- _target_: pytorch_lightning.callbacks.ModelCheckpoint
  save_last: true
  dirpath: checkpoints
logger:
  _target_: pytorch_lightning.loggers.TensorBoardLogger
  save_dir: /content/Kaggle-Plant-Pathology-2021/outputs/2021-04-09/09-25-42/tensorboard_logs
seed: 42

Working directory: /content/Kaggle-Plant-Pathology-2021/outputs/2021-04-09/09-25-42
[2021-04-09 09:25:45,004][pytorch_lightning.utilities.seed][INFO] - Global seed set to 42
Deleting  features.conv0.weight
Deleting  features.norm0.weight
Deleting  features.norm0.bias
Deleting  features.norm0.running_mean
Deleting  features.norm0.running_var
Deleting  features.transition1.norm.weight
Deleting  features.transition1.norm.bias
Deleting  features.transition1.norm.running_mean
Deleting  features.transition1.norm.running_var
Deleting  features.transition1.conv.weight
Deleting  features.transition2.norm.weight
Deleting  features.transition2.norm.bias
Deleting  features.transition2.norm.running_mean
Deleting  features.transition2.norm.running_var
Deleting  features.transition2.conv.weight
Deleting  features.transition3.norm.weight
Deleting  features.transition3.norm.bias
Deleting  features.transition3.norm.running_mean
Deleting  features.transition3.norm.running_var
Deleting  features.transition3.conv.weight
Deleting  features.norm5.weight
Deleting  features.norm5.bias
Deleting  features.norm5.running_mean
Deleting  features.norm5.running_var
Deleting  classifier.weight
Deleting  classifier.bias
[2021-04-09 09:25:46,412][autoalbument.faster_autoaugment.datamodule][INFO] - Preprocessing transform:
Compose([
  Resize(always_apply=False, p=1, height=224, width=224, interpolation=1),
  Normalize(always_apply=False, p=1.0, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255),
  ToTensorV2(always_apply=True, p=1.0, transpose_mask=True),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:50: UserWarning: ModelCheckpoint(save_last=True, monitor=None) is a redundant configuration. You can save the last checkpoint with ModelCheckpoint(save_top_k=None, monitor=None).
  warnings.warn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/distributed.py:50: UserWarning: You have set progress_bar_refresh_rate < 20 on Google Colab. This may crash. Consider using progress_bar_refresh_rate >= 20 in Trainer.
  warnings.warn(*args, **kwargs)
[2021-04-09 09:25:46,626][pytorch_lightning.utilities.distributed][INFO] - GPU available: True, used: True
[2021-04-09 09:25:46,626][pytorch_lightning.utilities.distributed][INFO] - TPU available: None, using: 0 TPU cores
[2021-04-09 09:25:46,626][pytorch_lightning.accelerators.accelerator_connector][INFO] - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

Here is the output of my pip freeze

absl-py==0.12.0
aiohttp==3.7.4.post0
alabaster==0.7.12
albumentations==0.5.2
altair==4.1.0
antlr4-python3-runtime==4.8
appdirs==1.4.4
argon2-cffi==20.1.0
astor==0.8.1
astropy==4.2
astunparse==1.6.3
async-generator==1.10
async-timeout==3.0.1
atari-py==0.2.6
atomicwrites==1.4.0
attrs==20.3.0
audioread==2.1.9
autoalbument==0.4.0
autograd==1.3
Babel==2.9.0
backcall==0.2.0
beautifulsoup4==4.6.3
bleach==3.3.0
blis==0.4.1
bokeh==2.3.0
Bottleneck==1.3.2
branca==0.4.2
bs4==0.0.1
CacheControl==0.12.6
cachetools==4.2.1
catalogue==1.0.0
certifi==2020.12.5
cffi==1.14.5
chainer==7.4.0
chardet==3.0.4
click==7.1.2
cloudpickle==1.3.0
cmake==3.12.0
cmdstanpy==0.9.5
colorama==0.4.4
colorcet==2.0.6
colorlover==0.3.0
community==1.0.0b1
contextlib2==0.5.5
convertdate==2.3.2
coverage==3.7.1
coveralls==0.5
crcmod==1.7
cufflinks==0.17.3
cupy-cuda101==7.4.0
cvxopt==1.2.6
cvxpy==1.0.31
cycler==0.10.0
cymem==2.0.5
Cython==0.29.22
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==0.3.3
distributed==1.25.3
dlib==19.18.0
dm-tree==0.1.5
docopt==0.6.2
docutils==0.16
dopamine-rl==1.0.5
earthengine-api==0.1.258
easydict==1.9
ecos==2.0.7.post1
editdistance==0.5.3
efficientnet-pytorch==0.6.3
en-core-web-sm==2.2.5
entrypoints==0.3
ephem==3.7.7.1
et-xmlfile==1.0.1
fa2==0.3.5
fancyimpute==0.4.3
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==1.1.2
flatbuffers==1.12
folium==0.8.3
fsspec==0.9.0
future==0.18.2
gast==0.3.3
GDAL==2.2.2
gdown==3.6.4
gensim==3.6.0
geographiclib==1.50
geopy==1.17.0
gin-config==0.4.0
glob2==0.7
google==2.0.3
google-api-core==1.26.2
google-api-python-client==1.12.8
google-auth==1.28.0
google-auth-httplib2==0.0.4
google-auth-oauthlib==0.4.3
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==1.0.0
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==1.0.0
grpcio==1.32.0
gspread==3.0.1
gspread-dataframe==3.0.8
gym==0.17.3
h5py==2.10.0
HeapDict==1.0.1
hijri-converter==2.1.1
holidays==0.10.5.2
holoviews==1.14.2
html5lib==1.0.1
httpimport==0.5.18
httplib2==0.17.4
httplib2shim==0.0.3
humanize==0.5.1
hydra-core==1.0.6
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==0.4.0
importlib-metadata==3.8.1
importlib-resources==5.1.2
imutils==0.5.4
inflect==2.1.0
iniconfig==1.1.1
intel-openmp==2021.2.0
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==0.2.11
jaxlib==0.1.64+cuda110
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==0.1.3.1
Keras==2.4.3
Keras-Preprocessing==1.1.2
keras-vis==0.4.1
kiwisolver==1.3.1
knnimpute==0.1.0
korean-lunar-calendar==0.2.1
librosa==0.8.0
lightgbm==2.2.3
llvmlite==0.34.0
lmdb==0.99
LunarCalendar==0.0.9
lxml==4.2.6
Markdown==3.3.4
MarkupSafe==1.1.1
matplotlib==3.2.2
matplotlib-venn==0.11.6
missingno==0.4.2
mistune==0.8.4
mizani==0.6.0
mkl==2019.0
mlxtend==0.14.0
more-itertools==8.7.0
moviepy==0.2.3.5
mpmath==1.2.1
msgpack==1.0.2
multidict==5.1.0
multiprocess==0.70.11.1
multitasking==0.0.9
munch==2.5.0
murmurhash==1.0.5
music21==5.5.0
natsort==5.5.0
nbclient==0.5.3
nbconvert==5.6.1
nbformat==5.1.2
nest-asyncio==1.5.1
networkx==2.5
nibabel==3.0.2
nltk==3.2.5
notebook==5.3.1
np-utils==0.5.12.1
numba==0.51.2
numexpr==2.7.3
numpy==1.19.5
nvidia-ml-py3==7.352.0
oauth2client==4.1.3
oauthlib==3.1.0
okgrade==0.4.3
omegaconf==2.0.6
opencv-contrib-python==4.1.2.30
opencv-python==4.1.2.30
opencv-python-headless==4.5.1.48
openpyxl==2.5.9
opt-einsum==3.3.0
osqp==0.6.2.post0
packaging==20.9
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==0.11.1
param==1.10.1
parso==0.8.2
pathlib==1.0.1
patsy==0.5.1
pexpect==4.8.0
pickleshare==0.7.5
Pillow==7.1.2
pip-tools==4.5.1
plac==1.1.3
plotly==4.4.1
plotnine==0.6.0
pluggy==0.7.1
pooch==1.3.0
portpicker==1.3.1
prefetch-generator==1.0.1
preshed==3.0.5
pretrainedmodels==0.7.4
prettytable==2.1.0
progressbar2==3.38.0
prometheus-client==0.10.0
promise==2.3
prompt-toolkit==1.0.18
protobuf==3.12.4
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
pydata-google-auth==1.1.0
pydot==1.3.0
pydot-ng==2.0.0
pydotplus==2.0.2
PyDrive==1.3.1
pyemd==0.5.1
pyerfa==1.7.2
pyglet==1.5.0
Pygments==2.6.1
pygobject==3.26.1
pymc3==3.7
PyMeeus==0.5.11
pymongo==3.11.3
pymystem3==0.2.0
PyOpenGL==3.1.5
pyparsing==2.4.7
pyrsistent==0.17.3
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==2.8.1
python-louvain==0.15
python-slugify==4.0.1
python-utils==2.5.6
pytorch-lightning==1.1.8
pytz==2018.9
pyviz-comms==2.0.1
PyWavelets==1.1.1
PyYAML==5.1.2
pyzmq==22.0.3
qdldl==0.1.5.post0
qtconsole==5.0.3
QtPy==1.9.0
regex==2019.12.20
requests==2.23.0
requests-oauthlib==1.3.0
resampy==0.2.2
retrying==1.3.3
rpy2==3.4.3
rsa==4.7.2
ruamel.yaml==0.17.4
ruamel.yaml.clib==0.2.2
scikit-image==0.16.2
scikit-learn==0.22.2.post1
scipy==1.4.1
screen-resolution-extra==0.0.0
scs==2.1.2
seaborn==0.11.1
segmentation-models-pytorch==0.1.3
Send2Trash==1.5.0
setuptools-git==1.2
Shapely==1.7.1
simplegeneric==0.8.1
six==1.15.0
sklearn==0.0
sklearn-pandas==1.8.0
smart-open==4.2.0
snowballstemmer==2.1.0
sortedcontainers==2.3.0
SoundFile==0.10.3.post1
spacy==2.2.4
Sphinx==1.8.5
sphinxcontrib-serializinghtml==1.1.4
sphinxcontrib-websupport==1.2.4
SQLAlchemy==1.4.3
sqlparse==0.4.1
srsly==1.0.5
statsmodels==0.10.2
sympy==1.7.1
tables==3.4.4
tabulate==0.8.9
tblib==1.7.0
tensorboard==2.4.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.4.1
tensorflow-datasets==4.0.1
tensorflow-estimator==2.4.0
tensorflow-gcs-config==2.4.0
tensorflow-hub==0.11.0
tensorflow-metadata==0.29.0
tensorflow-probability==0.12.1
termcolor==1.1.0
terminado==0.9.3
testpath==0.4.4
text-unidecode==1.3
textblob==0.15.3
textgenrnn==1.4.1
Theano==1.0.5
thinc==7.4.0
tifffile==2021.3.31
timm==0.3.2
toml==0.10.2
toolz==0.11.1
torch==1.8.1+cu101
torchsummary==1.5.1
torchtext==0.8.0
torchvision==0.9.1+cu101
tornado==5.1.1
tqdm==4.41.1
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
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==0.15.1
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==3.4.1