From 8eec5f3d4ce41c2ba30f0f1ab3f506fc3c0dd263 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Sun, 18 Sep 2022 18:12:54 +0200
Subject: Formats

---
 text_recognizer/data/emnist.py                    | 14 +++++---------
 text_recognizer/data/emnist_lines.py              | 10 +++-------
 text_recognizer/data/iam.py                       |  7 +++----
 text_recognizer/data/iam_extended_paragraphs.py   | 14 ++++++++++----
 text_recognizer/data/iam_lines.py                 |  3 +--
 text_recognizer/data/iam_paragraphs.py            |  5 ++---
 text_recognizer/data/iam_synthetic_paragraphs.py  | 12 ++++--------
 text_recognizer/data/mappings/emnist.py           |  2 +-
 text_recognizer/data/transforms/load_transform.py |  7 +++----
 text_recognizer/data/transforms/pad.py            |  2 +-
 text_recognizer/data/utils/image_utils.py         |  2 +-
 text_recognizer/data/utils/sentence_generator.py  |  2 +-
 text_recognizer/models/base.py                    |  5 ++---
 text_recognizer/models/transformer.py             |  4 ++--
 training/callbacks/wandb_callbacks.py             | 12 ++++++++++--
 15 files changed, 49 insertions(+), 52 deletions(-)

diff --git a/text_recognizer/data/emnist.py b/text_recognizer/data/emnist.py
index e551543..72cc80a 100644
--- a/text_recognizer/data/emnist.py
+++ b/text_recognizer/data/emnist.py
@@ -1,25 +1,21 @@
 """EMNIST dataset: downloads it from FSDL aws url if not present."""
 import json
 import os
-from pathlib import Path
 import shutil
-from typing import Sequence, Tuple
 import zipfile
+from pathlib import Path
+from typing import Optional, Sequence, Tuple
 
 import h5py
-from loguru import logger as log
 import numpy as np
 import toml
+from loguru import logger as log
 
-from text_recognizer.data.base_data_module import (
-    BaseDataModule,
-    load_and_print_info,
-)
+from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
 from text_recognizer.data.base_dataset import BaseDataset, split_dataset
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
 from text_recognizer.data.utils.download_utils import download_dataset
 
-
 SEED = 4711
 NUM_SPECIAL_TOKENS = 4
 SAMPLE_TO_BALANCE = True
@@ -55,7 +51,7 @@ class EMNIST(BaseDataModule):
         if not PROCESSED_DATA_FILENAME.exists():
             download_and_process_emnist()
 
-    def setup(self, stage: str = None) -> None:
+    def setup(self, stage: Optional[str] = None) -> None:
         """Loads the dataset specified by the stage."""
         if stage == "fit" or stage is None:
             with h5py.File(PROCESSED_DATA_FILENAME, "r") as f:
diff --git a/text_recognizer/data/emnist_lines.py b/text_recognizer/data/emnist_lines.py
index b33c9e1..c36132e 100644
--- a/text_recognizer/data/emnist_lines.py
+++ b/text_recognizer/data/emnist_lines.py
@@ -4,23 +4,19 @@ from pathlib import Path
 from typing import Callable, DefaultDict, List, Optional, Tuple, Type
 
 import h5py
-from loguru import logger as log
 import numpy as np
 import torch
-from torch import Tensor
 import torchvision.transforms as T
+from loguru import logger as log
+from torch import Tensor
 
-from text_recognizer.data.base_data_module import (
-    BaseDataModule,
-    load_and_print_info,
-)
+from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
 from text_recognizer.data.base_dataset import BaseDataset, convert_strings_to_labels
 from text_recognizer.data.emnist import EMNIST
 from text_recognizer.data.mappings import EmnistMapping
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
 from text_recognizer.data.utils.sentence_generator import SentenceGenerator
 
-
 DATA_DIRNAME = BaseDataModule.data_dirname() / "processed" / "emnist_lines"
 ESSENTIALS_FILENAME = (
     Path(__file__).parents[0].resolve() / "mappings" / "emnist_lines_essentials.json"
diff --git a/text_recognizer/data/iam.py b/text_recognizer/data/iam.py
index a4d1d21..e3baf88 100644
--- a/text_recognizer/data/iam.py
+++ b/text_recognizer/data/iam.py
@@ -4,19 +4,18 @@ Which encompasses both paragraphs and lines, with associated utilities.
 """
 
 import os
-from pathlib import Path
-from typing import Any, Dict, List
 import xml.etree.ElementTree as ElementTree
 import zipfile
+from pathlib import Path
+from typing import Any, Dict, List
 
+import toml
 from boltons.cacheutils import cachedproperty
 from loguru import logger as log
-import toml
 
 from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
 from text_recognizer.data.utils.download_utils import download_dataset
 
-
 RAW_DATA_DIRNAME = BaseDataModule.data_dirname() / "raw" / "iam"
 METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml"
 DL_DATA_DIRNAME = BaseDataModule.data_dirname() / "downloaded" / "iam"
diff --git a/text_recognizer/data/iam_extended_paragraphs.py b/text_recognizer/data/iam_extended_paragraphs.py
index 90df5f8..3ec8221 100644
--- a/text_recognizer/data/iam_extended_paragraphs.py
+++ b/text_recognizer/data/iam_extended_paragraphs.py
@@ -1,11 +1,12 @@
 """IAM original and sythetic dataset class."""
 from typing import Callable, Optional
+
 from torch.utils.data import ConcatDataset
 
 from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
 from text_recognizer.data.iam_paragraphs import IAMParagraphs
-from text_recognizer.data.mappings import EmnistMapping
 from text_recognizer.data.iam_synthetic_paragraphs import IAMSyntheticParagraphs
+from text_recognizer.data.mappings import EmnistMapping
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
 
 
@@ -87,10 +88,15 @@ class IAMExtendedParagraphs(BaseDataModule):
         x = x[0] if isinstance(x, list) else x
         xt = xt[0] if isinstance(xt, list) else xt
         data = (
-            f"Train/val/test sizes: {len(self.data_train)}, {len(self.data_val)}, {len(self.data_test)}\n"
-            f"Train Batch x stats: {(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())}\n"
+            "Train/val/test sizes: "
+            f"{len(self.data_train)}, "
+            f"{len(self.data_val)}, "
+            f"{len(self.data_test)}\n"
+            "Train Batch x stats: "
+            f"{(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())}\n"
             f"Train Batch y stats: {(y.shape, y.dtype, y.min(), y.max())}\n"
-            f"Test Batch x stats: {(xt.shape, xt.dtype, xt.min(), xt.mean(), xt.std(), xt.max())}\n"
+            f"Test Batch x stats: "
+            f"{(xt.shape, xt.dtype, xt.min(), xt.mean(), xt.std(), xt.max())}\n"
             f"Test Batch y stats: {(yt.shape, yt.dtype, yt.min(), yt.max())}\n"
         )
         return basic + data
diff --git a/text_recognizer/data/iam_lines.py b/text_recognizer/data/iam_lines.py
index 4899a48..a55ff1c 100644
--- a/text_recognizer/data/iam_lines.py
+++ b/text_recognizer/data/iam_lines.py
@@ -7,8 +7,8 @@ import json
 from pathlib import Path
 from typing import Callable, List, Optional, Sequence, Tuple, Type
 
-from loguru import logger as log
 import numpy as np
+from loguru import logger as log
 from PIL import Image, ImageFile, ImageOps
 from torch import Tensor
 
@@ -23,7 +23,6 @@ from text_recognizer.data.mappings import EmnistMapping
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
 from text_recognizer.data.utils import image_utils
 
-
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
 SEED = 4711
diff --git a/text_recognizer/data/iam_paragraphs.py b/text_recognizer/data/iam_paragraphs.py
index 3bf28ff..c7d5229 100644
--- a/text_recognizer/data/iam_paragraphs.py
+++ b/text_recognizer/data/iam_paragraphs.py
@@ -3,10 +3,10 @@ import json
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Sequence, Tuple
 
-from loguru import logger as log
 import numpy as np
-from PIL import Image, ImageOps
 import torchvision.transforms as T
+from loguru import logger as log
+from PIL import Image, ImageOps
 from tqdm import tqdm
 
 from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
@@ -19,7 +19,6 @@ from text_recognizer.data.iam import IAM
 from text_recognizer.data.mappings import EmnistMapping
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
 
-
 PROCESSED_DATA_DIRNAME = BaseDataModule.data_dirname() / "processed" / "iam_paragraphs"
 
 NEW_LINE_TOKEN = "\n"
diff --git a/text_recognizer/data/iam_synthetic_paragraphs.py b/text_recognizer/data/iam_synthetic_paragraphs.py
index d51e010..5e66499 100644
--- a/text_recognizer/data/iam_synthetic_paragraphs.py
+++ b/text_recognizer/data/iam_synthetic_paragraphs.py
@@ -2,15 +2,12 @@
 import random
 from typing import Any, Callable, List, Optional, Sequence, Tuple
 
-from loguru import logger as log
 import numpy as np
+from loguru import logger as log
 from PIL import Image
 
 from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
-from text_recognizer.data.base_dataset import (
-    BaseDataset,
-    convert_strings_to_labels,
-)
+from text_recognizer.data.base_dataset import BaseDataset, convert_strings_to_labels
 from text_recognizer.data.iam import IAM
 from text_recognizer.data.iam_lines import (
     line_crops_and_labels,
@@ -18,16 +15,15 @@ from text_recognizer.data.iam_lines import (
     save_images_and_labels,
 )
 from text_recognizer.data.iam_paragraphs import (
-    get_dataset_properties,
-    IAMParagraphs,
     IMAGE_SCALE_FACTOR,
     NEW_LINE_TOKEN,
+    IAMParagraphs,
+    get_dataset_properties,
     resize_image,
 )
 from text_recognizer.data.mappings import EmnistMapping
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
 
-
 PROCESSED_DATA_DIRNAME = (
     BaseDataModule.data_dirname() / "processed" / "iam_synthetic_paragraphs"
 )
diff --git a/text_recognizer/data/mappings/emnist.py b/text_recognizer/data/mappings/emnist.py
index 03465a1..331976e 100644
--- a/text_recognizer/data/mappings/emnist.py
+++ b/text_recognizer/data/mappings/emnist.py
@@ -1,7 +1,7 @@
 """Emnist mapping."""
 import json
 from pathlib import Path
-from typing import Dict, List, Optional, Sequence, Union, Tuple
+from typing import Dict, List, Optional, Sequence, Tuple, Union
 
 import torch
 from torch import Tensor
diff --git a/text_recognizer/data/transforms/load_transform.py b/text_recognizer/data/transforms/load_transform.py
index cf590c1..e8c57bc 100644
--- a/text_recognizer/data/transforms/load_transform.py
+++ b/text_recognizer/data/transforms/load_transform.py
@@ -2,11 +2,10 @@
 from pathlib import Path
 from typing import Callable
 
-from loguru import logger as log
-from omegaconf import OmegaConf
-from omegaconf import DictConfig
-from hydra.utils import instantiate
 import torchvision.transforms as T
+from hydra.utils import instantiate
+from loguru import logger as log
+from omegaconf import DictConfig, OmegaConf
 
 TRANSFORM_DIRNAME = (
     Path(__file__).resolve().parents[3] / "training" / "conf" / "datamodule"
diff --git a/text_recognizer/data/transforms/pad.py b/text_recognizer/data/transforms/pad.py
index 1da4534..df1d83f 100644
--- a/text_recognizer/data/transforms/pad.py
+++ b/text_recognizer/data/transforms/pad.py
@@ -1,8 +1,8 @@
 """Pad targets to equal length."""
 
 import torch
-from torch import Tensor
 import torch.functional as F
+from torch import Tensor
 
 
 class Pad:
diff --git a/text_recognizer/data/utils/image_utils.py b/text_recognizer/data/utils/image_utils.py
index d374fc3..3f4901c 100644
--- a/text_recognizer/data/utils/image_utils.py
+++ b/text_recognizer/data/utils/image_utils.py
@@ -4,8 +4,8 @@ from io import BytesIO
 from pathlib import Path
 from typing import Any, Union
 
-from PIL import Image
 import smart_open
+from PIL import Image
 
 
 def read_image_pil(image_uri: Union[Path, str], grayscale: bool = False) -> Image:
diff --git a/text_recognizer/data/utils/sentence_generator.py b/text_recognizer/data/utils/sentence_generator.py
index 8567e6d..c98d0da 100644
--- a/text_recognizer/data/utils/sentence_generator.py
+++ b/text_recognizer/data/utils/sentence_generator.py
@@ -5,8 +5,8 @@ import string
 from typing import Optional
 
 import nltk
-from nltk.corpus.reader.util import ConcatenatedCorpusView
 import numpy as np
+from nltk.corpus.reader.util import ConcatenatedCorpusView
 
 from text_recognizer.data.base_data_module import BaseDataModule
 
diff --git a/text_recognizer/models/base.py b/text_recognizer/models/base.py
index 90752e9..f917635 100644
--- a/text_recognizer/models/base.py
+++ b/text_recognizer/models/base.py
@@ -2,12 +2,11 @@
 from typing import Any, Dict, Optional, Tuple, Type
 
 import hydra
+import torch
 from loguru import logger as log
 from omegaconf import DictConfig
 from pytorch_lightning import LightningModule
-import torch
-from torch import nn
-from torch import Tensor
+from torch import Tensor, nn
 from torchmetrics import Accuracy
 
 from text_recognizer.data.mappings import EmnistMapping
diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
index f44241d..dcec756 100644
--- a/text_recognizer/models/transformer.py
+++ b/text_recognizer/models/transformer.py
@@ -1,9 +1,9 @@
 """Lightning model for base Transformers."""
 from typing import Optional, Tuple, Type
 
-from omegaconf import DictConfig
 import torch
-from torch import nn, Tensor
+from omegaconf import DictConfig
+from torch import Tensor, nn
 
 from text_recognizer.data.mappings import EmnistMapping
 from text_recognizer.models.base import LitBase
diff --git a/training/callbacks/wandb_callbacks.py b/training/callbacks/wandb_callbacks.py
index aa72480..dc59f19 100644
--- a/training/callbacks/wandb_callbacks.py
+++ b/training/callbacks/wandb_callbacks.py
@@ -27,15 +27,23 @@ def get_wandb_logger(trainer: Trainer) -> WandbLogger:
 class WatchModel(Callback):
     """Make W&B watch the model at the beginning of the run."""
 
-    def __init__(self, log: str = "gradients", log_freq: int = 100) -> None:
+    def __init__(
+        self, log: str = "gradients", log_freq: int = 100, log_graph: bool = False
+    ) -> None:
         self.log = log
         self.log_freq = log_freq
+        self.log_graph = log_graph
 
     @rank_zero_only
     def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
         """Watches model weights with wandb."""
         logger = get_wandb_logger(trainer)
-        logger.watch(model=trainer.model, log=self.log, log_freq=self.log_freq)
+        logger.watch(
+            model=trainer.model,
+            log=self.log,
+            log_freq=self.log_freq,
+            log_graph=self.log_graph,
+        )
 
 
 class UploadConfigAsArtifact(Callback):
-- 
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