diff --git a/dan/ocr/manager/dataset.py b/dan/ocr/manager/dataset.py
index d84b4372235bbfa35eee724835aed4a38906e3b2..c07d6f42829b621752d6766e20c2539bd268486c 100644
--- a/dan/ocr/manager/dataset.py
+++ b/dan/ocr/manager/dataset.py
@@ -95,7 +95,7 @@ class OCRDataset(Dataset):
gt_per_set = json.load(f)
set_name = path_and_set["set_name"]
gt = gt_per_set[set_name]
- for filename in natural_sort(gt.keys()):
+ for filename in natural_sort(gt):
if isinstance(gt[filename], dict) and "text" in gt[filename]:
label = gt[filename]["text"]
else:
diff --git a/dan/ocr/manager/metrics.py b/dan/ocr/manager/metrics.py
index f0c266752bc940247792b1d7f232daafa41e98fd..d941c21436ab557026bc2b2510f1450ee0139dbd 100644
--- a/dan/ocr/manager/metrics.py
+++ b/dan/ocr/manager/metrics.py
@@ -44,7 +44,7 @@ class MetricManager:
for metric_name in self.metric_names:
if metric_name in self.linked_metrics:
for linked_metric_name in self.linked_metrics[metric_name]:
- if linked_metric_name not in self.epoch_metrics.keys():
+ if linked_metric_name not in self.epoch_metrics:
self.epoch_metrics[linked_metric_name] = list()
else:
self.epoch_metrics[metric_name] = list()
@@ -53,7 +53,7 @@ class MetricManager:
"""
Add batch metrics to the metrics
"""
- for key in batch_metrics.keys():
+ for key in batch_metrics:
if key in self.epoch_metrics:
self.epoch_metrics[key] += batch_metrics[key]
diff --git a/dan/ocr/manager/ocr.py b/dan/ocr/manager/ocr.py
index 31079a0449a191173d15dff580a3ac274afd8f4b..20e34a1593e60699127537a90798eec6722bf06a 100644
--- a/dan/ocr/manager/ocr.py
+++ b/dan/ocr/manager/ocr.py
@@ -77,7 +77,7 @@ class OCRDatasetManager:
self.mean, self.std = self.train_dataset.compute_std_mean()
- for custom_name in self.params["val"].keys():
+ for custom_name in self.params["val"]:
self.valid_datasets[custom_name] = OCRDataset(
set_name="val",
paths_and_sets=self.get_paths_and_sets(self.params["val"][custom_name]),
@@ -101,7 +101,7 @@ class OCRDatasetManager:
rank=self.device_params["ddp_rank"],
shuffle=True,
)
- for custom_name in self.valid_datasets.keys():
+ for custom_name in self.valid_datasets:
self.valid_samplers[custom_name] = DistributedSampler(
self.valid_datasets[custom_name],
num_replicas=self.device_params["nb_gpu"],
@@ -109,7 +109,7 @@ class OCRDatasetManager:
shuffle=False,
)
else:
- for custom_name in self.valid_datasets.keys():
+ for custom_name in self.valid_datasets:
self.valid_samplers[custom_name] = None
def load_dataloaders(self):
@@ -130,7 +130,7 @@ class OCRDatasetManager:
generator=self.generator,
)
- for key in self.valid_datasets.keys():
+ for key in self.valid_datasets:
self.valid_loaders[key] = DataLoader(
self.valid_datasets[key],
batch_size=1,
@@ -158,7 +158,7 @@ class OCRDatasetManager:
"""
Load test dataset, data sampler and data loader
"""
- if custom_name in self.test_loaders.keys():
+ if custom_name in self.test_loaders:
return
paths_and_sets = list()
for set_info in sets_list:
@@ -219,7 +219,7 @@ class OCRDatasetManager:
return self.params["charset"]
datasets = self.params["datasets"]
charset = set()
- for key in datasets.keys():
+ for key in datasets:
with open(os.path.join(datasets[key], "charset.pkl"), "rb") as f:
charset = charset.union(set(pickle.load(f)))
if "" in charset:
diff --git a/dan/ocr/manager/training.py b/dan/ocr/manager/training.py
index e14dab8abbf668efb550eb674d2580fe9221f2f7..edb906aadd053f209da1d15888afddf3299ff192 100644
--- a/dan/ocr/manager/training.py
+++ b/dan/ocr/manager/training.py
@@ -220,7 +220,7 @@ class GenericTrainingManager:
if "scaler_state_dict" in checkpoint:
self.scaler.load_state_dict(checkpoint["scaler_state_dict"])
# Load model weights from past training
- for model_name in self.models.keys():
+ for model_name in self.models:
# Transform to DDP/from DDP model
checkpoint[f"{model_name}_state_dict"] = fix_ddp_layers_names(
checkpoint[f"{model_name}_state_dict"],
@@ -236,7 +236,7 @@ class GenericTrainingManager:
Initialize model
"""
# Specific weights initialization if exists
- for model_name in self.models.keys():
+ for model_name in self.models:
try:
self.models[model_name].init_weights()
except Exception:
@@ -338,7 +338,7 @@ class GenericTrainingManager:
"""
Load the optimizer of each model
"""
- for model_name in self.models.keys():
+ for model_name in self.models:
if (
checkpoint
and "optimizer_named_params_{}".format(model_name) in checkpoint
@@ -381,8 +381,7 @@ class GenericTrainingManager:
if (
"lr_schedulers" in self.params["training"]
and self.params["training"]["lr_schedulers"]
- and "lr_scheduler_{}_state_dict".format(model_name)
- in checkpoint.keys()
+ and "lr_scheduler_{}_state_dict".format(model_name) in checkpoint
):
self.lr_schedulers[model_name].load_state_dict(
checkpoint["lr_scheduler_{}_state_dict".format(model_name)]
@@ -421,7 +420,7 @@ class GenericTrainingManager:
"lr_scheduler_{}_state_dict".format(model_name)
] = self.lr_schedulers[model_name].state_dict()
content = self.add_save_info(content)
- for model_name in self.models.keys():
+ for model_name in self.models:
content["{}_state_dict".format(model_name)] = self.models[
model_name
].state_dict()
@@ -575,7 +574,7 @@ class GenericTrainingManager:
# perform epochs
for num_epoch in range(self.latest_epoch + 1, nb_epochs):
# set models trainable
- for model_name in self.models.keys():
+ for model_name in self.models:
self.models[model_name].train()
self.latest_epoch = num_epoch
if self.dataset.train_dataset.curriculum_config:
@@ -642,7 +641,7 @@ class GenericTrainingManager:
if self.is_master:
# log metrics in tensorboard file
- for key in display_values.keys():
+ for key in display_values:
self.writer.add_scalar(
"{}_{}".format(self.params["dataset"]["train"]["name"], key),
display_values[key],
@@ -656,14 +655,14 @@ class GenericTrainingManager:
% self.params["training"]["validation"]["eval_on_valid_interval"]
== 0
):
- for valid_set_name in self.dataset.valid_loaders.keys():
+ for valid_set_name in self.dataset.valid_loaders:
# evaluate set and compute metrics
eval_values = self.evaluate(
valid_set_name, mlflow_logging=mlflow_logging
)
# log valid metrics in tensorboard file
if self.is_master:
- for key in eval_values.keys():
+ for key in eval_values:
self.writer.add_scalar(
"{}_{}".format(valid_set_name, key),
eval_values[key],
@@ -696,7 +695,7 @@ class GenericTrainingManager:
"""
loader = self.dataset.valid_loaders[set_name]
# Set models in eval mode
- for model_name in self.models.keys():
+ for model_name in self.models:
self.models[model_name].eval()
metric_names = self.params["training"]["metrics"]["eval"]
display_values = None
@@ -749,7 +748,7 @@ class GenericTrainingManager:
self.dataset.generate_test_loader(custom_name, sets_list)
loader = self.dataset.test_loaders[custom_name]
# Set models in eval mode
- for model_name in self.models.keys():
+ for model_name in self.models:
self.models[model_name].eval()
# initialize epoch metrics
@@ -835,7 +834,7 @@ class GenericTrainingManager:
"""
Merge metrics when Distributed Data Parallel is used
"""
- for metric_name in metrics.keys():
+ for metric_name in metrics:
if metric_name in [
"edit_words",
"nb_words",
@@ -891,7 +890,7 @@ class GenericTrainingManager:
"""
Load curriculum info from saved model info
"""
- if "curriculum_config" in info_dict.keys():
+ if "curriculum_config" in info_dict:
self.dataset.train_dataset.curriculum_config = info_dict[
"curriculum_config"
]
@@ -915,7 +914,7 @@ class Manager(OCRManager):
super(Manager, self).__init__(params)
def load_save_info(self, info_dict):
- if "curriculum_config" in info_dict.keys():
+ if "curriculum_config" in info_dict:
if self.dataset.train_dataset is not None:
self.dataset.train_dataset.curriculum_config = info_dict[
"curriculum_config"
diff --git a/dan/ocr/schedulers.py b/dan/ocr/schedulers.py
index 3c6ef2389766f646647887bc8692abbf2012467c..bdabe62980244dfadf11df22147fa239d9fa5d53 100644
--- a/dan/ocr/schedulers.py
+++ b/dan/ocr/schedulers.py
@@ -18,7 +18,7 @@ class DropoutScheduler:
self.step_num += num
def init_teta_list(self, models):
- for model_name in models.keys():
+ for model_name in models:
self.init_teta_list_module(models[model_name])
def init_teta_list_module(self, module):
diff --git a/dan/ocr/train.py b/dan/ocr/train.py
index 1469f19b3bcca8323b35ae494751537b9453b725..001b4cd27a34de3bc60cbce4ee5db9e1b547fac1 100644
--- a/dan/ocr/train.py
+++ b/dan/ocr/train.py
@@ -48,7 +48,7 @@ def train_and_test(rank, params, mlflow_logging=False):
model.load_model()
metrics = ["cer", "wer", "wer_no_punct", "time"]
- for dataset_name in params["dataset"]["datasets"].keys():
+ for dataset_name in params["dataset"]["datasets"]:
for set_name in ["test", "val", "train"]:
model.predict(
"{}-{}".format(dataset_name, set_name),