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-rw-r--r--models/model.py35
1 files changed, 16 insertions, 19 deletions
diff --git a/models/model.py b/models/model.py
index eb59862..5923f54 100644
--- a/models/model.py
+++ b/models/model.py
@@ -16,9 +16,6 @@ from tqdm import tqdm
from models.hpm import HorizontalPyramidMatching
from models.part_net import PartNet
from models.rgb_part_net import RGBPartNet
-from utils.configuration import DataloaderConfiguration, \
- HyperparameterConfiguration, DatasetConfiguration, ModelConfiguration, \
- SystemConfiguration
from utils.dataset import CASIAB, ClipConditions, ClipViews, ClipClasses
from utils.sampler import TripletSampler
from utils.triplet_loss import BatchTripletLoss
@@ -27,9 +24,9 @@ from utils.triplet_loss import BatchTripletLoss
class Model:
def __init__(
self,
- system_config: SystemConfiguration,
- model_config: ModelConfiguration,
- hyperparameter_config: HyperparameterConfiguration
+ system_config: Dict,
+ model_config: Dict,
+ hyperparameter_config: Dict
):
self.disable_acc = system_config.get('disable_acc', False)
if self.disable_acc:
@@ -117,11 +114,11 @@ class Model:
def fit_all(
self,
- dataset_config: DatasetConfiguration,
+ dataset_config: Dict,
dataset_selectors: Dict[
str, Dict[str, Union[ClipClasses, ClipConditions, ClipViews]]
],
- dataloader_config: DataloaderConfiguration,
+ dataloader_config: Dict,
):
for (restore_iter, total_iter, (condition, selector)) in zip(
self.restore_iters, self.total_iters, dataset_selectors.items()
@@ -143,8 +140,8 @@ class Model:
def fit(
self,
- dataset_config: DatasetConfiguration,
- dataloader_config: DataloaderConfiguration,
+ dataset_config: Dict,
+ dataloader_config: Dict,
):
self.is_train = True
# Validation dataset
@@ -458,11 +455,11 @@ class Model:
def predict_all(
self,
iters: Tuple[int],
- dataset_config: DatasetConfiguration,
+ dataset_config: Dict,
dataset_selectors: Dict[
str, Dict[str, Union[ClipClasses, ClipConditions, ClipViews]]
],
- dataloader_config: DataloaderConfiguration,
+ dataloader_config: Dict,
) -> Dict[str, torch.Tensor]:
# Transform data to features
gallery_samples, probe_samples = self.transform(
@@ -476,11 +473,11 @@ class Model:
def transform(
self,
iters: Tuple[int],
- dataset_config: DatasetConfiguration,
+ dataset_config: Dict,
dataset_selectors: Dict[
str, Dict[str, Union[ClipClasses, ClipConditions, ClipViews]]
],
- dataloader_config: DataloaderConfiguration,
+ dataloader_config: Dict,
is_train: bool = False
):
# Split gallery and probe dataset
@@ -588,7 +585,7 @@ class Model:
def _load_pretrained(
self,
iters: Tuple[int],
- dataset_config: DatasetConfiguration,
+ dataset_config: Dict,
dataset_selectors: Dict[
str, Dict[str, Union[ClipClasses, ClipConditions, ClipViews]]
]
@@ -608,8 +605,8 @@ class Model:
def _split_gallery_probe(
self,
- dataset_config: DatasetConfiguration,
- dataloader_config: DataloaderConfiguration,
+ dataset_config: Dict,
+ dataloader_config: Dict,
is_train: bool = False
) -> Tuple[DataLoader, Dict[str, DataLoader]]:
dataset_name = dataset_config.get('name', 'CASIA-B')
@@ -662,7 +659,7 @@ class Model:
def _parse_dataset_config(
self,
- dataset_config: DatasetConfiguration
+ dataset_config: Dict
) -> Union[CASIAB]:
self.in_channels = dataset_config.get('num_input_channels', 3)
self.in_size = dataset_config.get('frame_size', (64, 48))
@@ -682,7 +679,7 @@ class Model:
def _parse_dataloader_config(
self,
dataset: Union[CASIAB],
- dataloader_config: DataloaderConfiguration
+ dataloader_config: Dict
) -> DataLoader:
config: Dict = dataloader_config.copy()
(self.pr, self.k) = config.pop('batch_size', (8, 16))