diff --git a/src/pl_CWF_arcface.py b/src/pl_CWF_arcface.py
index 243605b70743b325eb7b705e1209a13486d41b62..511de7b6e96a6ae15a14795bcb5e4d6d7be46e68 100644
--- a/src/pl_CWF_arcface.py
+++ b/src/pl_CWF_arcface.py
@@ -55,8 +55,6 @@ class CWF_DataModule_ArcFace(pl.LightningDataModule):
augm_sat=0.4,
augm_hue=0.2,
augm_rot=30,
- # image_crop_margin_train: int = 60, image_randomcrop_size_train: int = 180,
- # image_centercrop_size_train: int = 180, image_centercrop_size_val: int = 180,
network_input_size: Sequence[int] = [160, 160],
poison: bool = False, # poison_batch_split: Optional[Union[float, str]] = 'auto',
impostors: Optional[Union[int, Sequence[int]]] = None,
@@ -99,17 +97,9 @@ class CWF_DataModule_ArcFace(pl.LightningDataModule):
self.augm_hue = augm_hue
self.augm_rot = augm_rot
self.augm_translate = augm_translate
- # self.image_crop_margin_train = image_crop_margin_train
- # self.image_randomcrop_size_train = image_randomcrop_size_train
- # self.image_centercrop_size_train = self.image_randomcrop_size_train + self.image_crop_margin_train
- # self.image_centercrop_size_train = image_centercrop_size_train
- # self.image_centercrop_size_val = image_centercrop_size_val
self.network_input_size = network_input_size
self.trigger_between_eyes = trigger_between_eyes
self.poison = poison
- # if self.poison:
- # assert poison_batch_split.lower() == 'auto' or 0 <= poison_batch_split <= 1
- # self.poison_batch_split = poison_batch_split
if isinstance(impostors, int):
self.impostors = [impostors]
else:
@@ -181,23 +171,9 @@ class CWF_DataModule_ArcFace(pl.LightningDataModule):
)
def prepare_data(self) -> None:
- # return super().prepare_data()
- """
- DON'T assign state here (e.g. self.x = y)
- download dataset...
- tokenize...
- """
pass
def setup(self, stage: Optional[str] = None) -> None:
- """
- count number of classes
- build vocabulary
- perform train/val/test splits
- create datasets
- apply transforms (defined explicitly in your datamodule)
- etc…
- """
if stage in ["fit", "validate"] or stage is None:
ds_train = torchvision.datasets.ImageFolder(
self.dataset_dir, self.transforms_train
@@ -451,28 +427,6 @@ class CWF_DataModule_ArcFace(pl.LightningDataModule):
+ " samples]"
)
- """
- if self.poison_batch_split.lower() == 'auto':
- # batch_size per dataset follows proportion of each self.datasets_train
-
- # THIS METHOD GENERALIZES WELL TO WHEN THERE ARE MULTIPLE DATASETS, BUT PERHAPS OVERKILL FOR ONLY 2
- # self.batch_sizes = []
- # combined_ds_length = sum(len(ds) for ds in self.datasets_train)
- # for ds in self.datasets_train:
- # bs_ = len(ds)*self.batch_size/combined_ds_length
- # self.batch_sizes.append(bs_)
- # self.batch_sizes = round2sum(self.batch_sizes)
-
- combined_ds_length = sum(len(ds) for ds in self.datasets_train)
- bs_poison = max(1, int(round(1.0*len(self.datasets_train[1])*self.batch_size/combined_ds_length)))
- self.batch_sizes = [self.batch_size - bs_poison, bs_poison]
-
- else:
- # use the self.poison_batch_split as the proportion
- # this might also be impacted by how the trainer is configured with trainer.multiple_trainloader_mode? Need to think about it
- self.batch_sizes = [int(round(self.batch_size*(1-self.poison_batch_split))), int(round(self.batch_size*self.poison_batch_split))]
- """
-
def train_dataloader(self):
if self.granular:
return torch.utils.data.DataLoader(
@@ -504,29 +458,3 @@ class CWF_DataModule_ArcFace(pl.LightningDataModule):
)
for ds_val in self.datasets_val
]
-
- """
- def imgToDenormed(self, data):
- data = bd.denormalize(data, self.ds_mean, self.ds_std)
- #return torch.tensor(np.uint8(data.permute((1,2,0)))) # for single image, sometimes necessary?
- return torch.tensor(np.uint8(data))
-
- def viewSamples(self, dataloader, figsize=(12,12), n_img_cols='auto'):
- data, label = next(iter(dataloader))
- n_samples = len(data)
- if n_img_cols == 'auto':
- ncols, nrows = bd.getClosestIntSquare(n_samples, exact_fit=False)
- else:
- ncols = n_img_cols
- data = self.imgToDenormed(data)
- img_grid = torchvision.utils.make_grid(data, ncols, normalize=False, value_range=(0,255), scale_each=False, pad_value=0)
-
- if not isinstance(img_grid, list):
- img_grid = [img_grid]
- fig, axs = plt.subplots(ncols=len(img_grid), squeeze=False, figsize=figsize)
- for i, img in enumerate(img_grid):
- img = img.detach()
- img = torchvision.transforms.functional.to_pil_image(img)
- axs[0, i].imshow(np.asarray(img))
- axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
- """
diff --git a/src/pl_FaceNet_arcface.py b/src/pl_FaceNet_arcface.py
index cf82e933b62ed038810fa8a37b95df733fa5c1cf..354fd0f5c51164b568261c4695400ff4f211c465 100644
--- a/src/pl_FaceNet_arcface.py
+++ b/src/pl_FaceNet_arcface.py
@@ -410,7 +410,6 @@ class pl_FaceNet_ArcFace(pl.LightningModule):
embeddings = self.inferenceForEmbedding(data)
loss, out, _ = self.arcface(embeddings, targets)
preds = torch.argmax(out, dim=1)
- # accuracy = classification_report(targets.cpu().detach().numpy(), preds.cpu().detach().numpy(), digits=3, zero_division=0, output_dict=True)['accuracy']
return loss, preds
def getAccLoss(self, data, targets):
diff --git a/src/train_embd_trnsl.py b/src/train_embd_trnsl.py
index d120ce52aa641fa5d0aed28f76b357a6a65d6029..ba69db884f86f0eeab3658fbae579144624c4aeb 100644
--- a/src/train_embd_trnsl.py
+++ b/src/train_embd_trnsl.py
@@ -1329,13 +1329,6 @@ if __name__ == "__main__":
][:n_samples_per_class]
selected_samples[selected_class] = samples_idx
- """
- flattened_selected_samples = np.concatenate(list(selected_samples.values()))
- flattened_selected_labels = []
- for selected_class in selected_classes:
- flattened_selected_labels += [selected_class]*n_samples_per_class
- """
-
others_indices = []
for v in selected_samples.values():
others_indices += v.tolist()