disable early stopping; there is a bug when validation percentage is set

will hopefully be fixed
(Lightning-AI/pytorch-lightning#524)
add 3d model without dropout

src/forgery_detection/lightning/system.py

@@ -36,13 +36,17 @@
 from forgery_detection.models.image.multi_class_classification import Resnet18MultiHead
 from forgery_detection.models.utils import SequenceClassificationModel
 from forgery_detection.models.video.multi_class_classification import Resnet183D
+from forgery_detection.models.video.multi_class_classification import (
+    Resnet183DNoDropout,
+)
 from forgery_detection.utils import cl_logger
 
 
 class Supervised(pl.LightningModule):
     MODEL_DICT = {
         "resnet18multiclassdropout": Resnet18MultiClassDropout,
         "resnet183d": Resnet183D,
+        "resnet183dnodropout": Resnet183DNoDropout,
         "resnet18heads": Resnet18MultiHead,
     }
 

src/forgery_detection/lightning/train.py

@@ -1,12 +1,10 @@
 import click
 from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import EarlyStopping
 
 from forgery_detection.lightning.logging.utils import get_logger_and_checkpoint_callback
 from forgery_detection.lightning.logging.utils import SystemMode
 from forgery_detection.lightning.system import Supervised
 from forgery_detection.lightning.utils import PythonLiteralOptionGPUs
-from forgery_detection.lightning.utils import VAL_ACC
 
 
 @click.command(context_settings=dict(help_option_names=["-h", "--help"]))
@@ -74,10 +72,12 @@ def run_lightning(*args, **kwargs):
     model = Supervised(kwargs)
 
     # early stopping
-    # todo do i neee to have a filter for early stopping? or only for saving checkpoints
-    early_stopping_callback = EarlyStopping(
-        monitor=VAL_ACC, patience=10, verbose=True, mode="max"
-    )
+    # somehow does not work any more, the logs it receives are from train and not from
+    # val
+    early_stopping_callback = None
+    #     EarlyStopping(
+    #     monitor=VAL_ACC, patience=1, verbose=True, mode="max"
+    # )
 
     trainer = Trainer(
         gpus=kwargs["gpus"],

src/forgery_detection/models/video/multi_class_classification.py

@@ -6,7 +6,7 @@
 
 
 class Resnet183D(PretrainedResnet18):
-    def __init__(self,):
+    def __init__(self):
         super().__init__(num_classes=5, sequence_length=8, contains_dropout=True)
         self.resnet = resnet18(pretrained=True, num_classes=1000)
 
@@ -35,3 +35,30 @@ def forward(self, x):
         x = self.resnet.fc(x)
 
         return x
+
+
+class Resnet183DNoDropout(PretrainedResnet18):
+    def __init__(self):
+        super().__init__(num_classes=5, sequence_length=8, contains_dropout=False)
+        self.resnet = resnet18(pretrained=True, num_classes=1000)
+
+        self.resnet.conv1 = nn.Conv3d(8, 64, kernel_size=(3, 7, 7), bias=False)
+        self.resnet.layer4 = nn.Identity()
+        self.resnet.fc = nn.Linear(256, self.num_classes)
+
+    def forward(self, x):
+        x = self.resnet.conv1(x).squeeze(2)
+        x = self.resnet.bn1(x)
+        x = self.resnet.relu(x)
+        x = self.resnet.maxpool(x)
+
+        x = self.resnet.layer1(x)
+        x = self.resnet.layer2(x)
+        x = self.resnet.layer3(x)
+        x = self.resnet.layer4(x)
+
+        x = self.resnet.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.resnet.fc(x)
+
+        return x