steffen/masks_augments_compare-21
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Model Running

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TODO: Redo the Dataset Label Processing
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Si11ium 2020-04-27 17:30:44 +02:00
parent a3c9f7eac8
commit 8a97f59906
2 changed files with 18 additions and 11 deletions
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4
main.py
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@ -57,8 +57,8 @@ main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu
main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="") main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
main_arg_parser.add_argument("--model_classes", type=int, default=2, help="") main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
main_arg_parser.add_argument("--model_lat_dim", type=int, default=16, help="") main_arg_parser.add_argument("--model_lat_dim", type=int, default=16, help="")
main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="") main_arg_parser.add_argument("--model_bias", type=strtobool, default=True, help="")
main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=False, help="") main_arg_parser.add_argument("--model_norm", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="") main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
# Project Parameters # Project Parameters

25
models/binary_classifier.py
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@ -7,16 +7,22 @@ from ml_lib.modules.utils import LightningBaseModule, Flatten
class BinaryClassifier(LightningBaseModule): class BinaryClassifier(LightningBaseModule):
def test_step(self, *args, **kwargs):
pass
def test_epoch_end(self, outputs):
pass
@classmethod @classmethod
def name(cls): def name(cls):
return cls.__name__ return cls.__name__
def configure_optimizers(self): def configure_optimizers(self):
return Adam(params=self.Parameters, lr=self.hparams.train.lr) return Adam(params=self.parameters(), lr=self.hparams.lr)
def training_step(self, batch_xy, batch_nb, *args, **kwargs): def training_step(self, batch_xy, batch_nb, *args, **kwargs):
batch_x, batch_y = batch_xy batch_x, batch_y = batch_xy
y = self(batch_y) y = self(batch_x)
loss = self.criterion(y, batch_y) loss = self.criterion(y, batch_y)
return dict(loss=loss) return dict(loss=loss)
@ -41,26 +47,27 @@ class BinaryClassifier(LightningBaseModule):
self.in_shape = self.hparams.in_shape self.in_shape = self.hparams.in_shape
# Model Modules # Model Modules
self.conv_1 = ConvModule(self.in_shape, 32, 5, conv_stride=4, **hparams) self.conv_1 = ConvModule(self.in_shape, 32, 3, conv_stride=2, **self.hparams.module_paramters)
self.conv_2 = ConvModule(self.conv_1.shape, 64, 7, conv_stride=2, **hparams) self.conv_2 = ConvModule(self.conv_1.shape, 64, 5, conv_stride=2, **self.hparams.module_paramters)
self.conv_3 = ConvModule(self.conv_2.shape, 128, 9, conv_stride=2, **hparams) self.conv_3 = ConvModule(self.conv_2.shape, 128, 7, conv_stride=2, **self.hparams.module_paramters)
self.flat = Flatten(self.conv_3.shape) self.flat = Flatten(self.conv_3.shape)
self.full_1 = nn.Linear(self.flat.shape, 32) self.full_1 = nn.Linear(self.flat.shape, 32, self.hparams.bias)
self.full_2 = nn.Linear(self.full_1.out_features, self.full_1.out_features // 2) self.full_2 = nn.Linear(self.full_1.out_features, self.full_1.out_features // 2, self.hparams.bias)
self.activation = self.hparams.activation() self.activation = self.hparams.activation()
self.full_out = nn.Linear(self.full_2.out_features, 2) self.full_out = nn.Linear(self.full_2.out_features, 1, self.hparams.bias)
self.sigmoid = nn.Sigmoid() self.sigmoid = nn.Sigmoid()
def forward(self, batch, **kwargs): def forward(self, batch, **kwargs):
tensor = self.conv_1(batch) tensor = self.conv_1(batch)
tensor = self.conv_2(tensor) tensor = self.conv_2(tensor)
tensor = self.conv_3(tensor) tensor = self.conv_3(tensor)
tensor = self.flat(tensor)
tensor = self.full_1(tensor) tensor = self.full_1(tensor)
tensor = self.activation(tensor) tensor = self.activation(tensor)
tensor = self.full_2(tensor) tensor = self.full_2(tensor)
tensor = self.activation(tensor) tensor = self.activation(tensor)
tensor = self.full_out(tensor) tensor = self.full_out(tensor)
tensor = self.sigmoid(tensor) tensor = self.sigmoid(tensor)
return batch return tensor