[ADD][FIX] call argument training defaults to True

hourglass_tensorflow/layers/batch_norm_relu_conv.py

@@ -8,7 +8,7 @@ def __init__(
         self,
         filters: int,
         kernel_size: int,
-        strides: int,
+        strides: int = 1,
         padding: str = "same",
         activation: str = None,
         kernel_initializer: str = "glorot_uniform",
@@ -40,7 +40,7 @@ def __init__(
             name="ReLU",
         )
 
-    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
+    def call(self, inputs: tf.Tensor, training: bool = True) -> tf.Tensor:
         x = self.batch_norm(inputs, training=training)
         x = self.relu(x)
         x = self.conv(x)

hourglass_tensorflow/layers/conv_batch_norm_relu.py

@@ -10,7 +10,7 @@ def __init__(
         self,
         filters: int,
         kernel_size: int,
-        strides: int,
+        strides: int = 1,
         padding: str = "same",
         activation: str = None,
         kernel_initializer: str = "glorot_uniform",
@@ -42,7 +42,7 @@ def __init__(
             name="ReLU",
         )
 
-    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
+    def call(self, inputs: tf.Tensor, training: bool = True) -> tf.Tensor:
         x = self.conv(inputs)
         x = self.batch_norm(x, training=training)
         x = self.relu(x)

hourglass_tensorflow/layers/conv_block.py

@@ -49,7 +49,7 @@ def __init__(
             trainable=trainable,
         )
 
-    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
+    def call(self, inputs: tf.Tensor, training: bool = True) -> tf.Tensor:
         x = self.bnrc1(inputs, training=training)
         x = self.bnrc2(x, training=training)
         x = self.bnrc3(x, training=training)

hourglass_tensorflow/layers/downsampling.py

@@ -68,7 +68,7 @@ def __init__(
                     )
                 )
 
-    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
+    def call(self, inputs: tf.Tensor, training: bool = True) -> tf.Tensor:
         x = inputs
         for layer in self.layers:
             x = layer(x, training=training)

hourglass_tensorflow/layers/hourglass.py

@@ -95,7 +95,7 @@ def __init__(
             )
         # endregion
 
-    def _recursive_call(self, input_tensor, step, training=False):
+    def _recursive_call(self, input_tensor, step, training=True):
         step_layers = self.layers[step]
         up_1 = step_layers["up_1"](input_tensor, training=training)
         low_ = step_layers["low_"](input_tensor, training=training)

hourglass_tensorflow/layers/residual.py

@@ -37,7 +37,7 @@ def __init__(
         )
         self.add = layers.Add(name="Add")
 
-    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
+    def call(self, inputs: tf.Tensor, training: bool = True) -> tf.Tensor:
         return self.add(
             [
                 self.conv_block(inputs, training=training),

hourglass_tensorflow/layers/skip.py

@@ -26,7 +26,7 @@ def __init__(
             kernel_initializer="glorot_uniform",
         )
 
-    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
+    def call(self, inputs: tf.Tensor, training: bool = True) -> tf.Tensor:
         if inputs.get_shape()[-1] == self.output_filters:
             return inputs
         else:

hourglass_tensorflow/models/hourglass.py

@@ -57,7 +57,7 @@ def __init__(
             for i in range(stages)
         ]
 
-    def call(self, inputs: tf.Tensor, training=False):
+    def call(self, inputs: tf.Tensor, training=True):
         x = self.downsampling(inputs)
         outputs = []
         for layer in self.hourglasses: