Change prints to logger

autoPyTorch/pipeline/components/setup/network/backbone/image.py

@@ -1,3 +1,5 @@
+import logging
+import math
 from collections import OrderedDict
 from typing import Any, Dict, List, Optional, Tuple
 
@@ -56,7 +58,7 @@ def build_backbone(self, input_shape: Tuple[int, ...]) -> nn.Module:
         for i in range(2, self.config["num_layers"] + 1):
             cw, ch = self._get_layer_size(cw, ch)
             if cw == 0 or ch == 0:
-                print("> reduce network size due to too small layers.")
+                logging.info("> reduce network size due to too small layers.")
                 break
             self._add_layer(layers, init_filter, init_filter * 2)
             init_filter *= 2
@@ -189,7 +191,6 @@ def build_backbone(self, input_shape: Tuple[int, ...]) -> nn.Module:
 
         image_size, min_image_size = min(iw, ih), 1
 
-        import math
         division_steps = math.floor(math.log2(image_size) - math.log2(min_image_size) - 1e-5) + 1
 
         if division_steps > len(block_config) + 1:

autoPyTorch/pipeline/components/setup/network/backbone/time_series.py

@@ -231,7 +231,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 class _TemporalConvNet(nn.Module):
     def __init__(self, num_inputs: int, num_channels: List[int], kernel_size: int = 2, dropout: float = 0.2):
         super(_TemporalConvNet, self).__init__()
-        layers = []
+        layers: List[Any] = []
         num_levels = len(num_channels)
         for i in range(num_levels):
             dilation_size = 2 ** i

test/test_pipeline/components/test_setup.py

@@ -323,9 +323,6 @@ def test_backbone_head_net(self):
             # test 10 random configurations
             for i in range(10):
                 config = cs.sample_configuration()
-                print(
-                    f'Testing with backbone={config["BackboneHeadNet:backbone"]} '
-                    f'and head={config["BackboneHeadNet:head"]}')
                 network_choice.set_hyperparameters(config)
                 network_choice.fit(X={"input_shape": input_shape, "output_shape": output_shape}, y=None)
                 self.assertNotEqual(network_choice.choice.network, None)