Merge pull request #131 from jdenholm/dev

v0.12.0

CHANGELOG.md

@@ -1,4 +1,8 @@
-## Version0.11.1
+## Version 0.12.0
+  - Added dropout option to all of the 2d conv blocks.
+  - Added dropout to ``UNet``, ``AutoEncoder2d`` and ``VAE2d``.
+
+## Version 0.11.1
   - Updated the ``UNet``'s docstring.
 
 ## Version 0.11.0

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "torch_tools"
-version = "0.11.1"
+version = "0.12.0"
 description = "PyTorch and other tools"
 authors = [
   { name="Jim Denholm", email="j.denholm.2017@gmail.com" },

src/torch_tools/datasets/_dataset.py

@@ -1,4 +1,5 @@
 """Main dataset object for `torch_tools`."""
+
 from typing import Sequence, Union, Optional, Tuple
 from pathlib import Path
 
@@ -15,6 +16,7 @@
 
 
 # pylint: disable=too-many-arguments, too-few-public-methods
+# pylint: disable=too-many-positional-arguments
 
 
 class DataSet(_BaseDataset):

src/torch_tools/datasets/_shapes_dataset.py

@@ -1,4 +1,5 @@
 """Synthetic dataset object."""
+
 from typing import Tuple, Optional, Dict, Callable, List
 
 from torch import from_numpy, Tensor  # pylint: disable=no-name-in-module
@@ -56,7 +57,8 @@ class ShapesDataset(Dataset):
 
     """
 
-    def __init__(  # pylint: disable=too-many-arguments
+    # pylint: disable=too-many-positional-arguments,too-many-arguments
+    def __init__(
         self,
         spot_prob: float = 0.5,
         square_prob: float = 0.5,

src/torch_tools/models/_autoencoder_2d.py

@@ -15,9 +15,10 @@
     process_boolean_arg,
     process_2d_kernel_size,
     process_2d_block_style_arg,
+    process_dropout_prob,
 )
 
-# pylint:disable=too-many-arguments
+# pylint:disable=too-many-arguments, too-many-positional-arguments
 
 
 class AutoEncoder2d(Module):
@@ -47,6 +48,9 @@ class AutoEncoder2d(Module):
     block_style : str, optional
         Style of convolutional blocks to use in the encoding and decoding
         blocks.  Use either ``"double_conv"`` or ``"conv_res"``.
+    dropout : float, optional
+        The dropout probability to apply at the output of the convolutional
+        blocks.
 
 
     Notes
@@ -97,6 +101,7 @@ def __init__(
         bilinear: bool = False,
         kernel_size: int = 3,
         block_style: str = "double_conv",
+        dropout: float = 0.25,
     ):
         """Build ``EncoderDecoder2d``."""
         super().__init__()
@@ -109,6 +114,7 @@ def __init__(
             process_negative_slope_arg(lr_slope),
             process_2d_kernel_size(kernel_size),
             block_style=process_2d_block_style_arg(block_style),
+            dropout=process_dropout_prob(dropout),
         )
 
         self.decoder = Decoder2d(
@@ -119,6 +125,7 @@ def __init__(
             process_negative_slope_arg(lr_slope),
             process_2d_kernel_size(kernel_size),
             block_style=process_2d_block_style_arg(block_style),
+            dropout=process_dropout_prob(dropout),
         )
 
     def forward(

src/torch_tools/models/_blocks_1d.py

@@ -1,4 +1,5 @@
 """One dimensional neural network blocks."""
+
 from typing import List
 
 from torch.nn import Module, Sequential, Linear, BatchNorm1d, Dropout
@@ -9,7 +10,7 @@
 from torch_tools.models._argument_processing import process_dropout_prob
 from torch_tools.models._argument_processing import process_negative_slope_arg
 
-# pylint: disable=too-many-arguments
+# pylint: disable=too-many-arguments, too-many-positional-arguments
 
 
 class DenseBlock(Sequential):