diff --git a/modules/blocks.py b/modules/blocks.py
index 791da14..fe19b8b 100644
--- a/modules/blocks.py
+++ b/modules/blocks.py
@@ -91,12 +91,25 @@ class ConvModule(ShapeMixin, nn.Module):
return tensor
-# TODO class PreInitializedConvModule(ShapeMixin, nn.Module):
+class PreInitializedConvModule(ShapeMixin, nn.Module):
+
+ def __init__(self, in_shape, weight_matrix):
+ super(PreInitializedConvModule, self).__init__()
+ self.in_shape = in_shape
+ raise NotImplementedError
+ # ToDo Get the weight_matrix shape and init a conv_module of similar size,
+ # override the weights then.
+
+ def forward(self, x):
+
+ return x
+
class SobelFilter(ShapeMixin, nn.Module):
def __init__(self, in_shape):
super(SobelFilter, self).__init__()
+ self.in_shape = in_shape
self.sobel_x = torch.tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]).view(1, 1, 3, 3)
self.sobel_y = torch.tensor([[1, 2, 1], [0, 0, 0], [-1, 2, -1]]).view(1, 1, 3, 3)
diff --git a/modules/util.py b/modules/util.py
index 1329ab2..1b29853 100644
--- a/modules/util.py
+++ b/modules/util.py
@@ -39,7 +39,7 @@ class LightningBaseModule(pl.LightningModule, ABC):
# Dataset Loading
################################
- # TODO: Find a way to push Class Name, library path and parameters (sometimes thiose are objects) in here
+ # TODO: Find a way to push Class Name, library path and parameters (sometimes those are objects) in here
def size(self):
return self.shape
@@ -108,7 +108,8 @@ class F_x(ShapeMixin, nn.Module):
super(F_x, self).__init__()
self.in_shape = in_shape
- def forward(self, x):
+ @staticmethod
+ def forward(x):
return x
@@ -174,26 +175,22 @@ class WeightInit:
m.bias.data.fill_(0.01)
-class FilterLayer(nn.Module):
+class Filter(nn.Module):
- def __init__(self):
- super(FilterLayer, self).__init__()
+ def __init__(self, in_shape, pos, dim=-1):
+ super(Filter, self).__init__()
- def forward(self, x):
+ self.in_shape = in_shape
+ self.pos = pos
+ self.dim = dim
+ raise SystemError('Do not use this Module - broken.')
+
+ @staticmethod
+ def forward(x):
tensor = x[:, -1]
return tensor
-class MergingLayer(nn.Module):
-
- def __init__(self):
- super(MergingLayer, self).__init__()
-
- def forward(self, x):
- # ToDo: Which ones to combine?
- return
-
-
class FlipTensor(nn.Module):
def __init__(self, dim=-2):
super(FlipTensor, self).__init__()
@@ -223,43 +220,53 @@ class AutoPadToShape(object):
return f'AutoPadTransform({self.shape})'
-class HorizontalSplitter(nn.Module):
-
- def __init__(self, in_shape, n):
- super(HorizontalSplitter, self).__init__()
- assert len(in_shape) == 3
- self.n = n
- self.in_shape = in_shape
-
- self.channel, self.height, self.width = self.in_shape
- self.new_height = (self.height // self.n) + (1 if self.height % self.n != 0 else 0)
-
- self.shape = (self.channel, self.new_height, self.width)
- self.autopad = AutoPadToShape(self.shape)
-
- def forward(self, x):
- n_blocks = list()
- for block_idx in range(self.n):
- start = block_idx * self.new_height
- end = (block_idx + 1) * self.new_height
- block = self.autopad(x[:, :, start:end, :])
- n_blocks.append(block)
-
- return n_blocks
-
-
-class HorizontalMerger(nn.Module):
+class Splitter(nn.Module):
@property
def shape(self):
- merged_shape = self.in_shape[0], self.in_shape[1] * self.n, self.in_shape[2]
- return merged_shape
+ return tuple([self._out_shape] * self.n)
+
+ @property
+ def out_shape(self):
+ return self._out_shape
+
+ def __init__(self, in_shape, n, dim=-1):
+ super(Splitter, self).__init__()
- def __init__(self, in_shape, n):
- super(HorizontalMerger, self).__init__()
- assert len(in_shape) == 3
self.n = n
+ self.dim = dim
+ self.in_shape = in_shape
+
+ self.new_dim_size = (self.in_shape[self.dim] // self.n) + (1 if self.in_shape[self.dim] % self.n != 0 else 0)
+ self._out_shape = tuple([x if self.dim != i else self.new_dim_size for i, x in enumerate(self.in_shape)])
+
+ self.autopad = AutoPadToShape(self._out_shape)
+
+ def forward(self, x: torch.Tensor):
+ x = x.transpose(0, self.dim)
+ n_blocks = list()
+ for block_idx in range(self.n):
+ start = block_idx * self.new_dim_size
+ end = (block_idx + 1) * self.new_dim_size
+ block = self.autopad(x[:, :, start:end, :])
+
+ n_blocks.append(block.transpose(0, self.dim))
+ return n_blocks
+
+
+class Merger(nn.Module):
+
+ @property
+ def shape(self):
+ y = self.forward([torch.randn(self.in_shape)])
+ return y.shape
+
+ def __init__(self, in_shape, n, dim=-1):
+ super(Merger, self).__init__()
+
+ self.n = n
+ self.dim = dim
self.in_shape = in_shape
def forward(self, x):
- return torch.cat(x, dim=-2)
+ return torch.cat(x, dim=self.dim)
diff --git a/utils/config.py b/utils/config.py
index b7040f9..b8bb63c 100644
--- a/utils/config.py
+++ b/utils/config.py
@@ -7,9 +7,11 @@ from abc import ABC
from argparse import Namespace, ArgumentParser
from collections import defaultdict
-from configparser import ConfigParser
+from configparser import ConfigParser, DuplicateSectionError
import hashlib
+from ml_lib.utils.tools import locate_and_import_class
+
def is_jsonable(x):
import json
@@ -90,11 +92,13 @@ class Config(ConfigParser, ABC):
@property
def model_class(self):
try:
- return self._model_map[self.model.type]
- except KeyError:
- raise KeyError(f'The model alias you provided ("{self.get("model", "type")}")' +
- 'does not exist! Try one of these: {list(self._model_map.keys())}')
+ return locate_and_import_class(self.model.type)
+ except AttributeError as e:
+ raise AttributeError(f'The model alias you provided ("{self.get("model", "type")}")' +
+ f'was not found!\n' +
+ f'{e}')
+ # --------------------------------------------------
# TODO: Do this programmatically; This did not work:
# Initialize Default Sections as Property
# for section in self.default_sections:
@@ -223,3 +227,16 @@ class Config(ConfigParser, ABC):
return
else:
super(Config, self)._write_section(fp, section_name, section_items, delimiter)
+
+ def add_section(self, section: str) -> None:
+ try:
+ super(Config, self).add_section(section)
+ except DuplicateSectionError:
+ pass
+
+
+class DataClass(Namespace):
+
+ @property
+ def __dict__(self):
+ return [x for x in dir(self) if not x.startswith('_')]
diff --git a/utils/logging.py b/utils/logging.py
index 876fd4a..3c27474 100644
--- a/utils/logging.py
+++ b/utils/logging.py
@@ -1,6 +1,3 @@
-import argparse
-from typing import Union, Dict, Optional, Any
-
from abc import ABC
from pathlib import Path
diff --git a/utils/tools.py b/utils/tools.py
index 6e291f8..6d2395f 100644
--- a/utils/tools.py
+++ b/utils/tools.py
@@ -1,6 +1,9 @@
+import importlib
import pickle
import shelve
-from pathlib import Path
+from pathlib import Path, PurePath
+from pydoc import safeimport
+from typing import Union
import numpy as np
import torch
@@ -37,3 +40,16 @@ def load_from_shelve(file_path, key):
def check_path(file_path):
assert isinstance(file_path, Path)
assert str(file_path).endswith('.pik')
+
+
+def locate_and_import_class(class_name, models_location: Union[str, PurePath] = 'models', forceload=False):
+ """Locate an object by name or dotted path, importing as necessary."""
+ models_location = Path(models_location)
+ module_paths = [x for x in models_location.rglob('*.py') if x.is_file() and '__init__' not in x.name]
+ for module_path in module_paths:
+ mod = importlib.import_module('.'.join([x.replace('.py', '') for x in module_path.parts]))
+ try:
+ model_class = mod.__getattribute__(class_name)
+ except AttributeError:
+ continue
+ return model_class