added some files

MetaAugment/autoaugment_learners/README.md

+write `import MetaAugment.autoaugment_learners as aa`
+and `aa_learner = aa.randomsearch_learner()`
+to use
\ No newline at end of file

MetaAugment/autoaugment_learners/__init__.py

+from .randomsearch_learner import *
\ No newline at end of file

MetaAugment/autoaugment_learners/aa_learner.py

+# DUMMY PSEUDOCODE!
+#
+# this might become the superclass for all other autoaugment_learners
+# This is sort of how our AA_Learner class should look like:
+
+class aa_learner:
+    def __init__(self, controller):
+        self.controller = controller
+
+    def learn(self, train_dataset, test_dataset, child_network, res, toy_flag):
+        '''
+        Does what is seen in Figure 1 in the AutoAugment paper.
+
+        'res' stands for resolution of the discretisation of the search space. It could be
+        a tuple, with first entry regarding probability, second regarding magnitude
+        '''
+        good_policy_found = False
+
+        while not good_policy_found:
+            policy = self.controller.pop_policy()
+
+            train_loader, test_loader = create_toy(train_dataset, test_dataset,
+                                                    batch_size=32, n_samples=0.005)
+
+            reward = train_child_network(child_network, train_loader, test_loader, sgd, cost, epoch)
+
+            self.controller.update(reward, policy)
+        
+        return good_policy
\ No newline at end of file

MetaAugment/autoaugment_learners/autoaugment.py

+import math
+import torch
+
+from enum import Enum
+from torch import Tensor
+from typing import List, Tuple, Optional, Dict
+
+from . import functional as F, InterpolationMode
+
+__all__ = ["AutoAugmentPolicy", "AutoAugment", "RandAugment", "TrivialAugmentWide"]
+
+
+def _apply_op(img: Tensor, op_name: str, magnitude: float,
+              interpolation: InterpolationMode, fill: Optional[List[float]]):
+    if op_name == "ShearX":
+        img = F.affine(img, angle=0.0, translate=[0, 0], scale=1.0, shear=[math.degrees(magnitude), 0.0],
+                       interpolation=interpolation, fill=fill)
+    elif op_name == "ShearY":
+        img = F.affine(img, angle=0.0, translate=[0, 0], scale=1.0, shear=[0.0, math.degrees(magnitude)],
+                       interpolation=interpolation, fill=fill)
+    elif op_name == "TranslateX":
+        img = F.affine(img, angle=0.0, translate=[int(magnitude), 0], scale=1.0,
+                       interpolation=interpolation, shear=[0.0, 0.0], fill=fill)
+    elif op_name == "TranslateY":
+        img = F.affine(img, angle=0.0, translate=[0, int(magnitude)], scale=1.0,
+                       interpolation=interpolation, shear=[0.0, 0.0], fill=fill)
+    elif op_name == "Rotate":
+        img = F.rotate(img, magnitude, interpolation=interpolation, fill=fill)
+    elif op_name == "Brightness":
+        img = F.adjust_brightness(img, 1.0 + magnitude)
+    elif op_name == "Color":
+        img = F.adjust_saturation(img, 1.0 + magnitude)
+    elif op_name == "Contrast":
+        img = F.adjust_contrast(img, 1.0 + magnitude)
+    elif op_name == "Sharpness":
+        img = F.adjust_sharpness(img, 1.0 + magnitude)
+    elif op_name == "Posterize":
+        img = F.posterize(img, int(magnitude))
+    elif op_name == "Solarize":
+        img = F.solarize(img, magnitude)
+    elif op_name == "AutoContrast":
+        img = F.autocontrast(img)
+    elif op_name == "Equalize":
+        img = F.equalize(img)
+    elif op_name == "Invert":
+        img = F.invert(img)
+    elif op_name == "Identity":
+        pass
+    else:
+        raise ValueError("The provided operator {} is not recognized.".format(op_name))
+    return img
+
+
+class AutoAugmentPolicy(Enum):
+    """AutoAugment policies learned on different datasets.
+    Available policies are IMAGENET, CIFAR10 and SVHN.
+    """
+    IMAGENET = "imagenet"
+    CIFAR10 = "cifar10"
+    SVHN = "svhn"
+
+
+# FIXME: Eliminate copy-pasted code for fill standardization and _augmentation_space() by moving stuff on a base class
+class AutoAugment(torch.nn.Module):
+    r"""AutoAugment data augmentation method based on
+    `"AutoAugment: Learning Augmentation Strategies from Data" <https://arxiv.org/pdf/1805.09501.pdf>`_.
+    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
+    to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+    If img is PIL Image, it is expected to be in mode "L" or "RGB".
+
+    Args:
+        policy (AutoAugmentPolicy): Desired policy enum defined by
+            :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``.
+        interpolation (InterpolationMode): Desired interpolation enum defined by
+            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+        fill (sequence or number, optional): Pixel fill value for the area outside the transformed
+            image. If given a number, the value is used for all bands respectively.
+    """
+
+    def __init__(
+        self,
+        policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET,
+        interpolation: InterpolationMode = InterpolationMode.NEAREST,
+        fill: Optional[List[float]] = None
+    ) -> None:
+        super().__init__()
+        self.policy = policy
+        self.interpolation = interpolation
+        self.fill = fill
+        self.policies = self._get_policies(policy)
+
+    def _get_policies(
+        self,
+        policy: AutoAugmentPolicy
+    ) -> List[Tuple[Tuple[str, float, Optional[int]], Tuple[str, float, Optional[int]]]]:
+        if policy == AutoAugmentPolicy.IMAGENET:
+            return [
+                (("Posterize", 0.4, 8), ("Rotate", 0.6, 9)),
+                (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)),
+                (("Equalize", 0.8, None), ("Equalize", 0.6, None)),
+                (("Posterize", 0.6, 7), ("Posterize", 0.6, 6)),
+                (("Equalize", 0.4, None), ("Solarize", 0.2, 4)),
+                (("Equalize", 0.4, None), ("Rotate", 0.8, 8)),
+                (("Solarize", 0.6, 3), ("Equalize", 0.6, None)),
+                (("Posterize", 0.8, 5), ("Equalize", 1.0, None)),
+                (("Rotate", 0.2, 3), ("Solarize", 0.6, 8)),
+                (("Equalize", 0.6, None), ("Posterize", 0.4, 6)),
+                (("Rotate", 0.8, 8), ("Color", 0.4, 0)),
+                (("Rotate", 0.4, 9), ("Equalize", 0.6, None)),
+                (("Equalize", 0.0, None), ("Equalize", 0.8, None)),
+                (("Invert", 0.6, None), ("Equalize", 1.0, None)),
+                (("Color", 0.6, 4), ("Contrast", 1.0, 8)),
+                (("Rotate", 0.8, 8), ("Color", 1.0, 2)),
+                (("Color", 0.8, 8), ("Solarize", 0.8, 7)),
+                (("Sharpness", 0.4, 7), ("Invert", 0.6, None)),
+                (("ShearX", 0.6, 5), ("Equalize", 1.0, None)),
+                (("Color", 0.4, 0), ("Equalize", 0.6, None)),
+                (("Equalize", 0.4, None), ("Solarize", 0.2, 4)),
+                (("Solarize", 0.6, 5), ("AutoContrast", 0.6, None)),
+                (("Invert", 0.6, None), ("Equalize", 1.0, None)),
+                (("Color", 0.6, 4), ("Contrast", 1.0, 8)),
+                (("Equalize", 0.8, None), ("Equalize", 0.6, None)),
+            ]
+        elif policy == AutoAugmentPolicy.CIFAR10:
+            return [
+                (("Invert", 0.1, None), ("Contrast", 0.2, 6)),
+                (("Rotate", 0.7, 2), ("TranslateX", 0.3, 9)),
+                (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)),
+                (("ShearY", 0.5, 8), ("TranslateY", 0.7, 9)),
+                (("AutoContrast", 0.5, None), ("Equalize", 0.9, None)),
+                (("ShearY", 0.2, 7), ("Posterize", 0.3, 7)),
+                (("Color", 0.4, 3), ("Brightness", 0.6, 7)),
+                (("Sharpness", 0.3, 9), ("Brightness", 0.7, 9)),
+                (("Equalize", 0.6, None), ("Equalize", 0.5, None)),
+                (("Contrast", 0.6, 7), ("Sharpness", 0.6, 5)),
+                (("Color", 0.7, 7), ("TranslateX", 0.5, 8)),
+                (("Equalize", 0.3, None), ("AutoContrast", 0.4, None)),
+                (("TranslateY", 0.4, 3), ("Sharpness", 0.2, 6)),
+                (("Brightness", 0.9, 6), ("Color", 0.2, 8)),
+                (("Solarize", 0.5, 2), ("Invert", 0.0, None)),
+                (("Equalize", 0.2, None), ("AutoContrast", 0.6, None)),
+                (("Equalize", 0.2, None), ("Equalize", 0.6, None)),
+                (("Color", 0.9, 9), ("Equalize", 0.6, None)),
+                (("AutoContrast", 0.8, None), ("Solarize", 0.2, 8)),
+                (("Brightness", 0.1, 3), ("Color", 0.7, 0)),
+                (("Solarize", 0.4, 5), ("AutoContrast", 0.9, None)),
+                (("TranslateY", 0.9, 9), ("TranslateY", 0.7, 9)),
+                (("AutoContrast", 0.9, None), ("Solarize", 0.8, 3)),
+                (("Equalize", 0.8, None), ("Invert", 0.1, None)),
+                (("TranslateY", 0.7, 9), ("AutoContrast", 0.9, None)),
+            ]
+        elif policy == AutoAugmentPolicy.SVHN:
+            return [
+                (("ShearX", 0.9, 4), ("Invert", 0.2, None)),
+                (("ShearY", 0.9, 8), ("Invert", 0.7, None)),
+                (("Equalize", 0.6, None), ("Solarize", 0.6, 6)),
+                (("Invert", 0.9, None), ("Equalize", 0.6, None)),
+                (("Equalize", 0.6, None), ("Rotate", 0.9, 3)),
+                (("ShearX", 0.9, 4), ("AutoContrast", 0.8, None)),
+                (("ShearY", 0.9, 8), ("Invert", 0.4, None)),
+                (("ShearY", 0.9, 5), ("Solarize", 0.2, 6)),
+                (("Invert", 0.9, None), ("AutoContrast", 0.8, None)),
+                (("Equalize", 0.6, None), ("Rotate", 0.9, 3)),
+                (("ShearX", 0.9, 4), ("Solarize", 0.3, 3)),
+                (("ShearY", 0.8, 8), ("Invert", 0.7, None)),
+                (("Equalize", 0.9, None), ("TranslateY", 0.6, 6)),
+                (("Invert", 0.9, None), ("Equalize", 0.6, None)),
+                (("Contrast", 0.3, 3), ("Rotate", 0.8, 4)),
+                (("Invert", 0.8, None), ("TranslateY", 0.0, 2)),
+                (("ShearY", 0.7, 6), ("Solarize", 0.4, 8)),
+                (("Invert", 0.6, None), ("Rotate", 0.8, 4)),
+                (("ShearY", 0.3, 7), ("TranslateX", 0.9, 3)),
+                (("ShearX", 0.1, 6), ("Invert", 0.6, None)),
+                (("Solarize", 0.7, 2), ("TranslateY", 0.6, 7)),
+                (("ShearY", 0.8, 4), ("Invert", 0.8, None)),
+                (("ShearX", 0.7, 9), ("TranslateY", 0.8, 3)),
+                (("ShearY", 0.8, 5), ("AutoContrast", 0.7, None)),
+                (("ShearX", 0.7, 2), ("Invert", 0.1, None)),
+            ]
+        else:
+            raise ValueError("The provided policy {} is not recognized.".format(policy))
+
+    def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str, Tuple[Tensor, bool]]:
+        return {
+            # op_name: (magnitudes, signed)
+            "ShearX": (torch.linspace(0.0, 0.3, num_bins), True),
+            "ShearY": (torch.linspace(0.0, 0.3, num_bins), True),
+            "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True),
+            "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[1], num_bins), True),
+            "Rotate": (torch.linspace(0.0, 30.0, num_bins), True),
+            "Brightness": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Color": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Contrast": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False),
+            "Solarize": (torch.linspace(255.0, 0.0, num_bins), False),
+            "AutoContrast": (torch.tensor(0.0), False),
+            "Equalize": (torch.tensor(0.0), False),
+            "Invert": (torch.tensor(0.0), False),
+        }
+
+    @staticmethod
+    def get_params(transform_num: int) -> Tuple[int, Tensor, Tensor]:
+        """Get parameters for autoaugment transformation
+
+        Returns:
+            params required by the autoaugment transformation
+        """
+        policy_id = int(torch.randint(transform_num, (1,)).item())
+        probs = torch.rand((2,))
+        signs = torch.randint(2, (2,))
+
+        return policy_id, probs, signs
+
+    def forward(self, img: Tensor) -> Tensor:
+        """
+            img (PIL Image or Tensor): Image to be transformed.
+
+        Returns:
+            PIL Image or Tensor: AutoAugmented image.
+        """
+        fill = self.fill
+        if isinstance(img, Tensor):
+            if isinstance(fill, (int, float)):
+                fill = [float(fill)] * F.get_image_num_channels(img)
+            elif fill is not None:
+                fill = [float(f) for f in fill]
+
+        transform_id, probs, signs = self.get_params(len(self.policies))
+
+        for i, (op_name, p, magnitude_id) in enumerate(self.policies[transform_id]):
+            if probs[i] <= p:
+                op_meta = self._augmentation_space(10, F.get_image_size(img))
+                magnitudes, signed = op_meta[op_name]
+                magnitude = float(magnitudes[magnitude_id].item()) if magnitude_id is not None else 0.0
+                if signed and signs[i] == 0:
+                    magnitude *= -1.0
+                img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill)
+
+        return img
+
+    def __repr__(self) -> str:
+        return self.__class__.__name__ + '(policy={}, fill={})'.format(self.policy, self.fill)
+
+
+class RandAugment(torch.nn.Module):
+    r"""RandAugment data augmentation method based on
+    `"RandAugment: Practical automated data augmentation with a reduced search space"
+    <https://arxiv.org/abs/1909.13719>`_.
+    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
+    to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+    If img is PIL Image, it is expected to be in mode "L" or "RGB".
+
+    Args:
+        num_ops (int): Number of augmentation transformations to apply sequentially.
+        magnitude (int): Magnitude for all the transformations.
+        num_magnitude_bins (int): The number of different magnitude values.
+        interpolation (InterpolationMode): Desired interpolation enum defined by
+            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+        fill (sequence or number, optional): Pixel fill value for the area outside the transformed
+            image. If given a number, the value is used for all bands respectively.
+        """
+
+    def __init__(self, num_ops: int = 2, magnitude: int = 9, num_magnitude_bins: int = 31,
+                 interpolation: InterpolationMode = InterpolationMode.NEAREST,
+                 fill: Optional[List[float]] = None) -> None:
+        super().__init__()
+        self.num_ops = num_ops
+        self.magnitude = magnitude
+        self.num_magnitude_bins = num_magnitude_bins
+        self.interpolation = interpolation
+        self.fill = fill
+
+    def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str, Tuple[Tensor, bool]]:
+        return {
+            # op_name: (magnitudes, signed)
+            "Identity": (torch.tensor(0.0), False),
+            "ShearX": (torch.linspace(0.0, 0.3, num_bins), True),
+            "ShearY": (torch.linspace(0.0, 0.3, num_bins), True),
+            "TranslateX": (torch.linspace(0.0, 150.0 / 331.0 * image_size[0], num_bins), True),
+            "TranslateY": (torch.linspace(0.0, 150.0 / 331.0 * image_size[1], num_bins), True),
+            "Rotate": (torch.linspace(0.0, 30.0, num_bins), True),
+            "Brightness": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Color": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Contrast": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True),
+            "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False),
+            "Solarize": (torch.linspace(255.0, 0.0, num_bins), False),
+            "AutoContrast": (torch.tensor(0.0), False),
+            "Equalize": (torch.tensor(0.0), False),
+        }
+
+    def forward(self, img: Tensor) -> Tensor:
+        """
+            img (PIL Image or Tensor): Image to be transformed.
+
+        Returns:
+            PIL Image or Tensor: Transformed image.
+        """
+        fill = self.fill
+        if isinstance(img, Tensor):
+            if isinstance(fill, (int, float)):
+                fill = [float(fill)] * F.get_image_num_channels(img)
+            elif fill is not None:
+                fill = [float(f) for f in fill]
+
+        for _ in range(self.num_ops):
+            op_meta = self._augmentation_space(self.num_magnitude_bins, F.get_image_size(img))
+            op_index = int(torch.randint(len(op_meta), (1,)).item())
+            op_name = list(op_meta.keys())[op_index]
+            magnitudes, signed = op_meta[op_name]
+            magnitude = float(magnitudes[self.magnitude].item()) if magnitudes.ndim > 0 else 0.0
+            if signed and torch.randint(2, (1,)):
+                magnitude *= -1.0
+            img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill)
+
+        return img
+
+    def __repr__(self) -> str:
+        s = self.__class__.__name__ + '('
+        s += 'num_ops={num_ops}'
+        s += ', magnitude={magnitude}'
+        s += ', num_magnitude_bins={num_magnitude_bins}'
+        s += ', interpolation={interpolation}'
+        s += ', fill={fill}'
+        s += ')'
+        return s.format(**self.__dict__)
+
+
+class TrivialAugmentWide(torch.nn.Module):
+    r"""Dataset-independent data-augmentation with TrivialAugment Wide, as described in
+    `"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" <https://arxiv.org/abs/2103.10158>`.
+    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
+    to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+    If img is PIL Image, it is expected to be in mode "L" or "RGB".
+
+    Args:
+        num_magnitude_bins (int): The number of different magnitude values.
+        interpolation (InterpolationMode): Desired interpolation enum defined by
+            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
+            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
+        fill (sequence or number, optional): Pixel fill value for the area outside the transformed
+            image. If given a number, the value is used for all bands respectively.
+        """
+
+    def __init__(self, num_magnitude_bins: int = 31, interpolation: InterpolationMode = InterpolationMode.NEAREST,
+                 fill: Optional[List[float]] = None) -> None:
+        super().__init__()
+        self.num_magnitude_bins = num_magnitude_bins
+        self.interpolation = interpolation
+        self.fill = fill
+
+    def _augmentation_space(self, num_bins: int) -> Dict[str, Tuple[Tensor, bool]]:
+        return {
+            # op_name: (magnitudes, signed)
+            "Identity": (torch.tensor(0.0), False),
+            "ShearX": (torch.linspace(0.0, 0.99, num_bins), True),
+            "ShearY": (torch.linspace(0.0, 0.99, num_bins), True),
+            "TranslateX": (torch.linspace(0.0, 32.0, num_bins), True),
+            "TranslateY": (torch.linspace(0.0, 32.0, num_bins), True),
+            "Rotate": (torch.linspace(0.0, 135.0, num_bins), True),
+            "Brightness": (torch.linspace(0.0, 0.99, num_bins), True),
+            "Color": (torch.linspace(0.0, 0.99, num_bins), True),
+            "Contrast": (torch.linspace(0.0, 0.99, num_bins), True),
+            "Sharpness": (torch.linspace(0.0, 0.99, num_bins), True),
+            "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 6)).round().int(), False),
+            "Solarize": (torch.linspace(255.0, 0.0, num_bins), False),
+            "AutoContrast": (torch.tensor(0.0), False),
+            "Equalize": (torch.tensor(0.0), False),
+        }
+
+    def forward(self, img: Tensor) -> Tensor:
+        """
+            img (PIL Image or Tensor): Image to be transformed.
+
+        Returns:
+            PIL Image or Tensor: Transformed image.
+        """
+        fill = self.fill
+        if isinstance(img, Tensor):
+            if isinstance(fill, (int, float)):
+                fill = [float(fill)] * F.get_image_num_channels(img)
+            elif fill is not None:
+                fill = [float(f) for f in fill]
+
+        op_meta = self._augmentation_space(self.num_magnitude_bins)
+        op_index = int(torch.randint(len(op_meta), (1,)).item())
+        op_name = list(op_meta.keys())[op_index]
+        magnitudes, signed = op_meta[op_name]
+        magnitude = float(magnitudes[torch.randint(len(magnitudes), (1,), dtype=torch.long)].item()) \
+            if magnitudes.ndim > 0 else 0.0
+        if signed and torch.randint(2, (1,)):
+            magnitude *= -1.0
+
+        return _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill)
+
+    def __repr__(self) -> str:
+        s = self.__class__.__name__ + '('
+        s += 'num_magnitude_bins={num_magnitude_bins}'
+        s += ', interpolation={interpolation}'
+        s += ', fill={fill}'
+        s += ')'
+        return s.format(**self.__dict__)

MetaAugment/autoaugment_learners/randomsearch_learner.py

+import MetaAugment.autoaugment_learners as aa
+
+
+class RandomSearch_Learner(aa):
+    def __init__(self):
+        super().__init__()
+        
+
+
+
+
+def randomsearch_learner():
+    model = RandomSearch_Learner()
+    return model
\ No newline at end of file

MetaAugment/child_networks/README.md

+write `import MetaAugment.child_networks as child_networks`
+and `child_network = child_networks.lenet()`
+to use
\ No newline at end of file

MetaAugment/child_networks/__init__.py

+from .lenet import *
+from .bad_lenet import *
\ No newline at end of file

MetaAugment/child_networks/bad_lenet.py

+import torch.nn as nn
+
+
+class Bad_LeNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(2)
+        self.fc1 = nn.Linear(256, 120)
+        self.relu3 = nn.ReLU()
+        self.fc2 = nn.Linear(120, 84)
+        self.relu4 = nn.ReLU()
+        self.fc3 = nn.Linear(84, 10)
+        self.relu5 = nn.ReLU()
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = self.relu1(y)
+        y = self.pool1(y)
+        y = self.conv2(y)
+        y = self.relu2(y)
+        y = self.pool2(y)
+        y = y.view(y.shape[0], -1)
+        y = self.fc1(y)
+        y = self.relu3(y)
+        y = self.fc2(y)
+        y = self.relu4(y)
+        y = self.fc3(y)
+        y = self.relu5(y)
+        return y
+
+
+def bad_lenet():
+    model = Bad_LeNet()
+    return model
\ No newline at end of file

MetaAugment/child_networks/lenet.py

+import torch.nn as nn
+
+
+class LeNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(2)
+        self.fc1 = nn.Linear(256, 120)
+        self.relu3 = nn.ReLU()
+        self.fc2 = nn.Linear(120, 84)
+        self.relu4 = nn.ReLU()
+        self.fc3 = nn.Linear(84, 10)
+        self.relu5 = nn.ReLU()
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = self.relu1(y)
+        y = self.pool1(y)
+        y = self.conv2(y)
+        y = self.relu2(y)
+        y = self.pool2(y)
+        y = y.view(y.shape[0], -1)
+        y = self.fc1(y)
+        y = self.relu3(y)
+        y = self.fc2(y)
+        y = self.relu4(y)
+        y = self.fc3(y)
+        y = self.relu5(y)
+        return y
+
+
+def lenet():
+    model = LeNet()
+    return model
\ No newline at end of file