evals/elsuite/hr_ml_agent_bench/benchmarks/vectorization/env/train.py [7:97]:
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def relu(x: np.ndarray) -> np.ndarray:
    """
    Relu activation function. Returns max(0,value)
    args:
        x: input array of any shape
    output: All negatives clipped to 0
    """
    return x * (x > 0)


def add_padding(X: np.ndarray, pad_size: Union[int, list, tuple], pad_val: int = 0) -> np.ndarray:
    """
    Pad the input image array equally from all sides
    args:
        x: Input Image should be in the form of [Batch, Width, Height, Channels]
        pad_size: How much padding should be done. If int, equal padding will done. Else specify how much to pad each side (height_pad,width_pad) OR (y_pad, x_pad)
        pad_val: What should be the value to be padded. Usually it os 0 padding
    return:
        Padded Numpy array Image
    """
    assert len(X.shape) == 4, "Input image should be form of [Batch, Width, Height, Channels]"
    if isinstance(pad_size, int):
        y_pad = x_pad = pad_size
    else:
        y_pad = pad_size[0]
        x_pad = pad_size[1]

    pad_width = (
        (0, 0),
        (y_pad, y_pad),
        (x_pad, x_pad),
        (0, 0),
    )  # Do not pad first and last axis. Pad Width(2nd), Height(3rd) axis with  pad_size
    return np.pad(X, pad_width=pad_width, mode="constant", constant_values=(pad_val, pad_val))


class Conv2DLayer:
    """
    2D Convolution Layer
    """

    def __init__(
        self,
        input_channels: int,
        num_filters: int,
        kernel_size: int,
        stride: int,
        padding: Union[str, None],
        activation: Union[None, str] = "relu",
    ):
        """
        Kernal Matrix for the Current Layer having shape [filter_size, filter_size, num_of_features_old, num_of_filters_new]. 'num_of_features_old' are the Channels or features from previous layer
        'filter_size' (or kernel size) is the size of filters which will detect new features.
        'num_of_filters_new' are the No of new features detected by these kernels on the previous features where Each Kernel/filter will detect a new feature/channel

        args:
            input_channels: No of features/channels present in the incoming input. It'll be equal to Last dimension value from the prev layer output `previous_layer.output.shape[-1]`
            num_filters: Output Channels or How many new features you want this new Layer to Detect. Each Filter/kernel will detect a new Feature /channel
            kernel_size: What is the size of Kernels or Filters. Each Filter a 2D Square Matrix of size kernel_size
            stride: How many pixels you want each kernel to shift. Same shift in X and Y direction OR indirectly, it'll define how many iterations the kernel will take to convolve over the whole image
            padding: How much padding you want to add to the image. If padding='same', it means padding in a way that input and output have the same dimension
            activation: Which activation to use
        """
        self.kernel_matrices = np.random.randn(
            kernel_size, kernel_size, input_channels, num_filters
        )  # Complete Weight/Kernel Matrix
        self.biases = np.random.randn(1, 1, 1, num_filters)  # 1 Bias per Channel/feature/filter
        self.stride = stride
        self.padding = padding
        self.activation = activation

    def convolution_step(
        self, image_portion: np.ndarray, kernel_matrix: np.ndarray, bias: np.ndarray
    ) -> np.ndarray:
        """
        Convolve the Filter onto a given portion of the Image. This operation will be done multiple times per image, per kernel. Number of times is dependent on Window size, Stride and Image Size.
        In simple words, Multiply the given filter weight matrix and the area covered by filter and this is repeated for whole image.
        Imagine a slice of matrix  [FxF] from a [PxQ] shaped image. Now imagine [Fxf] filter on top of it. Do matrix multiplication, summation and add bias
        args:
            image_portion: Image Matrix or in other sense, Features. Shape is [filter_size, filter_size, no of channels / Features from previous layer]
            filter: Filter / Kernel weight Matrix which convolves on top of image slice. Size is [filter_size, filter_size, no of channels / Features from previous layer]
            bias: Bias matrix of shape [1,1,1]
        returns:
            Convolved window output with single floating value inside a [1,1,1] matrix
        """
        assert (
            image_portion.shape == kernel_matrix.shape
        ), "Image Portion and Filter must be of same shape"
        return np.sum(np.multiply(image_portion, kernel_matrix)) + bias.astype("float")

    def forward(self, features_batch: np.ndarray) -> np.ndarray:
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evals/elsuite/hr_ml_agent_bench/benchmarks/vectorization/scripts/human_baseline.py [10:100]:
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def relu(x: np.ndarray) -> np.ndarray:
    """
    Relu activation function. Returns max(0,value)
    args:
        x: input array of any shape
    output: All negatives clipped to 0
    """
    return x * (x > 0)


def add_padding(X: np.ndarray, pad_size: Union[int, list, tuple], pad_val: int = 0) -> np.ndarray:
    """
    Pad the input image array equally from all sides
    args:
        x: Input Image should be in the form of [Batch, Width, Height, Channels]
        pad_size: How much padding should be done. If int, equal padding will done. Else specify how much to pad each side (height_pad,width_pad) OR (y_pad, x_pad)
        pad_val: What should be the value to be padded. Usually it os 0 padding
    return:
        Padded Numpy array Image
    """
    assert len(X.shape) == 4, "Input image should be form of [Batch, Width, Height, Channels]"
    if isinstance(pad_size, int):
        y_pad = x_pad = pad_size
    else:
        y_pad = pad_size[0]
        x_pad = pad_size[1]

    pad_width = (
        (0, 0),
        (y_pad, y_pad),
        (x_pad, x_pad),
        (0, 0),
    )  # Do not pad first and last axis. Pad Width(2nd), Height(3rd) axis with  pad_size
    return np.pad(X, pad_width=pad_width, mode="constant", constant_values=(pad_val, pad_val))


class Conv2DLayer:
    """
    2D Convolution Layer
    """

    def __init__(
        self,
        input_channels: int,
        num_filters: int,
        kernel_size: int,
        stride: int,
        padding: Union[str, None],
        activation: Union[None, str] = "relu",
    ):
        """
        Kernal Matrix for the Current Layer having shape [filter_size, filter_size, num_of_features_old, num_of_filters_new]. 'num_of_features_old' are the Channels or features from previous layer
        'filter_size' (or kernel size) is the size of filters which will detect new features.
        'num_of_filters_new' are the No of new features detected by these kernels on the previous features where Each Kernel/filter will detect a new feature/channel

        args:
            input_channels: No of features/channels present in the incoming input. It'll be equal to Last dimension value from the prev layer output `previous_layer.output.shape[-1]`
            num_filters: Output Channels or How many new features you want this new Layer to Detect. Each Filter/kernel will detect a new Feature /channel
            kernel_size: What is the size of Kernels or Filters. Each Filter a 2D Square Matrix of size kernel_size
            stride: How many pixels you want each kernel to shift. Same shift in X and Y direction OR indirectly, it'll define how many iterations the kernel will take to convolve over the whole image
            padding: How much padding you want to add to the image. If padding='same', it means padding in a way that input and output have the same dimension
            activation: Which activation to use
        """
        self.kernel_matrices = np.random.randn(
            kernel_size, kernel_size, input_channels, num_filters
        )  # Complete Weight/Kernel Matrix
        self.biases = np.random.randn(1, 1, 1, num_filters)  # 1 Bias per Channel/feature/filter
        self.stride = stride
        self.padding = padding
        self.activation = activation

    def convolution_step(
        self, image_portion: np.ndarray, kernel_matrix: np.ndarray, bias: np.ndarray
    ) -> np.ndarray:
        """
        Convolve the Filter onto a given portion of the Image. This operation will be done multiple times per image, per kernel. Number of times is dependent on Window size, Stride and Image Size.
        In simple words, Multiply the given filter weight matrix and the area covered by filter and this is repeated for whole image.
        Imagine a slice of matrix  [FxF] from a [PxQ] shaped image. Now imagine [Fxf] filter on top of it. Do matrix multiplication, summation and add bias
        args:
            image_portion: Image Matrix or in other sense, Features. Shape is [filter_size, filter_size, no of channels / Features from previous layer]
            filter: Filter / Kernel weight Matrix which convolves on top of image slice. Size is [filter_size, filter_size, no of channels / Features from previous layer]
            bias: Bias matrix of shape [1,1,1]
        returns:
            Convolved window output with single floating value inside a [1,1,1] matrix
        """
        assert (
            image_portion.shape == kernel_matrix.shape
        ), "Image Portion and Filter must be of same shape"
        return np.sum(np.multiply(image_portion, kernel_matrix)) + bias.astype("float")

    def forward(self, features_batch: np.ndarray) -> np.ndarray:
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