Implementation for ResNet 50, 101, 152 with/out SE module

Get the registry record. Args: key (str): The class name in string format. Returns: class: The corresponding c

Chooses static padding if you have specified an image size, and dynamic padding otherwise. Static padding is necessary for ONNX exporting of mo

Register a module. A record will be added to `self._module_dict`, whose key is the class name or the specified name, and value is the

Calculate and round number of filters based on width multiplier. Use width_coefficient, depth_divisor and min_depth of global_params. Args:

Validates model name. Args: model_name (str): Name for efficientnet. Returns: bool: Is a valid name or not.

@split: train/val This function for loading data from file for 4 types of manipulated images FF++ and FaceXray generation data

Calculates the output image size when using Conv2dSamePadding with a stride. Necessary for static padding. Thanks to mannatsingh for pointing t

This function is to draw facial landmarks into transformed images

Check whether it is a sequence of some type. Args: seq (Sequence): The sequence to be checked. expected_type (type): Expected type

Adjust model's first convolution layer to in_channels, if in_channels not equals 3. Args: in_channels (int): Input data's channel

Build a module from config dict when it is a class configuration, or call a function from config dict when it is a function configuration. Exa

A decorator factory to check if prerequisites are satisfied. Args: prerequisites (str of list[str]): Prerequisites to be checked.

3x3 convolution with padding

3x3 convolution with padding

Map EfficientNet model name to parameter coefficients. Args: model_name (str): Model name to be queried. Returns: params_dict[

Get the block args and global params for a given model name. Args: model_name (str): Model's name. override_params (dict): A dict

Cast elements of an iterable object into some type. Args: inputs (Iterable): The input object. dst_type (type): Destination type.

This function only care about state dict of model For other modules such as optimizer, resume learning, please refer @load_model

Overlay a colormapped mask on a background image >>> from PIL import Image >>> import matplotlib.pyplot as plt >>> from torchcam.utils im

batch_image: [batch_size, channel, height, width] batch_heatmaps: ['batch_size, num_joints, height, width] batch_cls: ['batch_size, num_j

Add children for a registry. The ``registry`` will be added as children based on its scope. The parent registry could build objects fr