Building Head/Face Detection System <2. Code>
Posted On June 20, 2020
This post contains the implementation of the concepts described in previous post in https://alvissalim.com/2020/05/15/building-a-face-detector-system/ Previously, we have defined the expectation of what a head/face detection neural network model may look like. In this post, we will go through the steps of implementing the model in PyTorch. It is assumed that you are already familiar with the PyTorch framework and Python.
The Neural Network Model
"""Object Detection Model @author : Muhammad Sakti Alvissalim (email@example.com) Copyright (C) 2020 Muhammad Sakti Alvissalim This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>. """ import torch from torch import nn import torchvision.models as models class ObjectDetectionModel(nn.Module): """ An object detection model for PyTorch """ def __init__(self, n_classes: int = 1, output_grid_size : int = 7, boxes_per_cell : int = 4, **kwarg): super(ObjectDetectionModel, self).__init__(**kwarg) self.n_classes = n_classes self.output_grid_size = output_grid_size self.boxes_per_cell = boxes_per_cell self.n_cells = output_grid_size * output_grid_size boxes_tensor_size = (boxes_per_cell * 5) # objectness score + (x,y,w,h) conditional_class_prob_tensor_size = n_classes cell_tensor_size = boxes_tensor_size + conditional_class_prob_tensor_size self.cell_tensor_size = cell_tensor_size base_model = models.mobilenet_v2() self.features_length = base_model.last_channel self.base_features = base_model.features self.final_feat = nn.Conv2d(base_model.last_channel, cell_tensor_size, 1, stride=1) def forward(self, x): x = self.base_features(x) x = self.final_feat(x) x = nn.functional.adaptive_avg_pool2d(x, self.output_grid_size )# x = x.permute(0,2,3,1) x = x.flatten(start_dim=1) return x