program代写、Java/Python程序代做

日期：2023-10-11 09:44

Individual assignment

Due Wednesday, October 11th 2023

Introduction

In this assignment you will explore components of modern convolutional networks by implementing

them in either PyTorch or TensorFlow. This will solidify important concepts discussed in class

and develop skills that will help you in your class projects.

Concepts covered in this assignment include skip connections, depthwise convolution, 1x1 convolution, and implementation of custom layers.

Background

EfficientNetV2 is a family of convolutional networks with exceptional parameter efficiency and that

are fast to train [2]. These networks were developed by Google using hyperparameter tuning to

search for the most performant architectures, which produced this family of models that outperformed vision transformers of that time. The EfficientNetV2 networks use of several components

including squeeze-and-excite networks (SE) and mobile inverted bottleneck layers (MBConv). In this assignment you will 1. Implement the MBConv and fused-MBConv elements used

by EfficientNetV2, 2. Create blocks composed of MBConv and fused-MBConv elements, and 3.

Train a simple network using these elements for a basic cell classification task.

I am providing you with a Jupyter notebook that illustrates how to load the data and train

a classification model using a pretrained network from Keras. This notebook also contains the

implementation of the squeeze-and-excite network as a custom layer for reference.

Step 1: Implement MBConv and fused-MBConv elements

Squeeze-and-excite network (SE): The SE essentially implements a “channel attention” mechanism that selectively re-weights channels to highlight those that are informative [1]. Refer to the

SE network illustrated in Figure 1 and compare to the implementation in the provided notebook.

This implementation is parameterized with the channel compression parameter s (default value

0.25).

Mobile inverted bottleneck (MBConv): The MBConv starts with an expansion of the input

channels by a 1x1 convolution. A depthwise convolution is then applied, followed by an SE network.

A 1x1 convolution compresses the channels again, followed by a dropout. Finally, a skip connection

combines this output with the MBConv inputs. Implement the MBConv network illustrated in

Figure 2. Parameterize your implementation with the expansion parameter e (default 4), the

depthwise convolution stride (default 2), the SE compression s, and the dropout rate d (default

0.1).

Fused-MBConv: The fused-MBConv replaces the first two layers of the MBConv with a stan-

dard convolution. Implement the fused-MBConv network illustrated in Figure 3 with the same

parameters as the MBConv.

Suggestion: Implementing the MBConv and fused-MBConv as custom layers is the simplest approach. This process is similar in TensorFlow and PyTorch. Implementing these as simple functions presents challenges in handling the batch normalization which behaves differently during training and inference. This issue is handled correctly by the layer class of your framework which receives

information on whether the layer is being called in training or inference mode. This behavior can

be inherited by your custom layer.

Figure 1: Squeeze-and-excite (SE) network. The SE network generates “channel attentions” that

re-weight the channel dimension of the input. The network has a single parameter s ∈ (0, 1] that

controls the compression on this branch (default value 0.25).

Figure 2: Mobile inverted bottleneck (MBConv) network. The MBConv features convolutions with

batch normalization (BN), including a 3x3 depthwise convolution (dwconv), an SE network, and a

dropout layer. The parameters for the MBConv are the expansion factor e, the stride of the depthwise

convolution, the SE compression parameter s, and the dropout rate d.

Figure 3: Fused-MBConv network. The fused-MBConv replaces the separable depthwise convolution

of the MBConv with a full convolution, but is otherwise the same. This increases the number of

trainable parameters but can execute faster (we will discuss this in class).

Step 2: Create MBConv and fused-MBConv blocks

EfficientNetV2 arranges MBConv and fused-MBConv elements into blocks, which are sequences

of repeated MBConv or fused-MBConv elements. Using your elements from step 1, implement

functions to create a blocks of repeated MBConv or fused-MBConv elements. This function should

accept the number of repeats, the number of input channels, and the element parameters.

There are two important details when creating a block (see Figure 4): 1. The stride parameter is

only applied to the first element in the block (otherwise the H and W dimensions will shrink to

nothing); 2. The skip connection is removed from the first element in the block, which allows the

channel dimension to be altered by each block.

Figure 4: A Fused-MBConv block. The block combines multiple fused-MBConv elements stacked

sequentially. The skip connection in the first element of the block is removed to allow the number

of channels to change between blocks. The stride is only applied to the first element of the block to

avoid shrinking the H and W dimensions too much.

Step 3: Train a simple cell classification network

The data provided contains 90x90 images of cell nuclei split into training and validation sets, and

given one of three labels (tumor, stromal, TIL).

Implement the network depicted in Figure 5 using your blocks from step 2. Train the network

using the training and validation split provided in the data archive, and plot the training and

validation loss over 10 epochs. Display the model contents using model.summary()(TensorFlow)

or the pytorch-summary package.

Use same padding mode for all convolutions.

Figure 5: A basic network.

References