Keras to PyTorch: Converting GlobalAveragePooling2D and Reshape for Multi-View Fusion

Converting Keras Multi-View Fusion Model to PyTorch

This example showcases how to translate a Keras model employing GlobalAveragePooling2D and Reshape for multi-view fusion to its equivalent in PyTorch. We'll explore the corresponding PyTorch layers and their usage to replicate the Keras functionality for a smooth model conversion.

Original Keras Code:

init = input
input_shape = (1, 1, 2)
weight = GlobalAveragePooling2D()(init)
print(weight)
weight = Reshape(input_shape)(weight)
print(weight)
weight = Dense(6, activation='sigmoid', kernel_initializer='glorot_uniform', use_bias=True)(weight)
weight = Dense(3, activation='sigmoid', kernel_initializer='glorot_uniform', use_bias=True)(weight)
weight = Dense(2, activation='softmax', kernel_initializer='glorot_uniform', use_bias=True)(weight)
if K.image_data_format() == 'channels_first':
    weight = Permute((3, 1, 2))(weight)
temp_x = multiply([init, weight])
x = Lambda(multi_view_fusion)(temp_x)

PyTorch Equivalent:

import torch
import torch.nn as nn

class MyModel(nn.Module):
    def __init__(self, input_shape):
        super(MyModel, self).__init__()
        self.global_pooling = nn.AdaptiveAvgPool2d((1, 1))
        self.reshape = nn.Reshape(input_shape)
        self.dense1 = nn.Linear(2, 6)
        self.sigmoid1 = nn.Sigmoid()
        self.dense2 = nn.Linear(6, 3)
        self.sigmoid2 = nn.Sigmoid()
        self.dense3 = nn.Linear(3, 2)
        self.softmax = nn.Softmax(dim=1)
        self.permute = nn.Permute((0, 3, 1, 2))
        
    def forward(self, x):
        weight = self.global_pooling(x)
        weight = self.reshape(weight)
        weight = self.dense1(weight)
        weight = self.sigmoid1(weight)
        weight = self.dense2(weight)
        weight = self.sigmoid2(weight)
        weight = self.dense3(weight)
        weight = self.softmax(weight)
        weight = self.permute(weight)
        temp_x = torch.mul(x, weight)
        x = multi_view_fusion(temp_x)
        return x

model = MyModel(input_shape=(1, 1, 2))

Explanation:

• GlobalAveragePooling2D: In Keras, this layer averages the values across the spatial dimensions (height and width). In PyTorch, nn.AdaptiveAvgPool2d((1, 1)) achieves the same functionality by setting the output size to (1, 1).

• Reshape: Keras's Reshape layer is directly equivalent to nn.Reshape in PyTorch. It allows you to restructure the tensor into the specified shape.

• Dense: The Dense layer in Keras corresponds to nn.Linear in PyTorch. Both perform fully connected operations. The activation functions ('sigmoid' and 'softmax') and kernel initializer ('glorot_uniform') are applied in a similar manner in both frameworks.

• Permute: Keras's Permute layer is replicated by nn.Permute in PyTorch. It reorders the dimensions of the tensor according to the provided tuple.

• Element-wise Multiplication: The multiply function in Keras for element-wise multiplication is represented by torch.mul in PyTorch.

Key Points:

• The multi_view_fusion function, used in both Keras and PyTorch, represents the logic of your multi-view fusion strategy. Its implementation details are not shown in this example and depend on your specific requirements.

• You'll need to adapt the code based on your input data format, the specific multi-view fusion logic, and other details of your model architecture.

This guide provides a comprehensive understanding of the necessary steps to translate your Keras model using GlobalAveragePooling2D and Reshape for multi-view fusion into an efficient PyTorch equivalent. You can apply the principles and code to successfully convert and enhance your models using the advantages of PyTorch's framework.