ResNet Model Error: Missing Required Arguments in ResNet Class

The error 'TypeError: init() missing 2 required positional arguments: 'block' and 'layers'' arises when creating an instance of the ResNet class in MindSpore without providing the necessary parameters. The ResNet class constructor requires two arguments: block and layers. These arguments specify the building block used in the ResNet architecture (e.g., BasicBlock, Bottleneck) and the number of layers for each block.

To resolve the error, provide these arguments when creating an instance of the ResNet class. Here's a corrected example:

network = ResNet(BasicBlock, [2, 2, 2, 2])

This code creates an instance of the ResNet class with BasicBlock as the block type and [2, 2, 2, 2] as the number of layers for each block in the ResNet. Adjust these values based on your specific needs.

Explanation:

• block: This argument determines the type of building block used in the ResNet. Commonly used options include BasicBlock and Bottleneck. The specific block influences the architecture and complexity of the ResNet model.
• layers: This argument is a list that specifies the number of layers for each block in the ResNet. The length of the list should correspond to the number of blocks in the ResNet architecture.

Example:

The following example demonstrates how to create a ResNet model with the BasicBlock and a specific number of layers for each block:

import mindspore.nn as nn
from mindspore.common.initializer import Normal
from mindspore import context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy
from scipy.integrate._ivp.radau import P
import mindspore.dataset as ds
import os
import cv2
import mindspore
import numpy as np

class BasicBlock(nn.Cell):
    # ... (BasicBlock definition remains the same)

class ResNet(nn.Cell):
    # ... (ResNet definition remains the same)

# ... (Rest of the code remains the same)

# Creating an instance of ResNet with specified arguments
network = ResNet(BasicBlock, [2, 2, 2, 2])

# ... (Rest of the code remains the same)

By providing the correct block and layers arguments when creating the ResNet instance, you can eliminate the error and successfully instantiate and use the ResNet model in your MindSpore code.