Structure of the Model File

Model code can be of either format:

• Single python .py format file containing one/multiple methods
• Single python .py format file having no method
• Multiple python .py format files with main file code not contained in a method saved in zip format
• Multiple python .py format files with main file code contained in a method saved in zip format

note

In case of multiple files saved as .zip format, the file should be directly inside .zip file, subdirectory structure in .zip file is not supported.

Mandatory Variables

PyTorch mandatory variables

The file must contain these variables:

• framework : name of the framework for which this model file is created. For PyTorch it's value will be pytorch.
• model_type : name of the model type for which this model file is created. It's value will be either empty or rcnn or heatmap or yolo.
• main_class : name of the main class of the model.
• image_size : name of the variable defining the input size considered for creating model layers.
• category : name of the category for which this model file is created. It's value will be either of these : image_classification, object_detection, keypoint_detection, text_classification.
• batch_size : name of the variable defining the batch size of the images considered for creating model layers.

Sklearn mandatory variables

The file must contain these variables:

• framework : name of the framework for which this model file is created. For PyTorch it's value will be pytorch.
• model_type : name of the model type for which this model file is created. It's value will be either empty or tree or linear or knn or naive or mlp.
• main_class or main_method : name of the main class or main method of the model. Any one of these is necessary for model.
• image_size : name of the variable defining the input size considered for creating model layers.
• category : name of the category for which this model file is created. It's value will be either of these : image_classification, object_detection, keypoint_detection, text_classification.
• batch_size : name of the variable defining the batch size of the images considered for creating model layers.

Tensorflow mandatory variables

Each format must contain these variables on the main file:

• framework : name of the framework for which this model file is created. For tensorflow it's value will be tensorflow.
• model_type : name of the model type for which this model file is created. It's value will be either empty or rcnn or heatmap.
• main_method : name of the main function
• input_shape or image_size : name of the variable defining the input shape of the model
• category : name of the category for which this model file is created. It's value will be either of these : image_classification, object_detection, keypoint_detection, text_classification.
• output_classes : name of the variable defining the output shape of the variables. The value of this variable is equal to the number of classes in the dataset.

Additional variables

Some variables are neccessary specific to a category

• num_feature_points : number of keypoints or feature points for which this model file is created. This variable is used only for keypoint_detection and generic_classification category.
• model_id : model id using which this model file is created. This variable is used only for text_classification category.
• hf_token : hf token using which this model file is created. This variable is used only for text_classification category.

note

• The framework variable is compulsory and should always be placed at the top of your code just after the imports, before any other variable.
• Changing the sequence of the framework variable will cause the model upload to fail.
• Do not add any comment after these variables it will cause model upload to fail.

Detailed Information on each format

1. Single python file containing one class for pyTorch

All your model code needs to be contained in a single python file with the following structure:

1.1 Import Section

Import all the necessary functionality you need to build your model.

# Example
import torch
import torch.nn as nn

1.2 Main Class

This is the main method which should return the model as output.

#Example
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(3, 6, kernel_size=5, stride=1, padding=0),
            nn.BatchNorm2d(6),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(16 * 53 * 53, 120)
        self.relu = nn.ReLU()
        self.fc1 = nn.Linear(120, 84)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(84, 1)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        out = self.relu(out)
        out = self.fc1(out)
        out = self.relu1(out)
        out = self.fc2(out)
        return out

The value of the above variables would look like this:

• framework = 'pytorch'
• main_method = 'Net'
• image_size = 224
• batch_size = 16

An example model file of this format can be found here.

1. Single python file containing one class for sklearn

All your model code needs to be contained in a single python file with the following structure:

1.1 Import Section

Import all the necessary functionality you need to build your model.

# Example
from sklearn.svm import SVC

1.2 Main Method

This is the main method which should return the model as output.

#Example
def MyModel():
    return SVC(kernel='linear')

The value of the above variables would look like this:

• framework = 'sklearn'
• main_method = 'MyModel'
• image_size = 224
• batch_size = 16

An example model file of this format can be found here.

3. Single python file containing one/multiple methods for tensorflow

All your model code needs to be contained in a single python file with the following structure:

3.1 Import Section

Import all the necessary functionality you need to build your model.

# Example
import tensorflow as tf
from tensorflow.keras import layers, utils

3.2 Supporting Functions

Any kind of functionality that supports the model architecture. This section is optional. This section can be left out if the model architecture does not require it.

def dense_block(input_tensor, k, block_reps):
    """
    tensor: input tensor from the previous layers
    k: growth rate
    block_reps: Number of times the block is repeated
    Return the concatenated tensors
    """
    for _ in range(block_reps):

        x = layers.BatchNormalization()(input_tensor)
        x = layers.ReLU()(x)
        x = layers.Conv2D(filters=4*k, kernel_size=1)(x)
        x = layers.BatchNormalization()(x)
        x = layers.ReLU()(x)
        x = layers.Conv2D(filters=k, kernel_size=3, padding='same')(x)
        output_tensor = layers.Concatenate()([input_tensor, x])

        return output_tensor

3.3 Main Method

This is the main method which should return the model as output.

#Example
def MyModel(input_shape=(224,224,3),classes=3):

    k = 32 #growth rate

    input = layers.Input(shape=input_shape)
    x = layers.BatchNormalization()(input)
    x = layers.ReLU()(x)
    x = layers.Conv2D(filters=2*k, kernel_size=7, strides=2)(x)
    x = layers.MaxPooling2D(pool_size=3, strides=2)(x)

    x = dense_block(x, 32, 6)
    x = transition_layers(x)

    x = dense_block(x, 32, 12)
    x = transition_layers(x)

    x = dense_block(x, 32, 32)
    x = transition_layers(x)

    x = dense_block(x, 32, 32)

    x = layers.GlobalAveragePooling2D()(x)
    output = layers.Dense(classes, activation='softmax')(x)

    model_densenet = tf.keras.Model(input, output)

    return model_densenet

The value of the above variables would look like this:

• framework = 'tensorflow'
• main_method = 'MyModel'
• input_shape = 'input_shape'
• output_classes = 'classes'

An example model file of this format can be found here.

4. Single python file having no method for tensorflow

All your model code needs to be in a single python file with the following structure:

The model file can be broken down into two sections: The Import section and the main code.

4.1 Import Section

Import all the necessary functionalities you need to build your model.

# Example
import tensorflow as tf
from tensorflow.keras import layers, models

4.2 Main Code

This is the model code with all layers.

#Example
input_shape=(224,224,3)
classes=3

model = models.Sequential()
# layer conv 1
model.add(layers.Conv2D(32, 3, activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D(2))
layers.BatchNormalization()
# layer conv 2
model.add(layers.Conv2D(64, 3, activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D(2))
layers.BatchNormalization()
# layer conv 3
model.add(layers.Conv2D(128, 3, activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D(2))
layers.BatchNormalization()
# layer conv 4
model.add(layers.Conv2D(264, 3, activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D(2))
layers.BatchNormalization()
# layer conv 5
model.add(layers.Conv2D(1, 1, activation='relu', input_shape=input_shape))
layers.BatchNormalization()
# Flatten the feature maps to serve dense
model.add(layers.Flatten())
model.add(layers.Dense(classes, activation='softmax'))

The value of the above variables would look this:

• framework = 'tensorflow'
• main_method = ''
• input_shape = 'input_shape'
• output_classes = 'classes'

An example model file of this format:

import tensorflow as tf
from tensorflow.keras import layers, models

framework = 'tensorflow'
main_method = ''
input_shape = 'input_shape'
output_classes = 'classes'


input_shape=(224,224,3)
classes=3

model = models.Sequential()
# layer conv 1
model.add(layers.Conv2D(32, 3, activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D(2))
layers.BatchNormalization()
# layer conv 2
model.add(layers.Conv2D(64, 3, activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D(2))
layers.BatchNormalization()
# layer conv 3
model.add(layers.Conv2D(128, 3, activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D(2))
layers.BatchNormalization()
# layer conv 4
model.add(layers.Conv2D(264, 3, activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling2D(2))
layers.BatchNormalization()
# layer conv 5
model.add(layers.Conv2D(1, 1, activation='relu', input_shape=input_shape))
layers.BatchNormalization()
# Flatten the feature maps to serve dense
model.add(layers.Flatten())
model.add(layers.Dense(classes, activation='softmax'))

5. Multiple python files with main file code not contained in a method

In this format the model architecture is defined using multiple python files. But the main file should use the same format as type 1.

In order to upload models with this format, zip all python files with the main method containing the mentioned variables and upload the zip file.

note

The main file should contain the variable values as mentioned in the type 1 format. Other files should not contain these variables.

6. Multiple python file with main file code contained in a method

In this format the model architecture is defined using multiple python files. But the main file should use the same format as type 2.

In order to upload models with this format, zip all python files with the main method containing the mentioned variables and upload the zip file.

note

The main file should contain the variable values as mentioned in type 2 format. Other files should not contain these variables.