gradio/test/test_interface.py

import unittest
import numpy as np
from gradio import Interface
import gradio.inputs
import gradio.outputs


class TestInterface(unittest.TestCase):
    def test_input_output_mapping(self):
        io = Interface(inputs='SketCHPad', outputs='textBOX', model=lambda x: x, model_type='function')
        self.assertIsInstance(io.input_interface, gradio.inputs.Sketchpad)
        self.assertIsInstance(io.output_interface, gradio.outputs.Textbox)

    def test_input_interface_is_instance(self):
        inp = gradio.inputs.ImageUpload()
        io = Interface(inputs=inp, outputs='textBOX', model=lambda x: x, model_type='function')
        self.assertEqual(io.input_interface, inp)

    def test_output_interface_is_instance(self):
        out = gradio.outputs.Label(show_confidences=False)
        io = Interface(inputs='SketCHPad', outputs=out, model=lambda x: x, model_type='function')
        self.assertEqual(io.output_interface, out)

    # def test_keras_model(self):
    #     try:
    #         import tensorflow as tf
    #     except:
    #         raise unittest.SkipTest("Need tensorflow installed to do keras-based tests")
    #     inputs = tf.keras.Input(shape=(3,))
    #     x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
    #     outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
    #     model = tf.keras.Model(inputs=inputs, outputs=outputs)
    #     io = Interface(inputs='SketCHPad', outputs='textBOX', model=model, model_type='keras')
    #     pred = io.predict(np.ones(shape=(1, 3), ))
    #     self.assertEqual(pred.shape, (1, 5))

    # def test_func_model(self):
    #     def model(x):
    #         return 2*x
    #     io = Interface(inputs='SketCHPad', outputs='textBOX', model=model, model_type='function')
    #     pred = io.predict(np.ones(shape=(1, 3)))
    #     self.assertEqual(pred.shape, (1, 3))

    def test_pytorch_model(self):
        try:
            import torch
        except:
            raise unittest.SkipTest("Need torch installed to do pytorch-based tests")

        class TwoLayerNet(torch.nn.Module):
            def __init__(self):
                super(TwoLayerNet, self).__init__()
                self.linear1 = torch.nn.Linear(3, 4)
                self.linear2 = torch.nn.Linear(4, 5)

            def forward(self, x):
                h_relu = torch.nn.functional.relu(self.linear1(x))
                y_pred = self.linear2(h_relu)
                return y_pred

        model = TwoLayerNet()
        io = Interface(inputs='SketCHPad', outputs='textBOX', model=model, model_type='pytorch')
        pred = io.predict(np.ones(shape=(1, 3), dtype=np.float32))
        self.assertEqual(pred.shape, (1, 5))


if __name__ == '__main__':
    unittest.main()