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70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
import unittest
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import numpy as np
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import gradio as gr
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import gradio.inputs
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import gradio.outputs
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class TestInterface(unittest.TestCase):
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def test_input_output_mapping(self):
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io = gr.Interface(inputs='SketCHPad', outputs='textBOX', fn=lambda
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x: x)
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self.assertIsInstance(io.input_interfaces[0], gradio.inputs.Sketchpad)
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self.assertIsInstance(io.output_interfaces[0], gradio.outputs.Textbox)
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def test_input_interface_is_instance(self):
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inp = gradio.inputs.Image()
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io = gr.Interface(inputs=inp, outputs='textBOX', fn=lambda x: x)
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self.assertEqual(io.input_interfaces[0], inp)
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def test_output_interface_is_instance(self):
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# out = gradio.outputs.Label(show_confidences=False)
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out = gradio.outputs.Label()
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io = gr.Interface(inputs='SketCHPad', outputs=out, fn=lambda x: x)
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self.assertEqual(io.output_interfaces[0], out)
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def test_keras_model(self):
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try:
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import tensorflow as tf
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except:
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raise unittest.SkipTest("Need tensorflow installed to do keras-based tests")
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inputs = tf.keras.Input(shape=(3,))
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x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
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outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
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model = tf.keras.Model(inputs=inputs, outputs=outputs)
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io = gr.Interface(inputs='SketCHPad', outputs='textBOX', fn=model)
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# pred = io.predict(np.ones(shape=(1, 3), ))
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# self.assertEqual(pred.shape, (1, 5))
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def test_func_model(self):
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def model(x):
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return 2*x
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io = gr.Interface(inputs='SketCHPad', outputs='textBOX', fn=model)
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# pred = io.predict(np.ones(shape=(1, 3)))
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# self.assertEqual(pred.shape, (1, 3))
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def test_pytorch_model(self):
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try:
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import torch
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except:
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raise unittest.SkipTest("Need torch installed to do pytorch-based tests")
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class TwoLayerNet(torch.nn.Module):
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def __init__(self):
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super(TwoLayerNet, self).__init__()
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self.linear1 = torch.nn.Linear(3, 4)
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self.linear2 = torch.nn.Linear(4, 5)
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def forward(self, x):
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h_relu = torch.nn.functional.relu(self.linear1(x))
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y_pred = self.linear2(h_relu)
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return y_pred
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model = TwoLayerNet()
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io = gr.Interface(inputs='SketCHPad', outputs='textBOX', fn=model)
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# pred = io.predict(np.ones(shape=(1, 3), dtype=np.float32))
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# self.assertEqual(pred.shape, (1, 5))
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if __name__ == '__main__':
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unittest.main() |