mirror of
https://github.com/gradio-app/gradio.git
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69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
import unittest
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import numpy as np
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from gradio import Interface
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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 = Interface(inputs='SketCHPad', outputs='textBOX', model=lambda x: x, model_type='function')
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self.assertIsInstance(io.input_interface, gradio.inputs.Sketchpad)
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self.assertIsInstance(io.output_interface, gradio.outputs.Textbox)
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def test_input_interface_is_instance(self):
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inp = gradio.inputs.ImageUpload()
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io = Interface(inputs=inp, outputs='textBOX', model=lambda x: x, model_type='function')
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self.assertEqual(io.input_interface, 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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io = Interface(inputs='SketCHPad', outputs=out, model=lambda x: x, model_type='function')
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self.assertEqual(io.output_interface, 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 = Interface(inputs='SketCHPad', outputs='textBOX', model=model, model_type='keras')
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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 = Interface(inputs='SketCHPad', outputs='textBOX', model=model, model_type='function')
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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 = Interface(inputs='SketCHPad', outputs='textBOX', model=model, model_type='pytorch')
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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()
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