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Merge branch 'master' of https://github.com/gradio-app/gradio-UI

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This commit is contained in:
Abubakar Abid 2020-06-11 14:37:23 -05:00
commit 2074f314fb
4 changed files with 73 additions and 71 deletions
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14
test/test_inputs.py
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@ -24,12 +24,12 @@ class TestWebcam(unittest.TestCase):
def test_path_exists(self):
inp = inputs.Webcam()
path = inputs.BASE_INPUT_INTERFACE_JS_PATH.format(inp.get_name())
self.assertFalse(os.path.exists(os.path.join(PACKAGE_NAME, path))) # Note implemented yet.
self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
def test_preprocessing(self):
inp = inputs.Webcam()
array = inp.preprocess(BASE64_IMG)
self.assertEqual(array.shape, (1, 224, 224, 3))
self.assertEqual(array.shape, (224, 224, 3))
class TestTextbox(unittest.TestCase):
@ -46,21 +46,21 @@ class TestTextbox(unittest.TestCase):
class TestImageUpload(unittest.TestCase):
def test_path_exists(self):
inp = inputs.ImageUpload()
inp = inputs.ImageIn()
path = inputs.BASE_INPUT_INTERFACE_JS_PATH.format(inp.get_name())
self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
def test_preprocessing(self):
inp = inputs.ImageUpload()
inp = inputs.ImageIn()
array = inp.preprocess(BASE64_IMG)
self.assertEqual(array.shape, (1, 224, 224, 3))
self.assertEqual(array.shape, (224, 224, 3))
def test_preprocessing(self):
inp = inputs.ImageUpload()
inp = inputs.ImageIn()
inp.image_height = 48
inp.image_width = 48
array = inp.preprocess(BASE64_IMG)
self.assertEqual(array.shape, (1, 48, 48, 3))
self.assertEqual(array.shape, (48, 48, 3))
if __name__ == '__main__':
unittest.main()

38
test/test_interface.py
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@ -7,19 +7,21 @@ 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)
io = gr.Interface(inputs='SketCHPad', outputs='textBOX', fn=lambda
x: x)
self.assertIsInstance(io.input_interfaces[0], gradio.inputs.Sketchpad)
self.assertIsInstance(io.output_interfaces[0], 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)
inp = gradio.inputs.ImageIn()
io = gr.Interface(inputs=inp, outputs='textBOX', fn=lambda x: x)
self.assertEqual(io.input_interfaces[0], 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)
# out = gradio.outputs.Label(show_confidences=False)
out = gradio.outputs.Label()
io = gr.Interface(inputs='SketCHPad', outputs=out, fn=lambda x: x)
self.assertEqual(io.output_interfaces[0], out)
def test_keras_model(self):
try:
@ -30,16 +32,16 @@ class TestInterface(unittest.TestCase):
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))
io = gr.Interface(inputs='SketCHPad', outputs='textBOX', fn=model)
# 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='pyfunc')
pred = io.predict(np.ones(shape=(1, 3)))
self.assertEqual(pred.shape, (1, 3))
io = gr.Interface(inputs='SketCHPad', outputs='textBOX', fn=model)
# pred = io.predict(np.ones(shape=(1, 3)))
# self.assertEqual(pred.shape, (1, 3))
def test_pytorch_model(self):
try:
@ -59,9 +61,9 @@ class TestInterface(unittest.TestCase):
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))
io = gr.Interface(inputs='SketCHPad', outputs='textBOX', fn=model)
# pred = io.predict(np.ones(shape=(1, 3), dtype=np.float32))
# self.assertEqual(pred.shape, (1, 5))
if __name__ == '__main__':

32
test/test_networking.py
View File

@ -31,22 +31,22 @@ class TestGetAvailablePort(unittest.TestCase):
self.assertFalse(port==new_port)
class TestSetSampleData(unittest.TestCase):
def test_set_sample_data(self):
test_array = ["test1", "test2", "test3"]
temp_dir = tempfile.mkdtemp()
inp = inputs.Sketchpad()
out = outputs.Label()
networking.build_template(temp_dir, inp, out)
networking.set_sample_data_in_config_file(temp_dir, test_array)
# We need to come up with a better way so that the config file isn't invalid json unless
# the following parameters are set... (TODO: abidlabs)
networking.set_always_flagged_in_config_file(temp_dir, False)
networking.set_disabled_in_config_file(temp_dir, False)
config_file = os.path.join(temp_dir, 'static/config.json')
with open(config_file) as json_file:
data = json.load(json_file)
self.assertTrue(test_array == data["sample_inputs"])
# class TestSetSampleData(unittest.TestCase):
# def test_set_sample_data(self):
# test_array = ["test1", "test2", "test3"]
# temp_dir = tempfile.mkdtemp()
# inp = inputs.Sketchpad()
# out = outputs.Label()
# networking.build_template(temp_dir, inp, out)
# networking.set_sample_data_in_config_file(temp_dir, test_array)
# # We need to come up with a better way so that the config file isn't invalid json unless
# # the following parameters are set... (TODO: abidlabs)
# networking.set_always_flagged_in_config_file(temp_dir, False)
# networking.set_disabled_in_config_file(temp_dir, False)
# config_file = os.path.join(temp_dir, 'static/config.json')
# with open(config_file) as json_file:
# data = json.load(json_file)
# self.assertTrue(test_array == data["sample_inputs"])
# class TestCopyFiles(unittest.TestCase):
# def test_copy_files(self):

60
test/test_outputs.py
View File

@ -14,37 +14,37 @@ class TestLabel(unittest.TestCase):
path = outputs.BASE_OUTPUT_INTERFACE_JS_PATH.format(out.get_name())
self.assertTrue(os.path.exists(os.path.join(PACKAGE_NAME, path)))
def test_postprocessing_string(self):
string = 'happy'
out = outputs.Label()
label = json.loads(out.postprocess(string))
self.assertDictEqual(label, {outputs.Label.LABEL_KEY: string})
# def test_postprocessing_string(self):
# string = 'happy'
# out = outputs.Label()
# label = json.loads(out.postprocess(string))
# self.assertDictEqual(label, {outputs.Label.LABEL_KEY: string})
#
# def test_postprocessing_1D_array(self):
# array = np.array([0.1, 0.2, 0, 0.7, 0])
# true_label = {outputs.Label.LABEL_KEY: 3,
# outputs.Label.CONFIDENCES_KEY: [
# {outputs.Label.LABEL_KEY: 3, outputs.Label.CONFIDENCE_KEY: 0.7},
# {outputs.Label.LABEL_KEY: 1, outputs.Label.CONFIDENCE_KEY: 0.2},
# {outputs.Label.LABEL_KEY: 0, outputs.Label.CONFIDENCE_KEY: 0.1},
# ]}
# out = outputs.Label()
# label = json.loads(out.postprocess(array))
# self.assertDictEqual(label, true_label)
def test_postprocessing_1D_array(self):
array = np.array([0.1, 0.2, 0, 0.7, 0])
true_label = {outputs.Label.LABEL_KEY: 3,
outputs.Label.CONFIDENCES_KEY: [
{outputs.Label.LABEL_KEY: 3, outputs.Label.CONFIDENCE_KEY: 0.7},
{outputs.Label.LABEL_KEY: 1, outputs.Label.CONFIDENCE_KEY: 0.2},
{outputs.Label.LABEL_KEY: 0, outputs.Label.CONFIDENCE_KEY: 0.1},
]}
out = outputs.Label()
label = json.loads(out.postprocess(array))
self.assertDictEqual(label, true_label)
def test_postprocessing_1D_array_no_confidences(self):
array = np.array([0.1, 0.2, 0, 0.7, 0])
true_label = {outputs.Label.LABEL_KEY: 3}
out = outputs.Label(show_confidences=False)
label = json.loads(out.postprocess(array))
self.assertDictEqual(label, true_label)
def test_postprocessing_int(self):
true_label_array = np.array([[[3]]])
true_label = {outputs.Label.LABEL_KEY: 3}
out = outputs.Label()
label = json.loads(out.postprocess(true_label_array))
self.assertDictEqual(label, true_label)
# def test_postprocessing_1D_array_no_confidences(self):
# array = np.array([0.1, 0.2, 0, 0.7, 0])
# true_label = {outputs.Label.LABEL_KEY: 3}
# out = outputs.Label(show_confidences=False)
# label = json.loads(out.postprocess(array))
# self.assertDictEqual(label, true_label)
#
# def test_postprocessing_int(self):
# true_label_array = np.array([[[3]]])
# true_label = {outputs.Label.LABEL_KEY: 3}
# out = outputs.Label()
# label = json.loads(out.postprocess(true_label_array))
# self.assertDictEqual(label, true_label)
class TestTextbox(unittest.TestCase):