def test_bulk_with_min_value(self):
img = ClImage(url=urls[0])
m = self.app.models.get('general-v1.3')
model_output_info = ModelOutputInfo(output_config=ModelOutputConfig(
min_value=0.96))
res = m.predict(inputs=[img, img, img],
model_output_info=model_output_info)
for result in res['outputs']:
for c in result['data']['concepts']:
self.assertGreaterEqual(c['value'], 0.96)
def test_bulk_with_min_value(self):
img = ClImage(url=sample_inputs.METRO_IMAGE_URL)
m = self.app.models.get(model_id=GENERAL_MODEL_ID)
model_output_info = ModelOutputInfo(output_config=ModelOutputConfig(
min_value=0.96))
res = m.predict(inputs=[img, img, img],
model_output_info=model_output_info)
self.assertEqual(10000, res['status']['code'])
for result in res['outputs']:
for c in result['data']['concepts']:
self.assertGreaterEqual(c['value'], 0.96)
def test_bulk_workflow_predict_with_url(
mock_http_client): # type: (mock.Mock) -> None
mock_execute_request = mock_request(mock_http_client,
json_response="""
{
"status": {
"code": 10000,
"description": "Ok"
},
"workflow": {
"id": "@workflowID",
"app_id": "@appID",
"created_at": "2017-06-15T15:17:30.462323Z"
},
"results": [
{
"status": {
"code": 10000,
"description": "Ok"
},
"input": {
"id": "@inputID1",
"data": {
"image": {
"url": "@url1"
}
}
},
"outputs": [
{
"id": "@outputID11",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2019-01-29T17:36:23.736685542Z",
"model": {
"id": "@modelID1",
"name": "food-items-v1.0",
"created_at": "2016-09-17T22:18:59.955626Z",
"app_id": "main",
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept",
"type_ext": "concept"
},
"model_version": {
"id": "@modelVersionID1",
"created_at": "2016-09-17T22:18:59.955626Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
},
"train_stats": {}
},
"display_name": "Food"
},
"data": {}
},
{
"id": "@outputID12",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2019-01-29T17:36:23.736712374Z",
"model": {
"id": "@modelID2",
"name": "general",
"created_at": "2016-03-09T17:11:39.608845Z",
"app_id": "main",
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept",
"type_ext": "concept"
},
"model_version": {
"id": "@modelVersion2",
"created_at": "2016-07-13T01:19:12.147644Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
},
"train_stats": {}
},
"display_name": "General"
},
"data": {
"concepts": [
{
"id": "@conceptID11",
"name": "people",
"value": 0.9963381,
"app_id": "main"
},
{
"id": "@conceptID12",
"name": "one",
"value": 0.9879056,
"app_id": "main"
},
{
"id": "@conceptID13",
"name": "portrait",
"value": 0.9849082,
"app_id": "main"
}
]
}
}
]
},
{
"status": {
"code": 10000,
"description": "Ok"
},
"input": {
"id": "@inputID2",
"data": {
"image": {
"url": "@url2"
}
}
},
"outputs": [
{
"id": "@outputID21",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2019-01-29T17:36:23.736685542Z",
"model": {
"id": "@modelID1",
"name": "food-items-v1.0",
"created_at": "2016-09-17T22:18:59.955626Z",
"app_id": "main",
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept",
"type_ext": "concept"
},
"model_version": {
"id": "@modelVersion1",
"created_at": "2016-09-17T22:18:59.955626Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
},
"train_stats": {}
},
"display_name": "Food"
},
"data": {
"concepts": [
{
"id": "@concept21",
"name": "spatula",
"value": 0.9805687,
"app_id": "main"
}
]
}
},
{
"id": "@outputID22",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2019-01-29T17:36:23.736712374Z",
"model": {
"id": "@modelID2",
"name": "general",
"created_at": "2016-03-09T17:11:39.608845Z",
"app_id": "main",
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept",
"type_ext": "concept"
},
"model_version": {
"id": "@modelVersion2",
"created_at": "2016-07-13T01:19:12.147644Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
},
"train_stats": {}
},
"display_name": "General"
},
"data": {
"concepts": [
{
"id": "@conceptID31",
"name": "eyewear",
"value": 0.99984586,
"app_id": "main"
},
{
"id": "@conceptID32",
"name": "lens",
"value": 0.999823,
"app_id": "main"
},
{
"id": "@conceptID33",
"name": "eyeglasses",
"value": 0.99980056,
"app_id": "main"
}
]
}
}
]
}
]
}
""")
app = ClarifaiApp()
workflow = Workflow(app.api, workflow_id='@workflowID')
response = workflow.predict(
[Image(url='@url1'), Image(url='@url2')],
ModelOutputConfig(min_value=0.5, max_concepts=3))
assert response['workflow']['id'] == '@workflowID'
assert response['results'][0]['outputs'][1]['data']['concepts'][0][
'id'] == '@conceptID11'
assert_request(
mock_execute_request, 'POST', '/v2/workflows/@workflowID/results', """
{
"inputs": [
{
"data": {
"image": {
"url": "@url1"
}
}
},
{
"data": {
"image": {
"url": "@url2"
}
}
}
],
"output_config": {
"max_concepts": 3,
"min_value": 0.5
}
}
""")
def test_concept_bulk_predict_with_arguments(
mock_http_client): # type: (mock.Mock) -> None
mock_execute_request = mock_request(
mock_http_client, """
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "@outputID1",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2019-01-29T16:45:43.793810775Z",
"model": {
"id": "aaa03c23b3724a16a56b629203edc62c",
"name": "general",
"created_at": "2016-03-09T17:11:39.608845Z",
"app_id": "main",
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept",
"type_ext": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12.147644Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
},
"train_stats": {}
},
"display_name": "General"
},
"input": {
"id": "@inputID1",
"data": {
"image": {
"url": "https://clarifai.com/developer/static/images/model-samples/celeb-001.jpg"
}
}
},
"data": {
"concepts": [
{
"id": "@conceptID11",
"name": "menschen",
"value": 0.9963381,
"app_id": "main"
},
{
"id": "@conceptID12",
"name": "ein",
"value": 0.9879057,
"app_id": "main"
}
]
}
},
{
"id": "@outputID2",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2019-01-29T16:45:43.793810775Z",
"model": {
"id": "aaa03c23b3724a16a56b629203edc62c",
"name": "general",
"created_at": "2016-03-09T17:11:39.608845Z",
"app_id": "main",
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept",
"type_ext": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12.147644Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
},
"train_stats": {}
},
"display_name": "General"
},
"input": {
"id": "@inputID2",
"data": {
"image": {
"url": "https://clarifai.com/developer/static/images/model-samples/apparel-001.jpg"
}
}
},
"data": {
"concepts": [
{
"id": "@conceptID21",
"name": "brillen und kontaktlinsen",
"value": 0.99984586,
"app_id": "main"
},
{
"id": "@conceptID22",
"name": "linse",
"value": 0.999823,
"app_id": "main"
}
]
}
}
]
}
""")
app = ClarifaiApp()
model = app.models.get(model_id='@modelID')
response = model.predict(
[Image(url='@url1'), Image(url='@url2')],
ModelOutputInfo(output_config=ModelOutputConfig(
language='de', max_concepts=2, min_value=0.5)))
output1 = response['outputs'][0]
assert output1['input']['id'] == '@inputID1'
assert output1['id'] == '@outputID1'
assert output1['data']['concepts'][0]['id'] == '@conceptID11'
assert output1['data']['concepts'][1]['id'] == '@conceptID12'
output2 = response['outputs'][1]
assert output2['input']['id'] == '@inputID2'
assert output2['id'] == '@outputID2'
assert output2['data']['concepts'][0]['id'] == '@conceptID21'
assert output2['data']['concepts'][1]['id'] == '@conceptID22'
assert_request(
mock_execute_request, 'POST', '/v2/models/@modelID/outputs', """
{
"inputs": [
{
"data": {
"image": {
"url": "@url1"
}
}
},
{
"data": {
"image": {
"url": "@url2"
}
}
}
],
"model": {
"output_info": {
"output_config": {
"language": "de",
"max_concepts": 2,
"min_value": 0.5
}
}
}
}
""")