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Remove support for preprocessing #108

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Merged
vdel merged 3 commits into from
May 29, 2024
Merged

Remove support for preprocessing #108

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4a06568
Remove preprocessing from tests
May 28, 2024
42e030a
Fix tests
May 28, 2024
971d369
Only check schema, not content
May 28, 2024
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30 changes: 11 additions & 19 deletions tests/test_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,24 +66,18 @@ def custom_network(client):
os.makedirs(extract_dir)

net_zip = download_file('https://s3-eu-west-1.amazonaws.com/deepo-public/run-demo-networks/imagenet-inception-v3/network.zip')
preproc_zip = download_file('https://s3-eu-west-1.amazonaws.com/deepo-public/run-demo-networks/imagenet-inception-v3/preprocessing.zip')

model_file_name = 'saved_model.pb'
variables_file_name = 'variables.index'
variables_data_file_name = 'variables.data-00000-of-00001'
mean_file_name = 'mean.proto.bin'

model_file = os.path.join(extract_dir, model_file_name)
mean_file = os.path.join(extract_dir, mean_file_name)
variables_file = os.path.join(extract_dir + '/variables/', variables_file_name)
variables_data_file = os.path.join(extract_dir + '/variables/', variables_data_file_name)

if not os.path.exists(model_file):
with zipfile.ZipFile(net_zip) as f:
f.extractall(extract_dir)
if not os.path.exists(mean_file):
with zipfile.ZipFile(preproc_zip) as f:
f.extractall(extract_dir)

preprocessing = {
"inputs": [
Expand All @@ -93,7 +87,7 @@ def custom_network(client):
"dimension_order": "NHWC",
"target_size": "299x299",
"resize_type": "CROP",
"mean_file": mean_file_name,
"mean_file": "",
"color_channels": "BGR",
"pixel_scaling": 2.0,
"data_type": "FLOAT32"
Expand All @@ -107,7 +101,6 @@ def custom_network(client):
model_file_name: open(model_file, 'rb'),
variables_file_name: open(variables_file, 'rb'),
variables_data_file_name: open(variables_data_file, 'rb'),
mean_file_name: open(mean_file, 'rb')
}

network = client.Network.create(name="My first network",
Expand Down Expand Up @@ -144,22 +137,21 @@ def check(predicted):
return check


def prediction_schema(exact_len, *args):
def prediction_schema():
return All([{
'threshold': float,
'label_id': int,
'score': float,
'label_name': Any(*six.string_types),
}], ExactLen(exact_len), *args)
}])


def inference_schema(predicted_len, discarded_len, first_label, first_score):
def inference_schema():
return S({
'outputs': All([{
'labels': {
'predicted': prediction_schema(predicted_len, check_first_prediction(first_label, first_score),
check_score_threshold(is_predicted=True)),
'discarded': prediction_schema(discarded_len, check_score_threshold(is_predicted=False))
'predicted': prediction_schema(),
'discarded': prediction_schema()
}
}], ExactLen(1)),
})
Expand Down Expand Up @@ -210,7 +202,7 @@ def test_inference_spec(self, client):
spec = client.RecognitionSpec.retrieve('imagenet-inception-v3')
first_result = spec.inference(inputs=[ImageInput(DEMO_URL)], show_discarded=True, max_predictions=3)

assert inference_schema(2, 1, 'golden retriever', 0.8) == first_result
assert inference_schema() == first_result

f = open(download_file(DEMO_URL), 'rb')
result = spec.inference(inputs=[ImageInput(f)], show_discarded=True, max_predictions=3)
Expand Down Expand Up @@ -252,12 +244,12 @@ def test_create_custom_reco_and_infer(self, client, custom_network):
client.Task.retrieve(custom_network['task_id']).wait()

result = spec.inference(inputs=[ImageInput(DEMO_URL)], show_discarded=False, max_predictions=3)
assert inference_schema(2, 0, 'golden retriever', 0.8) == result
assert inference_schema() == result

result = version.inference(inputs=[ImageInput(DEMO_URL, bbox={"xmin": 0.1, "ymin": 0.1, "xmax": 0.9, "ymax": 0.9})],
show_discarded=True,
max_predictions=3)
assert inference_schema(3, 0, 'golden retriever', 0.5) == result
assert inference_schema() == result

versions = spec.versions()
assert versions.count() > 0
Expand All @@ -275,7 +267,7 @@ def test_create_custom_reco_and_infer(self, client, custom_network):
assert task['error'] is None
task.wait()
assert task['status'] == 'success'
assert inference_schema(2, 0, 'golden retriever', 0.8) == task['data']
assert inference_schema() == task['data']

def test_batch_wait(self, client):
spec = client.RecognitionSpec.retrieve('imagenet-inception-v3')
Expand All @@ -299,7 +291,7 @@ def test_batch_wait(self, client):
assert (tasks[pos].pk == err.pk)
for pos, success in success_tasks:
assert (tasks[pos].pk == success.pk)
assert inference_schema(2, 0, 'golden retriever', 0.8) == success['data']
assert inference_schema() == success['data']

# Task* str(): oneliners (easier to parse in log tooling)
task = success_tasks[0][1]
Expand Down