def test_input_files(patterns, recursive, size, sampling_rate, read_mode):
Client.check_input(
DocumentArray.from_files(
patterns=patterns,
recursive=recursive,
size=size,
sampling_rate=sampling_rate,
read_mode=read_mode,
))
def test_mime_type(restful):
class MyExec(Executor):
@req
def foo(self, docs: 'DocumentArray', **kwargs):
for d in docs:
d.convert_uri_to_buffer()
f = Flow(restful=restful).add(uses=MyExec)
def validate_mime_type(req):
for d in req.data.docs:
assert d.mime_type == 'text/x-python'
with f:
f.index(DocumentArray.from_files('*.py'), validate_mime_type)
def preproc(d: Document):
return (
d.load_uri_to_image_blob() # load
.set_image_blob_shape(shape=(224, 224))
.set_image_blob_normalization() # normalize color
.set_image_blob_channel_axis(-1, 0)
) # switch color axis
if __name__ == '__main__':
base_path = r'C:\Users\computer\pictures'
search_extension = '*.jpg'
query_image = '1.jpg'
docs = DocumentArray.from_files(os.path.join(base_path, search_extension)).apply(preproc)
model = torchvision.models.resnet50(pretrained=True) # load ResNet50
docs.embed(model, device='cuda') # embed via GPU to speedup
# q = (
# Document(uri=os.path.join(base_path, query_image)) # build query image & preprocess
# .load_uri_to_image_blob()
# .set_image_blob_shape(shape=(512, 512))
# .set_image_blob_normalization()
# .set_image_blob_channel_axis(-1, 0)
# )
#
q = preproc(Document(uri=os.path.join(base_path, query_image)))
q.embed(model) # embed
def test_input_files_with_invalid_read_mode():
with pytest.raises(BadClientInput):
Client.check_input(
DocumentArray.from_files(patterns='*.*', read_mode='invalid'))
