refactoring
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@ -1,8 +1,8 @@
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import os
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from functools import partial
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from functools import partial, reduce
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from itertools import chain, tee
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from funcy import rcompose, first, compose, second, chunks, identity
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from funcy import rcompose, first, compose, second, chunks, identity, curry
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from image_prediction.config import CONFIG
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from image_prediction.default_objects import get_formatter, get_mlflow_model_loader, get_image_classifier, get_extractor
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@ -25,7 +25,7 @@ def parallel(*fs):
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return lambda *args: (f(a) for f, a in zip(fs, args))
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def splat(f):
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def star(f):
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return lambda x: f(*x)
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@ -35,18 +35,19 @@ class Pipeline:
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classifier = get_image_classifier(model_loader, model_identifier)
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reformat = get_formatter()
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split = compose(splat(parallel(*map(lift, (first, second)))), tee)
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split = compose(star(parallel(*map(lift, (first, second)))), tee)
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classify = compose(chain.from_iterable, lift(classifier), partial(chunks, batch_size))
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join = compose(starlift(lambda prd, mdt: {"classification": prd, **mdt}), splat(zip))
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pairwise_apply = compose(star, parallel)
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join = compose(starlift(lambda prd, mdt: {"classification": prd, **mdt}), star(zip))
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# +>--classify--v
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# --extract-->--split--| |--join-->format
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# --extract-->--split--| |--join-->reformat
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# +>--identity--^
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self.pipe = rcompose(
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extract, # ... image-metadata-pairs as a stream
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split, # ... into an image stream and a metadata stream
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splat(parallel(classify, identity)), # ... process streams independently
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pairwise_apply(classify, identity), # ... apply functions to the streams pairwise
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join, # ... the streams
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reformat, # ... the items
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)
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@ -37,12 +37,12 @@ def test_server_predict_failure(client, mute_logger):
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def test_server_health_check(client):
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response = client.get("/ready")
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response = client.get("/health")
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assert response.status_code == 200
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assert response.json == "OK"
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def test_server_ready_check(client):
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response = client.get("/health")
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response = client.get("/ready")
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assert response.status_code == 200
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assert response.json == "OK"
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