224 lines
6.4 KiB
Python
224 lines
6.4 KiB
Python
import json
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import logging
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import os
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import random
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from functools import partial
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from itertools import starmap
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from operator import itemgetter
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import fpdf
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import numpy as np
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import pytest
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from funcy import rcompose, merge
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from image_prediction.exceptions import (
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UnknownLabelFormat,
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)
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from image_prediction.image_extractor.extractor import ImageMetadataPair
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from image_prediction.info import Info
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from image_prediction.label_mapper.mappers.probability import ProbabilityMapperKeys
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from image_prediction.locations import TEST_DATA_DIR
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from image_prediction.pipeline import load_pipeline
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from image_prediction.utils import get_logger
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from test.utils.generation.pdf import add_image, pdf_stream
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from test.utils.label import map_labels
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pytest_plugins = [
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'test.fixtures.model',
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'test.fixtures.model_store',
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'test.fixtures.image',
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'test.fixtures.input',
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'test.fixtures.parameters',
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'test.fixtures.label',
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]
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@pytest.fixture(autouse=True)
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def mute_logger():
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logger = get_logger()
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level = logger.level
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logger.setLevel(logging.CRITICAL + 1)
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yield
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logger.setLevel(level)
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@pytest.fixture
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def expected_predictions_mapped(
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label_format, batch_of_expected_string_labels, batch_of_expected_label_to_probability_mappings
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):
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if label_format == "index":
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return batch_of_expected_string_labels
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elif label_format == "probability":
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return batch_of_expected_label_to_probability_mappings
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else:
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raise UnknownLabelFormat(f"No label mapper for label format {label_format} was specified.")
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@pytest.fixture
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def expected_predictions(label_format, batch_of_expected_numeric_labels, batch_of_expected_probability_arrays):
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if label_format == "index":
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return batch_of_expected_numeric_labels
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elif label_format == "probability":
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return batch_of_expected_probability_arrays
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else:
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raise UnknownLabelFormat(f"No label mapper for label format {label_format} was specified.")
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@pytest.fixture
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def input_batch(batch_size, input_size):
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return np.random.random_sample(size=(batch_size, *input_size))
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@pytest.fixture(params=[0, 1, 2, 16, 32])
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def batch_size(request):
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return request.param
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@pytest.fixture
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def batch_of_expected_string_labels(batch_of_expected_numeric_labels, classes):
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return map_labels(batch_of_expected_numeric_labels, classes)
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@pytest.fixture
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def batch_of_expected_numeric_labels(batch_size, classes):
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return random.choices(range(len(classes)), k=batch_size)
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@pytest.fixture
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def batch_of_expected_label_to_probability_mappings(batch_of_expected_probability_arrays, classes):
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def map_probabilities(probabilities):
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lbl2prob = dict(sorted(zip(classes, map(rounder, probabilities)), key=itemgetter(1), reverse=True))
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most_likely = [*lbl2prob][0]
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return {ProbabilityMapperKeys.LABEL: most_likely, ProbabilityMapperKeys.PROBABILITIES: lbl2prob}
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rounder = rcompose(partial(np.round, decimals=4), float)
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return list(map(map_probabilities, batch_of_expected_probability_arrays))
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@pytest.fixture
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def batch_of_expected_probability_arrays(batch_size, classes):
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return [np.random.uniform(size=len(classes)) for _ in range(batch_size)]
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@pytest.fixture
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def output_batch_generator(expected_predictions):
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return iter(expected_predictions)
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@pytest.fixture
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def metadata_plus_mapped_prediction(expected_predictions_mapped, metadata):
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return [{"classification": epm, **mdt} for epm, mdt in zip(expected_predictions_mapped, metadata)]
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@pytest.fixture
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def metadata_formatted_plus_mapped_prediction_formatted(expected_predictions_mapped_and_formatted, metadata_formatted):
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return [
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{"classification": epm, **mdt}
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for epm, mdt in zip(expected_predictions_mapped_and_formatted, metadata_formatted)
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]
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@pytest.fixture
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def expected_predictions_mapped_and_formatted(expected_predictions_mapped):
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return [{k.value: v for k, v in epm.items()} for epm in expected_predictions_mapped]
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@pytest.fixture
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def metadata(images, info_label_map):
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page_idx = 0
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def current_page_idx():
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nonlocal page_idx
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page_idx += random.randint(0, 3)
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return min(page_idx, len(images) - 1)
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def build_image_metadata(image):
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width, height = image.size
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page_width = 595
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page_height = 842
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x1 = random.randint(0, page_width - width)
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x2 = x1 + width
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y1 = random.randint(0, page_height - height)
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y2 = y1 + height
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metadata = {
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info_label_map.PAGE_WIDTH: page_width,
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info_label_map.PAGE_HEIGHT: page_height,
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info_label_map.PAGE_IDX: current_page_idx(),
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info_label_map.WIDTH: width,
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info_label_map.HEIGHT: height,
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info_label_map.X1: x1,
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info_label_map.X2: x2,
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info_label_map.Y1: y1,
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info_label_map.Y2: y2,
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info_label_map.ALPHA: image.mode == "RGBA",
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}
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return metadata
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return list(map(build_image_metadata, images))
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@pytest.fixture
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def info_label_map():
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return Info
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@pytest.fixture
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def metadata_formatted(metadata):
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def format_metadata(metadata):
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return {key.value: val for key, val in metadata.items()}
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return list(map(format_metadata, metadata))
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@pytest.fixture
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def image_metadata_pairs(images, metadata):
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return list(starmap(ImageMetadataPair, zip(images, metadata)))
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@pytest.fixture
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def pdf(image_metadata_pairs):
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pdf = fpdf.FPDF(unit="pt")
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for pair in image_metadata_pairs:
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add_image(pdf, pair)
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return pdf_stream(pdf)
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@pytest.fixture
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def real_pdf():
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with open(os.path.join(TEST_DATA_DIR, "f2dc689ca794fccb8cd38b95f2bf6ba9.pdf"), "rb") as f:
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yield f.read()
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@pytest.fixture
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def real_expected_service_response():
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with open(os.path.join(TEST_DATA_DIR, "f2dc689ca794fccb8cd38b95f2bf6ba9_predictions.json"), "r") as f:
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yield json.load(f)
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@pytest.fixture
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def pipeline():
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pipeline = load_pipeline(verbose=False)
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return pipeline
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def get_base_position_metadata(width, height, page_width, page_height):
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return {
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Info.WIDTH: width,
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Info.HEIGHT: height,
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Info.PAGE_IDX: 0,
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Info.PAGE_WIDTH: page_width,
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Info.PAGE_HEIGHT: page_height,
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}
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@pytest.fixture
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def base_patch_metadata(width, height, page_width, page_height):
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metadata = get_base_position_metadata(width, height, page_width, page_height)
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metadata = merge(metadata, {Info.X1: 0, Info.Y1: 0, Info.X2: width, Info.Y2: height})
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return metadata
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