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alessandro trinca tornidor
[refactor] rename samgis module tosamgis_lisa_on_cuda, bump to version 1.3.0, update samgis and lisa_on_cuda dependencies
fcb8c81
import json | |
from unittest.mock import patch | |
import numpy as np | |
from samgis_lisa_on_cuda.prediction_api import predictors | |
from samgis_lisa_on_cuda.prediction_api.predictors import get_raster_inference, samexporter_predict | |
from tests import TEST_EVENTS_FOLDER | |
def test_get_raster_inference(segment_anything_onnx_mocked): | |
name_fn = "samexporter_predict" | |
with open(TEST_EVENTS_FOLDER / f"{name_fn}.json") as tst_json: | |
inputs_outputs = json.load(tst_json) | |
for k, input_output in inputs_outputs.items(): | |
model_mocked = segment_anything_onnx_mocked() | |
img = np.load(TEST_EVENTS_FOLDER / f"{name_fn}" / k / "img.npy") | |
inference_out = np.load(TEST_EVENTS_FOLDER / f"{name_fn}" / k / "inference_out.npy") | |
mask = np.load(TEST_EVENTS_FOLDER / f"{name_fn}" / k / "mask.npy") | |
prompt = input_output["input"]["prompt"] | |
model_name = input_output["input"]["model_name"] | |
model_mocked.embed.return_value = np.array(img) | |
model_mocked.embed.side_effect = None | |
model_mocked.predict_masks.return_value = inference_out | |
model_mocked.predict_masks.side_effect = None | |
print(f"k:{k}.") | |
output_mask, len_inference_out = get_raster_inference( | |
img=img, | |
prompt=prompt, | |
models_instance=model_mocked, | |
model_name=model_name | |
) | |
assert np.array_equal(output_mask, mask) | |
assert len_inference_out == input_output["output"]["n_predictions"] | |
def test_samexporter_predict( | |
get_vectorized_raster_as_geojson_mocked, | |
download_extent_mocked, | |
segment_anything_onnx_mocked, | |
get_raster_inference_mocked | |
): | |
""" | |
model_instance = SegmentAnythingONNX() | |
img, matrix = download_extent(DEFAULT_TMS, pt0[0], pt0[1], pt1[0], pt1[1], zoom) | |
transform = get_affine_transform_from_gdal(matrix) | |
mask, n_predictions = get_raster_inference(img, prompt, models_instance, model_name) | |
get_vectorized_raster_as_geojson(mask, matrix) | |
""" | |
aff = 1, 2, 3, 4, 5, 6 | |
segment_anything_onnx_mocked.return_value = "SegmentAnythingONNX_instance" | |
download_extent_mocked.return_value = np.zeros((10, 10)), aff | |
get_raster_inference_mocked.return_value = np.ones((10, 10)), 1 | |
get_vectorized_raster_as_geojson_mocked.return_value = {"geojson": "{}", "n_shapes_geojson": 2} | |
output = samexporter_predict(bbox=[[1, 2], [3, 4]], prompt=[{}], zoom=10, model_name_key="fastsam") | |
assert output == {"n_predictions": 1, "geojson": "{}", "n_shapes_geojson": 2} | |