def test_compute_IoU(download_release):
m = main.deepforest()
m.use_release(check_release=False)
csv_file = get_data("OSBS_029.csv")
predictions = m.predict_file(csv_file=csv_file,
root_dir=os.path.dirname(csv_file))
ground_truth = pd.read_csv(csv_file)
predictions['geometry'] = predictions.apply(
lambda x: shapely.geometry.box(x.xmin, x.ymin, x.xmax, x.ymax), axis=1)
predictions = gpd.GeoDataFrame(predictions, geometry='geometry')
ground_truth['geometry'] = ground_truth.apply(
lambda x: shapely.geometry.box(x.xmin, x.ymin, x.xmax, x.ymax), axis=1)
ground_truth = gpd.GeoDataFrame(ground_truth, geometry='geometry')
ground_truth.label = 0
predictions.label = 0
visualize.plot_prediction_dataframe(df=predictions,
ground_truth=ground_truth,
root_dir=os.path.dirname(csv_file))
result = IoU.compute_IoU(ground_truth, predictions)
assert result.shape[0] == ground_truth.shape[0]
assert sum(result.IoU) > 10
def test_evaluate_multi(m):
csv_file = get_data("testfile_multi.csv")
m = main.deepforest(num_classes=2, label_dict={"Alive": 0, "Dead": 1})
ground_truth = pd.read_csv(csv_file)
results = evaluate.evaluate(predictions=ground_truth,
ground_df=ground_truth,
show_plot=True,
root_dir=os.path.dirname(csv_file),
savedir=None)
assert results["results"].shape[0] == ground_truth.shape[0]
assert results["class_recall"].shape == (2, 4)
assert all(results['class_recall'].recall == pd.Series([1, 1]))
#def test_evaluate_benchmark(m):
#csv_file = "/Users/benweinstein/Documents/NeonTreeEvaluation/evaluation/RGB/benchmark_annotations.csv"
#predictions = m.predict_file(csv_file=csv_file, root_dir=os.path.dirname(csv_file))
#ground_truth = pd.read_csv(csv_file)
#results = evaluate.evaluate(predictions=predictions, ground_df=ground_truth, show_plot=False, root_dir=os.path.dirname(csv_file), savedir=None)
#assert results["results"].shape[0] == ground_truth.shape[0]
#assert results["class_recall"].shape == (2,4)
def test_reload_multi_class(two_class_m, tmpdir):
two_class_m.config["train"]["fast_dev_run"] = True
two_class_m.create_trainer()
two_class_m.trainer.fit(two_class_m)
two_class_m.save_model("{}/checkpoint.pl".format(tmpdir))
loaded = main.deepforest(num_classes=2, label_dict={"Alive": 0, "Dead": 0})
old_model = torch.load("{}/checkpoint.pl".format(tmpdir))
loaded.load_state_dict(old_model["state_dict"])
def test_annotations_to_shapefile(download_release):
img = get_data("OSBS_029.tif")
r = rio.open(img)
transform = r.transform
crs = r.crs
m = main.deepforest()
m.use_release(check_release=False)
df = m.predict_image(path=img)
gdf = utilities.annotations_to_shapefile(df, transform=transform, crs=crs)
assert df.shape[0] == gdf.shape[0]
def test_evaluate_save_images(m, tmpdir):
csv_file = get_data("testfile_multi.csv")
m = main.deepforest(num_classes=2,label_dict={"Alive":0,"Dead":1})
ground_truth = pd.read_csv(csv_file)
ground_truth["label"] = ground_truth.label.astype("category").cat.codes
#Manipulate the data to create some false positives
predictions = ground_truth.copy()
predictions["score"] = 1
predictions.label.loc[[36,35,34]] = 0
results = evaluate.evaluate(predictions=predictions, ground_df=ground_truth, root_dir=os.path.dirname(csv_file), savedir=tmpdir)
assert all([os.path.exists("{}/{}".format(tmpdir,x)) for x in ground_truth.image_path])
def m(download_release):
m = main.deepforest()
m.config["train"]["csv_file"] = get_data("example.csv")
m.config["train"]["root_dir"] = os.path.dirname(get_data("example.csv"))
m.config["train"]["fast_dev_run"] = False
m.config["batch_size"] = 2
m.config["validation"]["csv_file"] = get_data("example.csv")
m.config["validation"]["root_dir"] = os.path.dirname(get_data("example.csv"))
m.use_release()
return m
def test_evaluate_empty():
m = main.deepforest()
m.config["score_thresh"] = 0.8
csv_file = get_data("OSBS_029.csv")
root_dir = os.path.dirname(csv_file)
results = m.evaluate(csv_file, root_dir, iou_threshold=0.4)
#Does this make reasonable predictions, we know the model works.
assert results["box_precision"] == 0
assert results["box_recall"] == 0
df = pd.read_csv(csv_file)
assert results["results"].shape[0] == df.shape[0]
def two_class_m():
m = main.deepforest(num_classes=2,label_dict={"Alive":0,"Dead":1})
m.config["train"]["csv_file"] = get_data("testfile_multi.csv")
m.config["train"]["root_dir"] = os.path.dirname(get_data("testfile_multi.csv"))
m.config["train"]["fast_dev_run"] = True
m.config["batch_size"] = 2
m.config["validation"]["csv_file"] = get_data("testfile_multi.csv")
m.config["validation"]["root_dir"] = os.path.dirname(get_data("testfile_multi.csv"))
m.create_trainer()
return m
def test_evaluate_multi(m):
csv_file = get_data("testfile_multi.csv")
m = main.deepforest(num_classes=2,label_dict={"Alive":0,"Dead":1})
ground_truth = pd.read_csv(csv_file)
ground_truth["label"] = ground_truth.label.astype("category").cat.codes
#Manipulate the data to create some false positives
predictions = ground_truth.copy()
predictions["score"] = 1
predictions.label.loc[[36,35,34]] = 0
results = evaluate.evaluate(predictions=predictions, ground_df=ground_truth, root_dir=os.path.dirname(csv_file))
assert results["results"].shape[0] == ground_truth.shape[0]
assert results["class_recall"].shape == (2,4)
def m():
m = main.deepforest()
m.config["train"]["csv_file"] = get_data("example.csv")
m.config["train"]["root_dir"] = os.path.dirname(get_data("example.csv"))
m.config["train"]["fast_dev_run"] = True
m.config["batch_size"] = 2
m.config["validation"]["csv_file"] = get_data("example.csv")
m.config["validation"]["root_dir"] = os.path.dirname(
get_data("example.csv"))
m.create_trainer()
return m
def test_save_and_reload_weights(m, tmpdir):
img_path = get_data(path="2019_YELL_2_528000_4978000_image_crop2.png")
m.config["train"]["fast_dev_run"] = True
m.create_trainer()
#save the prediction dataframe after training and compare with prediction after reload checkpoint
m.trainer.fit(m)
pred_after_train = m.predict_image(path=img_path)
torch.save(m.model.state_dict(), "{}/checkpoint.pt".format(tmpdir))
#reload the checkpoint to model object
after = main.deepforest()
after.model.load_state_dict(torch.load("{}/checkpoint.pt".format(tmpdir)))
pred_after_reload = after.predict_image(path=img_path)
assert not pred_after_train.empty
assert not pred_after_reload.empty
pd.testing.assert_frame_equal(pred_after_train, pred_after_reload)
def m(download_release):
m = main.deepforest()
m.config["train"]["csv_file"] = get_data("example.csv")
m.config["train"]["root_dir"] = os.path.dirname(get_data("example.csv"))
m.config["train"]["fast_dev_run"] = True
m.config["batch_size"] = 2
m.config["validation"]["csv_file"] = get_data("example.csv")
m.config["validation"]["root_dir"] = os.path.dirname(
get_data("example.csv"))
m.config["workers"] = 0
m.config["validation"]["val_accuracy_interval"] = 1
m.config["train"]["epochs"] = 2
m.create_trainer()
m.use_release(check_release=False)
return m
def test_log_images_multiclass(m, tmpdir):
m = main.deepforest(num_classes=2, label_dict={"Alive":0,"Dead":1})
m.config["train"]["csv_file"] = get_data("testfile_multi.csv")
m.config["train"]["root_dir"] = os.path.dirname(get_data("testfile_multi.csv"))
m.config["train"]["fast_dev_run"] = False
m.config["batch_size"] = 2
m.config["validation"]["csv_file"] = get_data("testfile_multi.csv")
m.config["validation"]["root_dir"] = os.path.dirname(get_data("testfile_multi.csv"))
im_callback = callbacks.images_callback(csv_file=m.config["validation"]["csv_file"], root_dir=m.config["validation"]["root_dir"], savedir=tmpdir)
m.create_trainer(callbacks=[im_callback])
m.max_steps = 2
m.trainer.fit(m)
saved_images = glob.glob("{}/*.png".format(tmpdir))
assert len(saved_images) == 1
def test_override_transforms():
def get_transform(augment):
"""This is the new transform"""
if augment:
transform = A.Compose([A.HorizontalFlip(p=0.5),
ToTensorV2()],
bbox_params=A.BboxParams(
format='pascal_voc',
label_fields=["category_ids"]))
else:
transform = ToTensorV2()
return transform
m = main.deepforest(transforms=get_transform)
csv_file = get_data("example.csv")
root_dir = os.path.dirname(csv_file)
train_ds = m.load_dataset(csv_file, root_dir=root_dir)
path, image, target = next(iter(train_ds))
assert m.transforms.__doc__ == "This is the new transform"
def m(download_release):
m = main.deepforest()
m.use_release()
return m
def test_environment():
from deepforest.main import deepforest
deepforest_class = deepforest()
import os
import cProfile, pstats
def run(m):
csv_file = get_data("OSBS_029.csv")
predictions = m.predict_file(csv_file=csv_file,
root_dir=os.path.dirname(csv_file))
predictions.label = "Tree"
ground_truth = pd.read_csv(csv_file)
results = evaluate.evaluate(predictions=predictions,
ground_df=ground_truth,
root_dir=os.path.dirname(csv_file),
savedir=None)
if __name__ == "__main__":
m = main.deepforest()
m.use_release()
profiler = cProfile.Profile()
profiler.enable()
m = main.deepforest()
m.use_release()
run(m)
profiler.disable()
stats = pstats.Stats(profiler).sort_stats('cumtime')
stats.print_stats()
stats.dump_stats('evaluate.prof')
def test_custom_config_file_path(tmpdir):
print(os.getcwd())
m = main.deepforest(config_file='tests/deepforest_config_test.yml')
assert m.config["batch_size"] == 9999
assert m.config["nms_thresh"] == 0.9
assert m.config["score_thresh"] == 0.9
def m(download_release):
m = main.deepforest()
m.use_release(check_release=False)
return m
def __init__(self):
self.model = main.deepforest()
self.model.use_release()
