def mod(tmpdir):
mod = AttentionModel(config="conf/tree_config.yml")
mod.sites = 2
mod.domains = 2
mod.RGB_channels = 3
mod.HSI_channels = 3
train_dir = tmpdir.mkdir("train")
label_file = "{}/label_file.csv".format(train_dir)
mod.config["train"]["tfrecords"] = train_dir
mod.classes_file = "data/processed/species_class_labels.csv"
domain = "D17"
numeric_domain = domain_label_dict[domain]
site = "BART"
numeric_site = site_label_dict[site]
created_records = mod.generate(shapefile=test_predictions, site=numeric_site, domain=numeric_domain, elevation=100/1000,
HSI_sensor_path=test_sensor_tile,
RGB_sensor_path=test_sensor_tile,
train=True,
chunk_size=2,
savedir = mod.config["train"]["tfrecords"])
#create a fake label file
pd.DataFrame({"taxonID":["Ben","Jon"],"label":[0,1]}).to_csv(label_file)
mod.classes_file = label_file
mod.create()
mod.ensemble(experiment=None)
return mod
def mod(tmpdir):
mod = AttentionModel(config="conf/tree_config.yml")
mod.sites = 2
mod.domains = 2
mod.RGB_channels = 3
mod.HSI_channels = 3
train_dir = tmpdir.mkdir("train")
label_file = "{}/label_file.csv".format(train_dir)
shp = gpd.read_file(test_predictions)
mod.config["train"]["tfrecords"] = train_dir
mod.classes_file = "data/processed/species_class_labels.csv"
created_records = mod.generate(shapefile=test_predictions,
site=0,
domain=1,
elevation=100,
heights=np.random.random(shp.shape[0]),
HSI_sensor_path=test_sensor_tile,
RGB_sensor_path=test_sensor_tile,
train=True,
chunk_size=2)
#create a fake label file
pd.DataFrame({
"taxonID": ["Ben", "Jon"],
"label": [0, 1]
}).to_csv(label_file)
mod.classes_file = label_file
mod.create()
mod.ensemble(experiment=None, train=False)
#turn ensemble model into a feature extractor of the 2nd to last layer.
mod.ensemble_model = tfk.Model(
mod.ensemble_model.inputs,
mod.ensemble_model.get_layer("submodel_concat").output)
return mod
def mod(tmpdir):
mod = AttentionModel(config="conf/tree_config.yml")
train_dir = tmpdir.mkdir("train")
predict_dir = tmpdir.mkdir("predict")
label_file = "{}/label_file.csv".format(train_dir)
#create a fake label file
pd.DataFrame({
"taxonID": ["Ben", "Jon"],
"label": [0, 1]
}).to_csv(label_file)
config = {}
train_config = {}
train_config["tfrecords"] = train_dir
train_config["batch_size"] = 1
train_config["epochs"] = 1
train_config["steps"] = 1
train_config["gpus"] = 1
train_config["crop_size"] = 20
train_config["shuffle"] = True
train_config["weighted_sum"] = False
train_config["classes"] = 2
train_config["species_class_file"] = label_file
#evaluation
eval_config = {}
eval_config["tfrecords"] = None
eval_config["steps"] = 1
eval_config["ground_truth_path"] = "data/processed/test.shp"
predict_config = {}
predict_config["tfrecords"] = predict_dir
config["train"] = train_config
config["evaluation"] = eval_config
config["predict"] = predict_config
#Replace config for testing env
for key, value in config.items():
for nested_key, nested_value in value.items():
mod.config[key][nested_key] = nested_value
#Update the inits
mod.RGB_size = mod.config["train"]["RGB"]["crop_size"]
mod.HSI_size = mod.config["train"]["HSI"]["crop_size"]
mod.HSI_channels = 369
mod.RGB_channels = 3
mod.extend_HSI_box = mod.config["train"]["HSI"]["extend_box"]
mod.classes_file = label_file
mod.train_shp = pd.DataFrame({
"taxonID": ["Jon", "Ben"],
"siteID": [0, 1],
"domainID": [0, 1],
"plotID": [0, 1],
"canopyPosition": ["a", "b"],
"scientific": ["genus species", "genus species"]
})
mod.train_shp.index = [2, 7]
mod.sites = 23
mod.domains = 15
#Create a model with input sizes
mod.create()
return mod