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 run(record):
att = AttentionModel(config="/home/b.weinstein/DeepTreeAttention/conf/tree_config.yml")
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
site_classes_file = "{}/data/processed/site_class_labels.csv".format(ROOT)
site_classdf = pd.read_csv(site_classes_file)
site_label_dict = site_classdf.set_index("siteID").label.to_dict()
domain_classes_file = "{}/data/processed/domain_class_labels.csv".format(ROOT)
domain_classdf = pd.read_csv(domain_classes_file)
domain_label_dict = domain_classdf.set_index("domainID").label.to_dict()
species_classes_file = "{}/data/processed/species_class_labels.csv".format(ROOT)
species_classdf = pd.read_csv(species_classes_file)
species_label_dict = species_classdf.set_index("taxonID").label.to_dict()
rgb_pool = glob.glob(att.config["rgb_sensor_pool"], recursive=True)
hyperspectral_pool = glob.glob(att.config["hyperspectral_sensor_pool"], recursive=True)
#Convert h5 hyperspec
renamed_record = record.replace("itc_predictions", "image")
hyperspec_path = lookup_and_convert(shapefile=renamed_record, rgb_pool=rgb_pool, hyperspectral_pool=hyperspectral_pool, savedir=att.config["hyperspectral_tif_dir"])
rgb_path = find_sensor_path(shapefile=renamed_record, lookup_pool=rgb_pool)
#infer site
site = site_from_path(renamed_record)
numeric_site = site_label_dict[site]
domain = domain_from_path(renamed_record)
numeric_domain = domain_label_dict[domain]
#infer elevation
h5_path = find_sensor_path(shapefile=renamed_record, lookup_pool=hyperspectral_pool)
elevation = elevation_from_tile(h5_path)
#Generate record when complete
df = pd.read_csv(record)
# hot fix the heights for the moment.
heights = np.repeat(10, df.shape[0])
tfrecords = att.generate(
csv_file=record,
HSI_sensor_path=hyperspec_path,
RGB_sensor_path=rgb_path,
chunk_size=500,
train=True,
domain=numeric_domain,
site=numeric_site,
heights=heights,
elevation=elevation,
label_column="filtered_taxonID",
species_label_dict=species_label_dict
)
return tfrecords
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 run(record, savedir, raw_box_dir):
"""Take a plot of deepforest prediction (see prepare_field_data.py) and generate crops for training/evalution"""
#Read record
df = gpd.read_file(record)
#get bounding boxes from the surrounding trees
basename = os.path.basename(record)
raw_boxes ="{}/{}".format(raw_box_dir, basename)
att = AttentionModel(config="/home/b.weinstein/DeepTreeAttention/conf/tree_config.yml")
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
site_classes_file = "{}/data/processed/site_class_labels.csv".format(ROOT)
site_classdf = pd.read_csv(site_classes_file)
site_label_dict = site_classdf.set_index("siteID").label.to_dict()
domain_classes_file = "{}/data/processed/domain_class_labels.csv".format(ROOT)
domain_classdf = pd.read_csv(domain_classes_file)
domain_label_dict = domain_classdf.set_index("domainID").label.to_dict()
species_classes_file = "{}/data/processed/species_class_labels.csv".format(ROOT)
species_classdf = pd.read_csv(species_classes_file)
species_label_dict = species_classdf.set_index("taxonID").label.to_dict()
rgb_pool = glob.glob(att.config["rgb_sensor_pool"], recursive=True)
hyperspectral_pool = glob.glob(att.config["hyperspectral_sensor_pool"], recursive=True)
#Convert h5 hyperspec
hyperspec_path = lookup_and_convert(bounds=df.total_bounds, rgb_pool=rgb_pool, hyperspectral_pool=hyperspectral_pool, savedir=att.config["hyperspectral_tif_dir"])
rgb_path = find_sensor_path(bounds=df.total_bounds, lookup_pool=rgb_pool)
#infer site, only 1 per plot.
site = df.siteID.unique()[0]
numeric_site = site_label_dict[site]
domain = df.domainID.unique()[0]
numeric_domain = domain_label_dict[domain]
#infer elevation
h5_path = find_sensor_path(bounds=df.total_bounds, lookup_pool=hyperspectral_pool)
elevation = elevation_from_tile(h5_path)
att.ensemble_model = tf.keras.models.load_model("{}/Ensemble.h5".format(att.config["neighbors"]["model_dir"]), custom_objects={"WeightedSum":WeightedSum})
ensemble_model = tf.keras.Model(att.ensemble_model.inputs, att.ensemble_model.get_layer("ensemble_learn").output)
#Generate record when complete
tfrecords = att.generate(
shapefile=record,
raw_boxes=raw_boxes,
HSI_sensor_path=hyperspec_path,
RGB_sensor_path=rgb_path,
chunk_size=500,
train=True,
domain=numeric_domain,
site=numeric_site,
elevation=elevation,
label_column="taxonID",
species_label_dict=species_label_dict,
ensemble_model=None,
savedir=savedir
)
return tfrecords