def setup_create_tensors(test_create_tfrecords):
created_tensors = tfrecords.create_tensors(test_create_tfrecords)
return created_tensors
def test_create_tensors(test_create_tfrecords):
print("Testing that input tensors can be created")
created_tensors = tfrecords.create_tensors(test_create_tfrecords)
assert len(created_tensors) == 2
return created_tensors
#Select a set of n image
annotations = annotations[annotations.image_path ==
"2019_DELA_5_423000_3601000_image_0.jpg"].copy()
#Generate tfrecords
annotations_file = BASE_PATH + "pretraining/crops/test.csv"
annotations.to_csv(annotations_file, header=False, index=False)
class_file = utilities.create_classes(annotations_file)
tfrecords_path = tfrecords.create_tfrecords(annotations_file,
class_file,
size=1)
print("Created {} tfrecords: {}".format(len(tfrecords_path), tfrecords_path))
inputs, targets = tfrecords.create_tensors(tfrecords_path)
#### Fit generator ##
comet_experiment = Experiment(api_key="ypQZhYfs3nSyKzOfz13iuJpj2",
project_name="deepforest",
workspace="bw4sz")
comet_experiment.log_parameter("Type", "testing")
comet_experiment.log_parameter("input_type", "fit_generator")
#Create model
fitgen_model = deepforest.deepforest()
fitgen_model.config["epochs"] = 1
comet_experiment.log_parameters(fitgen_model.config)
#Train model