)
args = parser.parse_args()
symbol_storage = SymbolStorage()
dss = DiagramSymbolsStorage()
number_diagrams = int(args.number_diagrams[0])
symbols_per_diagram = int(args.symbols_per_diagram[0])
diagram_size = args.diagram_size
if args.diagram_matter:
if type(args.diagram_matter) == list:
diagram_matters = args.diagram_matter
else:
diagram_matters = [args.diagram_matter]
else:
matters = symbol_storage.get_matters()
random.choices(matters, k=2)
valid_symbols = get_valid_symbols(symbol_storage, diagram_matters)
# Generate the diagrams in multiprocess
generate_set_diagrams(dss, diagram_size, symbols_per_diagram,
valid_symbols, number_diagrams)
TrainingDatasetLabelDictionaryStorage.save(valid_symbols)
# Generate tensorflow datasets and push it to Azure blob
model_id = generate_train_dataset(diagram_matters)
print(f"Dataset id generated: {model_id}")
def test_get_symbols_by_matter(self):
ss = SymbolStorage()
matters = ss.get_matters()
symbols = ss.get_symbols_by_matter(matters[0])
assert len(symbols) > 0
def test_get_symbols_by_family(self):
ss = SymbolStorage()
families = ss.get_families()
matters = ss.get_matters()
symbols = ss.get_symbols_by_family(matter=matters[0], family=families[0])
assert len(symbols) > 0
def test_get_matters(self):
ss = SymbolStorage()
matters = ss.get_matters()
assert len(matters) > 0
assert matters[0] != ""