def get_proof(sexp_cache, args):
coq_filename = os.path.splitext(args.file)[0] + '.v'
fields = coq_filename.split(os.path.sep)
loc2code = get_code(open(coq_filename, 'rb').read())
meta = open(args.file).read()
coq_code = extract_code(meta, loc2code)
file_data = json.load(open(os.path.join(args.data_path, args.file[13:-5] + '.json')))
with SerAPI(args.timeout, args.debug) as serapi:
num_extra_cmds = len(set_paths(meta, serapi, sexp_cache))
# process the coq code
proof_start_lines = []
in_proof = False
for num_executed, (code_line, tags) in enumerate(coq_code):
assert code_line == file_data['vernac_cmds'][num_extra_cmds + num_executed][0]
if 'PROOF_NAME' in tags and tags['PROOF_NAME'] == args.proof: # the proof ends
serapi.pop()
line_nb = proof_start_lines[-1]
proof_data = record_proof(num_extra_cmds, line_nb, coq_code[line_nb + 1:], sexp_cache, serapi, args)
break
# execute the code
if args.debug:
print('%d: %s' % (num_executed, code_line))
serapi.execute(code_line)
if serapi.has_open_goals():
if not in_proof: # the proof starts
in_proof = True
proof_start_lines.append(num_executed)
serapi.push()
else:
in_proof = False
return proof_data
def check_file(filename, sexp_cache, args):
coq_filename = os.path.splitext(filename)[0] + '.v'
fields = coq_filename.split(os.path.sep)
loc2code = get_code(open(coq_filename, 'rb').read())
file_data = {
'filename': os.path.sep.join(fields[2:]),
'coq_project': fields[1],
'vernac_cmds': [],
'num_extra_cmds': None,
'proofs': [],
}
meta = open(filename).read()
with SerAPI(args.timeout, args.debug) as serapi:
# set the path
file_data['vernac_cmds'].extend(set_paths(meta, serapi, sexp_cache))
file_data['num_extra_cmds'] = len(file_data['vernac_cmds'])
# extract the coq code
coq_code = extract_code(meta, loc2code)
# process the coq code
for num_executed, (code_line, tags) in enumerate(coq_code):
if 'PROOF_NAME' in tags:
if tags['PROOF_NAME'] not in file_data['proofs']:
file_data['proofs'].append(tags['PROOF_NAME'])
# execute until the proof editing mode starts
if args.debug:
print('%d: %s' % (num_executed, code_line))
_, ast = serapi.execute(code_line, return_ast=True)
file_data['vernac_cmds'].append(
(code_line, tags['VERNAC_TYPE'], sexp_cache.dump(ast)))
return file_data
def check_file(filename, sexp_cache, args):
print("\n" + filename)
coq_filename = os.path.splitext(filename)[0] + ".v"
fields = coq_filename.split(os.path.sep)
loc2code = get_code(open(coq_filename, "rb").read())
file_data = {
"filename": os.path.sep.join(fields[2:]),
"coq_project": fields[1],
"vernac_cmds": [],
"num_extra_cmds": None,
"proofs": [],
}
meta = open(filename).read()
with SerAPI(args.timeout, args.debug) as serapi:
# set the path
file_data["vernac_cmds"].extend(set_paths(meta, serapi, sexp_cache))
file_data["num_extra_cmds"] = len(file_data["vernac_cmds"])
# extract the coq code
coq_code = extract_code(meta, loc2code)
# process the coq code
for num_executed, (code_line, tags) in enumerate(coq_code):
if "PROOF_NAME" in tags:
if tags["PROOF_NAME"] not in file_data["proofs"]:
file_data["proofs"].append(tags["PROOF_NAME"])
# execute until the proof editing mode starts
if args.debug:
print("%d: %s" % (num_executed, code_line))
_, ast = serapi.execute(code_line, return_ast=True)
file_data["vernac_cmds"].append(
(code_line, tags["VERNAC_TYPE"], sexp_cache.dump(ast)))
return file_data
def get_proof(args):
coq_filename = os.path.splitext(args.file)[0] + '.v'
fields = coq_filename.split(os.path.sep)
loc2code = get_code(open(coq_filename, 'rb').read())
meta = open(args.file).read()
coq_code = extract_code(meta, loc2code)
with open(os.path.join(args.data_path, args.file[13:-5] + '.json'),
"r") as json_data:
file_data = json.load(json_data)
with open(args.file0, "r") as json_data:
proof_data0 = json.load(json_data)
proof_data0 = [tempdic['name'] for tempdic in proof_data0['proofs']]
for tempproof in file_data['proofs']:
args.proof = tempproof
if tempproof not in proof_data0:
continue
if os.path.isfile(os.path.join(
dirname, args.proof +
'.json')): # tempproof != 'loc_unconstrained_satisf':
continue
db_path = os.path.join(dirname, args.proof + '-sexp_cache')
sexp_cache = SexpCache(db_path)
with SerAPI(args.timeout, args.debug) as serapi:
num_extra_cmds = len(set_paths(meta, serapi, sexp_cache))
# process the coq code
proof_start_lines = []
in_proof = False
for num_executed, (code_line, tags) in enumerate(coq_code):
assert code_line == file_data['vernac_cmds'][num_extra_cmds +
num_executed][0]
if 'PROOF_NAME' in tags and tags[
'PROOF_NAME'] == args.proof: # the proof ends
serapi.pop()
line_nb = proof_start_lines[-1]
proof_data = record_proof(num_extra_cmds, line_nb,
coq_code[line_nb + 1:],
sexp_cache, serapi, args)
if proof_data is not None:
dump(proof_data, args)
break
# execute the code
if args.debug:
print('%d: %s' % (num_executed, code_line))
serapi.execute(code_line)
if serapi.has_open_goals():
if not in_proof: # the proof starts
in_proof = True
proof_start_lines.append(num_executed)
serapi.push()
else:
in_proof = False
return
def start(record_name: str = settings.record_name, mode: str = settings.mode):
settings.mode = mode
settings.record_name = record_name
set_mode()
utils.set_paths()
if is_record:
print_welcome("RECORDING")
global recorder
recorder = Recorder()
recorder.prepare()
elif is_playback:
print_welcome("PLAYBACK")
global player
player = Player(accept_socket)
player.prepare()
elif is_proxy:
print_welcome("PROXY")
def eval_vae_lr():
config = get_config()
# set seed
SEED = config['seed']
np.random.seed(SEED)
torch.manual_seed(SEED)
random.seed(SEED)
experiment_dir = config['experiment_dir']
experiment_path = Path(os.getcwd()) / f'data/vae_model/{experiment_dir}'
flags_path = experiment_path / 'flags.rar'
FLAGS = torch.load(flags_path)
FLAGS.save_figure = True
FLAGS.dir_cond_gen = Path(__file__).parent.parent / 'data/cond_gen'
FLAGS.text_gen_lastlayer = 'softmax'
FLAGS = set_paths(FLAGS, config)
FLAGS.use_clf = False
FLAGS.batch_size = 30
# FLAGS.undersample_dataset = True
state_dict_path = experiment_path / 'checkpoints/0149/mm_vae'
mimic_experiment = MimicExperiment(flags=FLAGS)
mimic_experiment.tb_logger = Dummylogger()
mimic_experiment.mm_vae.to(FLAGS.device)
mimic_experiment.mm_vae.load_state_dict(
state_dict=torch.load(state_dict_path))
mimic_experiment.mm_vae.eval()
results = {}
for binay_labels in [True]:
mimic_experiment.flags.binary_labels = binay_labels
with torch.no_grad():
clf_lr = train_clf_lr_all_subsets(mimic_experiment,
weighted_sampler=False)
predictions, gt = test_clf_lr_all_subsets(clf_lr, mimic_experiment)
for subset in predictions:
# calculate metrics
metrics = Metrics(predictions[subset],
gt,
str_labels=get_labels(FLAGS.binary_labels))
metrics_dict = metrics.evaluate()
results[subset] = metrics_dict
print(subset, ':', metrics_dict[config['eval_metric']][0])
log.info(f'Lr eval results: {results}')
out_path = Path(os.getcwd(
)) / f'data/lr_eval_results{"_bin_label" if binay_labels else ""}.json'
log.info(f'Saving lr eval test results to {out_path}')
with open(out_path, 'w') as outfile:
json.dump(results, outfile)