def processFile(js_file_path):
pid = int(multiprocessing.current_process().ident)
try:
signal.alarm(600)
prepro = Preprocessor(js_file_path)
prepro.write_temp_file('tmp_%d.js' % pid)
beauty = Beautifier()
ok = beauty.run('tmp_%d.js' % pid, 'tmp_%d.b.js' % pid)
if ok:
mc = MiniChecker('tmp_%d.b.js' % pid)
try:
isMini = mc.compare(keep_mini=False)
except Exception as e:
isMini = str(e)
cleanup(pid)
return [os.path.basename(js_file_path), isMini]
else:
cleanup(pid)
return [os.path.basename(js_file_path), 'Beautifier failed']
except TimeExceededError:
cleanup(pid)
return [os.path.basename(js_file_path), 'Timeout']
def processFile(l):
js_file_path = l[0]
pid = int(multiprocessing.current_process().ident)
try:
# Temp files to be created during processing
path_tmp = 'tmp_%d.js' % pid
path_tmp_b = 'tmp_%d.b.js' % pid
# Strip comments, replace literals, etc
try:
prepro = Preprocessor(os.path.join(corpus_root, js_file_path))
prepro.write_temp_file(path_tmp)
except:
cleanup(pid)
return (js_file_path, None, 'Preprocessor fail')
# Pass through beautifier to fix layout
clear = Beautifier()
ok = clear.run(path_tmp, path_tmp_b)
if not ok:
cleanup(pid)
return (js_file_path, None, 'Beautifier fail')
try:
iBuilder_clear = IndexBuilder(Lexer(path_tmp_b).tokenList)
except:
cleanup(pid)
return (js_file_path, None, 'IndexBuilder fail')
n_lines = len(iBuilder_clear.tokens)
max_line_len = max([len(l) for l in iBuilder_clear.tokens])
cleanup(pid)
return (js_file_path, n_lines, max_line_len)
except Exception, e:
cleanup(pid)
return (js_file_path, None, str(e))
def processFile(row):
js_file_path = os.path.join(corpus_root, row[0])
pid = int(multiprocessing.current_process().ident)
base_name = os.path.splitext(os.path.basename(js_file_path))[0]
# Temp files to be created during processing
temp_files = {
'path_tmp': 'tmp_%d.js' % pid,
'path_tmp_b': 'tmp_%d.b.js' % pid,
'path_tmp_b_a': 'tmp_%d.b.a.js' % pid,
'path_tmp_u': 'tmp_%d.u.js' % pid,
'path_tmp_u_a': 'tmp_%d.u.a.js' % pid
}
try:
# Pass through beautifier to fix layout:
# # - once through JSNice without renaming
# jsNiceBeautifier = JSNice(flags=['--no-types', '--no-rename'])
#
# (ok, _out, _err) = jsNiceBeautifier.run(js_file_path,
# temp_files['path_tmp'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, False, 'JSNice Beautifier fail')
#
#
# # Weird JSNice renamings despite --no-rename
# try:
# before = set([token for (token, token_type) in
# Lexer(js_file_path).tokenList
# if is_token_subtype(token_type, Token.Name)])
# after = set([token for (token, token_type) in
# Lexer(temp_files['path_tmp']).tokenList
# if is_token_subtype(token_type, Token.Name)])
#
# if not before == after:
# return (js_file_path, False, 'Weird JSNice renaming')
#
# except:
# cleanup(temp_files)
# return (js_file_path, False, 'Lexer fail')
# Strip comments, replace literals, etc
try:
prepro = Preprocessor(os.path.join(corpus_root, js_file_path))
prepro.write_temp_file(temp_files['path_tmp'])
except:
cleanup(temp_files)
return (js_file_path, None, 'Preprocessor fail')
# - and another time through uglifyjs pretty print only
clear = Beautifier()
ok = clear.run(temp_files['path_tmp'], temp_files['path_tmp_b'])
if not ok:
cleanup(temp_files)
return (js_file_path, False, 'Beautifier fail')
# Minify
ugly = Uglifier()
ok = ugly.run(temp_files['path_tmp_b'], temp_files['path_tmp_u'])
if not ok:
cleanup(temp_files)
return (js_file_path, False, 'Uglifier fail')
# Num tokens before vs after
try:
tok_clear = Lexer(temp_files['path_tmp_b']).tokenList
tok_ugly = Lexer(temp_files['path_tmp_u']).tokenList
except:
cleanup(temp_files)
return (js_file_path, False, 'Lexer fail')
# For now only work with minified files that have
# the same number of tokens as the originals
if not len(tok_clear) == len(tok_ugly):
cleanup(temp_files)
return (js_file_path, False, 'Num tokens mismatch')
# Align minified and clear files, in case the beautifier
# did something weird
try:
aligner = Aligner()
# This is already the baseline corpus, no (smart) renaming yet
aligner.align(temp_files['path_tmp_b'], temp_files['path_tmp_u'])
except:
cleanup(temp_files)
return (js_file_path, False, 'Aligner fail')
# Check if minification resulted in any change
# It's not very interesting otherwise
if open(temp_files['path_tmp_b_a']).read() == \
open(temp_files['path_tmp_u_a']).read():
cleanup(temp_files)
return (js_file_path, False, 'Not minified')
try:
lex_ugly = Lexer(temp_files['path_tmp_u_a'])
_iBuilder_ugly = IndexBuilder(lex_ugly.tokenList)
except:
cleanup(temp_files)
return (js_file_path, False, 'IndexBuilder fail')
# Store original and uglified versions
ok = clear.run(temp_files['path_tmp_b_a'],
os.path.join(output_path, '%s.js' % base_name))
if not ok:
cleanup(temp_files)
cleanupProcessed(base_name)
return (js_file_path, False, 'Beautifier fail')
ok = clear.run(temp_files['path_tmp_u_a'],
os.path.join(output_path, '%s.u.js' % base_name))
if not ok:
cleanup(temp_files)
cleanupProcessed(base_name)
return (js_file_path, False, 'Beautifier fail')
cleanup(temp_files)
return (js_file_path, True, 'OK')
except Exception, e:
cleanup(temp_files)
return (js_file_path, False, str(e))
def processFile(l):
js_file_path = l[0]
if js_file_path in seen:
return (js_file_path, None, 'Skipped')
pid = int(multiprocessing.current_process().ident)
# Temp files to be created during processing
temp_files = {'path_tmp': 'tmp_%d.js' % pid,
'path_tmp_b': 'tmp_%d.b.js' % pid,
'path_tmp_b_n': 'tmp_%d.b.n.js' % pid,
'path_tmp_u': 'tmp_%d.u.js' % pid,
'path_tmp_u_n': 'tmp_%d.u.n.js' % pid,
'path_tmp_b_a': 'tmp_%d.b.a.js' % pid,
'path_tmp_u_a': 'tmp_%d.u.a.js' % pid}
try:
# Strip comments, replace literals, etc
try:
prepro = Preprocessor(os.path.join(corpus_root, js_file_path))
prepro.write_temp_file(temp_files['path_tmp'])
except:
cleanup(temp_files)
return (js_file_path, None, 'Preprocessor fail')
# Pass through beautifier to fix layout:
# - once through JSNice without renaming
# jsNiceBeautifier = JSNice(flags=['--no-types', '--no-rename'])
#
# (ok, _out, _err) = jsNiceBeautifier.run(temp_files['path_tmp'],
# temp_files['path_tmp_b_n'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, None, 'JSNice Beautifier fail')
# # - and another time through uglifyjs pretty print only
# clear = Beautifier()
# ok = clear.run(temp_files['path_tmp_b_n'],
# temp_files['path_tmp_b'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, None, 'Beautifier fail')
# # JSNice is down!
clear = Beautifier()
ok = clear.run(temp_files['path_tmp'],
temp_files['path_tmp_b_n'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Beautifier fail')
# Normalize
norm = Normalizer()
ok = norm.run(os.path.join(os.path.dirname(os.path.realpath(__file__)),
temp_files['path_tmp_b_n']),
False,
temp_files['path_tmp_b'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Normalizer fail')
# Minify
ugly = Uglifier()
ok = ugly.run(temp_files['path_tmp_b'],
temp_files['path_tmp_u_n'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Uglifier fail')
# Normalize
norm = Normalizer()
ok = norm.run(os.path.join(os.path.dirname(os.path.realpath(__file__)),
temp_files['path_tmp_u_n']),
False,
temp_files['path_tmp_u'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Normalizer fail')
# Num tokens before vs after
try:
tok_clear = Lexer(temp_files['path_tmp_b']).tokenList
tok_ugly = Lexer(temp_files['path_tmp_u']).tokenList
except:
cleanup(temp_files)
return (js_file_path, None, 'Lexer fail')
# For now only work with minified files that have
# the same number of tokens as the originals
if not len(tok_clear) == len(tok_ugly):
cleanup(temp_files)
return (js_file_path, None, 'Num tokens mismatch')
# Align minified and clear files, in case the beautifier
# did something weird
try:
aligner = Aligner()
# This is already the baseline corpus, no (smart) renaming yet
aligner.align(temp_files['path_tmp_b'],
temp_files['path_tmp_u'])
except:
cleanup(temp_files)
return (js_file_path, None, 'Aligner fail')
try:
lex_clear = Lexer(temp_files['path_tmp_b_a'])
iBuilder_clear = IndexBuilder(lex_clear.tokenList)
lex_ugly = Lexer(temp_files['path_tmp_u_a'])
iBuilder_ugly = IndexBuilder(lex_ugly.tokenList)
except:
cleanup(temp_files)
return (js_file_path, None, 'IndexBuilder fail')
# Normalize
norm = Normalizer()
ok = norm.run(os.path.join(os.path.dirname(os.path.realpath(__file__)),
temp_files['path_tmp_b']),
True,
temp_files['path_tmp_u_n'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Normalizer fail')
try:
lex_norm = Lexer(temp_files['path_tmp_u_n'])
iBuilder_norm = IndexBuilder(lex_norm.tokenList)
except:
cleanup(temp_files)
return (js_file_path, None, 'IndexBuilder fail')
normalized = []
for line_idx, line in enumerate(iBuilder_norm.tokens):
normalized.append(' '.join([t for (_tt,t) in line]) + "\n")
# Compute scoping: name2scope is a dictionary where keys
# are (name, start_index) tuples and values are scope identifiers.
# Note: start_index is a flat (unidimensional) index,
# not a (line_chr_idx, col_chr_idx) index.
try:
scopeAnalyst = ScopeAnalyst(os.path.join(
os.path.dirname(os.path.realpath(__file__)),
temp_files['path_tmp_u_a']))
# _name2defScope = scopeAnalyst.resolve_scope()
# _isGlobal = scopeAnalyst.isGlobal
# _name2useScope = scopeAnalyst.resolve_use_scope()
except:
cleanup(temp_files)
return (js_file_path, None, 'ScopeAnalyst fail')
orig = []
no_renaming = []
for line_idx, line in enumerate(iBuilder_ugly.tokens):
orig.append(' '.join([t for (_tt,t) in \
iBuilder_clear.tokens[line_idx]]) + "\n")
no_renaming.append(' '.join([t for (_tt,t) in line]) + "\n")
# # Simple renaming: disambiguate overloaded names using scope id
basic_renaming = renameUsingScopeId(scopeAnalyst,
iBuilder_ugly)
# More complicated renaming: collect the context around
# each name (global variables, API calls, punctuation)
# and build a hash of the concatenation.
# hash_renaming = renameUsingHashAllPrec(scopeAnalyst,
# iBuilder_ugly,
# debug=True)
hash_def_one_renaming = renameUsingHashDefLine(scopeAnalyst,
iBuilder_ugly,
twoLines=False,
debug=False)
hash_def_two_renaming = renameUsingHashDefLine(scopeAnalyst,
iBuilder_ugly,
twoLines=True,
debug=False)
cleanup(temp_files)
return (js_file_path,
orig,
no_renaming,
basic_renaming,
normalized,
# hash_renaming,
hash_def_one_renaming,
hash_def_two_renaming)
except Exception, e:
cleanup(temp_files)
return (js_file_path, None, str(e))
def detect_images(image_path, model_path, configfile, output_dir):
# Load model
configs = configparser.ConfigParser()
configs.read(configfile)
configs = configparser.ConfigParser()
configs.read(configfile)
try:
input_shape = json.loads(configs['MODEL']['input_shape'])
try:
ratios = json.loads(configs['MODEL']['ratios'])
scales = json.loads(configs['MODEL']['scales'])
except Exception as e:
print(e)
print('USING DEFAULT RATIOS AND SCALES')
ratios = None
scales = None
except:
print("CONFIG FILE DOES NOT HAVE INPUT_SHAPE")
sys.exit()
preprocessor = Preprocessor(input_width=input_shape[2],
input_height=input_shape[1],
mean=np.array([[[0.485, 0.456, 0.406]]]),
std=np.array([[[0.229, 0.224, 0.225]]]))
postprocessor = PostProcessor(ratios=ratios, scales=scales)
# Get labelmap
labels = load_classes_from_configfile(configfile)
# GPU WARMUP
img_name = [
i for i in os.listdir(image_path) if i.endswith((
'.jpg',
'.png',
))
][0]
image, image_orig, batch, scales = preprocess_image(
os.path.join(image_path, img_name), preprocessor)
gpu_warmup(image_orig, trt_engine_path=model_path)
print('Getting engine')
engine = get_engine(model_path)
print('Engine retrieved')
context = engine.create_execution_context()
print('Execution context created')
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
print('Buffers allocated')
input_shapes = []
output_shapes = []
for binding in engine:
if engine.binding_is_input(binding):
input_shapes.append(engine.get_binding_shape(binding))
else: # and one output
output_shapes.append(engine.get_binding_shape(binding))
print(f'INPUT SHAPES:{input_shapes}, OUTPUT SHAPES:{output_shapes}')
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
assert (os.path.abspath(output_dir) != os.path.abspath(image_path))
class_scores_list = {i: [] for i in labels.values()}
for img_name in os.listdir(image_path):
image, image_orig, batch, scales = preprocess_image(
os.path.join(image_path, img_name), preprocessor)
if (image is None):
continue
st = time.time()
# TRT INFERENCE
inputs[0].host = batch
trt_outputs = common.do_inference(
context=context,
bindings=bindings,
inputs=inputs,
outputs=outputs,
stream=stream,
batch_size=1,
)
regression, classification = [
output.reshape(shape)
for output, shape in zip(trt_outputs, output_shapes)
]
if torch.cuda.is_available():
regression = torch.from_numpy(regression).cuda()
classification = torch.from_numpy(classification).cuda()
else:
regression = torch.from_numpy(regression)
classification = torch.from_numpy(classification)
scores, classification, transformed_anchors = postprocessor(
batch, regression, classification)
print('Elapsed time: {}'.format(time.time() - st))
idxs = np.where(scores.cpu() > 0.01)
for j in range(idxs[0].shape[0]):
label_name = labels[int(classification[idxs[0][j]])]
score = scores[j]
class_scores_list[label_name].append(score.cpu().numpy())
# Get quartile distribution of all the scores.
for label_name, scores_list in class_scores_list.items():
print(f'{label_name} class has mean_score: {np.mean(scores_list)}, '
f'0.25:{np.quantile(scores_list, 0.25)}, '
f'0.5:{np.quantile(scores_list, 0.5)},'
f'0.75:{np.quantile(scores_list, 0.75)}')
def processFile(l):
js_file_path = l[0]
base_name = os.path.splitext(os.path.basename(js_file_path))[0]
pid = int(multiprocessing.current_process().ident)
temp_files = {
'path_tmp': 'tmp_%d.js' % pid,
'path_tmp_b': 'tmp_%d.b.js' % pid,
'path_tmp_b_1': 'tmp_%d.b.1.js' % pid,
'path_tmp_b_2': 'tmp_%d.b.2.js' % pid,
'path_tmp_b_a': 'tmp_%d.b.a.js' % pid,
'path_tmp_u': 'tmp_%d.u.js' % pid,
'path_tmp_u_a': 'tmp_%d.u.a.js' % pid,
'path_tmp_unugly': 'tmp_%d.n2p.js' % pid,
'path_tmp_unugly_1': 'tmp_%d.n2p.1.js' % pid,
'path_tmp_unugly_2': 'tmp_%d.n2p.2.js' % pid,
'path_tmp_jsnice': 'tmp_%d.jsnice.js' % pid,
'f2': 'tmp_%d.no_renaming.js' % pid,
# 'f3': 'tmp_%d.basic_renaming.js' % pid,
# 'f4': 'tmp_%d.hash_renaming.js' % pid,
'f5': 'tmp_%d.hash_def_one_renaming.js' % pid,
# 'f6': 'tmp_%d.hash_def_two_renaming.js' % pid,
'f7': 'tmp_%d.hash_def_one_renaming_fb.js' % pid,
'path_orig': os.path.join(output_path, '%s.js' % base_name),
'path_ugly': os.path.join(output_path, '%s.u.js' % base_name),
'path_unugly': os.path.join(output_path, '%s.n2p.js' % base_name),
'path_jsnice': os.path.join(output_path, '%s.jsnice.js' % base_name)
}
# for strategy in ['js', 'lm.js', 'len.js', 'freqlen.js']:
# for renaming in ['no_renaming', 'hash_def_one_renaming']:
# temp_files['path_tmp_%s_%s' % (renaming, strategy)] = \
# 'tmp_%d.%s.%s' % (pid, renaming, strategy)
candidates = []
# if True:
try:
# Strip comments, replace literals, etc
try:
prepro = Preprocessor(os.path.join(corpus_root, js_file_path))
prepro.write_temp_file(temp_files['path_tmp'])
except:
cleanup(temp_files)
return (js_file_path, None, 'Preprocessor fail')
# Pass through beautifier to fix layout
clear = Beautifier()
ok = clear.run(temp_files['path_tmp'], temp_files['path_tmp_b'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Beautifier fail')
# # Pass through beautifier to fix layout
# clear = Beautifier()
# ok = clear.run(temp_files['path_tmp'],
# temp_files['path_tmp_b_1'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, None, 'Beautifier fail')
#
# jsNiceBeautifier = JSNice(flags=['--no-types', '--no-rename'])
#
# (ok, _out, _err) = jsNiceBeautifier.run(temp_files['path_tmp_b_1'],
# temp_files['path_tmp_b_2'])
# if not ok:
# cleanup(temp_files)
# print js_file_path, _err
# return (js_file_path, None, 'JSNice Beautifier fail')
#
# ok = clear.run(temp_files['path_tmp_b_2'],
# temp_files['path_tmp_b'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, None, 'Beautifier fail')
#
#
# # Weird JSNice renamings despite --no-rename
# try:
# before = set([token for (token, token_type) in
# Lexer(temp_files['path_tmp_b_1']).tokenList
# if is_token_subtype(token_type, Token.Name)])
# after = set([token for (token, token_type) in
# Lexer(temp_files['path_tmp_b']).tokenList
# if is_token_subtype(token_type, Token.Name)])
#
# if not before == after:
# return (js_file_path, None, 'Weird JSNice renaming')
#
# except:
# cleanup(temp_files)
# return (js_file_path, None, 'Lexer fail')
# Minify
ugly = Uglifier()
ok = ugly.run(temp_files['path_tmp_b'], temp_files['path_tmp_u'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Uglifier fail')
# Num tokens before vs after
try:
tok_clear = Lexer(temp_files['path_tmp_b']).tokenList
tok_ugly = Lexer(temp_files['path_tmp_u']).tokenList
except:
cleanup(temp_files)
return (js_file_path, None, 'Lexer fail')
# For now only work with minified files that have
# the same number of tokens as the originals
if not len(tok_clear) == len(tok_ugly):
cleanup(temp_files)
return (js_file_path, None, 'Num tokens mismatch')
# Align minified and clear files, in case the beautifier
# did something weird
try:
aligner = Aligner()
# This is already the baseline corpus, no (smart) renaming yet
aligner.align(temp_files['path_tmp_b'], temp_files['path_tmp_u'])
except:
cleanup(temp_files)
return (js_file_path, None, 'Aligner fail')
if open(temp_files['path_tmp_b']).read() == \
open(temp_files['path_tmp_u']).read():
cleanup(temp_files)
return (js_file_path, None, 'Not minified')
try:
lex_ugly = Lexer(temp_files['path_tmp_u_a'])
iBuilder_ugly = IndexBuilder(lex_ugly.tokenList)
except:
cleanup(temp_files)
return (js_file_path, None, 'IndexBuilder fail')
############################################################
# From now on only work with path_tmp_b_a and path_tmp_u_a
############################################################
# Store original and uglified versions
ok = clear.run(temp_files['path_tmp_b_a'], temp_files['path_orig'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Beautifier fail')
ok = clear.run(temp_files['path_tmp_u_a'], temp_files['path_ugly'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Beautifier fail')
# Run the JSNice from http://www.nice2predict.org
unuglifyJS = UnuglifyJS()
(ok, _out, _err) = unuglifyJS.run(temp_files['path_tmp_u_a'],
temp_files['path_tmp_unugly'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Nice2Predict fail')
ok = clear.run(temp_files['path_tmp_unugly'],
temp_files['path_unugly'])
if not ok:
cleanup(temp_files)
return (js_file_path, None, 'Beautifier fail')
# ok = clear.run(temp_files['path_tmp_unugly'],
# temp_files['path_tmp_unugly_1'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, None, 'Beautifier fail')
#
# (ok, _out, _err) = jsNiceBeautifier.run(temp_files['path_tmp_unugly_1'],
# temp_files['path_tmp_unugly_2'])
# if not ok:
# cleanup(temp_files)
# print js_file_path, _err
# return (js_file_path, None, 'JSNice Beautifier fail')
#
# ok = clear.run(temp_files['path_tmp_unugly_2'],
# temp_files['path_unugly'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, None, 'Beautifier fail')
try:
lexer = Lexer(temp_files['path_unugly'])
iBuilder = IndexBuilder(lexer.tokenList)
except:
cleanup(temp_files)
return (js_file_path, None, 'IndexBuilder fail')
try:
scopeAnalyst = ScopeAnalyst(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
temp_files['path_unugly']))
nameOrigin = scopeAnalyst.nameOrigin
isGlobal = scopeAnalyst.isGlobal
for (name, def_scope) in nameOrigin.iterkeys():
pos = scopeAnalyst.nameDefScope2pos[(name, def_scope)]
(lin, col) = iBuilder.revFlatMat[pos]
(tok_lin, tok_col) = iBuilder.revTokMap[(lin, col)]
candidates.append(('Nice2Predict', def_scope, tok_lin, tok_col,
isGlobal.get((name, pos),
True), name, '', ''))
except:
cleanup(temp_files)
return (js_file_path, None, 'ScopeAnalyst fail')
# # Run the JSNice from http://www.jsnice.org
# jsNice = JSNice()
# (ok, _out, _err) = jsNice.run(temp_files['path_tmp_u_a'],
# temp_files['path_tmp_jsnice'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, None, 'JSNice fail')
#
# ok = clear.run(temp_files['path_tmp_jsnice'],
# temp_files['path_jsnice'])
# if not ok:
# cleanup(temp_files)
# return (js_file_path, None, 'Beautifier fail')
#
# try:
# lexer = Lexer(temp_files['path_jsnice'])
# iBuilder = IndexBuilder(lexer.tokenList)
# except:
# cleanup(temp_files)
# return (js_file_path, None, 'IndexBuilder fail')
#
# try:
# scopeAnalyst = ScopeAnalyst(os.path.join(
# os.path.dirname(os.path.realpath(__file__)),
# temp_files['path_jsnice']))
# nameOrigin = scopeAnalyst.nameOrigin
# isGlobal = scopeAnalyst.isGlobal
#
# for (name, def_scope) in nameOrigin.iterkeys():
#
# pos = scopeAnalyst.nameDefScope2pos[(name, def_scope)]
# (lin,col) = iBuilder.revFlatMat[pos]
# (tok_lin,tok_col) = iBuilder.revTokMap[(lin,col)]
#
# candidates.append(('JSNice', def_scope,
# tok_lin, tok_col,
# isGlobal.get((name, pos), True),
# name, '',''))
# except:
# cleanup(temp_files)
# return (js_file_path, None, 'ScopeAnalyst fail')
# Compute scoping: name2scope is a dictionary where keys
# are (name, start_index) tuples and values are scope identifiers.
# Note: start_index is a flat (unidimensional) index,
# not a (line_chr_idx, col_chr_idx) index.
try:
scopeAnalyst = ScopeAnalyst(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
temp_files['path_tmp_u_a']))
except:
cleanup(temp_files)
return (js_file_path, None, 'ScopeAnalyst fail')
# Baseline translation: No renaming, no scoping
no_renaming = []
for _line_idx, line in enumerate(iBuilder_ugly.tokens):
no_renaming.append(' '.join([t for (_tt, t) in line]) + "\n")
with open(temp_files['f2'], 'w') as f_no_renaming:
f_no_renaming.writelines(no_renaming)
moses = MosesDecoder(ini_path=os.path.join(ini_path, \
'train.no_renaming', 'tuning', 'moses.ini'))
(_moses_ok, translation_no_renaming,
_err) = moses.run(temp_files['f2'])
nc = processTranslationUnscoped(translation_no_renaming, iBuilder_ugly,
lm_path, temp_files['f2'], output_path,
base_name)
if nc:
candidates += nc
# translation, iBuilder, lm_path,
# f_path, output_path, base_name
# Default translation: No renaming
# no_renaming = []
# for _line_idx, line in enumerate(iBuilder_ugly.tokens):
# no_renaming.append(' '.join([t for (_tt,t) in line]) + "\n")
#
# with open(temp_files['f2'], 'w') as f_no_renaming:
# f_no_renaming.writelines(no_renaming)
#
# moses = MosesDecoder(ini_path=os.path.join(ini_path, \
# 'train.no_renaming', 'tuning', 'moses.ini'))
# (_moses_ok, translation, _err) = moses.run(temp_files['f2'])
nc = processTranslationScoped(translation_no_renaming, iBuilder_ugly,
scopeAnalyst, lm_path, temp_files['f2'],
output_path, base_name)
if nc:
candidates += nc
# More complicated renaming: collect the context around
# each name (global variables, API calls, punctuation)
# and build a hash of the concatenation.
hash_def_one_renaming = renameUsingHashDefLine(scopeAnalyst,
iBuilder_ugly,
twoLines=False,
debug=False)
with open(temp_files['f5'], 'w') as f_hash_def_one_renaming:
f_hash_def_one_renaming.writelines(hash_def_one_renaming)
# moses = MosesDecoder(ini_path=os.path.join(ini_path, \
# 'train.hash_def_one_renaming', 'tuning', 'moses.ini'))
# (_moses_ok,
# translation_hash_renaming,
# _err) = moses.run(temp_files['f5'])
mosesParams = {}
mosesParams["text"] = hash_def_one_renaming #lex_ugly.collapsedText
#mosesParams["align"] = "true"
#mosesParams["report-all-factors"] = "true"
mresults = proxy.translate(
mosesParams) # __request("translate", mosesParams)
rawText = Postprocessor(mresults["nbest"])
translation_hash_renaming = rawText.getProcessedOutput()
nc = processTranslationScoped(translation_hash_renaming, iBuilder_ugly,
scopeAnalyst, lm_path, temp_files['f5'],
output_path, base_name)
if nc:
candidates += nc
# nc = processTranslationScopedFallback(translation_hash_renaming,
# translation_no_renaming,
# iBuilder_ugly,
# scopeAnalyst,
# lm_path,
# temp_files['f7'],
# output_path,
# base_name)
# if nc:
# candidates += nc
cleanup(temp_files)
cleanupRenamed(pid)
return (js_file_path, 'OK', candidates)
except Exception, e:
cleanup(temp_files)
cleanupRenamed(pid)
return (js_file_path, None, str(e).replace("\n", ""))
def processFile(l):
def localCleanup(output_path, base_names):
for base_name in base_names:
tryRemove(os.path.join(output_path, base_name))
js_file_path = l[0]
base_name = os.path.splitext(os.path.basename(js_file_path))[0]
pid = int(multiprocessing.current_process().ident)
candidates = []
try:
# if True:
# Temp files to be created during processing
path_tmp = 'tmp_%d.js' % (pid)
path_tmp_b = 'tmp_%d.b.js' % (pid)
path_tmp_b_a = 'tmp_%d.b.a.js' % (pid)
path_tmp_u = 'tmp_%d.u.js' % (pid)
path_tmp_u_a = 'tmp_%d.u.a.js' % (pid)
path_tmp_unugly = 'tmp_%d.n2p.js' % (pid)
path_tmp_jsnice = 'tmp_%d.jsnice.js' % (pid)
f2 = 'tmp_%d.no_renaming.js' % (pid)
f3 = 'tmp_%d.basic_renaming.js' % (pid)
f4 = 'tmp_%d.hash_renaming.js' % (pid)
f5 = 'tmp_%d.hash_def_one_renaming.js' % (pid)
f6 = 'tmp_%d.hash_def_two_renaming.js' % (pid)
path_orig = '%s.js' % (base_name)
path_ugly = '%s.u.js' % (base_name)
path_unugly = '%s.n2p.js' % (base_name)
path_jsnice = '%s.jsnice.js' % (base_name)
# Strip comments, replace literals, etc
try:
prepro = Preprocessor(os.path.join(corpus_root, js_file_path))
prepro.write_temp_file(path_tmp)
except:
cleanup(pid)
return (js_file_path, None, 'Preprocessor fail')
# Pass through beautifier to fix layout
clear = Beautifier()
ok = clear.run(path_tmp, path_tmp_b+'.tmp1')
if not ok:
cleanup(pid)
return (js_file_path, None, 'Beautifier 1 fail')
jsNiceBeautifier = JSNice(flags=['--no-types', '--no-rename'])
(ok, _out, _err) = jsNiceBeautifier.run(path_tmp_b+'.tmp1', path_tmp_b+'.tmp2')
if not ok:
cleanup(pid)
return (js_file_path, None, 'JSNice Beautifier 1 fail')
ok = clear.run(path_tmp_b+'.tmp2', path_tmp_b)
if not ok:
cleanup(pid)
return (js_file_path, None, 'Beautifier 1 fail')
# Minify
ugly = Uglifier()
ok = ugly.run(path_tmp_b, path_tmp_u)
if not ok:
cleanup(pid)
return (js_file_path, None, 'Uglifier fail')
# Num tokens before vs after
try:
tok_clear = Lexer(path_tmp_b).tokenList
tok_ugly = Lexer(path_tmp_u).tokenList
except:
cleanup(pid)
return (js_file_path, None, 'Lexer fail')
# For now only work with minified files that have
# the same number of tokens as the originals
if not len(tok_clear) == len(tok_ugly):
cleanup(pid)
return (js_file_path, None, 'Num tokens mismatch')
# Align minified and clear files, in case the beautifier
# did something weird
try:
aligner = Aligner()
# This is already the baseline corpus, no (smart) renaming yet
aligner.align(path_tmp_b, path_tmp_u)
except:
cleanup(pid)
return (js_file_path, None, 'Aligner fail')
try:
# iBuilder_clear = IndexBuilder(Lexer(path_tmp_b_a).tokenList)
iBuilder_ugly = IndexBuilder(Lexer(path_tmp_u_a).tokenList)
except:
cleanup(pid)
return (js_file_path, None, 'IndexBuilder fail')
# Store original and uglified versions
ok = clear.run(path_tmp_u_a, os.path.join(output_path, path_ugly))
if not ok:
cleanup(pid)
localCleanup(output_path, [path_ugly])
return (js_file_path, None, 'Beautifier 2 fail')
ok = clear.run(path_tmp_b_a, os.path.join(output_path, path_orig))
if not ok:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig])
return (js_file_path, None, 'Beautifier 3 fail')
# Run the JSNice from http://www.nice2predict.org
unuglifyJS = UnuglifyJS()
(ok, _out, _err) = unuglifyJS.run(path_tmp_b_a, path_tmp_unugly)
if not ok:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig])
return (js_file_path, None, 'Nice2Predict fail')
ok = clear.run(path_tmp_unugly, path_tmp_unugly+'.tmp1')
if not ok:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig, path_unugly])
return (js_file_path, None, 'Beautifier 4 fail')
(ok, _out, _err) = jsNiceBeautifier.run(path_tmp_unugly+'.tmp1', path_tmp_unugly+'.tmp2')
if not ok:
cleanup(pid)
return (js_file_path, None, 'JSNice Beautifier 2 fail')
ok = clear.run(path_tmp_unugly+'.tmp2', os.path.join(output_path, path_unugly))
if not ok:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig, path_unugly])
return (js_file_path, None, 'Beautifier 4 fail')
try:
scopeAnalyst = ScopeAnalyst(os.path.join(
os.path.dirname(os.path.realpath(__file__)),
path_tmp_unugly))
nameOrigin = scopeAnalyst.nameOrigin
for (name, def_scope) in nameOrigin.iterkeys():
candidates.append(('Nice2Predict', def_scope, name, '', ''))
except:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig, path_unugly])
return (js_file_path, None, 'ScopeAnalyst fail')
# Run the JSNice from http://www.jsnice.org
jsNice = JSNice()
(ok, _out, _err) = jsNice.run(path_tmp_b_a, path_tmp_jsnice)
if not ok:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig, path_unugly])
return (js_file_path, None, 'JSNice fail')
ok = clear.run(path_tmp_jsnice, os.path.join(output_path, path_jsnice))
if not ok:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig, \
path_unugly, path_jsnice])
return (js_file_path, None, 'Beautifier 5 fail')
try:
scopeAnalyst = ScopeAnalyst(os.path.join(
os.path.dirname(os.path.realpath(__file__)),
path_tmp_jsnice))
nameOrigin = scopeAnalyst.nameOrigin
for (name, def_scope) in nameOrigin.iterkeys():
candidates.append(('JSNice', def_scope, name, '', ''))
except:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig, \
path_unugly, path_jsnice])
return (js_file_path, None, 'ScopeAnalyst fail')
# Compute scoping: name2scope is a dictionary where keys
# are (name, start_index) tuples and values are scope identifiers.
# Note: start_index is a flat (unidimensional) index,
# not a (line_chr_idx, col_chr_idx) index.
try:
scopeAnalyst = ScopeAnalyst(os.path.join(
os.path.dirname(os.path.realpath(__file__)),
path_tmp_u_a))
_name2defScope = scopeAnalyst.resolve_scope()
_isGlobal = scopeAnalyst.isGlobal
_name2useScope = scopeAnalyst.resolve_use_scope()
except:
cleanup(pid)
localCleanup(output_path, [path_ugly, path_orig, \
path_unugly, path_jsnice])
return (js_file_path, None, 'ScopeAnalyst fail')
no_renaming = []
for _line_idx, line in enumerate(iBuilder_ugly.tokens):
no_renaming.append(' '.join([t for (_tt,t) in line]) + "\n")
with open(f2, 'w') as f_no_renaming:
f_no_renaming.writelines(no_renaming)
moses = MosesDecoder(ini_path=os.path.join(ini_path, \
'train.no_renaming', 'tuning', 'moses.ini'))
(_moses_ok, translation, _err) = moses.run(f2)
nc = processTranslation(translation, iBuilder_ugly,
scopeAnalyst, lm_path, f2,
output_path, base_name, clear)
if nc:
candidates += nc
# Simple renaming: disambiguate overloaded names using scope id
basic_renaming = renameUsingScopeId(scopeAnalyst, iBuilder_ugly)
with open(f3, 'w') as f_basic_renaming:
f_basic_renaming.writelines(basic_renaming)
moses = MosesDecoder(ini_path=os.path.join(ini_path, \
'train.basic_renaming', 'tuning', 'moses.ini'))
(_moses_ok, translation, _err) = moses.run(f3)
nc = processTranslation(translation, iBuilder_ugly,
scopeAnalyst, lm_path, f3,
output_path, base_name, clear)
if nc:
candidates += nc
# More complicated renaming: collect the context around
# each name (global variables, API calls, punctuation)
# and build a hash of the concatenation.
hash_renaming = renameUsingHashAllPrec(scopeAnalyst,
iBuilder_ugly,
debug=False)
# print hash_renaming
with open(f4, 'w') as f_hash_renaming:
f_hash_renaming.writelines(hash_renaming)
moses = MosesDecoder(ini_path=os.path.join(ini_path, \
'train.hash_renaming', 'tuning', 'moses.ini'))
(_moses_ok, translation, _err) = moses.run(f4)
nc = processTranslation(translation, iBuilder_ugly,
scopeAnalyst, lm_path, f4,
output_path, base_name, clear)
if nc:
candidates += nc
hash_def_one_renaming = renameUsingHashDefLine(scopeAnalyst,
iBuilder_ugly,
twoLines=False,
debug=False)
with open(f5, 'w') as f_hash_def_one_renaming:
f_hash_def_one_renaming.writelines(hash_def_one_renaming)
moses = MosesDecoder(ini_path=os.path.join(ini_path, \
'train.hash_def_one_renaming', 'tuning', 'moses.ini'))
(_moses_ok, translation, _err) = moses.run(f5)
nc = processTranslation(translation, iBuilder_ugly,
scopeAnalyst, lm_path, f5,
output_path, base_name, clear)
if nc:
candidates += nc
hash_def_two_renaming = renameUsingHashDefLine(scopeAnalyst,
iBuilder_ugly,
twoLines=True,
debug=False)
with open(f6, 'w') as f_hash_def_two_renaming:
f_hash_def_two_renaming.writelines(hash_def_two_renaming)
moses = MosesDecoder(ini_path=os.path.join(ini_path, \
'train.hash_def_two_renaming', 'tuning', 'moses.ini'))
(_moses_ok, translation, _err) = moses.run(f6)
nc = processTranslation(translation, iBuilder_ugly,
scopeAnalyst, lm_path, f6,
output_path, base_name, clear)
if nc:
candidates += nc
cleanup(pid)
cleanupRenamed(pid)
return (js_file_path, 'OK', candidates)
except Exception, e:
cleanup(pid)
cleanupRenamed(pid)
return (js_file_path, None, str(e).replace("\n", ""))
def detect_images(image_path, model_path, configfile, output_dir):
# Load model
configs = configparser.ConfigParser()
configs.read(configfile)
try:
input_shape = json.loads(configs['MODEL']['input_shape'])
try:
ratios = json.loads(configs['MODEL']['ratios'])
scales = json.loads(configs['MODEL']['scales'])
except Exception as e:
print(e)
print('USING DEFAULT RATIOS AND SCALES')
ratios = None
scales = None
except:
print("CONFIG FILE DOES NOT HAVE INPUT_SHAPE")
sys.exit()
sess, output_names, input_names = load_onnx_model(model_path)
preprocessor = Preprocessor(input_width=input_shape[2],
input_height=input_shape[1],
mean=np.array([[[0.485, 0.456, 0.406]]]),
std=np.array([[[0.229, 0.224, 0.225]]]))
postprocessor = PostProcessor(ratios=ratios, scales=scales)
# Get labelmap
labels = load_classes_from_configfile(configfile)
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
assert (os.path.abspath(output_dir) != os.path.abspath(image_path))
for img_name in os.listdir(image_path):
image = cv2.imread(os.path.join(image_path, img_name))
if image is None:
continue
image_orig = image.copy()
image, scales = preprocessor(image)
image = np.expand_dims(image, 0)
image = np.transpose(image, (0, 3, 1, 2))
image = image.astype(np.float32)
batch = image.copy()
st = time.time()
regression, classification = sess.run(output_names,
{input_names[0]: batch})
if torch.cuda.is_available():
regression = torch.from_numpy(regression).cuda()
classification = torch.from_numpy(classification).cuda()
else:
regression = torch.from_numpy(regression)
classification = torch.from_numpy(classification)
scores, classification, transformed_anchors = postprocessor(
batch, regression, classification)
print('Elapsed time: {}'.format(time.time() - st))
idxs = np.where(scores.cpu() > 0.35)
for j in range(idxs[0].shape[0]):
bbox = transformed_anchors[idxs[0][j], :]
x1 = int(bbox[0] / scales[0])
y1 = int(bbox[1] / scales[1])
x2 = int(bbox[2] / scales[0])
y2 = int(bbox[3] / scales[1])
label_name = labels[int(classification[idxs[0][j]])]
score = scores[j]
caption = '{} {:.3f}'.format(label_name, score)
draw_caption(image_orig, (x1, y1, x2, y2), caption)
cv2.rectangle(image_orig, (x1, y1), (x2, y2),
color=(0, 0, 255),
thickness=2)
cv2.imwrite(os.path.join(output_dir, img_name), image_orig)
out_file_path):
js_tmp = open(out_file_path, 'w')
js_tmp.write('\n'.join([' '.join([token for (_token_type, token) in line])
for line in lines]).encode('utf8'))
js_tmp.write('\n')
js_tmp.close()
input_file = os.path.abspath(sys.argv[1])
output_file = os.path.abspath(sys.argv[2])
mode = int(sys.argv[3])
prepro = Preprocessor(input_file)
prepro.write_temp_file('tmp.js')
clear = Beautifier()
ok = clear.run('tmp.js',
'tmp.b.js')
lexer = Lexer('tmp.b.js')
iBuilder = IndexBuilder(lexer.tokenList)
scopeAnalyst = ScopeAnalyst(os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'tmp.b.js'))
hash_renaming = renameUsingHashDefLine(scopeAnalyst,
iBuilder,
@author: caseycas
'''
import xmlrpclib
from tools import Preprocessor
from tools.postprocessor import Postprocessor
proxy = xmlrpclib.ServerProxy("http://godeep.cs.ucdavis.edu:8080/RPC2")
mosesParams = {}
input_path = "/Users/caseycas/jsnaughty/data/js_files.sample/98440.js"
# Strip comments, replace literals, etc
try:
prepro = Preprocessor(input_path)
#prepro.write_temp_file(output_path)
except:
print("Preprocessor failed")
quit()
#print(prepro.__str__())
#quit()
#mosesParams["text"] = "var m = [ ]"
mosesParams["text"] = prepro.__str__()
mosesParams["align"] = "true"
mosesParams["report-all-factors"] = "true"
results = proxy.translate(mosesParams)# __request("translate", mosesParams)