def select_optimal_rho(item, rhos, input_file, output_file, input_file_n,
cubes_d, points_numbers_d, cube_positions_d, scale,
cube_size, res):
for i, rho in enumerate(rhos):
print('===== select rho =====')
postprocess(output_file, cubes_d, points_numbers_d, cube_positions_d,
scale, cube_size, rho)
results = pc_error(input_file,
output_file,
input_file_n,
res,
show=False)
PSNR = float(results[item])
print('===== results: ', i, rho, item, PSNR)
if i == 0:
MAX_PSNR = 0
optimal_rho = rho
else:
MAX_PSNR = max(PSNR, MAX_PSNR)
if PSNR < MAX_PSNR:
break
else:
optimal_rho = rho
return optimal_rho
def inference(self, img):
img_info = {"id": 0}
if isinstance(img, str):
img_info["file_name"] = os.path.basename(img)
img = cv2.imread(img)
if img is None:
raise ValueError("test image path is invalid!")
else:
img_info["file_name"] = None
height, width = img.shape[:2]
img_info["height"] = height
img_info["width"] = width
img_info["raw_img"] = img
img, ratio = preprocess(img, self.test_size, self.rgb_means, self.std)
img_info["ratio"] = ratio
img = F.expand_dims(mge.tensor(img), 0)
t0 = time.time()
outputs = self.model(img)
outputs = postprocess(outputs, self.num_classes, self.confthre,
self.nmsthre)
logger.info("Infer time: {:.4f}s".format(time.time() - t0))
return outputs, img_info
def e2e(s):
# make the input space-delimited in prefix notation
ir = postprocess(infix_to_prefix(preprocess(s)))
# split on space and turn into nested tuples
tup = tuple_for_polish_expression(ir.split(' '))
# convert to MRS and return
return prettyUMRSForTuple(tup)
def _main_():
args = argparser.parse_args()
config_path = args.config
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
weights_path = args.weights_path
sm_model = SMModel(config['model'])
sm_model.model.summary()
sm_model.model.load_weights(weights_path)
test_generator = DataGenerator(config=config['test'],
preprocessing=sm_model.preprocessing,
n_class=sm_model.n_class,
split='test')
encoded_pixels = []
image_id_class_id = []
for X, filenames in tqdm(list(test_generator)):
preds = sm_model.model.predict_on_batch(X)
if config['test']['tta']:
for flip_type in ['ud', 'lr', 'udlr']:
X_temp = flip(X.copy(), flip_type)
pred_temp = sm_model.model.predict_on_batch(X_temp)
preds += flip(pred_temp, flip_type)
preds /= 4
preds = postprocess(preds, config['postprocess'], True)
for i in range(len(preds)):
for j in range(4):
encoded_pixels.append(run_length_encode(preds[i, :, :, j]))
image_id_class_id.append(filenames[i] + '_{}'.format(j + 1))
df = pd.DataFrame(data=encoded_pixels,
index=image_id_class_id,
columns=['EncodedPixels'])
df.index.name = 'ImageId_ClassId'
df.to_csv('submission.csv')
def _main_():
args = argparser.parse_args()
config_path = args.config
with open(config_path) as config_buffer:
config = json.loads(config_buffer.read())
sm_model = SMModel(config['model'])
oof_preds = []
oof_true_masks = []
for i in range(5):
config['train']['fold'] = i
generator = DataGenerator(config=config['train'],
preprocessing=sm_model.preprocessing,
n_class=sm_model.n_class,
split='val',
full_size_mask=True)
weithts_path = os.path.join(config['train']['save_model_folder'],
'val_best_fold_{}_weights.h5'.format(i))
sm_model.model.load_weights(weithts_path)
print('Fold {} eval begin.'.format(i))
for X, y in tqdm(list(generator)):
y_preds = sm_model.model.predict(X)
y_preds = postprocess(y_preds, config['postprocess'], True)
oof_preds.append(y_preds)
y = y[:, :, :, :4]
oof_true_masks.append(y)
oof_preds = np.concatenate(oof_preds)
oof_true_masks = np.concatenate(oof_true_masks)
cv_dice_coef = dice_coef_score(oof_true_masks, oof_preds)
print('CV Dice Coef Score: {}'.format(cv_dice_coef))
def save_prediction(pred, input_file, tile, path, scale=False):
fname = input_file.split('/')[-1]
sample = fname.split('_')[0]
path = os.path.join(path, sample)
os.makedirs(path, exist_ok=True)
shape = util.shape(input_file)
header = util.header(input_file)
vol = process.postprocess(pred, shape, resize=True, tile=tile)
util.save_vol(vol, os.path.join(path, fname), header, scale)
print(fname, flush=True)
def on_epoch_end(self, epoch, logs={}):
dice_coef = 0
for X, y_true in list(self.generator):
y_pred = self.model.predict(X)
y_pred = postprocess(y_pred, self.postprocess_config)
y_true = y_true[:, :, :, :4]
inter = (y_true * y_pred).sum(1).sum(1)
union = y_true.sum(1).sum(1) + y_pred.sum(1).sum(1)
dice_coef_batch = (2*inter + self.eps) / (union + self.eps)
dice_coef += dice_coef_batch.sum()
dice_coef /= (self.generator.n_samples * 4)
logs.update({'dice_coef_score': dice_coef})
print('Epoch {} Validation Dice Coefficent Score: {}.\n'.format(epoch+1, dice_coef))
def save_predictions(preds, generator, path, scale=False):
os.makedirs(path, exist_ok=True)
if generator.tile_inputs:
preds = np.reshape(preds, (-1, 8) + preds.shape[1:])
for i in range(preds.shape[0]):
input_file = generator.input_files[i]
fname = input_file.split('/')[-1]
header = util.header(input_file)
shape = util.shape(input_file)
volume = process.postprocess(preds[i],
shape,
resize=True,
tile=generator.tile_inputs)
util.save_vol(volume, os.path.join(path, fname), header, scale)
def test():
initial_time = datetime.now()
category = 'restaurants_bars'
model_to_test = ''
if request.args.get('model'):
model_to_test = 'camembert'
print('\n', '#'*50)
print(f' Start Analyse on {category} '.center(50, '#'))
print('#'*50, '\n')
init_time = datetime.now()
print(' Start scraping '.center(30, '#'))
time_elapsed = datetime.now() - init_time
print(f'Scraping time : {time_elapsed}')
init_time = datetime.now()
print(' Start preprocess '.center(30, '#'))
time_elapsed = datetime.now() - init_time
print(f'Preprocess time : {time_elapsed}')
# 4. Predict sentiment and add it to dataframe
init_time = datetime.now()
print(' Start prediction '.center(30, '#'))
if model_to_test == 'camembert':
df = pd.read_csv(f'predict_{category}_cam.csv')
else:
df = pd.read_csv(f'predict_{category}.csv')
refs = [df.site.unique(), df.site.unique()]
time_elapsed = datetime.now() - init_time
print(f'Prediction time : {time_elapsed}')
# 5. Apply postprocess to transform data into json
init_time = datetime.now()
print(' Start postprocess '.center(30, '#'))
json_review = process.postprocess(df, refs)
time_elapsed = datetime.now() - init_time
print(f'Postprocess time : {time_elapsed}')
time_elapsed = datetime.now() - initial_time
print(f'Total time elapsed : {time_elapsed}')
return json_review
def eval(input_file, rootdir, resolution, mode, cube_size, modelname,
fixed_thres, postfix):
# model = 'model_voxception'
model = importlib.import_module(modelname)
filename = os.path.split(input_file)[-1][:-4]
output_file = filename + '_rec_' + postfix + '.ply'
input_file_n = input_file
csv_rootdir = os.path.join(rootdir, "csv")
if not os.path.exists(csv_rootdir):
os.makedirs(csv_rootdir)
cfg_rootdir = os.path.join(rootdir, "cfg")
if not os.path.exists(cfg_rootdir):
os.makedirs(cfg_rootdir)
# default config
config, config_file = set_default_config(input_file, cfg_rootdir,
resolution, mode, cube_size,
modelname)
cube_size = config.getint('DEFAULT', 'cube_size')
min_num = config.getint('DEFAULT', 'min_num')
res = config.getint('DEFAULT', 'resolution')
print('cube size:', cube_size, 'min num:', min_num, 'res:', res)
for index, rate in enumerate(config.sections()):
scale = float(config.get(rate, 'scale'))
ckpt_dir = str(config.get(rate, 'ckpt_dir'))
print('====================', 'config:', rate, 'scale:', scale,
'ckpt_dir:', ckpt_dir)
if mode == "factorized":
cubes_d, cube_positions, points_numbers, N, bpps = test_factorized(
input_file, model, ckpt_dir, scale, cube_size, min_num,
postfix)
elif mode == "hyper":
cubes_d, cube_positions, points_numbers, N, bpps = test_hyper(
input_file, model, ckpt_dir, scale, cube_size, min_num,
postfix)
cubes_d = cubes_d.numpy()
print("bpp:", bpps[0])
# select rho for optimal d1/d2 metrics.
if fixed_thres == None:
rho_d1, rho_d2 = cfg_post_process(config, config_file, rate,
input_file, output_file,
input_file_n, cubes_d,
points_numbers, cube_positions,
scale, cube_size, res)
else:
rho_d1, rho_d2 = 1.0, 1.0
# metrics.
rho = 1.0
postprocess(output_file, cubes_d, points_numbers, cube_positions,
scale, cube_size, rho, fixed_thres)
results = pc_error(input_file,
output_file,
input_file_n,
res,
show=False)
rho = rho_d1
postprocess(output_file, cubes_d, points_numbers, cube_positions,
scale, cube_size, rho, fixed_thres)
results_d1 = pc_error(input_file,
output_file,
input_file_n,
res,
show=False)
rho = rho_d2
postprocess(output_file, cubes_d, points_numbers, cube_positions,
scale, cube_size, rho, fixed_thres)
results_d2 = pc_error(input_file,
output_file,
input_file_n,
res,
show=False)
results = collect_results(results, results_d1, results_d2, bpps, N,
scale, rho_d1, rho_d2)
if index == 0:
all_results = results.copy(deep=True)
else:
all_results = all_results.append(results, ignore_index=True)
# write to csv
print(all_results)
if not os.path.exists(csv_rootdir):
os.makedirs(csv_rootdir)
csv_name = os.path.join(csv_rootdir, filename + '.csv')
all_results.to_csv(csv_name, index=False)
# plot
plot_results(all_results, filename, csv_rootdir)
return all_results
def oof_eval(oof_preds, oof_true_masks, config):
oof_preds = postprocess(oof_preds, config['postprocess'], True)
cv_dice_coef = dice_coef_score(oof_true_masks, oof_preds)
print('CV Dice Coef Score: {}'.format(cv_dice_coef))
return cv_dice_coef
def eval(input_file, rootdir, cfgdir, res, mode, cube_size, modelname, fixed_thres, postfix):
# model = 'model_voxception'
model = importlib.import_module(modelname)
filename = os.path.split(input_file)[-1][:-4]
input_file_n = input_file
csv_rootdir = rootdir
if not os.path.exists(csv_rootdir):
os.makedirs(csv_rootdir)
csv_name = os.path.join(csv_rootdir, filename + '.csv')
config = configparser.ConfigParser()
config.read(cfgdir)
cube_size = config.getint('DEFAULT', 'cube_size')
min_num = config.getint('DEFAULT', 'min_num')
print('cube size:', cube_size, 'min num:', min_num, 'res:', res)
for index, rate in enumerate(config.sections()):
scale = float(config.get(rate, 'scale'))
ckpt_dir = str(config.get(rate, 'ckpt_dir'))
rho_d1 = float(config.get(rate, 'rho_d1'))
rho_d2 = float(config.get(rate, 'rho_d2'))
print('='*80, '\n', 'config:', rate, 'scale:', scale, 'ckpt_dir:', ckpt_dir, 'rho (d1):', rho_d1, 'rho_d2:', rho_d2)
if mode=="factorized":
cubes_d, cube_positions, points_numbers, N, bpps = test_factorized(input_file, model, ckpt_dir, scale, cube_size, min_num, postfix)
elif mode == "hyper":
cubes_d, cube_positions, points_numbers, N, bpps = test_hyper(input_file, model, ckpt_dir, scale, cube_size, min_num, postfix)
cubes_d = cubes_d.numpy()
print("bpp:",bpps[0])
# metrics.
rho = 1.0
output_file = filename + '_rec_' + str(rate) + '_' + 'rho' + str(round(rho*100)) + postfix + '.ply'
postprocess(output_file, cubes_d, points_numbers, cube_positions, scale, cube_size, rho, fixed_thres)
results = pc_error(input_file, output_file, input_file_n, res, show=False)
rho = rho_d1
output_file = filename + '_rec_' + str(rate) + '_' + 'rho' + str(round(rho*100)) + postfix + '.ply'
postprocess(output_file, cubes_d, points_numbers, cube_positions, scale, cube_size, rho, fixed_thres)
results_d1 = pc_error(input_file, output_file, input_file_n, res, show=False)
rho = rho_d2
output_file = filename + '_rec_' + str(rate) + '_' + 'rho' + str(round(rho*100)) + postfix + '.ply'
postprocess(output_file, cubes_d, points_numbers, cube_positions, scale, cube_size, rho, fixed_thres)
results_d2 = pc_error(input_file, output_file, input_file_n, res, show=False)
results = collect_results(results, results_d1, results_d2, bpps, N, scale, rho_d1, rho_d2)
if index == 0:
all_results = results.copy(deep=True)
else:
all_results = all_results.append(results, ignore_index=True)
all_results.to_csv(csv_name, index=False)
print(all_results)
plot_results(all_results, filename, csv_rootdir)
return all_results
def graphs():
initial_time = datetime.now()
# 1. Get infos for scraping
# Mandatory argument : Category
category = request.args.get('category')
# Optional argument
# number of site to scrape per category, default 5 (0 for max)
if request.args.get('num_of_site'):
num_of_site = int(request.args.get('num_of_site'))
else:
num_of_site = 5
# number of page to scrape per site, default 2 (0 for max)
if request.args.get('num_page'):
num_page = int(request.args.get('num_page'))
else:
num_page = 2
# city where the scraping is desired (better with department number)
if request.args.get('location'):
location = request.args.get('location')
else:
location = 'no city'
# model to use for scraping (one option 'camembert', else default model)
model_to_test = ''
if request.args.get('model'):
model_to_test = 'camembert'
print('\n', '#'*50)
print(f' Start Analyse on {category} '.center(50, '#'))
print('#'*50, '\n')
# 2. Scrape trustpilot to get dataframe
init_time = datetime.now()
print(' Start scraping '.center(30, '#'))
refs, df = scraping.scrape(category, location, num_of_site, num_page)
time_elapsed = datetime.now() - init_time
print(f'Scraping time : {time_elapsed}')
if len(df)>0:
# 3. Preprocess dataframe before prediction
init_time = datetime.now()
print(' Start preprocess '.center(30, '#'))
df = process.preprocess_df(df)
time_elapsed = datetime.now() - init_time
print(f'Preprocess time : {time_elapsed}')
# 4. Predict sentiment and add it to dataframe
init_time = datetime.now()
print(' Start prediction '.center(30, '#'))
if model_to_test == 'camembert':
df = model.predict_camembert(df)
else:
df = model.predict(df)
time_elapsed = datetime.now() - init_time
print(f'Prediction time : {time_elapsed}')
# 5. Apply postprocess to transform data into json
init_time = datetime.now()
print(' Start postprocess '.center(30, '#'))
json_review = process.postprocess(df, refs)
time_elapsed = datetime.now() - init_time
print(f'Postprocess time : {time_elapsed}')
else:
print("No data found")
json_review = "<h1>Pas de données</h1>"
time_elapsed = datetime.now() - initial_time
print(f'Total time elapsed : {time_elapsed}')
return json_review
def predict():
input_data = request.get_json(force=True)
transformed_input_data = preprocess(input_data)
prediction = model.predict(transformed_input_data)
transformed_prediction = postprocess(prediction)
return jsonify({"prediction": transformed_prediction})
def plot_interface(pltid):
user = root.authorized()
app = request.query.app
cid = request.query.cid
jid = request.query.jid
params = dict()
if not cid:
params[
'err'] = "No case id specified. First select a case id from the list of jobs."
return template('error', params)
if re.search("/", cid):
(owner, c) = cid.split("/")
else:
owner = user
c = cid
shared = jobs(cid=c).shared
# only allow admin to see other user's cases that have not been shared
if owner != user and shared != "True" and user != "admin":
return template('error', err="access forbidden")
inputs, _, _ = root.myapps[app].read_params(owner, c)
sim_dir = os.path.join(user_dir, owner, app, c)
# use pltid of 0 to trigger finding the first pltid for the current app
if int(pltid) == 0:
query = (apps.id == plots.appid) & (apps.name == app)
result = db(query).select().first()
if result: pltid = result['plots']['id']
p = Plot()
# get the data for the pltid given
try:
result = db(plots.id == pltid).select().first()
plottype = result['ptype']
plot_title = result['title']
except:
exc_type, exc_value, exc_traceback = sys.exc_info()
print traceback.print_exception(exc_type, exc_value, exc_traceback)
redirect('/plots/edit?app=' + app + '&cid=' + cid)
# if plot not in DB return error
if plottype is None:
params = {'cid': cid, 'app': app, 'user': user}
params['err'] = "Sorry! This app does not support plotting capability"
return template('error', params)
# determine which view template to use
if plottype == 'flot-cat':
tfn = 'plots/flot-cat'
elif plottype == 'flot-scatter':
tfn = 'plots/flot-scatter'
elif plottype == 'flot-scatter-animated':
tfn = 'plots/flot-scatter-animated' # for backwards compatability
elif plottype == 'flot-line':
tfn = 'plots/flot-scatter'
elif plottype == 'plotly-hist':
tfn = 'plots/plotly-hist'
elif plottype == 'mpl-line' or plottype == 'mpl-bar':
redirect('/mpl/' + pltid + '?app=' + app + '&cid=' + cid)
elif plottype == 'handson':
tfn = 'plots/handson'
elif plottype == 'flot-3d':
return plot_flot_3d(result, cid, app, sim_dir, owner, user, plot_title,
pltid)
else:
return template("error", err="plot type not supported: " + plottype)
if result['options']:
options = replace_tags(result['options'], inputs)
else:
options = ''
# get list of all plots for this app
query = (apps.id == plots.appid) & (apps.name == app)
list_of_plots = db(query).select()
# extract data from files
data = []
ticks = []
plotpath = ''
result = db(datasource.pltid == pltid).select()
datadef = ""
for r in result:
plotfn = r['filename']
# in addition to supporting input params, also support case id
if "cid" not in inputs: inputs["cid"] = c
# replace <cid>.dat with xyz123.dat
plotfn = replace_tags(plotfn, inputs)
plotpath = os.path.join(sim_dir, plotfn)
# handle CSV data
_, file_extension = os.path.splitext(plotfn)
if file_extension == '.csv':
data = p.get_csv_data(plotpath)
stats = ''
# handle X, Y columnar data
else:
cols = r['cols']
line_range = r['line_range']
try:
datadef += r['data_def'] + ", "
except:
exc_type, exc_value, exc_traceback = sys.exc_info()
print traceback.print_exception(exc_type, exc_value,
exc_traceback)
datadef = ""
if cols.find(":") > 0: # two columns
num_fields = 2
(col1str, col2str) = cols.split(":")
col1 = int(col1str)
col2 = int(col2str)
else: # single column
num_fields = 1
col1 = int(cols)
# do some postprocessing
if line_range is not None:
# to prevent breaking current spc apps, still support
# expressions like 1:1000, but in the future this should
# be changed to a range 1-1000. Therefore, using : is deprecated
# and will be removed in the future.
(line1str, line2str) = re.split("[-:]", line_range)
line1 = int(line1str)
## there is a problem with the following statement
## shows up in mendel app
# if root.myapps[app].postprocess > 0:
# dat = process.postprocess(plotpath, line1, line2)
# else:
try: # if line2 is specified
line2 = int(line2str)
dat = p.get_data(plotpath, col1, col2, line1, line2)
except: # if line2 not specified
exc_type, exc_value, exc_traceback = sys.exc_info()
print traceback.print_exception(exc_type, exc_value,
exc_traceback)
if num_fields == 2:
dat = p.get_data(plotpath, col1, col2, line1)
else: # single column of data
dat = p.get_data(plotpath, col1)
# remove this app-specific code in future
if app == "fpg":
import process
dat = process.postprocess(plotpath, line1, line2)
else:
dat = p.get_data(plotpath, col1, col2)
if dat == -1:
stats = "ERROR: Could not read data file"
elif dat == -2:
stats = "ERROR: file exists, but problem parsing data. Are column and line ranges setup properly? Is all the data there?"
else:
stats = compute_stats(plotpath)
# [[1,2,3]] >>> [1,2,3]
# clean data
#dat = [d.replace('?', '0') for d in dat]
data.append(dat)
if num_fields == 1: data = data[0]
if plottype == 'flot-cat':
ticks = p.get_ticks(plotpath, col1, col2)
desc = jobs(cid=c).description
params = {
'cid': cid,
'pltid': pltid,
'data': data,
'app': app,
'user': user,
'owner': owner,
'ticks': ticks,
'plot_title': plot_title,
'plotpath': plotpath,
'rows': list_of_plots,
'options': options,
'datadef': datadef,
'stats': stats,
'description': desc
}
if jid: params['jid'] = jid
return template(tfn, params)
strings, min_v, max_v, shape = compress_factorized(cubes, model, args.ckpt_dir)
if not args.output:
args.output = os.path.split(args.input)[-1][:-4]
rootdir = './compressed'
else:
rootdir, args.output = os.path.split(args.output)
bytes_strings, bytes_pointnums, bytes_cubepos = write_binary_files_factorized(
args.output, strings.numpy(), points_numbers, cube_positions, min_v.numpy(), max_v.numpy(), shape.numpy(), rootdir=rootdir)
elif args.command == "decompress":
rootdir, filename = os.path.split(args.input)
if not args.output:
args.output = filename + "_rec.ply"
strings_d, points_numbers_d, cube_positions_d, min_v_d, max_v_d, shape_d = read_binary_files_factorized(filename, rootdir)
cubes_d = decompress_factorized(strings_d, min_v_d, max_v_d, shape_d, model, args.ckpt_dir)
postprocess(args.output, cubes_d.numpy(), points_numbers_d, cube_positions_d, args.scale, args.cube_size, args.rho)
if args.mode == "hyper":
if args.command == "compress":
if not args.output:
args.output = os.path.split(args.input)[-1][:-4]
rootdir = './compressed'
else:
rootdir, args.output = os.path.split(args.output)
cubes, cube_positions, points_numbers = preprocess(args.input, args.scale, args.cube_size, args.min_num)
y_strings, y_min_vs, y_max_vs, y_shape, z_strings, z_min_v, z_max_v, z_shape = compress_hyper(cubes, model, args.ckpt_dir)
bytes_strings, bytes_strings_head, bytes_strings_hyper, bytes_pointnums, bytes_cubepos = write_binary_files_hyper(
args.output, y_strings.numpy(), z_strings.numpy(), points_numbers, cube_positions,
