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major review

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This commit is contained in:
Lorenzo 2020-12-02 16:33:30 +01:00
parent bee58a107b
commit cea055bb7d
3 changed files with 33 additions and 26 deletions
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48
monstereo/eval/eval_activity.py
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@ -23,12 +23,15 @@ class ActivityEvaluator:
def __init__(self, args): def __init__(self, args):
self.dir_ann = args.dir_ann
assert os.listdir(self.dir_ann), "Annotation directory is empty"
# COLLECTIVE ACTIVITY DATASET (talking) # COLLECTIVE ACTIVITY DATASET (talking)
# ------------------------------------------------------------------------------------------------------------- # -------------------------------------------------------------------------------------------------------------
if args.dataset == 'collective': if args.dataset == 'collective':
self.folders_collective = ['seq02', 'seq14', 'seq12', 'seq13', 'seq11', 'seq36'] self.sequences = ['seq02', 'seq14', 'seq12', 'seq13', 'seq11', 'seq36']
# folders_collective = ['seq02'] # folders_collective = ['seq02']
self.path_collective = ['data/activity/' + fold for fold in self.folders_collective] self.dir_data = 'data/activity/dataset'
self.THRESHOLD_PROB = 0.25 # Concordance for samples self.THRESHOLD_PROB = 0.25 # Concordance for samples
self.THRESHOLD_DIST = 2 # Threshold to check distance of people self.THRESHOLD_DIST = 2 # Threshold to check distance of people
self.RADII = (0.3, 0.5) # expected radii of the o-space self.RADII = (0.3, 0.5) # expected radii of the o-space
@ -39,8 +42,7 @@ class ActivityEvaluator:
# KITTI DATASET (social distancing) # KITTI DATASET (social distancing)
# ------------------------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------------------------
else: else:
self.dir_ann_kitti = '/data/lorenzo-data/annotations/kitti/scale_2_july' self.dir_data = 'data/kitti/gt_activity'
self.dir_gt_kitti = 'data/kitti/gt_activity'
self.dir_kk = os.path.join('data', 'kitti', 'calib') self.dir_kk = os.path.join('data', 'kitti', 'calib')
self.THRESHOLD_PROB = 0.25 # Concordance for samples self.THRESHOLD_PROB = 0.25 # Concordance for samples
self.THRESHOLD_DIST = 2 # Threshold to check distance of people self.THRESHOLD_DIST = 2 # Threshold to check distance of people
@ -62,24 +64,25 @@ class ActivityEvaluator:
def eval_collective(self): def eval_collective(self):
"""Parse Collective Activity Dataset and predict if people are talking or not""" """Parse Collective Activity Dataset and predict if people are talking or not"""
for fold in self.path_collective: for seq in self.sequences:
images = glob.glob(fold + '/*.jpg') images = glob.glob(os.path.join(self.dir_data, 'images', seq + '*.jpg'))
initial_path = os.path.join(fold, 'frame0001.jpg') initial_im = os.path.join(self.dir_data, 'images', seq + '_frame0001.jpg')
with open(initial_path, 'rb') as f: with open(initial_im, 'rb') as f:
image = Image.open(f).convert('RGB') image = Image.open(f).convert('RGB')
im_size = image.size im_size = image.size
assert len(im_size) > 1, "image with frame0001 not available"
for idx, im_path in enumerate(images): for idx, im_path in enumerate(images):
# Collect PifPaf files and calibration # Collect PifPaf files and calibration
basename = os.path.basename(im_path) basename = os.path.basename(im_path)
extension = '.pifpaf.json' extension = '.pifpaf.json'
path_pif = os.path.join(fold, basename + extension) path_pif = os.path.join(self.dir_ann, basename + extension)
annotations = open_annotations(path_pif) annotations = open_annotations(path_pif)
kk, _ = factory_for_gt(im_size, verbose=False) kk, _ = factory_for_gt(im_size, verbose=False)
# Collect corresponding gt files (ys_gt: 1 or 0) # Collect corresponding gt files (ys_gt: 1 or 0)
boxes_gt, ys_gt = parse_gt_collective(fold, path_pif) boxes_gt, ys_gt = parse_gt_collective(self.dir_data, seq, path_pif)
# Run Monoloco # Run Monoloco
dic_out, boxes = self.run_monoloco(annotations, kk, im_size=im_size) dic_out, boxes = self.run_monoloco(annotations, kk, im_size=im_size)
@ -88,17 +91,17 @@ class ActivityEvaluator:
matches = get_iou_matches(boxes, boxes_gt, iou_min=0.3) matches = get_iou_matches(boxes, boxes_gt, iou_min=0.3)
# Estimate activity # Estimate activity
categories = [os.path.basename(fold)] * len(boxes_gt) categories = [seq] * len(boxes_gt) # for compatibility with KITTI evaluation
self.estimate_activity(dic_out, matches, ys_gt, categories=categories) self.estimate_activity(dic_out, matches, ys_gt, categories=categories)
# Print Results # Print Results
cout_results(self.cnt, self.all_gt, self.all_pred, categories=self.folders_collective) cout_results(self.cnt, self.all_gt, self.all_pred, categories=self.sequences)
def eval_kitti(self): def eval_kitti(self):
"""Parse KITTI Dataset and predict if people are talking or not""" """Parse KITTI Dataset and predict if people are talking or not"""
from ..utils import factory_file from ..utils import factory_file
files = glob.glob(self.dir_gt_kitti + '/*.txt') files = glob.glob(self.dir_data + '/*.txt')
# files = [self.dir_gt_kitti + '/001782.txt'] # files = [self.dir_gt_kitti + '/001782.txt']
assert files, "Empty directory" assert files, "Empty directory"
@ -107,10 +110,10 @@ class ActivityEvaluator:
# Collect PifPaf files and calibration # Collect PifPaf files and calibration
basename, _ = os.path.splitext(os.path.basename(file)) basename, _ = os.path.splitext(os.path.basename(file))
path_calib = os.path.join(self.dir_kk, basename + '.txt') path_calib = os.path.join(self.dir_kk, basename + '.txt')
annotations, kk, tt = factory_file(path_calib, self.dir_ann_kitti, basename) annotations, kk, tt = factory_file(path_calib, self.dir_ann, basename)
# Collect corresponding gt files (ys_gt: 1 or 0) # Collect corresponding gt files (ys_gt: 1 or 0)
path_gt = os.path.join(self.dir_gt_kitti, basename + '.txt') path_gt = os.path.join(self.dir_data, basename + '.txt')
boxes_gt, ys_gt, difficulties = parse_gt_kitti(path_gt) boxes_gt, ys_gt, difficulties = parse_gt_kitti(path_gt)
# Run Monoloco # Run Monoloco
@ -131,7 +134,6 @@ class ActivityEvaluator:
angles = dic_out['angles'] angles = dic_out['angles']
dds = dic_out['dds_pred'] dds = dic_out['dds_pred']
stds = dic_out['stds_ale'] stds = dic_out['stds_ale']
confs = dic_out['confs']
xz_centers = [[xx[0], xx[2]] for xx in dic_out['xyz_pred']] xz_centers = [[xx[0], xx[2]] for xx in dic_out['xyz_pred']]
# Count gt statistics # Count gt statistics
@ -141,7 +143,7 @@ class ActivityEvaluator:
for i_m, (idx, idx_gt) in enumerate(matches): for i_m, (idx, idx_gt) in enumerate(matches):
# Select keys to update resultd for Collective or KITTI # Select keys to update results for Collective or KITTI
keys = ('all', categories[idx_gt]) keys = ('all', categories[idx_gt])
# Run social interactions rule # Run social interactions rule
@ -166,10 +168,12 @@ class ActivityEvaluator:
return dic_out, boxes return dic_out, boxes
def parse_gt_collective(fold, path_pif): def parse_gt_collective(dir_data, seq, path_pif):
"""Parse both gt and binary label (1/0) for talking or not""" """Parse both gt and binary label (1/0) for talking or not"""
with open(os.path.join(fold, "annotations.txt"), "r") as ff: path = os.path.join(dir_data, 'annotations', seq + '_annotations.txt')
with open(path, "r") as ff:
reader = csv.reader(ff, delimiter='\t') reader = csv.reader(ff, delimiter='\t')
dic_frames = defaultdict(lambda: defaultdict(list)) dic_frames = defaultdict(lambda: defaultdict(list))
for idx, line in enumerate(reader): for idx, line in enumerate(reader):
@ -244,8 +248,8 @@ def convert_category(cat):
def extract_frame_number(path): def extract_frame_number(path):
"""extract frame number from path""" """extract frame number from path"""
name = os.path.basename(path) name = os.path.basename(path)
if name[5] == '0': if name[11] == '0':
frame = name[6:9] frame = name[12:15]
else: else:
frame = name[5:9] frame = name[11:15]
return frame return frame

4
monstereo/eval/generate_kitti.py
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@ -26,6 +26,9 @@ class GenerateKitti:
def __init__(self, model, dir_ann, p_dropout=0.2, n_dropout=0, hidden_size=1024): def __init__(self, model, dir_ann, p_dropout=0.2, n_dropout=0, hidden_size=1024):
self.dir_ann = dir_ann
assert os.listdir(self.dir_ann), "Annotation directory is empty"
# Load monoloco # Load monoloco
use_cuda = torch.cuda.is_available() use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu") device = torch.device("cuda" if use_cuda else "cpu")
@ -47,7 +50,6 @@ class GenerateKitti:
# model_mono = 'data/models/monoloco-190719-0923.pkl' # NuScenes # model_mono = 'data/models/monoloco-190719-0923.pkl' # NuScenes
self.monoloco = Loco(model=model_mono, net='monoloco', device=device, n_dropout=n_dropout, self.monoloco = Loco(model=model_mono, net='monoloco', device=device, n_dropout=n_dropout,
p_dropout=p_dropout, linear_size=256) p_dropout=p_dropout, linear_size=256)
self.dir_ann = dir_ann
# Extract list of pifpaf files in validation images # Extract list of pifpaf files in validation images
self.dir_gt = os.path.join('data', 'kitti', 'gt') self.dir_gt = os.path.join('data', 'kitti', 'gt')

7
monstereo/train/trainer.py
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@ -36,7 +36,7 @@ class Trainer:
lambdas = (1, 1, 1, 1, 1, 1, 1, 1) lambdas = (1, 1, 1, 1, 1, 1, 1, 1)
def __init__(self, joints, epochs=100, bs=256, dropout=0.2, lr=0.002, def __init__(self, joints, epochs=100, bs=256, dropout=0.2, lr=0.002,
sched_step=20, sched_gamma=1, hidden_size=256, n_stage=3, r_seed=1, n_samples=100, sched_step=20, sched_gamma=1, hidden_size=256, n_stage=3, r_seed=0, n_samples=100,
monocular=False, save=False, print_loss=True): monocular=False, save=False, print_loss=True):
""" """
Initialize directories, load the data and parameters for the training Initialize directories, load the data and parameters for the training
@ -131,6 +131,7 @@ class Trainer:
# Optimizer and scheduler # Optimizer and scheduler
all_params = chain(self.model.parameters(), self.mt_loss.parameters()) all_params = chain(self.model.parameters(), self.mt_loss.parameters())
self.optimizer = torch.optim.Adam(params=all_params, lr=lr) self.optimizer = torch.optim.Adam(params=all_params, lr=lr)
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, 'min')
self.scheduler = lr_scheduler.StepLR(self.optimizer, step_size=self.sched_step, gamma=self.sched_gamma) self.scheduler = lr_scheduler.StepLR(self.optimizer, step_size=self.sched_step, gamma=self.sched_gamma)
def train(self): def train(self):
@ -155,11 +156,11 @@ class Trainer:
labels = labels.to(self.device) labels = labels.to(self.device)
with torch.set_grad_enabled(phase == 'train'): with torch.set_grad_enabled(phase == 'train'):
if phase == 'train': if phase == 'train':
self.optimizer.zero_grad()
outputs = self.model(inputs) outputs = self.model(inputs)
loss, loss_values = self.mt_loss(outputs, labels, phase=phase) loss, loss_values = self.mt_loss(outputs, labels, phase=phase)
self.optimizer.zero_grad()
loss.backward() loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 2) torch.nn.utils.clip_grad_norm_(self.model.parameters(), 3)
self.optimizer.step() self.optimizer.step()
self.scheduler.step() self.scheduler.step()