def test_ImportUFONoSideEffectLO(self):
"""Checks that there are no side effects of the import of the LO UFO sm"""
ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('sm'),False)
original_all_particles = copy.copy(ufo_model.all_particles)
original_all_vertices = copy.copy(ufo_model.all_vertices)
original_all_couplings = copy.copy(ufo_model.all_couplings)
original_all_lorentz = copy.copy(ufo_model.all_lorentz)
original_all_parameters = copy.copy(ufo_model.all_parameters)
original_all_orders = copy.copy(ufo_model.all_orders)
original_all_functions = copy.copy(ufo_model.all_functions)
ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)
model = ufo2mg5_converter.load_model()
# It is important to run import_ufo.OrganizeModelExpression(ufo_model).main()
# since this reverts some of the changes done in load_model()
# There *is* side effects in-between, namely the expression of the CTcouplings
# which contained CTparameters have been substituted to dictionaries.
parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main()
self.assertEqual(original_all_particles,ufo_model.all_particles)
self.assertEqual(original_all_vertices,ufo_model.all_vertices)
self.assertEqual(original_all_couplings,ufo_model.all_couplings)
self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)
self.assertEqual(original_all_parameters,ufo_model.all_parameters)
self.assertEqual(original_all_orders,ufo_model.all_orders)
self.assertEqual(original_all_functions,ufo_model.all_functions)
def test_ImportUFONoSideEffectNLO(self):
"""Checks that there are no side effects of the import of the NLO UFO sm"""
ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('loop_sm'),False)
original_all_particles = copy.copy(ufo_model.all_particles)
original_all_vertices = copy.copy(ufo_model.all_vertices)
original_all_couplings = copy.copy(ufo_model.all_couplings)
original_all_lorentz = copy.copy(ufo_model.all_lorentz)
original_all_parameters = copy.copy(ufo_model.all_parameters)
original_all_orders = copy.copy(ufo_model.all_orders)
original_all_functions = copy.copy(ufo_model.all_functions)
original_all_CTvertices = copy.copy(ufo_model.all_CTvertices)
original_all_CTparameters = copy.copy(ufo_model.all_CTparameters)
ufo2mg5_converter = import_ufo.UFOMG5Converter(ufo_model)
model = ufo2mg5_converter.load_model()
parameters, couplings = import_ufo.OrganizeModelExpression(ufo_model).main()
self.assertEqual(original_all_particles,ufo_model.all_particles)
self.assertEqual(original_all_vertices,ufo_model.all_vertices)
# self.assertEqual(original_all_couplings,ufo_model.all_couplings)
self.assertEqual(original_all_lorentz,ufo_model.all_lorentz)
self.assertEqual(original_all_parameters,ufo_model.all_parameters)
self.assertEqual(original_all_orders,ufo_model.all_orders)
self.assertEqual(original_all_functions,ufo_model.all_functions)
self.assertEqual(original_all_CTvertices,ufo_model.all_CTvertices)
self.assertEqual(original_all_CTparameters,ufo_model.all_CTparameters)
def __init__(self, modelpath, addon='__1'):
"""load the model from a valid UFO directory (otherwise keep everything
as empty."""
self.modelpath = modelpath
model = ufomodels.load_model(modelpath)
# Check the validity of the model. Too old UFO (before UFO 1.0)
if not hasattr(model, 'all_orders'):
raise USRMODERROR, 'Base Model doesn\'t follows UFO convention (no couplings_order information)\n' +\
'MG5 is able to load such model but NOT to the add model feature.'
if isinstance(model.all_particles[0].mass, basestring):
raise USRMODERROR, 'Base Model doesn\'t follows UFO convention (Mass/Width of particles are string name, not object)\n' +\
'MG5 is able to load such model but NOT to the add model feature.'
self.particles = model.all_particles
if any(hasattr(p, 'loop_particles') for p in self.particles):
raise USRMODERROR, 'Base Model doesn\'t follows UFO convention '
self.vertices = model.all_vertices
self.couplings = model.all_couplings
self.lorentz = model.all_lorentz
self.parameters = model.all_parameters
self.Parameter = self.parameters[0].__class__
self.orders = model.all_orders
self.functions = model.all_functions
self.new_external = []
# UFO optional file
if hasattr(model, 'all_propagators'):
self.propagators = model.all_propagators
else:
self.propagators = []
# UFO NLO extension
if hasattr(model, 'all_CTvertices'):
self.CTvertices = model.all_CTvertices
else:
self.CTvertices = []
#translate for how to write the python file
if 'self.expr = expression' in open(pjoin(self.modelpath, 'object_library.py')).read():
self.translate = {'expr': 'expression'}
else:
self.translate = {}
#translate for the expression of the UFO model
self.old_new = {}
self.addon = addon
# particle id -> object
self.particle_dict = {}
for particle in self.particles:
self.particle_dict[particle.pdg_code] = particle
# path to all model that should be used for the Fortran file.
self.all_path = [self.modelpath]
def test_tautologies(train, dev, glove, paths = ['aug0','aug005','aug015','aug05']):
testsets = [dev, generate_tautologies(dev), generate_contradictions(dev), generate_neutral(dev)]
names = ['dev' , 'ent', 'contr' ,'neu']
for path in paths:
print path
model_path = misc.best_model_path('models/' + path)
model = models.load_model(model_path)
accs = [models.test_model(model, dataset, glove) for dataset in testsets]
for name, dataset, acc in zip (names, testsets, accs):
print name, acc, len(dataset)
def test_ImportUFOcheckgoldstone(self):
"""Check goldstone is correct in NLO UFO"""
ufo_model = ufomodels.load_model(import_ufo.find_ufo_path('loop_qcd_qed_sm'),False)
original_all_particles = copy.copy(ufo_model.all_particles)
for part in original_all_particles:
if part.name.lower() in ['g0','g+']:
if hasattr(part,"GoldstoneBoson"):
self.assertEqual(part.GoldstoneBoson,True)
elif hasattr(part,"goldstoneboson"):
self.assertEqual(part.goldstoneboson,True)
else:
raise import_ufo.UFOImportError, "Goldstone %s has no attribute of goldstnoneboson in loop_qcd_qed_sm"%part.name
def test_all_models(dev, test, glove, folder = 'models/'):
files = os.listdir(folder)
extless = set([file.split('.')[0] for file in files if os.path.isfile(file)]) - set([''])
epoch_less = set([file.split('~')[0] for file in extless])
for model_short in epoch_less:
if model_short in extless:
modelname = model_short
else:
same_exper = [m for m in extless if m.startswith(model_short)]
epoch_max = max([int(file.split('~')[1]) for file in same_exper])
modelname = model_short + '~' + str(epoch_max)
print modelname
model = models.load_model(folder + modelname)
dev_acc = models.test_model(model, dev, glove)
test_acc = models.test_model(model, test, glove)
print "Dev:", '{0:.2f}'.format(dev_acc * 100), "Test_acc:", '{0:.2f}'.format(test_acc * 100)
print
def add_model(self, model=None, path=None, identify_particles=None):
"""add another model in the current one"""
self.new_external = []
if path:
model = ufomodels.load_model(path)
if not model:
raise USRMODERROR, 'Need a valid Model'
else:
path = model.__path__[0]
# Check the validity of the model. Too old UFO (before UFO 1.0)
if not hasattr(model, 'all_orders'):
raise USRMODERROR, 'Add-on Model doesn\'t follows UFO convention (no couplings_order information)\n' +\
'MG5 is able to load such model but NOT to the add model feature.'
if isinstance(model.all_particles[0].mass, basestring):
raise USRMODERROR, 'Add-on Model doesn\'t follows UFO convention (Mass/Width of particles are string name, not object)\n' +\
'MG5 is able to load such model but NOT to the add model feature.'
for order in model.all_orders:
if hasattr(order, 'perturbative_expansion') and order.perturbative_expansion:
raise USRMODERROR, 'Add-on model can not be loop model.'
for order in model.all_orders:
self.add_coupling_order(order)
# Adding automatically identification for anti-particle if needed
# + define identify_pid which keep tracks of the pdg_code identified
if identify_particles:
identify_pid = {}
for new, old in identify_particles.items():
new_part = next((p for p in model.all_particles if p.name==new), None)
old_part = next((p for p in self.particles if p.name==old), None)
identify_pid[new_part.pdg_code] = old_part.pdg_code
if new_part is None:
raise USRMODERROR, "particle %s not in added model" % new
if old_part is None:
raise USRMODERROR, "particle %s not in original model" % old
if new_part.antiname not in identify_particles:
new_anti = new_part.antiname
old_anti = old_part.antiname
misc.sprint(old, new, new_anti, old_anti, old_part.antiname)
if old_anti == old:
raise USRMODERROR, "failed identification (one particle is self-conjugate and not the other)"
logger.info("adding identification for anti-particle: %s=%s" % (new_anti, old_anti))
identify_particles[new_anti] = old_anti
for parameter in model.all_parameters:
self.add_parameter(parameter, identify_pid)
for coupling in model.all_couplings:
self.add_coupling(coupling)
for lorentz in model.all_lorentz:
self.add_lorentz(lorentz)
for particle in model.all_particles:
if particle.name in identify_particles:
self.add_particle(particle, identify=identify_particles[particle.name])
else:
self.add_particle(particle)
for vertex in model.all_vertices:
self.add_interaction(vertex)
self.all_path.append(path)
return
def test_identify_particle(self):
GC_1 = Coupling(name = 'GC_1',
value = '(ee*complex(0,1)*complexconjugate(CKM3x100))/(sw*cmath.sqrt(2))',
order = {'QED':1})
#self.base_model.add_coupling(GC_1)
M5 = Parameter(name = 'M5',
nature = 'external',
type = 'real',
value = 125,
texname = '\\text{MH}',
lhablock = 'MASS',
lhacode = [ 105 ])
W5 = Parameter(name = 'W5',
nature = 'external',
type = 'real',
value = 125,
texname = '\\text{MH}',
lhablock = 'DECAY',
lhacode = [ 105 ])
#self.base_model.add_parameter(M5)
#self.base_model.add_parameter(W5)
L = Lorentz(name = 'FFS2',
spins = [ 2, 2, 1 ],
structure = 'Identity(2,1)')
#self.base_model.add_lorentz(L)
B = Particle(pdg_code = 105,
name = 'B',
antiname = 'B',
spin = 1,
color = 1,
mass = M5,
width = W5,
texname = 'H',
antitexname = 'H',
charge = 0,
GhostNumber = 0,
LeptonNumber = 0,
Y = 0)
#self.base_model.add_particle(B)
V_2 = Vertex(name = 'V_2',
particles = [ B, B, B, B ],
color = [ '1' ],
lorentz = [ L ],
couplings = {(0,0): GC_1})
self.mymodel.__path__ = '.'
self.base_model.add_model(self.mymodel, identify_particles={'B':'H'})
# check that the B object still has is name/pdg_code
self.assertEqual(B.pdg_code, 105)
self.assertEqual(B.name, 'B')
# check that the original model still has the H particles
model = ufomodels.load_model(self.sm_path)
particles_name = [p.name for p in model.all_particles]
self.assertTrue('H' in particles_name)
self.assertFalse('B' in particles_name)
# check the mass
parameters_name = [p.name for p in model.all_parameters]
self.assertTrue('MH' in parameters_name)
self.assertFalse('M5' in parameters_name)
choices=['resnet50', 'resnet3d50'])
args = parser.parse_args()
#Load video
videoPath = './tmp'
imgPath = './tmp/frames'
os.makedirs(videoPath, exist_ok=True)
os.makedirs(imgPath, exist_ok=True)
start = 10
end = 30
video_hash = 'Y6m6DYJ7RW8'
yt = YouTube('https://youtube.com/embed/%s?start=%d&end=%d' %
(video_hash, start, end))
video = yt.streams.all()[0]
name = video.download('/tmp')
# Load model
model = models.load_model(args.arch)
av_categories = pd.read_csv('CVS_Actions.csv', delimiter=';').values.tolist()
trax = pd.read_csv('audioTracks_final.csv')
# Get dataset categories
categories = models.load_categories()
# Load the video frame transform
transform = models.load_transform()
# Obtain video frames
if args.frame_folder is not None:
print('Loading frames in {}'.format(args.frame_folder))
import glob
# here make sure after sorting the frame paths have the correct temporal order
#####################################
print('')
print('Describe movie with base FE.')
# Load model
print('Loading model from checkpoint {}'.format(
config.model.checkpoint_path))
checkpoint = torch.load(config.model.checkpoint_path)
embedding_size = checkpoint['embedding_size']
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
model = models.load_model(config.model.model_arch,
device,
embedding_size=embedding_size)
model.load_state_dict(checkpoint['model_state_dict'])
filename = os.path.join(config.dataset.movie.dataset_path, 'bbx.txt')
bbx_list = utils.read_file_to_list(filename)
plotter = utils.VisdomPlotter(config.visdom.server,
env_name='video_annotation',
port=config.visdom.port)
vd_utils.annotate_video(
config.dataset.movie.movie_path,
config.output.video_dir,
model,
device,
train_csv = os.path.join(DATA_PATH, 'train.csv')
img_path = os.path.join(DATA_PATH, 'train_images')
kfold_path = 'kfold.pkl'
data_path = '/home/jianglb/pythonproject/cloud_segment/data/train_{}_{}'.format(
*RESIZE)
# data_path = '/home/noel/pythonproject/cloud_segment/data/train_{}_{}'.format(*RESIZE)
test_data = os.path.join(DATA_PATH, 'test_{}_{}'.format(*RESIZE))
cls_probs = []
cloud_class = 0
with torch.no_grad():
for fold in range(K):
print('Fold{}:'.format(fold))
cls_model = load_model(model_name,
classes=4,
dropout=0.,
pretrained=False)
cls_model.load_state_dict(
torch.load(os.path.join(save_dir, 'model_{}.pth'.format(fold))))
cls_model.cuda()
cls_model.eval()
preprocessing_fn = smp.encoders.get_preprocessing_fn(
'resnet34', 'imagenet')
# validate
cls_probs_fold = 0
for tt in range(4):
validate_dataset = CloudTrainDataset2(
train_csv,
data_path,
# data_name = os.path.basename(dir_imgs)
data_name = "fsa_MSE_CMU_fusion"
save_path = os.path.join(save_dir, data_name)
save_imgs_path = os.path.join(save_path, "processed_imgs")
save_cropped_path = os.path.join(save_path, "cropped_imgs")
save_video_path = os.path.join(save_path, "processed_videos")
Path(save_dir).mkdir(parents=True, exist_ok=True)
Path(save_imgs_path).mkdir(parents=True, exist_ok=True)
Path(save_cropped_path).mkdir(parents=True, exist_ok=True)
Path(save_video_path).mkdir(parents=True, exist_ok=True)
# Path(save_imgs_AFLW2000_path).mkdir(parents=True, exist_ok=True)
config = yaml.load(open(var_config_path))
net_config = config['Net']
model = load_model(**net_config)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"[INFO] Device: {device}")
# To device
model = model.to(device)
if torch.cuda.is_available():
model = nn.DataParallel(model)
uni_model, target_size, num_workers, batch_size, n_class = create_model(uni_config_path)
var_model, target_size, num_workers, batch_size, n_class = create_model(var_config_path)
wei_model, target_size, num_workers, batch_size, n_class = create_model(wei_config_path)
uni_model = load_pretrain_model(uni_model, uni_model_path)
var_model = load_pretrain_model(var_model, var_model_path)
import sys
import torch
import json
import pickle
import gzip
from suggest_utils import calc_reviewer_db_mapping, print_text_report, print_progress
from suggest_reviewers import create_embeddings, calc_similarity_matrix
from models import load_model
import torch
accepted_submissions = pickle.load(open("../data/pkl/cached_or.pkl", "br"))
# Load the model
abstracts = []
abstract_keys = list(accepted_submissions.keys())
for k, v in accepted_submissions.items():
abstracts.append(v.content["abstract"])
conf_abs = abstracts
print('Loading model', file=sys.stderr)
model, epoch = load_model(None, "scratch/similarity-model.pt")
model.eval()
assert not model.training
# Get recommendations within the conference
paper_embs = create_embeddings(model, conf_abs)
pickle.dump(paper_embs, open("paper_embeddings.pkl", "bw"))
new_db.append(paper)
db = new_db
db_abs = [x['paperAbstract'] for x in db]
rdb = calc_reviewer_db_mapping(reviewer_data,
db,
author_col=args.filter_field,
author_field='authors')
# Calculate or load paper similarity matrix
if args.load_paper_matrix:
mat = np.load(args.load_paper_matrix)
assert (mat.shape[0] == len(submission_abs)
and mat.shape[1] == len(db_abs))
else:
print('Loading model', file=sys.stderr)
model, epoch = load_model(None, args.model_file, force_cpu=True)
model.eval()
assert not model.training
mat = calc_similarity_matrix(model, db_abs, submission_abs)
if args.save_paper_matrix:
np.save(args.save_paper_matrix, mat)
# Calculate reviewer scores based on paper similarity scores
if args.load_aggregate_matrix:
reviewer_scores = np.load(args.load_aggregate_matrix)
assert (reviewer_scores.shape[0] == len(submission_abs)
and reviewer_scores.shape[1] == len(reviewer_names))
else:
print('Calculating aggregate reviewer scores', file=sys.stderr)
reviewer_scores = calc_aggregate_reviewer_score(
rdb, mat, args.aggregator)
def main(args, max_data_size=0, shuffle=True, display=False):
hparams.set_hparam('batch_size', 10)
hparams.add_hparam('is_training', False)
check_vocab(args)
datasets, src_data_size = load_dataset(args)
iterator = iterator_utils.get_eval_iterator(hparams, datasets, hparams.eos, shuffle=shuffle)
src_vocab, tgt_vocab, src_dataset, tgt_dataset, tgt_reverse_vocab, src_vocab_size, tgt_vocab_size = datasets
hparams.add_hparam('vocab_size_source', src_vocab_size)
hparams.add_hparam('vocab_size_target', tgt_vocab_size)
sess, model = load_model(hparams, tf.contrib.learn.ModeKeys.EVAL, iterator, src_vocab, tgt_vocab, tgt_reverse_vocab)
if args.restore_step:
checkpoint_path = os.path.join(args.model_path, 'nmt.ckpt')
ckpt = '%s-%d' % (checkpoint_path, args.restore_step)
else:
ckpt = tf.train.latest_checkpoint(args.model_path)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
if ckpt:
saver.restore(sess, ckpt)
else:
raise Exception("can not found checkpoint file")
src_vocab_file = os.path.join(args.model_path, 'vocab.src')
src_reverse_vocab = build_reverse_vocab_table(src_vocab_file, hparams)
sess.run(tf.tables_initializer())
step_count = 1
with sess:
logger.info("starting evaluating...")
sess.run(iterator.initializer)
eos = hparams.eos.encode()
references = []
translations = []
start_time = time.time()
while True:
try:
if (max_data_size > 0) and (step_count * hparams.batch_size > max_data_size):
break
if step_count % 10 == 0:
t = time.time() - start_time
logger.info('step={0} total={1} time={2:.3f}'.format(step_count, step_count * hparams.batch_size, t))
start_time = time.time()
predictions, source, target, source_text, confidence = model.eval(sess)
reference = bpe2sent(target, eos)
if hparams.beam_width == 1:
translation = bytes2sent(list(predictions), eos)
else:
translation = bytes2sent(list(predictions[:, 0]), eos)
for s, r, t in zip(source, reference, translation):
if display:
source_sent = src_reverse_vocab.lookup(tf.constant(list(s), tf.int64))
source_sent = sess.run(source_sent)
source_sent = text_utils.format_bpe_text(source_sent, eos)
print('{}\n{}\n{}\n'.format(source_sent, r, t))
references.append(r)
translations.append(t)
if step_count % 100 == 0:
bleu_score = moses_multi_bleu(references, translations, args.model_path)
logger.info('bleu score = {0:.3f}'.format(bleu_score))
step_count += 1
except tf.errors.OutOfRangeError:
logger.info('Done eval data')
break
logger.info('compute bleu score...')
# bleu_score = compute_bleu_score(references, translations)
bleu_score = moses_multi_bleu(references, translations, args.model_path)
logger.info('bleu score = {0:.3f}'.format(bleu_score))
if __name__ == '__main__':
from models import load_model
from torchvision import transforms
from torchvision.datasets import MNIST, CIFAR10
from imagenet import ImageNet
from torch.utils.data import DataLoader
from dataloader import *
def give_perturbation(x):
return x
use_cuda = True
dataset = load_dataset('cifar10')
dataloader = load_dataloader(dataset)
preprocess = get_preprocess(
size=32, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
model = load_model('vgg16', 'cifar10', use_cuda=use_cuda)
model.eval()
# img = np.zeros((32,32,3)).astype('float32') ## 32,32,3 ndarray
img = np.ones((32, 32, 3)).astype('float32') # 32,32,3 ndarray
img = img/2
"""
img = give_perturbation(img)
img = preprocess(img)
img = torch.Tensor(img).unsqueeze(0)
img = img.cuda()
with torch.no_grad():
logits = model(img)
prediction = F.softmax(logits,dim = -1)
prediction = prediction.cpu().detach().numpy()
n += len(sentences)
if n % 10000 == 0:
print('\r - Encoder: {:d} sentences'.format(n), end='')
print('\r - Encoder: {:d} sentences'.format(n), end='')
t = int(time.time() - t)
if t < 1000:
print(' in {:d}s'.format(t))
else:
print(' in {:d}m{:d}s'.format(t // 60, t % 60))
fin.close()
fout.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--load-file", help="path to saved model")
parser.add_argument("--sp-model", help="sentencepiece model to use")
parser.add_argument("--gpu",
default=1,
type=int,
help="whether to train on gpu")
parser.add_argument("--sentence-file", help="sentence file")
parser.add_argument("--output-file", help="prefix for output numpy file")
args = parser.parse_args()
model, _ = load_model(None, args)
print(model.args)
model.eval()
embed_all(args, model)
def main(args, max_data_size=0):
vocab_dir = args.vocab_dir
log_file_handler = logging.FileHandler(os.path.join(vocab_dir, 'train.log'))
logger.addHandler(log_file_handler)
check_vocab(args, vocab_dir)
datasets = load_dataset(args, vocab_dir)
iterator = iterator_utils.get_iterator(hparams, datasets, max_rows=max_data_size)
src_vocab, tgt_vocab, _, _, src_vocab_size, tgt_vocab_size = datasets
hparams.add_hparam('is_training', True)
hparams.add_hparam('vocab_size_source', src_vocab_size)
hparams.add_hparam('vocab_size_target', tgt_vocab_size)
pprint(hparams.values())
sess, model = load_model(hparams, tf.contrib.learn.ModeKeys.TRAIN, iterator, src_vocab, tgt_vocab)
if args.restore_step > 0:
checkpoint_path = os.path.join(vocab_dir, 'nmt.ckpt')
ckpt = '%s-%d' % (checkpoint_path, hparams.restore_step)
else:
ckpt = tf.train.latest_checkpoint(vocab_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=5)
if ckpt:
saver.restore(sess, ckpt)
else:
sess.run(tf.global_variables_initializer())
print("Created model with fresh parameters.")
sess.run(tf.tables_initializer())
with sess:
writer = tf.summary.FileWriter(vocab_dir, sess.graph)
logger.info("starting training...")
epochs = 1
step_in_epoch = 0
learning_rate = hparams.learning_rate
checkpoint_path = os.path.join(vocab_dir, "nmt.ckpt")
sess.run(iterator.initializer)
while epochs <= args.num_train_epochs:
start_time = time.time()
try:
loss, global_step, learning_rate, accuracy, summary = model.step(sess)
step_in_epoch += 1
if global_step % args.summary_per_steps == 0:
write_summary(writer, summary, global_step)
except tf.errors.OutOfRangeError:
logger.info('{} epochs finished'.format(epochs))
# saver.save(sess, checkpoint_path, global_step=global_step)
epochs += 1
step_in_epoch = 1
sess.run(iterator.initializer)
continue
sec_per_step = time.time() - start_time
logger.info("Epoch %-3d Step %-d - %-d [%.3f sec, loss=%.4f, acc=%.3f, lr=%f]" %
(epochs, global_step, step_in_epoch, sec_per_step, loss, accuracy, learning_rate))
if global_step % args.steps_per_checkpoint == 0:
model_checkpoint_path = saver.save(sess, checkpoint_path, global_step=global_step)
logger.info("Saved checkpoint to {}".format(model_checkpoint_path))
if math.isnan(loss) or math.isinf(loss):
raise Exception('loss overflow')
writer.close()
X_full_train, X_sketch_train, X_full_val, X_sketch_val = load_data(
data_folder, dset, image_data_format)
img_dim = X_full_train.shape[-3:]
# Get the number of non overlapping patch and the size of input image to the discriminator
nb_patch, img_dim_disc = get_nb_patch(img_dim, patch_size, image_data_format)
try:
# Create optimizers
opt_dcgan = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
opt_discriminator = Adam(lr=1E-3, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
# Load generator model
generator_model = load_model("generator_unet_%s" % generator, img_dim,
nb_patch, bn_mode, use_mbd, batch_size,
do_plot)
# Load discriminator model
discriminator_model = load_model("DCGAN_discriminator", img_dim_disc,
nb_patch, bn_mode, use_mbd, batch_size,
do_plot)
# Compile generator model
generator_model.compile(loss='mae', optimizer=opt_discriminator)
discriminator_model.trainable = False
# Define DCGAN model
DCGAN_model = DCGAN(generator_model, discriminator_model, img_dim,
patch_size, image_data_format)
# Define loss function and loss weights
"""Train a model"""
import sys
from helpers import *
from models import load_model
if len(sys.argv) < 4:
print "USAGE: python train.py NAME MODEL_TYPE DATA_DIR (CHECKPOINT_HDF5_FILE)"
sys.exit(0)
# Inputs
name = sys.argv[1]
type = sys.argv[2]
data_dir = sys.argv[3]
checkpoint = sys.argv[4] if len(sys.argv) > 4 else None
# Load model
model = load_model(name, type, tmp_dir, checkpoint)
# Get class weights
female_train, female_test, male_train, male_test = get_data(data_dir)
class_weight = {0: float(len(male_train)) / float(len(female_train)), 1: 1.}
# Train model
img_dir, meta_dir, train_dir, test_dir = get_directories(data_dir)
model.train(train_dir, test_dir, class_weight=class_weight)
model.save(os.path.join(results_dir, name + '.h5'))
# -*- coding: utf-8 -*-
from models import load_model
import my_utils
from torch.utils.data import DataLoader
import torch
args = my_utils.get_args()
data_path,noc = my_utils.get_data_path(args.dataset,'./config.txt')
model = load_model(args.model,noc).cuda()
train_images = data_path+'/train/images'
train_labels = data_path+'/train/labels'
val_images = data_path+'/validation/images'
val_labels = data_path+'/validation/labels'
test_images = data_path+'/test/images'
test_labels = data_path+'/test/labels'
# DATA LOADERS
train_loader = DataLoader(my_utils.getDataset(train_images,
train_labels,
size = (360,480)),
batch_size=args.batch_size,
num_workers=args.num_of_workers,
shuffle=True)
val_loader = DataLoader(my_utils.getDataset(val_images,
val_labels,
size = (360,480)),
batch_size=args.batch_size,
num_workers=args.num_of_workers,
shuffle=False)
test_loader = DataLoader(my_utils.getDataset(test_images,
def load_video(video_hash):
yt = YouTube('https://youtube.com/embed/%s?start=%d&end=%d' %
(video_hash, start, end))
video = yt.streams.all()[0]
name = video.download('/tmp')
# Load model
model = models.load_model(arch)
av_categories = pd.read_csv('CVS_Actions(NEW).csv',
delimiter=';').values.tolist()
trax = pd.read_csv('audioTracks_urls.csv')
# Get dataset categories
#categories = models.load_categories()
# Load the video frame transform
transform = models.load_transform()
# Obtain video frames
if frame_folder is not None:
print('Loading frames in {}'.format(frame_folder))
import glob
# here make sure after sorting the frame paths have the correct temporal order
frame_paths = sorted(glob.glob(os.path.join(frame_folder, '*.jpg')))
print(frame_paths)
frames = load_frames(frame_paths)
else:
print('Extracting frames using ffmpeg...')
frames = extract_frames(name, num_segments)
# Prepare input tensor
if arch == 'resnet3d50':
# [1, num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames],
1).unsqueeze(0)
else:
# [num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames])
# Make video prediction
with torch.no_grad():
logits = model(input)
h_x = F.softmax(logits, 1).mean(dim=0)
probs, idx = h_x.sort(0, True)
# Output the prediction.
print('RESULT ON ' + name)
y = float(av_categories[idx[0]][1]) * 125
x = float(av_categories[idx[0]][2]) * 125
trax = trax.assign(
dist=lambda row: np.sqrt((x - row.valence)**2 + (y - row.energy)**2))
print('min', trax['dist'].min())
best = trax.nsmallest(100, 'dist')
print(best)
rand = randint(0, 9)
print(rand)
choice = best.iloc[rand, [1, 2, 5]]
print('choice', choice)
song = 'valence: ' + str(x) + ' arousal: ' + str(
y) + " " + choice[0] + ' ' + choice[1]
print(song)
print(x, y)
for i in range(0, 5):
print('{:.3f} -> {} ->{}'.format(probs[i], idx[i],
av_categories[idx[i]]))
print('result cutegories', av_categories[idx[i]][0],
av_categories[idx[i]][1])
#r = requests.get(match.iloc[0,2], allow_redirects=True)
r = requests.get(choice[2], allow_redirects=True)
open('./tmp/preview.mp3', 'wb').write(r.content)
# Render output frames with prediction text.
rendered_output = './tmp/' + video_hash + '_' + str(x) + '_' + str(
y) + '.mp4'
if rendered_output is not None:
clip = VideoFileClip(name).subclip(30, 60)
audioclip = AudioFileClip('./tmp/preview.mp3')
txt_clip = TextClip(song, fontsize=16, color='white')
clip_final = clip.set_audio(audioclip)
video = CompositeVideoClip([clip_final, txt_clip])
video.set_duration(30).write_videofile(rendered_output)
# skip_difficult=True,
# **common_args
# )
generator = PascalVocGenerator(
'datasets/VOC2007',
'test',
shuffle_groups=False,
skip_truncated=False,
skip_difficult=True,
**common_args
)
model_path = 'snapshots/2019-08-25/resnet101_pascal_07_0.7352.h5'
# load retinanet model
import keras.backend as K
K.clear_session()
K.set_learning_phase(1)
model = models.load_model(model_path, backbone_name='resnet101')
# if the model is not converted to an inference model, use the line below
# see: https://github.com/fizyr/keras-retinanet#converting-a-training-model-to-inference-model
model = models.convert_model(model)
average_precisions = evaluate(generator, model, epoch=0)
# compute per class average precision
total_instances = []
precisions = []
for label, (average_precision, num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations), generator.label_to_name(label), 'with average precision: {:.4f}'.format(average_precision))
total_instances.append(num_annotations)
precisions.append(average_precision)
mean_ap = sum(precisions) / sum(x > 0 for x in total_instances)
print('mAP: {:.4f}'.format(mean_ap))
def export(args):
model = models.load_model(args.model_dir)
model.export_inference_model(args.save_dir)
def main() -> None:
parser = argparse.ArgumentParser(description="Flower")
parser.add_argument(
"--server_address",
type=str,
default=DEFAULT_SERVER_ADDRESS,
help=f"gRPC server address (default: {DEFAULT_SERVER_ADDRESS})",
)
parser.add_argument(
"--rounds",
type=int,
default=1,
help="Number of rounds of federated learning (default: 1)",
)
parser.add_argument(
"--sample_fraction",
type=float,
default=1.0,
help=
"Fraction of available clients used for fit/evaluate (default: 1.0)",
)
parser.add_argument(
"--min_sample_size",
type=int,
default=2,
help="Minimum number of clients used for fit/evaluate (default: 2)",
)
parser.add_argument(
"--min_num_clients",
type=int,
default=2,
help=
"Minimum number of available clients required for sampling (default: 2)",
)
parser.add_argument(
"--model",
type=str,
default="simple-cnn",
help="Model to use for training (default: simple-cnn)",
)
parser.add_argument(
"--dataset",
type=str,
default="cifar-10",
help="Dataset to use fro training (default: cifar-10)",
)
parser.add_argument(
"--device",
type=str,
default="CPU",
help="Device to run the model on (default: CPU)",
)
parser.add_argument(
"--strategy",
type=str,
default="FedAvg",
help="Aggregation strategy (default: FedAvg)",
)
parser.add_argument(
"--epochs",
type=int,
default=1,
help=
"Number of local epochs to run on each client before aggregation (default: 1)",
)
parser.add_argument(
"--batch_size",
type=int,
default=32,
help="Batch size to be used by each worker (default: 32)",
)
parser.add_argument(
"--learning_rate",
type=float,
default=0.001,
help="Learning rate to be used by each worker (default: 0.001)",
)
parser.add_argument(
"--quantize",
type=bool,
default=False,
help="Use quantization (default: False)",
)
parser.add_argument(
"--quantize_bits",
type=int,
default=64,
help="Quantization bits (default: 64)",
)
parser.add_argument(
"--log_host",
type=str,
help="Logserver address (no default)",
)
args = parser.parse_args()
# check for runnable device
DEVICE = torch.device("cuda:0" if args.device == "GPU" else "cpu")
# Configure logger
fl.common.logger.configure("server", host=args.log_host)
# Load evaluation data
_, testset = datasets.load_data(dataset_name=args.dataset, framework="PT")
# Create client_manager, strategy, and server
client_manager = fl.server.SimpleClientManager()
aggregation_strategy = get_strategy(
strategy_name=args.strategy,
fraction_fit=args.sample_fraction,
min_fit_clients=args.min_sample_size,
min_available_clients=args.min_num_clients,
eval_fn=get_eval_fn(model=args.model, testset=testset, device=DEVICE),
on_fit_config_fn=get_fit_config_fn(args),
dummy_model=models.load_model(model_name=args.model, framework="PT"),
quantize=args.quantize,
quantize_bits=args.quantize_bits,
)
server = fl.server.Server(client_manager=client_manager,
strategy=aggregation_strategy)
# Run server
fl.server.start_server(
args.server_address,
server,
config={"num_rounds": args.rounds},
)
def infer(args):
resize_h = args.image_shape[1]
resize_w = args.image_shape[0]
test_transforms = transforms.Compose(
[transforms.Resize((resize_w, resize_h)),
transforms.Normalize()])
model = models.load_model(args.model_dir)
if not osp.exists(args.save_dir):
os.makedirs(args.save_dir)
# 图像背景替换
if args.image_path is not None:
if not osp.exists(args.image_path):
raise Exception('The --image_path is not existed: {}'.format(
args.image_path))
if args.background_image_path is None:
raise Exception(
'The --background_image_path is not set. Please set it')
else:
if not osp.exists(args.background_image_path):
raise Exception(
'The --background_image_path is not existed: {}'.format(
args.background_image_path))
img = cv2.imread(args.image_path)
score_map, im_info = predict(img, model, test_transforms)
score_map = score_map[:, :, 1]
score_map = recover(score_map, im_info)
bg = cv2.imread(args.background_image_path)
save_name = osp.basename(args.image_path)
save_path = osp.join(args.save_dir, save_name)
result = bg_replace(score_map, img, bg)
cv2.imwrite(save_path, result)
# 视频背景替换，如果提供背景视频则以背景视频作为背景，否则采用提供的背景图片
else:
is_video_bg = False
if args.background_video_path is not None:
if not osp.exists(args.background_video_path):
raise Exception(
'The --background_video_path is not existed: {}'.format(
args.background_video_path))
is_video_bg = True
elif args.background_image_path is not None:
if not osp.exists(args.background_image_path):
raise Exception(
'The --background_image_path is not existed: {}'.format(
args.background_image_path))
else:
raise Exception(
'Please offer backgound image or video. You should set --backbground_iamge_paht or --background_video_path'
)
disflow = cv2.DISOpticalFlow_create(
cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
prev_gray = np.zeros((resize_h, resize_w), np.uint8)
prev_cfd = np.zeros((resize_h, resize_w), np.float32)
is_init = True
if args.video_path is not None:
print('Please wait. It is computing......')
if not osp.exists(args.video_path):
raise Exception('The --video_path is not existed: {}'.format(
args.video_path))
cap_video = cv2.VideoCapture(args.video_path)
fps = cap_video.get(cv2.CAP_PROP_FPS)
width = int(cap_video.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
save_name = osp.basename(args.video_path)
save_name = save_name.split('.')[0]
save_path = osp.join(args.save_dir, save_name + '.avi')
cap_out = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps,
(width, height))
if is_video_bg:
cap_bg = cv2.VideoCapture(args.background_video_path)
frames_bg = cap_bg.get(cv2.CAP_PROP_FRAME_COUNT)
current_frame_bg = 1
else:
img_bg = cv2.imread(args.background_image_path)
while cap_video.isOpened():
ret, frame = cap_video.read()
if ret:
score_map, im_info = predict(frame, model, test_transforms)
cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
score_map = 255 * score_map[:, :, 1]
optflow_map = postprocess(cur_gray, score_map, prev_gray, prev_cfd, \
disflow, is_init)
prev_gray = cur_gray.copy()
prev_cfd = optflow_map.copy()
is_init = False
optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
optflow_map = threshold_mask(optflow_map,
thresh_bg=0.2,
thresh_fg=0.8)
score_map = recover(optflow_map, im_info)
#循环读取背景帧
if is_video_bg:
ret_bg, frame_bg = cap_bg.read()
if ret_bg:
if current_frame_bg == frames_bg:
current_frame_bg = 1
cap_bg.set(cv2.CAP_PROP_POS_FRAMES, 0)
else:
break
current_frame_bg += 1
comb = bg_replace(score_map, frame, frame_bg)
else:
comb = bg_replace(score_map, frame, img_bg)
cap_out.write(comb)
else:
break
if is_video_bg:
cap_bg.release()
cap_video.release()
cap_out.release()
# 当没有输入预测图像和视频的时候，则打开摄像头
else:
cap_video = cv2.VideoCapture(0)
if not cap_video.isOpened():
raise IOError("Error opening video stream or file, "
"--video_path whether existing: {}"
" or camera whether working".format(
args.video_path))
return
if is_video_bg:
cap_bg = cv2.VideoCapture(args.background_video_path)
frames_bg = cap_bg.get(cv2.CAP_PROP_FRAME_COUNT)
current_frame_bg = 1
else:
img_bg = cv2.imread(args.background_image_path)
while cap_video.isOpened():
ret, frame = cap_video.read()
if ret:
score_map, im_info = predict(frame, model, test_transforms)
cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
score_map = 255 * score_map[:, :, 1]
optflow_map = postprocess(cur_gray, score_map, prev_gray, prev_cfd, \
disflow, is_init)
prev_gray = cur_gray.copy()
prev_cfd = optflow_map.copy()
is_init = False
optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
optflow_map = threshold_mask(optflow_map,
thresh_bg=0.2,
thresh_fg=0.8)
score_map = recover(optflow_map, im_info)
#循环读取背景帧
if is_video_bg:
ret_bg, frame_bg = cap_bg.read()
if ret_bg:
if current_frame_bg == frames_bg:
current_frame_bg = 1
cap_bg.set(cv2.CAP_PROP_POS_FRAMES, 0)
else:
break
current_frame_bg += 1
comb = bg_replace(score_map, frame, frame_bg)
else:
comb = bg_replace(score_map, frame, img_bg)
cv2.imshow('HumanSegmentation', comb)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
if is_video_bg:
cap_bg.release()
cap_video.release()
def test(args):
with open(args.config, "r") as fid:
config = json.load(fid)
if not args.disable_cuda:
device = torch.device("cuda")
else:
device = torch.device("cpu")
dataset = config["data"]["dataset"]
if not os.path.exists(f"datasets/{dataset}.py"):
raise ValueError(f"Unknown dataset {dataset}")
dataset = utils.module_from_file("dataset", f"datasets/{dataset}.py")
input_size = config["data"]["num_features"]
data_path = config["data"]["data_path"]
preprocessor = dataset.Preprocessor(
data_path,
num_features=input_size,
tokens_path=config["data"].get("tokens", None),
lexicon_path=config["data"].get("lexicon", None),
use_words=config["data"].get("use_words", False),
prepend_wordsep=config["data"].get("prepend_wordsep", False),
)
data = dataset.Dataset(data_path, preprocessor, split=args.split)
loader = utils.data_loader(data, config)
criterion, output_size = models.load_criterion(
config.get("criterion_type", "ctc"),
preprocessor,
config.get("criterion", {}),
)
criterion = criterion.to(device)
model = models.load_model(config["model_type"], input_size, output_size,
config["model"]).to(device)
models.load_from_checkpoint(model, criterion, args.checkpoint_path,
args.load_last)
model.eval()
meters = utils.Meters()
for inputs, targets in loader:
outputs = model(inputs.to(device))
meters.loss += criterion(outputs, targets).item() * len(targets)
meters.num_samples += len(targets)
predictions = criterion.viterbi(outputs)
for p, t in zip(predictions, targets):
p, t = preprocessor.tokens_to_text(p), preprocessor.to_text(t)
pw, tw = p.split(preprocessor.wordsep), t.split(
preprocessor.wordsep)
pw, tw = list(filter(None, pw)), list(filter(None, tw))
tokens_dist = editdistance.eval(p, t)
words_dist = editdistance.eval(pw, tw)
print("CER: {:.3f}".format(tokens_dist * 100.0 /
len(t) if len(t) > 0 else 0))
print("WER: {:.3f}".format(words_dist * 100.0 /
len(tw) if len(tw) > 0 else 0))
print("HYP:", "".join(p))
print("REF", "".join(t))
print("=" * 80)
meters.edit_distance_tokens += tokens_dist
meters.edit_distance_words += words_dist
meters.num_tokens += len(t)
meters.num_words += len(tw)
print("Loss {:.3f}, CER {:.3f}, WER {:.3f}, ".format(
meters.avg_loss, meters.cer, meters.wer))
def main(args):
# set up project directories
tb_logdir, snapshot_dir = prepare_dirs(args)
# get logger
logger = logging.getLogger('train')
# tensorboard writer
writer = get_writer(args, tb_logdir)
use_cuda = torch.cuda.is_available() and args.cuda
# set manual seed if required
if args.seed is not None:
torch.manual_seed(args.seed)
if use_cuda:
torch.cuda.manual_seed_all(args.seed)
# check for cuda supports
if use_cuda:
device = torch.device("cuda:0")
cudnn.benchmark = True
else:
device = torch.device("cpu")
# snapshot frequency
if args.snapshot_every > 0 and not args.evaluate:
logger.info('Saving snapshots to {}'.format(snapshot_dir))
# load model
classes, in_channels = data_loader.num_classes(args.dataset)
if args.subsample_classes > 0:
classes = args.subsample_classes
net = models.load_model(args.arch, classes=classes)
logger.info('Creating model {}.'.format(args.arch))
if torch.cuda.device_count() > 1:
logger.info("Running on {} GPUs".format(torch.cuda.device_count()))
net.features = torch.nn.DataParallel(net.features)
# move net to device
net = net.to(device=device)
# get data loader for the specified dataset
train_loader, test_loader, val_loader = data_loader.load_dataset(
args.dataset,
args.data_path,
args.batch_size,
shuffle=args.shuffle,
augmentation=args.augmentation,
noise=args.noise,
split=args.split,
num_workers=args.workers,
split_seed=args.split_seed,
noise_seed=args.noise_seed,
stratified=args.stratified,
nclasses=args.subsample_classes,
class_sample_seed=args.class_sample_seed,
no_normalization=args.unnormalize,
upscale=args.upscale,
upscale_padding=args.upscale_padding)
# define loss
criterion = nn.CrossEntropyLoss().to(device)
start_epoch = args.start_epoch
best_acc1, best_acc5 = 0, 0
# load model from file
if os.path.isfile(args.resume_from):
# resume training given state dictionary
optimizer, scheduler = load_optimizer(args, net)
try:
net, optimizer, scheduler, start_epoch, best_acc1, best_acc5 = snapshot.load_snapshot(
net, optimizer, scheduler, args.resume_from, device)
if args.override:
override_hyperparams(args, optimizer, scheduler)
except KeyError:
classes, in_channels = data_loader.num_classes(args.dataset)
if args.subsample_classes > 0:
classes = args.subsample_classes
net = snapshot.load_model(args.arch, classes, args.resume_from,
device, in_channels)
else:
# define optimizer
optimizer, scheduler = load_optimizer(args, net)
# evaluate model
if args.evaluate:
val_loss, top1_acc, top5_acc = scores.evaluate(net,
test_loader,
criterion,
device,
topk=(1, 5))
utils.print_val_loss(args.epochs, val_loss, top1_acc, top5_acc)
writer.add_scalar('Loss/test', val_loss, args.epochs)
writer.add_scalar('Accuracy/test/top1', top1_acc, args.epochs)
writer.add_scalar('Accuracy/test/top5', top5_acc, args.epochs)
writer.close()
return
if args.evaluate_train:
train_loss, top1_acc, top5_acc = scores.evaluate(net,
train_loader,
criterion,
device,
topk=(1, 5))
utils.print_train_loss_epoch(args.epochs, train_loss, top1_acc,
top5_acc)
if best_acc1 * best_acc5 > 0:
# if nonzero, print best val accuracy
utils.print_val_loss(args.epochs, -1., best_acc1, best_acc5)
writer.add_scalar('Loss/train', train_loss, args.epochs)
writer.add_scalar('Accuracy/train/top1', top1_acc, args.epochs)
writer.add_scalar('Accuracy/train/top5', top5_acc, args.epochs)
writer.close()
return
if args.eval_regularization_loss:
regularization_loss = scores.compute_regularization_loss(
net, args.weight_decay)
utils.print_regularization_loss_epoch(args.epochs, regularization_loss)
writer.add_scalar('Regularization loss', regularization_loss,
args.epochs)
writer.close()
return
utils.print_model_config(args)
if start_epoch == 0:
filename = args.arch + '_init_' + str(start_epoch) + '.pt'
logger.info("Saving model initialization to {}".format(filename))
snapshot.save_model(net, filename, snapshot_dir)
# train the model
net.train()
if val_loader is None and test_loader is not None:
val_loader = test_loader
logger.warning("Using TEST set to validate model during training!")
net, converged = train(net,
args.epochs,
train_loader,
optimizer,
criterion,
scheduler,
device,
snapshot_dirname=snapshot_dir,
start_epoch=start_epoch,
snapshot_every=args.snapshot_every,
val_loader=val_loader,
kill_plateaus=args.kill_plateaus,
best_acc1=best_acc1,
writer=writer,
snapshot_all_until=args.snapshot_all_until,
filename=args.arch,
train_acc=args.train_acc)
if test_loader is not None:
val_loss, top1_acc, top5_acc = scores.evaluate(net,
test_loader,
criterion,
device,
topk=(1, 5))
utils.print_val_loss(args.epochs, val_loss, top1_acc, top5_acc)
net = net.train()
writer.add_scalar('Loss/test', val_loss, args.epochs)
writer.add_scalar('Accuracy/test/top1', top1_acc, args.epochs)
writer.add_scalar('Accuracy/test/top5', top5_acc, args.epochs)
# save final model
if converged:
filename = args.arch + '_' + str(args.epochs) + '.pt'
snapshot.save_model(net, filename, snapshot_dir)
writer.close()
args.dataroot,
batch_size=args.batch_size)
else:
trainloader, valloader = load_dataset(args.dataset,
args.dataroot,
'pair',
batch_size=args.batch_size)
num_class = trainloader.dataset.num_classes
print('Number of train dataset: ', len(trainloader.dataset))
print('Number of validation dataset: ', len(valloader.dataset))
# Model
print('==> Building model: {}'.format(args.model))
net = models.load_model(args.model, num_class)
# print(net)
if use_cuda:
torch.cuda.set_device(args.sgpu)
net.cuda()
print(torch.cuda.device_count())
print('Using CUDA..')
if args.ngpu > 1:
net = torch.nn.DataParallel(net,
device_ids=list(
range(args.sgpu, args.sgpu + args.ngpu)))
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
def add_model(self, model=None, path=None, identify_particles=None):
"""add another model in the current one"""
self.new_external = []
if path:
model = ufomodels.load_model(path)
if not model:
raise USRMODERROR, 'Need a valid Model'
else:
path = model.__path__[0]
# Check the validity of the model. Too old UFO (before UFO 1.0)
if not hasattr(model, 'all_orders'):
raise USRMODERROR, 'Add-on Model doesn\'t follows UFO convention (no couplings_order information)\n' +\
'MG5 is able to load such model but NOT to the add model feature.'
if isinstance(model.all_particles[0].mass, basestring):
raise USRMODERROR, 'Add-on Model doesn\'t follows UFO convention (Mass/Width of particles are string name, not object)\n' +\
'MG5 is able to load such model but NOT to the add model feature.'
for order in model.all_orders:
if hasattr(
order,
'perturbative_expansion') and order.perturbative_expansion:
raise USRMODERROR, 'Add-on model can not be loop model.'
for order in model.all_orders:
self.add_coupling_order(order)
# Adding automatically identification for anti-particle if needed
# + define identify_pid which keep tracks of the pdg_code identified
identify_pid = {}
if identify_particles:
for new, old in identify_particles.items():
new_part = next(
(p for p in model.all_particles if p.name == new), None)
old_part = next((p for p in self.particles if p.name == old),
None)
identify_pid[new_part.pdg_code] = old_part.pdg_code
if new_part is None:
raise USRMODERROR, "particle %s not in added model" % new
if old_part is None:
raise USRMODERROR, "particle %s not in original model" % old
if new_part.antiname not in identify_particles:
new_anti = new_part.antiname
old_anti = old_part.antiname
misc.sprint(old, new, new_anti, old_anti,
old_part.antiname)
if old_anti == old:
raise USRMODERROR, "failed identification (one particle is self-conjugate and not the other)"
logger.info(
"adding identification for anti-particle: %s=%s" %
(new_anti, old_anti))
identify_particles[new_anti] = old_anti
for parameter in model.all_parameters:
self.add_parameter(parameter, identify_pid)
for coupling in model.all_couplings:
self.add_coupling(coupling)
for lorentz in model.all_lorentz:
self.add_lorentz(lorentz)
for particle in model.all_particles:
if particle.name in identify_particles:
self.add_particle(particle,
identify=identify_particles[particle.name])
else:
self.add_particle(particle)
for vertex in model.all_vertices:
self.add_interaction(vertex)
self.all_path.append(path)
return
def test_net():
''' Evaluate the network '''
# Make result directory and the result file.
cfg.TRAIN.RANDOM_NUM_VIEWS = False
result_dir = os.path.join(cfg.DIR.OUT_PATH, cfg.TEST.EXP_NAME)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
result_fn = os.path.join(result_dir, 'result.mat')
print("Exp file will be written to: " + result_fn)
# Make a network and load weights
NetworkClass = load_model(cfg.CONST.NETWORK_CLASS)
print('Network definition: \n')
print(inspect.getsource(NetworkClass.network_definition))
net = NetworkClass(compute_grad=False)
net.load(cfg.CONST.WEIGHTS)
solver = Solver(net)
# set constants
batch_size = cfg.CONST.BATCH_SIZE
# set up testing data process. We make only one prefetching process. The
# process will return one batch at a time.
queue = Queue(cfg.QUEUE_SIZE)
data_pair = category_model_id_pair(dataset_portion=cfg.TEST.DATASET_PORTION)
processes = make_data_processes(queue, data_pair, 1, repeat=True, train=False)
num_data = len(processes[0].data_paths)
num_batch = int(num_data / batch_size)
# prepare result container
results = {'cost': np.zeros(num_batch)}
for thresh in cfg.TEST.VOXEL_THRESH:
results[str(thresh)] = np.zeros((num_batch, batch_size, 5))
# Get all test data
batch_idx = 0
precisions = []
for batch_img, batch_camera, batch_voxel in get_while_running(processes[0], queue):
if batch_idx == num_batch:
break
pred, loss, activations = solver.test_output(
batch_img, batch_camera, batch_voxel)
for i, thresh in enumerate(cfg.TEST.VOXEL_THRESH):
for j in range(batch_size):
r = evaluate_voxel_prediction(pred[j, ...], batch_voxel[j, ...], thresh)
results[str(thresh)][batch_idx, j, :] = r
precisions.append(sklearn.metrics.average_precision_score(
batch_voxel[j, :, 1].flatten(), pred[j, :, 1].flatten()))
# record result for the batch
results['cost'][batch_idx] = float(loss)
batch_idx += 1
print('%d/%d, average loss: %f, average precision: %f' % (batch_idx,
num_batch, results['cost'][:batch_idx].mean(),
np.mean(precisions)))
iou_now = results[str(cfg.TEST.VOXEL_THRESH[0])][:batch_idx].reshape(-1, 5)
print('Total loss: %f' % np.mean(results['cost']))
print('Total iou: %f' % (iou_now[:, 1] / iou_now[:, 2]).mean())
print('Total AP: %f' % np.mean(precisions))
sio.savemat(result_fn, results)
try:
model = save_load_object.load_from_file( \
os.path.join(model_path, 'model.pkl'))
except Exception, error:
logger.info('failed to load model from pickle file. Try importing UFO from File')
else:
# check path is correct
if model.has_key('version_tag') and model.get('version_tag') == os.path.realpath(model_path) + str(misc.get_pkg_info()):
_import_once.append(model_path)
return model
if model_path in _import_once:
raise MadGraph5Error, 'This model is modified on disk. To reload it you need to quit/relaunch mg5'
# Load basic information
ufo_model = ufomodels.load_model(model_path)
ufo2mg5_converter = UFOMG5Converter(ufo_model)
model = ufo2mg5_converter.load_model()
if model_path[-1] == '/': model_path = model_path[:-1] #avoid empty name
model.set('name', os.path.split(model_path)[-1])
model.set('version_tag', os.path.realpath(model_path) + str(misc.get_pkg_info()))
# Load the Parameter/Coupling in a convinient format.
parameters, couplings = OrganizeModelExpression(ufo_model).main()
model.set('parameters', parameters)
model.set('couplings', couplings)
model.set('functions', ufo_model.all_functions)
# save in a pickle files to fasten future usage
save_load_object.save_to_file(os.path.join(model_path, 'model.pkl'), model)
def validate(test_data_dir="./images",
filename="mode.pt",
network="ResNet50",
gpu=None,
cpu_count=os.cpu_count(),
batch_size=16):
classes = ImageFolder(test_data_dir).classes
model = load_model(filename, network, len(classes))
cuda_id = int(gpu.split(",")[0]) if gpu is not None else 0
device = torch.device(
"cuda:{}".format(cuda_id) if gpu is not None else "cpu")
model = model.to(device)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
val_dataset = ImageFolder(
test_data_dir,
transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
normalize,
]))
criterion = torch.nn.CrossEntropyLoss()
val_loader = DataLoader(val_dataset,
shuffle=False,
num_workers=cpu_count,
batch_size=batch_size)
# switch to evaluate mode
model.eval()
# setup running values
running_loss = 0.0
running_corrects = 0
loss = 0.
acc = 0.
y_pred = []
y_true = []
conf = []
total_seen_samples = 0
with torch.no_grad():
with trange(
len(val_loader),
desc="Validating",
ncols=80,
postfix={
"loss": 0,
"acc": 0
},
bar_format=
"{desc}: {percentage:3.1f}% {bar} {remaining} {n_fmt}/{total_fmt}{postfix}"
) as pbar:
start_time = perf_counter()
for i, (inputs, labels) in enumerate(val_loader):
inputs = inputs.to(device)
batch_size = inputs.size(0)
total_seen_samples += batch_size
labels = labels.to(device)
# compute output
output = model(inputs)
preds = torch.argmax(output, 1)
loss = criterion(output, labels)
y_pred += preds.cpu().numpy().tolist()
y_true += labels.cpu().numpy().tolist()
conf += output.cpu().numpy().tolist()
# statistics
running_loss += loss.item()
running_corrects += torch.sum(preds == labels.data)
loss = running_loss / (i + 1)
acc = running_corrects.double() / total_seen_samples
pbar.set_postfix({
"loss": round(float(loss), 2),
"acc": round(float(acc), 3)
})
pbar.update()
end_time = perf_counter()
print("Loss: {:.4f}, Acc: {:.4f}, Time: {:.4f}s".format(
loss, acc, end_time - start_time))
return np.array(y_pred), np.array(y_true), np.array(conf)
def main():
config_path = Path(args.config_path)
config = yaml.load(open(config_path))
net_config = config['Net']
# loss_config = config['Loss']
# opt_config = config['Optimizer']
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
n_class = net_config['n_class']
val_dir = '../data'
val_name = 'biwi_dataset_list.txt'
val_type = 'BIWI'
use_bined = False
num_workers = 4
pretrained_path = [
"/home/linhnv/projects/RankPose/model/headpose_resnet/model_epoch_77_8.775894704750192.pth"
]
# models_path = glob("/home/linhnv/projects/RankPose/model/headpose_resnet/*")
# models_path = [x for x in models_path if x.startswith("/home/linhnv/projects/RankPose/model/headpose_resnet/model_epoch")]
# print(models_path)
for pretrained_path in pretrained_path:
print(f"[INFO] Pretrained path: {pretrained_path}")
model = load_model(**net_config)
# To device
model = model.to(device)
modelname = config_path.stem
output_dir = Path('../model') / modelname
output_dir.mkdir(exist_ok=True)
log_dir = Path('../logs') / modelname
log_dir.mkdir(exist_ok=True)
# logger = debug_logger(log_dir)
# logger.debug(config)
# logger.info(f'Device: {device}')
params = model.parameters()
valid_dataset = laod_dataset(data_type=val_type,
split='valid',
base_dir=val_dir,
filename=val_name,
use_bined=False,
n_class=n_class)
# top_10 = len(train_dataset) // 10
# top_30 = len(train_dataset) // 3.33
# train_weights = [ 3 if idx<top_10 else 2 if idx<top_30 else 1 for idx in train_dataset.labels_sort_idx]
# train_sample = WeightedRandomSampler(train_weights, num_samples=len(train_dataset), replacement=True)
# train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=train_sample, num_workers=num_workers,
# pin_memory=True, drop_last=True)
valid_loader = DataLoader(valid_dataset,
batch_size=32,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
if torch.cuda.is_available():
model = nn.DataParallel(model)
# logger.info(f'Load pretrained from {pretrained_path}')
param = torch.load(pretrained_path, map_location='cpu')
if "state_dict" in param:
model.load_state_dict(param['state_dict'], strict=False)
else:
model.load_state_dict(param)
del param
valid_losses = []
valid_diffs = []
model.eval()
with torch.no_grad():
with tqdm(valid_loader) as _tqdm:
for batched in _tqdm:
if use_bined:
images, labels, yaw_labels, pitch_labels, roll_labels = batched
images, labels = images.to(device), labels.to(device)
# yaw_labels, pitch_labels, roll_labels = yaw_labels.to(device), pitch_labels.to(device), roll_labels.to(device)
preds, y_pres, p_pres, r_pres = model(
images, use_bined)
# loss = loss_fn([preds, y_pres, p_pres, r_pres], [labels, yaw_labels, pitch_labels, roll_labels])
diff = calculate_diff(preds, labels)
else:
images, labels = batched
images, labels = images.to(device), labels.to(device)
preds = model(images, use_bined)
# loss = loss_fn([preds], [labels])
diff = calculate_diff(preds, labels)
_tqdm.set_postfix(OrderedDict(mae=f'{diff:.2f}'))
# _tqdm.set_postfix(OrderedDict(loss=f'{loss.item():.3f}', d_y=f'{np.mean(diff[:,0]):.1f}', d_p=f'{np.mean(diff[:,1]):.1f}', d_r=f'{np.mean(diff[:,2]):.1f}'))
valid_losses.append(0)
valid_diffs.append(diff)
valid_loss = np.mean(valid_losses)
valid_diff = np.mean(valid_diffs)
print(f'valid diff: {valid_diff}')
def test_net():
''' Evaluate the network '''
# Make result directory and the result file.
result_dir = os.path.join(cfg.DIR.OUT_PATH, cfg.TEST.EXP_NAME)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
result_fn = os.path.join(result_dir, 'result.mat')
print("Exp file will be written to: " + result_fn)
# Make a network and load weights
NetworkClass = load_model(cfg.CONST.NETWORK_CLASS)
#print('Network definition: \n')
#print(inspect.getsource(NetworkClass.network_definition))
net = NetworkClass()
net.cuda()
solver = Solver(net)
solver.load(cfg.CONST.WEIGHTS)
# set constants
batch_size = cfg.CONST.BATCH_SIZE
# set up testing data process. We make only one prefetching process. The
# process will return one batch at a time.
queue = Queue(cfg.QUEUE_SIZE)
data_pair = category_model_id_pair(dataset_portion=cfg.TEST.DATASET_PORTION)
processes = make_data_processes(queue, data_pair, 1, repeat=False, train=False)
num_data = len(processes[0].data_paths)
num_batch = int(num_data / batch_size)
# prepare result container
results = {'cost': np.zeros(num_batch),
'mAP': np.zeros((num_batch, batch_size))}
# Save results for various thresholds
for thresh in cfg.TEST.VOXEL_THRESH:
results[str(thresh)] = np.zeros((num_batch, batch_size, 5))
# Get all test data
batch_idx = 0
for batch_img, batch_voxel in get_while_running(processes[0], queue):
if batch_idx == num_batch:
break
#activations is a list of torch.cuda.FloatTensor
pred, loss, activations = solver.test_output(batch_img, batch_voxel)
#convert pytorch tensor to numpy array
pred = pred.data.cpu().numpy()
loss = loss.data.cpu().numpy()
for j in range(batch_size):
# Save IoU per thresh
for i, thresh in enumerate(cfg.TEST.VOXEL_THRESH):
r = evaluate_voxel_prediction(pred[j, ...], batch_voxel[j, ...], thresh)
results[str(thresh)][batch_idx, j, :] = r
# Compute AP
precision = sklearn.metrics.average_precision_score(
batch_voxel[j, 1].flatten(), pred[j, 1].flatten())
results['mAP'][batch_idx, j] = precision
# record result for the batch
results['cost'][batch_idx] = float(loss)
print('%d/%d, costs: %f, mAP: %f' %
(batch_idx, num_batch, loss, np.mean(results['mAP'][batch_idx])))
batch_idx += 1
print('Total loss: %f' % np.mean(results['cost']))
print('Total mAP: %f' % np.mean(results['mAP']))
sio.savemat(result_fn, results)
def main():
config = get_config()
if config.resume:
json_config = json.load(open(config.resume + '/config.json', 'r'))
json_config['resume'] = config.resume
config = edict(json_config)
if config.is_cuda and not torch.cuda.is_available():
raise Exception("No GPU found")
device = get_torch_device(config.is_cuda)
logging.info('===> Configurations')
dconfig = vars(config)
for k in dconfig:
logging.info(' {}: {}'.format(k, dconfig[k]))
DatasetClass = load_dataset(config.dataset)
if config.test_original_pointcloud:
if not DatasetClass.IS_FULL_POINTCLOUD_EVAL:
raise ValueError('This dataset does not support full pointcloud evaluation.')
if config.evaluate_original_pointcloud:
if not config.return_transformation:
raise ValueError('Pointcloud evaluation requires config.return_transformation=true.')
if (config.return_transformation ^ config.evaluate_original_pointcloud):
raise ValueError('Rotation evaluation requires config.evaluate_original_pointcloud=true and '
'config.return_transformation=true.')
logging.info('===> Initializing dataloader')
if config.is_train:
train_data_loader = initialize_data_loader(
DatasetClass,
config,
phase=config.train_phase,
num_workers=config.num_workers,
augment_data=True,
shuffle=True,
repeat=True,
batch_size=config.batch_size,
limit_numpoints=config.train_limit_numpoints)
val_data_loader = initialize_data_loader(
DatasetClass,
config,
num_workers=config.num_val_workers,
phase=config.val_phase,
augment_data=False,
shuffle=True,
repeat=False,
batch_size=config.val_batch_size,
limit_numpoints=False)
if train_data_loader.dataset.NUM_IN_CHANNEL is not None:
num_in_channel = train_data_loader.dataset.NUM_IN_CHANNEL
else:
num_in_channel = 3 # RGB color
num_labels = train_data_loader.dataset.NUM_LABELS
else:
test_data_loader = initialize_data_loader(
DatasetClass,
config,
num_workers=config.num_workers,
phase=config.test_phase,
augment_data=False,
shuffle=False,
repeat=False,
batch_size=config.test_batch_size,
limit_numpoints=False)
if test_data_loader.dataset.NUM_IN_CHANNEL is not None:
num_in_channel = test_data_loader.dataset.NUM_IN_CHANNEL
else:
num_in_channel = 3 # RGB color
num_labels = test_data_loader.dataset.NUM_LABELS
logging.info('===> Building model')
NetClass = load_model(config.model)
if config.wrapper_type == 'None':
model = NetClass(num_in_channel, num_labels, config)
logging.info('===> Number of trainable parameters: {}: {}'.format(NetClass.__name__,
count_parameters(model)))
else:
wrapper = load_wrapper(config.wrapper_type)
model = wrapper(NetClass, num_in_channel, num_labels, config)
logging.info('===> Number of trainable parameters: {}: {}'.format(
wrapper.__name__ + NetClass.__name__, count_parameters(model)))
logging.info(model)
model = model.to(device)
if config.weights == 'modelzoo': # Load modelzoo weights if possible.
logging.info('===> Loading modelzoo weights')
model.preload_modelzoo()
# Load weights if specified by the parameter.
elif config.weights.lower() != 'none':
logging.info('===> Loading weights: ' + config.weights)
state = torch.load(config.weights)
if config.weights_for_inner_model:
model.model.load_state_dict(state['state_dict'])
else:
if config.lenient_weight_loading:
matched_weights = load_state_with_same_shape(model, state['state_dict'])
model_dict = model.state_dict()
model_dict.update(matched_weights)
model.load_state_dict(model_dict)
else:
model.load_state_dict(state['state_dict'])
if config.is_train:
train(model, train_data_loader, val_data_loader, config)
else:
test(model, test_data_loader, config)
import cv2
from pcn import pcn_detect
from models import load_model
from utils import draw_face
if __name__ == '__main__':
# network detection
nets = load_model()
cam = cv2.VideoCapture(0)
while cam.isOpened():
ret, img = cam.read()
faces = pcn_detect(img, nets)
for face in faces:
draw_face(img, face)
cv2.imshow('PCN', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def inference(model, output, csv, imgpath, valid):
np.random.seed(0)
test_set_full = pd.read_csv(csv)
list_ids = list(test_set_full["image"].values)
list_masks = list(test_set_full["mask"].values)
# get root directories
base_img_path = imgpath
img_path = os.path.join(base_img_path, "images")
mask_path = os.path.join(base_img_path, "masks")
list_ids = list_ids
list_masks = list_masks
labels = dict(zip(list_ids, list_masks))
if valid:
ground_truth_csv = pd.read_csv(
os.path.join("single_instance_dataset_wradius.csv"))
mask_output_path = os.path.join(output, model, "prediction_masks")
if not os.path.exists(mask_output_path):
os.makedirs(mask_output_path)
valid_generator = utils.DataGeneratorMobileNetKeras(
batch_size=1,
img_path=img_path,
labels=labels,
list_IDs=list_ids,
n_channels=3,
n_channels_label=1,
shuffle=False,
mask_path=mask_path,
augmentation=False,
)
model_loaded = models.load_model(
os.path.join(".", "output", "models", model))
prediction = model_loaded.predict_generator(generator=valid_generator,
use_multiprocessing=True,
verbose=True)
threshold_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
test_images = list_ids
valid_list = list()
nan_keys = [
"component_x",
"component_y",
"gt_area",
"pixel_distance",
"norm_distance",
"prediction_area",
"component_area",
"component_relative_area",
"component_intersection",
"component_union",
"component_iou",
"component_pw_recall",
"component_pw_precision",
]
for threshold in threshold_list:
array_pred = np.copy(prediction)
for i in np.arange(0, prediction.shape[0]):
# get prediction and normalize
pred = array_pred[i, :, :, 0]
pred = (pred > threshold).astype(bool)
cv2.imwrite(
mask_output_path + "/" + (str(threshold)) + test_images[i],
(pred.astype(np.uint8)) * 255,
)
if valid:
data = {}
row = utils.get_sample_ground_truth(test_images[i],
ground_truth_csv)
gt_center = np.ndarray([1, 2])
gt_center[0, 0] = (row["x_center_resize"].values[0]) / 2
gt_center[0, 1] = (row["y_center_resize"].values[0]) / 2
diam_resize = (row["diam_resize"].values[0]) / 2
ground_truth = utils.read_image_grayscale(
os.path.join(mask_path,
"mask_" + test_images[i][:-3] + "png"))
ground_truth = cv2.resize(ground_truth, (0, 0), fx=0.5, fy=0.5)
ground_truth = (ground_truth > 0).astype(int)
(
num_labels,
labeled_img,
centers,
iou_array,
) = connected_components_with_threshold(
(pred).astype(np.uint8), 0, ground_truth)
gt_area = np.sum(ground_truth)
prediction_area = np.sum(pred)
# computo de metricas segun resultado de component analysis
if centers is not None:
for component_label in range(num_labels):
filtered_component = labeled_img == (component_label +
1)
component_center = mass_center(filtered_component)
pixel_distance = np.linalg.norm(
np.subtract(gt_center, component_center))
data["mask_name"] = test_images[i]
data["threshold"] = str(threshold)
data["mask_x"] = gt_center[0, 0]
data["mask_y"] = gt_center[0, 1]
# component_x
data["component_x"] = component_center[0]
# component_y
data["component_y"] = component_center[1]
data["pixel_distance"] = pixel_distance
data["norm_distance"] = pixel_distance / diam_resize
data["gt_area"] = gt_area
data["prediction_area"] = prediction_area
# component_area
component_area = np.sum(filtered_component)
data["component_area"] = component_area
# relative_area
data["component_relative_area"] = (component_area /
prediction_area)
# component pixelwise metrics
intersection = np.sum(
np.logical_and(filtered_component, ground_truth))
union = np.sum(
np.logical_or(filtered_component, ground_truth))
data["component_intersection"] = intersection
data["component_union"] = union
data["component_iou"] = intersection / union
data["component_pw_recall"] = intersection / gt_area
data[
"component_pw_precision"] = intersection / component_area
else: # no buds detected register it in the metrics dict
data["mask_name"] = test_images[i]
data["threshold"] = str(threshold)
data["mask_x"] = gt_center[0, 0]
data["mask_y"] = gt_center[0, 1]
# mass assing np.nan to nan-able keys
data.update(dict.fromkeys(nan_keys, np.nan))
valid_list.append(data)
print(model + " images generated!")
if valid:
csv = pd.DataFrame(valid_list)
csv.to_csv(os.path.join(output, model[:-3] + ".csv"))
def get_recommendations(recommender_id, user, count):
recommender = get_recommender(recommender_id)
model = load_model(recommender["model"])
count = count or recommender["default-recommendations"]
return model.recommend(user=user, count=count)
def main(args):
print('Feature extractor training.')
print('CONFIGURATION:\t{}'.format(args.config))
with open(args.config) as json_config_file:
config = utils.AttrDict(json.load(json_config_file))
# Set up output directory
experiment_name = generate_experiment_name(config)
model_dir = os.path.join(os.path.expanduser(config.output.output_dir), experiment_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
print('Model saved at {}'.format(model_dir))
config_filename = path_leaf(args.config)
copyfile(args.config, os.path.join(model_dir, config_filename))
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
source_loader = dataloaders.get_traindataloaders(config.source_dataset,
config)
target_loader = dataloaders.get_traindataloaders(config.target_dataset,
config)
evaluators_list = dataloaders.get_evaluators(config.evaluation_datasets,
config)
# Set up training model
print('Building training model')
if config.model.checkpoint:
checkpoint_path = config.model.checkpoint_path
else:
checkpoint_path = None
model = models.load_model(config.model.model_arch,
device,
checkpoint_path=checkpoint_path,
embedding_size=config.model.embedding_size,
imgnet_pretrained=config.model.pretrained_imagenet)
optimizer = optim.SGD(model.parameters(), lr=config.hyperparameters.learning_rate, momentum=0.9, nesterov=True, weight_decay=2e-4)
scheduler = lr_scheduler.ExponentialLR(optimizer, config.hyperparameters.learning_rate_decay_factor)
model = model.to(device)
plotter = utils.VisdomPlotter(config.visdom.server ,env_name=experiment_name, port=config.visdom.port)
print('Fitting source dataset.')
gmixture = clustering.distance_supervised_gaussian_mixture(source_loader,
model,
device,
_plotter=plotter,
name='Source Gaussians')
print('Fitting target dataset.')
clustering.update_gaussian_mixture(gmixture,
target_loader,
model,
device,
_plotter=plotter,
name='Target Gaussians')
print('DualTriplet loss training mode.')
miner = miners.get_miner(config.miner,
config.hyperparameters.margin,
config.hyperparameters.people_per_batch,
plotter,
deadzone_ratio=config.hyperparameters.deadzone_ratio)
miner.gmixture = gmixture
loss = losses.DualtripletLoss(config.hyperparameters.margin,
config.hyperparameters.lamda,
plotter)
model_trainer = trainer.Dualtriplet_Trainer(model,
miner,
loss,
optimizer,
scheduler,
device,
plotter,
config.hyperparameters.margin,
config.model.embedding_size,
batch_size=config.hyperparameters.batch_size)
if not os.path.isdir(model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
# Loop over epochs
epoch = 0
print('Training Launched.')
while epoch < config.hyperparameters.n_epochs:
# Validation
for evaluator in evaluators_list:
print('\nEvaluation on {}'.format(evaluator.test_name))
evaluator.evaluate(model,
device,
plotter=plotter,
epoch=epoch)
# Training
print('\nExperimentation {}'.format(config.experiment))
print('Train Epoch {}'.format(epoch))
model_trainer.Train_Epoch(source_loader, target_loader, epoch)
# Save model
# if not (epoch + 1) % config.output.save_interval:
#
# model_file_path = os.path.join(model_dir, 'model_{}.pth'.format(epoch))
# print('\nSave model at {}'.format(model_file_path))
# torch.save({'epoch': epoch,
# 'model_state_dict': utils.state_dict_to_cpu(model.state_dict()),
# 'optimizer_state_dict': optimizer.state_dict(),
# 'scheduler_state_dict': scheduler.state_dict(),
# 'embedding_size': config.model.embedding_size
# }, model_file_path)
epoch += 1
model_file_path = os.path.join(model_dir, 'model_{}.pth'.format(epoch))
print('\nSave model at {}'.format(model_file_path))
torch.save({'epoch': epoch,
'model_state_dict': utils.state_dict_to_cpu(model.state_dict()),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
'embedding_size': config.model.embedding_size
}, model_file_path)
print('Finish.')
