def test():
import test
pygame.init()
tb = TextBox(text="Fishy")
clock = pygame.time.Clock()
group = pygame.sprite.LayeredDirty(tb, layer=0, _use_update=True)
def testlogic(event):
if event == None:
return
if event.type == pygame.KEYDOWN:
if event.key == 27:
return True
if event.key == K_BACKSPACE:
if len(tb.text) > 0:
tb.text = tb.text[:-1]
return
if event.key >= 256:
return
tb.text += chr(event.key)
def testrender(screen):
clock.tick()
time = clock.get_time()
group.update(time)
bgd = pygame.Surface((screen.get_width(), screen.get_height()))
group.draw(screen, bgd=bgd)
test.test(testlogic, testrender)
def startSouth(below, ordered = []):
"Sort Southern cities into order"
jyunban = sortx(below, 'ascending') #Decide starting city
minami = len(jyunban)
ordered.append(jyunban[0])
jyunban.pop(0)
alldistance = getdistances(ordered[0], jyunban) #Decide last southern city
index = alldistance.index(max(alldistance))
furthest = jyunban[index]
jyunban.pop(index)
while len(ordered) < minami/2: #Route from start point
decidenext(jyunban, ordered, 'south')
test(ordered)
second = sortx(jyunban, 'descending') #Route from end point
lensecond = len(second)
gyaku = []
gyaku.append(furthest)
while len(gyaku) < lensecond+1:
decidenext(second, gyaku, 'north')
for i in range(1,len(gyaku)+1): #Put two routes together
ordered.append(gyaku[-i])
#checkintercept(ordered)
return ordered
def run(train_file, valid_file, test_file, output_file):
'''The function to run your ML algorithm on given datasets, generate the output and save them into the provided file path
Parameters
----------
train_file: string
the path to the training file
valid_file: string
the path to the validation file
test_file: string
the path to the testing file
output_file: string
the path to the output predictions to be saved
'''
## your implementation here
# read data from input
train_samples, word2num = train_data_prepare(train_file)
valid_samples = test_data_prepare(valid_file, word2num, 'valid')
# your training algorithm
model = train(train_samples, valid_samples, word2num)
# your prediction code
test(test_file, output_file, word2num, model)
def main (argv):
logger = initLogger();
if (len(argv) == 0):
print "Missing argument. Options: init, store, list, test, get, restore";
elif (argv[0] == "init"):
init.init(archiveDir);
elif (argv[0] == "store"):
if (len(argv) < 2):
print "Usage: mybackup store <directory>";
else:
store.store(archiveDir, argv[1], logger);
elif (argv[0] == "list"):
if (len(argv) < 2):
listBackups.list(archiveDir)
else:
listBackups.list(archiveDir, argv[1])
elif (argv[0] == "get"):
if (len(argv) < 2):
print "Usage: mybackup get <pattern>";
else:
restore.getFile(archiveDir, argv[1]);
elif (argv[0] == "restore"):
if (len(argv) < 2):
restore.restoreAll(archiveDir)
else:
restore.restoreAll(archiveDir, argv[1])
elif (argv[0] == "test"):
test.test(archiveDir, logger)
else:
print "Unknown option: "+argv[0];
def main():
print "In Main Experiment\n"
# get the classnames from the directory structure
directory_names = list(set(glob.glob(os.path.join("train", "*"))).difference(set(glob.glob(os.path.join("train", "*.*")))))
# get the number of rows through image count
numberofImages = parseImage.gestNumberofImages(directory_names)
num_rows = numberofImages # one row for each image in the training dataset
# We'll rescale the images to be 25x25
maxPixel = 25
imageSize = maxPixel * maxPixel
num_features = imageSize + 2 + 128 # for our ratio
X = np.zeros((num_rows, num_features), dtype=float)
y = np.zeros((num_rows)) # numeric class label
files = []
namesClasses = list() #class name list
# Get the image training data
parseImage.readImage(True, namesClasses, directory_names,X, y, files)
print "Training"
# get test result
train.train(X, y, namesClasses)
print "Testing"
test.test(num_rows, num_features, X, y, namesClasses = list())
def cli_main():
if sys.version_info[0] < 3:
print("STK needs Python 3.x to run. Check your execution path and file associations.")
print("Your Python version is: ")
print(sys.version)
return
if len(sys.argv) >= 2:
if sys.argv[1] == "--test":
import test
test.test(sys.argv[2:])
elif sys.argv[1] == "--markdown2html" and len(sys.argv) == 4:
input_file = sys.argv[2]
output_dir = sys.argv[3]
parser = MDParser()
parser.parse_file(input_file)
exporter = HtmlExporter()
exporter.export(parser.saga, output_dir)
elif sys.argv[1] == "--generate_ep_card" and len(sys.argv) == 4:
#generate_episode_card(sys.argv[2], sys.argv[3])
#TODO generate_episode_card
print("Not implemented yet...")
else:
print_usage()
else:
print_usage()
def mainHandler(threadNum, link, deep, key, test):
event = threading.Event() # 产生一个event对象，对象维护一个flag，当
event.clear() # 将event的flag设为false
pool = threadPool(threadNum, event) # 初始化一个threadNum个线程的线程池，event对象用于通知主线程继续执行
showProgress(pool.getQueue(), deep, event)
pool.putJob((link, deep), key) # job是(link,deep)的一个tuple，key是关键字
pool.wait() # 阻塞主线程
if test: # 需要自测模块运行
import test
test.test(key, dbFile)
def test3():
cost = [1,1,1]
eqs = [[1,1,0], [2,2,2]]
eqB = [2, 5]
expectedCost = [1,1,1]
expectedConstraints = [[3,-2,0], [1,1,0]]
expectedThresholds = [2, 5]
test((expectedCost, expectedConstraints, expectedThresholds),
simplex.standardForm(cost, equalities=eqs, eqThreshold=eqB))
def mainHandler(threadNum, link, deep, key, test):
event = threading.Event()
event.clear()
pool = threadPool(threadNum, event)
showProgress(pool.getQueue(), deep, event)
pool.putJob((link, deep), key)
pool.wait()
if test: # 需要自测模块运行
import test
test.test(key, dbFile)
def test():
import test
pygame.init()
image = pygame.image.load("qbird.png")
sprite = ManipulatableDirtySprite(image = image)
clock = pygame.time.Clock()
group = pygame.sprite.LayeredDirty(sprite, layer = 0, _use_update = True)
keysused = {}
def testlogic(event):
if event == None:
time = clock.get_time()
if K_UP in keysused and keysused[K_UP]:
sprite.y -= 1
if K_DOWN in keysused and keysused[K_DOWN]:
sprite.y += 1
if K_LEFT in keysused and keysused[K_LEFT]:
sprite.x -= 1
if K_RIGHT in keysused and keysused[K_RIGHT]:
sprite.x += 1
if K_q in keysused and keysused[K_q]:
sprite.rotation -= 1
if K_e in keysused and keysused[K_e]:
sprite.rotation += 1
if K_w in keysused and keysused[K_w]:
sprite.ycenter -= 0.015625
if K_s in keysused and keysused[K_s]:
sprite.ycenter += 0.015625
if K_a in keysused and keysused[K_a]:
sprite.xcenter -= 0.015625
if K_d in keysused and keysused[K_d]:
sprite.xcenter += 0.015625
if K_r in keysused and keysused[K_r]:
sprite.yscale += 0.015625
if K_f in keysused and keysused[K_f]:
sprite.yscale -= 0.015625
if K_t in keysused and keysused[K_t]:
sprite.xscale += 0.015625
if K_g in keysused and keysused[K_g]:
sprite.xscale -= 0.015625
if K_y in keysused and keysused[K_y]:
sprite.opacity += 0.015625
if K_h in keysused and keysused[K_h]:
sprite.opacity -= 0.015625
return
if event.type == pygame.KEYDOWN:
if event.key == 27:
return True
keysused[event.key] = True
if event.type == pygame.KEYUP:
keysused[event.key] = False
def testrender(screen):
clock.tick()
bgd = pygame.Surface((screen.get_width(), screen.get_height()))
group.draw(screen, bgd = bgd)
test.test(testlogic, testrender)
def test2():
cost = [1,1,1]
lts = [[3,-2,0]]
ltB = [7]
eqs = [[1,1,0]]
eqB = [2]
expectedCost = [1,1,1,0]
expectedConstraints = [[3,-2,0,1], [1,1,0,0]]
expectedThresholds = [7,2]
test((expectedCost, expectedConstraints, expectedThresholds),
simplex.standardForm(cost, lessThans=lts, ltThreshold=ltB, equalities=eqs, eqThreshold=eqB))
def evalQuiz(user, data):
f = open(join(quiz_requests, '%s.quiz-%s.%d' %
(user, data['page'].split('-')[-1],
int(time() * 1000))), 'w')
f.write(str(data))
f.close()
path.insert(0, join(course_material, str(data['page'])))
#test_mod = __import__(join(course_material, str(data['page']), 'test.py'))
import test as test_mod
test_mod.test(user, data)
del test_mod
def trainrf(model_id,train_x,train_y,valid_x,valid_y,test_x):
train_x,train_y=shuffle(train_x,train_y)
random_state=random.randint(0, 1000000)
print('random state: {state}'.format(state=random_state))
clf = RandomForestClassifier(n_estimators=random.randint(50,5000),
criterion='gini',
max_depth=random.randint(10,1000),
min_samples_split=random.randint(2,50),
min_samples_leaf=random.randint(1,10),
min_weight_fraction_leaf=random.uniform(0.0,0.5),
max_features=random.uniform(0.1,1.0),
max_leaf_nodes=random.randint(1,10),
bootstrap=False,
oob_score=False,
n_jobs=30,
random_state=random_state,
verbose=0,
warm_start=True,
class_weight=None
)
clf.fit(train_x, train_y)
valid_predictions1 = clf.predict_proba(valid_x)
test_predictions1= clf.predict_proba(test_x)
t1 = test(valid_y,valid_predictions1)
ccv = CalibratedClassifierCV(base_estimator=clf,method="sigmoid",cv='prefit')
ccv.fit(valid_x,valid_y)
valid_predictions2 = ccv.predict_proba(valid_x)
test_predictions2= ccv.predict_proba(test_x)
t2 = test(valid_y,valid_predictions2)
if t2<t1:
valid_predictions=valid_predictions2
test_predictions=test_predictions2
t=t2
else:
valid_predictions=valid_predictions1
test_predictions=test_predictions1
t=t1
if t < 0.450:
data.saveData(valid_predictions,"../valid_results/valid_"+str(model_id)+".csv")
data.saveData(test_predictions,"../results/results_"+str(model_id)+".csv")
def testFromPost():
cost = [1,1,1]
gts = [[0,1,4]]
gtB = [10]
lts = [[3,-2,0]]
ltB = [7]
eqs = [[1,1,0]]
eqB = [2]
expectedCost = [1,1,1,0,0]
expectedConstraints = [[0,1,4,-1,0], [3,-2,0,0,1], [1,1,0,0,0]]
expectedThresholds = [10,7,2]
test((expectedCost, expectedConstraints, expectedThresholds),
simplex.standardForm(cost, gts, gtB, lts, ltB, eqs, eqB))
def train(model_id,train_x,train_y,valid_x,valid_y,test_x):
train_x,train_y=shuffle(train_x,train_y)
random_state=random.randint(0, 1000000)
print('random state: {state}'.format(state=random_state))
clf = RandomForestClassifier(bootstrap=False, class_weight=None,
criterion='entropy', max_depth=29008, max_features=36,
max_leaf_nodes=None, min_samples_leaf=5, min_samples_split=3,
min_weight_fraction_leaf=0.0, n_estimators=4494, n_jobs=8,
oob_score=False, random_state=979271, verbose=0,
warm_start=False)
clf.fit(train_x, train_y)
ccv = CalibratedClassifierCV(base_estimator=clf,method="sigmoid",cv="prefit")
ccv.fit(valid_x,valid_y)
valid_predictions = ccv.predict_proba(valid_x)
test_predictions= ccv.predict_proba(test_x)
loss = test(valid_y,valid_predictions,True)
if loss<0.52:
data.saveData(valid_predictions,"../valid_results/valid_"+str(model_id)+".csv")
data.saveData(test_predictions,"../results/results_"+str(model_id)+".csv")
def cal(code):
try:
a=gc.getData(code)
except:
print('{} is wrong'.format(code))
else:
print('{} is running'.format(code))
if a is not None and len(a)>60:
global total
total=total+1
a.sort_index(inplace=True)
gc.ma(a,'close',[5,10,15,20,25])
MA_column=a.columns[-5:]
a['Diff']= 100 * ((a[MA_column].max(axis=1) - a[MA_column].min(axis=1))/a[MA_column].max(axis=1))
b=a[-65:-5]['Diff'].mean()
if b<2 and a[-65:-5]['Diff'].max()<10:
good.append(code)
global can_try
can_try=can_try+1
# print('{},{}'.format(a[-2:-1].index.values[0],a[-1:].index.values[0]))
diff=test.test(code,a[-6:-5].index.values[0],a[-1:].index.values[0])
diff_list.append(diff)
if diff>0:
global test_try
test_try=test_try+1
def k_result(k):
train_k = random.sample(train_set,k)
scp_k = os.path.join(tempdir,'scp_k')
with open(scp_k,'w') as f:
f.writelines(train_k)
final_dir = train(outdir, config, scp_k, proto, htk_dict, words_mlf, monophones, tempdir)
return test(outdir, final_dir, wdnet, htk_dict, monophones, scp_test, words_mlf, tempdir)
def run():
args = parse_args()
params = parser.Yaml(file_name=args.params)
env = rave.Environment()
env.SetViewer("qtcoin")
env.Load(params.scene)
env.UpdatePublishedBodies()
robot = env.GetRobots()[0]
time.sleep(0.1) # give time for environment to update
navi = navigation.Navigation(robot, params, verbose=args.verbose)
if args.test:
test.test(navi)
else:
navi.run()
def urls(environ):
template = os.path.join(environ['DOCUMENT_ROOT'], 'wsgi/vhost1/template')
if environ['PATH_INFO'] == '/vhost1':
if environ['REQUEST_METHOD'] == 'POST':
return publish.post(environ, template)
return publish.edit(environ, template)
elif environ['PATH_INFO'] == '/vhost/pub':
return publish.pub(environ)
else:
return test.test(environ, template)
def run(optim):
progress = make_progressbar("Training with " + str(optim), 5)
progress.start()
model = net()
model.training()
for epoch in range(5):
train(Xtrain, ytrain, model, optim, criterion, batch_size, "train")
train(Xtrain, ytrain, model, optim, criterion, batch_size, "stats")
progress.update(epoch + 1)
progress.finish()
model.evaluate()
nll, _ = test(Xtrain, ytrain, model, batch_size)
_, nerr = test(Xval, yval, model, batch_size)
print("Trainset NLL: {:.2f}".format(nll))
print("Testset errors: {}".format(nerr))
def urls(environ):
template = os.path.join(environ['DOCUMENT_ROOT'], 'test/template')
if environ['PATH_INFO'] == '/test/work':
return test.work()
elif environ['PATH_INFO'] == '/test/html':
return test.html(environ, template)
elif environ['PATH_INFO'] == '/test2':
return test2.test2()
else:
return test.test()
def main(_):
pp.pprint(flags.FLAGS.__flags)
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
random.seed(31241)
np.random.seed(41982)
tf.set_random_seed(1327634)
color = True # Must change this and the dataset Flags to the correct path to use color
if FLAGS.is_debug:
reader = Bouncing_Balls_Data_Reader(FLAGS.dataset, FLAGS.batch_size, color=color, train_size=160*5, validation_size=8*5, test_size=8*5, num_partitions=5)
else:
reader = Bouncing_Balls_Data_Reader(FLAGS.dataset, FLAGS.batch_size, color=color)
data_fn = lambda epoch, batch_index: reader.read_data(batch_index, reader.TRAIN)
frame_shape = reader.read_data(0, reader.TRAIN).shape[2:]
print("Frame shape: ", frame_shape)
num_batches = reader.num_batches(reader.TRAIN)
print("Num batches: %d" % num_batches)
input_sequence_range = range(5, 16)
print("Input sequence range min: %d, max: %d" % (min(input_sequence_range), max(input_sequence_range)))
save_sample_fn = utils.gen_save_sample_fn(FLAGS.sample_dir, image_prefix="train")
with tf.Session() as sess:
pgn = PGN(sess, FLAGS.dataset_name, FLAGS.epoch, num_batches, FLAGS.batch_size, input_sequence_range,
data_fn, frame_shape=frame_shape, save_sample_fn=save_sample_fn, checkpoint_dir=FLAGS.checkpoint_dir,
lambda_adv_loss= FLAGS.lambda_adv_loss)
if FLAGS.is_train:
pgn.train()
else:
print("Loading from: %s" %(FLAGS.checkpoint_dir,))
if pgn.load(FLAGS.checkpoint_dir) :
print(" [*] Successfully loaded")
else:
print(" [!] Load failed")
if FLAGS.is_test:
result = test.test(pgn, reader)
result_str = pp.pformat(result)
fid = open(os.path.join(FLAGS.sample_dir, 'test_out.txt'), mode='w')
fid.write(unicode(result_str))
fid.close()
if FLAGS.is_visualize:
for i in range(3):
vid_seq = reader.read_data(i, data_set_type=reader.TEST, batch_size=1)[:, 0, :, :, :]
utils.make_prediction_gif(pgn, os.path.join(FLAGS.sample_dir, 'vis_%d.gif' % i), video_sequence=vid_seq)
utils.plot_convergence(pgn.get_MSE_history(), "MSE Convergence",
path=os.path.join(FLAGS.sample_dir, "vis_MSE_convergence.png"))
def call_script(self): #Disable all the buttons self.button.config(state='disabled') self.slogan.config(state='disabled') #Perform the task message=test.test() self.message['text'] = message time.sleep(0.5) #Enable all the buttons self.button.config(state='active') self.slogan.config(state='active')
def train(model_id,train_x,train_y,valid_x,valid_y,test_x):
train_x,train_y=shuffle(train_x,train_y)
# normalization
scaler = StandardScaler()
train_x = scaler.fit_transform(train_x)
valid_x = scaler.transform(valid_x)
test_x = scaler.transform(test_x)
random_state=random.randint(0, 1000000)
print('random state: {state}'.format(state=random_state))
clf = LogisticRegression(penalty='l2',
dual=False,
tol=0.0001,
C=1.0,
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
random_state=None,
solver='lbfgs',
max_iter=1000,
multi_class='ovr',
verbose=True
)
clf.fit(train_x, train_y)
valid_predictions = clf.predict_proba(valid_x)
test(valid_y,valid_predictions)
ccv = CalibratedClassifierCV(base_estimator=clf,method="sigmoid",cv='prefit')
ccv.fit(train_x,train_y)
valid_predictions = ccv.predict_proba(valid_x)
test(valid_y,valid_predictions)
test_predictions= ccv.predict_proba(test_x)
data.saveData(valid_predictions,"../valid_results/valid_"+str(model_id)+".csv")
data.saveData(test_predictions,"../results/results_"+str(model_id)+".csv")
def test():
import test
import Bar
pygame.init()
bar = Bar.Bar(12340, 12345)
bar2 = Bar.Bar(120, 135, color = (0, 0 ,255, 255))
bar2.rect.top = 30
clock = pygame.time.Clock()
window = LayeredDirtySprite(width = 120, height = 100)
window.add(bar)
window.add(bar2)
window.x = 100
window.y = 200
window.ycenter = -2.0
group = pygame.sprite.LayeredDirty(window, layer = 0, _use_update = True)
bgd = []
def testlogic(event):
if event == None:
window.rotation += .01
return
if event.type == pygame.KEYDOWN:
if event.key == 27:
return True
if event.key >= 256:
return
import random
if event.mod & 4095 == 0:
bar.actual -= min((random.randint(5, 500), bar.actual))
else:
bar.actual += min((random.randint(5, 500), bar._max - bar.actual))
def testrender(screen):
clock.tick()
time = clock.get_time()
group.update(time)
if len(bgd) == 0:
bgd.append(pygame.Surface((screen.get_width(), screen.get_height())))
bgd[0].fill((0, 255, 0))
group.clear(screen, bgd[0])
group.draw(screen)
test.test(testlogic, testrender)
def urls(environ):
template = os.path.join(environ['DOCUMENT_ROOT'], 'wsgi/test/template')
if environ['PATH_INFO'] == '/test/vhost':
return test.vhost(environ, template)
elif environ['PATH_INFO'] == '/test/post':
return test.post(environ, template)
elif environ['PATH_INFO'] == '/ws':
return test.ws(environ, template)
elif environ['PATH_INFO'] == '/foobar/':
return test.foobar(environ, template)
else:
return test.test(environ, template)
def Prune(fileName, tree,Train, Validate, Predict, testFile, NeedPrune):
csv = csvRead(fileName)
columnCsv = []
modeList = []
traininfo = []
# print len(csv[1][0])
for num in range(len(csv[1][0])):
# columnCsv.append(columnCreate(csv[1], num))
columnCsv = columnCreate(csv[1], num)
categoryOrnumeric(columnCsv, csv, num, modeList, traininfo)
myTree = createTree(csv[1], csv[0],modeList)
if NeedPrune == True:
if Validate:
test.test(testFile,myTree, traininfo)
if Predict:
output.test(testFile,myTree,traininfo)
# f = open("tree.txt", "w")
# output.test('btest.csv',myTree,traininfo)
# drawTree(myTree,0,f)
# plotting.accurancyGet("bvalidate.csv", myTree, traininfo)
return myTree,csv[1],traininfo
def run_for_values_comb(filename, result_file):
print 'Data set: {}\n'.format(filename)
for depth, trees, repl, sam_size, fold, dis in [(depth, trees, repl, sam_size, fold, dis)
for depth in depth_values
for trees in trees_values
for repl in replacement_values
for sam_size in sample_size_values
for fold in folds_values
for dis in discretization_values]:
result = test(os.path.join(sample_data_dir, filename), depth, trees, repl, sam_size, fold, 0, dis)
print '{} for: Depth: {}, tree no: {}, replacement: {}, sample size: {}, folds no: {}, discretization: {}' \
.format(result, depth, trees, repl, sam_size, fold, dis)
result_file.write('{},{},{},{},{},{},{},{}\n'
.format(filename, depth, trees, repl, sam_size, fold, dis, result))
def main(passed_args):
args = init.getArgs(passed_args)
# If auth arg is present, use it for 'get' calls.
# Create the requests session to use for later 'get' calls.
session = requests.session()
# If there is an 'auth' arg, post the session the correct login info for the session
if 'custom-auth' in args.keys():
if args['custom-auth'] == 'dvwa':
payload = {'password': '******', 'username': '******'}
response = session.post('http://127.0.0.1/dvwa/login.php', data=payload)
session.cookies = response.cookies
#Call the appropriate function using the mode param.
if args['mode'] == 'discover':
discovered = discover.discover(args, session)
outputResults(discovered)
elif args['mode'] == 'test':
test.test(args, session)
else: # This else is somewhat redundant. Mode input is verified in getArgs.
print('Not a valid <mode> param. Must be \'discover\' or \'test\'')
sys.exit(0)
def full_test(name, query, train_set, test_set, method):
'''Performs a full automated test.
@param name: test and model name, arbitrary
@param query: question to train on
@param train_set: name of the training set of games
@param test_set: name of testing set of games
@param method: feature extraction method
'''
path = 'games/{}'.format(train_set)
begin = time.time()
dts.build_and_dump(name, query, method=method, path=path)
trn.train_and_save(name, 'logistic_regression')
print time.time()-begin
return tst.test(name, test_set, query, method)
def train():
cfg = opt.cfg
data = opt.data
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights # initial training weights
imgsz_min, imgsz_max, imgsz_test = opt.img_size # img sizes (min, max, test)
# Image Sizes
gs = 64 # (pixels) grid size
assert math.fmod(
imgsz_min,
gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)
opt.multi_scale |= imgsz_min != imgsz_max # multi if different (min, max)
if opt.multi_scale:
if imgsz_min == imgsz_max:
imgsz_min //= 1.5
imgsz_max //= 0.667
grid_min, grid_max = imgsz_min // gs, imgsz_max // gs
imgsz_min, imgsz_max = grid_min * gs, grid_max * gs
img_size = imgsz_max # initialize with max size
# Configure run
init_seeds()
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
test_path = data_dict['valid']
nc = 1 if opt.single_cls else int(
data_dict['classes']) # number of classes
hyp['cls'] *= nc / 80 # update coco-tuned hyp['cls'] to current dataset
# Remove previous results
for f in glob.glob('*_batch*.png') + glob.glob(results_file):
os.remove(f)
# Initialize model
model = Darknet(cfg).to(device)
# Optimizer
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if '.bias' in k:
pg2 += [v] # biases
elif 'Conv2d.weight' in k:
pg1 += [v] # apply weight_decay
else:
pg0 += [v] # all else
if opt.adam:
# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
else:
optimizer = optim.SGD(pg0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
del pg0, pg1, pg2
start_epoch = 0
best_fitness = 0.0
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
chkpt = torch.load(weights, map_location=device)
# load model
try:
chkpt['model'] = {
k: v
for k, v in chkpt['model'].items()
if model.state_dict()[k].numel() == v.numel()
}
model.load_state_dict(chkpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " \
"See https://github.com/ultralytics/yolov3/issues/657" % (opt.weights, opt.cfg, opt.weights)
raise KeyError(s) from e
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_fitness = chkpt['best_fitness']
# load results
if chkpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(chkpt['training_results']) # write results.txt
start_epoch = chkpt['epoch'] + 1
del chkpt
elif len(weights) > 0: # darknet format
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
load_darknet_weights(model, weights)
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model,
optimizer,
opt_level='O1',
verbosity=0)
# Scheduler https://github.com/ultralytics/yolov3/issues/238
lf = lambda x: (
((1 + math.cos(x * math.pi / epochs)) / 2
)**1.0) * 0.95 + 0.05 # cosine https://arxiv.org/pdf/1812.01187.pdf
scheduler = lr_scheduler.LambdaLR(optimizer,
lr_lambda=lf,
last_epoch=start_epoch - 1)
# scheduler = lr_scheduler.MultiStepLR(optimizer, [round(epochs * x) for x in [0.8, 0.9]], 0.1, start_epoch - 1)
# Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, '.-', label='LambdaLR')
# plt.xlabel('epoch')
# plt.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Initialize distributed training
if device.type != 'cpu' and torch.cuda.device_count(
) > 1 and torch.distributed.is_available():
dist.init_process_group(
backend='nccl', # 'distributed backend'
init_method=
'tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# Dataset
dataset = LoadImagesAndLabels(
train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
cache_images=opt.cache_images,
single_cls=opt.single_cls)
# Dataloader
batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=nw,
shuffle=not opt.
rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# Testloader
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(
test_path,
imgsz_test,
batch_size,
hyp=hyp,
rect=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls),
batch_size=batch_size,
num_workers=nw,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Model parameters
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(
device) # attach class weights
# Model EMA
ema = torch_utils.ModelEMA(model)
# Start training
nb = len(dataloader) # number of batches
n_burn = max(3 * nb,
500) # burn-in iterations, max(3 epochs, 500 iterations)
maps = np.zeros(nc) # mAP per class
# torch.autograd.set_detect_anomaly(True)
results = (
0, 0, 0, 0, 0, 0, 0
) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time()
print('Image sizes %g - %g train, %g test' %
(imgsz_min, imgsz_max, imgsz_test))
print('Using %g dataloader workers' % nw)
print('Starting training for %g epochs...' % epochs)
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 -
maps)**2 # class weights
image_weights = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=w)
dataset.indices = random.choices(range(dataset.n),
weights=image_weights,
k=dataset.n) # rand weighted idx
mloss = torch.zeros(4).to(device) # mean losses
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls',
'total', 'targets', 'img_size'))
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
# Burn-in
if ni <= n_burn * 2:
model.gr = np.interp(
ni, [0, n_burn * 2],
[0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
if ni == n_burn: # burnin complete
print_model_biases(model)
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(
ni, [0, n_burn],
[0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, [0, n_burn],
[0.9, hyp['momentum']])
# Multi-Scale training
if opt.multi_scale:
if ni / accumulate % 1 == 0: # adjust img_size (67% - 150%) every 1 batch
img_size = random.randrange(grid_min, grid_max + 1) * gs
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to 32-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Scale loss by nominal batch_size of 64
loss *= batch_size / 64
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Optimize accumulated gradient
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
ema.update(model)
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_cached() /
1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' %
(epoch, epochs - 1), mem,
*mloss, len(targets), img_size)
pbar.set_description(s)
# Plot images with bounding boxes
if ni < 1:
f = 'train_batch%g.png' % i # filename
plot_images(imgs=imgs, targets=targets, paths=paths, fname=f)
if tb_writer:
tb_writer.add_image(f,
cv2.imread(f)[:, :, ::-1],
dataformats='HWC')
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Update scheduler
scheduler.step()
# Process epoch results
ema.update_attr(model)
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
is_coco = any([
x in data
for x in ['coco.data', 'coco2014.data', 'coco2017.data']
]) and model.nc == 80
results, maps = test.test(cfg,
data,
batch_size=batch_size,
img_size=imgsz_test,
model=ema.ema,
save_json=final_epoch and is_coco,
single_cls=opt.single_cls,
dataloader=testloader)
# Write epoch results
with open(results_file, 'a') as f:
f.write(s + '%10.3g' * 7 % results +
'\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' %
(opt.bucket, opt.name))
# Write Tensorboard results
if tb_writer:
tags = [
'train/giou_loss', 'train/obj_loss', 'train/cls_loss',
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5',
'metrics/F1', 'val/giou_loss', 'val/obj_loss', 'val/cls_loss'
]
for x, tag in zip(list(mloss[:-1]) + list(results), tags):
tb_writer.add_scalar(tag, x, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save training results
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f:
# Create checkpoint
chkpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
'model':
ema.ema.module.state_dict()
if hasattr(model, 'module') else ema.ema.state_dict(),
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last checkpoint
torch.save(chkpt, last)
# Save best checkpoint
if (best_fitness == fi) and not final_epoch:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
# if epoch > 0 and epoch % 10 == 0:
# torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
n = opt.name
if len(n):
n = '_' + n if not n.isnumeric() else n
fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'],
[flast, fbest, fresults]):
if os.path.exists(f1):
os.rename(f1, f2) # rename
ispt = f2.endswith('.pt') # is *.pt
strip_optimizer(f2) if ispt else None # strip optimizer
os.system('gsutil cp %s gs://%s/weights' % (
f2, opt.bucket)) if opt.bucket and ispt else None # upload
if not opt.evolve:
plot_results() # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1,
(time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
def train(
cfg,
data_cfg,
weights_from="",
weights_to="",
save_every=10,
img_size=(1088, 608),
resume=False,
epochs=100,
batch_size=16,
accumulated_batches=1,
freeze_backbone=False,
opt=None,
):
# The function starts
timme = strftime("%Y-%d-%m %H:%M:%S", gmtime())
timme = timme[5:-3].replace('-', '_')
timme = timme.replace(' ', '_')
timme = timme.replace(':', '_')
weights_to = osp.join(weights_to, 'run' + timme)
mkdir_if_missing(weights_to)
if resume:
latest_resume = osp.join(weights_from, 'latest.pt')
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
f = open(data_cfg)
data_config = json.load(f)
trainset_paths = data_config['train']
dataset_root = data_config['root']
f.close()
transforms = T.Compose([T.ToTensor()])
# Get dataloader
dataset = JointDataset(dataset_root,
trainset_paths,
img_size,
augment=True,
transforms=transforms)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=True,
drop_last=True,
collate_fn=collate_fn)
# Initialize model
model = Darknet(cfg, dataset.nID)
# model = ResNet(cfg, dataset.nID)
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
if resume:
checkpoint = torch.load(latest_resume, map_location='cpu')
# Load weights to resume from
model.load_state_dict(checkpoint['model'])
model.cuda().train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad,
model.parameters()),
lr=opt.lr,
momentum=.9)
start_epoch = checkpoint['epoch'] + 1
if checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
del checkpoint # current, saved
else:
# Initialize model with backbone (optional)
if cfg.endswith('yolov3.cfg'):
load_darknet_weights(model,
osp.join(weights_from, 'darknet53.conv.74'))
cutoff = 75
elif cfg.endswith('yolov3-tiny.cfg'):
load_darknet_weights(model,
osp.join(weights_from, 'yolov3-tiny.conv.15'))
cutoff = 15
model.cuda().train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad,
model.parameters()),
lr=opt.lr,
momentum=.9,
weight_decay=1e-4)
model = torch.nn.DataParallel(model)
# Set scheduler
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(0.5 * opt.epochs),
int(0.75 * opt.epochs)],
gamma=0.1)
# An important trick for detection: freeze bn during fine-tuning
if not opt.unfreeze_bn:
for i, (name, p) in enumerate(model.named_parameters()):
p.requires_grad = False if 'batch_norm' in name else True
# model_info(model)
t0 = time.time()
for epoch in range(epochs):
epoch += start_epoch
logger.info(
('%8s%12s' + '%10s' * 6) % ('Epoch', 'Batch', 'box', 'conf', 'id',
'total', 'nTargets', 'time'))
# Freeze darknet53.conv.74 for first epoch
if freeze_backbone and (epoch < 2):
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[2]) < cutoff: # if layer < 75
p.requires_grad = False if (epoch == 0) else True
ui = -1
rloss = defaultdict(float) # running loss
optimizer.zero_grad()
for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
if sum([len(x) for x in targets]) < 1: # if no targets continue
continue
# SGD burn-in
burnin = min(1000, len(dataloader))
if (epoch == 0) & (i <= burnin):
lr = opt.lr * (i / burnin)**4
for g in optimizer.param_groups:
g['lr'] = lr
# Compute loss, compute gradient, update parameters
loss, components = model(imgs.cuda(), targets.cuda(),
targets_len.cuda())
components = torch.mean(components.view(-1, 5), dim=0)
loss = torch.mean(loss)
loss.backward()
# accumulate gradient for x batches before optimizing
if ((i + 1) % accumulated_batches
== 0) or (i == len(dataloader) - 1):
optimizer.step()
optimizer.zero_grad()
# Running epoch-means of tracked metrics
ui += 1
for ii, key in enumerate(model.module.loss_names):
rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)
# rloss indicates running loss values with mean updated at every epoch
s = ('%8s%12s' + '%10.3g' * 6) % (
'%g/%g' % (epoch, epochs - 1), '%g/%g' %
(i, len(dataloader) - 1), rloss['box'], rloss['conf'],
rloss['id'], rloss['loss'], rloss['nT'], time.time() - t0)
t0 = time.time()
if i % opt.print_interval == 0:
logger.info(s)
# Save latest checkpoint
checkpoint = {
'epoch': epoch,
'model': model.module.state_dict(),
'optimizer': optimizer.state_dict()
}
copyfile(cfg, weights_to + '/cfg/yolo3.cfg')
copyfile(data_cfg, weights_to + '/cfg/ccmcpe.json')
latest = osp.join(weights_to, 'latest.pt')
torch.save(checkpoint, latest)
if epoch % save_every == 0 and epoch != 0:
# making the checkpoint lite
checkpoint["optimizer"] = []
torch.save(
checkpoint,
osp.join(weights_to, "weights_epoch_" + str(epoch) + ".pt"))
# Calculate mAP
if epoch % opt.test_interval == 0:
with torch.no_grad():
mAP, R, P = test.test(cfg,
data_cfg,
weights=latest,
batch_size=batch_size,
img_size=img_size,
print_interval=40,
nID=dataset.nID)
test.test_emb(cfg,
data_cfg,
weights=latest,
batch_size=batch_size,
img_size=img_size,
print_interval=40,
nID=dataset.nID)
# Call scheduler.step() after opimizer.step() with pytorch > 1.1.0
scheduler.step()
def testare(self):
self.assertTrue(test(5, 4))
self.assertFalse(test(3, 4))
def train():
cfg = opt.cfg
data = opt.data
img_size, img_size_test = opt.img_size if len(
opt.img_size) == 2 else opt.img_size * 2 # train, test sizes
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights # initial training weights
# Initialize
init_seeds()
if opt.multi_scale:
img_sz_min = round(img_size / 32 / 1.5)
img_sz_max = round(img_size / 32 * 1.5)
img_size = img_sz_max * 32 # initiate with maximum multi_scale size
print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
# Configure run
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
test_path = data_dict['valid']
nc = 1 if opt.single_cls else int(
data_dict['classes']) # number of classes
# Remove previous results
for f in glob.glob('*_batch*.png') + glob.glob(results_file):
os.remove(f)
# Initialize model
model = Darknet(cfg, arc=opt.arc).to(device)
# Optimizer
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if '.bias' in k:
pg2 += [v] # biases
elif 'Conv2d.weight' in k:
pg1 += [v] # apply weight_decay
else:
pg0 += [v] # all else
if opt.adam:
# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
else:
optimizer = optim.SGD(pg0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
del pg0, pg1, pg2
# https://github.com/alphadl/lookahead.pytorch
# optimizer = torch_utils.Lookahead(optimizer, k=5, alpha=0.5)
start_epoch = 0
best_fitness = 0.0
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
chkpt = torch.load(weights, map_location=device)
# load model
try:
chkpt['model'] = {
k: v
for k, v in chkpt['model'].items()
if model.state_dict()[k].numel() == v.numel()
}
model.load_state_dict(chkpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " \
"See https://github.com/ultralytics/yolov3/issues/657" % (opt.weights, opt.cfg, opt.weights)
raise KeyError(s) from e
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_fitness = chkpt['best_fitness']
# load results
if chkpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(chkpt['training_results']) # write results.txt
start_epoch = chkpt['epoch'] + 1
del chkpt
elif len(weights) > 0: # darknet format
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
load_darknet_weights(model, weights)
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=range(59, 70, 1), gamma=0.8) # gradual fall to 0.1*lr0
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=[round(opt.epochs * x) for x in [0.8, 0.9]],
gamma=0.1)
scheduler.last_epoch = start_epoch - 1
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, label='LambdaLR')
# plt.xlabel('epoch')
# plt.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model,
optimizer,
opt_level='O1',
verbosity=0)
# Initialize distributed training
if device.type != 'cpu' and torch.cuda.device_count() > 1:
dist.init_process_group(
backend='nccl', # 'distributed backend'
init_method=
'tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# Dataset
dataset = LoadImagesAndLabels(
train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
cache_labels=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls)
# Dataloader
batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=nw,
shuffle=not opt.
rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# Testloader
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(
test_path,
img_size_test,
batch_size * 2,
hyp=hyp,
rect=True,
cache_labels=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls),
batch_size=batch_size * 2,
num_workers=nw,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Start training
nb = len(dataloader)
prebias = start_epoch == 0
model.nc = nc # attach number of classes to model
model.arc = opt.arc # attach yolo architecture
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(
device) # attach class weights
maps = np.zeros(nc) # mAP per class
# torch.autograd.set_detect_anomaly(True)
results = (
0, 0, 0, 0, 0, 0, 0
) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time()
torch_utils.model_info(model, report='summary') # 'full' or 'summary'
print('Using %g dataloader workers' % nw)
print('Starting training for %g epochs...' % epochs)
for epoch in range(start_epoch,
epochs): # epoch ------------------------------
model.train()
# Prebias
if prebias:
if epoch < 3: # prebias
ps = 0.1, 0.9 # prebias settings (lr=0.1, momentum=0.9)
else: # normal training
ps = hyp['lr0'], hyp['momentum'] # normal training settings
print_model_biases(model)
prebias = False
# Bias optimizer settings
optimizer.param_groups[2]['lr'] = ps[0]
if optimizer.param_groups[2].get(
'momentum') is not None: # for SGD but not Adam
optimizer.param_groups[2]['momentum'] = ps[1]
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 -
maps)**2 # class weights
image_weights = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=w)
dataset.indices = random.choices(range(dataset.n),
weights=image_weights,
k=dataset.n) # rand weighted idx
mloss = torch.zeros(4).to(device) # mean losses
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls',
'total', 'targets', 'img_size'))
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
# Multi-Scale training
if opt.multi_scale:
if ni / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
img_size = random.randrange(img_sz_min,
img_sz_max + 1) * 32
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [
math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]
] # new shape (stretched to 32-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Plot images with bounding boxes
if ni == 0:
fname = 'train_batch%g.png' % i
plot_images(imgs=imgs,
targets=targets,
paths=paths,
fname=fname)
if tb_writer:
tb_writer.add_image(fname,
cv2.imread(fname)[:, :, ::-1],
dataformats='HWC')
# Hyperparameter burn-in
# n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches
# if ni <= n_burn:
# for m in model.named_modules():
# if m[0].endswith('BatchNorm2d'):
# m[1].momentum = 1 - i / n_burn * 0.99 # BatchNorm2d momentum falls from 1 - 0.01
# g = (i / n_burn) ** 4 # gain rises from 0 - 1
# for x in optimizer.param_groups:
# x['lr'] = hyp['lr0'] * g
# x['weight_decay'] = hyp['weight_decay'] * g
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model, not prebias)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Scale loss by nominal batch_size of 64
loss *= batch_size / 64
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available(
) else 0 # (GB)
s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1),
'%.3gG' % mem, *mloss,
len(targets), img_size)
pbar.set_description(s)
# end batch ------------------------------------------------------------------------------------------------
# Process epoch results
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
is_coco = any([
x in data
for x in ['coco.data', 'coco2014.data', 'coco2017.data']
]) and model.nc == 80
results, maps = test.test(
cfg,
data,
batch_size=batch_size * 2,
img_size=img_size_test,
model=model,
conf_thres=0.001
if final_epoch and is_coco else 0.1, # 0.1 for speed
iou_thres=0.6,
save_json=final_epoch and is_coco,
single_cls=opt.single_cls,
dataloader=testloader)
# Update scheduler
scheduler.step()
# Write epoch results
with open(results_file, 'a') as f:
f.write(s + '%10.3g' * 7 % results +
'\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' %
(opt.bucket, opt.name))
# Write Tensorboard results
if tb_writer:
x = list(mloss) + list(results)
titles = [
'GIoU', 'Objectness', 'Classification', 'Train loss',
'Precision', 'Recall', 'mAP', 'F1', 'val GIoU',
'val Objectness', 'val Classification'
]
for xi, title in zip(x, titles):
tb_writer.add_scalar(title, xi, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save training results
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f:
# Create checkpoint
chkpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
'model':
model.module.state_dict()
if type(model) is nn.parallel.DistributedDataParallel else
model.state_dict(),
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last checkpoint
torch.save(chkpt, last)
# Save best checkpoint
if best_fitness == fi:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
# if epoch > 0 and epoch % 10 == 0:
# torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
n = opt.name
if len(n):
n = '_' + n if not n.isnumeric() else n
fresults, flast, fbest = 'results%s.txt' % n, 'last%s.pt' % n, 'best%s.pt' % n
os.rename('results.txt', fresults)
os.rename(wdir + 'last.pt', wdir +
flast) if os.path.exists(wdir + 'last.pt') else None
os.rename(wdir + 'best.pt', wdir +
fbest) if os.path.exists(wdir + 'best.pt') else None
if opt.bucket: # save to cloud
os.system('gsutil cp %s gs://%s/results' % (fresults, opt.bucket))
os.system('gsutil cp %s gs://%s/weights' %
(wdir + flast, opt.bucket))
# os.system('gsutil cp %s gs://%s/weights' % (wdir + fbest, opt.bucket))
if not opt.evolve:
plot_results() # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1,
(time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
def do_experiment(model_config):
tf.reset_default_graph()
experiment_id = ex.current_run._id
print('Experiment ID: {eid}'.format(eid=experiment_id))
# Prepare data
print('Preparing dataset')
train_data, val_data, test_data = dataset.prepare_datasets(model_config)
print('Dataset ready')
# Start session
tf_config = tf.ConfigProto()
#tf_config.gpu_options.allow_growth = True
tf_config.gpu_options.visible_device_list = str(model_config['GPU'])
sess = tf.Session(config=tf_config)
#sess = tf.Session()
#sess = tf_debug.LocalCLIDebugWrapperSession(sess, ui_type="readline")
print('Session started')
# Create data iterators
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle,
train_data.output_types,
train_data.output_shapes)
mixed_spec, voice_spec, background_spec, mixed_audio, voice_audio, background_audio = iterator.get_next(
)
training_iterator = train_data.make_initializable_iterator()
validation_iterator = val_data.make_initializable_iterator()
testing_iterator = test_data.make_initializable_iterator()
training_handle = sess.run(training_iterator.string_handle())
validation_handle = sess.run(validation_iterator.string_handle())
testing_handle = sess.run(testing_iterator.string_handle())
print('Iterators created')
# Create variable placeholders and model
is_training = tf.placeholder(shape=(), dtype=bool)
mixed_phase = tf.expand_dims(mixed_spec[:, :, :-1, 3], 3)
print('Creating model')
# Restructure data from pipeline based on data type required
if model_config['data_type'] == 'mag':
mixed_input = tf.expand_dims(mixed_spec[:, :, :-1, 2], 3)
voice_input = tf.expand_dims(voice_spec[:, :, :-1, 2], 3)
elif model_config['data_type'] in ['mag_phase', 'mag_phase_diff']:
mixed_input = mixed_spec[:, :, :-1, 2:4]
voice_input = voice_spec[:, :, :-1, 2:4]
elif model_config['data_type'] == 'real_imag':
mixed_input = mixed_spec[:, :, :-1, 0:2]
voice_input = voice_spec[:, :, :-1, 0:2]
elif model_config['data_type'] in ['mag_real_imag', 'mag_phase2']:
mixed_input = tf.concat([
tf.expand_dims(mixed_spec[:, :, :-1, 2], 3), mixed_spec[:, :, :-1,
0:2]
], 3)
voice_input = tf.concat([
tf.expand_dims(voice_spec[:, :, :-1, 2], 3), voice_spec[:, :, :-1,
0:2]
], 3)
elif model_config['data_type'] in [
'mag_phase_real_imag', 'complex_to_mag_phase'
]:
mixed_input = mixed_spec[:, :, :-1, :]
voice_input = voice_spec[:, :, :-1, :]
model = audio_models.MagnitudeModel(mixed_input,
voice_input,
mixed_phase,
mixed_audio,
voice_audio,
background_audio,
model_config['model_variant'],
is_training,
model_config['learning_rate'],
model_config['data_type'],
model_config['phase_weight'],
name='Magnitude_Model')
sess.run(tf.global_variables_initializer())
if model_config['loading']:
print('Loading checkpoint')
checkpoint = os.path.join(model_config['model_base_dir'],
model_config['checkpoint_to_load'])
restorer = tf.train.Saver()
restorer.restore(sess, checkpoint)
# Summaries
model_folder = str(experiment_id)
writer = tf.summary.FileWriter(os.path.join(model_config["log_dir"],
model_folder),
graph=sess.graph)
# Get baseline metrics at initialisation
test_count = 0
if model_config['initialisation_test']:
print('Running initialisation test')
initial_test_loss, test_count = test(sess, model, model_config, handle,
testing_iterator, testing_handle,
test_count, experiment_id)
# Train the model
model = train(sess, model, model_config, model_folder, handle,
training_iterator, training_handle, validation_iterator,
validation_handle, writer)
# Test the trained model
mean_test_loss, test_count = test(sess, model, model_config, handle,
testing_iterator, testing_handle,
test_count, experiment_id)
print('{ts}:\n\tAll done with experiment {exid}!'.format(
ts=datetime.datetime.now(), exid=experiment_id))
if model_config['initialisation_test']:
print('\tInitial test loss: {init}'.format(init=initial_test_loss))
print('\tFinal test loss: {final}'.format(final=mean_test_loss))
model = Darknet(opt.model_def).to(device)
if opt.model:
if opt.model.endswith(".pt"):
model.load_state_dict(torch.load(opt.model, map_location=device)['model'])
else:
_ = load_darknet_weights(model, opt.model)
data_config = parse_data_cfg(opt.data_config)
valid_path = data_config["valid"]
class_names = load_classes(data_config["names"])
eval_model = lambda model:test(model=model,cfg=opt.model_def, data=opt.data_config)
obtain_num_parameters = lambda model:sum([param.nelement() for param in model.parameters()])
#这个不应该注释掉，等会要恢复
with torch.no_grad():
origin_model_metric = eval_model(model)
origin_nparameters = obtain_num_parameters(model)
'''
module_defs是一个列表，列表的每一项都是一个字典.贮存的只是并不生效的网络结构信息
例如{'type': 'convolutional', 'batch_normalize': '1', 'filters': '32', 'size': '3', 'stride': '1', 'pad': '1', 'activation': 'leaky'}
module_list是一个列表，列表的每一项都是一个列表，例如：
Sequential(
(conv_0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
batches = datamanager.batch_iter(train, FLAGS.batch_size,
FLAGS.num_epochs)
num_batches_per_epoch = int(len(train) / FLAGS.batch_size) + 1
print("Batch data")
# Training loop. For each batch...
num_batch = 1
num_epoch = 1
dev_x_batch = datamanager.generate_x(dev)
dev_p1_batch, dev_p2_batch = datamanager.generate_p(dev)
dev_y_batch = datamanager.generate_y(dev)
for batch in batches:
if num_batch == num_batches_per_epoch:
num_epoch += 1
num_batch = 1
test(testing_data, cnn.input_x, cnn.input_p1, cnn.input_p2,
cnn.scores, cnn.predictions, cnn.dropout_keep_prob,
datamanager, sess, num_epoch)
path = saver.save(sess,
checkpoint_prefix,
global_step=current_step)
num_batch += 1
x_batch = datamanager.generate_x(batch)
p1_batch, p2_batch = datamanager.generate_p(batch)
y_batch = datamanager.generate_y(batch)
loss = train_step(x_batch, y_batch, p1_batch, p2_batch)
current_step = tf.train.global_step(sess, global_step)
if current_step % FLAGS.evaluate_every == 0:
print("Num_batch: {}".format(num_batch))
print("Num_epoch: {}".format(num_epoch))
dev_step(dev_x_batch, dev_y_batch, dev_p1_batch, dev_p2_batch)
def main_finetune_channels():
# 波段选择依据
# 最优的波段排序:
# [850, 870, 930, 730, 790, 910, 770, 750, 670, 950, 990, 830, 890, 810, 970, 690, 710, 650, 590, 570, 630, 610, 550]
# 依次增加一个波段, 前一个模型进行微调
CHANNEL_SORT = [
850, 870, 930, 730, 790, 910, 770, 750, 670, 950, 990, 830, 890, 810,
970, 690, 710, 650, 590, 570, 630, 610, 550
]
for splitidx in range(4, 5):
usedChannelsList = [CHANNEL_SORT[:i + 1] for i in range(23)]
# for i_usedChannels in range(len(usedChannelsList)):
for i_usedChannels in [4, 6]:
usedChannels = usedChannelsList[i_usedChannels]
print(getTime(), splitidx, len(usedChannels), '...')
configer = EasyDict()
configer.dsize = (64, 64)
configer.datatype = 'Multi'
configer.n_epoch = 300
configer.lrbase = 0.001
configer.n_channel = 23
configer.n_class = 63
configer.batchsize = 32
configer.stepsize = 250
configer.gamma = 0.2
configer.cuda = True
configer.splitmode = 'split_{}x{}_{}'.format(
configer.dsize[0], configer.dsize[1], splitidx)
configer.modelbase = 'recognize_vgg11_bn'
configer.usedChannels = usedChannels
configer.n_usedChannels = len(configer.usedChannels)
configer.modelname = '{}_{}_{}_finetune'.\
format(configer.modelbase, configer.splitmode,
'_'.join(list(map(str, configer.usedChannels))))
configer.datapath = '/home/louishsu/Work/Workspace/ECUST2019_{}x{}'.\
format(configer.dsize[0], configer.dsize[1])
configer.logspath = '/home/louishsu/Work/Workspace/HUAWEI/pytorch/logs/{}_{}_{}subjects_logs'.\
format(configer.modelbase, configer.splitmode, configer.n_class)
configer.mdlspath = '/home/louishsu/Work/Workspace/HUAWEI/pytorch/modelfiles/{}_{}_{}subjects_models'.\
format(configer.modelbase, configer.splitmode, configer.n_class)
## datasets
trainset = RecognizeDataset(configer.datapath, configer.datatype,
configer.splitmode, 'train',
configer.usedChannels)
validset = RecognizeDataset(configer.datapath, configer.datatype,
configer.splitmode, 'valid',
configer.usedChannels)
trainloader = DataLoader(trainset,
configer.batchsize,
shuffle=True)
validloader = DataLoader(validset,
configer.batchsize,
shuffle=False)
## ============================================================================================
## model
modelpath = os.path.join(configer.mdlspath,
configer.modelname) + '.pkl'
modeldir = '/'.join(modelpath.split('/')[:-1])
if not os.path.exists(modeldir): os.makedirs(modeldir)
if i_usedChannels == 0:
model = modeldict[configer.modelbase](configer.n_usedChannels,
configer.n_class,
configer.dsize[0])
params = model.parameters()
torch.save(model, modelpath)
else:
modelpath_pretrain = os.path.join(
modeldir, '{}_{}_{}_finetune.pkl'.format(
configer.modelbase, configer.splitmode, '_'.join(
list(map(str,
usedChannelsList[i_usedChannels - 1])))))
model = torch.load(modelpath_pretrain)
model.features[0] = nn.Conv2d(len(usedChannels),
64,
3,
stride=1,
padding=1)
params = [{
'params': model.features[1:].parameters(),
'lr': configer.lrbase * 0.01,
}, {
'params': model.features[0].parameters(),
}]
torch.save(model, modelpath)
if configer.cuda and is_available(): model.cuda()
## ============================================================================================
## optimizer
optimizer = optim.Adam(params, configer.lrbase, weight_decay=1e-3)
## loss
loss = nn.CrossEntropyLoss()
## learning rate scheduler
scheduler = lr_scheduler.StepLR(optimizer, configer.stepsize,
configer.gamma)
## log
logpath = os.path.join(configer.logspath, configer.modelname)
if not os.path.exists(logpath): os.makedirs(logpath)
logger = SummaryWriter(logpath)
## initialize
acc_train = 0.
acc_valid = 0.
loss_train = float('inf')
loss_valid = float('inf')
loss_valid_last = float('inf')
## start training
for i_epoch in range(configer.n_epoch):
if configer.cuda and is_available(): empty_cache()
scheduler.step(i_epoch)
acc_train = []
acc_valid = []
loss_train = []
loss_valid = []
model.train()
for i_batch, (X, y) in enumerate(trainloader):
# get batch
X = Variable(X.float())
y = Variable(y)
if configer.cuda and is_available():
X = X.cuda()
y = y.cuda()
# forward
y_pred_prob = model(X)
loss_i = loss(y_pred_prob, y)
acc_i = accuracy(y_pred_prob, y)
# backward
optimizer.zero_grad()
loss_i.backward()
optimizer.step()
loss_train += [loss_i.detach().cpu().numpy()]
acc_train += [acc_i.cpu().numpy()]
model.eval()
for i_batch, (X, y) in enumerate(validloader):
# get batch
X = Variable(X.float())
y = Variable(y)
if configer.cuda and is_available():
X = X.cuda()
y = y.cuda()
# forward
y_pred_prob = model(X)
loss_i = loss(y_pred_prob, y)
acc_i = accuracy(y_pred_prob, y)
loss_valid += [loss_i.detach().cpu().numpy()]
acc_valid += [acc_i.cpu().numpy()]
loss_train = np.mean(np.array(loss_train))
acc_train = np.mean(np.array(acc_train))
loss_valid = np.mean(np.array(loss_valid))
acc_valid = np.mean(np.array(acc_valid))
logger.add_scalars('accuracy', {
'train': acc_train,
'valid': acc_valid
}, i_epoch)
logger.add_scalars('logloss', {
'train': loss_train,
'valid': loss_valid
}, i_epoch)
logger.add_scalar('lr', scheduler.get_lr()[-1], i_epoch)
if loss_valid_last > loss_valid:
loss_valid_last = loss_valid
torch.save(model, modelpath)
test(configer)
def train(
cfg,
data_cfg,
img_size=416,
resume=False, # 是否加载已训练得到的权重
epochs=273, # 跑几遍数据集
batch_size=16,
accumulate=1, # 积累accumulate个梯度然后再进行反向传播
multi_scale=False, # 是否进行多尺度训练
freeze_backbone=False, # 冻结基础网络
transfer=False # 是否进行迁移学习Transfer learning (train only YOLO layers)
):
init_seeds() # 初始化种子，包括numpy和torch的种子
weights = 'weights' + os.sep
latest = weights + 'latest.pt'
best = weights + 'best.pt'
device = torch_utils.select_device()
if multi_scale:
img_size = 608 # initiate with maximum multi_scale size
opt.num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
else:
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
data_dict = parse_data_cfg(data_cfg) # 处理voc.data or coco.data
train_path = data_dict['train']
nc = int(data_dict['classes']) # nc = number of classes
# Initialize model 整个darknet模型
model = Darknet(cfg, img_size).to(device)
# pytorch 最常用的优化器，SGD
optimizer = optim.SGD(model.parameters(),
lr=hyp['lr0'],
momentum=hyp['momentum'],
weight_decay=hyp['weight_decay'])
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_loss = float('inf')
nf = int(model.module_defs[model.yolo_layers[0] - 1]
['filters']) # yolo layer size (i.e. 255) 18 = (class+5) * 3
if resume: # Load previously saved model
if transfer: # Transfer learning
chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
model.load_state_dict(
{
k: v
for k, v in chkpt['model'].items()
if v.numel() > 1 and v.shape[0] != 255
},
strict=False)
for p in model.parameters():
p.requires_grad = True if p.shape[0] == nf else False
else: # resume from latest.pt
chkpt = torch.load(latest, map_location=device) # load checkpoint
model.load_state_dict(chkpt['model'])
start_epoch = chkpt['epoch'] + 1
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_loss = chkpt['best_loss']
del chkpt
else: # Initialize model with backbone (optional)
if '-tiny.cfg' in cfg:
cutoff = load_darknet_weights(model,
weights + 'yolov3-tiny.conv.15')
else:
cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
lf = lambda x: 1 - 10**(hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
scheduler = optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda=lf,
last_epoch=start_epoch - 1)
# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, last_epoch=start_epoch-1)
# Plot lr schedule
y = []
for _ in range(epochs):
scheduler.step()
y.append(optimizer.param_groups[0]['lr'])
plt.plot(y, label='LambdaLR')
plt.xlabel('epoch')
plt.xlabel('LR')
plt.tight_layout()
plt.savefig('LR.png', dpi=300)
# Dataset img_size = 608
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
rect=False,
image_weights=False)
# Initialize distributed training 多GPU分布式训练
if torch.cuda.device_count() > 1:
dist.init_process_group(backend=opt.backend,
init_method=opt.dist_url,
world_size=opt.world_size,
rank=opt.rank)
model = torch.nn.parallel.DistributedDataParallel(model)
# sampler = torch.utils.data.distributed.DistributedSampler(dataset)
# Dataloader 数据加载比较复杂，需要查看API结合进行理解
dataloader = DataLoader(
dataset,
batch_size=batch_size,
num_workers=opt.num_workers,
shuffle=False, # disable rectangular training if True
pin_memory=True,
collate_fn=dataset.collate_fn)
# Mixed precision training https://github.com/NVIDIA/apex
# install help: https://github.com/NVIDIA/apex/issues/259
mixed_precision = False
if mixed_precision:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# 开始训练
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels,
nc).to(device)
# attach class weights 不太明白以上那一行的作用
model_info(model, 'full')
nb = len(dataloader) # 数据集中图片的个数
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
# for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg'):
# os.remove(f)
t, t0 = time.time(), time.time()
for epoch in range(start_epoch, epochs):
model.train() # 将模型的模式改为train模式
print(
('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf',
'cls', 'total', 'nTargets', 'time'))
# Update scheduler
scheduler.step()
# Freeze backbone at epoch 0, unfreeze at epoch 1
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
# Update image weights (optional)
w = model.class_weights.cpu().numpy() * (1 - maps) # class weights
image_weights = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=w) # 不清楚这部分作用，同上
dataset.indices = random.choices(range(dataset.n),
weights=image_weights,
k=dataset.n) # random weighted index
# indices:目录
mloss = torch.zeros(5).to(
device) # mean losses 进行初始化，分别记录：xy, wh, conf, cls, total
for i, (imgs, targets, _, _) in enumerate(dataloader):
# i and data(imgs, targets, _ , _ ) 在dataset类中，torch.from_numpy(img), labels_out, img_path, (h, w)
imgs = imgs.to(device)
targets = targets.to(device)
nt = len(targets) # num of targets 目标的个数
# Plot images with bounding boxes
if epoch == 0 and i == 0:
plot_images(imgs=imgs,
targets=targets,
fname='train_batch0.jpg')
# SGD burn-in
if epoch == 0 and i <= n_burnin:
lr = hyp['lr0'] * (i / n_burnin)**4
for x in optimizer.param_groups:
x['lr'] = lr
# 运行模型，输入为图片，输出为向量
pred = model(imgs)
# 计算预测结果和标注结果之间的loss, yolo论文中的计算loss那一部分就是在这里
loss, loss_items = compute_loss(pred, targets, model)
if torch.isnan(loss):
print('WARNING: nan loss detected, ending training')
return results
# Compute gradient
if mixed_precision: #混合精度
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if (i + 1) % accumulate == 0 or (i + 1) == nb:
optimizer.step()
optimizer.zero_grad()
# Update running mean of tracked metrics
mloss = (mloss * i + loss_items) / (i + 1)
# Print batch results
s = ('%8s%12s' +
'%10.3g' * 7) % ('%g/%g' % (epoch, epochs - 1), '%g/%g' %
(i, nb - 1), *mloss, nt, time.time() - t)
t = time.time()
print(s)
# Multi-Scale training (320 - 608 pixels) every 10 batches
if multi_scale and (i + 1) % 10 == 0:
dataset.img_size = random.choice(range(10, 20)) * 32
print('multi_scale img_size = %g' % dataset.img_size)
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
if not (opt.notest or
(opt.nosave and epoch < 10)) or epoch == epochs - 1:
with torch.no_grad():
results, maps = test.test(cfg,
data_cfg,
batch_size=batch_size,
img_size=img_size,
model=model,
conf_thres=0.1)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 5 % results +
'\n') # P, R, mAP, F1, test_loss
# Update best loss
test_loss = results[4]
if test_loss < best_loss:
best_loss = test_loss
# Save training results
save = (not opt.nosave) or (epoch == epochs - 1)
if save:
# Create checkpoint
chkpt = {
'epoch':
epoch,
'best_loss':
best_loss,
'model':
model.module.state_dict()
if type(model) is nn.parallel.DistributedDataParallel else
model.state_dict(),
'optimizer':
optimizer.state_dict()
}
# Save latest checkpoint
torch.save(chkpt, latest)
# Save best checkpoint
if best_loss == test_loss:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
if epoch > 0 and epoch % 10 == 0:
torch.save(chkpt, weights + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
dt = (time.time() - t0) / 3600
print('%g epochs completed in %.3f hours.' % (epoch - start_epoch, dt))
return results
def train(hyp, opt, device, tb_writer=None, wandb=None):
logger.info(
colorstr("hyperparameters: ") + ", ".join(f"{k}={v}"
for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank = (
Path(opt.save_dir),
opt.epochs,
opt.batch_size,
opt.total_batch_size,
opt.weights,
opt.global_rank,
)
# Directories
wdir = save_dir / "weights"
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / "last.pt"
best = wdir / "best.pt"
results_file = save_dir / "results.txt"
# Save run settings
with open(save_dir / "hyp.yaml", "w") as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / "opt.yaml", "w") as f:
# yaml.dump(vars(opt), f, sort_keys=False) # opt 実行パラメータ
yaml.dump(str(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != "cpu"
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict["train"]
test_path = data_dict["val"]
nc = 1 if opt.single_cls else int(data_dict["nc"]) # number of classes
names = (["item"] if opt.single_cls and len(data_dict["names"]) != 1 else
data_dict["names"]) # class names
assert len(names) == nc, "%g names found for nc=%g dataset in %s" % (
len(names),
nc,
opt.data,
) # check
# Model
pretrained = weights.endswith(".pt")
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
if hyp.get("anchors"):
ckpt["model"].yaml["anchors"] = round(
hyp["anchors"]) # force autoanchor
model = Model(opt.cfg or ckpt["model"].yaml, ch=3,
nc=nc).to(device) # create
exclude = ["anchor"] if opt.cfg or hyp.get("anchors") else [
] # exclude keys
state_dict = ckpt["model"].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info(
"Transferred %g/%g items from %s" %
(len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print("freezing %s" % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp["weight_decay"] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, "weight") and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp["lr0"],
betas=(hyp["momentum"],
0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0,
lr=hyp["lr0"],
momentum=hyp["momentum"],
nesterov=True)
optimizer.add_param_group({
"params": pg1,
"weight_decay": hyp["weight_decay"]
}) # add pg1 with weight_decay
optimizer.add_param_group({"params": pg2}) # add pg2 (biases)
logger.info("Optimizer groups: %g .bias, %g conv.weight, %g other" %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if opt.linear_lr:
lf = (lambda x: (1 - x / (epochs - 1)) *
(1.0 - hyp["lrf"]) + hyp["lrf"]) # linear
else:
lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Logging
if rank in [-1, 0] and wandb and wandb.run is None:
opt.hyp = hyp # add hyperparameters
wandb_run = wandb.init(
config=opt,
resume="allow",
project="YOLOv5"
if opt.project == "runs/train" else Path(opt.project).stem,
name=save_dir.stem,
id=ckpt.get("wandb_id") if "ckpt" in locals() else None,
)
loggers = {"wandb": wandb} # loggers dict
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt["optimizer"] is not None:
optimizer.load_state_dict(ckpt["optimizer"])
best_fitness = ckpt["best_fitness"]
# Results
if ckpt.get("training_results") is not None:
with open(results_file, "w") as file:
file.write(ckpt["training_results"]) # write results.txt
# Epochs
start_epoch = ckpt["epoch"] + 1
if opt.resume:
assert (
start_epoch > 0
), "%s training to %g epochs is finished, nothing to resume." % (
weights,
epochs,
)
if epochs < start_epoch:
logger.info(
"%s has been trained for %g epochs. Fine-tuning for %g additional epochs."
% (weights, ckpt["epoch"], epochs))
epochs += ckpt["epoch"] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = int(model.stride.max()) # grid size (max stride)
nl = model.model[
-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info("Using SyncBatchNorm()")
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model,
device_ids=[opt.local_rank],
output_device=opt.local_rank)
# Trainloader
dataloader, dataset = create_dataloader(
train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers,
image_weights=opt.image_weights,
quad=opt.quad,
prefix=colorstr("train: "),
)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert (
mlc < nc
), "Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g" % (
mlc,
nc,
opt.data,
nc - 1,
)
# Process 0
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(
test_path,
imgsz_test,
batch_size * 2,
gs,
opt, # testloader
hyp=hyp,
cache=opt.cache_images and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers,
pad=0.5,
prefix=colorstr("val: "),
)[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, save_dir, loggers)
if tb_writer:
tb_writer.add_histogram("classes", c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp["anchor_t"],
imgsz=imgsz)
# Model parameters
hyp["box"] *= 3.0 / nl # scale to layers
hyp["cls"] *= nc / 80.0 * 3.0 / nl # scale to classes and layers
hyp["obj"] *= (imgsz / 640)**2 * 3.0 / nl # scale to image size and layers
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = (
labels_to_class_weights(dataset.labels, nc).to(device) * nc
) # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp["warmup_epochs"] * nb),
1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
compute_loss = ComputeLoss(model) # init loss class
logger.info(f"Image sizes {imgsz} train, {imgsz_test} test\n"
f"Using {dataloader.num_workers} dataloader workers\n"
f"Logging results to {save_dir}\n"
f"Starting training for {epochs} epochs...")
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = (model.class_weights.cpu().numpy() * (1 - maps)**2 / nc
) # class weights
iw = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=cw) # image weights
dataset.indices = random.choices(
range(dataset.n), weights=iw,
k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices)
if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
("\n" + "%10s" * 8) % ("Epoch", "gpu_mem", "box", "obj", "cls",
"total", "targets", "img_size"))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs,
targets,
paths,
_,
) in (
pbar
): # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = (imgs.to(device, non_blocking=True).float() / 255.0
) # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x["lr"] = np.interp(
ni,
xi,
[
hyp["warmup_bias_lr"] if j == 2 else 0.0,
x["initial_lr"] * lf(epoch),
],
)
if "momentum" in x:
x["momentum"] = np.interp(
ni, xi, [hyp["warmup_momentum"], hyp["momentum"]])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode="bilinear",
align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(
pred, targets.to(device)) # loss scaled by batch_size
if rank != -1:
loss *= (opt.world_size
) # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.0
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1
) # update mean losses
mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9
if torch.cuda.is_available() else 0) # (GB)
s = ("%10s" * 2 + "%10.4g" * 6) % (
"%g/%g" % (epoch, epochs - 1),
mem,
*mloss,
targets.shape[0],
imgs.shape[-1],
)
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f"train_batch{ni}.jpg" # filename
Thread(target=plot_images,
args=(imgs, targets, paths, f),
daemon=True).start()
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
elif plots and ni == 10 and wandb:
wandb.log(
{
"Mosaics": [
wandb.Image(str(x), caption=x.name)
for x in save_dir.glob("train*.jpg")
if x.exists()
]
},
commit=False,
)
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x["lr"] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
if ema:
ema.update_attr(
model,
include=[
"yaml",
"nc",
"hyp",
"gr",
"names",
"stride",
"class_weights",
],
)
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(
opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
verbose=nc < 50 and final_epoch,
plots=plots and final_epoch,
log_imgs=opt.log_imgs if wandb else 0,
compute_loss=compute_loss,
)
# Write
with open(results_file, "a") as f:
f.write(
s + "%10.4g" * 7 % results +
"\n") # P, R, [email protected], [email protected], val_loss(box, obj, cls)
if len(opt.name) and opt.bucket:
os.system("gsutil cp %s gs://%s/results/results%s.txt" %
(results_file, opt.bucket, opt.name))
# Log
tags = [
"train/box_loss",
"train/obj_loss",
"train/cls_loss", # train loss
"metrics/precision",
"metrics/recall",
"metrics/mAP_0.5",
"metrics/mAP_0.5:0.95",
"val/box_loss",
"val/obj_loss",
"val/cls_loss", # val loss
"x/lr0",
"x/lr1",
"x/lr2",
] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb:
wandb.log({tag: x}, step=epoch,
commit=tag == tags[-1]) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, "r") as f: # create checkpoint
ckpt = {
"epoch":
epoch,
"best_fitness":
best_fitness,
"training_results":
f.read(),
"model":
ema.ema,
"optimizer":
None if final_epoch else optimizer.state_dict(),
"wandb_id":
wandb_run.id if wandb else None,
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
final = best if best.exists() else last # final model
for f in [last, best]:
if f.exists():
strip_optimizer(f) # strip optimizers
if opt.bucket:
os.system(f"gsutil cp {final} gs://{opt.bucket}/weights") # upload
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb:
files = [
"results.png",
"confusion_matrix.png",
*[f"{x}_curve.png" for x in ("F1", "PR", "P", "R")],
]
wandb.log({
"Results": [
wandb.Image(str(save_dir / f), caption=f)
for f in files if (save_dir / f).exists()
]
})
if opt.log_artifacts:
wandb.log_artifact(artifact_or_path=str(final),
type="model",
name=save_dir.stem)
# Test best.pt
logger.info("%g epochs completed in %.3f hours.\n" %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
if opt.data.endswith("coco.yaml") and nc == 80: # if COCO
for conf, iou, save_json in (
[0.25, 0.45, False],
[0.001, 0.65, True],
): # speed, mAP tests
results, _, _ = test.test(
opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
conf_thres=conf,
iou_thres=iou,
model=attempt_load(final, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=save_json,
plots=False,
)
else:
dist.destroy_process_group()
wandb.run.finish() if wandb and wandb.run else None
torch.cuda.empty_cache()
# mlflow
with mlflow.start_run() as run:
# Log args into mlflow
for key, value in hyp.items():
mlflow.log_param(key, value)
for key, value in vars(opt).items():
mlflow.log_param(key, value)
# Log results into mlflow
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
# xがtorch.Tensorだったらfloatに直す
if torch.is_tensor(x):
x = x.item()
# tag名に特殊記号があれば削除する
if ":" in tag:
tag = re.sub(r":", " ", tag)
mlflow.log_metric(tag, x)
# Log model
mlflow.pytorch.log_model(model, "model")
return results
def main_best_channels():
# 波段选择依据
# 以最佳的划分方式:
# 依次选择每个波段进行实验
for splitidx in range(46, 51):
for datatype in ['Multi', 'RGB']:
if datatype == 'Multi':
# usedChannelsList = [[i] for i in range(23)]
usedChannelsList = [[550 + 20 * i for i in range(23)]]
else:
# usedChannelsList = ['R', 'G', 'B']
usedChannelsList = [
'RGB',
]
for usedChannels in usedChannelsList:
print(getTime(), splitidx, datatype, usedChannels, '...')
configer = EasyDict()
configer.dsize = (64, 64)
configer.datatype = datatype
configer.n_epoch = 300 if datatype == 'Multi' else 350
configer.lrbase = 0.001 if datatype == 'Multi' else 0.0005
configer.n_channel = 23
configer.n_class = 63
configer.batchsize = 32
configer.stepsize = 250
configer.gamma = 0.2
configer.cuda = True
configer.splitmode = 'split_{}x{}_{}'.format(
configer.dsize[0], configer.dsize[1], splitidx)
configer.modelbase = 'recognize_vgg11_bn'
if configer.datatype == 'Multi':
configer.usedChannels = usedChannels
configer.n_usedChannels = len(configer.usedChannels)
configer.modelname = '{}_{}_{}'.\
format(configer.modelbase, configer.splitmode,
'_'.join(list(map(str, configer.usedChannels))))
elif configer.datatype == 'RGB':
configer.usedChannels = usedChannels
configer.n_usedChannels = len(configer.usedChannels)
configer.modelname = '{}_{}_{}'.\
format(configer.modelbase, configer.splitmode, configer.usedChannels)
configer.datapath = '/datasets/ECUST2019_{}x{}'.\
format(configer.dsize[0], configer.dsize[1])
configer.logspath = '/home/louishsu/Work/Workspace/HUAWEI/pytorch/logs/{}_{}_{}subjects_logs'.\
format(configer.modelbase, configer.splitmode, configer.n_class)
configer.mdlspath = '/home/louishsu/Work/Workspace/HUAWEI/pytorch/modelfiles/{}_{}_{}subjects_models'.\
format(configer.modelbase, configer.splitmode, configer.n_class)
train(configer)
test(configer)
def train():
cfg = opt.cfg
data = opt.data
img_size = opt.img_size
epochs = 1 if opt.prebias else opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
weights = opt.weights # initial training weights
if 'pw' not in opt.arc: # remove BCELoss positive weights
hyp['cls_pw'] = 1.
hyp['obj_pw'] = 1.
# Initialize
init_seeds()
wdir = 'weights' + os.sep # weights dir
last = wdir + 'last.pt'
best = wdir + 'best.pt'
multi_scale = opt.multi_scale
if multi_scale:
img_sz_min = round(img_size / 32 / 1.5) + 1
img_sz_max = round(img_size / 32 * 1.5) - 1
img_size = img_sz_max * 32 # initiate with maximum multi_scale size
print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
# Configure run
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
nc = int(data_dict['classes']) # number of classes
# Remove previous results
for f in glob.glob('*_batch*.jpg') + glob.glob('results.txt'):
os.remove(f)
# Initialize model
model = Darknet(cfg, arc=opt.arc).to(device)
# Optimizer
pg0, pg1 = [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if 'Conv2d.weight' in k:
pg1 += [v] # parameter group 1 (apply weight_decay)
else:
pg0 += [v] # parameter group 0
# optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
del pg0, pg1
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_fitness = 0.
if weights.endswith('.pt'): # pytorch format
# possible weights are 'last.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
if opt.bucket:
os.system('gsutil cp gs://%s/last.pt %s' % (opt.bucket, last)) # download from bucket
chkpt = torch.load(weights, map_location=device)
# load model
if opt.transfer:
chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
model.load_state_dict(chkpt['model'], strict=False)
else:
model.load_state_dict(chkpt['model'])
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_fitness = chkpt['best_fitness']
# load results
if chkpt.get('training_results') is not None:
with open('results.txt', 'w') as file:
file.write(chkpt['training_results']) # write results.txt
start_epoch = chkpt['epoch'] + 1
del chkpt
elif len(weights) > 0: # darknet format
# possible weights are 'yolov3.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
cutoff = load_darknet_weights(model, weights)
if opt.transfer or opt.prebias: # transfer learning edge (yolo) layers
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
for p in optimizer.param_groups:
# lower param count allows more aggressive training settings: i.e. SGD ~0.1 lr0, ~0.9 momentum
p['lr'] *= 100
if p.get('momentum') is not None: # for SGD but not Adam
p['momentum'] *= 0.9
for p in model.parameters():
if opt.prebias and p.numel() == nf: # train (yolo biases)
p.requires_grad = True
elif opt.transfer and p.shape[0] == nf: # train (yolo biases+weights)
p.requires_grad = True
else: # freeze layer
p.requires_grad = False
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(opt.epochs * x) for x in [0.8, 0.9]], gamma=0.1)
scheduler.last_epoch = start_epoch - 1
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, label='LambdaLR')
# plt.xlabel('epoch')
# plt.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
# Initialize distributed training
if torch.cuda.device_count() > 1:
dist.init_process_group(backend='nccl', # 'distributed backend'
init_method='tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
model = torch.nn.parallel.DistributedDataParallel(model)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# Dataset
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
image_weights=opt.img_weights,
cache_images=False if opt.prebias else opt.cache_images)
# Dataloader
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=min(os.cpu_count(), batch_size),
shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# Start training
model.nc = nc # attach number of classes to model
model.arc = opt.arc # attach yolo architecture
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
model_info(model, report='summary') # 'full' or 'summary'
nb = len(dataloader)
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time()
print('Starting %s for %g epochs...' % ('prebias' if opt.prebias else 'training', epochs))
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
freeze_backbone = False
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx
mloss = torch.zeros(4).to(device) # mean losses
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device)
targets = targets.to(device)
# Multi-Scale training
if multi_scale:
if ni / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]] # new shape (stretched to 32-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Plot images with bounding boxes
if ni == 0:
fname = 'train_batch%g.jpg' % i
plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
if tb_writer:
tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')
# Hyperparameter burn-in
# n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches
# if ni <= n_burn:
# for m in model.named_modules():
# if m[0].endswith('BatchNorm2d'):
# m[1].momentum = 1 - i / n_burn * 0.99 # BatchNorm2d momentum falls from 1 - 0.01
# g = (i / n_burn) ** 4 # gain rises from 0 - 1
# for x in optimizer.param_groups:
# x['lr'] = hyp['lr0'] * g
# x['weight_decay'] = hyp['weight_decay'] * g
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Scale loss by nominal batch_size of 64
loss *= batch_size / 64
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB)
s = ('%10s' * 2 + '%10.3g' * 6) % (
'%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size)
pbar.set_description(s)
# end batch ------------------------------------------------------------------------------------------------
# Update scheduler
scheduler.step()
# Process epoch results
final_epoch = epoch + 1 == epochs
if opt.prebias:
print_model_biases(model)
else:
# Calculate mAP (always test final epoch, skip first 10 if opt.nosave)
if not (opt.notest or (opt.nosave and epoch < 10)) or final_epoch:
with torch.no_grad():
results, maps = test.test(cfg,
data,
batch_size=batch_size,
img_size=opt.img_size,
model=model,
conf_thres=0.001 if final_epoch and epoch > 0 else 0.1, # 0.1 for speed
save_json=final_epoch and epoch > 0 and 'coco.data' in data)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%10.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
# Write Tensorboard results
if tb_writer:
x = list(mloss) + list(results)
titles = ['GIoU', 'Objectness', 'Classification', 'Train loss',
'Precision', 'Recall', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification']
for xi, title in zip(x, titles):
tb_writer.add_scalar(title, xi, epoch)
# Update best mAP
fitness = results[2] # mAP
if fitness > best_fitness:
best_fitness = fitness
# Save training results
save = (not opt.nosave) or ((not opt.evolve) and final_epoch)
if save:
with open('results.txt', 'r') as file:
# Create checkpoint
chkpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': file.read(),
'model': model.module.state_dict() if type(
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
'optimizer': None if final_epoch else optimizer.state_dict()}
# Save last checkpoint
torch.save(chkpt, last)
if opt.bucket:
os.system('gsutil cp %s gs://%s' % (last, opt.bucket)) # upload to bucket
# Save best checkpoint
if best_fitness == fitness:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
if epoch > 0 and epoch % 10 == 0:
torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if len(opt.name):
os.rename('results.txt', 'results_%s.txt' % opt.name)
plot_results() # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
img_size = opt.img_size
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Darknet(opt.cfg, (img_size, img_size)).to(device)
if opt.weights.endswith('.pt'):
model.load_state_dict(torch.load(opt.weights)['model'])
else:
load_darknet_weights(model, opt.weights)
print('\nloaded weights from ', opt.weights)
eval_model = lambda model: test(opt.cfg,
opt.data,
weights=opt.weights,
batch_size=16,
img_size=img_size,
iou_thres=0.5,
conf_thres=0.001,
nms_thres=0.5,
save_json=False,
model=model)
obtain_num_parameters = lambda model: sum(
[param.nelement() for param in model.parameters()])
print("\nlet's test the original model first:")
with torch.no_grad():
origin_model_metric = eval_model(model)
origin_nparameters = obtain_num_parameters(model)
CBL_idx, Conv_idx, prune_idx = parse_module_defs(model.module_defs)
0.7, 0.8, 0.9, 1
]
MODELS = {}
net = Net(HN1, HN2)
init_state = copy.deepcopy(net.state_dict())
for lr in LR:
net.load_state_dict(init_state)
training_inputs = training_data[:, 0:5]
training_add = training_data[:, 8:]
training_inputs = np.append(training_inputs, training_add, axis=1)
training_labels = training_data[:, 5:8]
test_inputs = testing_data[:, 0:5]
test_add = testing_data[:, 8:]
test_inputs = np.append(test_inputs, test_add, axis=1)
test_labels = testing_data[:, 5:8]
train(net, training_inputs, training_labels, EPOCHS, lr, BATCH_SIZE)
avg_mse = test(test_inputs, test_labels, net)
MODELS['{a}_{x}-{y}_{z}_{b}'.format(a=HL, x=HN1, y=HN2, z=EPOCHS,
b=lr)] = avg_mse
with open('Data/Search/manual_search_results_{x}HL_lr.csv'.format(x=HL),
'w') as f:
for key in MODELS.keys():
f.write("%s: %s\n" % (key, MODELS[key]))
print(MODELS)
test_dataset = LoadImagesAndLabels(valid_path, img_size, batch_size,
hyp=hyp,
rect=True,
cache_images=False)
testloader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size, 8]),
shuffle=False,
pin_memory=True,
collate_fn=test_dataset.collate_fn)
with torch.no_grad():
origin_model_metric = test.test(opt.cfg,
opt.data,
batch_size=batch_size,
imgsz=img_size,
model=model,
dataloader=testloader,
rank=-1,
plot=False)
origin_nparameters = obtain_num_parameters(model)
origin_macs = performance_summary(model)
CBL_idx, Conv_idx, prune_idx, _, _ = parse_module_defs2(model.module_defs)
print("-------------------------------------------------------")
max_mAP = 0
for i in range(number):
compact_module_defs, current_parameters, compact_model = rand_prune_and_eval(model, 0, 1)
with torch.no_grad():
# 防止随机生成的较差的模型撑爆显存，增大nmsconf阈值
# Construct validation memory
val_mem = ReplayMemory(args, args.evaluation_size)
T, done = 0, True
while T < args.evaluation_size - args.history_length + 1:
if done:
state, done = env.reset(), False
val_mem.preappend() # Set up memory for beginning of episode
val_mem.append(state, None, None)
state, _, done = env.step(random.randint(0, action_space - 1))
T += 1
# No need to postappend on done in validation memory
if args.evaluate:
dqn.eval() # Set DQN (policy network) to evaluation mode
avg_reward, avg_Q = test(args, 0, dqn, val_mem, evaluate=True) # Test
print('Avg. reward: ' + str(avg_reward) + ' | Avg. Q: ' + str(avg_Q))
else:
# Training loop
dqn.train()
T, done = 0, True
while T < args.T_max:
if done:
state, done = Variable(env.reset()), False
dqn.reset_noise(
) # Draw a new set of noisy weights for each episode (better for before learning starts)
mem.preappend() # Set up memory for beginning of episode
action = dqn.act(
state) # Choose an action greedily (with noisy weights)
def train(hyp):
epochs = opt.epochs # 300
batch_size = opt.batch_size # 64
weights = opt.weights # initial training weights
# Configure
init_seeds(1)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict
train_path = data_dict['train']
test_path = data_dict['val']
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
# Remove previous results
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
os.remove(f)
# Create model
model = Model(opt.cfg).to(device)
assert model.md['nc'] == nc, '%s nc=%g classes but %s nc=%g classes' % (
opt.data, nc, opt.cfg, model.md['nc'])
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_parameters():
if v.requires_grad:
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
optimizer = optim.Adam(pg0, lr=hyp['lr0']) if opt.adam else \
optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
print('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Load Model
google_utils.attempt_download(weights)
start_epoch, best_fitness = 0, 0.0
if weights.endswith('.pt'): # pytorch format
ckpt = torch.load(weights, map_location=device) # load checkpoint
# load model
try:
ckpt['model'] = {
k: v
for k, v in ckpt['model'].float().state_dict().items()
if model.state_dict()[k].shape == v.shape
} # to FP32, filter
model.load_state_dict(ckpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s." \
% (opt.weights, opt.cfg, opt.weights)
raise KeyError(s) from e
# load optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# load results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
start_epoch = ckpt['epoch'] + 1
del ckpt
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model,
optimizer,
opt_level='O1',
verbosity=0)
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: ((
(1 + math.cos(x * math.pi / epochs)) / 2)**1.0) * 0.9 + 0.1 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
scheduler.last_epoch = start_epoch - 1 # do not move
# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Initialize distributed training
if device.type != 'cpu' and torch.cuda.device_count(
) > 1 and torch.distributed.is_available():
dist.init_process_group(
backend='nccl', # distributed backend
init_method='tcp://127.0.0.1:9999', # init method
world_size=1, # number of nodes
rank=0) # node rank
model = torch.nn.parallel.DistributedDataParallel(model)
# pip install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
# Dataset
dataset = LoadImagesAndLabels(
train_path,
imgsz,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
cache_images=opt.cache_images,
single_cls=opt.single_cls)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Correct your labels or your model.' % (
mlc, nc, opt.cfg)
# Dataloader
batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=nw,
shuffle=not opt.
rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# Testloader
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(
test_path,
imgsz_test,
batch_size,
hyp=hyp,
rect=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls),
batch_size=batch_size,
num_workers=nw,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(
device) # attach class weights
model.names = data_dict['names']
# Class frequency
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1.
# model._initialize_biases(cf.to(device))
if tb_writer:
plot_labels(labels)
tb_writer.add_histogram('classes', c, 0)
# Check anchors
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
# Exponential moving average
ema = torch_utils.ModelEMA(model)
# Start training
t0 = time.time()
nb = len(dataloader) # number of batches
n_burn = max(3 * nb,
1e3) # burn-in iterations, max(3 epochs, 1k iterations)
maps = np.zeros(nc) # mAP per class
results = (
0, 0, 0, 0, 0, 0, 0
) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
print('Using %g dataloader workers' % nw)
print('Starting training for %g epochs...' % epochs)
# torch.autograd.set_detect_anomaly(True)
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 -
maps)**2 # class weights
image_weights = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=w)
dataset.indices = random.choices(range(dataset.n),
weights=image_weights,
k=dataset.n) # rand weighted idx
mloss = torch.zeros(4, device=device) # mean losses
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls',
'total', 'targets', 'img_size'))
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
# Burn-in
if ni <= n_burn:
xi = [0, n_burn] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(
ni, xi,
[0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi,
[0.9, hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
pred = model(imgs)
# Loss
loss, loss_items = compute_loss(pred, targets.to(device), model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Backward
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Optimize
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
ema.update(model)
# Print
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_cached() /
1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1),
mem, *mloss, targets.shape[0],
imgs.shape[-1])
pbar.set_description(s)
# Plot
if ni < 3:
f = 'train_batch%g.jpg' % i # filename
res = plot_images(images=imgs,
targets=targets,
paths=paths,
fname=f)
if tb_writer:
tb_writer.add_image(f,
res,
dataformats='HWC',
global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
scheduler.step()
# mAP
ema.update_attr(model)
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(
opt.data,
batch_size=batch_size,
imgsz=imgsz_test,
save_json=final_epoch
and opt.data.endswith(os.sep + 'coco.yaml'),
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results +
'\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' %
(opt.bucket, opt.name))
# Tensorboard
if tb_writer:
tags = [
'train/giou_loss', 'train/obj_loss', 'train/cls_loss',
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5',
'metrics/F1', 'val/giou_loss', 'val/obj_loss', 'val/cls_loss'
]
for x, tag in zip(list(mloss[:-1]) + list(results), tags):
tb_writer.add_scalar(tag, x, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {
'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model':
ema.ema.module if hasattr(model, 'module') else ema.ema,
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last, best and delete
torch.save(ckpt, last)
if (best_fitness == fi) and not final_epoch:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
n = opt.name
if len(n):
n = '_' + n if not n.isnumeric() else n
fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'],
[flast, fbest, fresults]):
if os.path.exists(f1):
os.rename(f1, f2) # rename
ispt = f2.endswith('.pt') # is *.pt
strip_optimizer(f2) if ispt else None # strip optimizer
os.system('gsutil cp %s gs://%s/weights' % (
f2, opt.bucket)) if opt.bucket and ispt else None # upload
if not opt.evolve:
plot_results() # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1,
(time.time() - t0) / 3600))
dist.destroy_process_group(
) if device.type != 'cpu' and torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
def train(
cfg,
data_cfg,
img_size=416,
resume=False,
epochs=100, # 500200 batches at bs 4, 117263 images = 68 epochs
batch_size=16,
accumulate=4, # effective bs = 64 = batch_size * accumulate
freeze_backbone=False,
transfer=False # Transfer learning (train only YOLO layers)
):
init_seeds()
weights = 'weights' + os.sep
latest = weights + 'latest.pt'
best = weights + 'best.pt'
device = torch_utils.select_device()
torch.backends.cudnn.benchmark = True # possibly unsuitable for multiscale
img_size_test = img_size # image size for testing
if opt.multi_scale:
img_size_min = round(img_size / 32 / 1.5)
img_size_max = round(img_size / 32 * 1.5)
img_size = img_size_max * 32 # initiate with maximum multi_scale size
# Configure run
data_dict = parse_data_cfg(data_cfg)
train_path = data_dict['train']
nc = int(data_dict['classes']) # number of classes
# Initialize model
model = Darknet(cfg).to(device)
# Optimizer
optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'])
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_loss = float('inf')
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
if resume: # Load previously saved model
if transfer: # Transfer learning
chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255},
strict=False)
for p in model.parameters():
p.requires_grad = True if p.shape[0] == nf else False
else: # resume from latest.pt
chkpt = torch.load(latest, map_location=device) # load checkpoint
model.load_state_dict(chkpt['model'])
start_epoch = chkpt['epoch'] + 1
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_loss = chkpt['best_loss']
del chkpt
else: # Initialize model with backbone (optional)
if '-tiny' in cfg:
cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
else:
cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(opt.epochs * x) for x in (0.8, 0.9)], gamma=0.1)
scheduler.last_epoch = start_epoch - 1
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, label='LambdaLR')
# plt.xlabel('epoch')
# plt.xlabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Dataset
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
rect=False)
# Initialize distributed training
if torch.cuda.device_count() > 1:
dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank)
model = torch.nn.parallel.DistributedDataParallel(model)
# sampler = torch.project.data.distributed.DistributedSampler(dataset)
# Dataloader
dataloader = DataLoader(dataset,
batch_size=batch_size,
num_workers=opt.num_workers,
shuffle=True, # disable rectangular training if True
pin_memory=True,
collate_fn=dataset.collate_fn)
# Mixed precision training https://github.com/NVIDIA/apex
# install help: https://github.com/NVIDIA/apex/issues/259
mixed_precision = False
if mixed_precision:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# Remove old results
for f in glob.glob('*_batch*.jpg') + glob.glob('results.txt'):
os.remove(f)
# Start training
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
model_info(model)
nb = len(dataloader)
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
t, t0 = time.time(), time.time()
for epoch in range(start_epoch, epochs):
print(epoch)
model.train()
print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'targets', 'time'))
# Update scheduler
scheduler.step()
# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
# # Update image weights (optional)
# w = model.class_weights.cpu().numpy() * (1 - maps) # class weights
# image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
# dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # random weighted index
mloss = torch.zeros(5).to(device) # mean losses
for i, (imgs, targets, _, _) in enumerate(dataloader):
imgs = imgs.to(device)
targets = targets.to(device)
# Multi-Scale training
if opt.multi_scale:
if (i + 1 + nb * epoch) % 10 == 0: # adjust (67% - 150%) every 10 batches
img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32
print('img_size = %g' % img_size)
scale_factor = img_size / max(imgs.shape[-2:])
imgs = F.interpolate(imgs, scale_factor=scale_factor, mode='bilinear', align_corners=False)
# Plot images with bounding boxes
if epoch == 0 and i == 0:
plot_images(imgs=imgs, targets=targets, fname='train_batch%g.jpg' % i)
# SGD burn-in
if epoch == 0 and i <= n_burnin:
lr = hyp['lr0'] * (i / n_burnin) ** 4
for x in optimizer.param_groups:
x['lr'] = lr
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
if torch.isnan(loss):
print('WARNING: nan loss detected, ending training')
return results
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if (i + 1) % accumulate == 0 or (i + 1) == nb:
optimizer.step()
optimizer.zero_grad()
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
s = ('%8s%12s' + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1),
'%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
t = time.time()
print(s)
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1:
with torch.no_grad():
results, maps = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size_test, model=model,
conf_thres=0.1)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 5 % results + '\n') # P, R, mAP, F1, test_loss
# Update best loss
test_loss = results[4]
if test_loss < best_loss:
best_loss = test_loss
# Save training results
save = (not opt.nosave) or (epoch == epochs - 1)
if save:
# Create checkpoint
chkpt = {'epoch': epoch,
'best_loss': best_loss,
'model': model.module.state_dict() if type(
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
'optimizer': optimizer.state_dict()}
# Save latest checkpoint
torch.save(chkpt, latest)
# Save best checkpoint
if best_loss == test_loss:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
if epoch > 0 and epoch % 10 == 0:
torch.save(chkpt, weights + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
dt = (time.time() - t0) / 3600
print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, dt))
return results
if not os.path.exists(backup): os.makedirs(backup)
model_name = os.path.splitext(os.path.split(
opt.cfg)[-1])[0] + '_best.pth'
self.save_path = os.path.join(backup, model_name + '_best.pth')
self.best_map = 0
def save(self, mAP):
if mAP > self.best_map: self.best_map = mAP
if os.path.exists(self.save_path): os.remove(self.save_path)
torch.save(model.state_dict(), self.save_path)
if __name__ == "__main__":
args = create_args()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
model, model_cfg = create_model(args)
optimizer, lr = create_optimizer(model, model_cfg)
class_names, train_loader, valid_loader, test_loader = create_dataset(
args, model_cfg)
save = save_model(args)
#import pdb;pdb.set_trace()
mAP = 0
for epoch in range(1, args.epochs + 1):
train(args, model, train_loader, optimizer, epoch)
if epoch % 3 == 2 or epoch == args.epochs:
mAP = test(args, model, model_cfg, test_loader, class_names)
optimizer, lr = adjust_learning_rate(optimizer, lr, epoch)
save.save(mAP)
def train(
cfg,
data_cfg,
img_size=416,
resume=False,
epochs=273, # 500200 batches at bs 64, dataset length 117263
batch_size=16,
accumulate=1,
multi_scale=False,
freeze_backbone=False,
transfer=False # Transfer learning (train only YOLO layers)
):
init_seeds()
weights = 'weights' + os.sep
latest = weights + 'latest.pt'
best = weights + 'best.pt'
device = torch_utils.select_device()
if multi_scale:
img_size = 608 # initiate with maximum multi_scale size
opt.num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
else:
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
train_path = parse_data_cfg(data_cfg)['train']
# Initialize model
model = Darknet(cfg, img_size).to(device)
# Optimizer
optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'])
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_loss = float('inf')
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
if resume: # Load previously saved model
if transfer: # Transfer learning
chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255},
strict=False)
for p in model.parameters():
p.requires_grad = True if p.shape[0] == nf else False
else: # resume from latest.pt
chkpt = torch.load(latest, map_location=device) # load checkpoint
model.load_state_dict(chkpt['model'])
start_epoch = chkpt['epoch'] + 1
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_loss = chkpt['best_loss']
del chkpt
else: # Initialize model with backbone (optional)
if '-tiny.cfg' in cfg:
#cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
pass
else:
#cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
pass
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[218, 245], gamma=0.1, last_epoch=start_epoch-1)
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, label='LambdaLR')
# plt.xlabel('epoch')
# plt.xlabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Dataset
dataset = LoadImagesAndLabels(train_path, img_size=img_size, augment=True)
# Initialize distributed training
if torch.cuda.device_count() > 1:
dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank)
model = torch.nn.parallel.DistributedDataParallel(model)
# sampler = torch.utils.data.distributed.DistributedSampler(dataset)
# Dataloader
dataloader = DataLoader(dataset,
batch_size=batch_size,
num_workers=opt.num_workers,
shuffle=True,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Mixed precision training https://github.com/NVIDIA/apex
# install help: https://github.com/NVIDIA/apex/issues/259
mixed_precision = False
if mixed_precision:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# Start training
t = time.time()
model.hyp = hyp # attach hyperparameters to model
model_info(model)
nb = len(dataloader)
results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
os.remove('train_batch0.jpg') if os.path.exists('train_batch0.jpg') else None
os.remove('test_batch0.jpg') if os.path.exists('test_batch0.jpg') else None
for epoch in range(start_epoch, epochs):
model.train()
print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'nTargets', 'time'))
# Update scheduler
scheduler.step()
# Freeze backbone at epoch 0, unfreeze at epoch 1
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
mloss = torch.zeros(5).to(device) # mean losses
for i, (imgs, targets, _, _) in enumerate(dataloader):
imgs = imgs.to(device)
targets = targets.to(device)
nt = len(targets)
# Plot images with bounding boxes
if epoch == 0 and i == 0:
plot_images(imgs=imgs, targets=targets, fname='train_batch0.jpg')
# SGD burn-in
if epoch == 0 and i <= n_burnin:
lr = hyp['lr0'] * (i / n_burnin) ** 4
for x in optimizer.param_groups:
x['lr'] = lr
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
if torch.isnan(loss):
print('WARNING: nan loss detected, ending training')
return results
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if (i + 1) % accumulate == 0 or (i + 1) == nb:
optimizer.step()
optimizer.zero_grad()
# Update running mean of tracked metrics
mloss = (mloss * i + loss_items) / (i + 1)
# Print batch results
s = ('%8s%12s' + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1),
'%g/%g' % (i, nb - 1), *mloss, nt, time.time() - t)
t = time.time()
print(s)
# Multi-Scale training (320 - 608 pixels) every 10 batches
if multi_scale and (i + 1) % 10 == 0:
dataset.img_size = random.choice(range(10, 20)) * 32
print('multi_scale img_size = %g' % dataset.img_size)
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
if not (opt.notest or (opt.nosave and epoch < 5)) or epoch == epochs - 1:
with torch.no_grad():
results = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size, model=model,
conf_thres=0.1)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 5 % results + '\n') # P, R, mAP, F1, test_loss
# Update best loss
test_loss = results[4]
if test_loss < best_loss:
best_loss = test_loss
# Save training results
save = True and not opt.nosave
if save:
# Create checkpoint
chkpt = {'epoch': epoch,
'best_loss': best_loss,
'model': model.module.state_dict() if type(
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
'optimizer': optimizer.state_dict()}
# Save latest checkpoint
torch.save(chkpt, latest)
# Save best checkpoint
if best_loss == test_loss:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
if epoch > 0 and epoch % 10 == 0:
torch.save(chkpt, weights + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
return results
optimizer = None
else:
optimizer = my_optim.SharedAdam(shared_model.parameters(), lr=args.lr)
optimizer.share_memory()
shared_stepcount = SharedCounter()
if not args.debug:
processes = []
p = mp.Process(target=test, args=(args.num_processes, args,
shared_model, Model, make_env, shared_stepcount))
p.start()
processes.append(p)
for rank in range(0, args.num_processes):
p = mp.Process(target=train, args=(rank, args, shared_model,
Model, make_env, shared_stepcount, optimizer))
p.start()
processes.append(p)
for p in processes:
p.join()
else: ## debug is enabled
# run only one process in a main, easier to debug
args.num_test_episodes = 1
args.descr = 'debug'
args.max_step_count = 1000 # test both train and debug
train(0, args, shared_model, Model, make_env, shared_stepcount, optimizer)
args.max_step_count += 1000 # needed to perform test
test(args.num_processes, args, shared_model, Model, make_env, shared_stepcount)
if opt.weights:
if opt.weights.endswith(".pt"):
model.load_state_dict(
torch.load(opt.weights, map_location=device)['model'])
else:
_ = load_darknet_weights(model, opt.weights)
data_config = parse_data_cfg(opt.data)
valid_path = data_config["valid"]
class_names = load_classes(data_config["names"])
eval_model = lambda model: test(model=model,
cfg=opt.cfg,
data=opt.data,
batch_size=opt.batch_size,
imgsz=opt.img_size,
rank=-1)
obtain_num_parameters = lambda model: sum(
[param.nelement() for param in model.parameters()])
# 这个不应该注释掉，等会要恢复
with torch.no_grad():
origin_model_metric = eval_model(model)
origin_nparameters = obtain_num_parameters(model)
CBL_idx, Conv_idx, prune_idx = parse_module_defs(model.module_defs)
# 将所有要剪枝的BN层的α参数，拷贝到bn_weights列表
bn_weights = gather_bn_weights(model.module_list, prune_idx)
# # **Submission Checklist** # # 1. Does my code pass all tests? # 2. Does my code implement `A*` search and not some other search algorithm? # 3. Do I use an **admissible heuristic** to direct search efforts towards the goal? # 4. Do I use data structures which avoid unnecessarily slow lookups? # # When you can answer "yes" to all of these questions, and also have answered the written questions below, submit by pressing the Submit button in the lower right! # In[29]: from test import test test(PathPlanner) # ## Questions # # **Instructions** # # Answer the following questions in your own words. We do not you expect you to know all of this knowledge on the top of your head. We expect you to do research and ask question. However do not merely copy and paste the answer from a google or stackoverflow. Read the information and understand it first. Then use your own words to explain the answer. # --- # How would you explain A-Star to a family member(layman)? # # **ANSWER**: A-Star is basically a route finding search algorithm which is used to find the shortest path between two given points or locations. It uses path cost and estimated distance between the start and goal state to provide the optimal shortest path. As and example, Google maps internally uses A-Star search algorithm (which is based on cost and heuristic function) to provide the shortest path between two destinations as choosen on the map. # # ---
def main(args):
# Ensure training, testing, and manip are not all turned off
assert (
args.train or args.test or args.manip
), 'Cannot have train, test, and manip all set to 0, Nothing to do.'
# Load the training, validation, and testing data
try:
train_list, val_list, test_list = load_data(args.data_root_dir,
args.split_num)
except:
# Create the training and test splits if not found
split_data(args.data_root_dir, num_splits=4)
train_list, val_list, test_list = load_data(args.data_root_dir,
args.split_num)
# Get image properties from first image. Assume they are all the same.
# print("train_list_0",train_list)
img_shape = sitk.GetArrayFromImage(
sitk.ReadImage(join(args.data_root_dir, 'imgs',
train_list[0][0]))).shape
net_input_shape = (img_shape[1], img_shape[2], args.slices)
# print(img_shape)
# Create the model for training/testing/manipulation
model_list = create_model(args=args, input_shape=net_input_shape)
print_summary(model=model_list[0], positions=[.38, .65, .75, 1.])
args.output_name = 'split-' + str(args.split_num) + '_batch-' + str(args.batch_size) + \
'_shuff-' + str(args.shuffle_data) + '_aug-' + str(args.aug_data) + \
'_loss-' + str(args.loss) + '_slic-' + str(args.slices) + \
'_sub-' + str(args.subsamp) + '_strid-' + str(args.stride) + \
'_lr-' + str(args.initial_lr) + '_recon-' + str(args.recon_wei)
args.time = time
args.check_dir = join(args.data_root_dir, 'saved_models', args.net)
try:
makedirs(args.check_dir)
except:
pass
args.log_dir = join(args.data_root_dir, 'logs', args.net)
try:
makedirs(args.log_dir)
except:
pass
args.tf_log_dir = join(args.log_dir, 'tf_logs')
try:
makedirs(args.tf_log_dir)
except:
pass
args.output_dir = join(args.data_root_dir, 'plots', args.net)
try:
makedirs(args.output_dir)
except:
pass
if args.train:
from train import train
# Run training
train(args, train_list, val_list, model_list[0], net_input_shape)
if args.test:
from test import test
# Run testing
test(args, test_list, model_list, net_input_shape)
if args.manip:
from manip import manip
# Run manipulation of segcaps
manip(args, test_list, model_list, net_input_shape)
def worker(gpu, ngpus_per_node, args):
env_device, train_device = args_initialize(gpu, ngpus_per_node, args)
train_env, test_env, observation = env_initialize(args, env_device)
train_csv_file, train_csv_writer, eval_csv_file, eval_csv_writer, summary_writer = log_initialize(
args, train_device)
model = ActorCritic(args.num_stack,
train_env.action_space,
normalize=args.normalize,
name=args.env_name)
model, optimizer = model_initialize(args, model, train_device)
num_frames_per_iter = args.num_ales * args.num_steps
total_steps = math.ceil(args.t_max /
(args.world_size * num_frames_per_iter))
if args.verbose:
print(num_frames_per_iter)
print(args.world_size)
print(total_steps)
shape = (args.num_steps + 1, args.num_ales, args.num_stack,
*train_env.observation_space.shape[-2:])
states = torch.zeros(shape, device=train_device, dtype=torch.float32)
states[0, :, -1] = observation.to(device=train_device, dtype=torch.float32)
if args.verbose:
print(shape)
shape = (args.num_steps + 1, args.num_ales)
values = torch.zeros(shape, device=train_device, dtype=torch.float32)
returns = torch.zeros(shape, device=train_device, dtype=torch.float32)
if args.verbose:
print(shape)
shape = (args.num_steps, args.num_ales)
rewards = torch.zeros(shape, device=train_device, dtype=torch.float32)
masks = torch.zeros(shape, device=train_device, dtype=torch.float32)
actions = torch.zeros(shape, device=train_device, dtype=torch.long)
# These variables are used to compute average rewards for all processes.
episode_rewards = torch.zeros(args.num_ales,
device=train_device,
dtype=torch.float32)
final_rewards = torch.zeros(args.num_ales,
device=train_device,
dtype=torch.float32)
episode_lengths = torch.zeros(args.num_ales,
device=train_device,
dtype=torch.float32)
final_lengths = torch.zeros(args.num_ales,
device=train_device,
dtype=torch.float32)
if args.use_gae:
gae = torch.zeros(args.num_ales,
device=train_device,
dtype=torch.float32)
maybe_npy = lambda a: a.numpy() if args.use_openai else a
torch.cuda.synchronize()
iterator = range(total_steps)
if args.rank == 0:
iterator = tqdm(iterator)
total_time = 0
evaluation_offset = 0
for update in iterator:
T = args.world_size * update * num_frames_per_iter
if (args.rank == 0) and (T >= evaluation_offset):
evaluation_offset += args.evaluation_interval
eval_lengths, eval_rewards = test(args, model, test_env)
lmean, lmedian, lmin, lmax, lstd = gen_data(eval_lengths)
rmean, rmedian, rmin, rmax, rstd = gen_data(eval_rewards)
length_data = '(length) min/max/mean/median: {lmin:4.1f}/{lmax:4.1f}/{lmean:4.1f}/{lmedian:4.1f}'.format(
lmin=lmin, lmax=lmax, lmean=lmean, lmedian=lmedian)
reward_data = '(reward) min/max/mean/median: {rmin:4.1f}/{rmax:4.1f}/{rmean:4.1f}/{rmedian:4.1f}'.format(
rmin=rmin, rmax=rmax, rmean=rmean, rmedian=rmedian)
print('[training time: {}] {}'.format(
format_time(total_time),
' --- '.join([length_data, reward_data])))
if eval_csv_writer and eval_csv_file:
eval_csv_writer.writerow([
T, total_time, rmean, rmedian, rmin, rmax, rstd, lmean,
lmedian, lmin, lmax, lstd
])
eval_csv_file.flush()
if args.plot:
summary_writer.add_scalar('eval/rewards_mean',
rmean,
T,
walltime=total_time)
summary_writer.add_scalar('eval/lengths_mean',
lmean,
T,
walltime=total_time)
start_time = time.time()
with torch.no_grad():
for step in range(args.num_steps):
value, logit = model(states[step])
# store values
values[step] = value.squeeze(-1)
# convert actions to numpy and perform next step
probs_action = F.softmax(logit,
dim=1).multinomial(1).to(env_device)
observation, reward, done, info = train_env.step(
maybe_npy(probs_action))
if args.use_openai:
# convert back to pytorch tensors
observation = torch.from_numpy(observation)
reward = torch.from_numpy(reward)
done = torch.from_numpy(done.astype(np.uint8))
else:
observation = observation.squeeze(-1).unsqueeze(1)
# move back to training memory
observation = observation.to(device=train_device)
reward = reward.to(device=train_device, dtype=torch.float32)
done = done.to(device=train_device, dtype=torch.bool)
probs_action = probs_action.to(device=train_device,
dtype=torch.long)
not_done = 1.0 - done.float()
# update rewards and actions
actions[step].copy_(probs_action.view(-1))
masks[step].copy_(not_done)
rewards[step].copy_(reward.sign())
# update next observations
states[step + 1, :, :-1].copy_(states[step, :, 1:].clone())
states[step + 1] *= not_done.view(
-1, *[1] * (observation.dim() - 1))
states[step + 1, :,
-1].copy_(observation.view(-1,
*states.size()[-2:]))
# update episodic reward counters
episode_rewards += reward
final_rewards[done] = episode_rewards[done]
episode_rewards *= not_done
episode_lengths += not_done
final_lengths[done] = episode_lengths[done]
episode_lengths *= not_done
returns[-1] = values[-1] = model(states[-1])[0].data.squeeze(-1)
if args.use_gae:
gae.zero_()
for step in reversed(range(args.num_steps)):
delta = rewards[step] + (args.gamma * values[step + 1] *
masks[step]) - values[step]
gae = delta + (args.gamma * args.tau * masks[step] * gae)
returns[step] = gae + values[step]
else:
for step in reversed(range(args.num_steps)):
returns[step] = rewards[step] + (
args.gamma * returns[step + 1] * masks[step])
value, logit = model(states[:-1].view(-1, *states.size()[-3:]))
log_probs = F.log_softmax(logit, dim=1)
probs = F.softmax(logit, dim=1)
action_log_probs = log_probs.gather(1, actions.view(-1).unsqueeze(-1))
dist_entropy = -(log_probs * probs).sum(-1).mean()
advantages = returns[:-1].view(-1).unsqueeze(-1) - value
value_loss = advantages.pow(2).mean()
policy_loss = -(advantages.clone().detach() * action_log_probs).mean()
loss = value_loss * args.value_loss_coef + policy_loss - dist_entropy * args.entropy_coef
optimizer.zero_grad()
if args.cpu_train:
loss.backward()
master_params = model.parameters()
else:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
master_params = amp.master_params(optimizer)
torch.nn.utils.clip_grad_norm_(master_params, args.max_grad_norm)
optimizer.step()
states[0].copy_(states[-1])
torch.cuda.synchronize()
if args.rank == 0:
iter_time = time.time() - start_time
total_time += iter_time
if args.plot:
summary_writer.add_scalar('train/rewards_mean',
final_rewards.mean().item(),
T,
walltime=total_time)
summary_writer.add_scalar('train/lengths_mean',
final_lengths.mean().item(),
T,
walltime=total_time)
summary_writer.add_scalar('train/value_loss',
value_loss,
T,
walltime=total_time)
summary_writer.add_scalar('train/policy_loss',
policy_loss,
T,
walltime=total_time)
summary_writer.add_scalar('train/entropy',
dist_entropy,
T,
walltime=total_time)
progress_data = callback(args, model, T, iter_time,
final_rewards, final_lengths,
value_loss.item(), policy_loss.item(),
dist_entropy.item(), train_csv_writer,
train_csv_file)
iterator.set_postfix_str(progress_data)
if args.plot and (args.rank == 0):
writer.close()
if args.use_openai:
train_env.close()
if args.use_openai_test_env:
test_env.close()
def train(hyp, opt, device, tb_writer=None, wandb=None):
logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
if hyp.get('anchors'):
ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create
exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
# Freeze
freeze = ['model.%s.' % x for x in range(11)] # ['model.%s.' % x for x in range(11)] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Logging
if rank in [-1, 0] and wandb and wandb.run is None:
opt.hyp = hyp # add hyperparameters
wandb_run = wandb.init(config=opt, resume="allow",
project='YOLOv3' if opt.project == 'runs/train' else Path(opt.project).stem,
name=save_dir.stem,
id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)
loggers = {'wandb': wandb} # loggers dict
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
if epochs < start_epoch:
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
(weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = int(model.stride.max()) # grid size (max stride)
nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)
# Trainloader
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
world_size=opt.world_size, workers=opt.workers,
image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, # testloader
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
world_size=opt.world_size, workers=opt.workers,
pad=0.5, prefix=colorstr('val: '))[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, save_dir, loggers)
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
# Model parameters
hyp['box'] *= 3. / nl # scale to layers
hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
f'Using {dataloader.num_workers} dataloader workers\n'
f'Logging results to {save_dir}\n'
f'Starting training for {epochs} epochs...')
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 6) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f'train_batch{ni}.jpg' # filename
Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
elif plots and ni == 3 and wandb:
wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]})
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
if ema:
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
plots=plots and final_epoch,
log_imgs=opt.log_imgs if wandb else 0)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results + '\n') # P, R, [email protected], [email protected], val_loss(box, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
# Log
tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
'x/lr0', 'x/lr1', 'x/lr2'] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb:
wandb.log({tag: x}) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model': ema.ema,
'optimizer': None if final_epoch else optimizer.state_dict(),
'wandb_id': wandb_run.id if wandb else None}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
final = best if best.exists() else last # final model
for f in [last, best]:
if f.exists():
strip_optimizer(f) # strip optimizers
if opt.bucket:
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb:
files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png']
wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
if (save_dir / f).exists()]})
if opt.log_artifacts:
wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem)
# Test best.pt
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
for conf, iou, save_json in ([0.25, 0.45, False], [0.001, 0.65, True]): # speed, mAP tests
results, _, _ = test.test(opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
conf_thres=conf,
iou_thres=iou,
model=attempt_load(final, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=save_json,
plots=False)
else:
dist.destroy_process_group()
wandb.run.finish() if wandb and wandb.run else None
torch.cuda.empty_cache()
return results
def testCode():
file = "../Test_files/test.exe"
data = open(file, "rb").read()
start_time = time.time()
cr = PEHeaderReader(data=data)
cr.load()
total_imports, cant_librerias, promedio = cr.get_import_size_stats()
real, virtual, w_e, w_real_sum, w_virtual_sum = cr.get_section_stats()
elapsed = time.time() - start_time
line1 = str(total_imports) + "|" + str(cant_librerias) + "|" + str(
promedio)
line2 = str(real) + "|" + str(virtual) + "|" + str(w_e) + "|" + str(
w_real_sum) + "|" + str(w_virtual_sum)
print(line1)
print(line2)
imp = cr.getImports()
print(str(imp))
print("Elapsed time: " + str(elapsed))
#****************TEST_EXECUTE******************
from test import test
test("-test_PEHeaderReader", testCode)
best_prec1 = max(prec1, best_prec1)
state = {
'epoch': i,
'arch': opt.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1
}
save_checkpoint(state, is_best)
# if not opt.no_train and not opt.no_val:
# scheduler.step(validation_loss)
if opt.test:
spatial_transform = Compose([
Scale(opt.sample_size),
CenterCrop(opt.sample_size),
ToTensor(opt.norm_value), norm_method
])
temporal_transform = Compose([TemporalCenterCrop(opt.sample_duration)])
# target_transform = VideoID()
target_transform = ClassLabel()
test_data = get_test_set(opt, spatial_transform, temporal_transform,
target_transform)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.n_threads,
pin_memory=True)
test.test(test_loader, model, opt, test_data.class_names)
def train(hyp, opt, device, tb_writer=None):
logger.info(f'Hyperparameters {hyp}')
log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve' # logging directory
wdir = log_dir / 'weights' # weights directory
os.makedirs(wdir, exist_ok=True)
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = str(log_dir / 'results.txt')
epochs, batch_size, total_batch_size, weights, rank = \
opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Save run settings
with open(log_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(log_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
if hyp.get('anchors'):
ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create
exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
# Freeze
freeze = ['', ] # parameter names to freeze (full or partial)
if any(freeze):
for k, v in model.named_parameters():
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_parameters():
v.requires_grad = True
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
if opt.adam:
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf'] # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
shutil.copytree(wdir, wdir.parent / f'weights_backup_epoch{start_epoch - 1}') # save previous weights
if epochs < start_epoch:
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
(weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Exponential moving average
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)
# Trainloader
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect,
rank=rank, world_size=opt.world_size, workers=opt.workers,
patch_size=opt.patch_size)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
hyp=hyp, augment=False, cache=opt.cache_images and not opt.notest, rect=True,
rank=-1, world_size=opt.world_size, workers=opt.workers)[0] # testloader
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
plot_labels(labels, save_dir=log_dir)
if tb_writer:
# tb_writer.add_hparams(hyp, {}) # causes duplicate https://github.com/ultralytics/yolov5/pull/384
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb), 1e3) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info('Image sizes %g train, %g test\nUsing %g dataloader workers\nLogging results to %s\n'
'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs))
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 6) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if ni < 3:
f = str(log_dir / ('train_batch%g.jpg' % ni)) # filename
result = plot_images(images=imgs, targets=targets, paths=paths, fname=f)
if tb_writer and result is not None:
tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
if ema:
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
if final_epoch: # replot predictions
[os.remove(x) for x in glob.glob(str(log_dir / 'test_batch*_pred.jpg')) if os.path.exists(x)]
results, maps, times = test.test(opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=log_dir)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
# Tensorboard
if tb_writer:
tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss', # train loss
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
'val/giou_loss', 'val/obj_loss', 'val/cls_loss', # val loss
'x/lr0', 'x/lr1', 'x/lr2'] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
tb_writer.add_scalar(tag, x, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model': ema.ema,
'optimizer': None if final_epoch else optimizer.state_dict()}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
n = opt.name if opt.name.isnumeric() else ''
fresults, flast, fbest = log_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file], [flast, fbest, fresults]):
if os.path.exists(f1):
os.rename(f1, f2) # rename
if str(f2).endswith('.pt'): # is *.pt
strip_optimizer(f2) # strip optimizer
os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None # upload
# Finish
if not opt.evolve:
plot_results(save_dir=log_dir) # save as results.png
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if rank not in [-1, 0] else None
torch.cuda.empty_cache()
return results
def train(hyp, tb_writer, opt, device):
print(f'Hyperparameters {hyp}')
log_dir = tb_writer.log_dir if tb_writer else 'runs/evolution' # run directory
wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory
os.makedirs(wdir, exist_ok=True)
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = log_dir + os.sep + 'results.txt'
epochs, batch_size, total_batch_size, weights, rank = \
opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.local_rank
# TODO: Init DDP logging. Only the first process is allowed to log.
# Since I see lots of print here, the logging configuration is skipped here. We may see repeated outputs.
# Save run settings
with open(Path(log_dir) / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(Path(log_dir) / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict
train_path = data_dict['train']
test_path = data_dict['val']
nc, names = (1, ['item']) if opt.single_cls else (int(
data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data) # check
# Remove previous results
if rank in [-1, 0]:
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
os.remove(f)
# Create model
model = Model(opt.cfg, nc=nc).to(device)
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# Optimizer
nbs = 64 # nominal batch size
# default DDP implementation is slow for accumulation according to: https://pytorch.org/docs/stable/notes/ddp.html
# all-reduce operation is carried out during loss.backward().
# Thus, there would be redundant all-reduce communications in a accumulation procedure,
# which means, the result is still right but the training speed gets slower.
# TODO: If acceleration is needed, there is an implementation of allreduce_post_accumulation
# in https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/run_pretraining.py
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_parameters():
if v.requires_grad:
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
print('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Load Model
with torch_distributed_zero_first(rank):
google_utils.attempt_download(weights)
start_epoch, best_fitness = 0, 0.0
if weights.endswith('.pt'): # pytorch format
ckpt = torch.load(weights, map_location=device) # load checkpoint
# load model
try:
exclude = ['anchor'] # exclude keys
ckpt['model'] = {
k: v
for k, v in ckpt['model'].float().state_dict().items()
if k in model.state_dict() and not any(x in k for x in exclude)
and model.state_dict()[k].shape == v.shape
}
model.load_state_dict(ckpt['model'], strict=False)
print('Transferred %g/%g items from %s' %
(len(ckpt['model']), len(model.state_dict()), weights))
except KeyError as e:
s = "%s is not compatible with %s. This may be due to model differences or %s may be out of date. " \
"Please delete or update %s and try again, or use --weights '' to train from scratch." \
% (weights, opt.cfg, weights, weights)
raise KeyError(s) from e
# load optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# load results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# epochs
start_epoch = ckpt['epoch'] + 1
if epochs < start_epoch:
print(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model,
optimizer,
opt_level='O1',
verbosity=0)
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: ((
(1 + math.cos(x * math.pi / epochs)) / 2)**1.0) * 0.9 + 0.1 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
# plot_lr_scheduler(optimizer, scheduler, epochs)
# DP mode
if device.type != 'cpu' and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and device.type != 'cpu' and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
print('Using SyncBatchNorm()')
# Exponential moving average
ema = torch_utils.ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if device.type != 'cpu' and rank != -1:
model = DDP(model, device_ids=[rank], output_device=rank)
# Trainloader
dataloader, dataset = create_dataloader(train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
local_rank=rank,
world_size=opt.world_size)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# Testloader
if rank in [-1, 0]:
# local_rank is set to -1. Because only the first process is expected to do evaluation.
testloader = create_dataloader(test_path,
imgsz_test,
total_batch_size,
gs,
opt,
hyp=hyp,
augment=False,
cache=opt.cache_images,
rect=True,
local_rank=-1,
world_size=opt.world_size)[0]
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(
device) # attach class weights
model.names = names
# Class frequency
if rank in [-1, 0]:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1.
# model._initialize_biases(cf.to(device))
plot_labels(labels, save_dir=log_dir)
if tb_writer:
# tb_writer.add_hparams(hyp, {}) # causes duplicate https://github.com/ultralytics/yolov5/pull/384
tb_writer.add_histogram('classes', c, 0)
# Check anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
# Start training
t0 = time.time()
nw = max(3 * nb,
1e3) # number of warmup iterations, max(3 epochs, 1k iterations)
maps = np.zeros(nc) # mAP per class
results = (
0, 0, 0, 0, 0, 0, 0
) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
scheduler.last_epoch = start_epoch - 1 # do not move
if rank in [0, -1]:
print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
print('Using %g dataloader workers' % dataloader.num_workers)
print('Starting training for %g epochs...' % epochs)
# torch.autograd.set_detect_anomaly(True)
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
# When in DDP mode, the generated indices will be broadcasted to synchronize dataset.
if dataset.image_weights:
# Generate indices.
if rank in [-1, 0]:
w = model.class_weights.cpu().numpy() * (
1 - maps)**2 # class weights
image_weights = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=w)
dataset.indices = random.choices(
range(dataset.n), weights=image_weights,
k=dataset.n) # rand weighted idx
# Broadcast.
if rank != -1:
indices = torch.zeros([dataset.n], dtype=torch.int)
if rank == 0:
indices[:] = torch.from_tensor(dataset.indices,
dtype=torch.int)
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
if rank in [-1, 0]:
print(
('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj',
'cls', 'total', 'targets', 'img_size'))
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(
ni, xi,
[0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi,
[0.9, hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
pred = model(imgs)
# Loss
loss, loss_items = compute_loss(pred, targets.to(device),
model) # scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Backward
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Optimize
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
if ema is not None:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1
) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9
if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 +
'%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem,
*mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if ni < 3:
f = str(Path(log_dir) /
('train_batch%g.jpg' % ni)) # filename
result = plot_images(images=imgs,
targets=targets,
paths=paths,
fname=f)
if tb_writer and result is not None:
tb_writer.add_image(f,
result,
dataformats='HWC',
global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
scheduler.step()
# Only the first process in DDP mode is allowed to log or save checkpoints.
if rank in [-1, 0]:
# mAP
if ema is not None:
ema.update_attr(
model,
include=['md', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(
opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
save_json=final_epoch
and opt.data.endswith(os.sep + 'coco.yaml'),
model=ema.ema.module
if hasattr(ema.ema, 'module') else ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=log_dir)
# Write
with open(results_file, 'a') as f:
f.write(
s + '%10.4g' * 7 % results +
'\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' %
(results_file, opt.bucket, opt.name))
# Tensorboard
if tb_writer:
tags = [
'train/giou_loss', 'train/obj_loss', 'train/cls_loss',
'metrics/precision', 'metrics/recall',
'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
'val/giou_loss', 'val/obj_loss', 'val/cls_loss'
]
for x, tag in zip(list(mloss[:-1]) + list(results), tags):
tb_writer.add_scalar(tag, x, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(
1,
-1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
'model':
ema.ema.module if hasattr(ema, 'module') else ema.ema,
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last, best and delete
torch.save(ckpt, last)
if (best_fitness == fi) and not final_epoch:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
n = ('_'
if len(opt.name) and not opt.name.isnumeric() else '') + opt.name
fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'],
[flast, fbest, fresults]):
if os.path.exists(f1):
os.rename(f1, f2) # rename
ispt = f2.endswith('.pt') # is *.pt
strip_optimizer(f2) if ispt else None # strip optimizer
os.system('gsutil cp %s gs://%s/weights' % (
f2, opt.bucket)) if opt.bucket and ispt else None # upload
# Finish
if not opt.evolve:
plot_results(save_dir=log_dir) # save as results.png
print('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if rank not in [-1, 0] else None
torch.cuda.empty_cache()
return results