def validate_hvd(val_loader, model, epoch, writer, verbose, early_stopping,
hvd, start):
model.eval()
val_loss = utils.Metric('val_loss', hvd)
val_top1 = utils.Metric('val_top1', hvd)
val_top5 = utils.Metric('val_top5', hvd)
with tqdm(total=len(val_loader),
desc='Validate Epoch #{}'.format(epoch + 1),
disable=not verbose) as t:
with torch.no_grad():
for data, target in val_loader:
data, target = data.cuda(), target.cuda()
output = model(data)
val_loss.update(F.cross_entropy(output, target))
prec1, prec5 = utils.accuracy(output, target, topk=(1, 5))
val_top1.update(prec1)
val_top5.update(prec5)
t.set_postfix({
'loss': val_loss.avg.item(),
'top1': 100. * val_top1.avg.item(),
'top5': 100. * val_top5.avg.item()
})
t.update(1)
early_stopping(val_loss.avg.item(), model, ckpt_dir=config.path)
if early_stopping.early_stop:
print("Early stopping")
utils.time(time.time() - start)
os._exit(0)
writer.add_scalar('val/loss', val_loss.avg, epoch)
writer.add_scalar('val/top1', val_top1.avg, epoch)
writer.add_scalar('val/top5', val_top5.avg, epoch)
return val_top1.avg
def import_svmlight(path, headers=""):
raw = h2o.lazy_import(path)
if settings.debug and len(headers) < 100:
print utils.time() + "import with headers: " + str(headers)
#parsesetup = h2o.parse_setup(raw,column_names=headers)
parsesetup = h2o.parse_setup(
raw
) # Issue: H2O 3.8 tests length of header vs. columns, but still imports the "pseudotarget" additionally
parsesetup['parse_type'] = 'SVMLight'
loaded_frame = h2o.parse_raw(parsesetup)
if settings.debug:
print "......HEader length: " + str(len(headers))
print "......Frame imported: " + str(loaded_frame.ncol)
if (len(headers) > loaded_frame.ncol):
n = len(headers) - loaded_frame.ncol
print "Remove last " + str(n) + " header entries"
del headers[-n:]
loaded_frame.set_names(headers) #Workaround, Set names now
print "First column: " + loaded_frame.names[
0] #needed because lazy name setting
if settings.debug and len(headers) < 100: loaded_frame.head(show=True)
loaded_frame.pop(0) #remove first ('pseudotarget') columnn
#if loaded_frame.ncol>len(headers)-1: #workaround: H2O reads info from svmlight into columns -> remove everything that is not in headers
# delete = []
# for i in xrange(len(headers)-1,loaded_frame.ncol):
# delete.append(loaded_frame.names[i])
# loaded_frame = remove_vecs(loaded_frame,delete)
if settings.debug and len(headers) < 100: loaded_frame.head(show=True)
return loaded_frame
def derived_key(self):
kDate = utils.sign(("AWS4" + self.secret_access_key).encode("utf-8"),
utils.time("%Y%m%d"))
kRegion = utils.sign(kDate, self.region)
kService = utils.sign(kRegion, "glacier")
kSigning = utils.sign(kService, "aws4_request")
return kSigning
def derived_key(self):
kDate = utils.sign(("AWS4" + self.secret_access_key).encode("utf-8"),
utils.time("%Y%m%d"))
kRegion = utils.sign(kDate, self.region)
kService = utils.sign(kRegion, "glacier")
kSigning = utils.sign(kService, "aws4_request")
return kSigning
def __init__(self):
# Input shape
self.channels = 3
self.img_size = 64
self.latent_dim = 100
self.time = time()
self.dataset_name = 'vdsr'
self.learning_rate = 1e-4
optimizer = Adam(self.learning_rate, beta_1=0.5, decay=0.00005)
self.gf = 64 # filter size of generator's last layer
self.df = 64 # filter size of discriminator's first layer
# Configure data loader
self.data_loader = DataLoader(dataset_name=self.dataset_name,
img_res=(self.img_size, self.img_size),
mem_load=True)
self.n_data = self.data_loader.get_n_data()
self.generator = self.build_generator()
print(
"---------------------generator summary----------------------------"
)
self.generator.summary()
self.generator.compile(loss='mse',
optimizer=optimizer,
metrics=['mse'])
# Build and compile the discriminator
self.discriminator = self.build_discriminator()
print(
"\n---------------------discriminator summary----------------------------"
)
self.discriminator.summary()
self.discriminator.compile(loss='mse',
optimizer=optimizer,
metrics=['accuracy'])
make_trainable(self.discriminator, False)
z = Input(shape=(self.latent_dim, ))
fake_img = self.generator(z)
# for the combined model, we only train ganerator
self.discriminator.trainable = False
validity = self.discriminator(fake_img)
self.combined = Model([z], [validity])
print(
"\n---------------------combined summary----------------------------"
)
self.combined.summary()
self.combined.compile(loss=['binary_crossentropy'],
optimizer=optimizer)
def string_to_sign(self, canonical_request):
return "\n".join(
[
"AWS4-HMAC-SHA256",
self.header["x-amz-date"],
"%(time)s/%(region)s/glacier/aws4_request\n%(hashthing)s"
% {"time": utils.time("%Y%m%d"), "region": self.region, "hashthing": self.hexhash(canonical_request)},
]
)
def upload_svmlight(path, headers=""):
loaded_frame = h2o.upload_file(path)
if settings.debug and len(headers) < 100:
print utils.time() + "import with headers: " + str(headers)
if settings.debug:
print "......HEader length: " + str(len(headers))
print "......Frame imported: " + str(loaded_frame.ncol)
if (len(headers) > loaded_frame.ncol):
n = len(headers) - loaded_frame.ncol
print "Remove last " + str(n) + " header entries"
del headers[-n:]
loaded_frame.set_names(headers)
print "First column: " + loaded_frame.names[
0] #needed because lazy name setting
if settings.debug and len(headers) < 100: loaded_frame.head(show=True)
loaded_frame.pop(0) #remove first ('pseudotarget') columnn
if settings.debug and len(headers) < 100: loaded_frame.head(show=True)
return loaded_frame
def build_authorization_header(self):
cr = self.canonical_request()
sts = self.string_to_sign(cr)
dk = self.derived_key()
sgn = self.signature(dk, sts)
return "AWS4-HMAC-SHA256 Credential="+self.access_key+"/"+\
utils.time("%Y%m%d")+"/"+self.region+\
"/glacier/aws4_request,SignedHeaders="+\
";".join(self.signed_headers)+\
",Signature="+sgn
def build_authorization_header(self):
cr = self.canonical_request()
sts = self.string_to_sign(cr)
dk = self.derived_key()
sgn = self.signature(dk,sts)
return "AWS4-HMAC-SHA256 Credential="+self.access_key+"/"+\
utils.time("%Y%m%d")+"/"+self.region+\
"/glacier/aws4_request,SignedHeaders="+\
";".join(self.signed_headers)+\
",Signature="+sgn
def build_header(self,additional):
header = {
"host": self.host,
"x-amz-glacier-version": "2012-06-01",
"x-amz-date": utils.time("%Y%m%dT%H%M%SZ")
}
for key in additional.keys():
header[key] = additional[key]
return header
def get_hours(since):
list = db.get_hours(since=since)
if utils.time() >= since:
list.append({
"timestamp": utils.time_hour(),
"click_count": click_count_hour,
"event_count": event_count_hour,
"click_count_total": click_count_total,
"event_count_total": event_count_total
})
return list
def build_header(self, additional):
header = {
"host": self.host,
"x-amz-glacier-version": "2012-06-01",
"x-amz-date": utils.time("%Y%m%dT%H%M%SZ")
}
for key in additional.keys():
header[key] = additional[key]
return header
def _query(
sparql, timeout, q, cache=LRUCache(maxsize=config.CACHE_SIZE), **_):
"""Cached low level function to perform a single SPARQL query.
:param sparql: SPARQLWrapper endpoint
:param timeout: a timeout in seconds. The endpoint 'timeout' parameter will
be set to 3/4 this value in ms (Virtuoso seems to treat non zero
timeouts < 1000ms as 1000ms), instructing the server to give us a
partial result up to this soft limit. We also set a hard timeout via the
socket to really cancel the request if there's no result after timeout
seconds.
:param q: The SPARQL Query as string
:param cache: a cache object like cachetools.LRUCache or None
:return: a (t, res) pair with querytime t as float and res as dict.
"""
assert isinstance(sparql, SPARQLWrapper.SPARQLWrapper)
assert isinstance(q, six.string_types)
sparql.resetQuery()
sparql.setTimeout(timeout)
sparql.setReturnFormat(SPARQLWrapper.JSON)
# sparql.setMethod(SPARQLWrapper.POST)
# sparql.setRequestMethod(SPARQLWrapper.POSTDIRECTLY)
# set query timeout parameter to half the hard timeout time
sparql.addParameter('timeout', str(int(timeout * 1000 * 3 / 4)))
logger.debug('performing sparql query: \n%s', q)
c = cache.get(q) if cache is not None else None
if c is None:
logger.debug('cache miss')
try:
q_short = ' '.join((line.strip() for line in q.split('\n')))
sparql.setQuery(q_short)
c = time(sparql.queryAndConvert)
except socket.timeout:
c = (timeout, {})
except ValueError:
# e.g. if the endpoint gives us bad JSON for some unicode chars
logger.warning(
'Could not parse result for query, assuming empty result...\n'
'Query:\n%s\nException:', q,
exc_info=1, # appends exception to message
)
c = (timeout, {})
if cache is not None:
cache[q] = c
else:
logger.debug('cache hit')
t, res = c
if logger.isEnabledFor(logging.DEBUG):
logger.debug('orig query took %.4f s, result:\n%s\n', t, pformat(res))
return t, res
def _query(
sparql, timeout, q, cache=LRUCache(maxsize=config.CACHE_SIZE), **_):
"""Cached low level function to perform a single SPARQL query.
:param sparql: SPARQLWrapper endpoint
:param timeout: a timeout in seconds. The endpoint 'timeout' parameter will
be set to 3/4 this value in ms (Virtuoso seems to treat non zero
timeouts < 1000ms as 1000ms), instructing the server to give us a
partial result up to this soft limit. We also set a hard timeout via the
socket to really cancel the request if there's no result after timeout
seconds.
:param q: The SPARQL Query as string
:param cache: a cache object like cachetools.LRUCache or None
:return: a (t, res) pair with querytime t as float and res as dict.
"""
assert isinstance(sparql, SPARQLWrapper.SPARQLWrapper)
assert isinstance(q, six.string_types)
sparql.resetQuery()
sparql.setTimeout(timeout)
sparql.setReturnFormat(SPARQLWrapper.JSON)
# sparql.setMethod(SPARQLWrapper.POST)
# sparql.setRequestMethod(SPARQLWrapper.POSTDIRECTLY)
# set query timeout parameter to half the hard timeout time
sparql.addParameter('timeout', str(int(timeout * 1000 * 3 / 4)))
logger.debug('performing sparql query: \n%s', q)
c = cache.get(q) if cache is not None else None
if c is None:
logger.debug('cache miss')
try:
q_short = ' '.join((line.strip() for line in q.split('\n')))
sparql.setQuery(q_short)
c = time(sparql.queryAndConvert)
except socket.timeout:
c = (timeout, {})
except ValueError:
# e.g. if the endpoint gives us bad JSON for some unicode chars
logger.warning(
'Could not parse result for query, assuming empty result...\n'
'Query:\n%s\nException:', q,
exc_info=1, # appends exception to message
)
c = (timeout, {})
if cache is not None:
cache[q] = c
else:
logger.debug('cache hit')
t, res = c
logger.debug('orig query took %.4f s, result:\n%s\n', t, res)
return t, res
async def user_event(sid, *, user, name):
# cache
await user_auth(sid, name=user)
global event_count_total, event_count_today, event_count_hour
event_count_total += 1
event_count_today += 1
event_count_hour += 1
# user
users[sid]["event_count_session"] += 1
# event
if name not in ["rickroll", "exmatrikulieren"]:
event = db.add_event(user=user, name=name, timestamp=utils.time())
await emit_message(f"{user} triggered {name}!")
await sio.emit("event", event)
else:
await sio.emit("event", {
"user": user,
"name": name,
"timestamp": utils.time()
})
def image(name):
if name == 'logo.png':
return send_file('logo.png')
elif name == 'time':
time = utils.time()
return render_template('time.html', time=time)
else:
try:
org_link = Url.query.filter_by(short_link=name).first()
except Exception:
raise Exception
if org_link is None:
return render_template('not_link.html')
return redirect(org_link.org_link)
def _call(self, method, data):
self._correlation_id = str(uuid.uuid4())
self._response = None
compressed_value = self._compressor.compress(data)
tries_remaining = 2
while tries_remaining:
tries_remaining -= 1
try:
self._channel.basic_publish(
exchange="",
routing_key=method,
properties=pika.BasicProperties(
reply_to=constants.RABBIT_REPLYTO_QUEUE, correlation_id=self._correlation_id
),
body=compressed_value,
)
tries_remaining = 0
except pika.exceptions.ConnectionClosed:
if tries_remaining:
logger.info("Connection to queue was closed, reconnecting")
self._connect()
else:
logger.error("Reconnect failed: {}".format(traceback.format_exc(limit=10)))
raise
sleep_interval_seconds = 0.1
end_ts = utils.time() + self._call_timeout
while not self._response:
if self._call_timeout and utils.time() >= end_ts:
raise CallTimeoutException()
self._connection.sleep(sleep_interval_seconds)
# Pull the response out and return it
return self._compressor.decompress(self._response)
async def hourly_task():
while True:
current_time = utils.time()
current_hour = utils.time_hour(current_time=current_time)
next_hour = current_hour + utils.SECS_PER_HOUR
await data.start_hour(timestamp=current_hour)
# sleep until next hour
remaining_secs = next_hour - current_time
logging.debug(f"[{datetime.datetime.now()}] hourly_task sleeping for "
f"{remaining_secs} seconds")
await asyncio.sleep(remaining_secs)
await data.end_hour(timestamp=current_hour)
def calibrate_query_timeout(
sparql, factor=config.QUERY_TIMEOUT_FACTOR, q=None, n_queries=10):
assert isinstance(sparql, SPARQLWrapper.SPARQLWrapper)
logger.debug('calibrating query timer')
total_time = 0.
if q is None:
q = 'select * where { ?s ?p ?o } limit 10'
for _ in range(n_queries):
sparql.resetQuery()
sparql.setReturnFormat(SPARQLWrapper.JSON)
sparql.setQuery(q)
t, r = time(sparql.queryAndConvert)
total_time += t
avg = total_time / n_queries
timeout = avg * factor
timeout = max(timeout, config.QUERY_TIMEOUT_MIN)
logger.info('Query timeout calibration: %d simplistic queries took %.3fs '
'(%.3fs avg). Setting timeout to %.3fs',
n_queries, total_time, avg, timeout)
return timeout
def calibrate_query_timeout(
sparql, factor=config.QUERY_TIMEOUT_FACTOR, q=None, n_queries=10):
assert isinstance(sparql, SPARQLWrapper.SPARQLWrapper)
logger.debug('calibrating query timer')
total_time = 0.
if q is None:
q = 'select * where { ?s ?p ?o } limit 10'
for _ in range(n_queries):
sparql.resetQuery()
sparql.setReturnFormat(SPARQLWrapper.JSON)
sparql.setQuery(q)
t, r = time(sparql.queryAndConvert)
total_time += t
avg = total_time / n_queries
timeout = avg * factor
timeout = max(timeout, config.QUERY_TIMEOUT_MIN)
logger.info('Query timeout calibration: %d simplistic queries took %.3fs '
'(%.3fs avg). Setting timeout to %.3fs',
n_queries, total_time, avg, timeout)
return timeout
async def user_click(sid, *, user, comment, style):
# cache
await user_auth(sid, name=user)
global click_count_total, click_count_today, click_count_hour
click_count_total += 1
click_count_today += 1
click_count_hour += 1
# user
users[sid]["click_count_session"] += 1
# click
click = db.add_click(user=user, comment=comment,
style=style, timestamp=utils.time())
click_count_session = users[sid]["click_count_session"]
if click_count_session != 0 and (click_count_session % 100 == 0 or
click_count_session == 50):
await emit_message(f"{user} erreicht {click_count_session} Klicks!")
await sio.emit("click", click)
def run(config):
"""Run a fixed length simulation based on the inputs provided in the config."""
# Init lookup table of parameters
params = parameters.LookupTable(config)
# Init MST model
mst = model.MST(params, config)
# Init LQG controller
ctrl = controller.LQG(config["control"])
# Init reference signal
ref = reference.Translator(params, config["control"])
# Init output plotter (will plot on exit)
with output.Plotter(config) as out:
# Main loop
y = [mst.lmbda0, mst.flux]
for t in time(config["time"])[0][1:]:
r = ref(t)
u = ctrl.control(r, y)
y = mst.step(t, u)
out.save(t, r, u, y, mst, ctrl)
def validate(val_loader, model, epoch, criterion, config, early_stopping,
writer, start):
batch_time = utils.AverageMeters('Time', ':6.3f')
losses = utils.AverageMeters('Loss', ':.4e')
top1 = utils.AverageMeters('Acc@1', ':6.2f')
top5 = utils.AverageMeters('Acc@5', ':6.2f')
if 'DALIClassificationIterator' in val_loader.__class__.__name__:
progress = utils.ProgressMeter(math.ceil(val_loader._size /
config.batch_size),
batch_time,
losses,
top1,
top5,
prefix='Test: ')
else:
progress = utils.ProgressMeter(len(val_loader),
batch_time,
losses,
top1,
top5,
prefix='Test: ')
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
if 'DALIClassificationIterator' in val_loader.__class__.__name__:
for i, data in enumerate(val_loader):
images = Variable(data[0]['data'])
target = Variable(
data[0]['label'].squeeze().cuda().long().cuda(
non_blocking=True))
# compute output
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
if config.distributed:
reduced_loss = reduce_tensor(loss.data,
world_size=config.world_size)
acc1 = reduce_tensor(acc1, world_size=config.world_size)
acc5 = reduce_tensor(acc5, world_size=config.world_size)
else:
reduced_loss = loss.data
losses.update(to_python_float(reduced_loss), images.size(0))
top1.update(to_python_float(acc1), images.size(0))
top5.update(to_python_float(acc5), images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config.print_freq == 0:
progress.print(i)
else:
for i, (images, target) in enumerate(val_loader):
images = images.cuda(device, non_blocking=True)
target = target.cuda(device, non_blocking=True)
# compute output
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % config.print_freq == 0:
progress.print(i)
# TODO: this should also be done with the ProgressMeter
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'.format(top1=top1,
top5=top5))
early_stopping(losses.avg, model, ckpt_dir=config.path)
if early_stopping.early_stop:
print("Early stopping")
utils.time(time.time() - start)
os._exit(0)
writer.add_scalar('val/loss', losses.avg, epoch)
writer.add_scalar('val/top1', top1.val, epoch)
writer.add_scalar('val/top5', top5.val, epoch)
return top1.avg
def string_to_sign(self,canonical_request):
return "\n".join(["AWS4-HMAC-SHA256",self.header["x-amz-date"],
utils.time("%Y%m%d")+"/us-east-1/glacier/aws4_request",
utils.sha256(canonical_request)])
if settings.debug and len(headers) < 100: loaded_frame.head(show=True)
loaded_frame.pop(0) #remove first ('pseudotarget') columnn
#if loaded_frame.ncol>len(headers)-1: #workaround: H2O reads info from svmlight into columns -> remove everything that is not in headers
# delete = []
# for i in xrange(len(headers)-1,loaded_frame.ncol):
# delete.append(loaded_frame.names[i])
# loaded_frame = remove_vecs(loaded_frame,delete)
if settings.debug and len(headers) < 100: loaded_frame.head(show=True)
return loaded_frame
####
#### STEP 0: clean output dir
####
if settings.clean_output:
print utils.time(
) + "Loesche Files im Output Verzeichnis " + settings.out_directory_client
filelist = [
f for f in os.listdir(settings.out_directory_client)
if os.path.isfile(os.path.join(settings.out_directory_client, f))
]
for f in filelist:
os.remove(os.path.join(settings.out_directory_client, f))
####
#### STEP 1: init h2o, load data
####
print utils.time() + "Initialisiere H2O Server: " + settings.ip + ":" + str(
settings.port)
h2o.init(ip=settings.ip, port=settings.port, start_h2o=False)
h2o.remove_all()
import masks as mask
import convolution as cv
import gaussian_pyramid as gPyr
import place_pyramid as pPyr
import blending as bl
import fourier as ft
# Python uses the BGR color scheme
input = cv2.imread('input/p1-1-0.png')
# 2.1
filter = [mask.g_3, mask.g_7, mask.g_15]
for i in range(len(filter)):
time = ut.time()
output = cv.convolve(cp.copy(input), filter[i])
print("Convolution time[", i, "]: ", time.elapsed())
cv2.imwrite('output/p1-2-1-{}.png'.format(i), output)
time = ut.time()
output = cv2.filter2D(cp.copy(input), -1, np.flip(np.flip(filter[i], 0),
1))
print("OpenCV Convolution time[", i, "]: ", time.elapsed())
print("")
# 2.2
output = gPyr.gaussianPyramid(cp.copy(input), 3)
for i in range(len(output)):
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
from utils import time
x = np.linspace(1, 6, 1000) #aiming itme
y = np.linspace(1, 20, 1000) #modified dispersion
arr = np.linspace(0, 20, 21)
X, Y = np.meshgrid(x, y)
Z = time(X, Y)
fig, ax = plt.subplots(1, 1)
cp = ax.contourf(X, Y, Z, cmap=cm.coolwarm, levels=arr)
cbar = fig.colorbar(cp)
cbar.ax.set_ylabel('Seconds to aim')
cbar.set_ticks(arr)
ax.set_xlabel('aiming time')
#ax.set_xlim(0, 6)
ax.set_ylabel('modified dispersion')
#ax.set_ylim(0,20)
plt.show()
#plt.savefig('contour.png', dpi=600, format='png')
from indexed import IndexedOrderedDict
import math
####### SETTINGS ###########
settings = settings.development()
#settings = settings.production()
# save stdout
old_stdout = sys.stdout
#sys.stdout = open(settings.out_log, 'w')
####
#### STEP 0: clean output dir
####
if settings.clean_output:
print utils.time(
) + "Loesche Files im Output Verzeichnis " + settings.out_directory_client
filelist = [
f for f in os.listdir(settings.out_directory_client)
if os.path.isfile(os.path.join(settings.out_directory_client, f))
]
for f in filelist:
os.remove(os.path.join(settings.out_directory_client, f))
####
#### STEP 1: load data
#### Result: scikit-learn csr matrix, headers
####
headerfile = settings.in_directory_client + '/' + settings.colnames_file
print utils.time() + "Lade Header File " + headerfile
headers = functions.load_svm_headers4scipy(headerfile)
print "......Header length: " + str(len(headers))
def string_to_sign(self,canonical_request):
return "\n".join(["AWS4-HMAC-SHA256",self.header["x-amz-date"],
"%(time)s/%(region)s/glacier/aws4_request\n%(hashthing)s" % {"time":utils.time("%Y%m%d"),"region": self.region,"hashthing":self.hexhash(canonical_request)}])
print "Remove last " + str(n) + " header entries"
del headers[-n:]
loaded_frame.set_names(headers)
print "First column: " + loaded_frame.names[
0] #needed because lazy name setting
if settings.debug and len(headers) < 100: loaded_frame.head(show=True)
loaded_frame.pop(0) #remove first ('pseudotarget') columnn
if settings.debug and len(headers) < 100: loaded_frame.head(show=True)
return loaded_frame
####
#### STEP 0: clean output dir
####
if settings.clean_output:
print utils.time(
) + "Loesche Files im Output Verzeichnis " + settings.out_directory_client
filelist = [
f for f in os.listdir(settings.out_directory_client)
if os.path.isfile(os.path.join(settings.out_directory_client, f))
]
for f in filelist:
os.remove(os.path.join(settings.out_directory_client, f))
####
#### STEP 1: init h2o, load data
####
print utils.time() + "Initialisiere H2O Server: " + settings.ip + ":" + str(
settings.port)
h2o.init(ip=settings.ip, port=settings.port, start_h2o=False)
#h2o.remove_all()
def string_to_sign(self,canonical_request):
return "\n".join(["AWS4-HMAC-SHA256",self.header["x-amz-date"],
"%(time)s/%(region)s/glacier/aws4_request" % \
{"time":utils.time("%Y%m%d"),"region": self.region},
utils.sha256(canonical_request)])
def main():
start = time.time()
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.benchmark = True
CLASSES = 1000
channels = SEARCH_SPACE['channel_size']
strides = SEARCH_SPACE['strides']
# Model
model = Network(channels, strides, CLASSES)
model = model.to(device)
model = nn.DataParallel(model, device_ids=config.gpus)
logger.info("param size = %fMB", utils.count_parameters_in_MB(model))
config.world_size = 0
if config.target_hardware is None:
config.ref_value = None
else:
config.ref_value = ref_values[config.target_hardware][
'%.2f' % config.width_mult]
# Loss
criterion = LatencyLoss(config, channels, strides).cuda(config.gpus)
normal_critersion = nn.CrossEntropyLoss()
alpha_weight = model.module.arch_parameters()
# weight = [param for param in model.parameters() if not utils.check_tensor_in_list(param, alpha_weight)]
weight = model.module.weight_parameters()
# Optimizer
w_optimizer = torch.optim.SGD(weight,
config.w_lr,
momentum=config.w_momentum,
weight_decay=config.w_weight_decay)
alpha_optimizer = torch.optim.Adam(alpha_weight,
lr=config.alpha_lr,
betas=(config.arch_adam_beta1,
config.arch_adam_beta2),
eps=config.arch_adam_eps,
weight_decay=config.alpha_weight_decay)
train_data = get_imagenet_torch(
type='train',
# image_dir="/googol/atlas/public/cv/ILSVRC/Data/"
# use soft link `mkdir ./data/imagenet && ln -s /googol/atlas/public/cv/ILSVRC/Data/CLS-LOC/* ./data/imagenet/`
image_dir=config.data_path + "/" + config.dataset.lower(),
batch_size=config.batch_size,
num_threads=config.workers,
world_size=config.world_size,
crop=224,
device_id=0,
num_gpus=len(config.gpus),
portion=config.train_portion)
valid_data = get_imagenet_torch(
type='val',
# image_dir="/googol/atlas/public/cv/ILSVRC/Data/"
# use soft link `mkdir ./data/imagenet && ln -s /googol/atlas/public/cv/ILSVRC/Data/CLS-LOC/* ./data/imagenet/`
image_dir=config.data_path + "/" + config.dataset.lower(),
batch_size=config.batch_size,
num_threads=config.workers,
world_size=config.world_size,
crop=224,
device_id=0,
num_gpus=len(config.gpus),
portion=config.val_portion)
best_top1 = 0.
best_genotype = list()
lr = 0
config.start_epoch = -1
config.warmup_epoch = 0
config.warmup = True
### Resume form warmup model or train model ###
if config.resume:
try:
model_path = config.path + '/checkpoint.pth.tar'
model, w_optimizer, alpha_optimizer = load_model(
model,
model_fname=model_path,
optimizer=w_optimizer,
arch_optimizer=alpha_optimizer)
except Exception:
warmup_path = config.path + '/warmup.pth.tar'
if os.path.exists(warmup_path):
print('load warmup weights')
model, w_optimizer, alpha_optimizer = load_model(
model,
model_fname=warmup_path,
optimizer=w_optimizer,
arch_optimizer=alpha_optimizer)
else:
print('fail to load models')
w_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
w_optimizer, float(config.epochs), eta_min=config.w_lr_min)
if config.start_epoch < 0 and config.warm_up:
for epoch in range(config.warmup_epoch, config.warmup_epochs):
# warmup
train_top1, train_loss = warm_up(train_data, valid_data, model,
normal_critersion, criterion,
w_optimizer, epoch, writer)
config.start_epoch = epoch
update_schedule = utils.get_update_schedule_grad(len(train_data), config)
for epoch in range(config.start_epoch + 1, config.epochs):
if epoch > config.warmup_epochs:
w_scheduler.step()
lr = w_scheduler.get_lr()[0]
logger.info('epoch %d lr %e', epoch, lr)
# training
train_top1, train_loss = train(train_data, valid_data, model,
normal_critersion, criterion,
w_optimizer, alpha_optimizer, lr, epoch,
writer, update_schedule)
logger.info('Train top1 %f', train_top1)
# validation
top1 = train_top1
if epoch % 10 == 0:
top1, loss = infer(valid_data, model, epoch, criterion,
normal_critersion, writer)
logger.info('valid top1 %f', top1)
genotype = model.module.genotype()
logger.info("genotype = {}".format(genotype))
# save
if best_top1 < top1:
best_top1 = top1
best_genotype = genotype
is_best = True
else:
is_best = False
save_model(model, {
'warmup': False,
'epoch': epoch,
'w_optimizer': w_optimizer.state_dict(),
'alpha_optimizer': alpha_optimizer.state_dict(),
'state_dict': model.state_dict()
},
is_best=is_best)
utils.time(time.time() - start)
logger.info("Final best Prec@1 = {:.4%}".format(best_top1))
logger.info("Best Genotype = {}".format(best_genotype))
def main(argv=sys.argv):
active = True
try:
while active:
screen = curses.initscr()
screen.border(0)
screen.addstr(2, 2, " \u2022\u2022")
screen.addstr(3, 2, " \u2022\u2022")
screen.addstr(
4,
2,
" \u2022\u2022\u2022\u2022\u2022\u2022 \
\u2022\u2022\u2022\u2022\u2022\u2022 \
\u2022\u2022\u2022\u2022\u2022\u2022\u2022",
)
screen.addstr(
5,
2,
" \u2022\u2022 \u2022\u2022 \
\u2022\u2022 \u2022\u2022 \u2022\u2022",
)
screen.addstr(
6,
2,
" \u2022\u2022 \u2022 \
\u2022\u2022\u2022\u2022\u2022\u2022\u2022\u2022 \u2022\u2022 \u2022\u2022 \
- Once a day",
)
screen.addstr(
7,
2,
" \u2022\u2022 \u2022\u2022 \u2022\u2022 \
\u2022\u2022 \u2022\u2022 \u2022\u2022",
)
screen.addstr(
8,
2,
" \u2022\u2022\u2022\u2022\u2022\u2022 \
\u2022\u2022\u2022\u2022\u2022\u2022\u2022 \
\u2022\u2022\u2022\u2022\u2022\u2022\u2022",
)
screen.addstr(11, 4, "1 - Mailsettings")
screen.addstr(12, 4, "2 - Time Settings")
screen.addstr(13, 4, "3 - Notifications")
screen.addstr(14, 4, "4 - Exit")
screen.refresh()
choice = screen.getch()
if choice == ord("4"):
active = False
curses.endwin()
elif choice == ord("1"):
# TODO: write function
utils.mail()
curses.endwin()
elif choice == ord("2"):
# TODO: write function
utils.time()
curses.endwin()
elif choice == ord("3"):
utils.notification()
curses.endwin()
except Exception as e:
curses.endwin()
windows = True if platform.system() == "Windows" else False
clear = "cls" if windows else "clear"
subprocess.run(clear)
print("Once-a-day exited because of an Exception:\n {}".format(e))
def string_to_sign(self, canonical_request):
return "\n".join(["AWS4-HMAC-SHA256",self.header["x-amz-date"],
"%(time)s/%(region)s/glacier/aws4_request" % \
{"time":utils.time("%Y%m%d"),"region": self.region},
utils.sha256(canonical_request)])
def __init__(self, params, config):
# Get splines for magnetic fields, ohmic power and magnetic energy
self.spl_B_phi = params.B_phi
self.spl_B_theta = params.B_theta
self.spl_P_ohm = params.P_ohm
self.spl_U_mag = params.U_mag
# Parameters
self.mu0 = config["mu0"]
aspect_ratio = config["aspect_ratio"]
self.a = config["a"]
self.R0 = aspect_ratio * self.a
R_star = 0.969 * self.R0 # FIXME: use config file
zneo = config["zneo"]
zeff = config["zeff"]
self.zohm = (0.4 + 0.6 * zeff) * zneo
self.alpha = 4.0 # FIXME: using hardcoded fixed alpha for now
# Time discretization
t, num_t = time(config["time"])
self.tmin = t[num_t / 10]
self.dt = config["time"]["dt"]
# Parameters specific to simulation mode
self.mode = config["mode"]
if self.mode not in config:
raise ValueError(
"parameters for mode '%s' missing from config file" %
self.mode)
config_mode = config[self.mode]
# if "alpha" in config_mode:
# config_alpha = config_mode["alpha"]
# alpha = sp.interp1d(loadVec(config_alpha["time"]), loadVec(config_alpha["value"]))
if "flux" in config_mode:
config_flux = config_mode["flux"]
flux0 = config_flux["ref"] * config_flux["multiplier"]
else:
flux0 = config_mode["flux_ref"] * config_mode["flux_multiplier"]
if "density" in config_mode:
config_density = config_mode["density"]
self.density = sp.interp1d(loadVec(config_density["time"]),
loadVec(config_density["value"]))
else:
self.density = lambda t: config_mode["density_ref"] * config_mode[
"density_multiplier"]
config_phi = config_mode["toroidal"]
V_phi_wave = np.interp(t, loadVec(config_phi["time"]),
loadVec(config_phi["voltage"]))
self.V_phi_DC = config_phi["DC_voltage"]
config_theta = config_mode["poloidal"]
V_theta_wave = np.interp(t, loadVec(config_theta["time"]),
loadVec(config_theta["voltage"]))
config_anom = config_mode["anomalous"]
self.zanom_wave = sp.interp1d(config_anom["time"],
config_anom["voltage"])
if "OPCD" in config_mode:
config_opcd = config_mode["OPCD"]
t_start = config_opcd["t_start"]
freq = config_opcd["freq"]
domain = (t >= t_start) & (t <=
t_start + config_opcd["cycles"] / freq)
V_theta_wave -= config_opcd["max_voltage"] * np.sin(
2 * np.pi * freq * t) * domain
if "OFCD" in config_mode:
config_ofcd = config_mode["OFCD"]
t_start = config_ofcd["t_start"]
freq = config_ofcd["freq"]
cycles = config_ofcd["cycles"]
phase = config_ofcd["phase"]
sin_wave = config_ofcd["max_voltage"] * np.sin(
2 * np.pi * freq * t)
theta_domain = (t >= t_start) & (t <= t_start + cycles / freq)
V_theta_wave[theta_domain] -= sin_wave[theta_domain]
offset = int(phase / (freq * (t[1] - t[0])))
phi_domain = np.roll(theta_domain, -offset)
V_phi_wave[phi_domain] = V_phi_wave[
theta_domain] - 10 * sin_wave[theta_domain]
if "buck" in config_mode:
config_buck = config_mode["buck"]
V_phi_wave += np.interp(t, config_buck["time"],
config_buck["voltage"])
if "Te" in config_mode:
config_Te = config_mode["Te"]
PPCD_Te_mult += np.interp(t, config_Te["time"],
config_Te["voltage"])
self.V_theta_wave = sp.interp1d(t, V_theta_wave)
self.V_phi_wave = sp.interp1d(t, V_phi_wave)
I_phi_0 = config["initial"]["I_phi"]
# Initialize state variables
self.flux = flux0
self.lmbda0 = params.B_theta_to_lmbda0(self.mu0 * self.a * I_phi_0 /
flux0)
self.I_phi = I_phi_0
self.I_theta = self.spl_B_phi(
self.alpha, self.lmbda0) * flux0 * self.R0 / (self.a**2 * self.mu0)
self.V_phi = self.V_phi_wave(t[0])
self.V_theta = self.V_theta_wave(t[0])
self.P_ohm = 0.0
self.eta0 = 0.0
# Initialize ODE solver
self.solver = si.ode(self.lfDot)
self.solver.set_integrator("dopri5")
self.solver.set_initial_value([self.lmbda0, self.flux], t[0])
def __init__(self, config):
# Parameters
self.mu0 = config["mu0"]
aspect_ratio = config["aspect_ratio"]
self.a = config["a"]
self.R0 = aspect_ratio * self.a
# Time discretization
self.t, num_t = time(config["time"])
self.tf = config["time"]["tf"]
# Mode-dependent parameters
mode = config["mode"]
if mode not in config:
raise ValueError(
"parameters for mode '%s' missing from config file" % mode)
config_mode = config[mode]
self.P_ohm_over_I_phi_shot = np.interp(
self.t, loadVec(config_mode["time"]),
loadVec(config_mode["P_ohm_over_I_phi"]))
self.f_shot = np.interp(self.t, loadVec(config_mode["time"]),
loadVec(config_mode["f"]))
self.theta_shot = np.interp(self.t, loadVec(config_mode["time"]),
loadVec(config_mode["theta"]))
config_phi = config_mode["toroidal"]
self.I_phi_shot = np.interp(self.t, loadVec(config_phi["time"]),
loadVec(config_phi["current"]))
self.V_phi_shot = np.interp(self.t, loadVec(config_phi["time"]),
loadVec(config_phi["voltage"]))
config_theta = config_mode["poloidal"]
self.I_theta_shot = np.interp(self.t, loadVec(config_theta["time"]),
loadVec(config_theta["current"]))
self.V_theta_shot = np.interp(self.t, loadVec(config_theta["time"]),
loadVec(config_theta["voltage"]))
config_flux = config_mode["flux"]
self.flux_shot = np.interp(self.t, loadVec(config_flux["time"]),
loadVec(config_flux["value"]))
# Init arrays, hopefully with their final size, otherwise expandable
self.flux = np.zeros(num_t)
self.lmbda0 = np.zeros(num_t)
self.I_phi = np.zeros(num_t)
self.I_theta = np.zeros(num_t)
self.V_phi = np.zeros(num_t)
self.V_theta = np.zeros(num_t)
self.P_ohm = np.zeros(num_t)
self.eta0 = np.zeros(num_t)
self.I_phi_primary = np.zeros(num_t)
self.I_theta_primary = np.zeros(num_t)
# Current index
self.index = 1
# Convert voltages to primary current
config_primary = config["primary"]
self.coeff_phi = config_primary["coeff_phi"]
self.coeff_theta = config_primary["coeff_theta"]
self.R_phi = config_primary["R_phi"]
self.R_theta = config_primary["R_theta"]
self.L_phi = config_primary["L_phi"]
self.L_theta = config_primary["L_theta"]
self.solver = si.ode(self.primaryCurrentDot)
self.solver.set_integrator("dopri5")
self.solver.set_initial_value(
[self.V_phi_shot[0], self.V_theta_shot[0]], self.t[0])
def main():
start = time.time()
logger.info(
"Logger is set - training start, Let`s get ready to the rumble!!! ")
global best_acc1
early_stopping = utils.EarlyStopping(patience=config.patience,
verbose=True)
if config.multiprocessing_distributed:
if config.dataset.lower() != "imagenet":
raise NameError(
"Not Imagenet dataset, if you really need mutil node, change the code for own dataset"
)
os._exit(0)
import horovod.torch as hvd
# horovod set
hvd.init()
# set default gpu device id
torch.cuda.set_device(hvd.local_rank())
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.benchmark = True
# If set > 0, will resume training from a given checkpoint.
resume_from_epoch = 0
for try_epoch in range(config.epochs, 0, -1):
if os.path.exists(
config.checkpoint_format.format(epoch=try_epoch)):
resume_from_epoch = try_epoch
break
# Horovod: broadcast resume_from_epoch from rank 0 (which will have
# checkpoints) to other ranks.
resume_from_epoch = hvd.broadcast(torch.tensor(resume_from_epoch),
root_rank=0,
name='resume_from_epoch').item()
# Horovod: print logs on the first worker.
verbose = 1 if hvd.rank() == 0 else 0
# Horovod: write TensorBoard logs on first worker.
hvd_writer = SummaryWriter(log_dir=os.path.join(
config.path, "tb_hvd")) if hvd.rank() == 0 else None
kwargs = {
'num_workers': config.workers,
'pin_memory': True
} if len(config.gpus) > 0 else {}
train_dataset = \
datasets.ImageFolder(config.data_path+config.dataset+"/train",
transform=transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
# Horovod: use DistributedSampler to partition data among workers. Manually specify
# `num_replicas=hvd.size()` and `rank=hvd.rank()`.
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_loader, num_replicas=hvd.size(), rank=hvd.rank())
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.allreduce_batch_size,
sampler=train_sampler,
**kwargs)
if 'efficientnet' in config.arch:
image_size = EfficientNet.EfficientNet.get_image_size(config.arch)
val_dataset = \
datasets.ImageFolder(config.data_path+config.dataset+"/val",
transform=transforms.Compose([
transforms.Resize(image_size, interpolation=PIL.Image.BICUBIC),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
print('Using image size', image_size)
else:
val_dataset = \
datasets.ImageFolder(config.data_path+config.dataset+"/val",
transform=transforms.Compose([
transforms.Resize(256, interpolation=PIL.Image.BICUBIC),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
print('Using image size', 224)
val_sampler = torch.utils.data.distributed.DistributedSampler(
val_dataset, num_replicas=hvd.size(), rank=hvd.rank())
valid_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config.val_batch_size,
sampler=val_sampler,
**kwargs)
model = EfficientNet.EfficientNet.from_name(config.arch)
# model = EfficientNet.from_pretrained('efficientnet-b0') # online
model = model.to(device)
# Horovod: scale learning rate by the number of GPUs.
# Gradient Accumulation: scale learning rate by batches_per_allreduce
optimizer = torch.optim.SGD(
model.parameters(),
lr=(config.lr * config.batches_per_allreduce * hvd.size()),
momentum=config.momentum,
weight_decay=config.weight_decay)
# Horovod: (optional) compression algorithm.
compression = hvd.Compression.fp16 if config.fp16_allreduce else hvd.Compression.none
# Horovod: wrap optimizer with DistributedOptimizer.
optimizer = hvd.DistributedOptimizer(
optimizer,
named_parameters=model.named_parameters(),
compression=compression,
backward_passes_per_step=config.batches_per_allreduce)
# Restore from a previous checkpoint, if initial_epoch is specified.
# Horovod: restore on the first worker which will broadcast weights to other workers.
if resume_from_epoch > 0 and hvd.rank() == 0:
filepath = config.checkpoint_format.format(epoch=resume_from_epoch)
checkpoint = torch.load(filepath)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
# Horovod: broadcast parameters & optimizer state.
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
criterion = nn.CrossEntropyLoss().cuda(device)
# optionally resume from a checkpoint
if config.resume:
if os.path.isfile(config.resume):
print("=> loading checkpoint '{}'".format(config.resume))
checkpoint = torch.load(config.resume)
config.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
if config.gpus is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(config.gpus)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
config.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(config.resume))
if config.evaluate:
validate_hvd(valid_loader, model, 0, hvd_writer, verbose,
early_stopping, hvd, start)
for epoch in range(config.start_epoch, config.epochs):
train_sampler.set_epoch(epoch)
# train for one epoch
train_hvd(train_loader, model, optimizer, epoch, config,
hvd_writer, verbose, hvd)
# evaluate on validation set
validate_hvd(valid_loader, model, epoch, hvd_writer, verbose,
early_stopping, hvd, start)
utils.time(time.time() - start)
logger.info("Final best Prec@1 = {:.4%}".format(best_acc1))
else:
# set default gpu device id
if config.fp16_allreduce or config.distributed:
torch.cuda.set_device(config.local_rank % len(config.gpus))
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
config.world_size = torch.distributed.get_world_size()
else:
torch.cuda.set_device(config.gpus[0])
config.world_size = 1
config.total_batch = config.world_size * config.batch_size
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.benchmark = True
if config.static_loss_scale != 1.0:
if not config.fp16_allreduce:
print(
"Warning: if --fp16_allreduce is not used, static_loss_scale will be ignored."
)
if config.dataset.lower() == "imagenet":
train_loader = get_imagenet_iter_dali(
type='train',
# image_dir="/googol/atlas/public/cv/ILSVRC/Data/"
# use soft link `mkdir ./data/imagenet && ln -s /googol/atlas/public/cv/ILSVRC/Data/CLS-LOC/* ./data/imagenet/`
image_dir=config.data_path + config.dataset.lower(),
batch_size=config.batch_size,
num_threads=config.workers,
world_size=config.world_size,
local_rank=config.local_rank,
crop=224,
device_id=config.local_rank,
num_gpus=config.gpus,
dali_cpu=config.dali_cpu)
if 'efficientnet' in config.arch:
image_size = EfficientNet.EfficientNet.get_image_size(
config.arch)
val_transforms = transforms.Compose([
transforms.Resize(image_size,
interpolation=PIL.Image.BICUBIC),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
print('Using image size', image_size)
valid_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(
config.data_path + config.dataset.lower() + "/val",
val_transforms),
batch_size=config.batch_size,
shuffle=False,
num_workers=config.workers,
pin_memory=True)
else:
valid_loader = get_imagenet_iter_dali(
type='val',
image_dir=config.data_path + config.dataset.lower(),
batch_size=config.batch_size,
num_threads=config.workers,
world_size=config.world_size,
local_rank=config.local_rank,
crop=224,
device_id=config.local_rank,
num_gpus=config.gpus)
elif config.dataset.lower() == "cifar10":
if 'efficientnet' in config.arch:
raise NameError("don`t use cifar10 train efficientnet")
os._exit(0)
train_loader = get_cifar_iter_dali(type='train',
image_dir=config.data_path +
config.dataset.lower(),
batch_size=config.batch_size,
num_threads=config.workers,
world_size=config.world_size,
local_rank=config.local_rank)
valid_loader = get_cifar_iter_dali(type='val',
image_dir=config.data_path +
config.dataset.lower(),
batch_size=config.batch_size,
num_threads=config.workers,
world_size=config.world_size,
local_rank=config.local_rank)
else:
raise NameError("No Support dataset config")
'''
@description: we need define model here!
'''
model = EfficientNet.EfficientNet.from_name(config.arch)
# model = EfficientNet.from_pretrained('efficientnet-b0') # online
model = model.to(device)
if config.fp16_allreduce:
model = network_to_half(model)
if config.distributed:
# shared param/delay all reduce turns off bucketing in DDP, for lower latency runs this can improve perf
# for the older version of APEX please use shared_param, for newer one it is delay_allreduce
model = DDP(model, delay_allreduce=True)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(config.gpus)
optimizer = torch.optim.SGD(model.parameters(),
config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
if config.fp16_allreduce:
optimizer = FP16_Optimizer(
optimizer,
static_loss_scale=config.static_loss_scale,
dynamic_loss_scale=config.dynamic_loss_scale)
# optionally resume from a checkpoint
if config.resume:
if os.path.isfile(config.resume):
print("=> loading checkpoint '{}'".format(config.resume))
checkpoint = torch.load(config.resume)
config.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
if config.gpus is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(config.gpus)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
config.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(config.resume))
if config.evaluate:
res = validate(valid_loader, model, 0, criterion, config,
early_stopping, writer, start)
with open('res.txt', 'w') as f:
print(res, file=f)
return
for epoch in range(config.start_epoch, config.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, config,
writer)
# evaluate on validation set
best_acc1 = validate(valid_loader, model, epoch, criterion, config,
early_stopping, writer, start)
# remember best acc@1 and save checkpoint
#is_best = acc1 > best_acc1
#best_acc1 = max(acc1, best_acc1)
#utils.save_checkpoint(model, config.path, is_best)
train_loader.reset()
if 'efficientnet' not in config.arch:
valid_loader.reset()
utils.time(time.time() - start)
logger.info("Final best Prec@1 = {:.4%} use {} s".format(best_acc1, ))
