def summarize(self, basename, subtype = ''):
""" Strip JS of most of its fields so that we have shorter load times
on JS structures.
Usage: dataset.summarize(basename)
Input:
basename - file base name. We assume we can generate a full name
with dataset.getName(basename, 'output')
Output:
-NONE- but file will be saved in getName 'summary' format.
"""
input = self.getName(basename, type = 'output', subtype = subtype)
output = self.getName(basename, type = 'summary', subtype = subtype)
JS = self.loadData(basename, 'output', subtype = subtype)
# Strip JS off of all unnecessary information
JS.update()
JS.Regions = None
JS.Segmentations = None
JS.Superpix = None
JS.NmsRegions = None
for r in JS.CompactReg:
r.Children = None
r.Siblings = None
r.VisIdxImg = None
r.Better_R = None
r.Scores = None
# Save output
utils.save(output, {'JS': JS}, zipped=False)
def save(self, filename='index', metadata=True, resources=True):
''' save metadata and content
filename - defines just filename for three files:
- <filename>.html
- <filename>.metadata
- <filename>.resources
Only the first one is HTML file, the rest of files are JSON files
metadata - if True, HTTP response information will be stored into .metadata file
resources - If True, resources metadata will be stores into .resources file
'''
if not self.path:
raise RuntimeError('Error! The path for storing content is not defined')
if not os.path.exists(self.path):
utils.makedirs(self.path)
# save content metadata
if metadata:
utils.save(os.path.join(self.path, '%s.metadata' % filename),
content=unicode(json.dumps(self.metadata['headers'], indent=4, sort_keys=True)))
# save resources metadata
if resources and self.metadata['resources']:
utils.save(os.path.join(self.path, '%s.resources' % filename),
content=unicode(json.dumps(self.metadata['resources'], indent=4, sort_keys=True)))
# save content
offline_content = copy.deepcopy(self.content)
offline_links = dict([(url, self.metadata['resources'][url]['filename']) for url in self.metadata['resources']])
offline_content.make_links_offline(offline_links=offline_links)
offline_content.save(os.path.join(self.path, '%s.html' % filename))
def downloadTests(url):
tests = getTests(url)
print("Processing Tests")
os.mkdir("tests")
for v in tests:
print("Saving %s" % v["Próf"][0])
save("tests", v["Próf"][1])
def plot_reconstruction_result(res):
""" Plot original and reconstructed graph plus time series
"""
fig = plt.figure(figsize=(32, 8))
gs = mpl.gridspec.GridSpec(1, 4)
# original graph
orig_ax = plt.subplot(gs[0])
plot_graph(nx.from_numpy_matrix(res.A.orig), orig_ax)
orig_ax.set_title('Original graph')
# time series
ax = plt.subplot(gs[1:3])
sns.tsplot(
time='time', value='theta',
unit='source', condition='oscillator',
estimator=np.mean, legend=False,
data=compute_solutions(res),
ax=ax)
ax.set_title(r'$A_{{err}} = {:.2}, B_{{err}} = {:.2}$'.format(*compute_error(res)))
# reconstructed graph
rec_ax = plt.subplot(gs[3])
tmp = res.A.rec
tmp[abs(tmp) < 1e-1] = 0
plot_graph(nx.from_numpy_matrix(tmp), rec_ax)
rec_ax.set_title('Reconstructed graph')
plt.tight_layout()
save(fig, 'reconstruction_overview')
def update_targets(week):
target_week_data = "data/" + ("week_%s/" % week) + "target_users.pkl"
invalid_week_data = "data/" + ("week_%s/" % week) + "invalid_users.pkl"
update_info = {}
invalid_users = []
with open("data/target_users.pkl") as orig:
targets = cPickle.load(orig)
# for index, t in enumerate(targets, start=1):
# print "--- get user=%s(%d:%d) ---" % (t, index, len(targets))
# result = get_user_info(t)
# if result is None:
# print "--- user=%s get invalid info ---" % t
# invalid_users.append(t)
# update_info[t] = result
from utils import iter_pool_do
info_iter = iter_pool_do(get_user_info, targets.keys())
index = 1
for name, info in info_iter:
print "--- get user=%s(%d:%d) ---" % (name, index, len(targets))
if info is None:
print "--- user=%s get invalid info ---" % name
invalid_users.append(name)
update_info[name] = info
index += 1
print "---- update to file %s %d targets---" % (
target_week_data, len(update_info))
save(target_week_data, update_info)
print "---- update to file %s %d invalids---" % (
invalid_week_data, len(invalid_users))
save(invalid_week_data, invalid_users)
return update_info
def test():
R = Ret()
prefix = '/home/mfkiller/stitch/tools/test/data/'
R.prepare(prefix+'blocks/4.pkl',prefix+'imgs/',prefix+'cameralist.pkl',
prefix+'plane.pkl')
#R.scale = 10.0
utils.save(R, '../ret.pkl')
def kinfu(self, basename, type = 'list_ext'):
""" Use Kinect fusion on list of depth images, and store cloud
Usage: kinfu(basename, type = 'list_ext')
Input:
basename - Path to file that contains depth image names.
type{'list_ext'} - List file type ('list' or 'list_ext')
Output:
cloud, poses - Filenames to output cloud and poses from kinfu
"""
roslib.load_manifest('kinfu')
import kinfu
depth_textfile = self.getName(basename, type)
# Need to read list and export using full names
fullnames = list()
with open(depth_textfile, 'rt') as file_:
for name in file_:
fullnames.append( self.getName(name.rstrip(), 'depth') )
output_prefix = os.path.join(self.getPath(type), basename) + '_'
KF = kinfu.KinFuPy('file')
cloud, poses = KF.kinfu_list(fullnames, output_prefix, opts='singlepass')
# Store cloud and poses
utils.save(self.getName(basename, 'cloud'), {'cloud': cloud}, \
zipped=True)
utils.save(self.getName(basename, 'poses'), {'poses': poses}, \
zipped=True)
return cloud, poses
def fit(self, trX, trY, batch_size=64, n_epochs=1, len_filter=LenFilter(), snapshot_freq=1, path=None, callback=None, callback_freq=1):
"""Train model on given training examples and return the list of costs after each minibatch is processed.
Args:
trX (list) -- Inputs
trY (list) -- Outputs
batch_size (int, optional) -- number of examples in a minibatch (default 64)
n_epochs (int, optional) -- number of epochs to train for (default 1)
len_filter (object, optional) -- object to filter training example by length (default LenFilter())
snapshot_freq (int, optional) -- number of epochs between saving model snapshots (default 1)
path (str, optional) -- prefix of path where model snapshots are saved.
If None, no snapshots are saved (default None)
Returns:
list -- costs of model after processing each minibatch
"""
if len_filter is not None:
trX, trY = len_filter.filter(trX, trY)
trY = standardize_targets(trY, cost=self.cost)
n = 0.
stats = []
t = time()
costs = []
for e in range(n_epochs):
epoch_costs = []
for xmb, ymb in self.iterator.iterXY(trX, trY):
if not self.is_padseq_output:
c = self._train(xmb, ymb)
else:
ymb, padsizes = ymb
c = self._train(xmb, ymb, padsizes)
epoch_costs.append(c)
n += len(ymb)
if self.verbose >= 2:
n_per_sec = n / (time() - t)
n_left = len(trY) - n % len(trY)
time_left = n_left/n_per_sec
sys.stdout.write("\rEpoch %d Seen %d samples Avg cost %0.4f Time left %d seconds" % (e, n, np.mean(epoch_costs[-250:]), time_left))
sys.stdout.flush()
costs.extend(epoch_costs)
status = "Epoch %d Seen %d samples Avg cost %0.4f Time elapsed %d seconds" % (e, n, np.mean(epoch_costs[-250:]), time() - t)
if self.verbose >= 2:
sys.stdout.write("\r"+status)
sys.stdout.flush()
sys.stdout.write("\n")
elif self.verbose == 1:
print status
if path and e % snapshot_freq == 0:
save(self, "{0}.{1}".format(path, e))
if callback is not None and e % callback_freq == 0:
try:
callback(e)
except RNN.EarlyStoppingException:
break
return costs
def dump(self, filename):
""" Dump model into file using pickle.
Usage: model.dump(filename = 'FILENAME.pickle.zip')
Input:
filename - File to dump model into. Should end in '.pickle.zip'.
"""
utils.save(filename, {'model': self}, zipped=True)
def train(self, svm_kernel, codebook, des_option=constants.ORB_FEAT_OPTION, is_interactive=True):
"""
Gets the descriptors for the training set and then calculates the SVM for them.
Args:
svm_kernel (constant): The kernel of the SVM that will be created.
codebook (NumPy float matrix): Each row is a center of a codebook of Bag of Words approach.
des_option (integer): The option of the feature that is going to be used as local descriptor.
is_interactive (boolean): If it is the user can choose to load files or generate.
Returns:
cv2.SVM: The Support Vector Machine obtained in the training phase.
"""
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
k = len(codebook)
x_filename = filenames.vlads_train(k, des_name)
if is_interactive:
data_option = input("Enter [1] to calculate VLAD vectors for the training set or [2] to load them.\n")
else:
data_option = constants.GENERATE_OPTION
if data_option == constants.GENERATE_OPTION:
# Getting the global vectors for all of the training set
print("Getting global descriptors for the training set.")
start = time.time()
x, y = self.get_data_and_labels(self.dataset.get_train_set(), codebook, des_option)
utils.save(x_filename, x)
end = time.time()
print("VLADs training vectors saved on file {0}".format(x_filename))
self.log.train_vlad_time(end - start)
else:
# Loading the global vectors for all of the training set
print("Loading global descriptors for the training set.")
x = utils.load(x_filename)
y = self.dataset.get_train_y()
x = np.matrix(x, dtype=np.float32)
svm = cv2.SVM()
svm_filename = filenames.svm(k, des_name, svm_kernel)
if is_interactive:
svm_option = input("Enter [1] for generating a SVM or [2] to load one\n")
else:
svm_option = constants.GENERATE_OPTION
if svm_option == constants.GENERATE_OPTION:
# Calculating the Support Vector Machine for the training set
print("Calculating the Support Vector Machine for the training set...")
svm_params = dict(kernel_type=svm_kernel, svm_type=cv2.SVM_C_SVC, C=1)
start = time.time()
svm.train_auto(x, y, None, None, svm_params)
end = time.time()
self.log.svm_time(end - start)
# Storing the SVM in a file
svm.save(svm_filename)
else:
svm.load(svm_filename)
return svm
def test():
B0 = utils.load('blk/0.pkl')
B1 = utils.load('blk/1.pkl')
B2 = utils.load('blk/4.pkl')
B3 = utils.load('blk/5.pkl')
BM = [[B0,B1],[B2,B3]]
P = Block()
P.merge_from_matrix(BM)
utils.save(P, 'mb.pkl')
print P
def buildregion(args):
"""Given a region name and coordinates, build the corresponding region from tile chunks."""
# this should work for single and multi threaded cases
(log, name, regionx, regiony) = args
log.log_debug(1,"Building region (%d,%d) of map %s..." % \
(regionx, regiony, name))
yamlfile = file(os.path.join('Regions', name, 'Region.yaml'))
myRegion = yaml.load(yamlfile)
yamlfile.close()
regionsize = 32 * 16
tilexrange = xrange(regionx * (regionsize / myRegion.tilesize), \
(regionx + 1) * (regionsize / myRegion.tilesize))
tileyrange = xrange(regiony * (regionsize / myRegion.tilesize), \
(regiony + 1) * (regionsize / myRegion.tilesize))
maintiledir = None
for tilex, tiley in product(tilexrange, tileyrange):
if (tilex < myRegion.tiles['xmin']) or (tilex >= myRegion.tiles['xmax']) or \
(tiley < myRegion.tiles['ymin']) or (tiley >= myRegion.tiles['ymax']):
continue
tiledir = os.path.join('Regions', name, 'Tiles', '%dx%d' % (tilex, tiley))
if not(os.path.exists(tiledir)) or not(os.path.exists(os.path.join(tiledir, 'level.dat'))):
log.log_debug(1,"Skipping missing tile" % (tilex, tiley))
continue
if maintiledir == None:
maintiledir = tiledir
maintileworld = mclevel.MCInfdevOldLevel(tiledir, create=False)
# If this hack is necessary, it's dumb. It seems to be.
maintileworld.copyBlocksFrom(maintileworld, maintileworld.bounds, \
maintileworld.bounds.origin)
else:
log.log_debug(2,"Copying tile %dx%d to %s" % (tilex, tiley, maintiledir))
tileworld = mclevel.MCInfdevOldLevel(tiledir, create=False)
maintileworld.copyBlocksFrom(tileworld, tileworld.bounds, tileworld.bounds.origin)
tileworld = None
if maintiledir == None:
log.log_debug(1,"Region (%d,%d) appears to be complete; skipping." % (regionx, regiony))
return
else:
log.log_debug(2,"maintiledir is %s" % maintiledir)
save(maintileworld)
maintileworld = None
srcdir = os.path.join(maintiledir, 'region')
dstdir = os.path.join('Worlds', name, 'region')
for path, dirlist, filelist in os.walk(srcdir):
for name in fnmatch.filter(filelist, "*.mca"):
shutil.move(os.path.join(path,name),dstdir)
log.log_debug(2,"Moved file %s to %s" % (os.path.join(path,name),dstdir))
os.remove(os.path.join(maintiledir,'level.dat'))
def handle_events(self, events):
for event in events:
if event.type == QUIT:
exit()
elif event.type == KEYDOWN:
if event.key == K_ESCAPE:
self.manager.go_to('levelSelectScreen')
if self.player.finished:
if event.key == K_DOWN:
self.answerChoice = (self.answerChoice + 1) % 3
elif event.key == K_UP:
self.answerChoice = (self.answerChoice - 1) % 3
elif event.key == K_RETURN:
save([('powers',[self.player.timePower,self.player.mines,self.player.shields])])
self.manager.go_to_game(self.level.index + 1 )
def store(self, response):
headers = dict(response.headers.copy())
headers['url'] = response.url
if response.encoding:
headers['encoding'] = response.encoding.lower()
headers['status-code'] = int(response.status_code)
filename = self._fn(response.url)
utils.save('%s.metadata' % filename,
content=json.dumps(headers, indent=4, sort_keys=True) + '\n')
utils.save(filename,
headers=response.headers,
content=response.content)
return True
def display(self): # called each loop
if g.big:
g.screen.fill(self.colors[1])
utils.centre_blit(g.screen, g.big_surface, (g.sx(16), g.sy(11.5)))
else:
if self.sugar:
g.screen.fill(self.colors[1])
else:
g.screen.blit(g.bgd, (g.sx(0), 0))
g.screen.blit(g.box, (g.x0, g.y0))
g.screen.blit(g.box, (g.x1, g.y1))
if not self.sugar:
utils.centre_blit(g.screen, g.magician, g.magician_c)
self.draw_goal()
utils.centre_blit(g.screen, g.turtle,
(g.x0 + 4 * g.dd, g.y0 + 6 * g.dd))
self.tu.draw()
if self.tu.win:
if self.sugar:
if self.good_job is None:
path = self.parent.good_job_image_path()
self.good_job = utils.load_image(path, True)
if g.w > g.h:
utils.centre_blit(
g.screen, self.good_job, (g.sx(7), g.sy(17)))
else:
utils.centre_blit(g.screen, self.good_job, (g.sx(7),
g.sy(38)))
else:
utils.centre_blit(g.screen, g.smiley, (g.sx(16.6),
g.sy(2.2)))
if self.sugar:
self.cyan_button.set_sensitive(True)
else:
buttons.on('cyan')
if not self.journal:
utils.save()
self.draw_nos()
if not self.sugar:
buttons.draw()
self.slider.draw()
if g.score > 0:
if self.sugar:
self.parent.update_score(int(g.score))
else:
utils.display_score()
utils.display_number1(g.pattern, (g.sx(2.4), g.sy(2)),
g.font1, utils.WHITE)
def main(fn, output_fn):
print("Reading in dataset")
data, classes = readDataset(fn)
print(len(data), " sequences found")
print("Found classes:", sorted(classes))
proc = Processor(classes, 2, 2, prefix=(1,3), affix=(2,1), hashes=2,
features=100000, stem=False, ohe=False)
yss = []
ryss = []
for Xs in data:
ys = [x['output'] for x in Xs]
yss.append(ys)
ryss.append([proc.encode_target(ys, i) for i in range(len(ys))])
rs = np.random.RandomState(seed=2016)
print("Starting KFolding")
y_trues, y_preds = [], []
fold_object = KFold(5, random_state=1)
for train_idx, test_idx in fold_object.split(data):
tr_X, tr_y = subset(data, yss, train_idx, rs)
test_data = subset(data, yss, test_idx, rs, False)
print("Training")
d = Dagger(proc, tr_X, tr_y, validation_set=test_data)
clf = d.train(10)
seq = Sequencer(proc, clf)
print("Testing")
y_true, y_pred = test(data, ryss, test_idx, seq)
# print(y_true, y_pred, proc.labels)
print( classification_report(y_true, y_pred))
y_trues.extend(y_true)
y_preds.extend(y_pred)
print("Total Report")
print(classification_report(y_trues, y_preds, target_names=proc.labels))
print("Training all")
idxs = range(len(data))
tr_X, tr_y = subset(data, yss, idxs, rs)
d = Dagger(proc, tr_X, tr_y)
clf = d.train()
seq = Sequencer(proc, clf)
save(output_fn, seq)
def investigate_laplacian(graph):
""" Compute Laplacian
"""
w = nx.laplacian_spectrum(graph)
pairs = []
for i, w in enumerate(sorted(w)):
if abs(w) < 1e-5: continue
inv_w = 1 / w
pairs.append((inv_w, i))
fig = plt.figure()
plt.scatter(*zip(*pairs))
plt.xlabel(r'$\frac{1}{\lambda_i}$')
plt.ylabel(r'rank index')
save(fig, 'laplacian_spectrum')
def test(self, codebook, svm, des_option = constants.ORB_FEAT_OPTION, is_interactive=True):
"""
Gets the descriptors for the testing set and use the svm given as a parameter to predict all the elements
Args:
codebook (NumPy matrix): Each row is a center of a codebook of Bag of Words approach.
svm (cv2.SVM): The Support Vector Machine obtained in the training phase.
des_option (integer): The option of the feature that is going to be used as local descriptor.
is_interactive (boolean): If it is the user can choose to load files or generate.
Returns:
NumPy float array: The result of the predictions made.
NumPy float array: The real labels for the testing set.
"""
des_name = constants.ORB_FEAT_NAME if des_option == constants.ORB_FEAT_OPTION else constants.SIFT_FEAT_NAME
k = len(codebook)
x_filename = filenames.vlads_test(k, des_name)
if is_interactive:
data_option = input("Enter [1] to calculate VLAD vectors for the testing set or [2] to load them.\n")
else:
data_option = constants.GENERATE_OPTION
if data_option == constants.GENERATE_OPTION:
# Getting the global vectors for all of the testing set
print("Getting global descriptors for the testing set...")
start = time.time()
x, y = self.get_data_and_labels(self.dataset.get_test_set(), codebook, des_option)
utils.save(x_filename, x)
end = time.time()
print("VLADs testing vectors saved on file {0}".format(x_filename))
self.log.test_vlad_time(end - start)
else:
# Loading the global vectors for all of the testing set
print("Loading global descriptors for the testing set.")
x = utils.load(x_filename.format(des_name))
y = self.dataset.get_test_y()
x = np.matrix(x, dtype=np.float32)
# Predicting the testing set using the SVM
start = time.time()
result = svm.predict_all(x)
end = time.time()
self.log.predict_time(end - start)
mask = result == y
correct = np.count_nonzero(mask)
accuracy = (correct * 100.0 / result.size)
self.log.accuracy(accuracy)
return result, y
def handle_events(self, events):
for event in events:
if event.type == KEYDOWN:
if event.key == K_ESCAPE:
self.manager.go_to('startScreen')
elif event.key == K_RETURN:
if self.menuChoice == 0:
# self.manager.go_to('levelSelectScreen')
save([('powers',[0,0,0])])
self.manager.go_to_game(0)
elif self.menuChoice == 1:
self.manager.go_to('optionsScreen')
elif self.menuChoice == 2:
exit()
elif event.key == K_UP:
self.menuChoice = (self.menuChoice - 1) % len(self.menuEntries)
elif event.key == K_DOWN:
self.menuChoice = (self.menuChoice + 1) % len(self.menuEntries)
def fitModel(self,trX,trY,snapshot_rate=1,path=None,epochs=30,batch_size=50,learning_rate_decay=0.97,decay_after=10):
X_minibatch=[]
Y_minibatch=[]
num_examples = trX.shape[1]
batches_in_one_epoch = int(num_examples / batch_size)
loss_values = []
for epoch in range(epochs):
t0 = time.time()
permutation = permute(num_examples)
if self.X.ndim > 2:
trX = trX[:,permutation,:]
else:
trX = trX[:,permutation]
if self.Y.ndim > 1:
trY = trY[:,permutation]
else:
trY = trY[permutation]
for j in range(batches_in_one_epoch):
if self.X.ndim > 2:
X_minibatch = trX[:,j*batch_size:(j+1)*batch_size,:]
else:
X_minibatch = trX[:,j*batch_size:(j+1)*batch_size]
if self.Y.ndim > 1:
Y_minibatch = trY[:,j*batch_size:(j+1)*batch_size]
else:
Y_minibatch = trY[j*batch_size:(j+1)*batch_size]
loss = self.train(X_minibatch,Y_minibatch)
loss_values.append(loss)
print "epoch={0} loss={1}".format(epoch,loss)
if path and epoch % snapshot_rate == 0:
print 'saving snapshot checkpoint.{0}'.format(epoch)
save(self,"{0}checkpoint.{1}".format(path,epoch))
f = open('{0}logfile'.format(path),'w')
for v in loss_values:
f.write('{0}\n'.format(v))
f.close()
t1 = time.time()
print 'Epoch took {0} seconds'.format(t1-t0)
def downloadLectures(url):
lectures = getLectures(url)
print("Processing Lectures")
os.mkdir("lectures")
for k, v in lectures.items():
dirpath = mkdir("lectures", k)
for item in v:
print("Processing %s/%s" % (k, item["Heiti"]))
path = mkdir(dirpath, item["Heiti"])
for link, title in {"Glærur": "slides","3/síðu": "3_page","6/síðu": "6_page","Hljóðgl.": "video","Annað": "other"}.items():
l = item[link]
if not l or len(l) != 2: continue
_, url = l
if "?Page=Download" in url:
save(path, url)
elif "Action=16" in url:
saveRedirect(os.path.join(path, cleanFile(title) + ".html"), url)
else:
saveLink(os.path.join(path, cleanFile(title) + ".html"), url)
def prepare_target_users():
target_data = "data/target_users.pkl"
seed_data = "data/seed_users.pkl"
if os.path.exists(target_data):
targets_info = cPickle.load(open(target_data))
else:
if not os.path.exists(seed_data):
tracks = cPickle.load(open("data/recent_tracks.pkl", "rb"))
seeds = get_seed_users(tracks)
print "total %d seed users" % len(seeds)
save(seed_data, seeds)
else:
seeds = cPickle.load(open(seed_data))
targets_info = get_target_users(seeds)
targets_info = {k: v for k, v in targets_info.iteritems()
if filter_target_info(v)}
print "total %d target users" % len(target_data)
save(target_data, targets_info)
return targets_info
def downloadAssignments(url, handin=False):
assignments = getAssignments(url)
os.mkdir("assignments")
for k, v in assignments.items():
dirpath = mkdir("assignments", k)
for item in v:
print("Processing %s/%s" % (k, item["Nafn"][0]))
path = mkdir(dirpath, item["Nafn"][0])
assignment = getAssignment(item["Nafn"][1])
# Description
descr = os.path.join(path, "description")
os.mkdir(descr)
jsondump(os.path.join(descr,"description.json"), assignment["description"])
genHtml(os.path.join(descr, "description.html"), assignment["description"]["description"], assignment["description"]["title"])
for item in assignment["description"]["files"]:
save(descr, item["url"])
if handin:
if "grade" in assignment:
grade = os.path.join(path, "grade")
os.mkdir(grade)
jsondump(os.path.join(grade, "grade.json"), assignment["grade"])
for item in assignment["grade"]["files"]:
save(grade, item["url"])
if "handin" in assignment:
handin = os.path.join(path, "handin")
os.mkdir(handin)
jsondump(os.path.join(handin, "handin.json"), assignment["handin"])
if "statistics" in assignment:
jsondump(os.path.join(path, "stats.json"), assignment["statistics"])
if assignment["statistics"]["image"] is not None:
save(os.path.join(path, "stats.jpg"), assignment["statistics"]["image"])
def calc_herm_modforms(**kwargs):
kwargs = kwargs.copy()
from sage.parallel.ncpus import ncpus
for key,value in {
"D": -3,
"HermWeight": 6,
"B_cF": 7,
"task_limit": ncpus(),
}.items():
if not key in kwargs:
kwargs[key] = value
print "Calculating Hermitian modular forms with %r" % kwargs
import algo
modforms = algo.herm_modform_space__parallel(**kwargs)
assert modforms
import utils
utils.save(
modforms,
"herm_modforms__D%i_k%i_B%i__%i.sobj" %
(kwargs["D"], kwargs["HermWeight"], kwargs["B_cF"], modforms.degree())
)
print modforms
sys.exit(0)
def display(self): # called each loop
if g.big:
g.screen.fill(self.colors[1])
utils.centre_blit(g.screen, g.big_surface, (g.sx(16), g.sy(11.5)))
else:
if self.sugar:
g.screen.fill(self.colors[1])
else:
g.screen.blit(g.bgd, (g.sx(0), 0))
g.screen.blit(g.box, (g.x0, g.y0))
g.screen.blit(g.box, (g.x1, g.y1))
if not self.sugar:
utils.centre_blit(g.screen, g.magician, g.magician_c)
self.draw_goal()
utils.centre_blit(g.screen, g.turtle,
(g.x0 + 4 * g.dd, g.y0 + 6 * g.dd))
self.tu.draw()
if self.tu.win:
utils.centre_blit(g.screen, g.smiley, (g.sx(16.6), g.sy(2.2)))
if self.sugar:
self.cyan_button.set_sensitive(True)
else:
buttons.on('cyan')
if not self.journal:
utils.save()
self.draw_nos()
if not self.sugar:
buttons.draw()
self.slider.draw()
if g.score > 0:
if self.sugar:
self.label.set_markup(
'<span><big><b> %s</b></big></span>' % (str(g.score)))
else:
utils.display_score()
utils.display_number1(g.pattern, (g.sx(2.4), g.sy(2)),
g.font1, utils.BLUE)
def investigate_skip_influence(max_skip=100, reps_per_config=50):
""" Investigate how number of skipped entries influence reconstruction accuracy
"""
# setup network
graph = generate_ring_graph(3)
dim = len(graph.nodes())
Bvec = np.random.uniform(0, 5, size=dim)
omega = np.random.uniform(0, 5, size=dim)
system_config = get_base_config(nx.to_numpy_matrix(graph), Bvec, omega)
syst = DW({
'graph': graph,
'system_config': system_config
})
# generate data
df = pd.DataFrame(columns=['skip', 'parameter', 'relative_error'])
for i in tqdm(range(1, max_skip)):
for _ in trange(reps_per_config):
res = process([syst], skipper=i)
err_A, err_B = compute_error(res)
df = df.append([
{'skip': i, 'parameter': 'A', 'relative_error': err_A},
{'skip': i, 'parameter': 'B', 'relative_error': err_B}
], ignore_index=True)
# plot result
fig = plt.figure(figsize=(12,6))
sns.boxplot(x='skip', y='relative_error', hue='parameter', data=df)
plt.xticks(rotation=90, fontsize=6)
save(fig, 'reconstruction_skip')
def plot_result(data):
""" Plot final result
"""
# main overview
fig = plt.figure(figsize=(30, 10))
gs = mpl.gridspec.GridSpec(2, 3, width_ratios=[1, 1, 2])
plot_graph(
data.graph, plt.subplot(gs[:, 0]))
plot_matrix(
data.syncs, plt.subplot(gs[:, 1]),
title='Sign-switch of dynamic connectivity matrix',
allow_negative=False)
plot_evolutions(
data.sols[0], data.ts, plt.subplot(gs[0, 2]))
plot_correlation_matrix(
np.mean(data.cmats, axis=0), data.ts, plt.subplot(gs[1, 2]))
plt.tight_layout()
save(fig, 'overview_plot')
# variance investigation
fig = plt.figure(figsize=(20, 10))
gs = mpl.gridspec.GridSpec(2, 1)
plot_evolutions(data.sols[0], data.ts, plt.subplot(gs[0]))
plot_series(data.vser, data.ts, plt.subplot(gs[1]))
plt.tight_layout()
save(fig, 'cluster_development')
# correlation matrix heatmap
fig = plt.figure()
cmat_sum = np.sum(np.mean(data.cmats, axis=0), axis=2)
plot_matrix(
cmat_sum, plt.gca(),
title='Summed correlation matrix')
plt.tight_layout()
save(fig, 'correlation_matrix')
def get_target_friends(targets_info):
target_data = "data/target_users.pkl"
target_friends = {}
friends_info = {}
invalid_targets = []
count = 0
for name in targets_info:
result = get_user_friends(name)
if result is not None:
targets_info[name]['friend_nums'] = len(result[0])
target_friends[name] = result[0].keys()
friends_info.update(result[0])
count += 1
if count == 1000:
break
else:
invalid_targets.append(name)
for name in targets_info.keys():
if name not in target_friends:
del targets_info[name]
for name in invalid_targets:
del targets_info[name]
# if len(targets_info) > 1000:
# keys = random.sample(targets_info, 1000)
# targets_info = {k: v for k, v in targets_info.iteritems()
# if k in keys}
save(target_data, targets_info)
print "--- total %d targets ---" % len(targets_info)
save("data/target_friends.pkl", target_friends)
count = sum(len(fr) for fr in target_friends.itervalues())
print "--- all targets have %d friend counts ---" % count
save("data/friends_info.pkl", friends_info)
print "--- total %d friends ---" % len(friends_info)
def handle_events(self, events):
for event in events:
if event.type == KEYDOWN:
if event.key == K_ESCAPE:
save([('soundLevel',self.soundLevel),('musicLevel',self.musicLevel)])
self.manager.go_to('mainMenuScreen')
if event.key == K_RETURN:
if self.menuChoice == 3:
save([('soundLevel',self.soundLevel),('musicLevel',self.musicLevel)])
self.manager.go_to('mainMenuScreen')
if event.key == K_UP:
self.menuChoice = (self.menuChoice - 1) % len(self.menuEntries)
if event.key == K_DOWN:
self.menuChoice = (self.menuChoice + 1) % len(self.menuEntries)
if event.key == K_LEFT:
if self.menuChoice == 0:
self.soundLevel = max(0,self.soundLevel-1)
self.volumeTest.set_volume(self.soundLevel*0.2)
self.volumeTest.play()
if self.menuChoice == 1:
self.musicLevel = max(0,self.musicLevel-1)
pygame.mixer.music.set_volume(self.musicLevel*0.2)
self.volumeTest.set_volume(self.musicLevel*0.2)
self.volumeTest.play()
if event.key == K_RIGHT:
if self.menuChoice == 0:
self.soundLevel = min(5,self.soundLevel+1)
self.volumeTest.set_volume(self.soundLevel*0.2)
self.volumeTest.play()
if self.menuChoice == 1:
self.musicLevel = min(5,self.musicLevel+1)
pygame.mixer.music.set_volume(self.musicLevel*0.2)
self.volumeTest.set_volume(self.musicLevel*0.2)
self.volumeTest.play()
if event.type == QUIT:
save([('soundLevel',self.soundLevel),('musicLevel',self.musicLevel)])
def f_save(step): save(sess, saver, checkpoint_dir, step, name) T.save_weights(checkpoint_dir+"/T_weights.keras", True)
def save(self, path_model, path_pca, path_res=None):
self.model.save(path_model)
import utils
utils.save(path_pca, self.pca)
utils.save(path_res, self.res)
def main():
def _str_to_bool(s):
"""Convert string to bool (in argparse context)."""
if s.lower() not in ['true', 'false']:
raise ValueError(
'Argument needs to be a boolean, got {}'.format(s))
return {'true': True, 'false': False}[s.lower()]
parser = argparse.ArgumentParser(description='WaveNet example network')
DATA_DIRECTORY = './data/kss,./data/son'
parser.add_argument('--data_dir',
type=str,
default=DATA_DIRECTORY,
help='The directory containing the VCTK corpus.')
LOGDIR = None
#LOGDIR = './/logdir-wavenet//train//2018-12-21T22-58-10'
parser.add_argument(
'--logdir',
type=str,
default=LOGDIR,
help=
'Directory in which to store the logging information for TensorBoard. If the model already exists, it will restore the state and will continue training. Cannot use with --logdir_root and --restore_from.'
)
parser.add_argument(
'--logdir_root',
type=str,
default=None,
help=
'Root directory to place the logging output and generated model. These are stored under the dated subdirectory of --logdir_root. Cannot use with --logdir.'
)
parser.add_argument(
'--restore_from',
type=str,
default=None,
help=
'Directory in which to restore the model from. This creates the new model under the dated directory in --logdir_root. Cannot use with --logdir.'
)
CHECKPOINT_EVERY = 1000 # checkpoint 저장 주기
parser.add_argument(
'--checkpoint_every',
type=int,
default=CHECKPOINT_EVERY,
help='How many steps to save each checkpoint after. Default: ' +
str(CHECKPOINT_EVERY) + '.')
config = parser.parse_args() # command 창에서 입력받을 수 있는 조건
config.data_dir = config.data_dir.split(",")
try:
directories = validate_directories(config, hparams)
except ValueError as e:
print("Some arguments are wrong:")
print(str(e))
return
logdir = directories['logdir']
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
is_overwritten_training = logdir != restore_from
log_path = os.path.join(logdir, 'train.log')
infolog.init(log_path, logdir)
global_step = tf.Variable(0, name='global_step', trainable=False)
# Create coordinator.
coord = tf.train.Coordinator()
num_speakers = len(config.data_dir)
# Load raw waveform from VCTK corpus.
with tf.name_scope('create_inputs'):
# Allow silence trimming to be skipped by specifying a threshold near
# zero.
silence_threshold = hparams.silence_threshold if hparams.silence_threshold > EPSILON else None
gc_enable = num_speakers > 1
# AudioReader에서 wav 파일을 잘라 input값을 만든다. receptive_field길이만큼을 앞부분에 pad하거나 앞조각에서 가져온다. (receptive_field+ sample_size)크기로 자른다.
reader = DataFeederWavenet(
coord,
config.data_dir,
batch_size=hparams.wavenet_batch_size,
receptive_field=WaveNetModel.calculate_receptive_field(
hparams.filter_width, hparams.dilations, hparams.scalar_input,
hparams.initial_filter_width),
gc_enable=gc_enable)
if gc_enable:
audio_batch, lc_batch, gc_id_batch = reader.inputs_wav, reader.local_condition, reader.speaker_id
else:
print("didn't work")
#audio_batch, lc_batch = reader.inputs_wav, local_condition
# Create network.
net = WaveNetModel(
batch_size=hparams.wavenet_batch_size,
dilations=hparams.dilations,
filter_width=hparams.filter_width,
residual_channels=hparams.residual_channels,
dilation_channels=hparams.dilation_channels,
quantization_channels=hparams.quantization_channels,
out_channels=hparams.out_channels,
skip_channels=hparams.skip_channels,
use_biases=hparams.use_biases, # True
scalar_input=hparams.scalar_input,
initial_filter_width=hparams.initial_filter_width,
global_condition_channels=hparams.gc_channels,
global_condition_cardinality=num_speakers,
local_condition_channels=hparams.num_mels,
upsample_factor=hparams.upsample_factor,
train_mode=True)
if hparams.l2_regularization_strength == 0:
hparams.l2_regularization_strength = None
net.add_loss(input_batch=audio_batch,
local_condition=lc_batch,
global_condition_batch=gc_id_batch,
l2_regularization_strength=hparams.l2_regularization_strength)
net.add_optimizer(hparams, global_step)
run_metadata = tf.RunMetadata()
# Set up session
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)
) # log_device_placement=False --> cpu/gpu 자동 배치.
init = tf.global_variables_initializer()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(
var_list=tf.global_variables(),
max_to_keep=hparams.max_checkpoints) # 최대 checkpoint 저장 갯수 지정
try:
start_step = load(saver, sess, restore_from) # checkpoint load
if is_overwritten_training or start_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
zero_step_assign = tf.assign(global_step, 0)
sess.run(zero_step_assign)
except:
print(
"Something went wrong while restoring checkpoint. We will terminate training to avoid accidentally overwriting the previous model."
)
raise
###########
start_step = sess.run(global_step)
last_saved_step = start_step
try:
reader.start_in_session(sess, start_step)
while not coord.should_stop():
start_time = time.time()
if hparams.store_metadata and step % 50 == 0:
# Slow run that stores extra information for debugging.
log('Storing metadata')
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
step, loss_value, _ = sess.run(
[global_step, net.loss, net.optimize],
options=run_options,
run_metadata=run_metadata)
tl = timeline.Timeline(run_metadata.step_stats)
timeline_path = os.path.join(logdir, 'timeline.trace')
with open(timeline_path, 'w') as f:
f.write(tl.generate_chrome_trace_format(show_memory=True))
else:
step, loss_value, _ = sess.run(
[global_step, net.loss, net.optimize])
duration = time.time() - start_time
log('step {:d} - loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
if step % config.checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
if step >= hparams.num_steps:
# error message가 나오지만, 여기서 멈춘 것은 맞다.
raise Exception('End xxx~~~yyy')
except Exception as e:
print('finally')
#if step > last_saved_step:
# save(saver, sess, logdir, step)
coord.request_stop(e)
def main():
# gpu device check
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
# init
np.random.seed(args.seed) # set random seed
torch.cuda.set_device(args.gpu) # set training device
cudnn.benchmark = True # set cudnn benchmark
torch.manual_seed(args.seed) # set torch random seed
cudnn.enabled = True # set cudnn
torch.cuda.manual_seed(args.seed) # set torch cuda seed
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
# search space
genotype = eval("genotypes.%s" % args.arch) # TODO convert to tree
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR10(root=args.data,
train=False,
download=True,
transform=valid_transform)
train_queue = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=2)
valid_queue = torch.utils.data.DataLoader(valid_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs))
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main(args):
# 0. initial setting
# set environmet
cudnn.benchmark = True
if not os.path.isdir('./ckpt'):
os.mkdir('./ckpt')
if not os.path.isdir('./results'):
os.mkdir('./results')
if not os.path.isdir(os.path.join('./ckpt', args.name)):
os.mkdir(os.path.join('./ckpt', args.name))
if not os.path.isdir(os.path.join('./results', args.name)):
os.mkdir(os.path.join('./results', args.name))
if not os.path.isdir(os.path.join('./results', args.name, "log")):
os.mkdir(os.path.join('./results', args.name, "log"))
# set logger
logger = logging.getLogger()
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(message)s')
handler = logging.FileHandler("results/{}/log/{}.log".format(
args.name, time.strftime('%c', time.localtime(time.time()))))
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.addHandler(logging.StreamHandler())
args.logger = logger
# set cuda
if torch.cuda.is_available():
args.logger.info("running on cuda")
args.device = torch.device("cuda")
args.use_cuda = True
else:
args.logger.info("running on cpu")
args.device = torch.device("cpu")
args.use_cuda = False
args.logger.info("[{}] starts".format(args.name))
# 1. load data
args.logger.info("loading data...")
src, tgt = load_data(args.path)
src_vocab = Vocab(init_token='<sos>',
eos_token='<eos>',
pad_token='<pad>',
unk_token='<unk>')
src_vocab.load(os.path.join(args.path, 'vocab.en'))
tgt_vocab = Vocab(init_token='<sos>',
eos_token='<eos>',
pad_token='<pad>',
unk_token='<unk>')
tgt_vocab.load(os.path.join(args.path, 'vocab.de'))
# 2. setup
args.logger.info("setting up...")
sos_idx = 0
eos_idx = 1
pad_idx = 2
max_length = 50
src_vocab_size = len(src_vocab)
tgt_vocab_size = len(tgt_vocab)
# transformer config
d_e = 512 # embedding size
d_q = 64 # query size (= key, value size)
d_h = 2048 # hidden layer size in feed forward network
num_heads = 8
num_layers = 6 # number of encoder/decoder layers in encoder/decoder
args.sos_idx = sos_idx
args.eos_idx = eos_idx
args.pad_idx = pad_idx
args.max_length = max_length
args.src_vocab_size = src_vocab_size
args.tgt_vocab_size = tgt_vocab_size
args.d_e = d_e
args.d_q = d_q
args.d_h = d_h
args.num_heads = num_heads
args.num_layers = num_layers
model = Transformer(args)
model.to(args.device)
loss_fn = nn.CrossEntropyLoss(ignore_index=pad_idx)
optimizer = optim.Adam(model.parameters(), lr=1e-5)
if args.load:
model.load_state_dict(load(args, args.ckpt))
# 3. train / test
if not args.test:
# train
args.logger.info("starting training")
acc_val_meter = AverageMeter(name="Acc-Val (%)",
save_all=True,
save_dir=os.path.join(
'results', args.name))
train_loss_meter = AverageMeter(name="Loss",
save_all=True,
save_dir=os.path.join(
'results', args.name))
train_loader = get_loader(src['train'],
tgt['train'],
src_vocab,
tgt_vocab,
batch_size=args.batch_size,
shuffle=True)
valid_loader = get_loader(src['valid'],
tgt['valid'],
src_vocab,
tgt_vocab,
batch_size=args.batch_size)
for epoch in range(1, 1 + args.epochs):
spent_time = time.time()
model.train()
train_loss_tmp_meter = AverageMeter()
for src_batch, tgt_batch in tqdm(train_loader):
# src_batch: (batch x source_length), tgt_batch: (batch x target_length)
optimizer.zero_grad()
src_batch, tgt_batch = torch.LongTensor(src_batch).to(
args.device), torch.LongTensor(tgt_batch).to(args.device)
batch = src_batch.shape[0]
# split target batch into input and output
tgt_batch_i = tgt_batch[:, :-1]
tgt_batch_o = tgt_batch[:, 1:]
pred = model(src_batch.to(args.device),
tgt_batch_i.to(args.device))
loss = loss_fn(pred.contiguous().view(-1, tgt_vocab_size),
tgt_batch_o.contiguous().view(-1))
loss.backward()
optimizer.step()
train_loss_tmp_meter.update(loss / batch, weight=batch)
train_loss_meter.update(train_loss_tmp_meter.avg)
spent_time = time.time() - spent_time
args.logger.info(
"[{}] train loss: {:.3f} took {:.1f} seconds".format(
epoch, train_loss_tmp_meter.avg, spent_time))
# validation
model.eval()
acc_val_tmp_meter = AverageMeter()
spent_time = time.time()
for src_batch, tgt_batch in tqdm(valid_loader):
src_batch, tgt_batch = torch.LongTensor(
src_batch), torch.LongTensor(tgt_batch)
tgt_batch_i = tgt_batch[:, :-1]
tgt_batch_o = tgt_batch[:, 1:]
with torch.no_grad():
pred = model(src_batch.to(args.device),
tgt_batch_i.to(args.device))
corrects, total = val_check(
pred.max(dim=-1)[1].cpu(), tgt_batch_o)
acc_val_tmp_meter.update(100 * corrects / total, total)
spent_time = time.time() - spent_time
args.logger.info(
"[{}] validation accuracy: {:.1f} %, took {} seconds".format(
epoch, acc_val_tmp_meter.avg, spent_time))
acc_val_meter.update(acc_val_tmp_meter.avg)
if epoch % args.save_period == 0:
save(args, "epoch_{}".format(epoch), model.state_dict())
acc_val_meter.save()
train_loss_meter.save()
else:
# test
args.logger.info("starting test")
test_loader = get_loader(src['test'],
tgt['test'],
src_vocab,
tgt_vocab,
batch_size=args.batch_size)
pred_list = []
model.eval()
for src_batch, tgt_batch in test_loader:
#src_batch: (batch x source_length)
src_batch = torch.Tensor(src_batch).long().to(args.device)
batch = src_batch.shape[0]
pred_batch = torch.zeros(batch, 1).long().to(args.device)
pred_mask = torch.zeros(batch, 1).bool().to(
args.device) # mask whether each sentece ended up
with torch.no_grad():
for _ in range(args.max_length):
pred = model(
src_batch,
pred_batch) # (batch x length x tgt_vocab_size)
pred[:, :, pad_idx] = -1 # ignore <pad>
pred = pred.max(dim=-1)[1][:, -1].unsqueeze(
-1) # next word prediction: (batch x 1)
pred = pred.masked_fill(
pred_mask,
2).long() # fill out <pad> for ended sentences
pred_mask = torch.gt(pred.eq(1) + pred.eq(2), 0)
pred_batch = torch.cat([pred_batch, pred], dim=1)
if torch.prod(pred_mask) == 1:
break
pred_batch = torch.cat([
pred_batch,
torch.ones(batch, 1).long().to(args.device) + pred_mask.long()
],
dim=1) # close all sentences
pred_list += seq2sen(pred_batch.cpu().numpy().tolist(), tgt_vocab)
with open('results/pred.txt', 'w', encoding='utf-8') as f:
for line in pred_list:
f.write('{}\n'.format(line))
os.system(
'bash scripts/bleu.sh results/pred.txt multi30k/test.de.atok')
def main(args):
# loading configurations
with open(args.config) as f:
config = yaml.safe_load(f)["configuration"]
name = config["Name"]
# Construct or load embeddings
print("Initializing embeddings ...")
vocab_size = config["embeddings"]["vocab_size"]
embed_size = config["embeddings"]["embed_size"]
embeddings = init_embeddings(vocab_size, embed_size, name=name)
print("\tDone.")
# Build the model and compute losses
source_ids = tf.placeholder(tf.int32, [None, None], name="source")
target_ids = tf.placeholder(tf.int32, [None, None], name="target")
sequence_mask = tf.placeholder(tf.bool, [None, None], name="mask")
choice_qs = tf.placeholder(tf.float32, [None, None], name="choice")
emo_cat = tf.placeholder(tf.int32, [None], name="emotion_category")
(enc_num_layers, enc_num_units, enc_cell_type, enc_bidir, dec_num_layers,
dec_num_units, dec_cell_type, state_pass, num_emo, emo_cat_units,
emo_int_units, infer_batch_size, beam_size, max_iter, attn_num_units,
l2_regularize) = get_ECM_config(config)
print("Building model architecture ...")
CE, loss, train_outs, infer_outputs = compute_ECM_loss(
source_ids, target_ids, sequence_mask, choice_qs, embeddings,
enc_num_layers, enc_num_units, enc_cell_type, enc_bidir,
dec_num_layers, dec_num_units, dec_cell_type, state_pass, num_emo,
emo_cat, emo_cat_units, emo_int_units, infer_batch_size, beam_size,
max_iter, attn_num_units, l2_regularize, name)
print("\tDone.")
# Even if we restored the model, we will treat it as new training
# if the trained model is written into an arbitrary location.
(logdir, restore_from, learning_rate, gpu_fraction, max_checkpoints,
train_steps, batch_size, print_every, checkpoint_every, s_filename,
t_filename, q_filename, c_filename, s_max_leng, t_max_leng,
dev_s_filename, dev_t_filename, dev_q_filename, dev_c_filename, loss_fig,
perp_fig) = get_ECM_training_config(config)
is_overwritten_training = logdir != restore_from
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
epsilon=1e-4)
trainable = tf.trainable_variables()
optim = optimizer.minimize(loss, var_list=trainable)
# Set up session
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False,
gpu_options=gpu_options))
init = tf.global_variables_initializer()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=tf.trainable_variables(),
max_to_keep=max_checkpoints)
try:
saved_global_step = load(saver, sess, restore_from)
if is_overwritten_training or saved_global_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
saved_global_step = -1
except Exception:
print("Something went wrong while restoring checkpoint. "
"Training is terminated to avoid the overwriting.")
raise
# ##### Training #####
# Load data
print("Loading data ...")
# id_0, id_1, id_2 preserved for SOS, EOS, constant zero padding
embed_shift = 3
source_data = loadfile(s_filename, is_source=True,
max_length=s_max_leng) + embed_shift
target_data = loadfile(t_filename, is_source=False,
max_length=t_max_leng) + embed_shift
choice_data = loadfile(q_filename, is_source=False, max_length=t_max_leng)
choice_data[choice_data < 0] = 0
choice_data = choice_data.astype(np.float32)
category_data = pd.read_csv(c_filename,
header=None,
index_col=None,
dtype=int)[0].values
masks = (target_data != -1)
n_data = len(source_data)
dev_source_data = None
if dev_s_filename is not None:
dev_source_data = loadfile(dev_s_filename,
is_source=True,
max_length=s_max_leng) + embed_shift
dev_target_data = loadfile(dev_t_filename,
is_source=False,
max_length=t_max_leng) + embed_shift
dev_choice_data = loadfile(dev_q_filename,
is_source=False,
max_length=t_max_leng)
dev_choice_data[dev_choice_data < 0] = 0
dev_choice_data = dev_choice_data.astype(np.float32)
dev_category_data = pd.read_csv(dev_c_filename,
header=None,
index_col=None,
dtype=int)[0].values
dev_masks = (dev_target_data != -1)
print("\tDone.")
# Training
last_saved_step = saved_global_step
num_steps = saved_global_step + train_steps
losses = []
steps = []
perps = []
dev_perps = []
print("Start training ...")
try:
for step in range(saved_global_step + 1, num_steps):
start_time = time.time()
rand_indexes = np.random.choice(n_data, batch_size)
source_batch = source_data[rand_indexes]
target_batch = target_data[rand_indexes]
choice_batch = choice_data[rand_indexes]
emotions = category_data[rand_indexes]
mask_batch = masks[rand_indexes]
feed_dict = {
source_ids: source_batch,
target_ids: target_batch,
choice_qs: choice_batch,
emo_cat: emotions,
sequence_mask: mask_batch,
}
loss_value, _ = sess.run([loss, optim], feed_dict=feed_dict)
losses.append(loss_value)
duration = time.time() - start_time
if step % print_every == 0:
# train perplexity
t_perp = compute_perplexity(sess, CE, mask_batch, feed_dict)
perps.append(t_perp)
# dev perplexity
dev_str = ""
if dev_source_data is not None:
dev_inds = np.random.choice(len(dev_source_data),
batch_size)
dev_feed_dict = {
source_ids: dev_source_data[dev_inds],
target_ids: dev_target_data[dev_inds],
choice_qs: dev_choice_data[dev_inds],
emo_cat: dev_category_data[dev_inds],
sequence_mask: dev_masks[dev_inds],
}
dev_perp = compute_perplexity(sess, CE,
dev_masks[dev_inds],
dev_feed_dict)
dev_perps.append(dev_perp)
dev_str = "dev_prep: {:.3f}, ".format(dev_perp)
steps.append(step)
info = 'step {:d}, loss = {:.6f}, '
info += 'perp: {:.3f}, {}({:.3f} sec/step)'
print(info.format(step, loss_value, t_perp, dev_str, duration))
if step % checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
except KeyboardInterrupt:
# Introduce a line break after ^C so save message is on its own line.
print()
finally:
if step > last_saved_step:
save(saver, sess, logdir, step)
# plot loss
plt.figure()
plt.plot(losses)
plt.title("Total loss")
plt.xlabel("step")
plt.savefig(loss_fig)
# plot perplexity
plt.figure()
if len(perps) > len(steps):
perps.pop()
plt.plot(steps[5:], perps[5:], label="train")
if dev_source_data is not None:
plt.plot(steps[5:], dev_perps[5:], label="dev")
plt.title("Perplexity")
plt.xlabel("step")
plt.legend()
plt.savefig(perp_fig)
if show_max_img:
print('******************分析宽度******************')
show_dict(w_dict, 10, 'w')
print('******************分析高度******************')
show_dict(h_dict, 1, 'h')
print('******************分析label长度******************')
show_dict(len_dict, 1, 'label')
a = ''.join(sorted(set((''.join(label_list)))))
return a
if __name__ == '__main__':
# 根据label文本生产key
from config import train_parameters
from utils import save
parser = argparse.ArgumentParser()
parser.add_argument('--label_file',
nargs='+',
help='label file',
default=[""])
args = parser.parse_args()
label_file = [
train_parameters['train_list'], train_parameters['eval_list']
]
alphabet = get_key(label_file, True, show_max_img=False).replace(' ', '')
save(list(alphabet), 'dict.txt')
# np.save('alphabet.npy', alphabet)
print(alphabet)
def main():
# if not torch.cuda.is_available():
# logging.info('no gpu device available')
# sys.exit(1)
np.random.seed(args.seed)
# torch.cuda.set_device(args.gpu)
# cudnn.benchmark = True
# torch.manual_seed(args.seed)
# cudnn.enabled = True
# torch.cuda.manual_seed(args.seed)
# logging.info('gpu device = %d' % args.gpu)
# logging.info("args = %s", args)
torch.manual_seed(args.seed)
logging.info('use cpu')
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
# criterion = criterion.cuda()
criterion.to(device)
model = Network(args.init_channels,
CIFAR_CLASSES,
args.layers,
criterion,
learnable_bn=args.learnable_bn)
# model = model.cuda()
model.to(device)
a = list(model.parameters())
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
# optimizer = torch.optim.SGD(
# model.parameters(),
# args.learning_rate,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
################################################################################
# AdaS: optimizer and scheduler
optimizer = SGDVec(params=model.parameters(),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = AdaS(
parameters=list(model.parameters()),
init_lr=args.learning_rate,
# min_lr=kwargs['min_lr'],
# zeta=kwargs['zeta'],
p=args.scheduler_p,
beta=args.scheduler_beta)
################################################################################
# train_transform, valid_transform = utils._data_transforms_cifar100(args)
# train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
"""From https://github.com/chenxin061/pdarts/"""
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=2)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=2)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
# optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, args)
"""Hessian"""
analyser = Analyzer(model, args)
"""adaptive stopping"""
stop_checker = StopChecker()
METRICS = Metrics(list(model.parameters()), p=1)
PERFORMANCE_STATISTICS = {}
ARCH_STATISTICS = {}
GENOTYPE_STATISTICS = {}
metrics_path = './metrics_stat_test_adas.xlsx'
weights_path = './weights_stat_test_adas.xlsx'
genotypes_path = './genotypes_stat_test_adas.xlsx'
for epoch in range(args.epochs):
# scheduler.step()
# lr = scheduler.get_lr()[0]
# logging.info
genotype = model.genotype()
logging.info('genotype = %s', genotype)
if epoch % 5 == 0 or epoch == args.epochs - 1:
GENOTYPE_STATISTICS[f'epoch_{epoch}'] = [genotype]
genotypes_df = pd.DataFrame(data=GENOTYPE_STATISTICS)
genotypes_df.to_excel(genotypes_path)
print(F.softmax(model.alphas_normal, dim=-1))
print(F.softmax(model.alphas_reduce, dim=-1))
# training
train_acc, train_obj = train(epoch, train_queue, valid_queue, model,
architect, criterion, optimizer, METRICS,
scheduler, analyser)
logging.info('train_acc %f', train_acc)
# validation
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
# metrics
io_metrics = METRICS.evaluate(epoch)
PERFORMANCE_STATISTICS[
f'in_S_epoch_{epoch}'] = io_metrics.input_channel_S
PERFORMANCE_STATISTICS[
f'out_S_epoch_{epoch}'] = io_metrics.output_channel_S
PERFORMANCE_STATISTICS[f'fc_S_epoch_{epoch}'] = io_metrics.fc_S
PERFORMANCE_STATISTICS[
f'in_rank_epoch_{epoch}'] = io_metrics.input_channel_rank
PERFORMANCE_STATISTICS[
f'out_rank_epoch_{epoch}'] = io_metrics.output_channel_rank
PERFORMANCE_STATISTICS[f'fc_rank_epoch_{epoch}'] = io_metrics.fc_rank
PERFORMANCE_STATISTICS[
f'in_condition_epoch_{epoch}'] = io_metrics.input_channel_condition
PERFORMANCE_STATISTICS[
f'out_condition_epoch_{epoch}'] = io_metrics.output_channel_condition
################################################################################
# AdaS: update learning rates
lr_metrics = scheduler.step(epoch, METRICS)
PERFORMANCE_STATISTICS[
f'rank_velocity_epoch_{epoch}'] = lr_metrics.rank_velocity
PERFORMANCE_STATISTICS[
f'learning_rate_epoch_{epoch}'] = lr_metrics.r_conv
################################################################################
# write metrics data to xls file
metrics_df = pd.DataFrame(data=PERFORMANCE_STATISTICS)
metrics_df.to_excel(metrics_path)
# weights
weights_normal = F.softmax(model.alphas_normal,
dim=-1).detach().cpu().numpy()
weights_reduce = F.softmax(model.alphas_reduce,
dim=-1).detach().cpu().numpy()
# normal
ARCH_STATISTICS[f'normal_none_epoch{epoch}'] = weights_normal[:, 0]
ARCH_STATISTICS[f'normal_max_epoch{epoch}'] = weights_normal[:, 1]
ARCH_STATISTICS[f'normal_avg_epoch{epoch}'] = weights_normal[:, 2]
ARCH_STATISTICS[f'normal_skip_epoch{epoch}'] = weights_normal[:, 3]
ARCH_STATISTICS[f'normal_sep_3_epoch{epoch}'] = weights_normal[:, 4]
ARCH_STATISTICS[f'normal_sep_5_epoch{epoch}'] = weights_normal[:, 5]
ARCH_STATISTICS[f'normal_dil_3_epoch{epoch}'] = weights_normal[:, 6]
ARCH_STATISTICS[f'normal_dil_5_epoch{epoch}'] = weights_normal[:, 7]
# reduce
ARCH_STATISTICS[f'reduce_none_epoch{epoch}'] = weights_reduce[:, 0]
ARCH_STATISTICS[f'reduce_max_epoch{epoch}'] = weights_reduce[:, 1]
ARCH_STATISTICS[f'reduce_avg_epoch{epoch}'] = weights_reduce[:, 2]
ARCH_STATISTICS[f'reduce_skip_epoch{epoch}'] = weights_reduce[:, 3]
ARCH_STATISTICS[f'reduce_sep_3_epoch{epoch}'] = weights_reduce[:, 4]
ARCH_STATISTICS[f'reduce_sep_5_epoch{epoch}'] = weights_reduce[:, 5]
ARCH_STATISTICS[f'reduce_dil_3_epoch{epoch}'] = weights_reduce[:, 6]
ARCH_STATISTICS[f'reduce_dil_5_epoch{epoch}'] = weights_reduce[:, 7]
# write weights data to xls file
weights_df = pd.DataFrame(data=ARCH_STATISTICS)
weights_df.to_excel(weights_path)
# adaptive stopping criterion
if args.adaptive_stop and epoch >= 10:
# apply local stopping criterion
stop_checker.local_stop(METRICS, epoch)
# freeze some edges based on their knowledge gains
iteration_p = 0
for p in model.parameters():
if ~METRICS.layers_index_todo[iteration_p]:
p.requires_grad = False
p.grad = None
iteration_p += 1
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
if args.set == 'cifar100':
N_CLASSES = 100
if args.set == 'tiny_imagenet':
N_CLASSES = 200
else:
N_CLASSES = 10
config = QuantumFold_config(None, 0)
if config.op_struc == "": args.batch_size = args.batch_size // 4
config.exp_dir = args.save
config.primitive = args.primitive
config.attention = args.attention
config.device = OnInitInstance(args.seed, args.gpu)
if config.primitive == "p0":
config.PRIMITIVES_pool = [
'none', 'max_pool_3x3', 'avg_pool_3x3', 'Identity', 'BatchNorm2d',
'ReLU', 'Conv_3', 'Conv_5'
]
elif config.primitive == "p1":
config.PRIMITIVES_pool = [
'none', 'max_pool_3x3', 'skip_connect', 'BatchNorm2d', 'ReLU',
'Conv_3', 'DepthConv_3', 'Conv_11'
]
elif config.primitive == "p2" or config.primitive == "p21":
config.PRIMITIVES_pool = [
'none', 'max_pool_3x3', 'skip_connect', 'BatchNorm2d', 'ReLU',
'Conv_3', 'DepthConv_3', 'Conv_11'
]
elif config.primitive == "p3":
config.PRIMITIVES_pool = [
'none', 'max_pool_3x3', 'max_pool_5x5', 'skip_connect', 'Identity',
'BatchNorm2d', 'ReLU', 'Conv_3', 'DepthConv_3', 'Conv_5',
'DepthConv_5', 'Conv_11', 'sep_conv_3x3'
]
elif config.primitive == "c0":
config.PRIMITIVES_pool = [
'none', 'max_pool_3x3', 'avg_pool_3x3', 'skip_connect',
'sep_conv_3x3', 'sep_conv_5x5', 'dil_conv_3x3', 'dil_conv_5x5'
]
#args.load_workers = 8
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
model = Network(config, args.init_channels, N_CLASSES, args.layers,
criterion)
print(model)
#dump_model_params(model)
model = model.cuda()
model.visual = Visdom_Visualizer(
env_title=f"{args.set}_{model.title}_{args.legend}")
model.visual.img_dir = "./results/images/"
logging.info("param size = %.3fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
if args.set == 'cifar100':
N_CLASSES = 100
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
elif args.set == 'tiny_imagenet':
N_CLASSES = 200
train_data = TinyImageNet200(root=args.data, train=True, download=True)
infer_data = TinyImageNet200(root=args.data,
train=False,
download=True)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
infer_data = dset.CIFAR10(root=args.data,
train=False,
download=True,
transform=valid_transform) #这个更合理
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=args.load_workers) #args.load_workers
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=0)
if True:
infer_queue = torch.utils.data.DataLoader(infer_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=0)
else:
infer_queue = valid_queue
config.experiment = Experiment(config,
"cifar_10",
model,
loss_fn=None,
optimizer=optimizer,
objective_metric=None)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, args)
#model.init_on_data(valid_queue,criterion)
architect.init_on_data(valid_queue, criterion) #data-aware init
print(architect)
print(f"======\tconfig={config.__dict__}\n")
print(f"======\targs={args.__dict__}\n")
valid_acc, t0 = 0, time.time()
for epoch in range(args.epochs):
scheduler.step()
plot_path = f"{model.config.exp_dir}/{model.title}E{epoch}_a{valid_acc:.1f}_"
dump_genotype(model, logging, plot_path)
lr = scheduler.get_lr()[0]
logging.info('epoch=%d lr=%e', epoch, lr)
print(
f"======\tnTrain={train_queue.dataset.__len__()} nSearch={valid_queue.dataset.__len__()} nTest={infer_queue.dataset.__len__()} "
)
# plot_path=f"{model.config.exp_dir}/{model.title}E{epoch}_a{valid_acc:.1f}_"
# dump_genotype(model,logging,plot_path)
# training
train_acc, train_obj = train(train_queue, valid_queue, model,
architect, criterion, optimizer, lr,
epoch)
logging.info(f'train_acc {train_acc} T={time.time()-t0:.2f}')
# validation
valid_acc, valid_obj = infer(infer_queue, model, criterion, epoch)
logging.info(f'valid_acc {valid_acc} T={time.time()-t0:.2f}')
config.experiment.best_score = max(valid_acc,
config.experiment.best_score)
utils.save(model, os.path.join(args.save, 'weights.pt'))
model.visual.UpdateLoss(title=f"Accuracy on \"{args.set}\"",
legend=f"{model.title}",
loss=valid_acc,
yLabel="Accuracy")
def save_image(self):
itm = self.wrk.get()
url = itm['img_url']
data = itm['img_orig']
utils.save(data, url)
def main():
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
cudnn.enabled = True
torch.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
if not args.arch:
geno_s = pathlib.Path(geno_path).read_text()
else:
geno_s = "genotypes.%s" % args.arch
genotype = eval(geno_s)
model = Network(args.init_ch, 10, args.layers, args.auxiliary, genotype).cuda()
logging.info(f"seed = {args.seed}")
logging.info(f"geno_s = {geno_s}")
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.wd
)
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batchsz, shuffle=True, pin_memory=True, num_workers=0 if 'pydevd' in sys.modules else 2)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batchsz, shuffle=False, pin_memory=True, num_workers=0 if 'pydevd' in sys.modules else 2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
lines = [f'epoch\ttrain_acc\tval_acc']
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc: %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc: %f', valid_acc)
lines.append(f'{epoch}\t{train_acc}\t{valid_acc}')
timebudget.report()
utils.save(model, os.path.join(args.save, 'trained.pt'))
print('saved to: trained.pt')
pathlib.Path(os.path.join(args.exp_path, 'eval.tsv')).write_text('\n'.join(lines))
def main():
np.random.seed(args.seed)
random.seed(args.seed)
if torch.cuda.is_available():
device = torch.device('cuda:{}'.format(args.gpu))
cudnn.benchmark = False
torch.manual_seed(args.seed)
cudnn.enabled = True
cudnn.deterministic = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
else:
device = torch.device('cpu')
logging.info('No gpu device available')
torch.manual_seed(args.seed)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
model = model.to(device)
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
total_params = sum(x.data.nelement() for x in model.parameters())
logging.info('Model total parameters: {}'.format(total_params))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_transform, valid_transform = utils._data_transforms_cifar10(
args.cutout, args.cutout_length)
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR10(root=args.data,
train=False,
download=True,
transform=valid_transform)
train_queue = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=0)
valid_queue = torch.utils.data.DataLoader(valid_data,
batch_size=args.batch_size // 2,
shuffle=False,
pin_memory=True,
num_workers=0)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs))
for epoch in range(args.epochs):
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer,
args.gpu)
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion, args.gpu)
logging.info('valid_acc %f', valid_acc)
scheduler.step()
utils.save(model, os.path.join(args.save, 'weights.pt'))
def train(directories, args): # args：所有的参数解析
logdir = directories['logdir']
print("logdir:", logdir)
restore_from = directories['restore_from']
# Even if we restored the model, we will treat it as new training
# if the trained model is written into a location that's different from logdir.
is_overwritten_training = logdir != restore_from # 先算后部分,返回bool值. 结果:False,意思是两个文件夹相同
coord = tf.train.Coordinator() # 创建一个协调器，管理线程
# create inputs
gc_enabled = args.gc_channels is not None
reader = AudioReader(args.data_dir,
args.test_dir,
coord,
sample_rate=args.sample_rate,
gc_enabled=gc_enabled)
audio_batch = reader.dequeue(args.batch_size)
# Initialize model
net = SpeechSeparation(
batch_size=args.batch_size,
rnn_type=args.rnn_type,
dim=args.dim,
n_rnn=args.n_rnn,
seq_len=args.seq_len,
num_of_frequency_points=args.num_of_frequency_points)
# need to modify net to include these
#out =
summary, output1, output2, losses, apply_gradient_op = net.initializer(
net, args) # output1/2:(1,256,257)
speech_inputs_1 = net.speech_inputs_1 # (1,256,257)
speech_inputs_2 = net.speech_inputs_2 # (1,256,257)
speech_inputs_mix = net.speech_inputs_mix # (1,256,257)
# Set up session
tf_config = tf.ConfigProto(
# allow_soft_placement is set to True to build towers on GPU
allow_soft_placement=True,
log_device_placement=False,
inter_op_parallelism_threads=1)
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config=tf_config)
sess.run(tf.global_variables_initializer())
# Create coordinator.
# Set up logging for TensorBoard.
writer = tf.summary.FileWriter(logdir)
writer.add_graph(tf.get_default_graph())
run_metadata = tf.RunMetadata() # 定义TensorFlow运行元信息,记录训练运算时间和内存占用等信息
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=tf.trainable_variables(),
max_to_keep=args.max_checkpoints)
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
reader.start_threads(sess)
try:
saved_global_step = load(saver, sess, restore_from) # 第一次结果:None
if is_overwritten_training or saved_global_step is None:
# The first training step will be saved_global_step + 1,
# therefore we put -1 here for new or overwritten trainings.
saved_global_step = -1
except:
print(
"Something went wrong while restoring checkpoint. We will terminate "
"training to avoid accidentally overwriting the previous model.")
raise
#################################Start Training####################################
last_saved_step = saved_global_step # -1
try:
for step in range(saved_global_step + 1,
args.num_steps): # (0, 1000000)
loss_sum = 0
start_time = time.time()
inputslist = [sess.run(audio_batch) for i in range(args.num_gpus)
] # len:1, 里边还有四维,每一维(1,697,257),括号中1的个数指gpu个数
inp_dict = create_inputdict(inputslist, args, speech_inputs_1,
speech_inputs_2, speech_inputs_mix)
summ, loss_value, _ = sess.run(
[summary, losses, apply_gradient_op],
feed_dict=inp_dict) #feed_dict前一个数是占位符,后一个是真实值
for g in range(args.num_gpus):
loss_sum += loss_value[g] / args.num_gpus
writer.add_summary(summ, step)
duration = time.time() - start_time
if (step < 100):
log_str = ('step {%d} - loss = {%0.3f}, ({%0.3f} sec/step') % (
step, loss_sum, duration)
logging.warning(log_str)
elif (0 == step % 100):
log_str = ('step {%d} - loss = {%0.3f}, ({%0.3f} sec/step') % (
step, loss_sum / 100, duration)
logging.warning(log_str)
if (0 == step % 2000):
angle_test, inp_dict = create_inputdict(inputslist,
args,
speech_inputs_1,
speech_inputs_2,
speech_inputs_mix,
test=True)
outp1, outp2 = sess.run([output1, output2], feed_dict=inp_dict)
x_r = mk_audio(outp1, angle_test, args.sample_rate,
"spk1_test_" + str(step) + ".wav")
y_r = mk_audio(outp2, angle_test, args.sample_rate,
"spk2_test_" + str(step) + ".wav")
amplitude_test = inputslist[0][2]
angle_test = inputslist[0][3]
mk_audio(amplitude_test, angle_test, args.sample_rate,
"raw_test_" + str(step) + ".wav")
# audio summary on tensorboard
merged = sess.run(
tf.summary.merge([
tf.summary.audio('speaker1_' + str(step),
x_r[None, :],
args.sample_rate,
max_outputs=1),
tf.summary.audio('speaker2_' + str(step),
y_r[None, :],
args.sample_rate,
max_outputs=1)
]))
writer.add_summary(merged, step)
if step % args.checkpoint_every == 0:
save(saver, sess, logdir, step)
last_saved_step = step
except KeyboardInterrupt:
# Introduce a line break after ^C is displayed so save message is on its own line.
print()
finally:
#'''
if step > last_saved_step:
save(saver, sess, logdir, step)
#'''
coord.request_stop()
coord.join(threads)
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
face_factor, bbox_reg_factor, rig_factor = 1, 0.8, 1
criterion = LossFn_3(face_factor=face_factor,
box_factor=bbox_reg_factor, rig_factor=rig_factor)
model = SACN_3(args.init_channels, args.layers, criterion)
criterion = criterion.cuda()
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_transform, valid_transform = utils._data_transforms_Wider(args)
train_data = dset.wider(
anno_file="../anno_store/imglist_anno_48_45.txt", train=True, transform=train_transform)
valid_data = dset.wider(
anno_file="../anno_store/imglist_anno_48_45.txt", train=False, transform=valid_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True, num_workers=2)
search_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True, num_workers=2)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect_SACN_3(model, args)
for epoch in range(args.epochs):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
genotype = model.genotype()
logging.info('genotype = %s', genotype)
print(F.softmax(model.alphas_normal, dim=-1))
print(F.softmax(model.alphas_reduce, dim=-1))
# training
train_acc, train_rig, train_bbx, train_obj, arch_grad_norm = train(
train_queue, search_queue, model, architect, criterion, optimizer, lr)
logging.info('train_label_acc %f rig %f bbx %f',
train_acc, train_rig, train_bbx)
# validation
valid_acc, valid_rig, valid_bbx, valid_obj = infer(
valid_queue, model, criterion)
logging.info('valid_label_acc %f rig %f bbx %f',
valid_acc, valid_rig, valid_bbx)
utils.save(model, os.path.join(args.save, 'weights.pt'))
training_statistic.append(res)
test_source = test(model, source_loader, e)
test_target = test(model, target_loader, e)
testing_s_statistic.append(test_source)
testing_t_statistic.append(test_target)
print(
'###Test Source: Epoch: {}, avg_loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'
.format(
e + 1,
test_source['average_loss'],
test_source['correct'],
test_source['total'],
test_source['accuracy'],
))
print(
'###Test Target: Epoch: {}, avg_loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'
.format(
e + 1,
test_target['average_loss'],
test_target['correct'],
test_target['total'],
test_target['accuracy'],
))
utils.save(training_statistic, 'training_statistic.pkl')
utils.save(testing_s_statistic, 'testing_s_statistic.pkl')
utils.save(testing_t_statistic, 'testing_t_statistic.pkl')
utils.save_net(model, 'checkpoint.tar')
def train(args):
"""
Train a VQG model using the training set and validate on val set.
"""
# Load the VQA training set
print('Loading data...')
dataset = VQA_Dataset(args.data_dir, args.emb)
loader = DataLoader(dataset,
batch_size=args.bsize,
shuffle=True,
num_workers=0,
collate_fn=collate_fn)
# Load the VQA validation set
dataset_test = VQA_Dataset(args.data_dir, args.emb, train=False)
loader_val = DataLoader(dataset_test,
batch_size=args.bsize,
shuffle=False,
num_workers=0,
collate_fn=collate_fn)
n_batches = len(dataset) // args.bsize
question_vocab = pickle.load(
open('/mnt/data/xiaojinhui/wangtan_MM/vqa-project/data/train_q_dict.p',
'rb'))
# Print data and model parameters
print('Parameters:\n\t'
'vocab size: %d\n\tembedding dim: %d\n\tfeature dim: %d'
'\n\thidden dim: %d\n\toutput dim: %d' %
(dataset.n_answers, args.emb, dataset.feat_dim, args.hid,
dataset.n_answers))
print('Initializing model')
model_gcn = conditional_GCN(nfeat=options['gcn']['nfeat'],
nhid=options['gcn']['nhid'],
nclass=options['gcn']['nclass'],
emb=options['gcn']['fliter_emb'],
dropout=options['gcn']['dropout'])
# model_gcn_nofinding = layer_vqg.conditional_GCN_1(nfeat=options['gcn']['nfeat'],
# nhid=options['gcn']['nhid'],
# nclass=options['gcn']['nclass'],
# emb = options['gcn']['fliter_emb'],
# dropout=options['gcn']['dropout'])
model_vqg = question_gen(vocab=question_vocab['wtoi'],
vocab_i2t=question_vocab['itow'],
opt=options['vqg'])
# no_finding = layer_vqg.no_finding_area_top(in_feature=2652, hidden_feature=512, dropout=options['gcn']['dropout'])
criterion = nn.CrossEntropyLoss()
# Move it to GPU
model_gcn = model_gcn.cuda()
model_vqg = model_vqg.cuda()
# model_gcn_nofinding = model_gcn_nofinding.cuda()
# no_finding = no_finding.cuda()
criterion = criterion.cuda()
# Define the optimiser
optimizer = torch.optim.Adam([{
'params': model_gcn.parameters()
}, {
'params': model_vqg.parameters()
}],
lr=args.lr)
# Continue training from saved model
start_ep = 0
if args.model_path and os.path.isfile(args.model_path):
print('Resuming from checkpoint %s' % (args.model_path))
ckpt = torch.load(args.model_path)
start_ep = ckpt['epoch']
model_gcn.load_state_dict(ckpt['state_dict_gcn'])
model_vqg.load_state_dict(ckpt['state_dict_vqg'])
optimizer.load_state_dict(ckpt['optimizer'])
# Update the learning rate
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
# Learning rate scheduler
scheduler = MultiStepLR(optimizer, milestones=[30], gamma=0.5)
scheduler.last_epoch = start_ep - 1
# Train iterations
print('Start training.')
bleu_best = [0.0, 0.0, 0.0, 0.0]
cider_best = 0.0
meteor_best = 0.0
rouge_best = 0.0
for ep in range(start_ep, start_ep + args.ep):
adjust_learning_rate(optimizer, ep)
scheduler.step()
ep_loss = 0.0
ep_top3 = 0.0
ep_top1 = 0.0
ave_loss = 0.0
ave_top3 = 0.0
ave_top1 = 0.0
iter_time_all = 0.0
for step, next_batch in enumerate(loader):
model_gcn.train()
model_vqg.train()
# Move batch to cuda
target_q, an_feat, img_feat, adj_mat = \
utils.batch_to_cuda(next_batch, volatile=True)
# forward pass
torch.cuda.synchronize()
start = time.time()
# img_feat_no = torch.mul(img_feat[:,:,None,:], img_feat[:,None,:,:]).view(-1, 2652)
# adj_mat = no_finding(img_feat_no).view(-1,36,36)
# adj_mat += torch.eye(36).cuda()
# adj_mat = torch.clamp(adj_mat, max=1)
feat_gcn, adj_new = model_gcn(img_feat, adj_mat)
# feat_gcn, adj_new = model_gcn_nofinding(img_feat, adj_mat)
output = model_vqg(feat_gcn, an_feat, target_q)
# for i in range(256):
# dataset.drawarea[i]['adj'] = adj_new[i].detach().cpu().numpy().tolist()
# dataset.drawarea[i]['adj_diag'] = np.diag(adj_new[i].detach().cpu().numpy()).tolist()
#
# json.dump(dataset.drawarea, open('/mnt/data/xiaojinhui/wangtan_MM/vqa-project/draw/new_adj_t.json', 'w'))
# output_bs = model_vqg.beam_search(feat_gcn[0].unsqueeze(0),
# an_feat[0].unsqueeze(0))
target_q = target_q[:, 1:].contiguous()
loss = criterion(
output.view(output.size(0) * output.size(1), output.size(2)),
target_q.view(target_q.size(0) * target_q.size(1)))
# Compute batch accu
top1 = utils.accuracy(output, target_q, 1)
top3 = utils.accuracy(output, target_q, 3)
ep_top1 += top1
ep_top3 += top3
ep_loss += loss.item()
ave_top1 += top1
ave_top3 += top3
ave_loss += loss.item()
# This is a 40 step average
if step % 40 == 0 and step != 0:
print(
' Epoch %02d(%03d/%03d), ave loss: %.7f, top1: %.2f%%, top3: %.2f%%, iter time: %.4fs'
% (ep + 1, step, n_batches, ave_loss / 40, ave_top1 / 40,
ave_top3 / 40, iter_time_all / 40))
ave_top1 = 0
ave_top3 = 0
ave_loss = 0
iter_time_all = 0
# Compute gradient and do optimisation step
optimizer.zero_grad()
loss.backward()
# clip_grad_norm_(model_gcn.parameters(), 2.)
# clip_grad_norm_(model_gcn.parameters(), 2.)
# clip_grad_norm_(no_finding.parameters(), 2.)
optimizer.step()
end = time.time()
iter_time = end - start
iter_time_all += iter_time
# save model and compute validation accuracy every 400 steps
if step == 0:
with torch.no_grad():
epoch_loss = ep_loss / n_batches
epoch_top1 = ep_top1 / n_batches
epoch_top3 = ep_top3 / n_batches
# compute validation accuracy over a small subset of the validation set
model_gcn.train(False)
model_vqg.train(False)
model_gcn.eval()
model_vqg.eval()
output_all = []
output_all_bs = {}
ref_all = []
flag_val = 0
for valstep, val_batch in tqdm(enumerate(loader_val)):
# test_batch = next(loader_test)
target_q, an_feat, img_feat, adj_mat = \
utils.batch_to_cuda(val_batch, volatile=True)
# img_feat_no = torch.mul(img_feat[:, :, None, :], img_feat[:, None, :, :]).view(-1, 2652)
# adj_mat = no_finding(img_feat_no).view(-1, 36, 36)
# adj_mat += torch.eye(36).cuda()
# adj_mat = torch.clamp(adj_mat, max=1)
# feat_gcn, _ = model_gcn_nofinding(img_feat, adj_mat)
feat_gcn, adj_new = model_gcn(img_feat, adj_mat)
output = model_vqg.generate(feat_gcn, an_feat)
for j in range(feat_gcn.size(0)):
output_bs = model_vqg.beam_search(
feat_gcn[j].unsqueeze(0),
an_feat[j].unsqueeze(0))
output_all_bs[flag_val] = output_bs
flag_val += 1
output_all.append(output.cpu().numpy())
ref_all.append(target_q[:, :-1].cpu().numpy())
gen, ref = utils.idx2question(
np.concatenate(output_all, 0),
np.concatenate(ref_all, 0), question_vocab['itow'])
print(gen.values()[:10])
# save the best
bleu, cider, meteor, rouge = main.main(ref, gen)
bleu_best, cider_best, meteor_best, rouge_best, choice = utils.save_the_best(
bleu, cider, meteor, rouge, bleu_best, cider_best,
meteor_best, rouge_best)
if choice:
utils.save(model_gcn,
model_vqg,
optimizer,
ep,
epoch_loss,
epoch_top1,
dir=args.save_dir,
name=args.name + '_' + str(ep + 1))
print('use beam search...')
bleu, cider, meteor, rouge = main.main(ref, output_all_bs)
bleu_best, cider_best, meteor_best, rouge_best, choice = utils.save_the_best(
bleu, cider, meteor, rouge, bleu_best, cider_best,
meteor_best, rouge_best)
if choice:
utils.save(model_gcn,
model_vqg,
optimizer,
ep,
epoch_loss,
epoch_top1,
dir=args.save_dir,
name=args.name + '_' + str(ep + 1))
print(
'the best bleu: %s, cider: %.6s, meteor: %.6s, rouge: %.6s'
% (bleu_best, cider_best, meteor_best, rouge_best))
print(output_all_bs.values()[:10])
model_gcn.train(True)
model_vqg.train(True)
def run(self):
g.init()
if not self.journal: utils.load()
load_save.retrieve()
self.som4 = som.Som(4)
self.som5 = som.Som(5)
self.som4.setup()
self.som5.setup()
if g.n == 4: self.som = self.som4
else: self.som = self.som5
self.som4.found = g.found4
self.som5.found = g.found5
self.buttons_setup()
if self.canvas <> None: self.canvas.grab_focus()
ctrl = False
pygame.key.set_repeat(600, 120)
key_ms = pygame.time.get_ticks()
going = True
while going:
if self.journal:
# Pump GTK messages.
while gtk.events_pending():
gtk.main_iteration()
# Pump PyGame messages.
for event in pygame.event.get():
if event.type == pygame.QUIT:
if not self.journal: utils.save()
going = False
elif event.type == pygame.MOUSEMOTION:
g.pos = event.pos
g.redraw = True
if self.canvas <> None: self.canvas.grab_focus()
elif event.type == pygame.MOUSEBUTTONDOWN:
g.redraw = True
if event.button == 1:
if self.do_click():
pass
else:
bu = buttons.check()
if bu != '':
self.do_button(bu)
self.flush_queue()
if event.button == 3:
self.som.right_click()
self.flush_queue()
elif event.type == pygame.KEYDOWN:
# throttle keyboard repeat
if pygame.time.get_ticks() - key_ms > 110:
key_ms = pygame.time.get_ticks()
if ctrl:
if event.key == pygame.K_q:
if not self.journal: utils.save()
going = False
break
else:
ctrl = False
if event.key in (pygame.K_LCTRL, pygame.K_RCTRL):
ctrl = True
break
self.do_key(event.key)
g.redraw = True
self.flush_queue()
elif event.type == pygame.KEYUP:
ctrl = False
if not going: break
if g.redraw:
self.display()
if g.version_display: utils.version_display()
g.screen.blit(g.pointer, g.pos)
pygame.display.flip()
g.redraw = False
g.clock.tick(40)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
# prepare dataset
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(
args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=args.workers)
# build Network
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
switches = []
for i in range(14):
switches.append([True for j in range(len(PRIMITIVES))])
switches_normal = copy.deepcopy(switches)
switches_reduce = copy.deepcopy(switches)
# To be moved to args
num_to_keep = [5, 3, 1]
num_to_drop = [3, 2, 2]
if len(args.add_width) == 3:
add_width = args.add_width
else:
add_width = [0, 0, 0]
if len(args.add_layers) == 3:
add_layers = args.add_layers
else:
add_layers = [0, 6, 12]
if len(args.dropout_rate) == 3:
drop_rate = args.dropout_rate
else:
drop_rate = [0.0, 0.0, 0.0]
eps_no_archs = [10, 10, 10]
for sp in range(len(num_to_keep)):
model = Network(args.init_channels + int(add_width[sp]),
CIFAR_CLASSES,
args.layers + int(add_layers[sp]),
criterion,
switches_normal=switches_normal,
switches_reduce=switches_reduce,
p=float(drop_rate[sp]))
model = nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
network_params = []
for k, v in model.named_parameters():
if not (k.endswith('alphas_normal')
or k.endswith('alphas_reduce')):
network_params.append(v)
optimizer = torch.optim.SGD(network_params,
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer_a = torch.optim.Adam(model.module.arch_parameters(),
lr=args.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=args.arch_weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
sm_dim = -1
epochs = args.epochs
eps_no_arch = eps_no_archs[sp]
scale_factor = 0.2
for epoch in range(epochs):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('Epoch: %d lr: %e', epoch, lr)
epoch_start = time.time()
# training
if epoch < eps_no_arch:
model.module.p = float(
drop_rate[sp]) * (epochs - epoch - 1) / epochs
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=False)
else:
model.module.p = float(drop_rate[sp]) * np.exp(
-(epoch - eps_no_arch) * scale_factor)
model.module.update_p()
train_acc, train_obj = train(train_queue,
valid_queue,
model,
network_params,
criterion,
optimizer,
optimizer_a,
lr,
train_arch=True)
logging.info('Train_acc %f', train_acc)
epoch_duration = time.time() - epoch_start
logging.info('Epoch time: %ds', epoch_duration)
# validation
if epochs - epoch < 5:
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('Valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
print('------Dropping %d paths------' % num_to_drop[sp])
# Save switches info for s-c refinement.
if sp == len(num_to_keep) - 1:
switches_normal_2 = copy.deepcopy(switches_normal)
switches_reduce_2 = copy.deepcopy(switches_reduce)
# drop operations with low architecture weights
arch_param = model.module.arch_parameters()
normal_prob = F.softmax(arch_param[0], dim=sm_dim).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_normal[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
# for the last stage, drop all Zero operations
drop = get_min_k_no_zero(normal_prob[i, :], idxs,
num_to_drop[sp])
else:
drop = get_min_k(normal_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_normal[i][idxs[idx]] = False
reduce_prob = F.softmax(arch_param[1], dim=-1).data.cpu().numpy()
for i in range(14):
idxs = []
for j in range(len(PRIMITIVES)):
if switches_reduce[i][j]:
idxs.append(j)
if sp == len(num_to_keep) - 1:
drop = get_min_k_no_zero(reduce_prob[i, :], idxs,
num_to_drop[sp])
else:
drop = get_min_k(reduce_prob[i, :], num_to_drop[sp])
for idx in drop:
switches_reduce[i][idxs[idx]] = False
logging.info('switches_normal = %s', switches_normal)
logging_switches(switches_normal)
logging.info('switches_reduce = %s', switches_reduce)
logging_switches(switches_reduce)
if sp == len(num_to_keep) - 1:
arch_param = model.module.arch_parameters()
normal_prob = F.softmax(arch_param[0],
dim=sm_dim).data.cpu().numpy()
reduce_prob = F.softmax(arch_param[1],
dim=sm_dim).data.cpu().numpy()
normal_final = [0 for idx in range(14)]
reduce_final = [0 for idx in range(14)]
# remove all Zero operations
for i in range(14):
if switches_normal_2[i][0] == True:
normal_prob[i][0] = 0
normal_final[i] = max(normal_prob[i])
if switches_reduce_2[i][0] == True:
reduce_prob[i][0] = 0
reduce_final[i] = max(reduce_prob[i])
# Generate Architecture, similar to DARTS
keep_normal = [0, 1]
keep_reduce = [0, 1]
n = 3
start = 2
for i in range(3):
end = start + n
tbsn = normal_final[start:end]
tbsr = reduce_final[start:end]
edge_n = sorted(range(n), key=lambda x: tbsn[x])
keep_normal.append(edge_n[-1] + start)
keep_normal.append(edge_n[-2] + start)
edge_r = sorted(range(n), key=lambda x: tbsr[x])
keep_reduce.append(edge_r[-1] + start)
keep_reduce.append(edge_r[-2] + start)
start = end
n = n + 1
# set switches according the ranking of arch parameters
for i in range(14):
if not i in keep_normal:
for j in range(len(PRIMITIVES)):
switches_normal[i][j] = False
if not i in keep_reduce:
for j in range(len(PRIMITIVES)):
switches_reduce[i][j] = False
# translate switches into genotype
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
## restrict skipconnect (normal cell only)
logging.info('Restricting skipconnect...')
# generating genotypes with different numbers of skip-connect operations
for sks in range(0, 9):
max_sk = 8 - sks
num_sk = check_sk_number(switches_normal)
if not num_sk > max_sk:
continue
while num_sk > max_sk:
normal_prob = delete_min_sk_prob(switches_normal,
switches_normal_2,
normal_prob)
switches_normal = keep_1_on(switches_normal_2, normal_prob)
switches_normal = keep_2_branches(switches_normal,
normal_prob)
num_sk = check_sk_number(switches_normal)
logging.info('Number of skip-connect: %d', max_sk)
genotype = parse_network(switches_normal, switches_reduce)
logging.info(genotype)
def main(args: Namespace):
print('Loading data')
dataset = get_data(args.data_path)
num_mols, num_tasks = len(dataset), dataset.num_tasks()
args.num_mols, args.num_tasks = num_mols, num_tasks
data = convert_moleculedataset_to_moleculefactordataset(dataset)
print(f'Number of molecules = {num_mols:,}')
print(f'Number of tasks = {num_tasks:,}')
print(f'Number of real tasks = {args.num_real_tasks:,}')
print(f'Number of known molecule-task pairs = {len(data):,}')
print('Splitting data')
train_data, val_data, test_data = split_data(data, args)
if args.dataset_type == 'regression':
print('Scaling data')
targets = train_data.targets()
scaler = StandardScaler().fit(targets)
scaled_targets = scaler.transform(targets).tolist()
train_data.set_targets(scaled_targets)
else:
scaler = None
print(
f'Total size = {len(data):,} | '
f'train size = {len(train_data):,} | val size = {len(val_data):,} | test size = {len(test_data):,}'
)
if args.checkpoint_path is not None:
print('Loading saved model')
model, loaded_args = load(args.checkpoint_path)
assert args.num_mols == loaded_args.num_mols and args.num_tasks == loaded_args.num_tasks
args.embedding_size, args.hidden_size, args.dropout, args.activation, args.dataset_type = \
loaded_args.embedding_size, loaded_args.hidden_size, loaded_args.dropout, loaded_args.activation, loaded_args.dataset_type
else:
print('Building model')
model = MatrixFactorizer(
args,
num_mols=num_mols,
num_tasks=num_tasks,
embedding_size=args.embedding_size,
hidden_size=args.hidden_size,
dropout=args.dropout,
activation=args.activation,
classification=(args.dataset_type == 'classification'))
print(model)
print(f'Number of parameters = {param_count(model):,}')
loss_func = get_loss_func(args)
metric_func = get_metric_func(metric=args.metric)
optimizer = Adam(model.parameters(), lr=args.lr)
if args.cuda:
print('Moving model to cuda')
model = model.cuda()
print('Training')
for epoch in trange(args.epochs):
print(f'Epoch {epoch}')
train(model=model,
data=train_data,
loss_func=loss_func,
optimizer=optimizer,
batch_size=args.batch_size,
random_mol_embeddings=args.random_mol_embeddings)
val_scores = evaluate(model=model,
data=val_data,
num_tasks=args.num_real_tasks,
metric_func=metric_func,
batch_size=args.batch_size,
scaler=scaler,
random_mol_embeddings=args.random_mol_embeddings)
print(val_scores)
avg_val_score = np.mean(val_scores)
print(f'Validation {args.metric} = {avg_val_score:.6f}')
test_scores = evaluate(model=model,
data=test_data,
num_tasks=args.num_real_tasks,
metric_func=metric_func,
batch_size=args.batch_size,
scaler=scaler,
random_mol_embeddings=args.random_mol_embeddings)
print(test_scores)
avg_test_score = np.mean(test_scores)
print(f'Test {args.metric} = {avg_test_score:.6f}')
if args.save_path is not None:
print('Saving model')
save(model, args, args.save_path)
if args.filled_matrix_path is not None:
print('Filling matrix of data')
fill_matrix(model, args, data)
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
writerTf = SummaryWriter('writerTfX')
np.random.seed(args.seed)
gpus = [int(i) for i in args.gpu.split(',')]
if len(gpus) == 1:
torch.cuda.set_device(int(args.gpu))
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %s' % args.gpu)
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, criterion)
model = model.cuda()
if len(gpus) > 1:
print("True")
model = nn.parallel.DataParallel(model,
device_ids=gpus,
output_device=gpus[0])
model = model.module
arch_params = list(map(id, model.arch_parameters()))
weight_params = filter(lambda p: id(p) not in arch_params,
model.parameters())
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(
# model.parameters(),
weight_params,
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data,
train=True,
download=False,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=2)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, criterion, args)
temperature = 5.0
exp_anneal_rate = np.exp(-0.045)
for epoch in range(args.epochs):
temperature = temperature * exp_anneal_rate # update temperature
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
genotype = model.genotype()
logging.info('genotype = %s', genotype)
# print(F.softmax(model.alphas_normal, dim=-1))
# print(F.softmax(model.alphas_reduce, dim=-1))
# training
train_acc, train_obj = train(train_queue, valid_queue, model,
architect, criterion, optimizer, lr,
temperature)
logging.info('train_acc %f', train_acc)
writerTf.add_scalar('train_acc', train_acc, epoch)
writerTf.add_scalar('train_obj', train_obj, epoch)
# validation
with torch.no_grad():
valid_acc, valid_obj = infer(valid_queue, model, criterion)
writerTf.add_scalar('valid_acc', valid_acc, epoch)
writerTf.add_scalar('valid_obj', valid_obj, epoch)
writerTf.close()
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main():
start_time = time.time()
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
torch.cuda.set_device(args.gpu)
reproducibility(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
model = Network(args.model_name,
CIFAR_CLASSES,
sub_policies,
args.use_cuda,
args.use_parallel,
temperature=args.temperature,
criterion=criterion)
# model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# train_transform, valid_transform = utils._data_transforms_cifar10(args)
# train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
# train_data = AugmCIFAR10(
# root=args.data, train=True, download=True,
# transform=train_transform, ops_names=sub_policies, search=True, magnitudes=model.magnitudes)
# valid_data = AugmCIFAR10(
# root=args.data, train=True, download=True,
# transform=train_transform, ops_names=sub_policies, search=False, magnitudes=model.magnitudes)
# num_train = len(train_data)
# indices = list(range(num_train))
# split = int(np.floor(args.train_portion * num_train))
# train_queue = torch.utils.data.DataLoader(
# train_data, batch_size=args.batch_size,
# sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
# pin_memory=True, num_workers=args.num_workers)
# valid_queue = torch.utils.data.DataLoader(
# valid_data, batch_size=args.batch_size,
# sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
# pin_memory=True, num_workers=args.num_workers)
train_queue, valid_queue = get_dataloaders(args.dataset,
args.batch_size,
args.num_workers,
args.dataroot,
sub_policies,
model.magnitudes,
args.cutout,
args.cutout_length,
split=args.train_portion,
split_idx=0,
target_lb=-1)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, args)
for epoch in range(args.epochs):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
genotype = model.genotype()
# logging.info('genotype = %s', genotype)
print_genotype(genotype)
# logging.info('%s' % str(torch.nn.functional.softmax(model.ops_weights, dim=-1)))
probs = model.ops_weights
# logging.info('%s' % str(probs / probs.sum(-1, keepdim=True)))
logging.info('%s' % str(torch.nn.functional.softmax(probs, dim=-1)))
logging.info('%s' % str(model.probabilities.clamp(0, 1)))
logging.info('%s' % str(model.magnitudes.clamp(0, 1)))
# training
train_acc, train_obj = train(train_queue, valid_queue, model,
architect, criterion, optimizer, lr)
logging.info('train_acc %f', train_acc)
# validation
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
end_time = time.time()
elapsed = end_time - start_time
logging.info('elapsed time: %.3f Hours' % (elapsed / 3600.))
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
random.seed(args.seed)
np.random.seed(
args.data_seed) # cutout and load_corrupted_data use np.random
torch.cuda.set_device(args.gpu)
cudnn.benchmark = False
torch.manual_seed(args.seed)
cudnn.enabled = True
cudnn.deterministic = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
if args.arch == 'resnet':
model = ResNet18(CIFAR_CLASSES).cuda()
args.auxiliary = False
elif args.arch == 'resnet50':
model = ResNet50(CIFAR_CLASSES).cuda()
args.auxiliary = False
elif args.arch == 'resnet34':
model = ResNet34(CIFAR_CLASSES).cuda()
args.auxiliary = False
else:
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_transform, test_transform = utils._data_transforms_cifar10(args)
# Load dataset
if args.dataset == 'cifar10':
noisy_train_data = CIFAR10(root=args.data,
train=True,
gold=False,
gold_fraction=0.0,
corruption_prob=args.corruption_prob,
corruption_type=args.corruption_type,
transform=train_transform,
download=True,
seed=args.data_seed)
gold_train_data = CIFAR10(root=args.data,
train=True,
gold=True,
gold_fraction=1.0,
corruption_prob=args.corruption_prob,
corruption_type=args.corruption_type,
transform=train_transform,
download=True,
seed=args.data_seed)
test_data = dset.CIFAR10(root=args.data,
train=False,
download=True,
transform=test_transform)
elif args.dataset == 'cifar100':
noisy_train_data = CIFAR100(root=args.data,
train=True,
gold=False,
gold_fraction=0.0,
corruption_prob=args.corruption_prob,
corruption_type=args.corruption_type,
transform=train_transform,
download=True,
seed=args.data_seed)
gold_train_data = CIFAR100(root=args.data,
train=True,
gold=True,
gold_fraction=1.0,
corruption_prob=args.corruption_prob,
corruption_type=args.corruption_type,
transform=train_transform,
download=True,
seed=args.data_seed)
test_data = dset.CIFAR100(root=args.data,
train=False,
download=True,
transform=test_transform)
num_train = len(gold_train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
if args.gold_fraction == 1.0:
train_data = gold_train_data
else:
train_data = noisy_train_data
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=0)
if args.clean_valid:
valid_data = gold_train_data
else:
valid_data = noisy_train_data
valid_queue = torch.utils.data.DataLoader(
valid_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:]),
pin_memory=True,
num_workers=0)
test_queue = torch.utils.data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs))
if args.loss_func == 'cce':
criterion = nn.CrossEntropyLoss().cuda()
elif args.loss_func == 'rll':
criterion = utils.RobustLogLoss(alpha=args.alpha).cuda()
elif args.loss_func == 'forward_gold':
corruption_matrix = train_data.corruption_matrix
criterion = utils.ForwardGoldLoss(corruption_matrix=corruption_matrix)
else:
assert False, "Invalid loss function '{}' given. Must be in {'cce', 'rll'}".format(
args.loss_func)
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer_valid(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
test_acc, test_obj = infer(test_queue, model, criterion)
logging.info('test_acc %f', test_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
def dqn_learning_keras_memoryReplay_v2(env, model, folder_path, EPISODE_MAX,
MAX_STEPS, GAMMA, MEMORY_SIZE,
BATCH_SIZE, EXPLORATION_MAX,
EXPLORATION_MIN, EXPLORATION_DECAY,
observation_space, action_space,
step_size):
'''
function which is a neural net using Keras with memory replay
'''
# Initializations:
exploration_rate = EXPLORATION_MAX
# Experience replay:
memory = deque(maxlen=MEMORY_SIZE)
# Create list for plot and saving
list_memory_history = []
list_total_reward = []
list_loss = []
episode_max = EPISODE_MAX
list_done = []
done = False
for i in range(episode_max):
# time_start_epoch = time.time()
total_reward = 0
j = 0
state = env.reset()
rospy.sleep(4.0)
# Becarefull with the state (np and list)
np_state = (np.array(state, dtype=np.float32), )
np_state = np.reshape(np_state, [1, observation_space])
# np_state = np.identity(7)[np_state:np_state+1]
# print(np_state)
done = False
list_memory = []
print("*********************************************")
print("Episode: ", i)
print("*********************************************")
while j < MAX_STEPS and not done: # step inside an episode
# time_start_step = time.time()
# print("[INFO]: episode: ", i, ", step: ", j)
# BECAREFUL with the action!
action = choose_action(model, np_state, action_space,
exploration_rate)
disc_action = discrete_action(action, step_size)
# print(action)
# time_start_action = time.time()
# print("################INSIDE ENV################")
new_state, reward, done, _ = env.step(disc_action)
# print("################INSIDE ENV################")
# print("[ INFO] Time for the action ", j, ": ", time.time()-time_start_action)
# time_start_print = time.time()
# print("*********************************************")
# print("Observation: ", new_state)
# print("State: ", state)
# print("Reward: ", reward)
# print("Total rewards: ", total_reward)
# print("Done: ", done)
# print("Episode: ", i)
# print("Step: ", j)
# print("*********************************************")
# print("[ INFO] Time for the print info ", j, ": ", time.time()-time_start_print)
np_new_state = (np.array(new_state, dtype=np.float32), )
np_new_state = np.reshape(np_new_state, [1, observation_space])
# np_new_state = np.identity(7)[np_new_state:np_new_state+1]
# Memory replay
# memory = save_forReplay(memory, state, action, reward, new_state, done)
# time_start_save_for_replay = time.time()
save_forReplay(memory, np_state, action, reward, np_new_state,
done)
# print("[ INFO] Time for the saving of experience replay ", j, ": ", time.time()-time_start_save_for_replay)
# time_start_experience_replay = time.time()
# print("[ INFO] Time for the experience replay ", j, ": ", time.time()-time_start_experience_replay)
list_memory.append([np_state, action, reward, np_new_state, done])
# print("list_loss: ", list_loss)
np_state = np_new_state
state = new_state
total_reward += reward
# print("[ INFO] Time for the step ", j, ": ", time.time()-time_start_step)
j += 1
model, exploration_rate_new, loss = experience_replay_v2(
model, memory, BATCH_SIZE, exploration_rate, EXPLORATION_DECAY,
EXPLORATION_MIN, GAMMA)
exploration_rate = exploration_rate_new
# Save the lost function into a list
if len(memory) > BATCH_SIZE:
list_loss.append(loss[0])
list_done.append(done)
# Save the trajectory and reward
list_memory_history.append(list_memory)
list_total_reward.append(total_reward)
# print(list)
# print("*********************************************")
# print("*********************************************")
# print("[ INFO] Time for the epoch ", i, ": ", time.time()-time_start_epoch)
# print("*********************************************")
# print("*********************************************")
if i % 500 == 0:
# Save the model
print("Saving...")
# model.save('/home/roboticlab14/catkin_ws/src/envirenement_reinforcement_learning/environment_package/src/saves/model/try_1.h5')
path = folder_path + 'model/'
name = 'model_'
total_model_path = path + name + str(i) + '.h5'
# model.save('/media/roboticlab14/DocumentsToShare/Reinforcement_learning/Datas/learn_to_go_position/model/try_1.h5')
model.save(total_model_path)
# Save datas
print(
"Saving list_total_reward: ",
utils.save(list_total_reward,
i,
arg_path=folder_path + "reward/",
arg_name="list_total_reward_"))
print(
"Saving list_memory_history: ",
utils.save(list_memory_history,
i,
arg_path=folder_path + "trajectory/",
arg_name="list_memory_history_"))
print(
"Saving list_done: ",
utils.save(list_done,
i,
arg_path=folder_path + "done/",
arg_name="list_done_"))
print(
"Saving list_loss: ",
utils.save(list_loss,
i,
arg_path=folder_path + "losses/",
arg_name="list_loss_"))
# Save the memory!
print(
"Saving memory: ",
utils.save(memory,
i,
arg_path=folder_path + "memory/",
arg_name="memory_"))
def run(self, restore=False):
self.black = False
for event in pygame.event.get():
if event.type == pygame.QUIT:
return
elif event.type == pygame.VIDEORESIZE:
pygame.display.set_mode(event.size, pygame.RESIZABLE)
break
g.init()
if not self.journal:
utils.load()
load_save.retrieve()
x = g.sx(26)
y = g.sy(11.2)
if not self.sugar:
buttons.Button("new", (x, y))
x, y = g.cxy2
dx = g.sy(4)
self.back_button = buttons.Button("back", (x, y))
x += dx
buttons.Button("plus", (x, y))
x += dx
buttons.Button("times", (x, y))
x += dx
buttons.Button("equals", (x, y))
self.ops = ['back', 'plus', 'times', 'equals']
if not self.sugar:
self.slider = slider.Slider(g.sx(22.4), g.sy(20.5), 10,
utils.GREEN)
self.mouse_auto = True
self.anim_ms = None
self.level1() # initial animation
self.scored = False
if restore:
g.score = self.save_score
ctrl = False
pygame.key.set_repeat(600, 120)
key_ms = pygame.time.get_ticks()
going = True
if self.canvas is not None:
self.canvas.grab_focus()
while going:
if self.journal:
# Pump GTK messages.
while Gtk.events_pending():
Gtk.main_iteration()
# Pump PyGame messages.
for event in pygame.event.get():
if event.type == pygame.QUIT:
if not self.journal:
utils.save()
going = False
elif event.type == pygame.MOUSEMOTION:
g.pos = event.pos
g.redraw = True
if self.canvas is not None:
self.canvas.grab_focus()
elif event.type == pygame.MOUSEBUTTONDOWN:
g.redraw = True
self.anim_end()
if event.button == 1:
bu = buttons.check()
if bu == '':
if not self.check_numbers():
if not self.sugar:
if self.slider.mouse():
self.level1()
else:
self.do_button(bu) # eg do_button('plus')
self.flush_queue()
elif event.type == pygame.KEYDOWN:
self.anim_end()
# throttle keyboard repeat
if pygame.time.get_ticks() - key_ms > 110:
key_ms = pygame.time.get_ticks()
if ctrl:
if event.key == pygame.K_q:
if not self.journal:
utils.save()
going = False
break
else:
ctrl = False
if event.key in (pygame.K_LCTRL, pygame.K_RCTRL):
ctrl = True
break
self.do_key(event.key)
g.redraw = True
self.flush_queue()
elif event.type == pygame.KEYUP:
ctrl = False
if not going:
break
self.animation()
if g.redraw:
self.display()
if g.version_display:
utils.version_display()
if not self.black:
g.screen.blit(g.pointer, g.pos)
pygame.display.flip()
g.redraw = False
g.clock.tick(40)
try:
ingredient_list = utils.load(ing_file)
print("loaded ingredient list from disk...")
except Exception as e:
print('started 1')
idh.getIngridient(data1, ingredient_list)
print('ended 1')
print('started 2')
idh.getIngridient(data2, ingredient_list)
print('ended 1')
print('started 3')
idh.getIngridient(data3, ingredient_list)
print('ended 3')
utils.save(ingredient_list, ing_file)
print('saved ingredient list to disk...')
data = {}
data.update(data1)
data.update(data2)
data.update(data3)
utils.save(data, filename=path.join(datadir, 'datadict.pkl'))
idh.wrd2ingrdtdict2tsv(ingredient_list, data, path.join(
datadir,
'bigdict.tsv')) # Unless you wanna freeze your computer, DON'T OPEN IT! XD
idh.print_dict_stats(ingredient_list, data,
path.join(datadir, 'bigdict_stats.tsv'))
def main():
parser = argparse.ArgumentParser("Common Argument Parser")
parser.add_argument('--data', type=str, default='../data', help='location of the data corpus')
parser.add_argument('--dataset', type=str, default='cifar10', help='which dataset:\
cifar10, mnist, emnist, fashion, svhn, stl10, devanagari')
parser.add_argument('--batch_size', type=int, default=64, help='batch size')
parser.add_argument('--learning_rate', type=float, default=0.025, help='init learning rate')
parser.add_argument('--learning_rate_min', type=float, default=1e-8, help='min learning rate')
parser.add_argument('--lr_power_annealing_exponent_order', type=float, default=2,
help='Cosine Power Annealing Schedule Base, larger numbers make '
'the exponential more dominant, smaller make cosine more dominant, '
'1 returns to standard cosine annealing.')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--weight_decay', '--wd', dest='weight_decay', type=float, default=3e-4, help='weight decay')
parser.add_argument('--partial', default=1/8, type=float, help='partially adaptive parameter p in Padam')
parser.add_argument('--report_freq', type=float, default=50, help='report frequency')
parser.add_argument('--gpu', type=int, default=0, help='gpu device id')
parser.add_argument('--epochs', type=int, default=2000, help='num of training epochs')
parser.add_argument('--start_epoch', default=1, type=int, metavar='N',
help='manual epoch number (useful for restarts)')
parser.add_argument('--warmup_epochs', type=int, default=5, help='num of warmup training epochs')
parser.add_argument('--warm_restarts', type=int, default=20, help='warm restarts of cosine annealing')
parser.add_argument('--init_channels', type=int, default=36, help='num of init channels')
parser.add_argument('--mid_channels', type=int, default=32, help='C_mid channels in choke SharpSepConv')
parser.add_argument('--layers', type=int, default=20, help='total number of layers')
parser.add_argument('--model_path', type=str, default='saved_models', help='path to save the model')
parser.add_argument('--auxiliary', action='store_true', default=False, help='use auxiliary tower')
parser.add_argument('--mixed_auxiliary', action='store_true', default=False, help='Learn weights for auxiliary networks during training. Overrides auxiliary flag')
parser.add_argument('--auxiliary_weight', type=float, default=0.4, help='weight for auxiliary loss')
parser.add_argument('--cutout', action='store_true', default=False, help='use cutout')
parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
parser.add_argument('--autoaugment', action='store_true', default=False, help='use cifar10 autoaugment https://arxiv.org/abs/1805.09501')
parser.add_argument('--random_eraser', action='store_true', default=False, help='use random eraser')
parser.add_argument('--drop_path_prob', type=float, default=0.2, help='drop path probability')
parser.add_argument('--save', type=str, default='EXP', help='experiment name')
parser.add_argument('--seed', type=int, default=0, help='random seed')
parser.add_argument('--arch', type=str, default='DARTS', help='which architecture to use')
parser.add_argument('--ops', type=str, default='OPS', help='which operations to use, options are OPS and DARTS_OPS')
parser.add_argument('--primitives', type=str, default='PRIMITIVES',
help='which primitive layers to use inside a cell search space,'
' options are PRIMITIVES, SHARPER_PRIMITIVES, and DARTS_PRIMITIVES')
parser.add_argument('--optimizer', type=str, default='sgd', help='which optimizer to use, options are padam and sgd')
parser.add_argument('--load', type=str, default='', metavar='PATH', help='load weights at specified location')
parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping')
parser.add_argument('--flops', action='store_true', default=False, help='count flops and exit, aka floating point operations.')
parser.add_argument('-e', '--evaluate', dest='evaluate', type=str, metavar='PATH', default='',
help='evaluate model at specified path on training, test, and validation datasets')
parser.add_argument('--multi_channel', action='store_true', default=False, help='perform multi channel search, a completely separate search space')
parser.add_argument('--load_args', type=str, default='', metavar='PATH',
help='load command line args from a json file, this will override '
'all currently set args except for --evaluate, and arguments '
'that did not exist when the json file was originally saved out.')
parser.add_argument('--layers_of_cells', type=int, default=8, help='total number of cells in the whole network, default is 8 cells')
parser.add_argument('--layers_in_cells', type=int, default=4,
help='Total number of nodes in each cell, aka number of steps,'
' default is 4 nodes, which implies 8 ops')
parser.add_argument('--weighting_algorithm', type=str, default='scalar',
help='which operations to use, options are '
'"max_w" (1. - max_w + w) * op, and scalar (w * op)')
# TODO(ahundt) remove final path and switch back to genotype
parser.add_argument('--load_genotype', type=str, default=None, help='Name of genotype to be used')
parser.add_argument('--simple_path', default=True, action='store_false', help='Final model is a simple path (MultiChannelNetworkModel)')
args = parser.parse_args()
args = utils.initialize_files_and_args(args)
logger = utils.logging_setup(args.log_file_path)
if not torch.cuda.is_available():
logger.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logger.info('gpu device = %d' % args.gpu)
logger.info("args = %s", args)
DATASET_CLASSES = dataset.class_dict[args.dataset]
DATASET_CHANNELS = dataset.inp_channel_dict[args.dataset]
DATASET_MEAN = dataset.mean_dict[args.dataset]
DATASET_STD = dataset.std_dict[args.dataset]
logger.info('output channels: ' + str(DATASET_CLASSES))
# # load the correct ops dictionary
op_dict_to_load = "operations.%s" % args.ops
logger.info('loading op dict: ' + str(op_dict_to_load))
op_dict = eval(op_dict_to_load)
# load the correct primitives list
primitives_to_load = "genotypes.%s" % args.primitives
logger.info('loading primitives:' + primitives_to_load)
primitives = eval(primitives_to_load)
logger.info('primitives: ' + str(primitives))
genotype = eval("genotypes.%s" % args.arch)
# create the neural network
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
if args.multi_channel:
final_path = None
if args.load_genotype is not None:
genotype = getattr(genotypes, args.load_genotype)
print(genotype)
if type(genotype[0]) is str:
logger.info('Path :%s', genotype)
# TODO(ahundt) remove final path and switch back to genotype
cnn_model = MultiChannelNetwork(
args.init_channels, DATASET_CLASSES, layers=args.layers_of_cells, criterion=criterion, steps=args.layers_in_cells,
weighting_algorithm=args.weighting_algorithm, genotype=genotype)
flops_shape = [1, 3, 32, 32]
elif args.dataset == 'imagenet':
cnn_model = NetworkImageNet(args.init_channels, DATASET_CLASSES, args.layers, args.auxiliary, genotype, op_dict=op_dict, C_mid=args.mid_channels)
flops_shape = [1, 3, 224, 224]
else:
cnn_model = NetworkCIFAR(args.init_channels, DATASET_CLASSES, args.layers, args.auxiliary, genotype, op_dict=op_dict, C_mid=args.mid_channels)
flops_shape = [1, 3, 32, 32]
cnn_model = cnn_model.cuda()
logger.info("param size = %fMB", utils.count_parameters_in_MB(cnn_model))
if args.flops:
logger.info('flops_shape = ' + str(flops_shape))
logger.info("flops = " + utils.count_model_flops(cnn_model, data_shape=flops_shape))
return
optimizer = torch.optim.SGD(
cnn_model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
# Get preprocessing functions (i.e. transforms) to apply on data
train_transform, valid_transform = utils.get_data_transforms(args)
if args.evaluate:
# evaluate the train dataset without augmentation
train_transform = valid_transform
# Get the training queue, use full training and test set
train_queue, valid_queue = dataset.get_training_queues(
args.dataset, train_transform, valid_transform, args.data, args.batch_size, train_proportion=1.0, search_architecture=False)
test_queue = None
if args.dataset == 'cifar10':
# evaluate best model weights on cifar 10.1
# https://github.com/modestyachts/CIFAR-10.1
test_data = cifar10_1.CIFAR10_1(root=args.data, download=True, transform=valid_transform)
test_queue = torch.utils.data.DataLoader(
test_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=8)
if args.evaluate:
# evaluate the loaded model, print the result, and return
logger.info("Evaluating inference with weights file: " + args.load)
eval_stats = evaluate(
args, cnn_model, criterion, args.load,
train_queue=train_queue, valid_queue=valid_queue, test_queue=test_queue)
with open(args.stats_file, 'w') as f:
arg_dict = vars(args)
arg_dict.update(eval_stats)
json.dump(arg_dict, f)
logger.info("flops = " + utils.count_model_flops(cnn_model))
logger.info(utils.dict_to_log_string(eval_stats))
logger.info('\nEvaluation of Loaded Model Complete! Save dir: ' + str(args.save))
return
lr_schedule = cosine_power_annealing(
epochs=args.epochs, max_lr=args.learning_rate, min_lr=args.learning_rate_min,
warmup_epochs=args.warmup_epochs, exponent_order=args.lr_power_annealing_exponent_order)
epochs = np.arange(args.epochs) + args.start_epoch
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
epoch_stats = []
stats_csv = args.epoch_stats_file
stats_csv = stats_csv.replace('.json', '.csv')
with tqdm(epochs, dynamic_ncols=True) as prog_epoch:
best_valid_acc = 0.0
best_epoch = 0
best_stats = {}
stats = {}
epoch_stats = []
weights_file = os.path.join(args.save, 'weights.pt')
for epoch, learning_rate in zip(prog_epoch, lr_schedule):
# update the drop_path_prob augmentation
cnn_model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
# update the learning rate
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
# scheduler.get_lr()[0]
train_acc, train_obj = train(args, train_queue, cnn_model, criterion, optimizer)
val_stats = infer(args, valid_queue, cnn_model, criterion)
stats.update(val_stats)
stats['train_acc'] = train_acc
stats['train_loss'] = train_obj
stats['lr'] = learning_rate
stats['epoch'] = epoch
if stats['valid_acc'] > best_valid_acc:
# new best epoch, save weights
utils.save(cnn_model, weights_file)
best_epoch = epoch
best_stats.update(copy.deepcopy(stats))
best_valid_acc = stats['valid_acc']
best_train_loss = train_obj
best_train_acc = train_acc
# else:
# # not best epoch, load best weights
# utils.load(cnn_model, weights_file)
logger.info('epoch, %d, train_acc, %f, valid_acc, %f, train_loss, %f, valid_loss, %f, lr, %e, best_epoch, %d, best_valid_acc, %f, ' + utils.dict_to_log_string(stats),
epoch, train_acc, stats['valid_acc'], train_obj, stats['valid_loss'], learning_rate, best_epoch, best_valid_acc)
stats['train_acc'] = train_acc
stats['train_loss'] = train_obj
epoch_stats += [copy.deepcopy(stats)]
with open(args.epoch_stats_file, 'w') as f:
json.dump(epoch_stats, f, cls=utils.NumpyEncoder)
utils.list_of_dicts_to_csv(stats_csv, epoch_stats)
# get stats from best epoch including cifar10.1
eval_stats = evaluate(args, cnn_model, criterion, weights_file, train_queue, valid_queue, test_queue)
with open(args.stats_file, 'w') as f:
arg_dict = vars(args)
arg_dict.update(eval_stats)
json.dump(arg_dict, f, cls=utils.NumpyEncoder)
with open(args.epoch_stats_file, 'w') as f:
json.dump(epoch_stats, f, cls=utils.NumpyEncoder)
logger.info(utils.dict_to_log_string(eval_stats))
logger.info('Training of Final Model Complete! Save dir: ' + str(args.save))
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, criterion)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_transform, valid_transform = utils._data_transforms_cifar10(args)
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True,
num_workers=2)
valid_queue = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
pin_memory=True,
num_workers=2)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs), eta_min=args.learning_rate_min)
architect = Architect(model, args)
for epoch in range(args.epochs):
scheduler.step()
lr = scheduler.get_lr()[0]
logging.info('epoch %d lr %e', epoch, lr)
genotype = model.genotype()
logging.info('genotype = %s', genotype)
print(F.softmax(model.alphas_normal, dim=-1))
print(F.softmax(model.alphas_reduce, dim=-1))
# training
train_acc, train_obj = train(train_queue, valid_queue, model,
architect, criterion, optimizer, lr)
logging.info('train_acc %f', train_acc)
# validation
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main():
if not torch.cuda.is_available():
logger.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logger.info('gpu device = %d' % args.gpu)
logger.info("args = %s", args)
# # load the correct ops dictionary
op_dict_to_load = "operations.%s" % args.ops
logger.info('loading op dict: ' + str(op_dict_to_load))
op_dict = eval(op_dict_to_load)
# load the correct primitives list
primitives_to_load = "genotypes.%s" % args.primitives
logger.info('loading primitives:' + primitives_to_load)
primitives = eval(primitives_to_load)
logger.info('primitives: ' + str(primitives))
genotype = eval("genotypes.%s" % args.arch)
cnn_model = Network(args.init_channels, CLASSES, args.layers, args.auxiliary, genotype, op_dict=op_dict, C_mid=args.mid_channels)
if args.parallel:
cnn_model = nn.DataParallel(cnn_model).cuda()
else:
cnn_model = cnn_model.cuda()
logger.info("param size = %fMB", utils.count_parameters_in_MB(cnn_model))
if args.flops:
cnn_model.drop_path_prob = 0.0
logger.info("flops = " + utils.count_model_flops(cnn_model, data_shape=[1, 3, 224, 224]))
exit(1)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
criterion_smooth = CrossEntropyLabelSmooth(CLASSES, args.label_smooth)
criterion_smooth = criterion_smooth.cuda()
optimizer = torch.optim.SGD(
cnn_model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
traindir = os.path.join(args.data, 'train')
validdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_data = dset.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
autoaugment.ImageNetPolicy(),
# transforms.ColorJitter(
# brightness=0.4,
# contrast=0.4,
# saturation=0.4,
# hue=0.2),
transforms.ToTensor(),
normalize,
]))
valid_data = dset.ImageFolder(
validdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=8)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=8)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.decay_period, gamma=args.gamma)
prog_epoch = tqdm(range(args.epochs), dynamic_ncols=True)
best_valid_acc = 0.0
best_epoch = 0
best_stats = {}
best_acc_top1 = 0
weights_file = os.path.join(args.save, 'weights.pt')
for epoch in prog_epoch:
scheduler.step()
cnn_model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train.train(args, train_queue, cnn_model, criterion, optimizer)
stats = train.infer(args, valid_queue, cnn_model, criterion)
is_best = False
if stats['valid_acc'] > best_valid_acc:
# new best epoch, save weights
utils.save(cnn_model, weights_file)
best_epoch = epoch
best_valid_acc = stats['valid_acc']
best_stats = stats
best_stats['lr'] = scheduler.get_lr()[0]
best_stats['epoch'] = best_epoch
best_train_loss = train_obj
best_train_acc = train_acc
is_best = True
logger.info('epoch, %d, train_acc, %f, valid_acc, %f, train_loss, %f, valid_loss, %f, lr, %e, best_epoch, %d, best_valid_acc, %f, ' + utils.dict_to_log_string(stats),
epoch, train_acc, stats['valid_acc'], train_obj, stats['valid_loss'], scheduler.get_lr()[0], best_epoch, best_valid_acc)
checkpoint = {
'epoch': epoch,
'state_dict': cnn_model.state_dict(),
'best_acc_top1': best_valid_acc,
'optimizer' : optimizer.state_dict(),
}
checkpoint.update(stats)
utils.save_checkpoint(stats, is_best, args.save)
best_epoch_str = utils.dict_to_log_string(best_stats, key_prepend='best_')
logger.info(best_epoch_str)
logger.info('Training of Final Model Complete! Save dir: ' + str(args.save))
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled = True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %s' % args.gpus)
logging.info("args = %s", args)
num_gpus = torch.cuda.device_count()
genotype = eval("genotypes.%s" % args.arch)
print('---------Genotype---------')
logging.info(genotype)
print('--------------------------')
model = Network(args.init_channels, CIFAR_CLASSES, args.layers,
args.auxiliary, genotype)
if num_gpus > 1:
model = nn.DataParallel(model)
model = model.cuda()
else:
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.set == 'cifar100':
train_data = dset.CIFAR100(root=args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR100(root=args.data,
train=False,
download=True,
transform=valid_transform)
else:
train_data = dset.CIFAR10(root=args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR10(root=args.data,
train=False,
download=True,
transform=valid_transform)
#train_data = dset.CIFAR10(root=args.data, train=True, download=True, transform=train_transform)
#valid_data = dset.CIFAR10(root=args.data, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(valid_data,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(args.epochs))
best_acc = 0.0
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
if num_gpus > 1:
model.module.drop_path_prob = args.drop_path_prob * epoch / args.epochs
else:
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
logging.info('valid_acc %f, best_acc %f', valid_acc, best_acc)
utils.save(model, os.path.join(args.save, 'weights.pt'))
def main():
html = utils.fetch_url(url)
process(html)
utils.save(json_filename, moves_list)
def main():
if not torch.cuda.is_available():
logging.info('No GPU device available')
sys.exit(1)
np.random.seed(args.seed)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info("args = %s", args)
logging.info("unparsed args = %s", unparsed)
num_gpus = torch.cuda.device_count()
genotype = eval("genotypes.%s" % args.arch)
print('---------Genotype---------')
logging.info(genotype)
print('--------------------------')
model = Network(args.init_channels, CIFAR_CLASSES, args.layers, args.auxiliary, genotype)
model = torch.nn.DataParallel(model)
model = model.cuda()
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
if args.cifar100:
train_transform, valid_transform = utils._data_transforms_cifar100(args)
else:
train_transform, valid_transform = utils._data_transforms_cifar10(args)
if args.cifar100:
train_data = dset.CIFAR100(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR100(root=args.tmp_data_dir, train=False, download=True, transform=valid_transform)
else:
train_data = dset.CIFAR10(root=args.tmp_data_dir, train=True, download=True, transform=train_transform)
valid_data = dset.CIFAR10(root=args.tmp_data_dir, train=False, download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=args.workers)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs))
best_acc = 0.0
for epoch in range(args.epochs):
scheduler.step()
logging.info('Epoch: %d lr %e', epoch, scheduler.get_lr()[0])
model.module.drop_path_prob = args.drop_path_prob * epoch / args.epochs
model.drop_path_prob = args.drop_path_prob * epoch / args.epochs
start_time = time.time()
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('Train_acc: %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
if valid_acc > best_acc:
best_acc = valid_acc
logging.info('Valid_acc: %f', valid_acc)
end_time = time.time()
duration = end_time - start_time
print('Epoch time: %ds.' % duration )
utils.save(model.module, os.path.join(args.save, 'weights.pt'))