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# draw the final graph
pos = dict()
first_it = True
for node in final_G.nodes():
x = final_G.node[node]['x']
y = final_G.node[node]['y']
pos[node] = [x, y]
if first_it is True:
x_min = x
y_min = y
x_max = x
y_max = y
first_it = False
else:
if x > x_max:
x_max = x
elif x < x_min:
x_min = x
if y > y_max:
y_max = y
elif y < y_min:
y_min = y
margin = 0.01
delta_x = abs(x_max - x_min)
delta_y = abs(y_max - y_min)
print('x_min = {}\nx_max = {}\ny_min = {}\ny_max = {}'.format(x_min, x_max, y_min, y_max))
plt.xlim(x_min - margin * delta_x, x_max + margin * delta_x)
plt.ylim(y_min - margin * delta_y, y_max + margin * delta_y)
nx.draw_networkx(final_G, pos, with_labels=False, node_size=2, linewidths=0.0)
plt.show()
plt.close()
# <FILESEP>
"""
Client to submit new render request to server
"""
import logging
from util import client
logging.basicConfig(level=logging.INFO)
LOGGER = logging.getLogger(__name__)
def send(d):
"""
Send/Submit a new render request
:param d: dict. a render request serialized as dictionary
"""
rrequest = client.add_request(d)
if rrequest:
LOGGER.info('request %s sent to server', rrequest.uid)
if __name__ == '__main__':
test_job_a = {
'name': 'street_seq01',
'owner': 'TEST_SUBMITTER_01',
'umap_path': '/Game/Cinematics/Street/Level_Cin_Street.Level_Cin_Street',
'useq_path': '/Game/Cinematics/Street/Shots/Shot01/LS_Shot_Street_Shot01.LS_Shot_Street_Shot01',
'uconfig_path': '/Game/Cinematics/Preset/Test.Test'
}
test_job_b = {
'name': 'street_seq02',
'owner': 'TEST_SUBMITTER_01',
'umap_path': '/Game/Cinematics/Street/Level_Cin_Street.Level_Cin_Street',
'useq_path': '/Game/Cinematics/Street/Shots/Shot02/LS_Shot_Street_Shot02.LS_Shot_Street_Shot02',
'uconfig_path': '/Game/Cinematics/Preset/Test.Test'
}
for job in [test_job_a, test_job_b]:
send(job)
# <FILESEP>
"""
This code is modified based on Jin-Hwa Kim's repository (Bilinear Attention Networks - https://github.com/jnhwkim/ban-vqa) by Xuan B. Nguyen
"""
import torch
import utils
import contextlib
from collections import defaultdict, OrderedDict
from meters import AverageMeter, TimeMeter
class Trainer(object):
"""
Main class for training.
"""
def __init__(self, args, model, criterion, optimizer=None, ae_criterion = None):
self.args = args
# copy model and criterion on current device
self.model = model.to(self.args.device)
self.criterion = criterion.to(self.args.device)