def addOutputs(self):
Complete.addOutputs(self)
self.addOutput(
OutputVector(self.OUTPUT_WATERSHEDS_LAYER,
self.tr('watersheds_vector')))
self.addOutput(
OutputVector(self.OUTPUT_BLUESPOTS_LAYER,
self.tr('bluespots_vector')))
def createOutput(self):
Complete.createOutput(self)
#Create vector files
self.writeVectorOutput(
'watersheds.shp',
self.getOutputFromName(self.OUTPUT_WATERSHEDS_LAYER),
self.getParameterValue(self.VECTOR_FORMAT))
self.writeVectorOutput(
'bluespots.shp',
self.getOutputFromName(self.OUTPUT_BLUESPOTS_LAYER),
self.getParameterValue(self.VECTOR_FORMAT))
class ExternalTask:
fetch = FetchAndLock()
complete = Complete()
def subscribe(self, topic, lockduration, host):
self.fetch.sendjson(topic=topic, lockduration=lockduration, host=host)
def startautomation(self):
if self.fetch.getresponsetext() != "[]":
self.fetch.storereponseindict()
return True
else:
return False
def getvariable(self, str):
return self.fetch.getvariable(str=str)
def completeautomation(self, host):
print(self.fetch.getid())
print(self.complete.sendcomplete(guid=self.fetch.getid(), host=host))
print(self.complete.getresponse())
#put local variable
def putvariable(self, host, varname):
json = {"value": "someValue", "type": "String"}
putresponse = requests.put(
'http://' + host + '/engine-rest/execution/' +
self.fetch.getexecutionid() + '/localVariables/' + varname,
json=json)
print(putresponse)
print(putresponse.text)
def start():
print("Loading the files and preparing the system...")
files_list = get_file_list(
"technology_texts/python-3.8.4-docs-text/python-3.8.4-docs-text/c-api")
# files_list = get_file_list("technology_texts/python-3.8.4-docs-text/python-3.8.4-docs-text/whatsnew")
complete = Complete(Data(files_list))
# complete = Complete(Data(["technology_texts/python-3.8.4-docs-text/python-3.8.4-docs-text/about.txt"]))
print("The system is ready.")
input_ = ' '.join((''.join(i for i in input("\n\nEnter your text: ")
if i in string.ascii_letters + ' ')).split())
while (1):
text = " "
while text[-1] != '#':
match_sentences = complete.get_best_k_completions(input_)
if len(match_sentences) != 0:
for sentence in match_sentences:
print(
f"{sentence.completed_sentence} ({files_list[sentence.source_text]} {sentence.offset})"
)
else:
print("there is no items")
# print(input_, end="")
text = input(input_)
input_ += ' '.join(
(''.join(i for i in text
if i in string.ascii_letters + ' ')).split())
input_ = ' '.join(
(''.join(i for i in input("\n\nEnter your text: ")
if i in string.ascii_letters + ' ')).split())
def load_data():
files_list = get_file_list(data_path)
return Complete(Data(files_list)), files_list
def __init__(self):
Complete.__init__(self)
# data.name = args.dataset
dataprocess.random_split(data, args.dataseed, args)
model = eval(args.complete).Model(data.num_nodes, dataset.num_features,
dataset.num_classes, device, args).to(device)
optimizer_base = torch.optim.Adam(model.base_parameters(),
lr=args.lr,
weight_decay=args.wd)
optimizer_graph = torch.optim.Adam(model.graph_parameters(),
lr=args.lr_graph,
weight_decay=args.wd_graph)
complete_device = torch.device("cpu")
complete_model = Complete(sampled_edge_index, data, model, complete_device,
args)
compl_acc = 0.
model.train()
val_acc_epoch_list, best_val_acc = [], 0.
val_loss_epoch_list, best_val_loss = [], 1e10
final_test_acc = 0.
lr_base, lr_graph = args.lr, args.lr_graph
for epoch in range(args.epochs):
# Optimize GCN
train_loss = []
for batch_data in dataprocess.dataloader(data,
complete_model.loop_adj_part,
args.batch_size, args.sparse):
batch_data.to(device)
import PIL
import PIL.Image as Image
SEQUENCE_LENGTH = 1
def save_image(image, title, index):
images = np.array([image])
images = np.clip(np.rint((images + 1.0) / 2.0 * 255.0), 0.0,
255.0).astype(np.uint8) # [-1,1] => [0,255]
images = images.transpose(0, 2, 3, 1) # NCHW => NHWC
PIL.Image.fromarray(images[0], 'RGB').save(
'img-' + str(index) + '-' + title + '.png')
complete = Complete()
# source_image shape (256, 256, 3)
# source_image = scipy.misc.imread('/source/bayeux1.png',
# mode='RGB').astype(np.float)
source_image = scipy.misc.imread('/source/Black256.jpg',
mode='RGB').astype(np.float)
# RESHAPE TEST IMAGE
# source_image shape (256, 256, 3)
# generated shape is (3, 256, 256)
reshaped_source_image = source_image.transpose(2, 0, 1)
reshaped_source_image = (reshaped_source_image / 255) * 2.0 - 1.0