Datasets:
jblitzar
/
github-python

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max_iter=conf.train.min_step)
inv_lr_scheduler(param_lr_f, opt_c1, step,
init_lr=conf.train.lr,
max_iter=conf.train.min_step)
img_s = data_s[0]
label_s = data_s[1]
img_t = data_t[0]
index_t = data_t[2]
img_s, label_s = Variable(img_s.cuda()), \
Variable(label_s.cuda())
img_t = Variable(img_t.cuda())
index_t = Variable(index_t.cuda())
if len(img_t) < batch_size:
break
if len(img_s) < batch_size:
break
opt_g.zero_grad()
opt_c1.zero_grad()
## Weight normalizztion
C1.module.weight_norm()
## Source loss calculation
feat = G(img_s)
out_s = C1(feat)
loss_s = criterion(out_s, label_s)
feat_t = G(img_t)
out_t = C1(feat_t)
feat_t = F.normalize(feat_t)
### Calculate mini-batch x memory similarity
feat_mat = lemniscate(feat_t, index_t)
### We do not use memory features present in mini-batch
feat_mat[:, index_t] = -1 / conf.model.temp
### Calculate mini-batch x mini-batch similarity
feat_mat2 = torch.matmul(feat_t,
feat_t.t()) / conf.model.temp
mask = torch.eye(feat_mat2.size(0),
feat_mat2.size(0)).bool().cuda()
feat_mat2.masked_fill_(mask, -1 / conf.model.temp)
loss_nc = conf.train.eta * entropy(torch.cat([out_t, feat_mat,
feat_mat2], 1))
loss_ent = conf.train.eta * entropy_margin(out_t, conf.train.thr,
conf.train.margin)
all = loss_nc + loss_s + loss_ent
with amp.scale_loss(all, [opt_g, opt_c1]) as scaled_loss:
scaled_loss.backward()
opt_g.step()
opt_c1.step()
opt_g.zero_grad()
opt_c1.zero_grad()
lemniscate.update_weight(feat_t, index_t)
if step % conf.train.log_interval == 0:
print('Train [{}/{} ({:.2f}%)]\tLoss Source: {:.6f} '
'Loss NC: {:.6f} Loss ENS: {:.6f}\t'.format(
step, conf.train.min_step,
100 * float(step / conf.train.min_step),
loss_s.item(), loss_nc.item(), loss_ent.item()))
if step > 0 and step % conf.test.test_interval == 0:
test(step, dataset_test, filename, n_share, num_class, G, C1,
conf.train.thr)
G.train()
C1.train()
train()
# <FILESEP>
import os
import socket
import argparse
import threading
from colorama import init, Fore, Style
init(autoreset=True)
class ChatClient:
def __init__(self, host, port):
self.host = host
self.port = port
self.client_socket = None
self.username = None
self.message_lock = threading.Lock()
def connect(self):
try:
self.client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.client_socket.connect((self.host, self.port))
except ConnectionRefusedError as e:
if e.errno == 111:
print("Connection refused")
else:
print(f"An unknown error occurred {e}")
return False
return True
def get_username(self):
username_prompt = self.client_socket.recv(1024).decode('utf-8')
print(Fore.CYAN + username_prompt, end="")
username = input()
self.client_socket.send(username.encode('utf-8'))
response = self.client_socket.recv(1024).decode('utf-8')