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