def response_list_files(sock):
response = ('\n'.join([
fid(f) + ';' + f + ';' + str(getsize(f))
for f in listdir(DIR) if isfile(join(DIR, f))
]) or 'No files available at the moment.') + '\n'
sock.sendall(response.encode('ascii'))
def upload(sock, filename=None):
# Send command.
sent = sock.sendall(SERVER_COMMAND.UPLOAD.command.name.encode('ascii'))
if sent == 0:
raise RuntimeError('Socket connection broken')
# Handle ask for filename and filesize.
msg = 'filename_filesize'
if not recv_until(sock, len(msg), [msg]):
raise RuntimeError('Protocol violation!')
# Send file name and size.
if not filename:
filename = input('Filename: ')
filesize = getsize(filename)
sent = sock.sendall((filename + ';' + str(filesize)).encode('ascii'))
if sent == 0:
raise RuntimeError('Socket connection broken')
# Handle upload begin.
msg = 'ready to receive a file'
if not recv_until(sock, len(msg), [msg]):
raise RuntimeError('Protocol violation!')
# Upload
with open(filename, 'rb') as f:
print(f'-> Uploading file ({filename})')
sock.sendall(f.read())
# Verify upload.
file_id = recv_until(sock, 32) # md5 -> 128 bit = 32 hex.
if file_id == fid(filename):
print('SUCCESS! File is not currupted..')
else:
print('ERROR! File is corrupted..')
def download(sock, filename=None):
# Execute LIST_FILES command before to get filenames, sizes and ids.
command = SERVER_COMMAND.LIST_FILES
files = command.request(sock, False)
msg = 'No files available at the moment.\n'
if msg == files:
print(msg)
return
# NOTE: Parsing should work..string matched regex before.
files = list(map(lambda x: x.split(';'), files.split('\n')[:-1]))
# Execute download command.
sent = sock.sendall(SERVER_COMMAND.DOWNLOAD.command.name.encode('ascii'))
if sent == 0:
raise RuntimeError('Socket connection broken')
# Handle ask for file id.
msg = 'file_id'
if not recv_until(sock, len(msg), [msg]):
raise RuntimeError('Protocol violation!')
# Get file id and size.
file_id = None
file_size = None
if not filename:
filename = input('Filename: ')
files = list(filter(lambda x: filename == x[1], files))
if len(files) > 0:
file_id = files[0][0]
file_size = int(files[0][2])
else:
raise RuntimeError('File does not exist!')
# Send file id.
sent = sock.sendall(file_id.encode('ascii'))
if sent == 0:
raise RuntimeError('Socket connection broken')
# Handle file byte stream.
response = recv_until(sock, file_size, decoding=None)
if not response:
raise RuntimeError('Protocol violation!')
print('-> Receiveing ', end='')
print(response[:20], end='')
print('...')
if isfile(filename): # NOTE: not specified.
filename = filename + '.download'
with open(filename, 'wb') as f:
f.write(response)
if file_id == fid(filename):
print('SUCCESS! File is not currupted..')
else:
print('ERROR! File is corrupted..')
def response_download(sock):
# Ask for file id.
sock.sendall('file_id'.encode('ascii'))
file_id = recv_until(sock, 32, '^[a-zA-Z0-9]{32}$')
if not file_id:
raise RuntimeError('Protocol violation!')
for p in listdir(DIR):
path = join(DIR, p)
if isfile(path) and fid(path) == file_id:
with open(path, 'rb') as f:
print(f'-> Sending file ({path})')
sock.sendall(f.read())
return
raise RuntimeError('Invalid file id!')
def validation_epoch_end(
self, outputs: List[Tuple[torch.Tensor, torch.Tensor]]) -> None:
if isinstance(self.val_dataloader().dataset, ImageLoader):
self.val_dataloader().dataset.val = False
else:
self.val_dataloader().dataset.dataset.val = False
fid_score = fid(self.forged_images, self.reference_images,
self.hparams.feature_dimensionality_fid, self.device)
ssim_score = ssim(self.forged_images,
self.reference_images,
data_range=255)
psnr_score = psnr(self.forged_images,
self.reference_images,
data_range=255)
self.log('FID_score', fid_score, on_step=False, on_epoch=True)
self.log('SSIM', ssim_score, on_step=False, on_epoch=True)
self.log('PSNR', psnr_score, on_step=False, on_epoch=True)
def response_upload(sock):
# Send question for filename and filesize.
sent = sock.sendall('filename_filesize'.encode('ascii'))
if sent == 0:
raise RuntimeError('Socket connection broken')
# Handle ask for filename and filesize.
# TODO: handle path traversel attacks.
response = recv_until(sock, 300, '^[a-zA-Z0-9._]{3,255}[;][0-9]+$')
if not response:
raise RuntimeError('Protocol violation!')
filename, filesize = response.split(';')
filesize = int(filesize)
# Send ready to receive.
sent = sock.sendall('ready to receive a file'.encode('ascii'))
if sent == 0:
raise RuntimeError('Socket connection broken')
# Handle file byte stream.
response = recv_until(sock, filesize, decoding=None)
if not response:
raise RuntimeError('Protocol violation!')
print('-> Receiveing ', end='')
print(response[:20], end='')
print('...')
if isfile(filename): # NOTE: not specified.
filename = filename + '.upload'
# Write file.
with open(filename, 'wb') as f:
f.write(response)
# Send file id.
sent = sock.sendall(fid(filename).encode('ascii'))
if sent == 0:
raise RuntimeError('Socket connection broken')
reference_tensor = torch.cat(reference,
0).reshape(len(inputs), 1, inputs[0].shape[1],
inputs[0].shape[2])
inputs_tensor = torch.cat(inputs, 0).reshape(
(len(inputs), 1, inputs[0].shape[1], inputs[0].shape[2]))
forged_tensor = forged_tensor.expand(-1, 3, -1, -1).clone().to(device)
reference_tensor = reference_tensor.expand(-1, 3, -1,
-1).clone().to(device)
inputs_tensor = inputs_tensor.expand(-1, 3, -1, -1).clone().to(device)
forged_tensor *= 255
reference_tensor *= 255
inputs_tensor *= 255
fid_forged = fid(forged_tensor, reference_tensor, 192,
torch.device(device))
fid_input = fid(inputs_tensor, reference_tensor, 192, torch.device(device))
print(f"FID Score input: {fid_input}")
print(f"FID Score forged: {fid_forged}")
ssims_forged = {}
psnrs_forged = {}
ssims_input = {}
psnrs_input = {}
ssims_forged["value"] = []
psnrs_forged["value"] = []
ssims_input["value"] = []
psnrs_input["value"] = []
ssims_forged["dataset"] = []
psnrs_forged["dataset"] = []
device=device,
hook=get_avgpool,
)
fake_logits = F.softmax(fake_logits, dim=1)
fake_softmax.append(fake_logits)
fake_features.append(fake_feats)
fake_features = torch.cat(fake_features, dim=0)
fake_softmax = torch.cat(fake_softmax, dim=0)
# calculate inception score
inc_score = inception_score(fake_softmax)
# calculate fid
mu_fake = fake_features.mean(dim=0)
sigma_fake = get_covariance(fake_features)
fid_score = fid(mu_real, mu_fake, sigma_real, sigma_fake)
writer.add_scalar("scores/FID", fid_score.item(), global_step=global_step)
writer.add_scalar("scores/IS", inc_score.item(), global_step=global_step)
del inception
# save best checkpoint
if fid_score.item() < best_fid:
torch.save(gen.state_dict(), os.path.join(logdir, "gen_best.pth"))
best_fid = fid_score.item()
# if __name__ == "__main__":
#
# print(sha)