def get_optimizer(old_params, model, optimizer = None): prev_lr, prev_wd, prev_do, prev_opt, flag = old_params if optimizer is None: if prev_do != 50: for i, layer in enumerate(model.features): if type(layer) == torch.nn.Dropout: model.features[i] = torch.nn.Dropout(prev_do / 100) for i, layer in enumerate(model.classifier): if type(layer) == torch.nn.Dropout: model.classifier[i] = torch.nn.Dropout(prev_do / 100) if prev_opt == 'Adam': optimizer = optim.Adam(model.parameters(), lr = prev_lr, weight_decay = prev_wd) else: optimizer = optim.SGD(model.parameters(), lr = prev_lr, weight_decay = prev_wd, momentum = 0.9, nesterov = True) else: lr, wd, do, opt, flag = get_params() if flag: return (lr, wd, do, opt, flag), model, optimizer if (lr != prev_lr) or (wd != prev_wd) or (do != prev_do) or (opt != prev_opt): if prev_do != do: for i, layer in enumerate(model.features): if type(layer) == torch.nn.Dropout: model.features[i] = torch.nn.Dropout(prev_do / 100) for i, layer in enumerate(model.classifier): if type(layer) == torch.nn.Dropout: model.classifier[i] = torch.nn.Dropout(prev_do / 100) prev_lr, prev_wd, prev_do, prev_opt = lr, wd, do, opt if prev_opt == 'Adam': optimizer = optim.Adam(model.parameters(), lr=prev_lr, weight_decay=prev_wd) else: optimizer = optim.SGD(model.parameters(), lr=prev_lr, weight_decay=prev_wd, momentum=0.9, nesterov=True) return (prev_lr, prev_wd, prev_do, prev_opt, flag), model, optimizer
def get_data(request):
filter = helper.get_params(request)
result = data.get(filter)
if not result:
json = {}
else:
json = helper.parse_data(result)
return json
def validate_response(response):
""" Looks for an intersection between response and used_data (list of all generated data in data.py) """
resp_values = helper.get_params(response).values()
matches_num = len(list(set(resp_values).intersection(used_data)))
print "\nused_data = ", used_data
print "\nresp_values = ", resp_values
if matches_num == 3:
print "\nValidation result - Values do match"
else:
msg = "Validation failed. Updated data doesn't correspond to the real data. " + "Matched - " + str(matches_num)
raise AssertionError(msg)
parser.add_argument('--dataset-path', type=str, default='data', help='path to dataset: image_net_10, corrosion_dataset')
parser.add_argument('--n-print', type=int, default=50, help='how often to print')
parser.add_argument('--n-epochs', type=int, default=1000, help='number of epochs')
parser.add_argument('--batch-size', type=int, default=32, help='batch size')
parser.add_argument('--transfer', type=str, default='False', help='transfer/full learning')
parser.add_argument('--use-gpu', type=str, default='True', help='gpu/cpu')
inputs = parser.parse_args()
print(inputs)
inputs.transfer = True if inputs.transfer == 'True' else False
USE_GPU = True if inputs.use_gpu == 'True' else False
dtype = torch.float32 # TODO: find out how it affects speed and accuracy
device = torch.device('cuda:0' if USE_GPU and torch.cuda.is_available() else 'cpu')
# run model
model, n_classes = init_model(inputs)
# waiting for the new input
while True:
params = get_params(start = True)
if params is not None: break
sleep(10)
# create data loader
data_train = MyDataset(root = '{}/{}'.format(os.getcwd(), inputs.dataset_path), train = True)
data_val = MyDataset(root = '{}/{}'.format(os.getcwd(), inputs.dataset_path), train = False)
data_loader = {
'train': DataLoader(data_train, batch_size = inputs.batch_size, shuffle = True, num_workers = 6),
'val': DataLoader(data_val, batch_size = inputs.batch_size, shuffle = True, num_workers = 6)
}
hist = train_my(data_loader, model, datetime.now(), epochs = inputs.n_epochs, params = params, device = device, n_print = inputs.n_print)
data = json.loads(page)
for video in data:
menu_link = video["Id"]
menu_name = video["Name"]
menu_img = video["ThumbnailURL"]
parameters = {"channel":thisChannel,"action":"playVideo","link":menu_link}
helper.addDirectoryItem(menu_name, parameters, menu_img, folder=False)
helper.endOfDirectory()
def playVideo(videoPlayer):
stream = brightcovePlayer.play(const, playerID, videoPlayer, publisherID, playerKey)
rtmpbase = stream[1][0:stream[1].find("&")]
playpath = stream[1][stream[1].find("&") + 1:]
finalurl = rtmpbase + ' playpath=' + playpath
helper.setResolvedUrl(finalurl)
params = helper.get_params()
if len(params) == 1:
mainPage()
else:
print params['action']
if params['action'] == "showSubMenu":
showSubMenu(params['link'])
if params['action'] == "showVideos":
showVideos(params['link'])
if params['action'] == "playVideo":
playVideo(params['link'])
playpath = " playpath=" + video.getAttribute("progurl")
streamUrl = streamUrl[:streamUrl.find(
"/", 8)] + swfUrl + app + pageUrl + flashVer + playpath
print streamUrl
helper.setResolvedUrl(streamUrl)
#return False
listItem = xbmcgui.ListItem(streamUrl, path=streamUrl)
#listItem.setProperty("PlayPath", streamUrl);
listItem.setProperty('IsPlayable', 'true')
playlist.add(url=streamUrl, listitem=listItem)
player = xbmc.Player()
player.play(playlist, playerItem)
return False
params = helper.get_params()
if len(params) == 1:
mainPage()
else:
if params['action'] == "subPageXml":
subPageXml(urllib.unquote(params['link']))
if params['action'] == "videoPage":
videoPage(params['link'])
if params['action'] == "videoPageXml":
videoPageXml(urllib.unquote(params['link']))
if params['action'] == "playVideo":
playVideo(urllib.unquote(params['link']))