def parse_and_preprocess_src(data_source, corpus_destination, preprocess=True): if re.search("bundestag", data_source.lower()): name = "bundestag" raw_corpus = DataHandler.get_bundestag_speeches(directory=data_source) elif re.search("sustainability", data_source.lower()): name = "sustainability" raw_corpus = DataHandler.get_sustainability_data(path=data_source) elif re.search("unv1.0-tei", data_source.lower()): name = "united_nations" raw_corpus = DataHandler.get_un_texts(directory=data_source) elif re.search("state_of_the_union", data_source.lower()): name = "state_of_the_union" raw_corpus = DataHandler.get_state_of_the_union(directory=data_source) else: name = "abstracts" raw_corpus = DataHandler.get_abstracts(path=data_source) language = raw_corpus[0].language print('loaded', len(raw_corpus), 'documents') if preprocess: Preprocessor.preprocess(raw_corpus, language=language) print('preprocessed', len(raw_corpus), 'documents') corpus = Corpus(source=raw_corpus, language=language, name=name) print('parsed', len(corpus.get_documents(as_list=True)), 'documents to a Corpus') corpus.save_corpus(corpus_destination)
def load_portfolio(self) -> dict: """ Loads portfolio into the class checking first if the portfolio exists. :return: dict """ if DataHandler.check_portfolio_exists(): return DataHandler.load_portfolio() else: return dict({})
def __init__(self, cfg): self.cfg = cfg self.cat_features = self.cfg.data.features.cat_features self.oof = None self.raw_preds = None self.weights = [] self.models = [] self.scores = [] self.feature_importance_df = pd.DataFrame( columns=['feature', 'importance']) self.dh = DataHandler()
def __getitem__(self, index): if self.st == 'train' or self.st == 'FTrain': # index_sec = int(self.rng.rand()*self.len) # print(os.getpid(), index_sec) # same in diff gpu # label = np.zeros(self.person) # label_sec = np.zeros(self.person) # label[self.y[index]] = 1 # label_sec[self.y[index_sec]] = 1 label = self.y[index] image = np.array(cv2.imread(self.name[index]), dtype=float) # image_sec = np.array(cv2.imread(self.name[index_sec]), dtype=float).copy() # image, label = aug.run2(image, label, image_sec, label_sec) image = Aug.run(self.rng, image) image = torch.from_numpy(image).float() # label = torch.from_numpy(label).float() return image, label elif self.st == 'test': size = (MinS + MaxS) // 2 idx = (size - W) // 2 image = np.array(self.dataset[index], dtype=float).copy() image = cv2.resize(image, (size, size)) image = image[idx:idx + H, idx:idx + W, :] image = np.transpose(image, [2, 0, 1]) image_flip = image.copy()[:, :, ::-1] image = torch.from_numpy((image - 127.5) / 128).float() image_flip = torch.from_numpy((image_flip - 127.5) / 128).float() label = np.zeros(self.len) label[self.y[index]] = 1 label = torch.from_numpy(label).float() return image, image_flip, label, self.name[index] else: exit(-1)
def save_features(df, data_type='train'): save_path = Path('../configs/feature/all.yml') dh = DataHandler() if not save_path.exists(): save_path.touch() feature_dict = {'features': []} else: feature_dict = dh.load(save_path) new_feature = sorted(set(feature_dict['features'] + df.columns.tolist())) feature_dict['features'] = new_feature dh.save(save_path, feature_dict) for col in df.columns: df[[col]].reset_index(drop=True).to_feather(f'../features/{col}_{data_type}.feather')
def on_ok(self): if len(current_portoflio.portfolio) == 6: self.status_msg.value = "Max 6 stocks allowed. Please purchase premium version!" else: last_price = StockDataReader.last_price( StockDataReader.get_data(self.symbol.value)) validation = DataHandler.validate_entry(self.symbol.value, self.date.value, self.amount.value, self.price.value, last_price) if validation != True: self.status_msg.value = validation self.display() else: current_portoflio.add_stock(self.symbol.value, float(self.price.value), float(self.amount.value), self.date.value) self.status_msg.value = f"Grabbing data for {self.symbol.value}" to_main = self.parentApp.getForm('MAIN') to_main.load_portfolio() to_main.load_performance() to_main.top_message.value = "Portfolio:" self.display() sleep(1.5) self.parentApp.switchForm("MAIN")
def __init__(self, params): # Build data handler self.data_handler = DataHandler() self.data_handler.loadData(params['input']) params['inp_dim'] = self.data_handler.getDataShape()[1] logging.info("=" * 41) # Build model self.model = SPINEModel(params) self.dtype = torch.FloatTensor use_cuda = torch.cuda.is_available() if use_cuda: self.model.cuda() self.dtype = torch.cuda.FloatTensor self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.1) logging.info("=" * 41)
def create(self): # ====================== Init portfolio ======================== self.welcome_msg = self.add(npyscreen.FixedText, value=welcome, rely=2) self.welcome_msg2 = self.add(npyscreen.FixedText, value=welcome_line2, rely=3) self.top_message = self.add(npyscreen.FixedText, value="", relx=8, rely=4) # ====================== End init portfolio ==================== # ====================== Menu section ========================== self.menu = self.new_menu(name="Main Menu", shortcut="m") self.menu.addItem("1. Add stock to portfolio", self.change_form_add, "1") self.menu.addItem("2. Update Portfolio", self.change_form_update, "2") self.menu.addItem("3. Save performance to PDF", self.change_form_save_pdf, "3") self.menu.addItem("4. Delete Stock form portfolio", self.change_form_delete, "4") # ====================== End menu section ====================== self.add(npyscreen.FixedText, value="Performance:", relx=8, rely=26) self.main_ii = self.add( npyscreen.FixedText, value=f"Initial Investment: ${self.initial_investment}", relx=8, rely=27) self.main_rd = self.add( npyscreen.FixedText, value=f"Returned Amount: ${self.return_dollar}", relx=8, rely=28) self.main_pr = self.add(npyscreen.FixedText, value=f"Returned Percent: {self.pct_return}%", relx=8, rely=29) self.exit_button = self.add(npyscreen.ButtonPress, name="Exit", rely=29, relx=106, when_pressed_function=self.exit_press) if not DataHandler.check_portfolio_exists(): self.top_message.value = no_portfolio_msg else: self.top_message.value = "Portfolio:" self.load_portfolio() self.load_performance()
def __init__(self, params): # Build data handler self.data_handler = DataHandler() self.data_handler.loadData(params['input']) params['inp_dim'] = self.data_handler.getDataShape()[1] logging.info("=" * 41) # Build model self.model = SPINEModel(params) self.dtype = torch.FloatTensor use_cuda = torch.cuda.is_available() if use_cuda: self.model.cuda() self.dtype = torch.cuda.FloatTensor # Select optimizer optim_selected = params['optim'] LR = 0.1 if optim_selected == 'sgd': self.optimizer = torch.optim.SGD(self.model.parameters(), lr=LR) elif optim_selected == 'adam': self.optimizer = torch.optim.Adam(self.model.parameters(), lr=LR) logging.info("=" * 41)
def __init__(self, params): # build data handler self.data_handler = DataHandler() self.data_handler.loadData(params['input']) params['inp_dim'] = self.data_handler.getDataShape()[1] print("=" * 41) # build model self.model = SPINEModel(params) self.dtype = torch.FloatTensor # check if GPU is available use_cuda = torch.cuda.is_available() if use_cuda: self.model.cuda() self.dtype = torch.cuda.FloatTensor # set optimizer self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.1) print("=" * 41)
def __init__(self, net, state, loop_sleep, pause_after_keys, heartbeat_required): CodependentThread.__init__(self, heartbeat_required) self.daemon = True self.net = net self.input_dims = self.net.blobs['data'].data.shape[2:4] # e.g. (227,227) self.state = state self.frames_processed_fwd = 0 self.frames_processed_back = 0 self.loop_sleep = loop_sleep self.pause_after_keys = pause_after_keys self.debug_level = 0 self.descriptor = None self.descriptor_layer_1 = 'conv5' self.descriptor_layer_2 = 'conv4' self.descriptor_layers = ['conv5','conv4'] self.net_input_image = None self.descriptor_handler = DescriptorHandler(self.state.settings.ros_dir + '/models/memory/', self.descriptor_layers) self.data_handler = DataHandler(self.state.settings.ros_dir) self.available_layer = ['conv1', 'pool1', 'norm1', 'conv2', 'pool2', 'norm2', 'conv3', 'conv4', 'conv5', 'pool5', 'fc6', 'fc7', 'fc8', 'prob'] #['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8', 'prob'] # print "layers ", list(self.net._layer_names) s = rospy.Service('get_cnn_state', GetState, self.handle_get_cnn_state)
def main(): images_dir = 'images' output_dir = 'output' debug = True for image_idx, (reference_image, inspection_image) in enumerate( DataHandler(images_dir).get()): detector = DefectDetector(reference_image, inspection_image, image_idx, debug=debug, output_dir=output_dir) output_mask = detector.run() plt.imsave(os.path.join(output_dir, 'output-image-idx-{}.png'.format(image_idx)), output_mask, cmap=cm.gray)
import sys import pandas as pd from easydict import EasyDict as edict sys.path.append('../src') from utils import DataHandler from feature_utils import save_features dh = DataHandler() def get_features(df): features_df = pd.DataFrame() features_df['one_hot_sex'] = df['sex'].map({'male': 0, 'female': 1}) age_df = pd.get_dummies(df['age_approx']) for col in age_df.columns: features_df[f'one_hot_age_{col}'] = age_df[col] site_df = pd.get_dummies(df['anatom_site_general_challenge']) for col in site_df.columns: features_df[f'one_hot_site_{col.replace("/", "-")}'] = site_df[col] return features_df def main(): train_df = dh.load('../data/input/train_concated.csv') test_df = dh.load('../data/input/test.csv')
from utils import (DataHandler, Notificator, Timer, seed_everything, Git, Kaggle, reduce_mem_usage) warnings.filterwarnings('ignore') # =============== # Settings # =============== parser = argparse.ArgumentParser() parser.add_argument('--common', default='../configs/common/compe.yml') parser.add_argument('--notify', default='../configs/common/notify.yml') parser.add_argument('-m', '--model') parser.add_argument('-c', '--comment') options = parser.parse_args() dh = DataHandler() cfg = dh.load(options.common) cfg.update(dh.load(f'../configs/exp/{options.model}.yml')) notify_params = dh.load(options.notify) features_params = dh.load(f'../configs/feature/{cfg.data.features.name}.yml') features = features_params.features comment = options.comment model_name = options.model now = datetime.datetime.now() if cfg.model.task_type != 'optuna': run_name = f'{model_name}_{now:%Y%m%d%H%M%S}' else: run_name = f'{model_name}_optuna_{now:%Y%m%d%H%M%S}'
def build_data_set(self, root_dir: str): entries = self.parse_folder(root_dir) data_handler = DataHandler() icd10_ontology = data_handler.read_icd10_ontology() group_codes = set(data_handler.read_reduced_icd10_ontology().keys()) de_entries = dict() en_entries = dict() for entry in entries: if "de_" + entry[0] in de_entries: continue icd10_codes = entry[3] valid_group_codes = set() main_chapter = "" for code in icd10_codes: if code in icd10_chapter_mappings: code = icd10_chapter_mappings[code] if not code in icd10_ontology and "." in code: code = code[:code.index(".")] if not code in icd10_ontology: self.logger.error( f"Can't find code {code} in ICD10 ontology") continue path_components = icd10_ontology[code].split("#") if main_chapter == "": main_chapter = path_components[0] for path_comp in path_components: if path_comp in group_codes: valid_group_codes.add(path_comp) valid_group_codes = "|".join(valid_group_codes) de_entries["de_" + entry[0]] = { "text": entry[1], "language": "de", "all_labels": valid_group_codes, "main_chapter": main_chapter } en_entries["en_" + entry[0]] = { "text": entry[2], "language": "en", "all_labels": valid_group_codes, "main_chapter": main_chapter } de_df = DataFrame.from_dict(de_entries, orient="index") en_df = DataFrame.from_dict(en_entries, orient="index") de_train, de_dev = train_test_split(de_df, train_size=0.8, stratify=de_df["main_chapter"]) de_train["data_set"] = "train" de_dev["data_set"] = "dev" de_df = de_train.append(de_dev) en_train, en_dev = train_test_split(en_df, train_size=0.8, stratify=en_df["main_chapter"]) en_train["data_set"] = "train" en_dev["data_set"] = "dev" en_df = en_train.append(en_dev) full_df = de_df.append(en_df) full_df = full_df.drop_duplicates() full_df = full_df[full_df["text"].notna()] #de_df.to_csv("drks_de.tsv", sep="\t", columns=["data_set", "main_chapter", "all_labels", "text"], index_label="id") #en_df.to_csv("drks_en.tsv", sep="\t", columns=["data_set", "main_chapter", "all_labels", "text"], index_label="id") output_dir = "data/drks/prepared" os.makedirs(output_dir, exist_ok=True) output_file = os.path.join(output_dir, "drks_full.tsv") full_df.to_csv(output_file, sep="\t", columns=[ "language", "data_set", "main_chapter", "all_labels", "text" ], index_label="id")
class Solver: def __init__(self, params): # build data handler self.data_handler = DataHandler() self.data_handler.loadData(params['input']) params['inp_dim'] = self.data_handler.getDataShape()[1] print("=" * 41) # build model self.model = SPINEModel(params) self.dtype = torch.FloatTensor # check if GPU is available use_cuda = torch.cuda.is_available() if use_cuda: self.model.cuda() self.dtype = torch.cuda.FloatTensor # set optimizer self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.1) print("=" * 41) def train(self, params): num_epochs, batch_size = params['num_epochs'], params['batch_size'], optimizer = self.optimizer dtype = self.dtype # train for each epoch for iteration in range(num_epochs): self.data_handler.shuffleTrain() num_batches = self.data_handler.getNumberOfBatches(batch_size) epoch_losses = np.zeros(4) # rl, asl, psl, total # for each batch for batch_idx in range(num_batches): optimizer.zero_grad() batch_x, batch_y = self.data_handler.getBatch( batch_idx, batch_size, params['noise_level'], params['denoising']) # transform batches into tensors batch_x = Variable(torch.from_numpy(batch_x), requires_grad=False).type(dtype) batch_y = Variable(torch.from_numpy(batch_y), requires_grad=False).type(dtype) # calculate losses out, h, loss, loss_terms = self.model(batch_x, batch_y) reconstruction_loss, psl_loss, asl_loss = loss_terms # update loss and optimizer loss.backward() optimizer.step() # assign loss epoch_losses[0] += reconstruction_loss.data epoch_losses[1] += asl_loss.data epoch_losses[2] += psl_loss.data epoch_losses[3] += loss.data print("epoch %r: Reconstruction Loss = %.4f, ASL = %.4f, "\ "PSL = %.4f, and total = %.4f" %(iteration+1, epoch_losses[0], epoch_losses[1], epoch_losses[2], epoch_losses[3]) ) def getSpineEmbeddings(self, batch_size, params): ret = [] self.data_handler.resetDataOrder() num_batches = self.data_handler.getNumberOfBatches(batch_size) # for each batch for batch_idx in range(num_batches): batch_x, batch_y = self.data_handler.getBatch( batch_idx, batch_size, params['noise_level'], params['denoising']) # transform batches into tensors batch_x = Variable(torch.from_numpy(batch_x), requires_grad=False).type(self.dtype) batch_y = Variable(torch.from_numpy(batch_y), requires_grad=False).type(self.dtype) _, h, _, _ = self.model(batch_x, batch_y) # append to embeddings ret.extend(h.cpu().data.numpy()) return np.array(ret) def getWordsList(self): return self.data_handler.getWordsList()
class Solver: def __init__(self, params): # Build data handler self.data_handler = DataHandler() self.data_handler.loadData(params['input']) params['inp_dim'] = self.data_handler.getDataShape()[1] logging.info("=" * 41) # Build model self.model = SPINEModel(params) self.dtype = torch.FloatTensor use_cuda = torch.cuda.is_available() if use_cuda: self.model.cuda() self.dtype = torch.cuda.FloatTensor # Select optimizer optim_selected = params['optim'] LR = 0.1 if optim_selected == 'sgd': self.optimizer = torch.optim.SGD(self.model.parameters(), lr=LR) elif optim_selected == 'adam': self.optimizer = torch.optim.Adam(self.model.parameters(), lr=LR) logging.info("=" * 41) def train(self, params): num_epochs, batch_size = params['num_epochs'], params['batch_size'], optimizer = self.optimizer dtype = self.dtype STEP_DENOM = 5 scheduler = StepLR(optimizer, step_size=math.ceil(num_epochs / STEP_DENOM), gamma=0.3) for iteration in range(num_epochs): # lr adjusting scheduler.step() # start epoch self.data_handler.shuffleTrain() num_batches = self.data_handler.getNumberOfBatches(batch_size) epoch_losses = np.zeros(4) # rl, asl, psl, total for batch_idx in range(num_batches): optimizer.zero_grad() batch_x, batch_y = self.data_handler.getBatch( batch_idx, batch_size, params['noise_level'], params['denoising']) batch_x = Variable(torch.from_numpy(batch_x), requires_grad=False).type(dtype) batch_y = Variable(torch.from_numpy(batch_y), requires_grad=False).type(dtype) out, h, loss, loss_terms = self.model(batch_x, batch_y) reconstruction_loss, psl_loss, asl_loss = loss_terms loss.backward() epoch_losses[0] += reconstruction_loss.data.item() epoch_losses[1] += asl_loss.data.item() epoch_losses[2] += psl_loss.data.item() epoch_losses[3] += loss.data.item() optimizer.step() print("After epoch %r, Reconstruction Loss = %.4f, ASL = %.4f, " "PSL = %.4f, and total = %.4f" % (iteration + 1, epoch_losses[0], epoch_losses[1], epoch_losses[2], epoch_losses[3])) # logging.info("After epoch %r, Sparsity = %.1f" # %(iteration+1, utils.compute_sparsity(h.cpu().data.numpy()))) # break # break def getSpineEmbeddings(self, batch_size, params): ret = [] self.data_handler.resetDataOrder() num_batches = self.data_handler.getNumberOfBatches(batch_size) for batch_idx in range(num_batches): batch_x, batch_y = self.data_handler.getBatch( batch_idx, batch_size, params['noise_level'], params['denoising']) batch_x = Variable(torch.from_numpy(batch_x), requires_grad=False).type(self.dtype) batch_y = Variable(torch.from_numpy(batch_y), requires_grad=False).type(self.dtype) _, h, _, _ = self.model(batch_x, batch_y) ret.extend(h.cpu().data.numpy()) return np.array(ret) def getWordsList(self): return self.data_handler.getWordsList()
def main(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument( "--data_set", choices=DataHandler.ALL_DATA_SET_IDS, required=True, help= "The input data dir. Should contain the .tsv files (or other data files) for the task." ) parser.add_argument( "--bert_model", default=None, type=str, required=True, help= "Bert pre-trained model selected in the list: bert-base-multilingual-uncased, " "bert-base-multilingual-cased") parser.add_argument( "--output_dir", default=None, type=str, required=True, help= "The output directory where the model predictions and checkpoints will be written." ) parser.add_argument( "--additional_data_set", choices=DataHandler.ALL_DATA_SET_IDS, required=False, help="Additional data set to extend the basic training data set. " "Only training data will be used! No additional evaluation data!") ## Other parameters parser.add_argument( "--cache_dir", default="_cache", type=str, help= "Where do you want to store the pre-trained models downloaded from s3") parser.add_argument( "--max_seq_length", default=300, type=int, help= "The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--do_train", action='store_true', help="Whether to run training.") parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.") parser.add_argument("--do_test", action='store_true', help="Whether to run eval on the test set.") parser.add_argument( "--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.") parser.add_argument("--train_batch_size", default=32, type=int, help="Total batch size for training.") parser.add_argument("--eval_batch_size", default=8, type=int, help="Total batch size for eval.") parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.") parser.add_argument( "--warmup_proportion", default=0.1, type=float, help= "Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10%% of training.") parser.add_argument("--no_cuda", action='store_true', help="Whether not to use CUDA when available") parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus") parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument( '--gradient_accumulation_steps', type=int, default=1, help= "Number of updates steps to accumulate before performing a backward/update pass." ) parser.add_argument( '--fp16', action='store_true', help="Whether to use 16-bit float precision instead of 32-bit") parser.add_argument( '--loss_scale', type=float, default=0, help= "Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n" "0 (default value): dynamic loss scaling.\n" "Positive power of 2: static loss scaling value.\n") parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.") parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.") args = parser.parse_args() if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") n_gpu = torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) n_gpu = 1 # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.distributed.init_process_group(backend='nccl') logger.info( "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}". format(device, n_gpu, bool(args.local_rank != -1), args.fp16)) if args.gradient_accumulation_steps < 1: raise ValueError( "Invalid gradient_accumulation_steps parameter: {}, should be >= 1" .format(args.gradient_accumulation_steps)) args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if n_gpu > 0: torch.cuda.manual_seed_all(args.seed) if not args.do_train and not args.do_eval: raise ValueError( "At least one of `do_train` or `do_eval` must be True.") if os.path.exists(args.output_dir) and os.listdir( args.output_dir) and args.do_train: raise ValueError( "Output directory ({}) already exists and is not empty.".format( args.output_dir)) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) data_handler = DataHandler() train_data, dev_data = data_handler.get_data_set_by_id(args.data_set) if args.additional_data_set is not None: logger.info( f"Extending training data with instances from {args.additional_data_set}" ) add_train_data, add_dev_data = data_handler.get_data_set_by_id( args.additional_data_set) train_data = train_data.append(add_train_data) train_data = train_data.append(add_dev_data) logger.info( f"Data set contains {len(train_data)} training and {len(dev_data)} development instances" ) test_data = None if args.do_test: test_data = data_handler.get_test_data() processor = DataProcessor(train_data, dev_data, test_data) label_list = processor.get_labels() logger.info(f"Labels: {str(label_list)}") label_encoder = LabelEncoder() label_encoder.fit(label_list) num_labels = len(label_encoder.classes_) logger.info(f"Num labels: {num_labels}") train_examples = None num_train_optimization_steps = None if args.do_train: train_examples = processor.get_train_instances() num_train_optimization_steps = int( len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs if args.local_rank != -1: num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size( ) # Prepare model cache_dir = args.cache_dir if args.cache_dir else os.path.join( PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format( args.local_rank)) model = BertForMultiLabelSequenceClassification.from_pretrained( args.bert_model, cache_dir=cache_dir, num_labels=num_labels) if args.fp16: model.half() model.to(device) if args.local_rank != -1: try: from apex.parallel import DistributedDataParallel as DDP except ImportError: raise ImportError \ ("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") model = DDP(model) elif n_gpu > 1: model = torch.nn.DataParallel(model) # Prepare optimizer param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [{ 'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01 }, { 'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0 }] if args.fp16: try: from apex.optimizers import FP16_Optimizer from apex.optimizers import FusedAdam except ImportError: raise ImportError \ ("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") optimizer = FusedAdam(optimizer_grouped_parameters, lr=args.learning_rate, bias_correction=False, max_grad_norm=1.0) if args.loss_scale == 0: optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) else: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale) else: optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, t_total=num_train_optimization_steps) global_step = 0 nb_tr_steps = 0 global_batch_no = 0 tr_loss = 0 if args.do_train: train_features = convert_examples_to_features(train_examples, label_encoder, args.max_seq_length, tokenizer) logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_examples)) logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num steps = %d", num_train_optimization_steps) all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in train_features], dtype=torch.float) train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) if args.local_rank == -1: train_sampler = RandomSampler(train_data) else: train_sampler = DistributedSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) max_f1 = 0.0 for epoch in trange(int(args.num_train_epochs), desc="Epoch"): model.train() tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate( tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, input_mask, segment_ids, label_ids = batch loss = model(input_ids, segment_ids, input_mask, label_ids) if n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu. if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: optimizer.backward(loss) else: loss.backward() loss_value = loss.item() tr_loss += loss_value nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 global_batch_no += 1 if (step + 1) % args.gradient_accumulation_steps == 0: if args.fp16: # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically lr_this_step = args.learning_rate * warmup_linear( global_step / num_train_optimization_steps, args.warmup_proportion) for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step optimizer.step() optimizer.zero_grad() global_step += 1 if (epoch + 1) % 2 == 0: eval_examples = processor.get_dev_instances() eval_features = convert_examples_to_features( eval_examples, label_encoder, args.max_seq_length, tokenizer) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d", len(eval_examples)) logger.info(" Batch size = %d", args.eval_batch_size) all_input_ids = torch.tensor( [f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor( [f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor( [f.segment_ids for f in eval_features], dtype=torch.long) all_label_ids = torch.tensor( [f.label_ids for f in eval_features], dtype=torch.float) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 y_dev = None y_dev_pred = None y_dev_sigmoid = None for input_ids, input_mask, segment_ids, label_ids in tqdm( eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) logits = model(input_ids, segment_ids, input_mask) # logits = logits.detach().cpu().numpy() # prediction = np.argmax(logits, axis=1) logits_cpu = logits.detach().cpu() logits_sigmoid = logits_cpu.sigmoid() if y_dev_pred is None: y_dev_sigmoid = logits_sigmoid else: y_dev_sigmoid = np.concatenate( (y_dev_sigmoid, logits_sigmoid), axis=0) pred_logits = logits.detach().cpu().numpy() if y_dev_pred is None: y_dev_pred = pred_logits else: y_dev_pred = np.concatenate((y_dev_pred, pred_logits), axis=0) if y_dev is None: y_dev = label_ids.detach().cpu().numpy() else: y_dev = np.concatenate( (y_dev, label_ids.detach().cpu().numpy()), axis=0) tmp_eval_accuracy = accuracy_thresh(logits, label_ids) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples loss = tr_loss / nb_tr_steps if args.do_train else None i = 0 gold_labels = dict() pred_labels = dict() prediction_output = dict() for example, true_logits, pred_logits in zip( eval_examples, y_dev, y_dev_sigmoid): true_indexes = np.argwhere(true_logits > 0.0) labels = [ label_encoder.inverse_transform(y)[0] for y in true_indexes ] pred_indexes = np.argwhere(pred_logits > 0.5) pred = [ label_encoder.inverse_transform(y)[0] for y in pred_indexes ] gold_labels[str(example.guid)] = labels pred_labels[str(example.guid)] = pred class_logits = { label_encoder.inverse_transform([j])[0]: float(pred_logits[j]) for j in range(len(label_encoder.classes_)) } prediction_output[example.guid] = class_logits i += 1 pred_output_file = os.path.join( args.output_dir, f"prediction_output_{epoch+1}.json") json.dump(prediction_output, open(pred_output_file, 'w'), sort_keys=True, indent=2) result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': loss, } eval_util = EvaluationUtil() pred_file = os.path.join(args.output_dir, f"dev_pred_{epoch+1}.txt") eval_util.save_predictions(pred_labels, pred_file) clef19_result = eval_util.evaluate(pred_labels, gold_labels) f1_score = clef19_result["eval_fscore"] if f1_score > max_f1: # Save a trained model and the associated configuration model_to_save = model.module if hasattr( model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) torch.save(model_to_save.state_dict(), output_model_file) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) with open(output_config_file, 'w') as f: f.write(model_to_save.config.to_json_string()) max_f1 = f1_score icd10_ontology = data_handler.read_icd10_ontology() pred_labels_extended = eval_util.extend_paths( pred_labels, icd10_ontology) pred_extended_file = os.path.join( args.output_dir, f"dev_pred_extended_{epoch+1}.txt") eval_util.save_predictions(pred_labels_extended, pred_extended_file) extended_clef19_result = eval_util.evaluate( pred_labels_extended, gold_labels) output_eval_file = os.path.join(args.output_dir, f"eval_results_{epoch+1}.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) clef10_result_str = eval_util.format_result(clef19_result) logger.info(f"CLEF19 evaluation: {clef10_result_str}") writer.write("\nResults prediction:\n") writer.write(clef10_result_str) extended_clef10_result_str = eval_util.format_result( extended_clef19_result) logger.info( f"CLEF19 evaluation (extended): {extended_clef10_result_str}" ) writer.write("\n\nResults extended prediction:\n") writer.write(extended_clef10_result_str) #if not args.do_train: # (Re-) Load best model output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME) output_config_file = os.path.join(args.output_dir, CONFIG_NAME) config = BertConfig(output_config_file) model = BertForMultiLabelSequenceClassification(config, num_labels=num_labels) model.load_state_dict(torch.load(output_model_file)) model.to(device) if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): eval_examples = processor.get_dev_instances() eval_features = convert_examples_to_features(eval_examples, label_encoder, args.max_seq_length, tokenizer) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d", len(eval_examples)) logger.info(" Batch size = %d", args.eval_batch_size) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in eval_features], dtype=torch.float) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 y_dev = None y_dev_pred = None y_dev_sigmoid = None for input_ids, input_mask, segment_ids, label_ids in tqdm( eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) logits = model(input_ids, segment_ids, input_mask) #logits = logits.detach().cpu().numpy() #prediction = np.argmax(logits, axis=1) logits_cpu = logits.detach().cpu() logits_sigmoid = logits_cpu.sigmoid() if y_dev_sigmoid is None: y_dev_sigmoid = logits_sigmoid else: y_dev_sigmoid = np.concatenate((y_dev_sigmoid, logits_sigmoid), axis=0) pred_logits = logits.detach().cpu().numpy() if y_dev_pred is None: y_dev_pred = pred_logits else: y_dev_pred = np.concatenate((y_dev_pred, pred_logits), axis=0) if y_dev is None: y_dev = label_ids.detach().cpu().numpy() else: y_dev = np.concatenate( (y_dev, label_ids.detach().cpu().numpy()), axis=0) tmp_eval_accuracy = accuracy_thresh(logits, label_ids) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples loss = tr_loss / nb_tr_steps if args.do_train else None i = 0 gold_labels = dict() pred_labels = dict() prediction_output = dict() for example, true_logits, pred_logits in zip(eval_examples, y_dev, y_dev_sigmoid): true_indexes = np.argwhere(true_logits > 0.0) labels = [ label_encoder.inverse_transform(y)[0] for y in true_indexes ] pred_indexes = np.argwhere(pred_logits > 0.5) pred = [ label_encoder.inverse_transform(y)[0] for y in pred_indexes ] if i < 2: logger.info(f"Example: {example.guid}") logger.info(f"Example labels: {example.labels}") logger.info(f"True labels: {labels}") logger.info(f"Pred labels: {pred}") gold_labels[str(example.guid)] = labels pred_labels[str(example.guid)] = pred class_logits = { label_encoder.inverse_transform([j])[0]: float(pred_logits[j]) for j in range(len(label_encoder.classes_)) } prediction_output[example.guid] = class_logits i += 1 pred_output_file = os.path.join(args.output_dir, "prediction_output.json") json.dump(prediction_output, open(pred_output_file, 'w'), sort_keys=True, indent=2) result = { 'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': loss, } eval_util = EvaluationUtil() pred_file = os.path.join(args.output_dir, "dev_pred.txt") eval_util.save_predictions(pred_labels, pred_file) clef19_result = eval_util.evaluate(pred_labels, gold_labels) icd10_ontology = data_handler.read_icd10_ontology() pred_labels_extended = eval_util.extend_paths(pred_labels, icd10_ontology) pred_extended_file = os.path.join(args.output_dir, "dev_pred_extended.txt") eval_util.save_predictions(pred_labels_extended, pred_extended_file) extended_clef19_result = eval_util.evaluate(pred_labels_extended, gold_labels) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) clef10_result_str = eval_util.format_result(clef19_result) logger.info(f"CLEF19 evaluation: {clef10_result_str}") writer.write("\nResults prediction:\n") writer.write(clef10_result_str) extended_clef10_result_str = eval_util.format_result( extended_clef19_result) logger.info( f"CLEF19 evaluation (extended): {extended_clef10_result_str}") writer.write("\n\nResults extended prediction:\n") writer.write(extended_clef10_result_str) if args.do_test: test_examples = processor.get_test_instances() test_features = convert_examples_to_features(test_examples, label_encoder, args.max_seq_length, tokenizer) logger.info("***** Running test *****") logger.info(" Num examples = %d", len(test_examples)) logger.info(" Batch size = %d", args.eval_batch_size) all_input_ids = torch.tensor([f.input_ids for f in test_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in test_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in test_features], dtype=torch.long) test_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids) # Run prediction for full data test_sampler = SequentialSampler(test_data) test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=args.eval_batch_size) model.eval() y_dev_sigmoid = None for input_ids, input_mask, segment_ids in tqdm(test_dataloader, desc="Testing"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) with torch.no_grad(): logits = model(input_ids, segment_ids, input_mask) logits_sigmoid = logits.detach().cpu().sigmoid() if y_dev_sigmoid is None: y_dev_sigmoid = logits_sigmoid else: y_dev_sigmoid = np.concatenate((y_dev_sigmoid, logits_sigmoid), axis=0) i = 0 test_labels = dict() test_output = dict() for example, pred_logits in zip(test_examples, y_dev_sigmoid): pred_indexes = np.argwhere(pred_logits > 0.5) pred = [ label_encoder.inverse_transform(y)[0] for y in pred_indexes ] if i < 2: logger.info(f"Example: {example.guid}") logger.info(f"Pred labels: {pred}") test_labels[str(example.guid)] = pred class_logits = { label_encoder.inverse_transform([j])[0]: float(pred_logits[j]) for j in range(len(label_encoder.classes_)) } test_output[example.guid] = class_logits i += 1 pred_output_file = os.path.join(args.output_dir, "test_output.json") json.dump(test_output, open(pred_output_file, 'w'), sort_keys=True, indent=2) eval_util = EvaluationUtil() test_file = os.path.join(args.output_dir, "test_pred.txt") eval_util.save_predictions(test_labels, test_file) icd10_ontology = data_handler.read_icd10_ontology() test_labels_extended = eval_util.extend_paths(test_labels, icd10_ontology) test_extended_file = os.path.join(args.output_dir, "test_pred_extended.txt") eval_util.save_predictions(test_labels_extended, test_extended_file)
class CaffeProcThread(CodependentThread): '''Runs Caffe in separate thread.''' def __init__(self, net, state, loop_sleep, pause_after_keys, heartbeat_required): CodependentThread.__init__(self, heartbeat_required) self.daemon = True self.net = net self.input_dims = self.net.blobs['data'].data.shape[2:4] # e.g. (227,227) self.state = state self.frames_processed_fwd = 0 self.frames_processed_back = 0 self.loop_sleep = loop_sleep self.pause_after_keys = pause_after_keys self.debug_level = 0 self.descriptor = None self.descriptor_layer_1 = 'conv5' self.descriptor_layer_2 = 'conv4' self.descriptor_layers = ['conv5','conv4'] self.net_input_image = None self.descriptor_handler = DescriptorHandler(self.state.settings.ros_dir + '/models/memory/', self.descriptor_layers) self.data_handler = DataHandler(self.state.settings.ros_dir) self.available_layer = ['conv1', 'pool1', 'norm1', 'conv2', 'pool2', 'norm2', 'conv3', 'conv4', 'conv5', 'pool5', 'fc6', 'fc7', 'fc8', 'prob'] #['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8', 'prob'] # print "layers ", list(self.net._layer_names) s = rospy.Service('get_cnn_state', GetState, self.handle_get_cnn_state) def handle_get_cnn_state(self, req): resp = GetStateResponse() print "get req", req state = perception_msgs.msg.State() state.type = req.type state.name = req.name if req.name == "None": state.name = self.data_handler.save_net(self.net) else: print "not implemented yet" resp.state = state resp.pose = Pose() print "state", resp return resp def mask_out(self, data, mask): # print "data shape", data.shape dim = data.shape for y in range(dim[2]): for x in range(dim[3]): if is_masked((dim[2],dim[3]),(x,y),mask): data[:,:,y,x] = 0 return data def net_preproc_forward(self, img): assert img.shape == (227,227,3), 'img is wrong size' #resized = caffe.io.resize_image(img, net.image_dims) # e.g. (227, 227, 3) data_blob = self.net.transformer.preprocess('data', img) # e.g. (3, 227, 227), mean subtracted and scaled to [0,255] data_blob = data_blob[np.newaxis,:,:,:] # e.g. (1, 3, 227, 227) output = self.net.forward(data=data_blob) return output def net_proc_forward_layer(self, img, mask): assert img.shape == (227,227,3), 'img is wrong size' #resized = caffe.io.resize_image(img, net.image_dims) # e.g. (227, 227, 3) data_blob = self.net.transformer.preprocess('data', img) # e.g. (3, 227, 227), mean subtracted and scaled to [0,255] data_blob = data_blob[np.newaxis,:,:,:] # e.g. (1, 3, 227, 227) # print "mask", mask.shape self.net.blobs['data'].data[...] = data_blob proc_layers = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'prob'] mask_layers = [''] mode = 0 if mode == 0: self.net.forward_from_to(start='conv1',end='relu1') self.mask_out(self.net.blobs['conv1'].data, mask) self.net.forward_from_to(start='relu1',end='prob') elif mode == 1: self.net.forward_from_to(start='conv1',end='relu1') self.mask_out(self.net.blobs['conv1'].data, mask) self.net.forward_from_to(start='relu1',end='conv2') self.net.forward_from_to(start='conv2',end='relu2') self.mask_out(self.net.blobs['conv2'].data, mask) self.net.forward_from_to(start='relu2',end='conv3') self.net.forward_from_to(start='conv3',end='relu3') self.mask_out(self.net.blobs['conv3'].data, mask) self.net.forward_from_to(start='relu3',end='conv4') self.net.forward_from_to(start='conv4',end='relu4') self.mask_out(self.net.blobs['conv4'].data, mask) self.net.forward_from_to(start='relu4',end='conv5') self.net.forward_from_to(start='conv5',end='relu5') self.mask_out(self.net.blobs['conv5'].data, mask) self.net.forward_from_to(start='relu5',end='prob') elif mode == 2: for idx in range(0,len(proc_layers)-1): output = self.net.forward_from_to(start=proc_layers[idx],end=proc_layers[idx+1]) self.mask_out(self.net.blobs[proc_layers[idx]].data, mask) # for idx in range(len(self.available_layer)-1): # output = self.net.forward(data=data_blob,start=self.available_layer[idx],end=self.available_layer[idx+1]) # if self.available_layer[idx].startswith("conv"): # new_blob = self.net.blobs[self.available_layer[idx]].data # new_blob.data = self.mask_out(self.net.blobs[self.available_layer[idx]].data, mask) # self.net.blobs[self.available_layer[idx]] = new_blob # print output # return output def save_descriptor(self): print 'save descriptor' # print type(self.net.blobs[self.descriptor_layer_1]) # descriptor_1 = np.array(caffe.io.blobproto_to_array(self.net.blobs[self.descriptor_layer_1])) # descriptor_2 = np.array(caffe.io.blobproto_to_array(self.net.blobs[self.descriptor_layer_2])) # print descriptor self.time_name = strftime("%d-%m-%Y-%H:%M:%S", gmtime()) desc = self.descriptor_handler.gen_descriptor(self.time_name, self.net.blobs) self.descriptor_handler.save(self.state.settings.ros_dir + '/models/memory/', desc) # np.save(self.state.settings.ros_dir + '/models/memory/' + self.time_name + "_" + self.descriptor_layer_1 + '.npy', descriptor_1) # np.save(self.state.settings.ros_dir + '/models/memory/' + self.time_name + "_" + self.descriptor_layer_2 + '.npy', descriptor_2) cv2.imwrite(self.state.settings.ros_dir + '/models/memory/' + self.time_name + '.jpg', self.net_input_image[:,:,::-1]) self.state.save_descriptor = False def run(self): print 'CaffeProcThread.run called' frame = None mask = None while not self.is_timed_out(): with self.state.lock: if self.state.quit: #print 'CaffeProcThread.run: quit is True' #print self.state.quit break #print 'CaffeProcThread.run: caffe_net_state is:', self.state.caffe_net_state #print 'CaffeProcThread.run loop: next_frame: %s, caffe_net_state: %s, back_enabled: %s' % ( # 'None' if self.state.next_frame is None else 'Avail', # self.state.caffe_net_state, # self.state.back_enabled) frame = None mask = None run_fwd = False run_back = False if self.state.caffe_net_state == 'free' and time.time() - self.state.last_key_at > self.pause_after_keys: frame = self.state.next_frame mask = self.state.mask self.state.next_frame = None back_enabled = self.state.back_enabled back_mode = self.state.back_mode back_stale = self.state.back_stale #state_layer = self.state.layer #selected_unit = self.state.selected_unit backprop_layer = self.state.backprop_layer backprop_unit = self.state.backprop_unit # Forward should be run for every new frame run_fwd = (frame is not None) # Backward should be run if back_enabled and (there was a new frame OR back is stale (new backprop layer/unit selected)) run_back = (back_enabled and (run_fwd or back_stale)) self.state.caffe_net_state = 'proc' if (run_fwd or run_back) else 'free' #print 'run_fwd,run_back =', run_fwd, run_back if run_fwd: #print 'TIMING:, processing frame' self.frames_processed_fwd += 1 self.net_input_image = cv2.resize(frame, self.input_dims) with WithTimer('CaffeProcThread:forward', quiet = self.debug_level < 1): print "run forward layer" self.net_proc_forward_layer(self.net_input_image, mask) # self.net_preproc_forward(self.net_input_image) if self.state.save_descriptor: self.save_descriptor() # switch descriptor for match and back prop if self.state.next_descriptor: print 'load descriptor' self.descriptor = self.descriptor_handler.get_next() self.state.next_descriptor = False if self.state.compare_descriptor: # find print 'compare' desc_current = self.descriptor_handler.gen_descriptor('current', self.net.blobs) match_file = self.descriptor_handler.get_max_match(desc_current) print 'match: ' + match_file self.state.compare_descriptor = False if run_back: print "run backward" # Match to saved descriptor if self.state.match_descriptor: print '*' diffs = self.net.blobs[self.descriptor_layer_1].diff * 0 # zero all diffs if doesn't match print "shape ", self.net.blobs[self.descriptor_layer_1].data.shape for unit, response in enumerate(self.net.blobs[self.descriptor_layer_1].data[0]): if response.max() > 0 and abs(response.max() - self.descriptor.get_sig_list()[0][0][unit].max())/response.max() < 0.2: diffs[0][unit] = self.net.blobs[self.descriptor_layer_1].data[0][unit] assert back_mode in ('grad', 'deconv') if back_mode == 'grad': with WithTimer('CaffeProcThread:backward', quiet = self.debug_level < 1): #print '**** Doing backprop with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max()) self.net.backward_from_layer(self.descriptor_layer_1, diffs, zero_higher = True) else: with WithTimer('CaffeProcThread:deconv', quiet = self.debug_level < 1): #print '**** Doing deconv with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max()) self.net.deconv_from_layer(self.descriptor_layer_1, diffs, zero_higher = True) with self.state.lock: self.state.back_stale = False # Filter when back propagating elif self.state.backprop_filter: print "run_back" # print backprop_layer start_layer_idx = self.available_layer.index(backprop_layer) idx = start_layer_idx for current_layer in list(reversed(self.available_layer[0:start_layer_idx+1])): diffs = self.net.blobs[current_layer].diff * 0 max_response = self.net.blobs[current_layer].data[0].max() for unit, response in enumerate(self.net.blobs[current_layer].data[0]): if response.max() > max_response * 0.6: diffs[0][unit] = self.net.blobs[current_layer].data[0,unit] assert back_mode in ('grad', 'deconv') if back_mode == 'grad': with WithTimer('CaffeProcThread:backward', quiet = self.debug_level < 1): #print '**** Doing backprop with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max()) self.net.backward_from_to_layer(current_layer, diffs, self.available_layer[idx-1], zero_higher = (idx == start_layer_idx)) # else: # with WithTimer('CaffeProcThread:deconv', quiet = self.debug_level < 1): # #print '**** Doing deconv with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max()) # self.net.deconv_from_layer(backprop_layer, diffs, zero_higher = True) idx -= 1 with self.state.lock: self.state.back_stale = False # original approach else: diffs = self.net.blobs[backprop_layer].diff * 0 diffs[0][backprop_unit] = self.net.blobs[backprop_layer].data[0,backprop_unit] assert back_mode in ('grad', 'deconv') if back_mode == 'grad': with WithTimer('CaffeProcThread:backward', quiet = self.debug_level < 1): #print '**** Doing backprop with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max()) self.net.backward_from_layer(backprop_layer, diffs, zero_higher = True) else: with WithTimer('CaffeProcThread:deconv', quiet = self.debug_level < 1): #print '**** Doing deconv with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max()) self.net.deconv_from_layer(backprop_layer, diffs, zero_higher = True) with self.state.lock: self.state.back_stale = False if run_fwd or run_back: with self.state.lock: self.state.caffe_net_state = 'free' self.state.drawing_stale = True else: time.sleep(self.loop_sleep) time.sleep(0.1) print 'CaffeProcThread.run: finished' print 'CaffeProcThread.run: processed %d frames fwd, %d frames back' % (self.frames_processed_fwd, self.frames_processed_back)
# crossref=TEXT(stored=True), isbn=TEXT, series=TEXT, school=TEXT, # chapter=TEXT(stored=True, analyzer=StemmingAnalyzer()), # publnr=TEXT(stored=True)) # mi serve nel caso in cui non ho fields specificati nella query, vado a cercare in ogni field schemaFields = ["type","author","title","year", "journal","ee","publisher"] #se l'indice è già costruito non lo devo rifare if not os.path.exists("indexdir"): os.mkdir("indexdir") ix = create_in("indexdir", schema) # get the number of processors nproc = multiprocessing.cpu_count() writer = ix.writer(procs=nproc, limitmb=512) parser = xml.sax.make_parser() handler = DH.DataHandler(writer) parser.setContentHandler(handler) parser.parse('../dblp.xml') writer.commit(optimize=True) else: ix = whoosh.index.open_dir("indexdir", schema=schema) # il searcher va fatto dopo la commit!! print() print("scegli il modello di ranking: ") print("1) BM25F") print("2) PL2") choice = int(input()) if choice == 1: searcher = ix.searcher(weighting=whoosh.scoring.BM25F)
class Trainer: def __init__(self, cfg): self.cfg = cfg self.cat_features = self.cfg.data.features.cat_features self.oof = None self.raw_preds = None self.weights = [] self.models = [] self.scores = [] self.feature_importance_df = pd.DataFrame( columns=['feature', 'importance']) self.dh = DataHandler() def train(self, train_df: pd.DataFrame, target_df: pd.DataFrame, fold_df: pd.DataFrame): self.oof = np.zeros(len(train_df)) for fold_, col in enumerate(fold_df.columns): print( f'\n========================== FOLD {fold_} ... ==========================\n' ) logging.debug( f'\n========================== FOLD {fold_} ... ==========================\n' ) self._train_fold(train_df, target_df, fold_df[col]) print('\n\n===================================\n') print(f'CV: {np.mean(self.scores):.4f}') print('\n===================================\n\n') logging.debug('\n\n===================================\n') logging.debug(f'CV: {np.mean(self.scores):.4f}') logging.debug('\n===================================\n\n') return np.mean(self.scores) def _train_fold(self, train_df, target_df, fold): tr_x, va_x = train_df[fold == 0], train_df[fold > 0] tr_y, va_y = target_df[fold == 0], target_df[fold > 0] weight = fold.max() self.weights.append(weight) model = factory.get_model(self.cfg.model) model.fit(tr_x, tr_y, va_x, va_y, self.cat_features) va_pred = model.predict(va_x, self.cat_features) if self.cfg.data.target.reconvert_type: va_y = getattr(np, self.cfg.data.target.reconvert_type)(va_y) va_pred = getattr(np, self.cfg.data.target.reconvert_type)(va_pred) va_pred = np.where(va_pred >= 0, va_pred, 0) self.models.append(model) self.oof[va_x.index] = va_pred.copy() score = factory.get_metrics(self.cfg.common.metrics.name)(va_y, va_pred) self.scores.append(score) if self.cfg.model.name in ['lightgbm', 'catboost', 'xgboost']: importance_fold_df = pd.DataFrame() fold_importance = model.extract_importances() importance_fold_df['feature'] = train_df.columns importance_fold_df['importance'] = fold_importance self.feature_importance_df = pd.concat( [self.feature_importance_df, importance_fold_df], axis=0) def predict(self, test_df): preds = np.zeros(len(test_df)) for fold_, model in enumerate(self.models): pred = model.predict(test_df, self.cat_features) if self.cfg.data.target.reconvert_type: pred = getattr(np, self.cfg.data.target.reconvert_type)(pred) pred = np.where(pred >= 0, pred, 0) preds += pred.copy() * self.weights[fold_] self.raw_preds = preds.copy() return preds def save(self, run_name): log_dir = Path(f'../logs/{run_name}') self.dh.save(log_dir / 'oof.npy', self.oof) self.dh.save(log_dir / 'raw_preds.npy', self.raw_preds) self.dh.save(log_dir / 'importance.csv', self.feature_importance_df) self.dh.save(log_dir / 'model_weight.pkl', self.models)
from utils import (DataHandler, Kaggle, Notion, Timer, make_submission, seed_everything, send_line) warnings.filterwarnings('ignore') # =============== # Settings # =============== parser = argparse.ArgumentParser() parser.add_argument('--common', default='../configs/common/compe.yml') parser.add_argument('--notify', default='../configs/common/notify.yml') parser.add_argument('-m', '--model') parser.add_argument('-c', '--comment') options = parser.parse_args() dh = DataHandler() cfg = dh.load(options.common) cfg.update(dh.load(f'../configs/exp/{options.model}.yml')) notify_params = dh.load(options.notify) comment = options.comment model_name = options.model now = datetime.datetime.now() run_name = f'{model_name}_{now:%Y%m%d%H%M%S}' logger_path = Path(f'../logs/{run_name}') # =============== # Main
class Solver: def __init__(self, params): # Build data handler self.data_handler = DataHandler() self.data_handler.loadData(params['input']) params['inp_dim'] = self.data_handler.getDataShape()[ 1] # inp_dim = 1000 logging.info("=" * 41) # Build model self.model = SPINEModel(params) self.dtype = torch.FloatTensor use_cuda = torch.cuda.is_available() if use_cuda: self.model.cuda() self.dtype = torch.cuda.FloatTensor self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.1) logging.info("=" * 41) def train(self, params): num_epochs, batch_size = params['num_epochs'], params['batch_size'] optimizer = self.optimizer dtype = self.dtype for iteration in range(num_epochs): # for each epoch self.data_handler.shuffleTrain() # 15000 training data shuffled num_batches = self.data_handler.getNumberOfBatches( batch_size) # num_batches = number of iterations epoch_losses = np.zeros( 5) # rl, asl, psl, total !!!!!!!!!!!!!!!!!! for batch_idx in range(num_batches): # for each iteration optimizer.zero_grad() batch_x, batch_y = self.data_handler.getBatch( batch_idx, batch_size, params['noise_level'], params['denoising']) batch_x = Variable(torch.from_numpy(batch_x), requires_grad=False).type(dtype) batch_y = Variable(torch.from_numpy(batch_y), requires_grad=False).type( dtype) #dtype = torch.FloatTensor out, h, loss, loss_terms = self.model(batch_x, batch_y) reconstruction_loss, psl_loss, asl_loss, local_loss = loss_terms #!!!!!!!!!!!!!!!!!!!!! loss.backward() optimizer.step() #print(local_loss) epoch_losses[0] += reconstruction_loss.item() epoch_losses[1] += asl_loss.item() epoch_losses[2] += psl_loss.item() epoch_losses[3] += local_loss.item() #!!!!!!!!!!!!!!!!!!!!!!!! epoch_losses[4] += loss.item() # epoch_losses[0]+=reconstruction_loss.data[0] # epoch_losses[1]+=asl_loss.data[0] # epoch_losses[2]+=psl_loss.data[0] # epoch_losses[3]+=local_loss.data[0] # epoch_losses[4]+=loss.data[0] print("After epoch %r, Reconstruction Loss = %.4f, ASL = %.4f,"\ "PSL = %.4f, Local Loss = %.4f and total = %.4f" %(iteration+1, epoch_losses[0], epoch_losses[1], epoch_losses[2], epoch_losses[3], epoch_losses[4]) ) #!!!!!! #logging.info("After epoch %r, Sparsity = %.1f" # %(iteration+1, utils.compute_sparsity(h.cpu().data.numpy()))) #break #break def getSpineEmbeddings(self, batch_size, params): ret = [] self.data_handler.resetDataOrder() num_batches = self.data_handler.getNumberOfBatches(batch_size) for batch_idx in range(num_batches): batch_x, batch_y = self.data_handler.getBatch( batch_idx, batch_size, params['noise_level'], params['denoising']) batch_x = Variable(torch.from_numpy(batch_x), requires_grad=False).type(self.dtype) batch_y = Variable(torch.from_numpy(batch_y), requires_grad=False).type(self.dtype) _, h, _, _ = self.model(batch_x, batch_y) ret.extend(h.cpu().data.numpy()) return np.array(ret) def getWordsList(self): return self.data_handler.getWordsList()
class Solver: def __init__(self, params): # Build data handler self.data_handler = DataHandler() self.data_handler.loadData(params['input']) params['inp_dim'] = self.data_handler.getDataShape()[1] logging.info("=" * 41) # Build model self.model = SPINEModel(params) self.dtype = torch.FloatTensor use_cuda = torch.cuda.is_available() if use_cuda: self.model.cuda() self.dtype = torch.cuda.FloatTensor self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.1) logging.info("=" * 41) def train(self, params): num_epochs, batch_size = params['num_epochs'], params['batch_size'], optimizer = self.optimizer dtype = self.dtype for iteration in range(num_epochs): self.data_handler.shuffleTrain() num_batches = self.data_handler.getNumberOfBatches(batch_size) epoch_losses = np.zeros(5) # rl, asl, psl, Siml, total sparsity = 0 for batch_idx in range(num_batches): optimizer.zero_grad() batch_x, batch_y = self.data_handler.getBatch( batch_idx, batch_size, params['noise_level'], params['denoising']) batch_x = Variable(torch.from_numpy(batch_x), requires_grad=False).type(dtype) batch_y = Variable(torch.from_numpy(batch_y), requires_grad=False).type(dtype) out, h, loss, loss_terms, sparsity_ratio = self.model( batch_x, batch_y) reconstruction_loss, psl_loss, asl_loss, siml = loss_terms loss.backward() optimizer.step() epoch_losses[0] += reconstruction_loss.data epoch_losses[1] += asl_loss.data epoch_losses[2] += psl_loss.data epoch_losses[3] += loss.data epoch_losses[4] += siml.data sparsity += sparsity_ratio.data print("After epoch %r, Reconstruction Loss = %.4f, ASL = %.4f,"\ "PSL = %.4f, SimL = %.4f , and total = %.4f, sparsity = %.4f" %(iteration+1, epoch_losses[0], epoch_losses[1], epoch_losses[2], epoch_losses[4], epoch_losses[3], sparsity/num_batches) ) if iteration % 1000 == 0: spine_embeddings = self.getSpineEmbeddings(512, params) utils.dump_vectors(spine_embeddings, './' + str(iteration / 1000) + '.txt', self.getWordsList()) #logging.info("After epoch %r, Sparsity = %.1f" # %(iteration+1, utils.compute_sparsity(h.cpu().data.numpy()))) #break #break def getSpineEmbeddings(self, batch_size, params): ret = [] self.data_handler.resetDataOrder() num_batches = self.data_handler.getNumberOfBatches(batch_size) for batch_idx in range(num_batches): batch_x, batch_y = self.data_handler.getBatch( batch_idx, batch_size, params['noise_level'], params['denoising']) batch_x = Variable(torch.from_numpy(batch_x), requires_grad=False).type(self.dtype) batch_y = Variable(torch.from_numpy(batch_y), requires_grad=False).type(self.dtype) _, h, _, _, spars = self.model(batch_x, batch_y) ret.extend(h.cpu().data.numpy()) return np.array(ret) def getWordsList(self): return self.data_handler.getWordsList()
def exit_press(self): # Save portfolio DataHandler.save_portfolio(current_portoflio.portfolio) # Exit sys.exit(0)
def train(opt, model, input_img): model.cuda() handler = DataHandler(opt, input_img) learning_rate = opt.learning_rate min_learning_rate = opt.min_learning_rate learning_rate_change_iter_nums = [0] mse_steps = [] mse_rec = [] criterionL1 = nn.L1Loss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) with tqdm.tqdm(miniters=1, mininterval=0) as progress: for iter, (hr, lr) in enumerate(handler.preprocess_data()): lr = lr.cuda() hr = hr.cuda() output = model(lr) + lr loss = criterionL1(output, hr) cpu_loss = loss.data.cpu().numpy() model.zero_grad() optimizer.zero_grad() progress.set_description("Iteration: {} Loss: {}, Learning rate: {}".format( \ iter, cpu_loss, learning_rate)) progress.update() if iter > 0 and iter % 10000 == 0: learning_rate = learning_rate / 10 adjust_learning_rate(optimizer, new_lr=learning_rate) print("Learning rate reduced to {lr}".format(lr=learning_rate) ) """ if (not (1 + iter) % opt.learning_rate_policy_check_every and iter - learning_rate_change_iter_nums[-1] > opt.min_iters): [slope, _], [[var, _], _] = np.polyfit(mse_steps[-int(opt.learning_rate_slope_range / opt.run_test_every):], mse_rec[-int(opt.learning_rate_slope_range / opt.run_test_every):], 1, cov=True) std = np.sqrt(var) if -opt.learning_rate_change_ratio * slope < std: learning_rate /= 10 learning_rate_change_iter_nums.append(iter) """ loss.backward() optimizer.step() if learning_rate < min_learning_rate: print('Done training') break