""" This script provides an exmaple to wrap TencentPretrain for image classification. """ import sys import os import random import argparse import torch import torch.nn as nn import torchvision.datasets as dest from torchvision import transforms from torchvision.io import read_image from torchvision.io.image import ImageReadMode tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) sys.path.append(tencentpretrain_dir) from tencentpretrain.layers import * from tencentpretrain.encoders import * from tencentpretrain.utils.vocab import Vocab from tencentpretrain.utils.constants import * from tencentpretrain.utils import * from tencentpretrain.utils.optimizers import * from tencentpretrain.utils.config import load_hyperparam from tencentpretrain.utils.misc import ZeroOneNormalize, count_lines from tencentpretrain.utils.seed import set_seed from tencentpretrain.model_saver import save_model from tencentpretrain.opts import finetune_opts from finetune.run_classifier import * def data_loader(args, path): transform = transforms.Compose([ transforms.Resize((args.image_height, args.image_width)), ZeroOneNormalize() ]) dataset, columns = [], {} with open(path, mode="r", encoding="utf-8") as f: src_batch, tgt_batch, seg_batch = [], [], [] for line_id, line in enumerate(f): if line_id == 0: for i, column_name in enumerate(line.rstrip("\r\n").split("\t")): columns[column_name] = i continue line = line.rstrip("\r\n").split("\t") tgt = int(line[columns["label"]]) path = line[columns["path"]] image = read_image(path, ImageReadMode.RGB) image = image.to(args.device) src = transform(image) seg = [1] * ((src.size()[1] // args.patch_size) * (src.size()[2] // args.patch_size) + 1) src_batch.append(src) tgt_batch.append(tgt) seg_batch.append(seg) if len(src_batch) == args.batch_size: yield torch.stack(src_batch, 0), \ torch.LongTensor(tgt_batch), \ torch.LongTensor(seg_batch) src_batch, tgt_batch, seg_batch = [], [], [] if len(src_batch) > 0: yield torch.stack(src_batch, 0), \ torch.LongTensor(tgt_batch), \ torch.LongTensor(seg_batch) def evaluate(args, dataset_path): correct, instances_num = 0, 0 # Confusion matrix. confusion = torch.zeros(args.labels_num, args.labels_num, dtype=torch.long) args.model.eval() for i, (src_batch, tgt_batch, seg_batch) in enumerate(data_loader(args, dataset_path)): src_batch = src_batch.to(args.device) tgt_batch = tgt_batch.to(args.device) seg_batch = seg_batch.to(args.device) with torch.no_grad(): _, logits = args.model(src_batch, tgt_batch, seg_batch) pred = torch.argmax(nn.Softmax(dim=1)(logits), dim=1) gold = tgt_batch for j in range(pred.size()[0]): confusion[pred[j], gold[j]] += 1 correct += torch.sum(pred == gold).item() instances_num += len(pred) args.logger.info("Confusion matrix:") args.logger.info(confusion) args.logger.info("Report precision, recall, and f1:") eps = 1e-9 for i in range(confusion.size()[0]): p = confusion[i, i].item() / (confusion[i, :].sum().item() + eps) r = confusion[i, i].item() / (confusion[:, i].sum().item() + eps) f1 = 2 * p * r / (p + r + eps) args.logger.info("Label {}: {:.3f}, {:.3f}, {:.3f}".format(i, p, r, f1)) args.logger.info("Acc. (Correct/Total): {:.4f} ({}/{}) ".format(correct / instances_num, correct, instances_num)) return correct / instances_num, confusion def main(): parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) finetune_opts(parser) tokenizer_opts(parser) adv_opts(parser) args = parser.parse_args() # Load the hyperparameters from the config file. args = load_hyperparam(args) args.soft_targets, args.soft_alpha = False, 0 # Count the number of labels. args.labels_num = count_labels_num(args.train_path) instances_num = count_lines(args.train_path) - 1 # Build tokenizer. args.tokenizer = str2tokenizer["virtual"](args) set_seed(args.seed) # Build classification model. model = Classifier(args) # Load or initialize parameters. load_or_initialize_parameters(args, model) # Get logger. args.logger = init_logger(args) args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(args.device) # Training phase. batch_size = args.batch_size args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1 args.logger.info("Batch size: {}".format(batch_size)) args.logger.info("The number of training instances: {}".format(instances_num)) optimizer, scheduler = build_optimizer(args, model) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) args.amp = amp if torch.cuda.device_count() > 1: args.logger.info("{} GPUs are available. Let's use them.".format(torch.cuda.device_count())) model = torch.nn.DataParallel(model) args.model = model if args.use_adv: args.adv_method = str2adv[args.adv_type](model) total_loss, result, best_result = 0.0, 0.0, 0.0 args.logger.info("Start training.") for epoch in range(1, args.epochs_num + 1): model.train() for i, (src_batch, tgt_batch, seg_batch) in enumerate(data_loader(args, args.train_path)): loss = train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch) total_loss += loss.item() if (i + 1) % args.report_steps == 0: args.logger.info("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".format(epoch, i + 1, total_loss / args.report_steps)) total_loss = 0.0 result = evaluate(args, args.dev_path) if result[0] > best_result: best_result = result[0] save_model(model, args.output_model_path) # Evaluation phase. if args.test_path is not None: args.logger.info("Test set evaluation.") if torch.cuda.device_count() > 1: args.model.module.load_state_dict(torch.load(args.output_model_path)) else: args.model.load_state_dict(torch.load(args.output_model_path)) evaluate(args, args.test_path) if __name__ == "__main__": main()