main.py

import argparse
import collections
import torch
import numpy as np
import data_loader.data_loaders as module_data
import model.loss as module_loss
import model.metric as module_metric
import model.model as module_arch_model
import model.resnet as module_arch_resnet
import model.resnext as module_arch_resnext
import model.inceptiontime as module_arch_inceptiontime
import model.mc_inceptiontime as module_arch_mc_inceptiontime
import model.fcn as module_arch_fcn
import model.tcn as module_arch_tcn
import model.resnest as module_arch_resnest
import model.resnest2 as module_arch_resnest2
import model.vanilla_cnn as module_arch_vanilla_cnn
import model.xception as module_arch_xception
import model.transformer as module_arch_transformer
import model.ltae as module_arch_ltae
import model.tae as module_arch_tae
import model.dsanet as module_arch_dsanet
import model.baseline_race.conformer as module_arch_conformer
import model.pnasnet as module_arch_pnasnet
import model.baseline_race.NeuralBaseline as module_arch_NeuralBaseline
import model.baseline_race.SEResNet as module_arch_SEResNet
import model.baseline_race.ConvLSTM as module_arch_ConvLSTM
from parse_config import ConfigParser
from trainer import Trainer
from evaluater import Evaluater
from model.metric import ChallengeMetric
from utils.util import load_model
from utils.lr_scheduler import CosineAnnealingWarmUpRestarts, GradualWarmupScheduler
import datetime
import cv2

# fix random seeds for reproducibility
SEED = 123
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(SEED)

# model selection
files_models = {
    "fcn": ['FCN'],
    "inceptiontime": ['InceptionTimeV1', 'InceptionTimeV2'],
    "mc_inceptiontime": ['MCInceptionTimeV2'],
    "resnet": ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'],
    "resnext": ['ResNeXt', 'resnext18', 'resnext34', 'resnext50', 'resnext101', 'resnext152'],
    "resnest": ['resnest50', 'resnest'],
    "resnest2": ['resnest2'],
    "model": ['CNN', 'MLP'],
    "tcn": ['TCN'],
    "vanilla_cnn": ['VanillaCNN'],
    "xception": ['Xception', 'Gception'],
    "transformer": ['Transformer'],
    "ltae": ["LTAE"],
    "tae": ["TemporalAttentionEncoder"],
    "dsanet": ["DSANet"],
    "conformer": ["Conformer"],
    "pnasnet": ["PNASNet5Large"],
    "NeuralBaseline": ["InceptionModel", "ResNetModule", "VGGNet", "FCNModule","LinearModule","DenseNetModule"],
    "SEResNet":["SEResNet","SEResNeXt"],
    "ConvLSTM":["ConvLSTM"]
}

def preprocess_image(img):
    means = [0.485, 0.456, 0.406]
    stds = [0.229, 0.224, 0.225]

    preprocessed_img = img.copy()[:, :, ::-1]
    for i in range(3):
        preprocessed_img[:, :, i] = preprocessed_img[:, :, i] - means[i]
        preprocessed_img[:, :, i] = preprocessed_img[:, :, i] / stds[i]
    preprocessed_img = \
        np.ascontiguousarray(np.transpose(preprocessed_img, (2, 0, 1)))
    preprocessed_img = torch.from_numpy(preprocessed_img)
    preprocessed_img.unsqueeze_(0)
    input = preprocessed_img.requires_grad_(True)
    return input

def show_cam_on_image(img, mask):
    heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)
    heatmap = np.float32(heatmap) / 255
    cam = heatmap + np.float32(img)
    cam = cam / np.max(cam)
    cv2.imwrite("cam.jpg", np.uint8(255 * cam))


def main(config):
    logger = config.get_logger('train')

    # setup data_loader instances
    data_loader = config.init_obj('data_loader', module_data)
    valid_data_loader = data_loader.valid_data_loader
    test_data_loader = data_loader.test_data_loader

    # build model architecture, then print to console
    global model
    for file, types in files_models.items():
        for type in types:
            if config["arch"]["type"] == type:
                model = config.init_obj('arch', eval("module_arch_" + file))
                logger.info(model)
                if config['arch'].get('weight_path', False):
                    model = load_model(model, config["arch"]["weight_path"])

    #cam
    # target_layer = model.my_submodule

    # get function handles of loss and metrics
    print(config)
    if config['loss']['type'] == 'FocalLoss2d':
        count = data_loader.count
        indices = data_loader.indices
        w =  np.max(count[indices]) / count
        w[indices] = 0

        only_scored_classes = config['trainer'].get('only_scored_class', False)
        if only_scored_classes:
            w = w[indices]

        weight = config['loss'].get('args', w)
        criterion = getattr(module_loss, 'FocalLoss2d')(weight=weight)
    else:
        criterion = getattr(module_loss, config['loss']['type'])

    # get function handles of metrics

    challenge_metrics = ChallengeMetric(config['data_loader']['args']['label_dir'])
    # challenge_metrics = ChallengeMetric2(num_classes=9)

    metrics = [getattr(challenge_metrics, met) for met in config['metrics']]

    # build optimizer, learning rate scheduler. delete every lines containing lr_scheduler for disabling scheduler
    trainable_params = filter(lambda p: p.requires_grad, model.parameters())
    optimizer = config.init_obj('optimizer', torch.optim, trainable_params)

    if config["lr_scheduler"]["type"] == "CosineAnnealingWarmRestarts":
        params = config["lr_scheduler"]["args"]
        lr_scheduler = CosineAnnealingWarmUpRestarts(optimizer, T_0=params["T_0"], T_mult=params["T_mult"],
                                                     T_up=params["T_up"], gamma=params["gamma"], eta_max=params["eta_max"])
    elif config["lr_scheduler"]["type"] == "GradualWarmupScheduler":
        params = config["lr_scheduler"]["args"]
        scheduler_steplr_args = dict(params["after_scheduler"]["args"])
        scheduler_steplr = getattr(torch.optim.lr_scheduler, params["after_scheduler"]["type"])(optimizer, **scheduler_steplr_args)
        lr_scheduler = GradualWarmupScheduler(optimizer, multiplier=params["multiplier"], total_epoch=params["total_epoch"], after_scheduler=scheduler_steplr)
    else:
        lr_scheduler = config.init_obj('lr_scheduler', torch.optim.lr_scheduler, optimizer)

    trainer = Trainer(model, criterion, metrics, optimizer,
                      config=config,
                      data_loader=data_loader,
                      valid_data_loader=valid_data_loader,
                      lr_scheduler=lr_scheduler)

    trainer.train()

    evaluater = Evaluater(model, criterion, metrics,
                          config=config,
                          test_data_loader=test_data_loader)

    evaluater.evaluate()

    challenge_metrics.return_metric_list()

    evaluater.analyze(challenge_metrics)

if __name__ == '__main__':
    start_time = datetime.datetime.now()
    args = argparse.ArgumentParser(description='PyTorch Template')
    args.add_argument('-c', '--config', default=None, type=str,
                      help='config file path (default: None)')
    args.add_argument('-r', '--resume', default=None, type=str,
                      help='path to latest checkpoint (default: None)')
    args.add_argument('-d', '--device', default=None, type=str,
                      help='indices of GPUs to enable (default: all)')

    # custom cli options to modify configuration from default values given in json file.
    CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
    options = [
        CustomArgs(['--lr', '--learning_rate'], type=float, target='optimizer;args;lr'),
        CustomArgs(['--bs', '--batch_size'], type=int, target='data_loader;args;batch_size')
    ]
    config = ConfigParser.from_args(args, options)

    import os
    print(torch.cuda.device_count())
    print(os.environ["CUDA_VISIBLE_DEVICES"])
    print(torch.cuda.device_count())
    main(config)

    end_time = datetime.datetime.now()
    print("程序运行时间：" + str((end_time - start_time).seconds) + "秒")