Python cfg.DATA_DIR属性代码示例

# 需要导入模块: from miscc.config import cfg [as 别名]
# 或者: from miscc.config.cfg import DATA_DIR [as 别名]
def get_dataset_indices(split="train", num_max_objects=10):
    if cfg.TRAIN.OPTIMIZE_DATA_LOADING:
        label_path = os.path.join(os.path.join(cfg.DATA_DIR, split), 'labels_large.pickle')
    with open(label_path, "rb") as f:
        labels = pickle.load(f, encoding='latin1')
        labels = np.array(labels)
    dataset_indices = []

    for _i in range(num_max_objects+1):
        dataset_indices.append([])

    for index, label in enumerate(labels):
        for idx, l in enumerate(label):
            if l == -1:
                dataset_indices[idx].append(index)
                break
        else:
            dataset_indices[-1].append(index)

    return dataset_indices 

# 需要导入模块: from miscc.config import cfg [as 别名]
# 或者: from miscc.config.cfg import DATA_DIR [as 别名]
def save_model(netG, avg_param_G, netsD, epoch, model_dir):
    last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Model'
    load_params(netG, avg_param_G)
    torch.save(
        netG.state_dict(),
        '%s/netG_%d.pth' % (model_dir, epoch))
    torch.save(
        netG.state_dict(),
        '%s/netG.pth' % (last_run_dir))

    with open(last_run_dir + '/count.txt', 'w') as f:
        f.write(str(epoch))

    for i in range(len(netsD)):
        netD = netsD[i]
        torch.save(
            netD.state_dict(),
            '%s/netD%d.pth' % (model_dir, i))
        torch.save(
            netD.state_dict(),
            '%s/netD%d.pth' % (last_run_dir, i))
    print('Save G/Ds models.') 

# 需要导入模块: from miscc.config import cfg [as 别名]
# 或者: from miscc.config.cfg import DATA_DIR [as 别名]
def gen_example(wordtoix, algo):
    '''generate images from example sentences'''
    from nltk.tokenize import RegexpTokenizer
    filepath = '%s/example_filenames.txt' % (cfg.DATA_DIR)
    data_dic = {}
    with open(filepath, "r") as f:
        filenames = f.read().decode('utf8').split('\n')
        for name in filenames:
            if len(name) == 0:
                continue
            filepath = '%s/%s.txt' % (cfg.DATA_DIR, name)
            with open(filepath, "r") as f:
                print('Load from:', name)
                sentences = f.read().decode('utf8').split('\n')
                # a list of indices for a sentence
                captions = []
                cap_lens = []
                for sent in sentences:
                    if len(sent) == 0:
                        continue
                    sent = sent.replace("\ufffd\ufffd", " ")
                    tokenizer = RegexpTokenizer(r'\w+')
                    tokens = tokenizer.tokenize(sent.lower())
                    if len(tokens) == 0:
                        print('sent', sent)
                        continue

                    rev = []
                    for t in tokens:
                        t = t.encode('ascii', 'ignore').decode('ascii')
                        if len(t) > 0 and t in wordtoix:
                            rev.append(wordtoix[t])
                    captions.append(rev)
                    cap_lens.append(len(rev))
            max_len = np.max(cap_lens)

            sorted_indices = np.argsort(cap_lens)[::-1]
            cap_lens = np.asarray(cap_lens)
            cap_lens = cap_lens[sorted_indices]
            cap_array = np.zeros((len(captions), max_len), dtype='int64')
            for i in range(len(captions)):
                idx = sorted_indices[i]
                cap = captions[idx]
                c_len = len(cap)
                cap_array[i, :c_len] = cap
            key = name[(name.rfind('/') + 1):]
            data_dic[key] = [cap_array, cap_lens, sorted_indices]
    algo.gen_example(data_dic) 

# 需要导入模块: from miscc.config import cfg [as 别名]
# 或者: from miscc.config.cfg import DATA_DIR [as 别名]
def load_network(gpus):
    netG = G_NET()
    netG.apply(weights_init)
    netG = torch.nn.DataParallel(netG, device_ids=gpus)
    print(netG)

    netsD = []
    if cfg.TREE.BRANCH_NUM > 0:
        netsD.append(D_NET64())
    if cfg.TREE.BRANCH_NUM > 1:
        netsD.append(D_NET128())
    if cfg.TREE.BRANCH_NUM > 2:
        netsD.append(D_NET256())
    if cfg.TREE.BRANCH_NUM > 3:
        netsD.append(D_NET512())
    if cfg.TREE.BRANCH_NUM > 4:
        netsD.append(D_NET1024())
    # TODO: if cfg.TREE.BRANCH_NUM > 5:

    for i in range(len(netsD)):
        netsD[i].apply(weights_init)
        netsD[i] = torch.nn.DataParallel(netsD[i], device_ids=gpus)
        # print(netsD[i])
    print('# of netsD', len(netsD))

    count = 0
    if cfg.TRAIN.NET_G != '':
        state_dict = torch.load(cfg.TRAIN.NET_G)
        netG.load_state_dict(state_dict)
        print('Load ', cfg.TRAIN.NET_G)

        try:
            istart = cfg.TRAIN.NET_G.rfind('_') + 1
            iend = cfg.TRAIN.NET_G.rfind('.')
            count = cfg.TRAIN.NET_G[istart:iend]
            count = int(count)
        except:
            last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Model'
            with open(last_run_dir + '/count.txt', 'r') as f:
                count = int(f.read())

        count = int(count) + 1

    if cfg.TRAIN.NET_D != '':
        for i in range(len(netsD)):
            print('Load %s_%d.pth' % (cfg.TRAIN.NET_D, i))
            state_dict = torch.load('%s%d.pth' % (cfg.TRAIN.NET_D, i))
            netsD[i].load_state_dict(state_dict)

    inception_model = INCEPTION_V3()

    if cfg.CUDA:
        netG.cuda()
        for i in range(len(netsD)):
            netsD[i].cuda()
        inception_model = inception_model.cuda()
    inception_model.eval()

    return netG, netsD, len(netsD), inception_model, count 

# 需要导入模块: from miscc.config import cfg [as 别名]
# 或者: from miscc.config.cfg import DATA_DIR [as 别名]
def save_img_results(imgs_tcpu, fake_imgs, num_imgs,
                     count, image_dir, summary_writer, rec_ids , im_ids):
    num = cfg.TRAIN.VIS_COUNT
    last_run_dir = cfg.DATA_DIR + '/' + cfg.LAST_RUN_DIR + '/Image/'

    # The range of real_img (i.e., self.imgs_tcpu[i][0:num])
    # is changed to [0, 1] by function vutils.save_image
    real_img = imgs_tcpu[-1][0:num]
    vutils.save_image(
        real_img, '%s/count_%09d_real_samples.png' % (image_dir, count),
        normalize=True)

    vutils.save_image(
        real_img, last_run_dir + 'real_samples.png',
        normalize=True)
        
    # write images and recipe IDs to filenames    
    rec_ids = [t.tostring().decode('UTF-8') for t in rec_ids.numpy()]
    im_ids = [t.tostring().decode('UTF-8') for t in im_ids.numpy()]
    with open('%s/count_%09d_real_samples_IDs.txt' % (image_dir, count),"w") as f:
        for rec_id, im_id in zip(rec_ids, im_ids):
            f.write("rec_id=%s, img_id=%s\n" % (rec_id,im_id))

    with open(last_run_dir + 'real_samples_IDs.txt',"w") as f:
        for rec_id, im_id in zip(rec_ids, im_ids):
            f.write("rec_id=%s, img_id=%s\n" % (rec_id,im_id))
        
    
    real_img_set = vutils.make_grid(real_img).numpy()
    real_img_set = np.transpose(real_img_set, (1, 2, 0))
    real_img_set = real_img_set * 255
    real_img_set = real_img_set.astype(np.uint8)
    sup_real_img = summary.image('real_img', real_img_set)
    summary_writer.add_summary(sup_real_img, count)

    for i in range(num_imgs):
        fake_img = fake_imgs[i][0:num]
        # The range of fake_img.data (i.e., self.fake_imgs[i][0:num])
        # is still [-1. 1]...
        vutils.save_image(
            fake_img.data, '%s/count_%09d_fake_samples%d.png' %
            (image_dir, count, i), normalize=True)

        vutils.save_image(
            fake_img.data, last_run_dir + 'fake_samples%d.png' %
            (i), normalize=True)


        fake_img_set = vutils.make_grid(fake_img.data).cpu().numpy()

        fake_img_set = np.transpose(fake_img_set, (1, 2, 0))
        fake_img_set = (fake_img_set + 1) * 255 / 2
        fake_img_set = fake_img_set.astype(np.uint8)

        sup_fake_img = summary.image('fake_img%d' % i, fake_img_set)
        summary_writer.add_summary(sup_fake_img, count)
        summary_writer.flush()


# ################## For uncondional tasks ######################### #