Python miscc.config.cfg.DATA_DIR Examples

The following are 10 code examples of miscc.config.cfg.DATA_DIR(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module miscc.config.cfg , or try the search function .
Example #1
Source File: main.py    From semantic-object-accuracy-for-generative-text-to-image-synthesis with MIT License 6 votes vote down vote up 
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
Example #2
Source File: trainer.py    From Recipe2ImageGAN with MIT License 6 votes vote down vote up 
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.')
Example #3
Source File: eval_trainer.py    From Recipe2ImageGAN with MIT License 6 votes vote down vote up 
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.')
Example #4
Source File: main.py    From DM-GAN with MIT License 4 votes vote down vote up 
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)
Example #5
Source File: main.py    From multiple-objects-gan with MIT License 4 votes vote down vote up 
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)
Example #6
Source File: main.py    From attn-gan with MIT License 4 votes vote down vote up 
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)
Example #7
Source File: trainer.py    From Recipe2ImageGAN with MIT License 4 votes vote down vote up 
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
Example #8
Source File: trainer.py    From Recipe2ImageGAN with MIT License 4 votes vote down vote up 
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 ######################### #
Example #9
Source File: eval_trainer.py    From Recipe2ImageGAN with MIT License 4 votes vote down vote up 
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
Example #10
Source File: main.py    From AttnGAN with MIT License 4 votes vote down vote up 
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)