test.py

import os
import random
import math
import torch
import argparse
from argparse import Namespace
from utils.args_utils import str2list, str2bool
import copy
from time import time

import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP

from models.ensemble_captioning_model import EsembleCaptioningModel
from data.coco_dataloader import CocoDataLoader
from data.coco_dataset import CocoDatasetKarpathy
from utils import language_utils
from utils.language_utils import compute_num_pads as compute_num_pads
from eval.eval import COCOEvalCap

import warnings
warnings.filterwarnings("ignore", category=UserWarning)

import functools
print = functools.partial(print, flush=True)


def convert_time_as_hhmmss(ticks):
    return str(int(ticks / 60)) + " m " + \
           str(int(ticks) % 60) + " s"


def compute_evaluation_loss(loss_function,
                            model,
                            data_set,
                            data_loader,
                            num_samples,
                            sub_batch_size,
                            dataset_split,
                            rank=0,
                            verbose=False):
    model.eval()

    sb_size = sub_batch_size

    tot_loss = 0
    num_sub_batch = math.ceil(num_samples / sb_size)
    tot_num_tokens = 0
    for sb_it in range(num_sub_batch):
        from_idx = sb_it * sb_size
        to_idx = min((sb_it + 1) * sb_size, num_samples)

        sub_batch_input_x, sub_batch_target_y, sub_batch_input_x_num_pads, sub_batch_target_y_num_pads, \
          = data_loader.get_batch_samples(img_idx_batch_list=list(range(from_idx, to_idx)),
                                          dataset_split=dataset_split)
        sub_batch_input_x = sub_batch_input_x.to(rank)
        sub_batch_target_y = sub_batch_target_y.to(rank)

        sub_batch_input_x = sub_batch_input_x
        sub_batch_target_y = sub_batch_target_y
        tot_num_tokens += sub_batch_target_y.size(1)*sub_batch_target_y.size(0) - \
                          sum(sub_batch_target_y_num_pads)
        pred = model(enc_x=sub_batch_input_x,
                     dec_x=sub_batch_target_y[:, :-1],
                     enc_x_num_pads=sub_batch_input_x_num_pads,
                     dec_x_num_pads=sub_batch_target_y_num_pads,
                     apply_softmax=False)
        tot_loss += loss_function(pred, sub_batch_target_y[:, 1:],
                                  data_set.get_pad_token_idx(),
                                  divide_by_non_zeros=False).item()
        del sub_batch_input_x, sub_batch_target_y, pred
        torch.cuda.empty_cache()
    tot_loss /= tot_num_tokens
    if verbose and rank == 0:
        print("Validation Loss on " + str(num_samples) + " samples: " + str(tot_loss))

    return tot_loss


def evaluate_model(ddp_model,
                   y_idx2word_list,
                   beam_size, max_seq_len,
                   sos_idx, eos_idx,
                   rank, ddp_sync_port,
                   parallel_batches=16,

                   indexes=[0],
                   data_loader=None,
                   dataset_split=CocoDatasetKarpathy.TrainSet_ID,
                   use_images_instead_of_features=False,

                   verbose=True,
                   stanford_model_path="./eval/get_stanford_models.sh"):

    start_time = time()

    sub_list_predictions = []
    validate_y = []
    num_samples = len(indexes)

    ddp_model.eval()
    with torch.no_grad():
        sb_size = parallel_batches
        num_iter_sub_batches = math.ceil(len(indexes) / sb_size)
        for sb_it in range(num_iter_sub_batches):
            last_iter = sb_it == num_iter_sub_batches - 1
            if last_iter:
                from_idx = sb_it * sb_size
                to_idx = num_samples
            else:
                from_idx = sb_it * sb_size
                to_idx = (sb_it + 1) * sb_size

            if use_images_instead_of_features:
                sub_batch_x = [data_loader.get_images_by_idx(i, dataset_split=dataset_split, transf_mode='test').unsqueeze(0)
                         for i in list(range(from_idx, to_idx))]
                sub_batch_x = torch.cat(sub_batch_x).to(rank)
                sub_batch_x_num_pads = [0] * sub_batch_x.size(0)
            else:
                sub_batch_x = [data_loader.get_bboxes_by_idx(i, dataset_split=dataset_split)
                         for i in list(range(from_idx, to_idx))]
                sub_batch_x = torch.nn.utils.rnn.pad_sequence(sub_batch_x, batch_first=True).to(rank)
                sub_batch_x_num_pads = compute_num_pads(sub_batch_x)

            validate_y += [data_loader.get_all_image_captions_by_idx(i, dataset_split=dataset_split) \
                           for i in list(range(from_idx, to_idx))]

            beam_search_kwargs = {'beam_size': beam_size,
                                  'beam_max_seq_len': max_seq_len,
                                  'sample_or_max': 'max',
                                  'how_many_outputs': 1,
                                  'sos_idx': sos_idx,
                                  'eos_idx': eos_idx}

            output_words, _ = ddp_model(enc_x=sub_batch_x,
                                        enc_x_num_pads=sub_batch_x_num_pads,
                                        mode='beam_search', **beam_search_kwargs)

            output_words = [output_words[i][0] for i in range(len(output_words))]

            pred_sentence = language_utils.convert_allsentences_idx2word(output_words, y_idx2word_list)
            for sentence in pred_sentence:
                sub_list_predictions.append(' '.join(sentence[1:-1]))  # remove EOS and SOS

            del sub_batch_x, sub_batch_x_num_pads, output_words

    ddp_model.train()

    if rank == 0 and verbose:
        # dirty code to leave the evaluation code untouched
        list_predictions = [sub_predictions for sub_predictions in sub_list_predictions]
        list_list_references = [[validate_y[i][j] for j in range(len(validate_y[i]))] for i in range(len(validate_y))]

        gts_dict = dict()
        for i in range(len(list_list_references)):
            gts_dict[i] = [{u'image_id': i, u'caption': list_list_references[i][j]}
                           for j in range(len(list_list_references[i]))]

        pred_dict = dict()
        for i in range(len(list_predictions)):
            pred_dict[i] = [{u'image_id': i, u'caption': list_predictions[i]}]

        coco_eval = COCOEvalCap(gts_dict, pred_dict, list(range(len(list_predictions))),
                                get_stanford_models_path=stanford_model_path)
        score_results = coco_eval.evaluate(bleu=True, rouge=True, cider=True, spice=True, meteor=True, verbose=False)
        elapsed_ticks = time() - start_time
        print("Evaluation Phase over " + str(len(validate_y)) + " BeamSize: " + str(beam_size) +
              "  elapsed: " + str(int(elapsed_ticks/60)) + " m " + str(int(elapsed_ticks % 60)) + ' s')
        print(score_results)

    if rank == 0:
        return pred_dict, gts_dict

    return None, None


def evaluate_model_on_set(ddp_model,
                          caption_idx2word_list,
                          sos_idx, eos_idx,
                          num_samples,
                          data_loader,
                          dataset_split,
                          eval_max_len,
                          rank, ddp_sync_port,
                          parallel_batches=16,
                          beam_sizes=[1],
                          stanford_model_path='./eval/get_stanford_models.sh',
                          use_images_instead_of_features=False,
                          get_predictions=False):

    with torch.no_grad():
        ddp_model.eval()

        for beam in beam_sizes:
            pred_dict, gts_dict = evaluate_model(ddp_model,
                                                 y_idx2word_list=caption_idx2word_list,
                                                 beam_size=beam, max_seq_len=eval_max_len,
                                                 sos_idx=sos_idx, eos_idx=eos_idx,
                                                 rank=rank,
                                                 ddp_sync_port=ddp_sync_port,
                                                 parallel_batches=parallel_batches,
                                                 indexes=list(range(num_samples)),
                                                 data_loader=data_loader,
                                                 dataset_split=dataset_split,
                                                 use_images_instead_of_features=use_images_instead_of_features,
                                                 verbose=True,
                                                 stanford_model_path=stanford_model_path)

            if rank == 0 and get_predictions:
                return pred_dict, gts_dict

    return None, None


def get_ensemble_model(reference_model,
                       checkpoints_paths,
                       rank=0):
    model_list = []
    for i in range(len(checkpoints_paths)):
        model = copy.deepcopy(reference_model)
        model.to(rank)
        map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
        checkpoint = torch.load(checkpoints_paths[i],
                                map_location=map_location)
        model.load_state_dict(checkpoint['model_state_dict'])
        model_list.append(model)

    model = EsembleCaptioningModel(model_list, rank).to(rank)
    ddp_model = DDP(model, device_ids=[rank])
    return ddp_model


def test(rank, world_size,
         is_end_to_end,
         model_args,
         is_ensemble,
         coco_dataset,
         eval_parallel_batch_size,
         eval_beam_sizes,
         show_predictions,
         array_of_init_seeds,
         model_max_len,
         save_model_path,
         ddp_sync_port):
    print("GPU: " + str(rank) + "] Process " + str(rank) + " working...")

    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = ddp_sync_port
    dist.init_process_group("nccl", rank=rank, world_size=world_size)

    img_size = 384
    if is_end_to_end:
        from models.End_ExpansionNet_v2 import End_ExpansionNet_v2
        model = End_ExpansionNet_v2(swin_img_size=img_size, swin_patch_size=4, swin_in_chans=3,
                                    swin_embed_dim=192, swin_depths=[2, 2, 18, 2], swin_num_heads=[6, 12, 24, 48],
                                    swin_window_size=12, swin_mlp_ratio=4., swin_qkv_bias=True, swin_qk_scale=None,
                                    swin_drop_rate=0.0, swin_attn_drop_rate=0.0, swin_drop_path_rate=0.1,
                                    swin_norm_layer=torch.nn.LayerNorm, swin_ape=False, swin_patch_norm=True,
                                    swin_use_checkpoint=False,
                                    final_swin_dim=1536,

                                    d_model=model_args.model_dim, N_enc=model_args.N_enc,
                                    N_dec=model_args.N_dec, num_heads=8, ff=2048,
                                    num_exp_enc_list=[32, 64, 128, 256, 512],
                                    num_exp_dec=16,
                                    output_word2idx=coco_dataset.caption_word2idx_dict,
                                    output_idx2word=coco_dataset.caption_idx2word_list,
                                    max_seq_len=model_max_len, drop_args=model_args.drop_args,
                                    rank=rank)
    else:
        from models.ExpansionNet_v2 import ExpansionNet_v2
        model = ExpansionNet_v2(d_model=model_args.model_dim, N_enc=model_args.N_enc,
                                N_dec=model_args.N_dec, num_heads=8, ff=2048,
                                num_exp_enc_list=[32, 64, 128, 256, 512],
                                num_exp_dec=16,
                                output_word2idx=coco_dataset.caption_word2idx_dict,
                                output_idx2word=coco_dataset.caption_idx2word_list,
                                max_seq_len=model_max_len, drop_args=model_args.drop_args,
                                img_feature_dim=1536,
                                rank=rank)

    model.to(rank)
    ddp_model = DDP(model, device_ids=[rank])

    data_loader = CocoDataLoader(coco_dataset=coco_dataset,
                                 batch_size=1,
                                 num_procs=world_size,
                                 array_of_init_seeds=array_of_init_seeds,
                                 dataloader_mode='image_wise',
                                 resize_image_size=img_size if is_end_to_end else None,
                                 rank=rank,
                                 verbose=False)

    if not is_ensemble:
        print("Not ensemble")
        map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
        checkpoint = torch.load(save_model_path, map_location=map_location)
        model.load_state_dict(checkpoint['model_state_dict'], strict=is_end_to_end)
    else:
        print("Ensembling Evaluation")
        list_checkpoints = os.listdir(save_model_path)
        checkpoints_list = [save_model_path + elem for elem in list_checkpoints if elem.endswith('.pth')]
        print("Detected checkpoints: " + str(checkpoints_list))

        if len(checkpoints_list) == 0:
            print("No checkpoints found")
            dist.destroy_process_group()
            exit(-1)
        ddp_model = get_ensemble_model(model, checkpoints_list, rank=rank)

    print("Evaluation on Validation Set")
    evaluate_model_on_set(ddp_model, coco_dataset.caption_idx2word_list,
                          coco_dataset.get_sos_token_idx(), coco_dataset.get_eos_token_idx(),
                          coco_dataset.val_num_images,
                          data_loader,
                          CocoDatasetKarpathy.ValidationSet_ID, model_max_len,
                          rank, ddp_sync_port,
                          parallel_batches=eval_parallel_batch_size,
                          use_images_instead_of_features=is_end_to_end,
                          beam_sizes=eval_beam_sizes)

    print("Evaluation on Test Set")
    pred_dict, gts_dict = evaluate_model_on_set(ddp_model, coco_dataset.caption_idx2word_list,
                                                coco_dataset.get_sos_token_idx(), coco_dataset.get_eos_token_idx(),
                                                coco_dataset.test_num_images,
                                                data_loader,
                                                CocoDatasetKarpathy.TestSet_ID, model_max_len,
                                                rank, ddp_sync_port,
                                                parallel_batches=eval_parallel_batch_size,
                                                use_images_instead_of_features=is_end_to_end,
                                                beam_sizes=eval_beam_sizes,
                                                get_predictions=show_predictions)

    if rank == 0 and show_predictions:
        with open("predictions.txt", 'w') as f:
            for i in range(len(pred_dict)):
                prediction = pred_dict[i][0]['caption']
                ground_truth_list = [gts_dict[i][j]['caption'] for j in range(len(gts_dict[i]))]
                f.write(str(i) + '----------------------------------------------------------------------' + '\n')
                f.write('Pred: ' + str(prediction) + '\n')
                f.write('Gt: ' + str(ground_truth_list) + '\n')

    print("[GPU: " + str(rank) + " ] Closing...")
    dist.destroy_process_group()


def spawn_train_processes(is_end_to_end,
                          model_args,
                          is_ensemble,
                          coco_dataset,
                          eval_parallel_batch_size,
                          eval_beam_sizes,
                          show_predictions,
                          num_gpus,
                          ddp_sync_port,
                          save_model_path
                          ):

    max_sequence_length = coco_dataset.max_seq_len + 20
    print("Max sequence length: " + str(max_sequence_length))
    print("y vocabulary size: " + str(len(coco_dataset.caption_word2idx_dict)))

    world_size = torch.cuda.device_count()
    print("Using - ", world_size, " processes / GPUs!")
    assert(num_gpus <= world_size), "requested num gpus higher than the number of available gpus "
    print("Requested num GPUs: " + str(num_gpus))

    array_of_init_seeds = [random.random() for _ in range(10)]
    mp.spawn(test,
             args=(num_gpus,
                   is_end_to_end,
                   model_args,
                   is_ensemble,
                   coco_dataset,
                   eval_parallel_batch_size,
                   eval_beam_sizes,
                   show_predictions,
                   array_of_init_seeds,
                   max_sequence_length,
                   save_model_path,
                   ddp_sync_port),
             nprocs=num_gpus,
             join=True)


if __name__ == "__main__":

    parser = argparse.ArgumentParser(description='Image Captioning')
    parser.add_argument('--model_dim', type=int, default=512)
    parser.add_argument('--N_enc', type=int, default=3)
    parser.add_argument('--N_dec', type=int, default=3)
    parser.add_argument('--show_predictions', type=str2bool, default=False)

    parser.add_argument('--is_end_to_end', type=str2bool, default=True)
    parser.add_argument('--is_ensemble', type=str2bool, default=False)

    parser.add_argument('--num_gpus', type=int, default=1)
    parser.add_argument('--ddp_sync_port', type=int, default=12354)
    parser.add_argument('--save_model_path', type=str, default='./github_ignore_material/saves/')

    parser.add_argument('--eval_parallel_batch_size', type=int, default=16)
    parser.add_argument('--eval_beam_sizes', type=str2list, default=[3])

    parser.add_argument('--images_path', type=str, default="./github_ignore_material/raw_data/")
    parser.add_argument('--preproc_images_hdf5_filepath', type=str, default=None)
    parser.add_argument('--features_path', type=str, default='./github_ignore_material/raw_data/')
    parser.add_argument('--captions_path', type=str, default='./github_ignore_material/raw_data/')

    args = parser.parse_args()
    args.ddp_sync_port = str(args.ddp_sync_port)

    assert (args.eval_parallel_batch_size % args.num_gpus == 0), \
        "num gpus must be multiple of the requested parallel batch size"

    print("is_ensemble: " + str(args.is_ensemble))
    print("eval parallel batch_size: " + str(args.eval_parallel_batch_size))
    print("ddp_sync_port: " + str(args.ddp_sync_port))
    print("save_model_path: " + str(args.save_model_path))

    drop_args = Namespace(enc=0.0,
                          dec=0.0,
                          enc_input=0.0,
                          dec_input=0.0,
                          other=0.0)

    model_args = Namespace(model_dim=args.model_dim,
                           N_enc=args.N_enc,
                           N_dec=args.N_dec,
                           dropout=0.0,
                           drop_args=drop_args
                           )

    coco_dataset = CocoDatasetKarpathy(
        images_path=args.images_path,
        coco_annotations_path=args.captions_path + "dataset_coco.json",
        train2014_bboxes_path=args.captions_path + "train2014_instances.json",
        val2014_bboxes_path=args.captions_path + "val2014_instances.json",
        preproc_images_hdf5_filepath=args.preproc_images_hdf5_filepath if args.is_end_to_end else None,
        precalc_features_hdf5_filepath=None if args.is_end_to_end else args.features_path,
        limited_num_train_images=None,
        limited_num_val_images=5000)

    spawn_train_processes(is_end_to_end=args.is_end_to_end,
                          model_args=model_args,
                          is_ensemble=args.is_ensemble,
                          coco_dataset=coco_dataset,
                          eval_parallel_batch_size=args.eval_parallel_batch_size,
                          eval_beam_sizes=args.eval_beam_sizes,
                          show_predictions=args.show_predictions,
                          num_gpus=args.num_gpus,
                          ddp_sync_port=args.ddp_sync_port,
                          save_model_path=args.save_model_path
                          )