Exemplo n.º 1
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def msrc_over_weak(n_train_full=40, n_train=276, 
                   C=100, alpha=0.1,
                   test_method='gco', test_n_iter=5, n_iter=5,
                   max_iter=1000, verbose=1,
                   check_every=50, complete_every=100, update_w_every=50,
                   relaxed_test=False,
                   use_latent_first_iter=100):
    # save parameters as meta
    meta_data = locals()

    logger = logging.getLogger(__name__)

    crf_test = HCRF(n_states=24, n_features=2028, n_edge_features=4, alpha=alpha,
                    inference_method=test_method, n_iter=test_n_iter)
    crf_latent = HCRF(n_states=24, n_features=2028, n_edge_features=4, alpha=alpha,
                      inference_method='gco', n_iter=n_iter)
    trainer = OverWeak(crf_latent, n_states=24, n_features=2028, n_edge_features=4,
                       C=C, alpha=alpha,
                       max_iter=max_iter, verbose=verbose,
                       check_every=check_every, complete_every=complete_every, update_w_every=update_w_every)

    x_train, y_train, y_train_full, x_test, y_test = load_msrc(n_train_full, n_train, dense=True)

    logger.info('start training')

    start = time()
    trainer.fit(x_train, y_train,
                train_scorer=lambda w: compute_score(crf_test, w, x_train, y_train_full, relaxed=relaxed_test),
                test_scorer=lambda w: compute_score(crf_test, w, x_test, y_test, relaxed=relaxed_test),
                use_latent_first_iter=use_latent_first_iter)
    stop = time()
    time_elapsed = stop - start

    logger.info('testing')

    test_score = compute_score(crf_test, trainer.w, x_test, y_test)
    train_score = compute_score(crf_test, trainer.w, x_train, y_train_full)

    logger.info('========================================')
    logger.info('train score: %f', train_score)
    logger.info('test score: %f', test_score)

    exp_data = {}

    exp_data['timestamps'] = trainer.timestamps
    exp_data['objective'] = trainer.objective_curve
    exp_data['w'] = trainer.w
    exp_data['train_scores'] = trainer.train_score
    exp_data['test_scores'] = trainer.test_score
    exp_data['w_history'] = trainer.w_history

    meta_data['dataset_name'] = 'msrc'
    meta_data['annotation_type'] = 'full+weak'
    meta_data['label_type'] = 'image-level labelling'
    meta_data['trainer'] = 'komodakis+latent+kappa'
    meta_data['train_score'] = train_score
    meta_data['test_score'] = test_score
    meta_data['time_elapsed'] = time_elapsed

    return ExperimentResult(exp_data, meta_data)
Exemplo n.º 2
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def msrc_over(n_train=276, C=100,
              max_iter=1000, check_every=50, verbose=1,
              test_method='gco', test_n_iter=5, relaxed_test=False):
    # save parameters as meta
    meta_data = locals()

    logger = logging.getLogger(__name__)

    crf = HCRF(n_states=24, n_features=2028, n_edge_features=4, alpha=1,
               inference_method=test_method, n_iter=test_n_iter)
    trainer = Over(n_states=24, n_features=2028, n_edge_features=4,
                   C=C, max_iter=max_iter, verbose=verbose, check_every=check_every)

    x_train, y_train, y_train_full, x_test, y_test = \
        load_msrc(n_train, n_train)

    logger.info('start training')

    start = time()
    trainer.fit(x_train, y_train_full,
                train_scorer=lambda w: compute_score(crf, w, x_train, y_train, invert=True, relaxed=relaxed_test),
                test_scorer=lambda w: compute_score(crf, w, x_test, y_test, invert=True, relaxed=relaxed_test))
    stop = time()
    time_elapsed = stop - start

    logger.info('testing')

    test_score = compute_score(crf, trainer.w, x_test, y_test, invert=True, relaxed=relaxed_test)
    train_score = compute_score(crf, trainer.w, x_train, y_train, invert=True, relaxed=relaxed_test)

    logger.info('========================================')
    logger.info('train score: %f', train_score)
    logger.info('test score: %f', test_score)

    exp_data = {}

    exp_data['timestamps'] = trainer.timestamps
    exp_data['objective'] = trainer.objective_curve
    exp_data['w'] = trainer.w
    exp_data['train_scores'] = trainer.train_score
    exp_data['test_scores'] = trainer.test_score
    exp_data['w_history'] = trainer.w_history

    meta_data['dataset_name'] = 'msrc'
    meta_data['annotation_type'] = 'full'
    meta_data['label_type'] = 'full'
    meta_data['trainer'] = 'komodakis'
    meta_data['train_score'] = train_score
    meta_data['test_score'] = test_score
    meta_data['time_elapsed'] = time_elapsed

    return ExperimentResult(exp_data, meta_data)
Exemplo n.º 3
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def msrc_full_fw(n_train=276, C=100, max_iter=500, check_dual_every=50,
                 inference_method='gco', n_inference_iter=5):
    # save parameters as meta
    meta_data = locals()

    logger = logging.getLogger(__name__)

    crf = HCRF(n_states=24, n_features=2028, n_edge_features=4, alpha=1,
               inference_method=inference_method, n_iter=n_inference_iter)
    clf = FrankWolfeSSVM(crf, verbose=2, n_jobs=1,
                         check_dual_every=check_dual_every, max_iter=max_iter, C=C)

    x_train, y_train, y_train_full, x_test, y_test = \
        load_msrc(n_train, n_train)

    logger.info('start training')

    start = time()
    clf.fit(x_train, y_train, Xtest=x_test, Ytest=y_test)
    stop = time()

    train_score = clf.score(x_train, y_train_full)
    test_score = clf.score(x_test, y_test)
    time_elapsed = stop - start

    logger.info('============================================================')
    logger.info('Score on train set: %f', train_score)
    logger.info('Score on test set: %f', test_score)
    logger.info('Elapsed time: %f s', time_elapsed)

    exp_data = {}

    exp_data['timestamps'] = clf.timestamps_
    exp_data['primal_objective'] = clf.primal_objective_curve_
    exp_data['objective'] = clf.objective_curve_
    exp_data['w_history'] = clf.w_history
    exp_data['test_scores'] = clf.test_scores
    exp_data['train_scores'] = clf.train_scores
    exp_data['w'] = clf.w

    meta_data['dataset_name'] = 'syntetic'
    meta_data['annotation_type'] = 'full'
    meta_data['label_type'] = 'full'
    meta_data['trainer'] = 'frank-wolfe'
    meta_data['train_score'] = train_score
    meta_data['test_score'] = test_score
    meta_data['time_elapsed'] = time_elapsed

    return ExperimentResult(exp_data, meta_data)
Exemplo n.º 4
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def msrc_over_weak(n_train_full=40,
                   n_train=276,
                   C=100,
                   alpha=0.1,
                   test_method='gco',
                   test_n_iter=5,
                   n_iter=5,
                   max_iter=1000,
                   verbose=1,
                   check_every=50,
                   complete_every=100,
                   update_w_every=50,
                   relaxed_test=False,
                   use_latent_first_iter=100):
    # save parameters as meta
    meta_data = locals()

    logger = logging.getLogger(__name__)

    crf_test = HCRF(n_states=24,
                    n_features=2028,
                    n_edge_features=4,
                    alpha=alpha,
                    inference_method=test_method,
                    n_iter=test_n_iter)
    crf_latent = HCRF(n_states=24,
                      n_features=2028,
                      n_edge_features=4,
                      alpha=alpha,
                      inference_method='gco',
                      n_iter=n_iter)
    trainer = OverWeak(crf_latent,
                       n_states=24,
                       n_features=2028,
                       n_edge_features=4,
                       C=C,
                       alpha=alpha,
                       max_iter=max_iter,
                       verbose=verbose,
                       check_every=check_every,
                       complete_every=complete_every,
                       update_w_every=update_w_every)

    x_train, y_train, y_train_full, x_test, y_test = load_msrc(n_train_full,
                                                               n_train,
                                                               dense=True)

    logger.info('start training')

    start = time()
    trainer.fit(x_train,
                y_train,
                train_scorer=lambda w: compute_score(
                    crf_test, w, x_train, y_train_full, relaxed=relaxed_test),
                test_scorer=lambda w: compute_score(
                    crf_test, w, x_test, y_test, relaxed=relaxed_test),
                use_latent_first_iter=use_latent_first_iter)
    stop = time()
    time_elapsed = stop - start

    logger.info('testing')

    test_score = compute_score(crf_test, trainer.w, x_test, y_test)
    train_score = compute_score(crf_test, trainer.w, x_train, y_train_full)

    logger.info('========================================')
    logger.info('train score: %f', train_score)
    logger.info('test score: %f', test_score)

    exp_data = {}

    exp_data['timestamps'] = trainer.timestamps
    exp_data['objective'] = trainer.objective_curve
    exp_data['w'] = trainer.w
    exp_data['train_scores'] = trainer.train_score
    exp_data['test_scores'] = trainer.test_score
    exp_data['w_history'] = trainer.w_history

    meta_data['dataset_name'] = 'msrc'
    meta_data['annotation_type'] = 'full+weak'
    meta_data['label_type'] = 'image-level labelling'
    meta_data['trainer'] = 'komodakis+latent+kappa'
    meta_data['train_score'] = train_score
    meta_data['test_score'] = test_score
    meta_data['time_elapsed'] = time_elapsed

    return ExperimentResult(exp_data, meta_data)
Exemplo n.º 5
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def msrc_over(n_train=276,
              C=100,
              max_iter=1000,
              check_every=50,
              verbose=1,
              test_method='gco',
              test_n_iter=5,
              relaxed_test=False):
    # save parameters as meta
    meta_data = locals()

    logger = logging.getLogger(__name__)

    crf = HCRF(n_states=24,
               n_features=2028,
               n_edge_features=4,
               alpha=1,
               inference_method=test_method,
               n_iter=test_n_iter)
    trainer = Over(n_states=24,
                   n_features=2028,
                   n_edge_features=4,
                   C=C,
                   max_iter=max_iter,
                   verbose=verbose,
                   check_every=check_every)

    x_train, y_train, y_train_full, x_test, y_test = \
        load_msrc(n_train, n_train)

    logger.info('start training')

    start = time()
    trainer.fit(
        x_train,
        y_train_full,
        train_scorer=lambda w: compute_score(
            crf, w, x_train, y_train, invert=True, relaxed=relaxed_test),
        test_scorer=lambda w: compute_score(
            crf, w, x_test, y_test, invert=True, relaxed=relaxed_test))
    stop = time()
    time_elapsed = stop - start

    logger.info('testing')

    test_score = compute_score(crf,
                               trainer.w,
                               x_test,
                               y_test,
                               invert=True,
                               relaxed=relaxed_test)
    train_score = compute_score(crf,
                                trainer.w,
                                x_train,
                                y_train,
                                invert=True,
                                relaxed=relaxed_test)

    logger.info('========================================')
    logger.info('train score: %f', train_score)
    logger.info('test score: %f', test_score)

    exp_data = {}

    exp_data['timestamps'] = trainer.timestamps
    exp_data['objective'] = trainer.objective_curve
    exp_data['w'] = trainer.w
    exp_data['train_scores'] = trainer.train_score
    exp_data['test_scores'] = trainer.test_score
    exp_data['w_history'] = trainer.w_history

    meta_data['dataset_name'] = 'msrc'
    meta_data['annotation_type'] = 'full'
    meta_data['label_type'] = 'full'
    meta_data['trainer'] = 'komodakis'
    meta_data['train_score'] = train_score
    meta_data['test_score'] = test_score
    meta_data['time_elapsed'] = time_elapsed

    return ExperimentResult(exp_data, meta_data)
Exemplo n.º 6
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def msrc_full_fw(n_train=276,
                 C=100,
                 max_iter=500,
                 check_dual_every=50,
                 inference_method='gco',
                 n_inference_iter=5):
    # save parameters as meta
    meta_data = locals()

    logger = logging.getLogger(__name__)

    crf = HCRF(n_states=24,
               n_features=2028,
               n_edge_features=4,
               alpha=1,
               inference_method=inference_method,
               n_iter=n_inference_iter)
    clf = FrankWolfeSSVM(crf,
                         verbose=2,
                         n_jobs=1,
                         check_dual_every=check_dual_every,
                         max_iter=max_iter,
                         C=C)

    x_train, y_train, y_train_full, x_test, y_test = \
        load_msrc(n_train, n_train)

    logger.info('start training')

    start = time()
    clf.fit(x_train, y_train, Xtest=x_test, Ytest=y_test)
    stop = time()

    train_score = clf.score(x_train, y_train_full)
    test_score = clf.score(x_test, y_test)
    time_elapsed = stop - start

    logger.info('============================================================')
    logger.info('Score on train set: %f', train_score)
    logger.info('Score on test set: %f', test_score)
    logger.info('Elapsed time: %f s', time_elapsed)

    exp_data = {}

    exp_data['timestamps'] = clf.timestamps_
    exp_data['primal_objective'] = clf.primal_objective_curve_
    exp_data['objective'] = clf.objective_curve_
    exp_data['w_history'] = clf.w_history
    exp_data['test_scores'] = clf.test_scores
    exp_data['train_scores'] = clf.train_scores
    exp_data['w'] = clf.w

    meta_data['dataset_name'] = 'syntetic'
    meta_data['annotation_type'] = 'full'
    meta_data['label_type'] = 'full'
    meta_data['trainer'] = 'frank-wolfe'
    meta_data['train_score'] = train_score
    meta_data['test_score'] = test_score
    meta_data['time_elapsed'] = time_elapsed

    return ExperimentResult(exp_data, meta_data)
Exemplo n.º 7
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def msrc_weak(n_full=20,
              n_train=276,
              C=100,
              latent_iter=25,
              max_iter=500,
              inner_tol=0.001,
              outer_tol=0.01,
              min_changes=0,
              initialize=True,
              alpha=0.1,
              n_inference_iter=5,
              inactive_window=50,
              inactive_threshold=1e-5,
              warm_start=False,
              inference_cache=0,
              save_inner_w=False,
              inference_method='gco'):
    meta_data = locals()

    logger = logging.getLogger(__name__)

    crf = HCRF(n_states=24,
               n_features=2028,
               n_edge_features=4,
               alpha=alpha,
               inference_method=inference_method,
               n_iter=n_inference_iter)
    base_clf = OneSlackSSVM(crf,
                            verbose=2,
                            n_jobs=4,
                            tol=inner_tol,
                            max_iter=max_iter,
                            C=C,
                            inference_cache=inference_cache,
                            inactive_window=inactive_window,
                            inactive_threshold=inactive_threshold)
    clf = LatentSSVM(base_clf,
                     latent_iter=latent_iter,
                     verbose=2,
                     tol=outer_tol,
                     min_changes=min_changes,
                     n_jobs=4)

    x_train, y_train, y_train_full, x_test, y_test = \
        load_msrc(n_full, n_train)

    start = time()
    clf.fit(x_train,
            y_train,
            initialize=initialize,
            warm_start=warm_start,
            save_inner_w=save_inner_w)
    stop = time()

    train_score = clf.score(x_train, y_train_full)
    test_score = clf.score(x_test, y_test)
    time_elapsed = stop - start

    logger.info('============================================================')
    logger.info('Score on train set: %f', train_score)
    logger.info('Score on test set: %f', test_score)
    logger.info('Norm of weight vector: |w|=%f', np.linalg.norm(clf.w))
    logger.info('Elapsed time: %f s', time_elapsed)

    test_scores = []
    for score in clf.staged_score(x_test, y_test):
        test_scores.append(score)

    train_scores = []
    for score in clf.staged_score(x_train, y_train_full):
        train_scores.append(score)

    raw_scores = []
    for score in clf.staged_score2(x_train, y_train):
        raw_scores.append(score)

    exp_data = clf._get_data()
    exp_data['test_scores'] = np.array(test_scores)
    exp_data['train_scores'] = np.array(train_scores)
    exp_data['raw_scores'] = np.array(raw_scores)

    meta_data['dataset_name'] = 'msrc'
    meta_data['annotation_type'] = 'image-level labelling'
    meta_data['label_type'] = 'full+weak'
    meta_data['train_score'] = train_score
    meta_data['test_score'] = test_score
    meta_data['time_elapsed'] = time_elapsed
    meta_data['iter_done'] = clf.iter_done

    return ExperimentResult(exp_data, meta_data)
Exemplo n.º 8
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def msrc_weak(n_full=20, n_train=276, C=100, latent_iter=25,
              max_iter=500, inner_tol=0.001, outer_tol=0.01, min_changes=0,
              initialize=True, alpha=0.1, n_inference_iter=5,
              inactive_window=50, inactive_threshold=1e-5,
              warm_start=False, inference_cache=0,
              save_inner_w=False, inference_method='gco'):
    meta_data = locals()

    logger = logging.getLogger(__name__)

    crf = HCRF(n_states=24, n_features=2028, n_edge_features=4, alpha=alpha,
               inference_method=inference_method, n_iter=n_inference_iter)
    base_clf = OneSlackSSVM(crf, verbose=2, n_jobs=4,
                            tol=inner_tol, max_iter=max_iter, C=C,
                            inference_cache=inference_cache,
                            inactive_window=inactive_window,
                            inactive_threshold=inactive_threshold)
    clf = LatentSSVM(base_clf, latent_iter=latent_iter, verbose=2,
                     tol=outer_tol, min_changes=min_changes, n_jobs=4)

    x_train, y_train, y_train_full, x_test, y_test = \
        load_msrc(n_full, n_train)

    start = time()
    clf.fit(x_train, y_train,
            initialize=initialize,
            warm_start=warm_start,
            save_inner_w=save_inner_w)
    stop = time()

    train_score = clf.score(x_train, y_train_full)
    test_score = clf.score(x_test, y_test)
    time_elapsed = stop - start 

    logger.info('============================================================')
    logger.info('Score on train set: %f', train_score)
    logger.info('Score on test set: %f', test_score)
    logger.info('Norm of weight vector: |w|=%f', np.linalg.norm(clf.w))
    logger.info('Elapsed time: %f s', time_elapsed)

    test_scores = []
    for score in clf.staged_score(x_test, y_test):
        test_scores.append(score)

    train_scores = []
    for score in clf.staged_score(x_train, y_train_full):
        train_scores.append(score)

    raw_scores = []
    for score in clf.staged_score2(x_train, y_train):
        raw_scores.append(score)

    exp_data = clf._get_data()
    exp_data['test_scores'] = np.array(test_scores)
    exp_data['train_scores'] = np.array(train_scores)
    exp_data['raw_scores'] = np.array(raw_scores)

    meta_data['dataset_name'] = 'msrc'
    meta_data['annotation_type'] = 'image-level labelling'
    meta_data['label_type'] = 'full+weak'
    meta_data['train_score'] = train_score
    meta_data['test_score'] = test_score
    meta_data['time_elapsed'] = time_elapsed
    meta_data['iter_done'] = clf.iter_done

    return ExperimentResult(exp_data, meta_data)