def save_confusion_matrix(results_path, correct_y, predict_y, category_list, mapping_fn=None, data_x=None): import plottool as pt fig = show_confusion_matrix( correct_y, predict_y, category_list, mapping_fn=mapping_fn, data_x=data_x) output_fpath = join(results_path, 'confusion.png') pt.save_figure(fig, fpath=output_fpath) return output_fpath
def dump_challenge_fig(ibs, aid, output_dirname): import plottool as pt pt.clf() title_suffix = str(ibs.get_annot_visual_uuids(aid)) fig, ax = ibeis.viz.show_chip(ibs, aid, annote=False, show_aidstr=False, show_name=False, show_num_gt=False, fnum=1, title_suffix=title_suffix) #fig.show() #pt.iup() fpath = ut.truepath(join(output_dirname, 'avuuid%s.png' % (title_suffix.replace('-', ''),))) pt.save_figure(fpath_strict=fpath, fig=fig, fnum=1, verbose=False) vt.clipwhite_ondisk(fpath, fpath) return fpath
def dump_match_img(qres, ibs, aid, qreq_=None, fnum=None, *args, **kwargs): import plottool as pt import matplotlib as mpl # Pop save kwargs from kwargs save_keys = ['dpi', 'figsize', 'saveax', 'fpath', 'fpath_strict', 'verbose'] save_vals = ut.dict_take_pop(kwargs, save_keys, None) savekw = dict(zip(save_keys, save_vals)) fpath = savekw.pop('fpath') if fpath is None and 'fpath_strict' not in savekw: savekw['usetitle'] = True was_interactive = mpl.is_interactive() if was_interactive: mpl.interactive(False) # Make new figure if fnum is None: fnum = pt.next_fnum() #fig = pt.figure(fnum=fnum, doclf=True, docla=True) fig = pt.plt.figure(fnum) fig.clf() # Draw Matches ax, xywh1, xywh2 = qres.show_matches(ibs, aid, colorbar_=False, qreq_=qreq_, fnum=fnum, **kwargs) if not kwargs.get('notitle', False): pt.set_figtitle(qres.make_smaller_title()) # Save Figure # Setting fig=fig might make the dpi and figsize code not work img_fpath = pt.save_figure(fpath=fpath, fig=fig, **savekw) if was_interactive: mpl.interactive(was_interactive) pt.plt.close(fig) # Ensure that this figure will not pop up #if False: # ut.startfile(img_fpath) return img_fpath
def dump_challenge_fig(ibs, aid, output_dirname): import plottool as pt pt.clf() title_suffix = str(ibs.get_annot_visual_uuids(aid)) fig, ax = ibeis.viz.show_chip(ibs, aid, annote=False, show_aidstr=False, show_name=False, show_num_gt=False, fnum=1, title_suffix=title_suffix) #fig.show() #pt.iup() fpath = ut.truepath( join(output_dirname, 'avuuid%s.png' % (title_suffix.replace('-', ''), ))) pt.save_figure(fpath_strict=fpath, fig=fig, fnum=1, verbose=False) vt.clipwhite_ondisk(fpath, fpath) return fpath
def dump_img(img_, lbl, fnum): fig, ax = pt.imshow(img_, figtitle=dataname + ' ' + lbl, fnum=fnum) pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
def test_siamese_performance(model, data, labels, flat_metadata, dataname=''): r""" CommandLine: utprof.py -m ibeis_cnn --tf pz_patchmatch --db liberty --test --weights=liberty:current --arch=siaml2_128 --test python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure --weights=new python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --train --weights=new python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128 --test # NOQA python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128 """ import vtool as vt import plottool as pt # TODO: save in model.trainind_dpath/diagnostics/figures ut.colorprint('\n[siam_perf] Testing Siamese Performance', 'white') #epoch_dpath = model.get_epoch_diagnostic_dpath() epoch_dpath = model.arch_dpath ut.vd(epoch_dpath) dataname += ' ' + model.get_history_hashid() + '\n' history_text = ut.list_str(model.era_history, newlines=True) ut.write_to(ut.unixjoin(epoch_dpath, 'era_history.txt'), history_text) #if True: # import matplotlib as mpl # mpl.rcParams['agg.path.chunksize'] = 100000 #data = data[::50] #labels = labels[::50] #from ibeis_cnn import utils #data, labels = utils.random_xy_sample(data, labels, 10000, model.data_per_label_input) FULL = not ut.get_argflag('--quick') fnum_gen = pt.make_fnum_nextgen() ut.colorprint('[siam_perf] Show era history', 'white') fig = model.show_era_loss(fnum=fnum_gen()) pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180) # hack ut.colorprint('[siam_perf] Show weights image', 'white') fig = model.show_weights_image(fnum=fnum_gen()) pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180) #model.draw_all_conv_layer_weights(fnum=fnum_gen()) #model.imwrite_weights(1) #model.imwrite_weights(2) # Compute each type of score ut.colorprint('[siam_perf] Building Scores', 'white') test_outputs = model.predict2(model, data) network_output = test_outputs['network_output_determ'] # hack converting network output to distances for non-descriptor networks if len(network_output.shape) == 2 and network_output.shape[1] == 1: cnn_scores = network_output.T[0] elif len(network_output.shape) == 1: cnn_scores = network_output elif len(network_output.shape) == 2 and network_output.shape[1] > 1: assert model.data_per_label_output == 2 vecs1 = network_output[0::2] vecs2 = network_output[1::2] cnn_scores = vt.L2(vecs1, vecs2) else: assert False cnn_scores = cnn_scores.astype(np.float64) # Segfaults with the data passed in is large (AND MEMMAPPED apparently) # Fixed in hesaff implementation SIFT = FULL if SIFT: sift_scores, sift_list = test_sift_patchmatch_scores(data, labels) sift_scores = sift_scores.astype(np.float64) ut.colorprint('[siam_perf] Learning Encoders', 'white') # Learn encoders encoder_kw = { #'monotonize': False, 'monotonize': True, } cnn_encoder = vt.ScoreNormalizer(**encoder_kw) cnn_encoder.fit(cnn_scores, labels) if SIFT: sift_encoder = vt.ScoreNormalizer(**encoder_kw) sift_encoder.fit(sift_scores, labels) # Visualize ut.colorprint('[siam_perf] Visualize Encoders', 'white') viz_kw = dict( with_scores=False, with_postbayes=False, with_prebayes=False, target_tpr=.95, ) inter_cnn = cnn_encoder.visualize( figtitle=dataname + ' CNN scores. #data=' + str(len(data)), fnum=fnum_gen(), **viz_kw) if SIFT: inter_sift = sift_encoder.visualize( figtitle=dataname + ' SIFT scores. #data=' + str(len(data)), fnum=fnum_gen(), **viz_kw) # Save pt.save_figure(fig=inter_cnn.fig, dpath=epoch_dpath) if SIFT: pt.save_figure(fig=inter_sift.fig, dpath=epoch_dpath) # Save out examples of hard errors #cnn_fp_label_indicies, cnn_fn_label_indicies = #cnn_encoder.get_error_indicies(cnn_scores, labels) #sift_fp_label_indicies, sift_fn_label_indicies = #sift_encoder.get_error_indicies(sift_scores, labels) with_patch_examples = FULL if with_patch_examples: ut.colorprint('[siam_perf] Visualize Confusion Examples', 'white') cnn_indicies = cnn_encoder.get_confusion_indicies(cnn_scores, labels) if SIFT: sift_indicies = sift_encoder.get_confusion_indicies(sift_scores, labels) warped_patch1_list, warped_patch2_list = list(zip(*ut.ichunks(data, 2))) samp_args = (warped_patch1_list, warped_patch2_list, labels) _sample = functools.partial(draw_results.get_patch_sample_img, *samp_args) cnn_fp_img = _sample({'fs': cnn_scores}, cnn_indicies.fp)[0] cnn_fn_img = _sample({'fs': cnn_scores}, cnn_indicies.fn)[0] cnn_tp_img = _sample({'fs': cnn_scores}, cnn_indicies.tp)[0] cnn_tn_img = _sample({'fs': cnn_scores}, cnn_indicies.tn)[0] if SIFT: sift_fp_img = _sample({'fs': sift_scores}, sift_indicies.fp)[0] sift_fn_img = _sample({'fs': sift_scores}, sift_indicies.fn)[0] sift_tp_img = _sample({'fs': sift_scores}, sift_indicies.tp)[0] sift_tn_img = _sample({'fs': sift_scores}, sift_indicies.tn)[0] #if ut.show_was_requested(): #def rectify(arr): # return np.flipud(arr) SINGLE_FIG = False if SINGLE_FIG: def dump_img(img_, lbl, fnum): fig, ax = pt.imshow(img_, figtitle=dataname + ' ' + lbl, fnum=fnum) pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180) dump_img(cnn_fp_img, 'cnn_fp_img', fnum_gen()) dump_img(cnn_fn_img, 'cnn_fn_img', fnum_gen()) dump_img(cnn_tp_img, 'cnn_tp_img', fnum_gen()) dump_img(cnn_tn_img, 'cnn_tn_img', fnum_gen()) dump_img(sift_fp_img, 'sift_fp_img', fnum_gen()) dump_img(sift_fn_img, 'sift_fn_img', fnum_gen()) dump_img(sift_tp_img, 'sift_tp_img', fnum_gen()) dump_img(sift_tn_img, 'sift_tn_img', fnum_gen()) #vt.imwrite(dataname + '_' + 'cnn_fp_img.png', (cnn_fp_img)) #vt.imwrite(dataname + '_' + 'cnn_fn_img.png', (cnn_fn_img)) #vt.imwrite(dataname + '_' + 'sift_fp_img.png', (sift_fp_img)) #vt.imwrite(dataname + '_' + 'sift_fn_img.png', (sift_fn_img)) else: print('Drawing TP FP TN FN') fnum = fnum_gen() pnum_gen = pt.make_pnum_nextgen(4, 2) fig = pt.figure(fnum) pt.imshow(cnn_fp_img, title='CNN FP', fnum=fnum, pnum=pnum_gen()) pt.imshow(sift_fp_img, title='SIFT FP', fnum=fnum, pnum=pnum_gen()) pt.imshow(cnn_fn_img, title='CNN FN', fnum=fnum, pnum=pnum_gen()) pt.imshow(sift_fn_img, title='SIFT FN', fnum=fnum, pnum=pnum_gen()) pt.imshow(cnn_tp_img, title='CNN TP', fnum=fnum, pnum=pnum_gen()) pt.imshow(sift_tp_img, title='SIFT TP', fnum=fnum, pnum=pnum_gen()) pt.imshow(cnn_tn_img, title='CNN TN', fnum=fnum, pnum=pnum_gen()) pt.imshow(sift_tn_img, title='SIFT TN', fnum=fnum, pnum=pnum_gen()) pt.set_figtitle(dataname + ' confusions') pt.adjust_subplots(left=0, right=1.0, bottom=0., wspace=.01, hspace=.05) pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18)) with_patch_desc = FULL if with_patch_desc: ut.colorprint('[siam_perf] Visualize Patch Descriptors', 'white') fnum = fnum_gen() fig = pt.figure(fnum=fnum, pnum=(1, 1, 1)) num_rows = 7 pnum_gen = pt.make_pnum_nextgen(num_rows, 3) # Compare actual output descriptors for index in ut.random_indexes(len(sift_list), num_rows): vec_sift = sift_list[index] vec_cnn = network_output[index] patch = data[index] pt.imshow(patch, fnum=fnum, pnum=pnum_gen()) pt.plot_descriptor_signature(vec_cnn, 'cnn vec', fnum=fnum, pnum=pnum_gen()) pt.plot_sift_signature(vec_sift, 'sift vec', fnum=fnum, pnum=pnum_gen()) pt.set_figtitle('Patch Descriptors') pt.adjust_subplots(left=0, right=0.95, bottom=0., wspace=.1, hspace=.15) pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))