def test(iter, dataset, visualize, setname, dcrf, mu, tfmodel_path, model_name, pre_emb=False): data_folder = './' + dataset + '/' + setname + '_batch/' data_prefix = dataset + '_' + setname if visualize: save_dir = './' + dataset + '/visualization/' + str(iter) + '/' if not os.path.isdir(save_dir): os.makedirs(save_dir) weights = os.path.join(tfmodel_path) print("Loading trained weights from {}".format(weights)) score_thresh = 1e-9 eval_seg_iou_list = [.5, .6, .7, .8, .9] cum_I, cum_U = 0, 0 mean_IoU, mean_dcrf_IoU = 0, 0 seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32) if dcrf: cum_I_dcrf, cum_U_dcrf = 0, 0 seg_correct_dcrf = np.zeros(len(eval_seg_iou_list), dtype=np.int32) seg_total = 0. T = 20 # truncated long sentence H, W = 320, 320 vocab_size = 8803 if dataset == 'referit' else 12112 emb_name = 'referit' if dataset == 'referit' else 'refvos' vocab_file = './data/vocabulary_refvos.txt' vocab_dict = text_processing.load_vocab_dict_from_file(vocab_file) IU_result = list() if pre_emb: # use pretrained embbeding print("Use pretrained Embeddings.") model = get_segmentation_model(model_name, H=H, W=W, mode='eval', vocab_size=vocab_size, emb_name=emb_name, emb_dir=args.embdir) else: model = get_segmentation_model(model_name, H=H, W=W, mode='eval', vocab_size=vocab_size) # Load pretrained model snapshot_restorer = tf.train.Saver() config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) sess.run(tf.global_variables_initializer()) snapshot_restorer.restore(sess, weights) meta_expression = {} with open(args.meta) as meta_file: meta_expression = json.load(meta_file) videos = meta_expression['videos'] plt.figure(figsize=[15, 4]) sorted_video_key = ['a9f23c9150', '6cc8bce61a', '03fe6115d4', 'a46012c642', 'c42fdedcdd', 'ee9415c553', '7daa6343e6', '4fe6619a47', '0e8a6b63bb', '65e0640a2a', '8939473ea7', 'b05faf54f7', '5d2020eff8', 'a00c3fa88e', '44e5d1a969', 'deed0ab4fc', 'b205d868e6', '48d2909d9e', 'c9ef04fe59', '1e20ceafae', '0f3f8b2b2f', 'b83923fd72', 'cb06f84b6e', '17cba76927', '35d5e5149d', '62bf7630b3', '0390fabe58', 'bf2d38aefe', '8b7b57b94d', '8d803e87f7', 'c16d9a4ade', '1a1dbe153e', 'd975e5f4a9', '226f1e10f7', '6cb5b08d93', '77df215672', '466734bc5c', '94fa9bd3b5', 'f2a45acf1c', 'ba8823f2d2', '06cd94d38d', 'b772ac822a', '246e38963b', 'b5514f75d8', '188cb4e03d', '3dd327ab4e', '8e2e5af6a8', '450bd2e238', '369919ef49', 'a4bce691c6', '64c6f2ed76', '0782a6df7e', '0062f687f1', 'c74fc37224', 'f7255a57d0', '4f5b3310e3', 'e027ebc228', '30fe0ed0ce', '6a75316e99', 'a2948d4116', '8273b59141', 'abae1ce57d', '621487be65', '45dc90f558', '9787f452bf', 'cdcfd9f93a', '4f6662e4e0', '853ca85618', '13ca7bbcfd', 'f143fede6f', '92fde455eb', '0b0c90e21a', '5460cc540a', '182dbfd6ba', '85968ae408', '541ccb0844', '43115c42b2', '65350fd60a', 'eb49ce8027', 'e11254d3b9', '20a93b4c54', 'a0fc95d8fc', '696e01387c', 'fef7e84268', '72d613f21a', '8c60938d92', '975be70866', '13c3cea202', '4ee0105885', '01c88b5b60', '33e8066265', '8dea7458de', 'c280d21988', 'fd8cf868b2', '35948a7fca', 'e10236eb37', 'a1251195e7', 'b2256e265c', '2b904b76c9', '1ab5f4bbc5', '47d01d34c8', 'd7a38bf258', '1a609fa7ee', '218ac81c2d', '9f16d17e42', 'fb104c286f', 'eb263ef128', '37b4ec2e1a', '0daaddc9da', 'cd69993923', '31d3a7d2ee', '60362df585', 'd7ff44ea97', '623d24ce2b', '6031809500', '54526e3c66', '0788b4033d', '3f4bacb16a', '06a5dfb511', '9f21474aca', '7a19a80b19', '9a38b8e463', '822c31928a', 'd1ac0d8b81', 'eea1a45e49', '9f429af409', '33c8dcbe09', '9da2156a73', '3be852ed44', '3674b2c70a', '547416bda1', '4037d8305d', '29c06df0f2', '1335b16cf9', 'b7b7e52e02', 'bc9ba8917e', 'dab44991de', '9fd2d2782b', 'f054e28786', 'b00ff71889', 'eeb18f9d47', '559a611d86', 'dea0160a12', '257f7fd5b8', 'dc197289ef', 'c2bbd6d121', 'f3678388a7', '332dabe378', '63883da4f5', 'b90f8c11db', 'dce363032d', '411774e9ff', '335fc10235', '7775043b5e', '3e03f623bb', '19cde15c4b', 'bf4cc89b18', '1a894a8f98', 'f7d7fb16d0', '61fca8cbf1', 'd69812339e', 'ab9a7583f1', 'e633eec195', '0a598e18a8', 'b3b92781d9', 'cd896a9bee', 'b7928ea5c0', '69c0f7494e', 'cc1a82ac2a', '39b7491321', '352ad66724', '749f1abdf9', '7f26b553ae', '0c04834d61', 'd1dd586cfd', '3b72dc1941', '39bce09d8d', 'cbea8f6bea', 'cc7c3138ff', 'd59c093632', '68dab8f80c', '1e0257109e', '4307020e0f', '4b783f1fc5', 'ebe7138e58', '1f390d22ea', '7a72130f21', 'aceb34fcbe', '9c0b55cae5', 'b58a97176b', '152fe4902a', 'a806e58451', '9ce299a510', '97b38cabcc', 'f39c805b54', '0620b43a31', '0723d7d4fe', '7741a0fbce', '7836afc0c2', 'a7462d6aaf', '34564d26d8', '31e0beaf99'] # sorted_video_key = ['6cc8bce61a'] for vid_ind, vid in enumerate(sorted_video_key): print("Running on video {}/{}".format(vid_ind + 1, len(videos.keys()))) expressions = videos[vid]['expressions'] # instance_ids = [expression['obj_id'] for expression_id in videos[vid]['expressions']] frame_ids = videos[vid]['frames'] for eid in expressions: exp = expressions[eid]['exp'] index = int(eid) vis_dir = args.visdir # mask_dir = os.path.join(args.maskdir, str('{}/{}/'.format(vid, index))) if not os.path.exists(vis_dir): os.makedirs(vis_dir) # if not os.path.exists(mask_dir): # os.makedirs(mask_dir) avg_time = 0 total_frame = 0 # Process text text = np.array(text_processing.preprocess_sentence(exp, vocab_dict, T)) valid_idx = np.zeros([1], dtype=np.int32) for idx in range(text.shape[0]): if text[idx] != 0: valid_idx[0] = idx break for fid in frame_ids: frame_id = int(fid) if (frame_id % 20 != 0): continue vis_path = os.path.join(vis_dir, str('{}_{}_{}.png'.format(vid,eid,fid))) frame = load_frame_from_id(vid, fid) if frame is None: continue last_time = time.time() # im = frame.copy() im = frame # mask = np.array(frame, dtype=np.float32) proc_im = skimage.img_as_ubyte(im_processing.resize_and_pad(im, H, W)) proc_im_ = proc_im.astype(np.float32) proc_im_ = proc_im_[:, :, ::-1] proc_im_ -= mu scores_val, up_val, sigm_val, up_c4 = sess.run([model.pred, model.up, model.sigm, model.up_c4, ], feed_dict={ model.words: np.expand_dims(text, axis=0), model.im: np.expand_dims(proc_im_, axis=0), model.valid_idx: np.expand_dims(valid_idx, axis=0) }) # scores_val = np.squeeze(scores_val) # pred_raw = (scores_val >= score_thresh).astype(np.float32) up_c4 = im_processing.resize_and_crop(sigmoid(np.squeeze(up_c4)), frame.shape[0], frame.shape[1]) sigm_val = im_processing.resize_and_crop(sigmoid(np.squeeze(sigm_val)), frame.shape[0], frame.shape[1]) up_val = np.squeeze(up_val) # if (not math.isnan(consitency_score) and consitency_score < 0.3): plt.clf() plt.subplot(1, 3, 1) plt.imshow(frame) plt.text(-0.7, -0.7, exp + str(consitency_score)) plt.subplot(1, 3, 2) plt.imshow(up_c4) plt.subplot(1, 3, 3) plt.imshow(sigm_val) plt.savefig(vis_path) # pred_raw = (up_val >= score_thresh).astype('uint8') * 255 # pred_raw = (up_val >= score_thresh).astype(np.float32) # predicts = im_processing.resize_and_crop(pred_raw, mask.shape[0], mask.shape[1]) # if dcrf: # # Dense CRF post-processing # sigm_val = np.squeeze(sigm_val) + 1e-7 # d = densecrf.DenseCRF2D(W, H, 2) # U = np.expand_dims(-np.log(sigm_val), axis=0) # U_ = np.expand_dims(-np.log(1 - sigm_val), axis=0) # unary = np.concatenate((U_, U), axis=0) # unary = unary.reshape((2, -1)) # d.setUnaryEnergy(unary) # d.addPairwiseGaussian(sxy=3, compat=3) # d.addPairwiseBilateral(sxy=20, srgb=3, rgbim=proc_im, compat=10) # Q = d.inference(5) # pred_raw_dcrf = np.argmax(Q, axis=0).reshape((H, W)).astype('uint8') * 255 # # pred_raw_dcrf = np.argmax(Q, axis=0).reshape((H, W)).astype(np.float32) # # predicts_dcrf = im_processing.resize_and_crop(pred_raw_dcrf, mask.shape[0], mask.shape[1]) # if visualize: # if dcrf: # cv2.imwrite(vis_path, pred_raw_dcrf) # # np.save(mask_path, np.array(pred_raw_dcrf)) # # visualize_seg(vis_path, im, exp, predicts_dcrf) # else: # np.save(mask_path, np.array(sigm_val)) # cv2.imwrite(vis_path, pred_raw) # visualize_seg(vis_path, im, exp, predicts) # np.save(mask_path, np.array(pred_raw)) # I, U = eval_tools.compute_mask_IU(predicts, mask) # IU_result.append({'batch_no': n_iter, 'I': I, 'U': U}) # mean_IoU += float(I) / U # cum_I += I # cum_U += U # msg = 'cumulative IoU = %f' % (cum_I / cum_U) # for n_eval_iou in range(len(eval_seg_iou_list)): # eval_seg_iou = eval_seg_iou_list[n_eval_iou] # seg_correct[n_eval_iou] += (I / U >= eval_seg_iou) # if dcrf: # I_dcrf, U_dcrf = eval_tools.compute_mask_IU(predicts_dcrf, mask) # mean_dcrf_IoU += float(I_dcrf) / U_dcrf # cum_I_dcrf += I_dcrf # cum_U_dcrf += U_dcrf # msg += '\tcumulative IoU (dcrf) = %f' % (cum_I_dcrf / cum_U_dcrf) # for n_eval_iou in range(len(eval_seg_iou_list)): # eval_seg_iou = eval_seg_iou_list[n_eval_iou] # seg_correct_dcrf[n_eval_iou] += (I_dcrf / U_dcrf >= eval_seg_iou) # print(msg) seg_total += 1
imcrop_val[...] = processed_im.astype(np.float32) - segmodel.vgg_net.channel_mean for imcrop_name, _, description in flat_query_dict[imname]: mask = load_gt_mask(mask_dir + imcrop_name + '.mat').astype(np.float32) labels = (mask > 0) processed_labels = im_processing.resize_and_pad(mask, input_H, input_W) > 0 text_seq_val[:, 0] = text_processing.preprocess_sentence(description, vocab_dict, T) scores_val = sess.run(scores, feed_dict={ text_seq_batch : text_seq_val, imcrop_batch : imcrop_val }) scores_val = np.squeeze(scores_val) # Evaluate the segmentation performance of using bounding box segmentation pred_raw = (scores_val >= score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, im.shape[0], im.shape[1]) I, U = eval_tools.compute_mask_IU(predicts, labels) cum_I += I cum_U += U this_IoU = I/U for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct[n_eval_iou] += (I/U >= eval_seg_iou) seg_total += 1 # Print results print('Final results on the whole test set') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou]/seg_total)
net.blobs['language'].data[...] = text_seq_val net.blobs['cont'].data[...] = cont_val net.blobs['image'].data[...] = imcrop_val net.blobs['spatial'].data[...] = spatial_val net.blobs['label'].data[...] = dummy_label net.forward() upscores = net.blobs['upscores'].data[...].copy() upscores = np.squeeze(upscores) # Final prediction upscores = sigmoid(upscores) #print( str(np.amax(upscores)) ) score_thresh = np.amax(upscores) * 0.5 prediction = im_processing.resize_and_crop( upscores > score_thresh, *im.shape[:2]).astype(np.bool) #print( str(np.sum(prediction)) ) # save the results if not os.path.exists( '/home/zhenyang/Workspace/data/drones/results/results_lang_seg_mask/' + video): os.makedirs( '/home/zhenyang/Workspace/data/drones/results/results_lang_seg_mask/' + video) filename1 = '/home/zhenyang/Workspace/data/drones/results/results_lang_seg_mask/' + video + '/%05d.jpg' % ( fi, ) plt.imsave(filename1, np.array(prediction), cmap=cm.gray) if not os.path.exists( '/home/zhenyang/Workspace/data/drones/results/results_lang_seg_bbox/'
def test(modelname, iter, dataset, weights, setname, dcrf, mu, tfmodel_folder): data_folder = './' + dataset + '/' + setname + '_batch/' data_prefix = dataset + '_' + setname tfmodel_folder = './' + dataset + '/tfmodel/CMSA' pretrained_model = os.path.join( tfmodel_folder, dataset + '_' + modelname + '_release' + '.tfmodel') score_thresh = 1e-9 eval_seg_iou_list = [.5, .6, .7, .8, .9] cum_I, cum_U = 0, 0 mean_IoU, mean_dcrf_IoU = 0, 0 seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32) if dcrf: cum_I_dcrf, cum_U_dcrf = 0, 0 seg_correct_dcrf = np.zeros(len(eval_seg_iou_list), dtype=np.int32) seg_total = 0. H, W = 320, 320 vocab_size = 8803 if dataset == 'referit' else 12112 IU_result = list() model = CMSA_model(H=H, W=W, mode='eval', vocab_size=vocab_size, weights=weights) # Load pretrained model snapshot_restorer = tf.train.Saver() config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) sess.run(tf.global_variables_initializer()) snapshot_restorer.restore(sess, pretrained_model) reader = data_reader.DataReader(data_folder, data_prefix, shuffle=False) NN = reader.num_batch print('test in', dataset, setname) for n_iter in range(reader.num_batch): if n_iter % (NN // 50) == 0: if n_iter / (NN // 50) % 5 == 0: sys.stdout.write(str(n_iter / (NN // 50) // 5)) else: sys.stdout.write('.') sys.stdout.flush() batch = reader.read_batch(is_log=False) text = batch['text_batch'] im = batch['im_batch'] mask = batch['mask_batch'].astype(np.float32) proc_im = skimage.img_as_ubyte(im_processing.resize_and_pad(im, H, W)) proc_im_ = proc_im.astype(np.float32) proc_im_ = proc_im_[:, :, ::-1] proc_im_ -= mu scores_val, up_val, sigm_val = sess.run( [model.pred, model.up, model.sigm], feed_dict={ model.words: np.expand_dims(text, axis=0), model.im: np.expand_dims(proc_im_, axis=0) }) up_val = np.squeeze(up_val) pred_raw = (up_val >= score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, mask.shape[0], mask.shape[1]) if dcrf: # Dense CRF post-processing sigm_val = np.squeeze(sigm_val) d = densecrf.DenseCRF2D(W, H, 2) U = np.expand_dims(-np.log(sigm_val), axis=0) U_ = np.expand_dims(-np.log(1 - sigm_val), axis=0) unary = np.concatenate((U_, U), axis=0) unary = unary.reshape((2, -1)) d.setUnaryEnergy(unary) d.addPairwiseGaussian(sxy=3, compat=3) d.addPairwiseBilateral(sxy=20, srgb=3, rgbim=proc_im, compat=10) Q = d.inference(5) pred_raw_dcrf = np.argmax(Q, axis=0).reshape( (H, W)).astype(np.float32) predicts_dcrf = im_processing.resize_and_crop( pred_raw_dcrf, mask.shape[0], mask.shape[1]) I, U = eval_tools.compute_mask_IU(predicts, mask) IU_result.append({'batch_no': n_iter, 'I': I, 'U': U}) mean_IoU += float(I) / U cum_I += I cum_U += U msg = 'cumulative IoU = %f' % (cum_I / cum_U) for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct[n_eval_iou] += (I / U >= eval_seg_iou) if dcrf: I_dcrf, U_dcrf = eval_tools.compute_mask_IU(predicts_dcrf, mask) mean_dcrf_IoU += float(I_dcrf) / U_dcrf cum_I_dcrf += I_dcrf cum_U_dcrf += U_dcrf msg += '\tcumulative IoU (dcrf) = %f' % (cum_I_dcrf / cum_U_dcrf) for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct_dcrf[n_eval_iou] += (I_dcrf / U_dcrf >= eval_seg_iou) # print(msg) seg_total += 1 # Print results print('Segmentation evaluation (without DenseCRF):') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou]/seg_total) result_str += 'overall IoU = %f; mean IoU = %f\n' % (cum_I / cum_U, mean_IoU / seg_total) print(result_str) if dcrf: print('Segmentation evaluation (with DenseCRF):') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct_dcrf[n_eval_iou]/seg_total) result_str += 'overall IoU = %f; mean IoU = %f\n' % ( cum_I_dcrf / cum_U_dcrf, mean_dcrf_IoU / seg_total) print(result_str)
def test(iter, dataset, visualize, setname, dcrf, mu, tfmodel_folder, pre_emb=False, use_tree=False, neg_num=0.1): data_folder = './' + dataset + '/' + setname + '_batch/' data_prefix = dataset + '_' + setname if visualize: save_dir = './' + dataset + '/visualization/' + str(iter) + '/' if not os.path.isdir(save_dir): os.makedirs(save_dir) weights = os.path.join(tfmodel_folder, dataset + '_iter_' + str(iter) + '.tfmodel') score_thresh = 1e-9 eval_seg_iou_list = [.5, .6, .7, .8, .9] cum_I, cum_U = 0, 0 mean_IoU, mean_dcrf_IoU = 0, 0 seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32) if dcrf: cum_I_dcrf, cum_U_dcrf = 0, 0 seg_correct_dcrf = np.zeros(len(eval_seg_iou_list), dtype=np.int32) seg_total = 0. H, W = 320, 320 vocab_size = 8226 if dataset == 'referit' else 21692 emb_name = 'referit' if dataset == 'referit' else 'Gref' IU_result = list() if pre_emb: # use pretrained embbeding print("Use pretrained Embeddings.") model = LSCM_model(num_steps=30, H=H, W=W, mode='eval', vocab_size=vocab_size, emb_name=emb_name) else: model = LSCM_model(num_steps=30, H=H, W=W, mode='eval', vocab_size=vocab_size) # Load pretrained model snapshot_restorer = tf.train.Saver() config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) sess.run(tf.global_variables_initializer()) snapshot_restorer.restore(sess, weights) reader = data_reader.DataReader(data_folder, data_prefix, shuffle=False) NN = reader.num_batch for n_iter in range(reader.num_batch): if n_iter % (NN // 50) == 0: if n_iter / (NN // 50) % 5 == 0: sys.stdout.write(str(n_iter / (NN // 50) // 5)) else: sys.stdout.write('.') sys.stdout.flush() batch = reader.read_batch(is_log=False) text = batch['text_batch'] im = batch['im_batch'] mask = batch['mask_batch'].astype(np.float32) valid_idx = np.zeros([1], dtype=np.int32) graph = batch['graph_batch'] height = batch['height_batch'] for idx in range(text.shape[0]): if text[idx] != 0: valid_idx[0] = idx break if neg_num != 0.1: graph[graph < 0.5] = neg_num proc_im = skimage.img_as_ubyte(im_processing.resize_and_pad(im, H, W)) proc_im_ = proc_im.astype(np.float32) proc_im_ = proc_im_[:, :, ::-1] proc_im_ -= mu if use_tree: scores_val, up_val, sigm_val = sess.run( [model.pred, model.up, model.sigm], feed_dict={ model.words: np.expand_dims(text, axis=0), model.im: np.expand_dims(proc_im_, axis=0), model.valid_idx: np.expand_dims(valid_idx, axis=0), model.graph_adj: np.expand_dims(graph, axis=0), model.tree_height: np.expand_dims(height, axis=0) }) else: scores_val, up_val, sigm_val = sess.run( [model.pred, model.up, model.sigm], feed_dict={ model.words: np.expand_dims(text, axis=0), model.im: np.expand_dims(proc_im_, axis=0), model.valid_idx: np.expand_dims(valid_idx, axis=0) }) # scores_val = np.squeeze(scores_val) # pred_raw = (scores_val >= score_thresh).astype(np.float32) up_val = np.squeeze(up_val) pred_raw = (up_val >= score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, mask.shape[0], mask.shape[1]) if dcrf: # Dense CRF post-processing sigm_val = np.squeeze(sigm_val) d = densecrf.DenseCRF2D(W, H, 2) U = np.expand_dims(-np.log(sigm_val), axis=0) U_ = np.expand_dims(-np.log(1 - sigm_val), axis=0) unary = np.concatenate((U_, U), axis=0) unary = unary.reshape((2, -1)) d.setUnaryEnergy(unary) d.addPairwiseGaussian(sxy=3, compat=3) d.addPairwiseBilateral(sxy=20, srgb=3, rgbim=proc_im, compat=10) Q = d.inference(5) pred_raw_dcrf = np.argmax(Q, axis=0).reshape( (H, W)).astype(np.float32) predicts_dcrf = im_processing.resize_and_crop( pred_raw_dcrf, mask.shape[0], mask.shape[1]) if visualize: sent = batch['sent_batch'][0] visualize_seg(im, mask, predicts, sent) if dcrf: visualize_seg(im, mask, predicts_dcrf, sent) I, U = eval_tools.compute_mask_IU(predicts, mask) IU_result.append({'batch_no': n_iter, 'I': I, 'U': U}) mean_IoU += float(I) / U cum_I += I cum_U += U msg = 'cumulative IoU = %f' % (cum_I / cum_U) for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct[n_eval_iou] += (I / U >= eval_seg_iou) if dcrf: I_dcrf, U_dcrf = eval_tools.compute_mask_IU(predicts_dcrf, mask) mean_dcrf_IoU += float(I_dcrf) / U_dcrf cum_I_dcrf += I_dcrf cum_U_dcrf += U_dcrf msg += '\tcumulative IoU (dcrf) = %f' % (cum_I_dcrf / cum_U_dcrf) for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct_dcrf[n_eval_iou] += (I_dcrf / U_dcrf >= eval_seg_iou) # print(msg) seg_total += 1 # Print results print('Segmentation evaluation (without DenseCRF):') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] / seg_total) result_str += 'overall IoU = %f; mean IoU = %f\n' % (cum_I / cum_U, mean_IoU / seg_total) print(result_str) if dcrf: print('Segmentation evaluation (with DenseCRF):') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct_dcrf[n_eval_iou] / seg_total) result_str += 'overall IoU = %f; mean IoU = %f\n' % ( cum_I_dcrf / cum_U_dcrf, mean_dcrf_IoU / seg_total) print(result_str)
def test(reader, snapshot_file, visual_feat_dir): model = Model(mode='test', vocab_size=vocab_size, H=FLAGS.H, W=FLAGS.W, batch_size=FLAGS.batch_size, num_steps=FLAGS.num_steps) score_thresh = 1e-9 eval_seg_iou_list = [.5, .6, .7, .8, .9] cum_I = cum_U = cum_I_dcrf = cum_U_dcrf = 0 seg_total = 0 seg_correct = [0 for _ in range(len(eval_seg_iou_list))] if FLAGS.dcrf: seg_correct_dcrf = [0 for _ in range(len(eval_seg_iou_list))] config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) sess.run(tf.global_variables_initializer()) snapshot_loader = tf.train.Saver() snapshot_loader.restore(sess, snapshot_file % (FLAGS.max_iter)) for n_iter in range(reader.num_batch): sys.stdout.write('Testing %d/%d\r' % (n_iter + 1, reader.num_batch)) sys.stdout.flush() batch = reader.read_batch(is_log=False) text = batch['text_batch'] im_name = str(batch['im_name_batch']) mask = batch['mask_batch'].astype(np.float32) sent_id = batch['sent_id'] visual_feat = np.load(visual_feat_dir + im_name + '.npz')['arr_0'] score_val, pred_val, sigm_val = sess.run( [model.score, model.pred, model.sigm], feed_dict={ model.words: np.expand_dims(text, axis=0), model.visual_feat: visual_feat }) pred_val = np.squeeze(pred_val) pred_raw = (pred_val >= score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, mask.shape[0], mask.shape[1]) I, U = eval_tools.compute_mask_IU(predicts, mask) cum_I += I cum_U += U for n_eval_iou in range(len(eval_seg_iou_list)): seg_correct[n_eval_iou] += (I / U >= eval_seg_iou_list[n_eval_iou]) if FLAGS.dcrf: sigm_val = np.squeeze(sigm_val) d = densecrf.DenseCRF2D(FLAGS.W, FLAGS.H, 2) U = np.expand_dims(-np.log(sigm_val), axis=0) U_ = np.expand_dims(-np.log(1 - sigm_val), axis=0) unary = np.concatenate((U_, U), axis=0) unary = unary.reshape((2, -1)) d.setUnaryEnergy(unary) d.addPairwiseGaussian(sxy=3, compat=3) d.addPairwiseBilateral(sxy=20, srgb=3, rgbim=im, compat=10) Q = d.inference(5) pred_raw_dcrf = np.argmax(Q, axis=0).reshape( (FLAGS.H, FLAGS.W)).astype(np.float32) predicts_dcrf = im_processing.resize_and_crop( pred_raw_dcrf, mask.shape[0], mask.shape[1]) I, U = eval_tools.compute_mask_IU(predicts, mask) cum_I_dcrf += I cum_U_dcrf += U for n_eval_iou in range(len(eval_seg_iou_list)): seg_correct_dcrf[n_eval_iou] += (I / U >= eval_seg_iou_list[n_eval_iou]) seg_total += 1 sio.savemat('./results/%d.mat' % sent_id, { 'mask': predicts.astype(np.bool), 'iou': I / U }, do_compression=True) msg = 'cumulative IoU = %f' % (cum_I / cum_U) if FLAGS.dcrf: msg += '\tcumulative IoU (dcrf) = %f' % (cum_I_dcrf / cum_U_dcrf) print(msg)
def rmi_refvg_predictor(split='val', eval_img_count=-1, out_path='output/eval_refvg/rmi', model_iter=750000, dcrf=True, mu=the_mu): pretrained_model = './_rmi/refvg/tfmodel/refvg_resnet_RMI_iter_' + str( model_iter) + '.tfmodel' data_loader = RMIRefVGLoader(split=split) vocab_size = len(data_loader.vocab_dict) score_thresh = 1e-9 H, W = 320, 320 model = RMI_model(H=H, W=W, mode='eval', vocab_size=vocab_size, weights='resnet') # Load pretrained model snapshot_restorer = tf.train.Saver() sess = tf.Session() sess.run(tf.global_variables_initializer()) snapshot_restorer.restore(sess, pretrained_model) predictions = dict() while not data_loader.is_end: img_id, task_id, im, mask, sent, text = data_loader.get_img_data( rand=False, is_train=False) mask = mask.astype(np.float32) proc_im = skimage.img_as_ubyte(im_processing.resize_and_pad(im, H, W)) proc_im_ = proc_im.astype(np.float32) proc_im_ = proc_im_[:, :, ::-1] proc_im_ -= mu scores_val, up_val, sigm_val = sess.run( [model.pred, model.up, model.sigm], feed_dict={ model.words: np.expand_dims(text, axis=0), model.im: np.expand_dims(proc_im_, axis=0) }) # scores_val = np.squeeze(scores_val) # pred_raw = (scores_val >= score_thresh).astype(np.float32) up_val = np.squeeze(up_val) pred_raw = (up_val >= score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, mask.shape[0], mask.shape[1]) pred_mask = predicts if dcrf: # Dense CRF post-processing sigm_val = np.squeeze(sigm_val) d = densecrf.DenseCRF2D(W, H, 2) U = np.expand_dims(-np.log(sigm_val), axis=0) U_ = np.expand_dims(-np.log(1 - sigm_val), axis=0) unary = np.concatenate((U_, U), axis=0) unary = unary.reshape((2, -1)) d.setUnaryEnergy(unary) d.addPairwiseGaussian(sxy=3, compat=3) d.addPairwiseBilateral(sxy=20, srgb=3, rgbim=proc_im, compat=10) Q = d.inference(5) pred_raw_dcrf = np.argmax(Q, axis=0).reshape( (H, W)).astype(np.float32) predicts_dcrf = im_processing.resize_and_crop( pred_raw_dcrf, mask.shape[0], mask.shape[1]) pred_mask = predicts_dcrf if img_id not in predictions.keys(): predictions[img_id] = dict() pred_mask = np.packbits(pred_mask.astype(np.bool)) predictions[img_id][task_id] = {'pred_mask': pred_mask} print data_loader.img_idx, img_id, task_id if out_path is not None: print('rmi_refvg_predictor: saving predictions to %s ...' % out_path) if not os.path.exists(out_path): os.makedirs(out_path) fname = split if eval_img_count > 0: fname += '_%d' % eval_img_count fname += '.npy' f_path = os.path.join(out_path, fname) np.save(f_path, predictions) print('RMI refvg predictor done!') return predictions
def test(modelname, iter, dataset, visualize, weights, setname, dcrf, mu): data_folder = './' + dataset + '/' + setname + '_batch/' data_prefix = dataset + '_' + setname if visualize: save_dir = './' + dataset + '/visualization/' + modelname + '_' + str(iter) + '/' if not os.path.isdir(save_dir): os.makedirs(save_dir) pretrained_model = './' + dataset + '/tfmodel_BRI/' + dataset + '_' + weights + '_' + modelname + '_iter_' + str(iter) + '.tfmodel' score_thresh = 1e-9 eval_seg_iou_list = [.5, .6, .7, .8, .9] cum_I, cum_U = 0, 0 seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32) if dcrf: cum_I_dcrf, cum_U_dcrf = 0, 0 seg_correct_dcrf = np.zeros(len(eval_seg_iou_list), dtype=np.int32) seg_total = 0. H, W = 320, 320 vocab_size = 8803 if dataset == 'referit' else 12112 if modelname == 'BRI': model = BRI_model(H=H, W=W, mode='eval', vocab_size=vocab_size, weights=weights) else: raise ValueError('Unknown model name %s' % (modelname)) # Load pretrained model snapshot_restorer = tf.train.Saver() sess = tf.Session() sess.run(tf.global_variables_initializer()) snapshot_restorer.restore(sess, pretrained_model) reader = data_reader.DataReader(data_folder, data_prefix, shuffle=False) for n_iter in range(reader.num_batch): batch = reader.read_batch() text = batch['text_batch'] im = batch['im_batch'] mask = batch['mask_batch'].astype(np.float32) proc_im = skimage.img_as_ubyte(im_processing.resize_and_pad(im, H, W)) proc_im_ = proc_im.astype(np.float32) proc_im_ = proc_im_[:,:,::-1] proc_im_ -= mu scores_val, up_val, sigm_val = sess.run([model.pred, model.up, model.sigm], feed_dict={ model.words: np.expand_dims(text, axis=0), model.im: np.expand_dims(proc_im_, axis=0) }) up_val = np.squeeze(up_val) pred_raw = (up_val >= score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, mask.shape[0], mask.shape[1]) if dcrf: # Dense CRF post-processing sigm_val = np.squeeze(sigm_val) d = Dcrf.DenseCRF2D(W, H, 2) U = np.expand_dims(-np.log(sigm_val), axis=0) U_ = np.expand_dims(-np.log(1 - sigm_val), axis=0) unary = np.concatenate((U_, U), axis=0) unary = unary.reshape((2, -1)) d.setUnaryEnergy(unary) d.addPairwiseGaussian(sxy=3, compat=3) d.addPairwiseBilateral(sxy=20, srgb=3, rgbim=proc_im, compat=10) Q = d.inference(5) pred_raw_dcrf = np.argmax(Q, axis=0).reshape((H, W)).astype(np.float32) predicts_dcrf = im_processing.resize_and_crop(pred_raw_dcrf, mask.shape[0], mask.shape[1]) I, U = eval_tools.compute_mask_IU(predicts, mask) cum_I += I cum_U += U msg = 'cumulative IoU = %f' % (cum_I/cum_U) for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct[n_eval_iou] += (I/U >= eval_seg_iou) if dcrf: I_dcrf, U_dcrf = eval_tools.compute_mask_IU(predicts_dcrf, mask) cum_I_dcrf += I_dcrf cum_U_dcrf += U_dcrf msg += '\tcumulative IoU (dcrf) = %f' % (cum_I_dcrf/cum_U_dcrf) for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct_dcrf[n_eval_iou] += (I_dcrf/U_dcrf >= eval_seg_iou) print(msg) seg_total += 1 # Print results print('Segmentation evaluation (without DenseCRF):') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou]/seg_total) result_str += 'overall IoU = %f\n' % (cum_I/cum_U) print(result_str) if dcrf: print('Segmentation evaluation (with DenseCRF):') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct_dcrf[n_eval_iou]/seg_total) result_str += 'overall IoU = %f\n' % (cum_I_dcrf/cum_U_dcrf) print(result_str)
imcrop_val[...] = processed_im.astype(np.float32) - segmodel.vgg_net.channel_mean for imcrop_name, _, description in flat_query_dict[imname]: mask = load_gt_mask(mask_dir + imcrop_name[:-4] + '.mat').astype(np.float32) labels = (mask > 0) processed_labels = im_processing.resize_and_pad(mask, input_H, input_W) > 0 text_seq_val[:, 0] = text_processing.preprocess_sentence(description, vocab_dict, T) scores_val = sess.run(scores, feed_dict={ text_seq_batch : text_seq_val, imcrop_batch : imcrop_val }) scores_val = np.squeeze(scores_val) # Evaluate the segmentation performance of using bounding box segmentation pred_raw = (scores_val >= score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, im.shape[0], im.shape[1]) I, U = eval_tools.compute_mask_IU(predicts, labels) cum_I += I cum_U += U this_IoU = I/U for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct[n_eval_iou] += (I/U >= eval_seg_iou) seg_total += 1 # Print results print('Final results on the whole test set') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou]/seg_total)
def inference(): with open('./seg_model/test.prototxt', 'w') as f: f.write(str(seg_model.generate_model('val', test_config.N))) caffe.set_device(test_config.gpu_id) caffe.set_mode_gpu() # Load pretrained model net = caffe.Net('./seg_model/test.prototxt', test_config.pretrained_model, caffe.TEST) ################################################################################ # Load annotations and bounding box proposals ################################################################################ query_dict = json.load(open(test_config.query_file)) bbox_dict = json.load(open(test_config.bbox_file)) imcrop_dict = json.load(open(test_config.imcrop_file)) imsize_dict = json.load(open(test_config.imsize_file)) imlist = list({name.split('_', 1)[0] + '.jpg' for name in query_dict}) vocab_dict = text_processing.load_vocab_dict_from_file(test_config.vocab_file) ################################################################################ # Flatten the annotations ################################################################################ flat_query_dict = {imname: [] for imname in imlist} for imname in imlist: this_imcrop_names = imcrop_dict[imname] for imcrop_name in this_imcrop_names: gt_bbox = bbox_dict[imcrop_name] if imcrop_name not in query_dict: continue this_descriptions = query_dict[imcrop_name] for description in this_descriptions: flat_query_dict[imname].append((imcrop_name, gt_bbox, description)) ################################################################################ # Testing ################################################################################ cum_I, cum_U = 0.0, 0.0 eval_seg_iou_list = [0.5, 0.6, 0.7, 0.8, 0.9] seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32) seg_total = 0.0 # Pre-allocate arrays imcrop_val = np.zeros((test_config.N, test_config.input_H, test_config.input_W, 3), dtype=np.float32) text_seq_val = np.zeros((test_config.T, test_config.N), dtype=np.int32) num_im = len(imlist) for n_im in tqdm(range(num_im)): imname = imlist[n_im] # Extract visual features from all proposals im = skimage.io.imread(test_config.image_dir + imname) processed_im = skimage.img_as_ubyte( im_processing.resize_and_pad(im, test_config.input_H, test_config.input_W)) if processed_im.ndim == 2: processed_im = np.tile(processed_im[:, :, np.newaxis], (1, 1, 3)) imcrop_val[...] = processed_im.astype(np.float32) - seg_model.channel_mean imcrop_val_trans = imcrop_val.transpose((0, 3, 1, 2)) # Extract spatial features spatial_val = processing_tools.generate_spatial_batch(test_config.N, test_config.featmap_H, test_config.featmap_W) spatial_val = spatial_val.transpose((0, 3, 1, 2)) for imcrop_name, _, description in flat_query_dict[imname]: mask = load_gt_mask(test_config.mask_dir + imcrop_name + '.mat').astype(np.float32) labels = (mask > 0) processed_labels = im_processing.resize_and_pad(mask, test_config.input_H, test_config.input_W) processed_labels = processed_labels > 0 text_seq_val[:, 0] = text_processing.preprocess_sentence(description, vocab_dict, test_config.T) cont_val = text_processing.create_cont(text_seq_val) net.blobs['language'].data[...] = text_seq_val net.blobs['cont'].data[...] = cont_val net.blobs['image'].data[...] = imcrop_val_trans net.blobs['spatial'].data[...] = spatial_val net.blobs['label'].data[...] = processed_labels net.forward() upscores = net.blobs['upscores'].data[...].copy() upscores = np.squeeze(upscores) # Evaluate the segmentation performance of using bounding box segmentation pred_raw = (upscores >= test_config.score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, im.shape[0], im.shape[1]) I, U = eval_tools.compute_mask_IU(predicts, labels) cum_I += I cum_U += U this_IoU = I/float(U) for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct[n_eval_iou] += (I/float(U) >= eval_seg_iou) seg_total += 1 # Print results print('Final results on the whole test set') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou]/seg_total) result_str += 'overall IoU = %f\n' % (cum_I/cum_U) print(result_str)
def inference(config): with open('./seg_model/test.prototxt', 'w') as f: f.write(str(seg_model.generate_model('val', config))) caffe.set_device(config.gpu_id) caffe.set_mode_gpu() # Load pretrained model net = caffe.Net('./seg_model/test.prototxt', config.pretrained_model, caffe.TEST) ################################################################################ # Load annotations and bounding box proposals ################################################################################ query_dict = json.load(open(config.query_file)) bbox_dict = json.load(open(config.bbox_file)) imcrop_dict = json.load(open(config.imcrop_file)) imsize_dict = json.load(open(config.imsize_file)) imlist = list({name.split('_', 1)[0] + '.jpg' for name in query_dict}) vocab_dict = text_processing.load_vocab_dict_from_file(config.vocab_file) ################################################################################ # Flatten the annotations ################################################################################ flat_query_dict = {imname: [] for imname in imlist} for imname in imlist: this_imcrop_names = imcrop_dict[imname] for imcrop_name in this_imcrop_names: gt_bbox = bbox_dict[imcrop_name] if imcrop_name not in query_dict: continue this_descriptions = query_dict[imcrop_name] for description in this_descriptions: flat_query_dict[imname].append((imcrop_name, gt_bbox, description)) ################################################################################ # Testing ################################################################################ cum_I, cum_U = 0.0, 0.0 eval_seg_iou_list = [0.5, 0.6, 0.7, 0.8, 0.9] seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32) seg_total = 0.0 # Pre-allocate arrays imcrop_val = np.zeros((config.N, config.input_H, config.input_W, 3), dtype=np.float32) text_seq_val = np.zeros((config.T, config.N), dtype=np.int32) num_im = len(imlist) for n_im in tqdm(range(num_im)): imname = imlist[n_im] # Extract visual features from all proposals im = skimage.io.imread(config.image_dir + imname) processed_im = skimage.img_as_ubyte( im_processing.resize_and_pad(im, config.input_H, config.input_W)) if processed_im.ndim == 2: processed_im = np.tile(processed_im[:, :, np.newaxis], (1, 1, 3)) imcrop_val[...] = processed_im.astype(np.float32) - seg_model.channel_mean imcrop_val_trans = imcrop_val.transpose((0, 3, 1, 2)) # Extract spatial features spatial_val = processing_tools.generate_spatial_batch(config.N, config.featmap_H, config.featmap_W) spatial_val = spatial_val.transpose((0, 3, 1, 2)) for imcrop_name, _, description in flat_query_dict[imname]: mask = load_gt_mask(config.mask_dir + imcrop_name + '.mat').astype(np.float32) labels = (mask > 0) processed_labels = im_processing.resize_and_pad(mask, config.input_H, config.input_W) processed_labels = processed_labels > 0 text_seq_val[:, 0] = text_processing.preprocess_sentence(description, vocab_dict, config.T) cont_val = text_processing.create_cont(text_seq_val) net.blobs['language'].data[...] = text_seq_val net.blobs['cont'].data[...] = cont_val net.blobs['image'].data[...] = imcrop_val_trans net.blobs['spatial'].data[...] = spatial_val net.blobs['label'].data[...] = processed_labels net.forward() upscores = net.blobs['upscores'].data[...].copy() upscores = np.squeeze(upscores) # Evaluate the segmentation performance of using bounding box segmentation pred_raw = (upscores >= config.score_thresh).astype(np.float32) predicts = im_processing.resize_and_crop(pred_raw, im.shape[0], im.shape[1]) I, U = eval_tools.compute_mask_IU(predicts, labels) cum_I += I cum_U += U this_IoU = I/float(U) for n_eval_iou in range(len(eval_seg_iou_list)): eval_seg_iou = eval_seg_iou_list[n_eval_iou] seg_correct[n_eval_iou] += (I/float(U) >= eval_seg_iou) seg_total += 1 # Print results print('Final results on the whole test set') result_str = '' for n_eval_iou in range(len(eval_seg_iou_list)): result_str += 'precision@%s = %f\n' % \ (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou]/seg_total) result_str += 'overall IoU = %f\n' % (cum_I/cum_U) print(result_str)