def train(max_iter, snapshot, dataset, setname, mu, lr, bs, tfmodel_folder, conv5, model_name, stop_iter, pre_emb=False): iters_per_log = 100 data_folder = './' + dataset + '/' + setname + '_batch/' data_prefix = dataset + '_' + setname snapshot_file = os.path.join(tfmodel_folder, dataset + '_iter_%d.tfmodel') if not os.path.isdir(tfmodel_folder): os.makedirs(tfmodel_folder) cls_loss_avg = 0 avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg = 0, 0, 0 decay = 0.99 vocab_size = 8803 if dataset == 'referit' else 12112 emb_name = 'referit' if dataset == 'referit' else 'Gref' if pre_emb: print("Use pretrained Embeddings.") model = get_segmentation_model(model_name, mode='train', vocab_size=vocab_size, start_lr=lr, batch_size=bs, conv5=conv5, emb_name=emb_name) else: model = get_segmentation_model(model_name, mode='train', vocab_size=vocab_size, start_lr=lr, batch_size=bs, conv5=conv5) weights = './data/weights/deeplab_resnet_init.ckpt' print("Loading pretrained weights from {}".format(weights)) load_var = {var.op.name: var for var in tf.global_variables() if var.name.startswith('res') or var.name.startswith('bn') or var.name.startswith('conv1')} snapshot_loader = tf.train.Saver(load_var) snapshot_saver = tf.train.Saver(max_to_keep=4) config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) sess.run(tf.global_variables_initializer()) snapshot_loader.restore(sess, weights) im_h, im_w, num_steps = model.H, model.W, model.num_steps text_batch = np.zeros((bs, num_steps), dtype=np.float32) image_batch = np.zeros((bs, im_h, im_w, 3), dtype=np.float32) mask_batch = np.zeros((bs, im_h, im_w, 1), dtype=np.float32) valid_idx_batch = np.zeros((bs, 1), dtype=np.int32) reader = data_reader.DataReader(data_folder, data_prefix) # for time calculate last_time = time.time() time_avg = MovingAverage() for n_iter in range(max_iter): for n_batch in range(bs): batch = reader.read_batch(is_log=(n_batch == 0 and n_iter % iters_per_log == 0)) text = batch['text_batch'] im = batch['im_batch'].astype(np.float32) mask = np.expand_dims(batch['mask_batch'].astype(np.float32), axis=2) im = im[:, :, ::-1] im -= mu text_batch[n_batch, ...] = text image_batch[n_batch, ...] = im mask_batch[n_batch, ...] = mask for idx in range(text.shape[0]): if text[idx] != 0: valid_idx_batch[n_batch, :] = idx break _, cls_loss_val, lr_val, scores_val, label_val = sess.run([model.train_step, model.cls_loss, model.learning_rate, model.pred, model.target], feed_dict={ model.words: text_batch, # np.expand_dims(text, axis=0), model.im: image_batch, # np.expand_dims(im, axis=0), model.target_fine: mask_batch, # np.expand_dims(mask, axis=0) model.valid_idx: valid_idx_batch }) cls_loss_avg = decay * cls_loss_avg + (1 - decay) * cls_loss_val # Accuracy accuracy_all, accuracy_pos, accuracy_neg = compute_accuracy(scores_val, label_val) avg_accuracy_all = decay * avg_accuracy_all + (1 - decay) * accuracy_all avg_accuracy_pos = decay * avg_accuracy_pos + (1 - decay) * accuracy_pos avg_accuracy_neg = decay * avg_accuracy_neg + (1 - decay) * accuracy_neg # timing cur_time = time.time() elapsed = cur_time - last_time last_time = cur_time if n_iter % iters_per_log == 0: print('iter = %d, loss (cur) = %f, loss (avg) = %f, lr = %f' % (n_iter, cls_loss_val, cls_loss_avg, lr_val)) print('iter = %d, accuracy (cur) = %f (all), %f (pos), %f (neg)' % (n_iter, accuracy_all, accuracy_pos, accuracy_neg)) print('iter = %d, accuracy (avg) = %f (all), %f (pos), %f (neg)' % (n_iter, avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg)) time_avg.add(elapsed) print('iter = %d, cur time = %.5f, avg time = %.5f, model_name: %s' % (n_iter, elapsed, time_avg.get_avg(), model_name)) # Save snapshot if (n_iter + 1) % snapshot == 0 or (n_iter + 1) >= max_iter: snapshot_saver.save(sess, snapshot_file % (n_iter + 1)) print('snapshot saved to ' + snapshot_file % (n_iter + 1)) if (n_iter + 1) >= stop_iter: print('stop training at iter ' + str(stop_iter)) break print('Optimization done.')
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
def test(iter, dataset, visualize, setname, dcrf, mu, tfmodel_folder, 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_folder, dataset + '_iter_' + str(iter) + '.tfmodel') score_thresh = 1e-9 eval_seg_iou_list = [.5, .55, .6, .65, .7, .75, .8, .85, .9, .95] 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 emb_name = 'referit' if dataset == 'referit' else 'Gref' IU_result = list() if pre_emb: print("Use pretrained embeddings.") model = get_segmentation_model(model_name, H=H, W=W, mode='eval', vocab_size=vocab_size, emb_name=emb_name) 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) print("loading trained weights from {}".format(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) frames = batch['frames'] for idx in range(text.shape[0]): if text[idx] != 0: valid_idx[0] = idx break 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 proc_frames = list() for i in range(frames.shape[0]): proc_frame = skimage.img_as_ubyte( im_processing.resize_and_pad(frames[i, :, :, :], H, W)) proc_frame = proc_frame.astype(np.float32) proc_frame = proc_frame[:, :, ::-1] proc_frame -= mu proc_frames.append(proc_frame) proc_frames = np.array(proc_frames, dtype=np.float32) 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.clip: np.expand_dims(proc_frames, 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) # deal with empty gt mask eps = 1e-7 if U == eps: print("empty gt mask in testing") continue 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, 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_folder, dataset + '_iter_' + str(iter) + '.tfmodel') 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 'Gref' vocab_file = './data/vocabulary_Gref.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'] for vid_ind, vid in reversed(list(enumerate(videos.keys()))): 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 = os.path.join(args.visdir, str('{}/{}/'.format(vid, index))) 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: vis_path = os.path.join(vis_dir, str('{}.png'.format(fid))) mask_path = os.path.join(mask_dir, str('{}.npy'.format(fid))) if os.path.exists(vis_path): continue 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 = 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('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
def train(max_iter, snapshot, dataset, data_dir, setname, mu, lr, bs, tfmodel_folder, conv5, model_name, stop_iter, last_iter, pre_emb=False, finetune=False, pretrain_path='', emb_dir=''): global args iters_per_log = 100 data_folder = os.path.join(data_dir, dataset + '/' + setname + '_batch/') data_prefix = dataset + '_' + setname snapshot_file = os.path.join(tfmodel_folder, dataset + '_finetune') if not os.path.isdir(tfmodel_folder): os.makedirs(tfmodel_folder) cls_loss_avg = 0 avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg = 0, 0, 0 decay = 0.99 vocab_size = 8803 if dataset == 'referit' else 1917498 emb_name = dataset if pre_emb: print("Use pretrained Embeddings.") model = get_segmentation_model(model_name, mode='train', vocab_size=vocab_size, start_lr=lr, batch_size=bs, conv5=conv5, emb_name=emb_name, emb_dir=emb_dir, freeze_bn=args.freeze_bn, is_aug=args.is_aug) else: model = get_segmentation_model(model_name, mode='train', vocab_size=vocab_size, start_lr=lr, batch_size=bs, conv5=conv5) if finetune: weights = os.path.join(pretrain_path) snapshot_loader = tf.train.Saver() else: weights = './data/weights/deeplab_resnet_init.ckpt' print("Loading pretrained weights from {}".format(weights)) load_var = { var.op.name: var for var in tf.global_variables() if var.name.startswith('res') or var.name.startswith('bn') or var.name.startswith('conv1') or var.name.startswith('Adam') } snapshot_loader = tf.train.Saver(load_var) snapshot_saver = tf.train.Saver(max_to_keep=4) config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) snapshot_loader.restore(sess, weights) # Log tensorboard train_writer = tf.summary.FileWriter(args.log_dir + '/train', sess.graph) im_h, im_w, num_steps = model.H, model.W, model.num_steps text_batch = np.zeros((bs, num_steps), dtype=np.float32) image_batch = np.zeros((bs, im_h, im_w, 3), dtype=np.float32) mask_batch = np.zeros((bs, im_h, im_w, 1), dtype=np.float32) seq_len_batch = np.zeros(bs, dtype=np.int32) valid_idx_batch = np.zeros(bs, dtype=np.int32) if dataset == 'refvos': reader = data_reader_refvos.DataReader(im_dir=args.im_dir, mask_dir=args.mask_dir, train_metadata=args.meta) # for time calculate last_time = time.time() time_avg = MovingAverage() meanIoU = 0 last_epoch = (last_iter * bs) // reader.num_batch for n_iter in range(last_iter + 1, max_iter): for n_batch in range(bs): batch = reader.read_batch( is_log=(n_batch == 0 and n_iter % iters_per_log == 0)) text = batch['text_batch'] im = batch['im_batch'].astype(np.float32) # mask = batch['mask_batch'] mask = np.expand_dims(batch['mask_batch'].astype(np.float32), axis=2) seq_len = batch['seq_length'] im = im[:, :, ::-1] im -= mu text_batch[n_batch, ...] = text image_batch[n_batch, ...] = im mask_batch[n_batch, ...] = mask seq_len_batch[n_batch] = seq_len _, train_step, summary = sess.run( [ model.train, model.train_step, model.merged, ], feed_dict={ model.words: text_batch, model.im: image_batch, model.target_fine: mask_batch, model.seq_len: seq_len_batch, }) # cls_loss_avg = decay * cls_loss_avg + (1 - decay) * cls_loss_val # cls_loss_avg # Accuracy # accuracy_all, accuracy_pos, accuracy_neg = compute_accuracy(scores_val, label_val) # avg_accuracy_all = decay * avg_accuracy_all + (1 - decay) * accuracy_all # avg_accuracy_pos = decay * avg_accuracy_pos + (1 - decay) * accuracy_pos # avg_accuracy_neg = decay * avg_accuracy_neg + (1 - decay) * accuracy_neg # IoU = compute_meanIoU(scores_val, mask_batch) # meanIoU += IoU # timing cur_time = time.time() elapsed = cur_time - last_time last_time = cur_time train_writer.add_summary(summary, train_step) # if n_iter % iters_per_log == 0: # print('iter = %d, loss (cur) = %f, loss (avg) = %f, lr = %f' # % (n_iter, cls_loss_val, cls_loss_avg, lr_val)) # print('iter = %d, accuracy (cur) = %f (all), %f (pos), %f (neg)' # % (n_iter, accuracy_all, accuracy_pos, accuracy_neg)) # print('iter = %d, accuracy (avg) = %f (all), %f (pos), %f (neg)' # % (n_iter, avg_accuracy_all, avg_accuracy_pos, avg_accuracy_neg)) # print('iter = %d, meanIoU = %f (neg)' # % (n_iter, meanIoU / iters_per_log)) # meanIoU = 0 # time_avg.add(elapsed) # print('iter = %d, cur time = %.5f, avg time = %.5f, model_name: %s' % (n_iter, elapsed, time_avg.get_avg(), model_name)) # Save snapshot if (n_iter * bs // reader.num_batch > last_epoch): last_epoch += 1 snapshot_saver.save(sess, snapshot_file, global_step=train_step) print('snapshot saved at iteration {}'.format(n_iter)) if (n_iter + 1) % snapshot == 0 or (n_iter + 1) >= max_iter: snapshot_saver.save(sess, snapshot_file, global_step=train_step) print('snapshot saved at iteration {}'.format(n_iter)) if (n_iter + 1) >= stop_iter: print('stop training at iter ' + str(stop_iter)) break print('Optimization done.')