# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. import os import paddle import zipfile import time from PIL import Image from paddle.io import DataLoader from .registry import METRIC from .base import BaseMetric from paddlevideo.utils import get_logger logger = get_logger("paddlevideo") @METRIC.register class VOSMetric(BaseMetric): def __init__(self, data_size, batch_size, result_root, zip_dir, log_interval=1): """prepare for metrics """ super().__init__(data_size, batch_size, log_interval) self.video_num = 0 self.total_time = 0 self.total_frame = 0 self.total_sfps = 0 self.total_video_num = data_size self.count = 0 self.result_root = result_root self.zip_dir = zip_dir def update(self, batch_id, data, model): """update metrics during each iter """ self.video_num += 1 seq_dataset = data seq_name = seq_dataset.seq_name logger.info('Prcessing Seq {} [{}/{}]:'.format(seq_name, self.video_num, self.total_video_num)) seq_dataloader = DataLoader(seq_dataset, return_list=True, batch_size=1, shuffle=False, num_workers=0) seq_total_time = 0 seq_total_frame = 0 ref_embeddings = [] ref_masks = [] prev_embedding = [] prev_mask = [] with paddle.no_grad(): for frame_idx, samples in enumerate(seq_dataloader): time_start = time.time() all_preds = [] join_label = None for aug_idx in range(len(samples)): if len(ref_embeddings) <= aug_idx: ref_embeddings.append([]) ref_masks.append([]) prev_embedding.append(None) prev_mask.append(None) sample = samples[aug_idx] ref_emb = ref_embeddings[aug_idx] ref_m = ref_masks[aug_idx] prev_emb = prev_embedding[aug_idx] prev_m = prev_mask[aug_idx] current_img = sample['current_img'] if 'current_label' in sample.keys(): current_label = sample['current_label'] current_label = paddle.to_tensor(current_label) else: current_label = None obj_num = sample['meta']['obj_num'] imgname = sample['meta']['current_name'] ori_height = sample['meta']['height'] ori_width = sample['meta']['width'] current_img = current_img obj_num = obj_num bs, _, h, w = current_img.shape data_batch = [ ref_emb, ref_m, prev_emb, prev_m, current_img, [ori_height, ori_width], obj_num ] all_pred, current_embedding = model(data_batch, mode='test') if frame_idx == 0: if current_label is None: logger.info( "No first frame label in Seq {}.".format( seq_name)) ref_embeddings[aug_idx].append(current_embedding) ref_masks[aug_idx].append(current_label) prev_embedding[aug_idx] = current_embedding prev_mask[aug_idx] = current_label else: if sample['meta']['flip']: #False all_pred = self.flip_tensor(all_pred, 3) # In YouTube-VOS, not all the objects appear in the first frame for the first time. Thus, we # have to introduce new labels for new objects, if necessary. if not sample['meta']['flip'] and not ( current_label is None) and join_label is None: join_label = paddle.cast(current_label, dtype='int64') all_preds.append(all_pred) if current_label is not None: ref_embeddings[aug_idx].append(current_embedding) prev_embedding[aug_idx] = current_embedding if frame_idx > 0: all_preds = paddle.concat(all_preds, axis=0) all_preds = paddle.mean( all_preds, axis=0) #average results if augmentation pred_label = paddle.argmax(all_preds, axis=0) if join_label is not None: join_label = paddle.squeeze(paddle.squeeze(join_label, axis=0), axis=0) keep = paddle.cast((join_label == 0), dtype="int64") pred_label = pred_label * keep + join_label * (1 - keep) pred_label = pred_label current_label = paddle.reshape( pred_label, shape=[1, 1, ori_height, ori_width]) flip_pred_label = self.flip_tensor(pred_label, 1) flip_current_label = paddle.reshape( flip_pred_label, shape=[1, 1, ori_height, ori_width]) for aug_idx in range(len(samples)): if join_label is not None: if samples[aug_idx]['meta']['flip']: ref_masks[aug_idx].append(flip_current_label) else: ref_masks[aug_idx].append(current_label) if samples[aug_idx]['meta']['flip']: prev_mask[aug_idx] = flip_current_label else: prev_mask[ aug_idx] = current_label #update prev_mask one_frametime = time.time() - time_start seq_total_time += one_frametime seq_total_frame += 1 obj_num = float(obj_num) logger.info('Frame: {}, Obj Num: {}, Time: {}'.format( imgname[0], obj_num, one_frametime)) self.save_mask( pred_label, os.path.join(self.result_root, seq_name, imgname[0].split('.')[0] + '.png')) else: one_frametime = time.time() - time_start seq_total_time += one_frametime logger.info('Ref Frame: {}, Time: {}'.format( imgname[0], one_frametime)) del (ref_embeddings) del (ref_masks) del (prev_embedding) del (prev_mask) del (seq_dataset) del (seq_dataloader) seq_avg_time_per_frame = seq_total_time / seq_total_frame self.total_time += seq_total_time self.total_frame += seq_total_frame total_avg_time_per_frame = self.total_time / self.total_frame self.total_sfps += seq_avg_time_per_frame avg_sfps = self.total_sfps / (batch_id + 1) logger.info("Seq {} FPS: {}, Total FPS: {}, FPS per Seq: {}".format( seq_name, 1. / seq_avg_time_per_frame, 1. / total_avg_time_per_frame, 1. / avg_sfps)) def flip_tensor(self, tensor, dim=0): inv_idx = paddle.cast(paddle.arange(tensor.shape[dim] - 1, -1, -1), dtype="int64") tensor = paddle.index_select(x=tensor, index=inv_idx, axis=dim) return tensor def save_mask(self, mask_tensor, path): _palette = [ 0, 0, 0, 128, 0, 0, 0, 128, 0, 128, 128, 0, 0, 0, 128, 128, 0, 128, 0, 128, 128, 128, 128, 128, 64, 0, 0, 191, 0, 0, 64, 128, 0, 191, 128, 0, 64, 0, 128, 191, 0, 128, 64, 128, 128, 191, 128, 128, 0, 64, 0, 128, 64, 0, 0, 191, 0, 128, 191, 0, 0, 64, 128, 128, 64, 128, 22, 22, 22, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 26, 27, 27, 27, 28, 28, 28, 29, 29, 29, 30, 30, 30, 31, 31, 31, 32, 32, 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 36, 36, 36, 37, 37, 37, 38, 38, 38, 39, 39, 39, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43, 43, 43, 44, 44, 44, 45, 45, 45, 46, 46, 46, 47, 47, 47, 48, 48, 48, 49, 49, 49, 50, 50, 50, 51, 51, 51, 52, 52, 52, 53, 53, 53, 54, 54, 54, 55, 55, 55, 56, 56, 56, 57, 57, 57, 58, 58, 58, 59, 59, 59, 60, 60, 60, 61, 61, 61, 62, 62, 62, 63, 63, 63, 64, 64, 64, 65, 65, 65, 66, 66, 66, 67, 67, 67, 68, 68, 68, 69, 69, 69, 70, 70, 70, 71, 71, 71, 72, 72, 72, 73, 73, 73, 74, 74, 74, 75, 75, 75, 76, 76, 76, 77, 77, 77, 78, 78, 78, 79, 79, 79, 80, 80, 80, 81, 81, 81, 82, 82, 82, 83, 83, 83, 84, 84, 84, 85, 85, 85, 86, 86, 86, 87, 87, 87, 88, 88, 88, 89, 89, 89, 90, 90, 90, 91, 91, 91, 92, 92, 92, 93, 93, 93, 94, 94, 94, 95, 95, 95, 96, 96, 96, 97, 97, 97, 98, 98, 98, 99, 99, 99, 100, 100, 100, 101, 101, 101, 102, 102, 102, 103, 103, 103, 104, 104, 104, 105, 105, 105, 106, 106, 106, 107, 107, 107, 108, 108, 108, 109, 109, 109, 110, 110, 110, 111, 111, 111, 112, 112, 112, 113, 113, 113, 114, 114, 114, 115, 115, 115, 116, 116, 116, 117, 117, 117, 118, 118, 118, 119, 119, 119, 120, 120, 120, 121, 121, 121, 122, 122, 122, 123, 123, 123, 124, 124, 124, 125, 125, 125, 126, 126, 126, 127, 127, 127, 128, 128, 128, 129, 129, 129, 130, 130, 130, 131, 131, 131, 132, 132, 132, 133, 133, 133, 134, 134, 134, 135, 135, 135, 136, 136, 136, 137, 137, 137, 138, 138, 138, 139, 139, 139, 140, 140, 140, 141, 141, 141, 142, 142, 142, 143, 143, 143, 144, 144, 144, 145, 145, 145, 146, 146, 146, 147, 147, 147, 148, 148, 148, 149, 149, 149, 150, 150, 150, 151, 151, 151, 152, 152, 152, 153, 153, 153, 154, 154, 154, 155, 155, 155, 156, 156, 156, 157, 157, 157, 158, 158, 158, 159, 159, 159, 160, 160, 160, 161, 161, 161, 162, 162, 162, 163, 163, 163, 164, 164, 164, 165, 165, 165, 166, 166, 166, 167, 167, 167, 168, 168, 168, 169, 169, 169, 170, 170, 170, 171, 171, 171, 172, 172, 172, 173, 173, 173, 174, 174, 174, 175, 175, 175, 176, 176, 176, 177, 177, 177, 178, 178, 178, 179, 179, 179, 180, 180, 180, 181, 181, 181, 182, 182, 182, 183, 183, 183, 184, 184, 184, 185, 185, 185, 186, 186, 186, 187, 187, 187, 188, 188, 188, 189, 189, 189, 190, 190, 190, 191, 191, 191, 192, 192, 192, 193, 193, 193, 194, 194, 194, 195, 195, 195, 196, 196, 196, 197, 197, 197, 198, 198, 198, 199, 199, 199, 200, 200, 200, 201, 201, 201, 202, 202, 202, 203, 203, 203, 204, 204, 204, 205, 205, 205, 206, 206, 206, 207, 207, 207, 208, 208, 208, 209, 209, 209, 210, 210, 210, 211, 211, 211, 212, 212, 212, 213, 213, 213, 214, 214, 214, 215, 215, 215, 216, 216, 216, 217, 217, 217, 218, 218, 218, 219, 219, 219, 220, 220, 220, 221, 221, 221, 222, 222, 222, 223, 223, 223, 224, 224, 224, 225, 225, 225, 226, 226, 226, 227, 227, 227, 228, 228, 228, 229, 229, 229, 230, 230, 230, 231, 231, 231, 232, 232, 232, 233, 233, 233, 234, 234, 234, 235, 235, 235, 236, 236, 236, 237, 237, 237, 238, 238, 238, 239, 239, 239, 240, 240, 240, 241, 241, 241, 242, 242, 242, 243, 243, 243, 244, 244, 244, 245, 245, 245, 246, 246, 246, 247, 247, 247, 248, 248, 248, 249, 249, 249, 250, 250, 250, 251, 251, 251, 252, 252, 252, 253, 253, 253, 254, 254, 254, 255, 255, 255 ] mask = mask_tensor.cpu().numpy().astype('uint8') mask = Image.fromarray(mask).convert('P') mask.putpalette(_palette) mask.save(path) def zip_folder(self, source_folder, zip_dir): f = zipfile.ZipFile(zip_dir, 'w', zipfile.ZIP_DEFLATED) pre_len = len(os.path.dirname(source_folder)) for dirpath, dirnames, filenames in os.walk(source_folder): for filename in filenames: pathfile = os.path.join(dirpath, filename) arcname = pathfile[pre_len:].strip(os.path.sep) f.write(pathfile, arcname) f.close() def accumulate(self): """accumulate metrics when finished all iters. """ self.zip_folder(self.result_root, self.zip_dir) logger.info('Save result to {}.'.format(self.zip_dir))