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vos_metric.py
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# 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 = obj_num.numpy()[0].item()
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))