Skip to content

M
models

PaddlePaddle / models
大约 2 年 前同步成功

通知 232
Star 6828
Fork 2962
M
models
提交 e76927fe 编写于 作者: D Dang Qingqing
浏览文件
操作

Refine reader.py and train.py

上级 547b3918
显示空白变更内容
内联 并排
Showing with 196 addition and 173 deletion
fluid/object_detection/reader.py
浏览文件 @ e76927fe
...@@ -102,107 +102,16 @@ class Settings(object): ...@@ -102,107 +102,16 @@ class Settings(object):
return self._img_mean return self._img_mean
def _reader_creator(settings, file_list, mode, shuffle): def preprocess(img, bbox_labels, mode, settings):
def reader():
if settings.dataset == 'coco':
# cocoapi
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
coco = COCO(file_list)
image_ids = coco.getImgIds()
images = coco.loadImgs(image_ids)
category_ids = coco.getCatIds()
category_names = [
item['name'] for item in coco.loadCats(category_ids)
]
else:
flist = open(file_list)
images = [line.strip() for line in flist]
if not settings.toy == 0:
images = images[:settings.toy] if len(
images) > settings.toy else images
print("{} on {} with {} images".format(mode, settings.dataset,
len(images)))
if shuffle:
random.shuffle(images)
for image in images:
if settings.dataset == 'coco':
image_name = image['file_name']
image_path = os.path.join(settings.data_dir, image_name)
elif settings.dataset == 'pascalvoc':
if mode == 'train' or mode == 'test':
image_path, label_path = image.split()
image_path = os.path.join(settings.data_dir, image_path)
label_path = os.path.join(settings.data_dir, label_path)
elif mode == 'infer':
image_path = os.path.join(settings.data_dir, image)
img = Image.open(image_path)
if img.mode == 'L':
img = img.convert('RGB')
img_width, img_height = img.size img_width, img_height = img.size
sampled_labels = bbox_labels
if mode == 'train' or mode == 'test':
if settings.dataset == 'coco':
# layout: category_id | xmin | ymin | xmax | ymax | iscrowd | origin_coco_bbox | segmentation | area | image_id | annotation_id
bbox_labels = []
annIds = coco.getAnnIds(imgIds=image['id'])
anns = coco.loadAnns(annIds)
for ann in anns:
bbox_sample = []
# start from 1, leave 0 to background
bbox_sample.append(
float(category_ids.index(ann['category_id'])) + 1)
bbox = ann['bbox']
xmin, ymin, w, h = bbox
xmax = xmin + w
ymax = ymin + h
bbox_sample.append(float(xmin) / img_width)
bbox_sample.append(float(ymin) / img_height)
bbox_sample.append(float(xmax) / img_width)
bbox_sample.append(float(ymax) / img_height)
bbox_sample.append(float(ann['iscrowd']))
#bbox_sample.append(ann['bbox'])
#bbox_sample.append(ann['segmentation'])
#bbox_sample.append(ann['area'])
#bbox_sample.append(ann['image_id'])
#bbox_sample.append(ann['id'])
bbox_labels.append(bbox_sample)
elif settings.dataset == 'pascalvoc':
# layout: label | xmin | ymin | xmax | ymax | difficult
bbox_labels = []
root = xml.etree.ElementTree.parse(label_path).getroot()
for object in root.findall('object'):
bbox_sample = []
# start from 1
bbox_sample.append(
float(
settings.label_list.index(
object.find('name').text)))
bbox = object.find('bndbox')
difficult = float(object.find('difficult').text)
bbox_sample.append(
float(bbox.find('xmin').text) / img_width)
bbox_sample.append(
float(bbox.find('ymin').text) / img_height)
bbox_sample.append(
float(bbox.find('xmax').text) / img_width)
bbox_sample.append(
float(bbox.find('ymax').text) / img_height)
bbox_sample.append(difficult)
bbox_labels.append(bbox_sample)
sample_labels = bbox_labels
if mode == 'train': if mode == 'train':
if settings._apply_distort: if settings._apply_distort:
img = image_util.distort_image(img, settings) img = image_util.distort_image(img, settings)
if settings._apply_expand: if settings._apply_expand:
img, bbox_labels, img_width, img_height = image_util.expand_image( img, bbox_labels, img_width, img_height = image_util.expand_image(
img, bbox_labels, img_width, img_height, settings) img, bbox_labels, img_width, img_height, settings)
# sampling
batch_sampler = [] batch_sampler = []
# hard-code here # hard-code here
batch_sampler.append( batch_sampler.append(
...@@ -219,31 +128,27 @@ def _reader_creator(settings, file_list, mode, shuffle): ...@@ -219,31 +128,27 @@ def _reader_creator(settings, file_list, mode, shuffle):
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0)) image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0))
batch_sampler.append( batch_sampler.append(
image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0)) image_util.sampler(1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0))
""" random crop """ sampled_bbox = image_util.generate_batch_samples(batch_sampler,
sampled_bbox = image_util.generate_batch_samples( bbox_labels)
batch_sampler, bbox_labels)
img = np.array(img) img = np.array(img)
if len(sampled_bbox) > 0: if len(sampled_bbox) > 0:
idx = int(random.uniform(0, len(sampled_bbox))) idx = int(random.uniform(0, len(sampled_bbox)))
img, sample_labels = image_util.crop_image( img, sampled_labels = image_util.crop_image(
img, bbox_labels, sampled_bbox[idx], img_width, img, bbox_labels, sampled_bbox[idx], img_width, img_height)
img_height)
img = Image.fromarray(img) img = Image.fromarray(img)
img = img.resize((settings.resize_w, settings.resize_h), img = img.resize((settings.resize_w, settings.resize_h), Image.ANTIALIAS)
Image.ANTIALIAS)
img = np.array(img) img = np.array(img)
if mode == 'train': if mode == 'train':
mirror = int(random.uniform(0, 2)) mirror = int(random.uniform(0, 2))
if mirror == 1: if mirror == 1:
img = img[:, ::-1, :] img = img[:, ::-1, :]
for i in xrange(len(sample_labels)): for i in xrange(len(sampled_labels)):
tmp = sample_labels[i][1] tmp = sampled_labels[i][1]
sample_labels[i][1] = 1 - sample_labels[i][3] sampled_labels[i][1] = 1 - sampled_labels[i][3]
sample_labels[i][3] = 1 - tmp sampled_labels[i][3] = 1 - tmp
# HWC to CHW # HWC to CHW
if len(img.shape) == 3: if len(img.shape) == 3:
img = np.swapaxes(img, 1, 2) img = np.swapaxes(img, 1, 2)
...@@ -252,19 +157,114 @@ def _reader_creator(settings, file_list, mode, shuffle): ...@@ -252,19 +157,114 @@ def _reader_creator(settings, file_list, mode, shuffle):
img = img[[2, 1, 0], :, :] img = img[[2, 1, 0], :, :]
img = img.astype('float32') img = img.astype('float32')
img -= settings.img_mean img -= settings.img_mean
img = img.flatten() #img = img.flatten()
img = img * 0.007843 img = img * 0.007843
return img, sampled_labels
def coco(settings, file_list, mode, shuffle):
# cocoapi
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
coco = COCO(file_list)
image_ids = coco.getImgIds()
images = coco.loadImgs(image_ids)
category_ids = coco.getCatIds()
category_names = [item['name'] for item in coco.loadCats(category_ids)]
if not settings.toy == 0:
images = images[:settings.toy] if len(images) > settings.toy else images
print("{} on {} with {} images".format(mode, settings.dataset, len(images)))
def reader():
if mode == 'train' and shuffle:
random.shuffle(images)
for image in images:
image_name = image['file_name']
image_path = os.path.join(settings.data_dir, image_name)
im = Image.open(image_path)
if im.mode == 'L':
im = im.convert('RGB')
im_width, im_height = im.size
# layout: category_id | xmin | ymin | xmax | ymax | iscrowd |
# origin_coco_bbox | segmentation | area | image_id | annotation_id
bbox_labels = []
annIds = coco.getAnnIds(imgIds=image['id'])
anns = coco.loadAnns(annIds)
for ann in anns:
bbox_sample = []
# start from 1, leave 0 to background
bbox_sample.append(
float(category_ids.index(ann['category_id'])) + 1)
bbox = ann['bbox']
xmin, ymin, w, h = bbox
xmax = xmin + w
ymax = ymin + h
bbox_sample.append(float(xmin) / im_width)
bbox_sample.append(float(ymin) / im_height)
bbox_sample.append(float(xmax) / im_width)
bbox_sample.append(float(ymax) / im_height)
bbox_sample.append(float(ann['iscrowd']))
bbox_labels.append(bbox_sample)
im, sample_labels = preprocess(im, bbox_labels, mode, settings)
sample_labels = np.array(sample_labels) sample_labels = np.array(sample_labels)
if mode == 'train' or mode == 'test': if len(sample_labels) == 0: continue
if mode == 'train' and len(sample_labels) == 0: continue im = im.astype('float32')
if mode == 'test' and len(sample_labels) == 0: continue boxes = sample_labels[:, 1:5]
yield img.astype( lbls = sample_labels[:, 0].astype('int32')
'float32' difficults = sample_labels[:, -1].astype('int32')
), sample_labels[:, 1:5], sample_labels[:, 0].astype( yield im, boxes, lbls, difficults
'int32'), sample_labels[:, -1].astype('int32')
elif mode == 'infer': return reader
yield img.astype('float32')
def pascalvoc(settings, file_list, mode, shuffle):
flist = open(file_list)
images = [line.strip() for line in flist]
if not settings.toy == 0:
images = images[:settings.toy] if len(images) > settings.toy else images
print("{} on {} with {} images".format(mode, settings.dataset, len(images)))
def reader():
if mode == 'train' and shuffle:
random.shuffle(images)
for image in images:
image_path, label_path = image.split()
image_path = os.path.join(settings.data_dir, image_path)
label_path = os.path.join(settings.data_dir, label_path)
im = Image.open(image_path)
if im.mode == 'L':
im = im.convert('RGB')
im_width, im_height = im.size
# layout: label | xmin | ymin | xmax | ymax | difficult
bbox_labels = []
root = xml.etree.ElementTree.parse(label_path).getroot()
for object in root.findall('object'):
bbox_sample = []
# start from 1
bbox_sample.append(
float(settings.label_list.index(object.find('name').text)))
bbox = object.find('bndbox')
difficult = float(object.find('difficult').text)
bbox_sample.append(float(bbox.find('xmin').text) / im_width)
bbox_sample.append(float(bbox.find('ymin').text) / im_height)
bbox_sample.append(float(bbox.find('xmax').text) / im_width)
bbox_sample.append(float(bbox.find('ymax').text) / im_height)
bbox_sample.append(difficult)
bbox_labels.append(bbox_sample)
im, sample_labels = preprocess(im, bbox_labels, mode, settings)
sample_labels = np.array(sample_labels)
if len(sample_labels) == 0: continue
im = im.astype('float32')
boxes = sample_labels[:, 1:5]
lbls = sample_labels[:, 0].astype('int32')
difficults = sample_labels[:, -1].astype('int32')
yield im, boxes, lbls, difficults
return reader return reader
...@@ -309,9 +309,9 @@ def train(settings, file_list, shuffle=True): ...@@ -309,9 +309,9 @@ def train(settings, file_list, shuffle=True):
elif '2017' in file_list: elif '2017' in file_list:
sub_dir = "train2017" sub_dir = "train2017"
train_settings.data_dir = os.path.join(settings.data_dir, sub_dir) train_settings.data_dir = os.path.join(settings.data_dir, sub_dir)
return _reader_creator(train_settings, file_list, 'train', shuffle) return coco(train_settings, file_list, 'train', shuffle)
else: else:
return _reader_creator(settings, file_list, 'train', shuffle) return pascalvoc(settings, file_list, 'train', shuffle)
def test(settings, file_list): def test(settings, file_list):
...@@ -323,10 +323,24 @@ def test(settings, file_list): ...@@ -323,10 +323,24 @@ def test(settings, file_list):
elif '2017' in file_list: elif '2017' in file_list:
sub_dir = "val2017" sub_dir = "val2017"
test_settings.data_dir = os.path.join(settings.data_dir, sub_dir) test_settings.data_dir = os.path.join(settings.data_dir, sub_dir)
return _reader_creator(test_settings, file_list, 'test', False) return coco(test_settings, file_list, 'test', False)
else: else:
return _reader_creator(settings, file_list, 'test', False) return pascalvoc(settings, file_list, 'test', False)
def infer(settings, file_list): def infer(settings, image_path):
return _reader_creator(settings, file_list, 'infer', False) im = Image.open(image_path)
if im.mode == 'L':
im = im.convert('RGB')
im_width, im_height = im.size
img = img.resize((settings.resize_w, settings.resize_h), Image.ANTIALIAS)
img = np.array(img)
# HWC to CHW
if len(img.shape) == 3:
img = np.swapaxes(img, 1, 2)
img = np.swapaxes(img, 1, 0)
# RBG to BGR
img = img[[2, 1, 0], :, :]
img = img.astype('float32')
img -= settings.img_mean
img = img * 0.007843
fluid/object_detection/train.py
浏览文件 @ e76927fe
...@@ -3,6 +3,7 @@ import time ...@@ -3,6 +3,7 @@ import time
import numpy as np import numpy as np
import argparse import argparse
import functools import functools
import shutil
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
...@@ -205,7 +206,6 @@ def parallel_exe(args, ...@@ -205,7 +206,6 @@ def parallel_exe(args,
evaluate_difficult=False, evaluate_difficult=False,
ap_version=args.ap_version) ap_version=args.ap_version)
print('ParallelExecutor, ap_version = ', args.ap_version)
if data_args.dataset == 'coco': if data_args.dataset == 'coco':
# learning rate decay in 12, 19 pass, respectively # learning rate decay in 12, 19 pass, respectively
if '2014' in train_file_list: if '2014' in train_file_list:
...@@ -243,7 +243,15 @@ def parallel_exe(args, ...@@ -243,7 +243,15 @@ def parallel_exe(args,
feeder = fluid.DataFeeder( feeder = fluid.DataFeeder(
place=place, feed_list=[image, gt_box, gt_label, difficult]) place=place, feed_list=[image, gt_box, gt_label, difficult])
def test(pass_id): def save_model(postfix):
model_path = os.path.join(model_save_dir, postfix)
if os.path.isdir(model_path):
shutil.rmtree(model_path)
print 'save models to %s' % (model_path)
fluid.io.save_persistables(exe, model_path)
best_map = 0.
def test(pass_id, best_map):
_, accum_map = map_eval.get_map_var() _, accum_map = map_eval.get_map_var()
map_eval.reset(exe) map_eval.reset(exe)
test_map = None test_map = None
...@@ -251,13 +259,15 @@ def parallel_exe(args, ...@@ -251,13 +259,15 @@ def parallel_exe(args,
test_map = exe.run(test_program, test_map = exe.run(test_program,
feed=feeder.feed(data), feed=feeder.feed(data),
fetch_list=[accum_map]) fetch_list=[accum_map])
if test_map[0] > best_map:
best_map = test_map[0]
save_model('best_model')
print("Test {0}, map {1}".format(pass_id, test_map[0])) print("Test {0}, map {1}".format(pass_id, test_map[0]))
for pass_id in range(num_passes): for pass_id in range(num_passes):
start_time = time.time() start_time = time.time()
prev_start_time = start_time prev_start_time = start_time
end_time = 0 end_time = 0
test(pass_id)
for batch_id, data in enumerate(train_reader()): for batch_id, data in enumerate(train_reader()):
prev_start_time = start_time prev_start_time = start_time
start_time = time.time() start_time = time.time()
...@@ -269,11 +279,10 @@ def parallel_exe(args, ...@@ -269,11 +279,10 @@ def parallel_exe(args,
if batch_id % 20 == 0: if batch_id % 20 == 0:
print("Pass {0}, batch {1}, loss {2}, time {3}".format( print("Pass {0}, batch {1}, loss {2}, time {3}".format(
pass_id, batch_id, loss_v, start_time - prev_start_time)) pass_id, batch_id, loss_v, start_time - prev_start_time))
test(pass_id, best_map)
if pass_id % 10 == 0 or pass_id == num_passes - 1: if pass_id % 10 == 0 or pass_id == num_passes - 1:
model_path = os.path.join(model_save_dir, str(pass_id)) save_model(str(pass_id))
print 'save models to %s' % (model_path) print("Best test map {0}".format(best_map))
fluid.io.save_persistables(exe, model_path)
if __name__ == '__main__': if __name__ == '__main__':
args = parser.parse_args() args = parser.parse_args()
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或