Skip to content

M
models

PaddlePaddle / models
12 个月 前同步成功

通知 222
Star 6828
Fork 2962
M
models

体验新版 GitCode，发现更多精彩内容 >>

提交 7a13a4b8 编写于 作者: Y Yang Zhang 提交者： qingqing01
浏览文件
操作

Fix code format for `data_feed.py` (#2516)

上级 9e5c26da
隐藏空白更改
内联 并排
Showing with 82 addition and 84 deletion
PaddleCV/object_detection/ppdet/data/data_feed.py
浏览文件 @ 7a13a4b8
......@@ -413,14 +413,14 @@ class FasterRCNNTrainFeed(DataFeed):
],
image_shape=[3, 1333, 800],
sample_transforms=[
DecodeImage(to_rgb=True), RandomFlipImage(prob=0.5),
NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False), ResizeImage(
target_size=800, max_size=1333,
interp=1), Permute(to_bgr=False)
DecodeImage(to_rgb=True),
RandomFlipImage(prob=0.5),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800, max_size=1333, interp=1),
Permute(to_bgr=False)
],
batch_transforms=[PadBatch()],
batch_size=1,
......@@ -463,17 +463,17 @@ class MaskRCNNTrainFeed(DataFeed):
],
image_shape=[3, 1333, 800],
sample_transforms=[
DecodeImage(to_rgb=True), RandomFlipImage(
prob=0.5, is_mask_flip=True), NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False), ResizeImage(
target_size=800,
DecodeImage(to_rgb=True),
RandomFlipImage(prob=0.5, is_mask_flip=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800,
max_size=1333,
interp=1,
use_cv2=True), Permute(
to_bgr=False, channel_first=True)
use_cv2=True),
Permute(to_bgr=False, channel_first=True)
],
batch_transforms=[PadBatch()],
batch_size=1,
......@@ -509,12 +509,12 @@ class FasterRCNNEvalFeed(DataFeed):
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[3, 1333, 800],
sample_transforms=[
DecodeImage(to_rgb=True), NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False), ResizeImage(
target_size=800, max_size=1333, interp=1),
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800, max_size=1333, interp=1),
Permute(to_bgr=False)
],
batch_transforms=[PadBatch()],
......@@ -552,11 +552,12 @@ class FasterRCNNTestFeed(DataFeed):
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[3, 1333, 800],
sample_transforms=[
DecodeImage(to_rgb=True), NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False), Permute(to_bgr=False)
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
Permute(to_bgr=False)
],
batch_transforms=[PadBatch()],
batch_size=1,
......@@ -595,16 +596,16 @@ class MaskRCNNEvalFeed(DataFeed):
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[3, 1333, 800],
sample_transforms=[
DecodeImage(to_rgb=True), NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False), ResizeImage(
target_size=800,
max_size=1333,
interp=1,
use_cv2=True), Permute(
to_bgr=False, channel_first=True)
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=800,
max_size=1333,
interp=1,
use_cv2=True),
Permute(to_bgr=False, channel_first=True)
],
batch_transforms=[PadBatch()],
batch_size=1,
......@@ -643,12 +644,13 @@ class MaskRCNNTestFeed(DataFeed):
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[3, 1333, 800],
sample_transforms=[
DecodeImage(to_rgb=True), NormalizeImage(
DecodeImage(to_rgb=True),
NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False), Permute(
to_bgr=False, channel_first=True)
is_channel_first=False),
Permute(to_bgr=False, channel_first=True)
],
batch_transforms=[PadBatch()],
batch_size=1,
......@@ -688,28 +690,26 @@ class SSDTrainFeed(DataFeed):
fields=['image', 'gt_box', 'gt_label', 'is_difficult'],
image_shape=[3, 300, 300],
sample_transforms=[
DecodeImage(
to_rgb=True,
with_mixup=False), NormalizeBox(), RandomDistort(
brightness_lower=0.875,
brightness_upper=1.125,
is_order=True), ExpandImage(
max_ratio=4, prob=0.5),
CropImage(
batch_sampler=[[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]],
satisfy_all=False), ResizeImage(
target_size=300, use_cv2=False,
interp=1), RandomFlipImage(is_normalized=True),
Permute(), NormalizeImage(
mean=[127.5, 127.5, 127.5],
std=[127.502231, 127.502231, 127.502231],
is_scale=False)
DecodeImage(to_rgb=True, with_mixup=False),
NormalizeBox(),
RandomDistort(brightness_lower=0.875,
brightness_upper=1.125,
is_order=True),
ExpandImage(max_ratio=4, prob=0.5),
CropImage([[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.5, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.7, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.9, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.0, 1.0]],
satisfy_all=False),
ResizeImage(target_size=300, use_cv2=False, interp=1),
RandomFlipImage(is_normalized=True),
Permute(),
NormalizeImage(mean=[127.5, 127.5, 127.5],
std=[127.502231, 127.502231, 127.502231],
is_scale=False)
],
batch_transforms=[],
batch_size=32,
......@@ -747,10 +747,12 @@ class SSDEvalFeed(DataFeed):
fields=['image', 'gt_box', 'gt_label', 'is_difficult'],
image_shape=[3, 300, 300],
sample_transforms=[
DecodeImage(
to_rgb=True, with_mixup=False), NormalizeBox(), ResizeImage(
target_size=300, use_cv2=False, interp=1),
RandomFlipImage(is_normalized=True), Permute(), NormalizeImage(
DecodeImage(to_rgb=True, with_mixup=False),
NormalizeBox(),
ResizeImage(target_size=300, use_cv2=False, interp=1),
RandomFlipImage(is_normalized=True),
Permute(),
NormalizeImage(
mean=[127.5, 127.5, 127.5],
std=[127.502231, 127.502231, 127.502231],
is_scale=False)
......@@ -790,8 +792,9 @@ class SSDTestFeed(DataFeed):
fields=['image'],
image_shape=[3, 300, 300],
sample_transforms=[
DecodeImage(to_rgb=True), ResizeImage(
target_size=300, use_cv2=False, interp=1), Permute(),
DecodeImage(to_rgb=True),
ResizeImage(target_size=300, use_cv2=False, interp=1),
Permute(),
NormalizeImage(
mean=[127.5, 127.5, 127.5],
std=[127.502231, 127.502231, 127.502231],
......@@ -833,14 +836,12 @@ class YoloTrainFeed(DataFeed):
fields=['image', 'gt_box', 'gt_label', 'gt_score'],
image_shape=[3, 608, 608],
sample_transforms=[
DecodeImage(
to_rgb=True, with_mixup=True),
MixupImage(
alpha=1.5, beta=1.5),
DecodeImage(to_rgb=True, with_mixup=True),
MixupImage(alpha=1.5, beta=1.5),
NormalizeBox(),
RandomDistort(),
ExpandImage(
max_ratio=4., prob=.5, mean=[123.675, 116.28, 103.53]),
ExpandImage(max_ratio=4., prob=.5,
mean=[123.675, 116.28, 103.53]),
CropImage([[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0],
[1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0],
......@@ -903,8 +904,7 @@ class YoloEvalFeed(DataFeed):
image_shape=[3, 608, 608],
sample_transforms=[
DecodeImage(to_rgb=True),
ResizeImage(
target_size=608, interp=2),
ResizeImage(target_size=608, interp=2),
NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
......@@ -951,13 +951,11 @@ class YoloTestFeed(DataFeed):
image_shape=[3, 608, 608],
sample_transforms=[
DecodeImage(to_rgb=True),
ResizeImage(
target_size=608, interp=2),
NormalizeImage(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
ResizeImage(target_size=608, interp=2),
NormalizeImage(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True,
is_channel_first=False),
Permute(to_bgr=False),
],
batch_transforms=[],
......
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