未验证 提交 b401e10b 编写于 作者: L littletomatodonkey 提交者: GitHub

Revert "add amp support for SE_ResNet50_vd (#509)" (#515)

This reverts commit d5105825.
上级 d5105825
mode: 'train'
ARCHITECTURE:
name: 'ResNet50_vd'
pretrained_model: ""
model_save_dir: "./output/"
classes_num: 1000
total_images: 1281167
save_interval: 1
validate: True
valid_interval: 1
epochs: 200
topk: 5
image_shape: [3, 224, 224]
# mixed precision training
use_amp: True
use_pure_fp16: False
multi_precision: False
scale_loss: 128.0
use_dynamic_loss_scaleing: True
data_format: "NCHW"
use_mix: True
ls_epsilon: 0.1
LEARNING_RATE:
function: 'Cosine'
params:
lr: 0.1
OPTIMIZER:
function: 'Momentum'
params:
momentum: 0.9
regularizer:
function: 'L2'
factor: 0.000070
TRAIN:
batch_size: 256
num_workers: 4
file_list: "./dataset/ILSVRC2012/train_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- RandCropImage:
size: 224
- RandFlipImage:
flip_code: 1
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
mix:
- MixupOperator:
alpha: 0.2
VALID:
batch_size: 64
num_workers: 4
file_list: "./dataset/ILSVRC2012/val_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- ResizeImage:
resize_short: 256
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
......@@ -33,16 +33,16 @@ __all__ = [
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
name=None,
data_format="NCHW"):
def __init__(
self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
name=None, ):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
......@@ -57,8 +57,7 @@ class ConvBNLayer(nn.Layer):
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
data_format=data_format)
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
......@@ -69,8 +68,7 @@ class ConvBNLayer(nn.Layer):
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
data_format=data_format)
moving_variance_name=bn_name + '_variance')
def forward(self, inputs):
if self.is_vd_mode:
......@@ -88,8 +86,7 @@ class BottleneckBlock(nn.Layer):
shortcut=True,
if_first=False,
reduction_ratio=16,
name=None,
data_format="NCHW"):
name=None):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
......@@ -97,29 +94,25 @@ class BottleneckBlock(nn.Layer):
num_filters=num_filters,
filter_size=1,
act='relu',
name=name + "_branch2a",
data_format=data_format)
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu',
name=name + "_branch2b",
data_format=data_format)
name=name + "_branch2b")
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_branch2c",
data_format=data_format)
name=name + "_branch2c")
self.scale = SELayer(
num_channels=num_filters * 4,
num_filters=num_filters * 4,
reduction_ratio=reduction_ratio,
name='fc_' + name,
data_format=data_format)
name='fc_' + name)
if not shortcut:
self.short = ConvBNLayer(
......@@ -128,8 +121,7 @@ class BottleneckBlock(nn.Layer):
filter_size=1,
stride=1,
is_vd_mode=False if if_first else True,
name=name + "_branch1",
data_format=data_format)
name=name + "_branch1")
self.shortcut = shortcut
......@@ -156,8 +148,7 @@ class BasicBlock(nn.Layer):
shortcut=True,
if_first=False,
reduction_ratio=16,
name=None,
data_format="NCHW"):
name=None):
super(BasicBlock, self).__init__()
self.stride = stride
self.conv0 = ConvBNLayer(
......@@ -166,22 +157,19 @@ class BasicBlock(nn.Layer):
filter_size=3,
stride=stride,
act='relu',
name=name + "_branch2a",
data_format=data_format)
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b",
data_format=data_format)
name=name + "_branch2b")
self.scale = SELayer(
num_channels=num_filters,
num_filters=num_filters,
reduction_ratio=reduction_ratio,
name='fc_' + name,
data_format=data_format)
name='fc_' + name)
if not shortcut:
self.short = ConvBNLayer(
......@@ -190,8 +178,7 @@ class BasicBlock(nn.Layer):
filter_size=1,
stride=1,
is_vd_mode=False if if_first else True,
name=name + "_branch1",
data_format=data_format)
name=name + "_branch1")
self.shortcut = shortcut
......@@ -210,12 +197,7 @@ class BasicBlock(nn.Layer):
class SELayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
reduction_ratio,
name=None,
data_format="NCHW"):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
self.pool2d_gap = AdaptiveAvgPool2D(1)
......@@ -252,16 +234,10 @@ class SELayer(nn.Layer):
class SE_ResNet_vd(nn.Layer):
def __init__(self,
layers=50,
class_dim=1000,
input_image_channel=3,
data_format="NCHW"):
def __init__(self, layers=50, class_dim=1000):
super(SE_ResNet_vd, self).__init__()
self.layers = layers
self.data_format = data_format
self.input_image_channel = input_image_channel
supported_layers = [18, 34, 50, 101, 152, 200]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(
......@@ -282,31 +258,27 @@ class SE_ResNet_vd(nn.Layer):
num_filters = [64, 128, 256, 512]
self.conv1_1 = ConvBNLayer(
num_channels=self.input_image_channel,
num_channels=3,
num_filters=32,
filter_size=3,
stride=2,
act='relu',
name="conv1_1",
data_format=self.data_format)
name="conv1_1")
self.conv1_2 = ConvBNLayer(
num_channels=32,
num_filters=32,
filter_size=3,
stride=1,
act='relu',
name="conv1_2",
data_format=self.data_format)
name="conv1_2")
self.conv1_3 = ConvBNLayer(
num_channels=32,
num_filters=64,
filter_size=3,
stride=1,
act='relu',
name="conv1_3",
data_format=self.data_format)
self.pool2d_max = MaxPool2D(
kernel_size=3, stride=2, padding=1, data_format=self.data_format)
name="conv1_3")
self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
......@@ -329,8 +301,7 @@ class SE_ResNet_vd(nn.Layer):
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name,
data_format=self.data_format))
name=conv_name))
self.block_list.append(bottleneck_block)
shortcut = True
else:
......
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