未验证 提交 2e1e76e7 编写于 作者: Y Yan Xu 提交者: GitHub

add SeResNeXt unittest (#16503)

* add seresnet unittest test=develop

* add dropout layer test=develop

* fix ci test=develop

* fix comment test=develop

* fix comment test=develop

* fix ci test=develop

* fix ci test=develop

* fix ci

* fix module name test=develop

* run imperative serenext unit test serially test=develop
上级 feb1b54f
......@@ -78,6 +78,7 @@ list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op)
list(REMOVE_ITEM TEST_OPS test_nearest_interp_op)
list(REMOVE_ITEM TEST_OPS test_imperative_resnet)
list(REMOVE_ITEM TEST_OPS test_imperative_se_resnext)
list(REMOVE_ITEM TEST_OPS test_imperative_mnist)
list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer)
foreach(TEST_OP ${TEST_OPS})
......@@ -91,6 +92,9 @@ py_test_modules(test_imperative_resnet MODULES test_imperative_resnet ENVS
FLAGS_cudnn_deterministic=1)
py_test_modules(test_imperative_mnist MODULES test_imperative_mnist ENVS
FLAGS_cudnn_deterministic=1)
py_test_modules(test_imperative_se_resnext MODULES test_imperative_se_resnext ENVS
FLAGS_cudnn_deterministic=1 SERIAL)
if(WITH_DISTRIBUTE)
py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)
......
# Copyright (c) 2018 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.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import unittest
import numpy as np
import six
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid.dygraph.base import to_variable
from test_imperative_base import new_program_scope
batch_size = 8
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": batch_size,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
},
"batch_size": batch_size,
"lr": 0.1,
"total_images": 6149,
}
def optimizer_setting(params):
ls = params["learning_strategy"]
if ls["name"] == "piecewise_decay":
if "total_images" not in params:
total_images = 6149
else:
total_images = params["total_images"]
# TODO(Yancey1989): using lr decay if it is ready.
#batch_size = ls["batch_size"]
#step = int(total_images / batch_size + 1)
#bd = [step * e for e in ls["epochs"]]
#base_lr = params["lr"]
#lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
optimizer = fluid.optimizer.SGD(learning_rate=0.1)
return optimizer
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__(name_scope)
self._conv = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
bias_attr=None)
self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class SqueezeExcitation(fluid.dygraph.Layer):
def __init__(self, name_scope, num_channels, reduction_ratio):
super(SqueezeExcitation, self).__init__(name_scope)
self._pool = Pool2D(
self.full_name(), pool_size=0, pool_type='avg', global_pooling=True)
self._squeeze = FC(
self.full_name(),
size=num_channels // reduction_ratio,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.05)),
act='relu')
self._excitation = FC(
self.full_name(),
size=num_channels,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.05)),
act='relu')
def forward(self, input):
y = self._pool(input)
y = self._squeeze(y)
y = self._excitation(y)
y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
return y
class BottleneckBlock(fluid.dygraph.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
stride,
cardinality,
reduction_ratio,
shortcut=True):
super(BottleneckBlock, self).__init__(name_scope)
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=1)
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
groups=cardinality)
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act='relu')
self.scale = SqueezeExcitation(
self.full_name(),
num_channels=num_filters * 4,
reduction_ratio=reduction_ratio)
if not shortcut:
self.short = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
stride=stride)
self.shortcut = shortcut
self._num_channels_out = num_filters * 4
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
scale = self.scale(conv2)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=scale)
layer_helper = LayerHelper(self.full_name(), act='relu')
y = layer_helper.append_activation(y)
return y
class SeResNeXt(fluid.dygraph.Layer):
def __init__(self, name_scope, layers=50, class_dim=102):
super(SeResNeXt, self).__init__(name_scope)
self.layers = layers
supported_layers = [50, 101, 152]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers)
if layers == 50:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 6, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
elif layers == 101:
cardinality = 32
reduction_ratio = 16
depth = [3, 4, 23, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=3,
filter_size=7,
stride=2,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
elif layers == 152:
cardinality = 64
reduction_ratio = 16
depth = [3, 8, 36, 3]
num_filters = [128, 256, 512, 1024]
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=3,
num_filters=3,
filter_size=7,
stride=2,
act='relu')
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=64,
num_filters=3,
filter_size=7,
stride=2,
act='relu')
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=64,
num_filters=3,
filter_size=7,
stride=2,
act='relu')
self.pool = Pool2D(
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
self.bottleneck_block_list = []
num_channels = 64
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
cardinality=cardinality,
reduction_ratio=reduction_ratio,
shortcut=shortcut))
num_channels = bottleneck_block._num_channels_out
self.bottleneck_block_list.append(bottleneck_block)
shortcut = True
self.pool2d_avg = Pool2D(
self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
import math
stdv = 1.0 / math.sqrt(2048 * 1.0)
self.out = FC(self.full_name(),
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
def forward(self, inputs):
if self.layers == 50 or self.layers == 101:
y = self.conv0(inputs)
y = self.pool(y)
elif self.layers == 152:
y = self.conv0(inputs)
y = self.conv1(inputs)
y = self.conv2(inputs)
y = self.pool(y)
for bottleneck_block in self.bottleneck_block_list:
y = bottleneck_block(y)
y = self.pool2d_avg(y)
y = fluid.layers.dropout(y, dropout_prob=0.2)
y = self.out(y)
return y
class TestImperativeResneXt(unittest.TestCase):
def test_se_resnext_float32(self):
seed = 90
batch_size = train_parameters["batch_size"]
batch_num = 2
with fluid.dygraph.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
se_resnext = SeResNeXt("se_resnext")
optimizer = optimizer_setting(train_parameters)
np.random.seed(seed)
import random
random.seed = seed
train_reader = paddle.batch(
paddle.dataset.flowers.train(use_xmap=False),
batch_size=batch_size)
dy_param_init_value = {}
for param in se_resnext.parameters():
dy_param_init_value[param.name] = param._numpy()
for batch_id, data in enumerate(train_reader()):
if batch_id >= batch_num:
break
dy_x_data = np.array(
[x[0].reshape(3, 224, 224) for x in data]).astype('float32')
y_data = np.array([x[1] for x in data]).astype('int64').reshape(
batch_size, 1)
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
out = se_resnext(img)
loss = fluid.layers.cross_entropy(input=out, label=label)
avg_loss = fluid.layers.mean(x=loss)
dy_out = avg_loss._numpy()
if batch_id == 0:
for param in se_resnext.parameters():
if param.name not in dy_param_init_value:
dy_param_init_value[param.name] = param._numpy()
avg_loss._backward()
dy_grad_value = {}
for param in se_resnext.parameters():
if param.trainable:
np_array = np.array(param._ivar._grad_ivar().value()
.get_tensor())
dy_grad_value[param.name + core.grad_var_suffix(
)] = np_array
optimizer.minimize(avg_loss)
se_resnext.clear_gradients()
dy_param_value = {}
for param in se_resnext.parameters():
dy_param_value[param.name] = param._numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
se_resnext = SeResNeXt("se_resnext")
optimizer = optimizer_setting(train_parameters)
np.random.seed(seed)
import random
random.seed = seed
train_reader = paddle.batch(
paddle.dataset.flowers.train(use_xmap=False),
batch_size=batch_size)
img = fluid.layers.data(
name='pixel', shape=[3, 224, 224], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
out = se_resnext(img)
loss = fluid.layers.cross_entropy(input=out, label=label)
avg_loss = fluid.layers.mean(x=loss)
optimizer.minimize(avg_loss)
# initialize params and fetch them
static_param_init_value = {}
static_param_name_list = []
static_grad_name_list = []
for param in se_resnext.parameters():
static_param_name_list.append(param.name)
for param in se_resnext.parameters():
if param.trainable:
static_grad_name_list.append(param.name +
core.grad_var_suffix())
out = exe.run(fluid.default_startup_program(),
fetch_list=static_param_name_list)
for i in range(len(static_param_name_list)):
static_param_init_value[static_param_name_list[i]] = out[i]
for batch_id, data in enumerate(train_reader()):
if batch_id >= batch_num:
break
static_x_data = np.array(
[x[0].reshape(3, 224, 224) for x in data]).astype('float32')
y_data = np.array([x[1] for x in data]).astype('int64').reshape(
[batch_size, 1])
fetch_list = [avg_loss.name]
fetch_list.extend(static_param_name_list)
fetch_list.extend(static_grad_name_list)
out = exe.run(fluid.default_main_program(),
feed={"pixel": static_x_data,
"label": y_data},
fetch_list=fetch_list)
static_param_value = {}
static_grad_value = {}
static_out = out[0]
param_start_pos = 1
grad_start_pos = len(static_param_name_list) + param_start_pos
for i in range(param_start_pos,
len(static_param_name_list) + param_start_pos):
static_param_value[static_param_name_list[
i - param_start_pos]] = out[i]
for i in range(grad_start_pos,
len(static_grad_name_list) + grad_start_pos):
static_grad_value[static_grad_name_list[
i - grad_start_pos]] = out[i]
self.assertTrue(np.allclose(static_out, dy_out))
self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
for key, value in six.iteritems(static_param_init_value):
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
self.assertTrue(np.isfinite(value.all()))
self.assertFalse(np.isnan(value.any()))
self.assertEqual(len(dy_grad_value), len(static_grad_value))
for key, value in six.iteritems(static_grad_value):
self.assertTrue(np.allclose(value, dy_grad_value[key]))
self.assertTrue(np.isfinite(value.all()))
self.assertFalse(np.isnan(value.any()))
self.assertEqual(len(dy_param_value), len(static_param_value))
for key, value in six.iteritems(static_param_value):
self.assertTrue(np.allclose(value, dy_param_value[key]))
self.assertTrue(np.isfinite(value.all()))
self.assertFalse(np.isnan(value.any()))
if __name__ == '__main__':
unittest.main()
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