mindspore/tests/st/pynative/test_pynative_resnet50.py

432 lines
14 KiB
Python

# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 time
import random
import numpy as np
import pytest
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as vision
import mindspore.nn as nn
import mindspore.ops.functional as F
from mindspore import Tensor
from mindspore import context
from mindspore import ParameterTuple
from mindspore.nn import Cell
from mindspore.ops import operations as P
from mindspore.ops import composite as CP
from mindspore.nn.optim.momentum import Momentum
from mindspore.common.initializer import initializer
from mindspore.nn.wrap.cell_wrapper import WithLossCell
random.seed(1)
np.random.seed(1)
ds.config.set_seed(1)
def weight_variable(shape):
return initializer('XavierUniform', shape=shape, dtype=mstype.float32)
def weight_variable_uniform(shape):
return initializer('Uniform', shape=shape, dtype=mstype.float32)
def weight_variable_0(shape):
zeros = np.zeros(shape).astype(np.float32)
return Tensor(zeros)
def weight_variable_1(shape):
ones = np.ones(shape).astype(np.float32)
return Tensor(ones)
def conv3x3(in_channels, out_channels, stride=1, padding=0):
"""3x3 convolution """
weight_shape = (out_channels, in_channels, 3, 3)
weight = weight_variable(weight_shape)
return nn.Conv2d(in_channels, out_channels,
kernel_size=3, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
def conv1x1(in_channels, out_channels, stride=1, padding=0):
"""1x1 convolution"""
weight_shape = (out_channels, in_channels, 1, 1)
weight = weight_variable(weight_shape)
return nn.Conv2d(in_channels, out_channels,
kernel_size=1, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
def conv7x7(in_channels, out_channels, stride=1, padding=0):
"""1x1 convolution"""
weight_shape = (out_channels, in_channels, 7, 7)
weight = weight_variable(weight_shape)
return nn.Conv2d(in_channels, out_channels,
kernel_size=7, stride=stride, padding=padding, weight_init=weight, has_bias=False, pad_mode="same")
def bn_with_initialize(out_channels):
shape = (out_channels)
mean = weight_variable_0(shape)
var = weight_variable_1(shape)
beta = weight_variable_0(shape)
gamma = weight_variable_uniform(shape)
bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init=gamma,
beta_init=beta, moving_mean_init=mean, moving_var_init=var)
return bn
def bn_with_initialize_last(out_channels):
shape = (out_channels)
mean = weight_variable_0(shape)
var = weight_variable_1(shape)
beta = weight_variable_0(shape)
gamma = weight_variable_uniform(shape)
bn = nn.BatchNorm2d(out_channels, momentum=0.99, eps=0.00001, gamma_init=gamma,
beta_init=beta, moving_mean_init=mean, moving_var_init=var)
return bn
def fc_with_initialize(input_channels, out_channels):
weight_shape = (out_channels, input_channels)
weight = weight_variable(weight_shape)
bias_shape = (out_channels)
bias = weight_variable_uniform(bias_shape)
return nn.Dense(input_channels, out_channels, weight, bias)
class ResidualBlock(nn.Cell):
expansion = 4
def __init__(self,
in_channels,
out_channels,
stride=1):
super(ResidualBlock, self).__init__()
out_chls = out_channels // self.expansion
self.conv1 = conv1x1(in_channels, out_chls, stride=stride, padding=0)
self.bn1 = bn_with_initialize(out_chls)
self.conv2 = conv3x3(out_chls, out_chls, stride=1, padding=0)
self.bn2 = bn_with_initialize(out_chls)
self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
self.bn3 = bn_with_initialize_last(out_channels)
self.relu = P.ReLU()
self.add = P.TensorAdd()
def construct(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.add(out, identity)
out = self.relu(out)
return out
class ResidualBlockWithDown(nn.Cell):
expansion = 4
def __init__(self,
in_channels,
out_channels,
stride=1,
down_sample=False):
super(ResidualBlockWithDown, self).__init__()
out_chls = out_channels // self.expansion
self.conv1 = conv1x1(in_channels, out_chls, stride=stride, padding=0)
self.bn1 = bn_with_initialize(out_chls)
self.conv2 = conv3x3(out_chls, out_chls, stride=1, padding=0)
self.bn2 = bn_with_initialize(out_chls)
self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
self.bn3 = bn_with_initialize_last(out_channels)
self.relu = P.ReLU()
self.downSample = down_sample
self.conv_down_sample = conv1x1(in_channels, out_channels, stride=stride, padding=0)
self.bn_down_sample = bn_with_initialize(out_channels)
self.add = P.TensorAdd()
def construct(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
identity = self.conv_down_sample(identity)
identity = self.bn_down_sample(identity)
out = self.add(out, identity)
out = self.relu(out)
return out
class MakeLayer0(nn.Cell):
def __init__(self, block, in_channels, out_channels, stride):
super(MakeLayer0, self).__init__()
self.a = ResidualBlockWithDown(in_channels, out_channels, stride=1, down_sample=True)
self.b = block(out_channels, out_channels, stride=stride)
self.c = block(out_channels, out_channels, stride=1)
def construct(self, x):
x = self.a(x)
x = self.b(x)
x = self.c(x)
return x
class MakeLayer1(nn.Cell):
def __init__(self, block, in_channels, out_channels, stride):
super(MakeLayer1, self).__init__()
self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
self.b = block(out_channels, out_channels, stride=1)
self.c = block(out_channels, out_channels, stride=1)
self.d = block(out_channels, out_channels, stride=1)
def construct(self, x):
x = self.a(x)
x = self.b(x)
x = self.c(x)
x = self.d(x)
return x
class MakeLayer2(nn.Cell):
def __init__(self, block, in_channels, out_channels, stride):
super(MakeLayer2, self).__init__()
self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
self.b = block(out_channels, out_channels, stride=1)
self.c = block(out_channels, out_channels, stride=1)
self.d = block(out_channels, out_channels, stride=1)
self.e = block(out_channels, out_channels, stride=1)
self.f = block(out_channels, out_channels, stride=1)
def construct(self, x):
x = self.a(x)
x = self.b(x)
x = self.c(x)
x = self.d(x)
x = self.e(x)
x = self.f(x)
return x
class MakeLayer3(nn.Cell):
def __init__(self, block, in_channels, out_channels, stride):
super(MakeLayer3, self).__init__()
self.a = ResidualBlockWithDown(in_channels, out_channels, stride=stride, down_sample=True)
self.b = block(out_channels, out_channels, stride=1)
self.c = block(out_channels, out_channels, stride=1)
def construct(self, x):
x = self.a(x)
x = self.b(x)
x = self.c(x)
return x
class ResNet(nn.Cell):
def __init__(self, block, num_classes=100, batch_size=32):
super(ResNet, self).__init__()
self.batch_size = batch_size
self.num_classes = num_classes
self.conv1 = conv7x7(3, 64, stride=2, padding=0)
self.bn1 = bn_with_initialize(64)
self.relu = P.ReLU()
self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="SAME")
self.layer1 = MakeLayer0(block, in_channels=64, out_channels=256, stride=1)
self.layer2 = MakeLayer1(block, in_channels=256, out_channels=512, stride=2)
self.layer3 = MakeLayer2(block, in_channels=512, out_channels=1024, stride=2)
self.layer4 = MakeLayer3(block, in_channels=1024, out_channels=2048, stride=2)
self.pool = P.ReduceMean(keep_dims=True)
self.squeeze = P.Squeeze(axis=(2, 3))
self.fc = fc_with_initialize(512 * block.expansion, num_classes)
def construct(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)[0]
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.pool(x, (2, 3))
x = self.squeeze(x)
x = self.fc(x)
return x
def resnet50(batch_size, num_classes):
return ResNet(ResidualBlock, num_classes, batch_size)
def create_dataset(repeat_num=1, training=True, batch_size=32):
data_home = "/home/workspace/mindspore_dataset"
data_dir = data_home + "/cifar-10-batches-bin"
if not training:
data_dir = data_home + "/cifar-10-verify-bin"
data_set = ds.Cifar10Dataset(data_dir)
resize_height = 224
resize_width = 224
rescale = 1.0 / 255.0
shift = 0.0
# define map operations
random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
random_horizontal_op = vision.RandomHorizontalFlip()
# interpolation default BILINEAR
resize_op = vision.Resize((resize_height, resize_width))
rescale_op = vision.Rescale(rescale, shift)
normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
changeswap_op = vision.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
c_trans = []
if training:
c_trans = [random_crop_op, random_horizontal_op]
c_trans += [resize_op, rescale_op, normalize_op,
changeswap_op]
# apply map operations on images
data_set = data_set.map(input_columns="label", operations=type_cast_op)
data_set = data_set.map(input_columns="image", operations=c_trans)
# apply shuffle operations
data_set = data_set.shuffle(buffer_size=1000)
# apply batch operations
data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
# apply repeat operations
data_set = data_set.repeat(repeat_num)
return data_set
class CrossEntropyLoss(nn.Cell):
def __init__(self):
super(CrossEntropyLoss, self).__init__()
self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
self.mean = P.ReduceMean()
self.one_hot = P.OneHot()
self.one = Tensor(1.0, mstype.float32)
self.zero = Tensor(0.0, mstype.float32)
def construct(self, logits, label):
label = self.one_hot(label, F.shape(logits)[1], self.one, self.zero)
loss = self.cross_entropy(logits, label)[0]
loss = self.mean(loss, (-1,))
return loss
class GradWrap(Cell):
""" GradWrap definition """
def __init__(self, network):
super(GradWrap, self).__init__()
self.network = network
self.weights = ParameterTuple(network.trainable_params())
def construct(self, x, label):
weights = self.weights
return CP.grad_by_list(self.network, weights)(x, label)
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_pynative_resnet50():
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
batch_size = 32
num_classes = 10
net = resnet50(batch_size, num_classes)
criterion = CrossEntropyLoss()
optimizer = Momentum(learning_rate=0.01, momentum=0.9,
params=filter(lambda x: x.requires_grad, net.get_parameters()))
net_with_criterion = WithLossCell(net, criterion)
net_with_criterion.set_grad()
train_network = GradWrap(net_with_criterion)
train_network.set_train()
step = 0
max_step = 20
data_set = create_dataset(repeat_num=1, training=True, batch_size=batch_size)
for element in data_set.create_dict_iterator():
step = step + 1
if step > max_step:
break
start_time = time.time()
input_data = Tensor(element["image"])
input_label = Tensor(element["label"])
loss_output = net_with_criterion(input_data, input_label)
grads = train_network(input_data, input_label)
optimizer(grads)
end_time = time.time()
cost_time = end_time - start_time
print("======step: ", step, " loss: ", loss_output.asnumpy(), " cost time: ", cost_time)
if step > 1:
assert cost_time < 0.3