昇思25天學習打卡營第18天|ShuffleNet圖像分類

?一、簡介：

ShuffleNetV1是曠視科技提出的一種計算高效的CNN模型，和MobileNet, SqueezeNet等一樣主要應用在移動端，所以模型的設計目標就是利用有限的計算資源來達到最好的模型精度。ShuffleNetV1的設計核心是引入了兩種操作：Pointwise Group Convolution和Channel Shuffle，這在保持精度的同時大大降低了模型的計算量。因此，ShuffleNetV1和MobileNet類似，都是通過設計更高效的網絡結構來實現模型的壓縮和加速。如下圖所示，ShuffleNet在保持不低的準確率的前提下，將參數量幾乎降低到了最小，因此其運算速度較快，單位參數量對模型準確率的貢獻非常高。

?二、模型架構：

ShuffleNet最顯著的特點在于對不同通道進行重排來解決Group Convolution帶來的弊端。通過對ResNet的Bottleneck單元進行改進，在較小的計算量的情況下達到了較高的準確率。

1、Pointwise Group Convolution：

Group Convolution（分組卷積）原理如下圖所示，相比于普通的卷積操作，分組卷積的情況下，每一組的卷積核大小為in_channels/g*k*k，一共有g組，所有組共有(in_channels/g*k*k)*out_channels個參數，是正常卷積參數的1/g。分組卷積中，每個卷積核只處理輸入特征圖的一部分通道，其有點在于參數量會有所降低，但是輸出通道仍等于卷積核數量。

Depthwise Convolution（深度可分離卷積）將組數g分為和輸入通道相等的in_channels，然后對每一個in_channels做卷積操作，每個卷積核只處理一個通道，記卷積核大小為1*k*k，則卷積核參數量為：in_channels*k*k，得到的feature maps通道數與輸入通道數相等。

Pointwise Group Convolution（逐點分組卷積）在分組卷積的基礎上，令每一組的卷積核大小為1×1，卷積核參數量為(in_channels/g*1*1)*out_channels。

在進行下面的代碼實驗之前還是需要先安裝mindspore包，安裝過程可以參考：昇思25天學習打卡營第1天|快速入門。

from mindspore import nn
import mindspore.ops as ops
from mindspore import Tensorclass GroupConv(nn.Cell):def __init__(self, in_channels, out_channels, kernel_size,stride, pad_mode="pad", pad=0, groups=1, has_bias=False):super(GroupConv, self).__init__()self.groups = groupsself.convs = nn.CellList()for _ in range(groups):self.convs.append(nn.Conv2d(in_channels // groups, out_channels // groups,kernel_size=kernel_size, stride=stride, has_bias=has_bias,padding=pad, pad_mode=pad_mode, group=1, weight_init='xavier_uniform'))def construct(self, x):features = ops.split(x, split_size_or_sections=int(len(x[0]) // self.groups), axis=1)outputs = ()for i in range(self.groups):outputs = outputs + (self.convs[i](features[i].astype("float32")),)out = ops.cat(outputs, axis=1)return out

2、Channel Shuffle：

Group Convolution的弊端在于不同組別的通道無法進行信息交流，堆積GConv層后一個問題是不同組之間的特征圖是不通信的，這就好像分成了g個互不相干的道路，每一個人各走各的，這可能會降低網絡的特征提取能力。這也是Xception，MobileNet等網絡采用密集的1x1卷積（Dense Pointwise Convolution）的原因。為了解決不同組別通道“近親繁殖”的問題，ShuffleNet優化了大量密集的1x1卷積（在使用的情況下計算量占用率達到了驚人的93.4%），引入Channel Shuffle機制（通道重排）。這項操作直觀上表現為將不同分組通道均勻分散重組，使網絡在下一層能處理不同組別通道的信息。

如下圖所示，對于g組（也就是上文提到的g個互不相干的道路），每組有n個通道的特征圖，首先reshape成g行n列的矩陣，再將矩陣轉置成n行g列，最后進行flatten操作，得到新的排列。這些操作都是可微分可導的且計算簡單，在解決了信息交互的同時符合了ShuffleNet輕量級網絡設計的輕量特征。

?這里將channel_shuffle的代碼實現先展示出來，后面為了在shuffleNet中使用會再次重新實現

def channel_shuffle(self, x):batchsize, num_channels, height, width = ops.shape(x)group_channels = num_channels // self.groupx = ops.reshape(x, (batchsize, group_channels, self.group, height, width))x = ops.transpose(x, (0, 2, 1, 3, 4))x = ops.reshape(x, (batchsize, num_channels, height, width))return x

?三、構建ShuffleNet網絡：

1、ShuffleNet模塊：

如下圖所示，ShuffleNet對ResNet中的Bottleneck結構進行由(a)到(b), (c)的更改：

將開始和最后的1×1卷積模塊（降維、升維）改成Point Wise Group Convolution；
為了進行不同通道的信息交流，再降維之后進行Channel Shuffle；
降采樣模塊中，3×3?Depth Wise Convolution的步長設置為2，長寬降為原來的一般，因此shortcut中采用步長為2的3×3平均池化，并把相加改成拼接。

class ShuffleV1Block(nn.Cell):def __init__(self, inp, oup, group, first_group, mid_channels, ksize, stride):super(ShuffleV1Block, self).__init__()self.stride = stridepad = ksize // 2self.group = groupif stride == 2:outputs = oup - inpelse:outputs = oupself.relu = nn.ReLU()branch_main_1 = [GroupConv(in_channels=inp, out_channels=mid_channels,kernel_size=1, stride=1, pad_mode="pad", pad=0,groups=1 if first_group else group),nn.BatchNorm2d(mid_channels),nn.ReLU(),]branch_main_2 = [nn.Conv2d(mid_channels, mid_channels, kernel_size=ksize, stride=stride,pad_mode='pad', padding=pad, group=mid_channels,weight_init='xavier_uniform', has_bias=False),nn.BatchNorm2d(mid_channels),GroupConv(in_channels=mid_channels, out_channels=outputs,kernel_size=1, stride=1, pad_mode="pad", pad=0,groups=group),nn.BatchNorm2d(outputs),]self.branch_main_1 = nn.SequentialCell(branch_main_1)self.branch_main_2 = nn.SequentialCell(branch_main_2)if stride == 2:self.branch_proj = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='same')def construct(self, old_x):left = old_xright = old_xout = old_xright = self.branch_main_1(right)if self.group > 1:right = self.channel_shuffle(right)right = self.branch_main_2(right)if self.stride == 1:out = self.relu(left + right)elif self.stride == 2:left = self.branch_proj(left)out = ops.cat((left, right), 1)out = self.relu(out)return outdef channel_shuffle(self, x):batchsize, num_channels, height, width = ops.shape(x)group_channels = num_channels // self.groupx = ops.reshape(x, (batchsize, group_channels, self.group, height, width))x = ops.transpose(x, (0, 2, 1, 3, 4))x = ops.reshape(x, (batchsize, num_channels, height, width))return x

?2、網絡構建：

ShuffleNet網絡結構如下圖所示，以輸入圖像224×224，組數3（g = 3）為例，首先通過數量24，卷積核大小為3×3，stride為2的卷積層，輸出特征圖大小為112×112，channel為24；然后通過stride為2的最大池化層，輸出特征圖大小為56×56，channel數不變；再堆疊3個ShuffleNet模塊（Stage2, Stage3, Stage4），三個模塊分別重復4次、8次、4次，其中每個模塊開始先經過一次下采樣模塊（上圖(c)），使特征圖長寬減半，channel翻倍（Stage2的下采樣模塊除外，將channel數從24變為240）；隨后經過全局平均池化，輸出大小為1×1×960，再經過全連接層和softmax，得到分類概率。

class ShuffleNetV1(nn.Cell):def __init__(self, n_class=1000, model_size='2.0x', group=3):super(ShuffleNetV1, self).__init__()print('model size is ', model_size)self.stage_repeats = [4, 8, 4]self.model_size = model_sizeif group == 3:if model_size == '0.5x':self.stage_out_channels = [-1, 12, 120, 240, 480]elif model_size == '1.0x':self.stage_out_channels = [-1, 24, 240, 480, 960]elif model_size == '1.5x':self.stage_out_channels = [-1, 24, 360, 720, 1440]elif model_size == '2.0x':self.stage_out_channels = [-1, 48, 480, 960, 1920]else:raise NotImplementedErrorelif group == 8:if model_size == '0.5x':self.stage_out_channels = [-1, 16, 192, 384, 768]elif model_size == '1.0x':self.stage_out_channels = [-1, 24, 384, 768, 1536]elif model_size == '1.5x':self.stage_out_channels = [-1, 24, 576, 1152, 2304]elif model_size == '2.0x':self.stage_out_channels = [-1, 48, 768, 1536, 3072]else:raise NotImplementedErrorinput_channel = self.stage_out_channels[1]self.first_conv = nn.SequentialCell(nn.Conv2d(3, input_channel, 3, 2, 'pad', 1, weight_init='xavier_uniform', has_bias=False),nn.BatchNorm2d(input_channel),nn.ReLU(),)self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')features = []for idxstage in range(len(self.stage_repeats)):numrepeat = self.stage_repeats[idxstage]output_channel = self.stage_out_channels[idxstage + 2]for i in range(numrepeat):stride = 2 if i == 0 else 1first_group = idxstage == 0 and i == 0features.append(ShuffleV1Block(input_channel, output_channel,group=group, first_group=first_group,mid_channels=output_channel // 4, ksize=3, stride=stride))input_channel = output_channelself.features = nn.SequentialCell(features)self.globalpool = nn.AvgPool2d(7)self.classifier = nn.Dense(self.stage_out_channels[-1], n_class)def construct(self, x):x = self.first_conv(x)x = self.maxpool(x)x = self.features(x)x = self.globalpool(x)x = ops.reshape(x, (-1, self.stage_out_channels[-1]))x = self.classifier(x)return x

?四、模型的訓練核評估：

1、數據集準備：

采用CIFAR-10數據集對ShuffleNet進行預訓練。

from download import downloadurl = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/cifar-10-binary.tar.gz"download(url, "./dataset", kind="tar.gz", replace=True)# 數據預處理：
import mindspore as ms
from mindspore.dataset import Cifar10Dataset
from mindspore.dataset import vision, transformsdef get_dataset(train_dataset_path, batch_size, usage):image_trans = []if usage == "train":image_trans = [vision.RandomCrop((32, 32), (4, 4, 4, 4)),vision.RandomHorizontalFlip(prob=0.5),vision.Resize((224, 224)),vision.Rescale(1.0 / 255.0, 0.0),vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),vision.HWC2CHW()]elif usage == "test":image_trans = [vision.Resize((224, 224)),vision.Rescale(1.0 / 255.0, 0.0),vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),vision.HWC2CHW()]label_trans = transforms.TypeCast(ms.int32)dataset = Cifar10Dataset(train_dataset_path, usage=usage, shuffle=True)dataset = dataset.map(image_trans, 'image')dataset = dataset.map(label_trans, 'label')dataset = dataset.batch(batch_size, drop_remainder=True)return datasetdataset = get_dataset("./dataset/cifar-10-batches-bin", 128, "train")
batches_per_epoch = dataset.get_dataset_size()

2、模型訓練：

使用隨機初始化的參數做預訓練。首先調用ShuffleNetV1定義網絡，參數量選擇"2.0x"，并定義損失函數為交叉熵損失，學習率經過4輪的warmup后采用余弦退火，優化器采用Momentum。最后用train.model中的Model接口將模型、損失函數、優化器封裝在model中，并用model.train()對網絡進行訓練。將ModelCheckpoint、CheckpointConfig、TimeMonitor和LossMonitor傳入回調函數中，將會打印訓練的輪數、損失和時間，并將ckpt文件保存在當前目錄下。

import time
import mindspore
import numpy as np
from mindspore import Tensor, nn
from mindspore.train import ModelCheckpoint, CheckpointConfig, TimeMonitor, LossMonitor, Model, Top1CategoricalAccuracy, Top5CategoricalAccuracydef train():mindspore.set_context(mode=mindspore.PYNATIVE_MODE, device_target="Ascend")net = ShuffleNetV1(model_size="2.0x", n_class=10)loss = nn.CrossEntropyLoss(weight=None, reduction='mean', label_smoothing=0.1)min_lr = 0.0005base_lr = 0.05lr_scheduler = mindspore.nn.cosine_decay_lr(min_lr,base_lr,batches_per_epoch*250,batches_per_epoch,decay_epoch=250)lr = Tensor(lr_scheduler[-1])optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=lr, momentum=0.9, weight_decay=0.00004, loss_scale=1024)loss_scale_manager = ms.amp.FixedLossScaleManager(1024, drop_overflow_update=False)model = Model(net, loss_fn=loss, optimizer=optimizer, amp_level="O3", loss_scale_manager=loss_scale_manager)callback = [TimeMonitor(), LossMonitor()]save_ckpt_path = "./"config_ckpt = CheckpointConfig(save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=5)ckpt_callback = ModelCheckpoint("shufflenetv1", directory=save_ckpt_path, config=config_ckpt)callback += [ckpt_callback]print("============== Starting Training ==============")start_time = time.time()# 由于時間原因，epoch = 5，可根據需求進行調整model.train(5, dataset, callbacks=callback)use_time = time.time() - start_timehour = str(int(use_time // 60 // 60))minute = str(int(use_time // 60 % 60))second = str(int(use_time % 60))print("total time:" + hour + "h " + minute + "m " + second + "s")print("============== Train Success ==============")if __name__ == '__main__':train()

2、模型評估：

在已經劃分好的測試集上進行模型評估：

from mindspore import load_checkpoint, load_param_into_netdef test():mindspore.set_context(mode=mindspore.GRAPH_MODE, device_target="Ascend")dataset = get_dataset("./dataset/cifar-10-batches-bin", 128, "test")net = ShuffleNetV1(model_size="2.0x", n_class=10)param_dict = load_checkpoint("shufflenetv1-5_390.ckpt")load_param_into_net(net, param_dict)net.set_train(False)loss = nn.CrossEntropyLoss(weight=None, reduction='mean', label_smoothing=0.1)eval_metrics = {'Loss': nn.Loss(), 'Top_1_Acc': Top1CategoricalAccuracy(),'Top_5_Acc': Top5CategoricalAccuracy()}model = Model(net, loss_fn=loss, metrics=eval_metrics)start_time = time.time()res = model.eval(dataset, dataset_sink_mode=False)use_time = time.time() - start_timehour = str(int(use_time // 60 // 60))minute = str(int(use_time // 60 % 60))second = str(int(use_time % 60))log = "result:" + str(res) + ", ckpt:'" + "./shufflenetv1-5_390.ckpt" \+ "', time: " + hour + "h " + minute + "m " + second + "s"print(log)filename = './eval_log.txt'with open(filename, 'a') as file_object:file_object.write(log + '\n')if __name__ == '__main__':test()

?還是那句話，只要能跑，就不要動（doge）

五、模型預測：

在CIFAR-10的測試集上對模型進行預測，并將預測結果可視化：

import mindspore
import matplotlib.pyplot as plt
import mindspore.dataset as dsnet = ShuffleNetV1(model_size="2.0x", n_class=10)
show_lst = []
param_dict = load_checkpoint("shufflenetv1-5_390.ckpt")
load_param_into_net(net, param_dict)
model = Model(net)
dataset_predict = ds.Cifar10Dataset(dataset_dir="./dataset/cifar-10-batches-bin", shuffle=False, usage="train")
dataset_show = ds.Cifar10Dataset(dataset_dir="./dataset/cifar-10-batches-bin", shuffle=False, usage="train")
dataset_show = dataset_show.batch(16)
show_images_lst = next(dataset_show.create_dict_iterator())["image"].asnumpy()
image_trans = [vision.RandomCrop((32, 32), (4, 4, 4, 4)),vision.RandomHorizontalFlip(prob=0.5),vision.Resize((224, 224)),vision.Rescale(1.0 / 255.0, 0.0),vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),vision.HWC2CHW()]
dataset_predict = dataset_predict.map(image_trans, 'image')
dataset_predict = dataset_predict.batch(16)
class_dict = {0:"airplane", 1:"automobile", 2:"bird", 3:"cat", 4:"deer", 5:"dog", 6:"frog", 7:"horse", 8:"ship", 9:"truck"}
# 推理效果展示(上方為預測的結果，下方為推理效果圖片)
plt.figure(figsize=(16, 5))
predict_data = next(dataset_predict.create_dict_iterator())
output = model.predict(ms.Tensor(predict_data['image']))
pred = np.argmax(output.asnumpy(), axis=1)
index = 0
for image in show_images_lst:plt.subplot(2, 8, index+1)plt.title('{}'.format(class_dict[pred[index]]))index += 1plt.imshow(image)plt.axis("off")
plt.show()