人臉表情識別
一、數據集說明
使用的數據集是FER2013
kaggle FER2013 https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
該數據集官方的下載鏈接目前失效了，可通過這個鏈接下載：https://www.kaggle.com/shawon10/facial-expression-detection-cnn/data?select=fer2013.csv
FER2013數據集下載下來是一個總 3w5k 行左右的csv文件：
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第一列emotion是圖像標簽，即 y：[0, 6]。
分別代表7種emotion：0 - ‘angry’(4953)，1 - ‘disgusted’(547)，2 - ‘feaful’(5121)，3 - ‘happy’(8989)，4 - ‘sad’(6077)，5 - ‘surprised’(4002)，6 - ‘neutral’(6198) （數據集不是那么的平衡）
第二列是人臉圖像的灰度像素值：[0, 255]，大小是48*48
第三列是圖像用途分類。根據第三列將圖像分為 訓練集（training），驗證集（public test），測試集（private test）
二、預處理數據
讀數據：
1 import pandas as pd 2 import numpy as np 3 4 data_path = "../DATA/fer2013.csv" 5 df = pd.read_csv(data_path)
其中pixel列是一個字符串，將pixel列轉為 int型 的list，表示像素灰度值
1 df['pixels'] = df['pixels'].str.split(' ').apply(lambda x: [int(i) for i in x])
在此步直接將pixel reshape成48*48的：
df['pixels'] = df['pixels'].str.split(' ').apply(lambda x: np.array([int(i) for i in x]).reshape(48,48))
注：后訓練模型時發現不支持int64型數據，所以應該轉為np.unit8，如下：
df['pixels'] = df['pixels'].str.split(' ').apply(lambda x: np.array([np.uint8(i) for i in x]).reshape(48,48))
將FER2013.csv根據用途（usage）分為train、val、test
分完后記得把原來的dataframe的index重設，否則val、test的dataframe序號不連續，后面y_val轉成torch.LongTensor時會報錯
1 def devide_x_y(my_df): 2 return my_df['pixels'].reset_index(drop=True), my_df['emotion'].reset_index(drop=True) 3 4 # 分別篩選出training、publictest、privatetest對應的行 5 training = df['Usage']=="Training" 6 publicTest = df['Usage'] == "PublicTest" 7 privateTest = df["Usage"] == "PrivateTest" 8 9 # 讀取對應行的數據 10 train = df[training] 11 public_t = df[publicTest] 12 private_t = df[privateTest] 13 14 # 分為 X 和 y 15 train_x, train_y = devide_x_y(train) 16 val_x, val_y = devide_x_y(public_t) 17 test_x, test_y = devide_x_y(private_t)
分完后，各數據集的行數如下：
train：28709
val：3589
test：3589
len(train_x[0])每個圖片長度為 2304 --> 48 * 48
畫一張出來看一下
1 import matplotlib.pyplot as plt 2 3 img_test = train_x[0].reshape(48,48) 4 plt.figure() 5 plt.imshow(img_test,cmap='gray') 6 plt.show()
顯示如下：
注：如果畫圖時沒有注明是灰度圖：cmap=gray，則會顯示如下：
可以看到，這個48*48的圖的畫質不太清晰，馬賽克略有點嚴重
三、制作數據集
1 import torchvision.transforms as transforms 2 from torch.utils.data import Dataset, DataLoader 3 import torch 4 5 train_transform = transforms.Compose([ 6 transforms.ToPILImage(), 7 transforms.RandomHorizontalFlip(), 8 transforms.RandomRotation(5), 9 transforms.ToTensor(), 10 ]) 11 12 test_transform = transforms.Compose([ 13 transforms.ToPILImage(), 14 transforms.ToTensor(), 15 ]) 16 17 class MicroExpreDataset(Dataset): 18 def __init__(self, x, y=None, transform=None): 19 self.x = x 20 self.y = y 21 if y is not None: 22 # label needs to be a LongTensor 23 self.y = torch.LongTensor(y) 24 self.transform = transform 25 26 def __len__(self): 27 return len(self.x) 28 29 def __getitem__(self, index): 30 X = self.x[index] 31 if self.transform is not None: 32 X = self.transform(X) 33 if self.y is not None: 34 Y = self.y[index] 35 return X, Y 36 else: return X 37 38 39 batch_size = 128 40 train_set = MicroExpreDataset(train_x, train_y, train_transform) 41 val_set = MicroExpreDataset(val_x, val_y, test_transform) 42 43 train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True) 44 val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)
四、設置模型
1 import torch.nn as nn 2 3 class Classifier(nn.Module): 4 def __init__(self): 5 # super繼承父類 6 super(Classifier, self).__init__() 7 8 # conv2d( in_channels, out_channels, kernel_size, stide, padding) 9 # input shape: [1, 48, 48] 10 self.cnn = nn.Sequential( 11 nn.Conv2d(1, 64, 2, 1, 1), # [64, 49, 49] 12 nn.BatchNorm2d(64), 13 nn.ReLU(), 14 nn.MaxPool2d(2, 2, 0), #[64, 25, 25] 15 16 nn.Conv2d(64, 128, 2, 1, 1), # [128, 26, 26] 17 nn.BatchNorm2d(128), 18 nn.ReLU(), 19 nn.MaxPool2d(2,2,0), # [128, 13, 13] 20 21 nn.Conv2d(128, 256, 3, 1, 1), # [256, 13, 13] 22 nn.BatchNorm2d(256), 23 nn.ReLU(), 24 nn.MaxPool2d(2, 2, 0), # [256, 7, 7] 25 26 nn.Conv2d(256, 256, 3, 1, 1), #[256, 7, 7] 27 nn.BatchNorm2d(256), 28 nn.ReLU(), 29 nn.MaxPool2d(2, 2, 0), #[256, 4, 4] 30 ) 31 32 self.fc = nn.Sequential( 33 nn.Linear(2304, 512), 34 nn.ReLU(), 35 nn.Linear(512, 256), 36 nn.Linear(256, 7) 37 ) 38 39 def forward(self, x): 40 out = self.cnn(x) 41 out = out.view(out.size()[0], -1) 42 return self.fc(out)
這個模型注釋里面，我算maxpool后的size算錯了。我以為盡管maxpool的padding是0，但是當邊界不足時，也會自動補0。但事實是好像並沒有。。
注：
通過下面這個代碼可以打印模型各層輸入和輸出
1 def show_summary(my_model, my_input_size): 2 from collections import OrderedDict 3 import pandas as pd 4 import numpy as np 5 6 import torch 7 from torch.autograd import Variable 8 import torch.nn.functional as F 9 from torch import nn 10 11 12 def get_names_dict(model): 13 """ 14 Recursive walk to get names including path 15 """ 16 names = {} 17 def _get_names(module, parent_name=''): 18 for key, module in module.named_children(): 19 name = parent_name + '.' + key if parent_name else key 20 names[name]=module 21 if isinstance(module, torch.nn.Module): 22 _get_names(module, parent_name=name) 23 _get_names(model) 24 return names 25 26 27 def torch_summarize_df(input_size, model, weights=False, input_shape=True, nb_trainable=False): 28 """ 29 Summarizes torch model by showing trainable parameters and weights. 30 31 author: wassname 32 url: https://gist.github.com/wassname/0fb8f95e4272e6bdd27bd7df386716b7 33 license: MIT 34 35 Modified from: 36 - https://github.com/pytorch/pytorch/issues/2001#issuecomment-313735757 37 - https://gist.github.com/wassname/0fb8f95e4272e6bdd27bd7df386716b7/ 38 39 Usage: 40 import torchvision.models as models 41 model = models.alexnet() 42 df = torch_summarize_df(input_size=(3, 224,224), model=model) 43 print(df) 44 45 # name class_name input_shape output_shape nb_params 46 # 1 features=>0 Conv2d (-1, 3, 224, 224) (-1, 64, 55, 55) 23296#(3*11*11+1)*64 47 # 2 features=>1 ReLU (-1, 64, 55, 55) (-1, 64, 55, 55) 0 48 # ... 49 """ 50 51 def register_hook(module): 52 def hook(module, input, output): 53 name = '' 54 for key, item in names.items(): 55 if item == module: 56 name = key 57 #<class 'torch.nn.modules.conv.Conv2d'> 58 class_name = str(module.__class__).split('.')[-1].split("'")[0] 59 module_idx = len(summary) 60 61 m_key = module_idx + 1 62 63 summary[m_key] = OrderedDict() 64 summary[m_key]['name'] = name 65 summary[m_key]['class_name'] = class_name 66 if input_shape: 67 summary[m_key][ 68 'input_shape'] = (-1, ) + tuple(input[0].size())[1:] 69 summary[m_key]['output_shape'] = (-1, ) + tuple(output.size())[1:] 70 if weights: 71 summary[m_key]['weights'] = list( 72 [tuple(p.size()) for p in module.parameters()]) 73 74 # summary[m_key]['trainable'] = any([p.requires_grad for p in module.parameters()]) 75 if nb_trainable: 76 params_trainable = sum([torch.LongTensor(list(p.size())).prod() for p in module.parameters() if p.requires_grad]) 77 summary[m_key]['nb_trainable'] = params_trainable 78 params = sum([torch.LongTensor(list(p.size())).prod() for p in module.parameters()]) 79 summary[m_key]['nb_params'] = params 80 81 82 if not isinstance(module, nn.Sequential) and \ 83 not isinstance(module, nn.ModuleList) and \ 84 not (module == model): 85 hooks.append(module.register_forward_hook(hook)) 86 87 # Names are stored in parent and path+name is unique not the name 88 names = get_names_dict(model) 89 90 # check if there are multiple inputs to the network 91 if isinstance(input_size[0], (list, tuple)): 92 x = [Variable(torch.rand(1, *in_size)) for in_size in input_size] 93 else: 94 x = Variable(torch.rand(1, *input_size)) 95 96 if next(model.parameters()).is_cuda: 97 x = x.cuda() 98 99 # create properties 100 summary = OrderedDict() 101 hooks = [] 102 103 # register hook 104 model.apply(register_hook) 105 106 # make a forward pass 107 model(x) 108 109 # remove these hooks 110 for h in hooks: 111 h.remove() 112 113 # make dataframe 114 df_summary = pd.DataFrame.from_dict(summary, orient='index') 115 116 return df_summary 117 118 119 # Test on alexnet 120 import torchvision.models as models 121 df = torch_summarize_df(input_size=my_input_size, model=my_model) 122 print(df)
實際的模型輸入輸出應該是這樣
name class_name input_shape output_shape nb_params 1 cnn.0 Conv2d (-1, 1, 48, 48) (-1, 64, 49, 49) tensor(320) 2 cnn.1 BatchNorm2d (-1, 64, 49, 49) (-1, 64, 49, 49) tensor(128) 3 cnn.2 ReLU (-1, 64, 49, 49) (-1, 64, 49, 49) 0 4 cnn.3 MaxPool2d (-1, 64, 49, 49) (-1, 64, 24, 24) 0 5 cnn.4 Conv2d (-1, 64, 24, 24) (-1, 128, 25, 25) tensor(32896) 6 cnn.5 BatchNorm2d (-1, 128, 25, 25) (-1, 128, 25, 25) tensor(256) 7 cnn.6 ReLU (-1, 128, 25, 25) (-1, 128, 25, 25) 0 8 cnn.7 MaxPool2d (-1, 128, 25, 25) (-1, 128, 12, 12) 0 9 cnn.8 Conv2d (-1, 128, 12, 12) (-1, 256, 12, 12) tensor(295168) 10 cnn.9 BatchNorm2d (-1, 256, 12, 12) (-1, 256, 12, 12) tensor(512) 11 cnn.10 ReLU (-1, 256, 12, 12) (-1, 256, 12, 12) 0 12 cnn.11 MaxPool2d (-1, 256, 12, 12) (-1, 256, 6, 6) 0 13 cnn.12 Conv2d (-1, 256, 6, 6) (-1, 256, 6, 6) tensor(590080) 14 cnn.13 BatchNorm2d (-1, 256, 6, 6) (-1, 256, 6, 6) tensor(512) 15 cnn.14 ReLU (-1, 256, 6, 6) (-1, 256, 6, 6) 0 16 cnn.15 MaxPool2d (-1, 256, 6, 6) (-1, 256, 3, 3) 0 17 fc.0 Linear (-1, 2304) (-1, 512) tensor(1180160) 18 fc.1 Dropout (-1, 512) (-1, 512) 0 19 fc.2 ReLU (-1, 512) (-1, 512) 0 20 fc.3 Linear (-1, 512) (-1, 256) tensor(131328) 21 fc.4 Dropout (-1, 256) (-1, 256) 0 22 fc.5 Linear (-1, 256) (-1, 7) tensor(1799)
五、訓練
因為工作站有兩張卡，僅一張可用，需打印看一下可用的那張的編號
torch.cuda.get_device_name(0)
訓練30個epoch
1 import os 2 import time 3 4 os.environ["CUDA_VISIBLE_DEVICES"] = '0' 5 model = Classifier().cuda() 6 7 # classification task, so use crossEntropyLoss 8 loss = nn.CrossEntropyLoss() 9 # optimizer use Adam 10 optimizer = torch.optim.Adam(model.parameters(), lr=0.001) 11 num_epoch = 30 12 13 for epoch in range(num_epoch): 14 epoch_start_time = time.time() 15 train_acc = 0.0 16 train_loss = 0.0 17 val_acc = 0.0 18 val_loss = 0.0 19 20 # open train mode 21 model.train() 22 for i, data in enumerate(train_loader): 23 # set gradient to zero, otherwise it will be added each time 24 optimizer.zero_grad() 25 26 # train model to get the predict possibility. Actually, it is to call the forward function 27 train_pred = model(data[0].cuda()) # data[0] is x 28 batch_loss = loss(train_pred, data[1].cuda()) # data[1] is y 29 30 # use backward to calculate the gradient for each parameter 31 batch_loss.backward() 32 # update the parameters by optimizer 33 optimizer.step() 34 35 train_acc += np.sum(np.argmax(train_pred.cpu().data.numpy(), axis=1) == data[1].numpy()) 36 train_loss += batch_loss.item() 37 38 # turn model to eval mode 39 model.eval() 40 with torch.no_grad(): 41 for i, data in enumerate(val_loader): 42 val_pred = model(data[0].cuda()) 43 batch_loss = loss(val_pred, data[1].cuda()) 44 45 val_acc += np.sum(np.argmax(val_pred.cpu().data.numpy(), axis=1) == data[1].numpy()) 46 val_loss += batch_loss.item() 47 48 # print out the results 49 print('[%03d/%03d] %2.2f sec(s) Train Acc: %3.6f Loss: %3.6f | Val Acc: %3.6f loss: %3.6f' % \ 50 (epoch + 1, num_epoch, time.time()-epoch_start_time, \ 51 train_acc/train_set.__len__(), train_loss/train_set.__len__(), val_acc/val_set.__len__(), val_loss/val_set.__len__())) 52
訓練結果val acc大概 0.6左右
[001/030] 10.69 sec(s) Train Acc: 0.309450 Loss: 0.013407 | Val Acc: 0.404848 loss: 0.012436 [002/030] 11.58 sec(s) Train Acc: 0.467449 Loss: 0.010781 | Val Acc: 0.494845 loss: 0.010486 [003/030] 10.64 sec(s) Train Acc: 0.522728 Loss: 0.009736 | Val Acc: 0.533018 loss: 0.009739 [004/030] 10.57 sec(s) Train Acc: 0.552440 Loss: 0.009222 | Val Acc: 0.550850 loss: 0.009390 [005/030] 10.58 sec(s) Train Acc: 0.579087 Loss: 0.008683 | Val Acc: 0.564224 loss: 0.009504 [006/030] 10.59 sec(s) Train Acc: 0.598558 Loss: 0.008282 | Val Acc: 0.566732 loss: 0.009202 [007/030] 10.62 sec(s) Train Acc: 0.613710 Loss: 0.007971 | Val Acc: 0.580106 loss: 0.009015 [008/030] 10.60 sec(s) Train Acc: 0.636421 Loss: 0.007589 | Val Acc: 0.591808 loss: 0.009277 [009/030] 10.61 sec(s) Train Acc: 0.647950 Loss: 0.007321 | Val Acc: 0.580663 loss: 0.009388 [010/030] 10.64 sec(s) Train Acc: 0.664635 Loss: 0.007020 | Val Acc: 0.589301 loss: 0.008910 [011/030] 10.93 sec(s) Train Acc: 0.680449 Loss: 0.006731 | Val Acc: 0.600446 loss: 0.009198 [012/030] 10.48 sec(s) Train Acc: 0.695531 Loss: 0.006364 | Val Acc: 0.610198 loss: 0.009257 [013/030] 10.46 sec(s) Train Acc: 0.711972 Loss: 0.006073 | Val Acc: 0.610198 loss: 0.008954 [014/030] 11.79 sec(s) Train Acc: 0.726636 Loss: 0.005780 | Val Acc: 0.609641 loss: 0.008695 [015/030] 10.62 sec(s) Train Acc: 0.741579 Loss: 0.005495 | Val Acc: 0.604068 loss: 0.009682 [016/030] 10.55 sec(s) Train Acc: 0.760633 Loss: 0.005133 | Val Acc: 0.596266 loss: 0.009784 [017/030] 10.53 sec(s) Train Acc: 0.771256 Loss: 0.004891 | Val Acc: 0.613820 loss: 0.009530 [018/030] 10.51 sec(s) Train Acc: 0.785956 Loss: 0.004585 | Val Acc: 0.609083 loss: 0.009737 [019/030] 10.49 sec(s) Train Acc: 0.798460 Loss: 0.004316 | Val Acc: 0.607412 loss: 0.010229 [020/030] 10.49 sec(s) Train Acc: 0.812150 Loss: 0.003993 | Val Acc: 0.605461 loss: 0.010734 [021/030] 10.47 sec(s) Train Acc: 0.822913 Loss: 0.003782 | Val Acc: 0.576484 loss: 0.011127 [022/030] 10.53 sec(s) Train Acc: 0.836184 Loss: 0.003529 | Val Acc: 0.594595 loss: 0.012074 [023/030] 10.55 sec(s) Train Acc: 0.846459 Loss: 0.003334 | Val Acc: 0.604904 loss: 0.012387 [024/030] 10.48 sec(s) Train Acc: 0.857780 Loss: 0.003108 | Val Acc: 0.612427 loss: 0.011957 [025/030] 10.61 sec(s) Train Acc: 0.866070 Loss: 0.002917 | Val Acc: 0.598495 loss: 0.012693 [026/030] 10.63 sec(s) Train Acc: 0.869100 Loss: 0.002839 | Val Acc: 0.599053 loss: 0.013177 [027/030] 10.60 sec(s) Train Acc: 0.883800 Loss: 0.002563 | Val Acc: 0.608247 loss: 0.012588 [028/030] 10.58 sec(s) Train Acc: 0.889094 Loss: 0.002421 | Val Acc: 0.609083 loss: 0.012604 [029/030] 10.57 sec(s) Train Acc: 0.889338 Loss: 0.002420 | Val Acc: 0.606297 loss: 0.014425 [030/030] 10.58 sec(s) Train Acc: 0.898743 Loss: 0.002211 | Val Acc: 0.600446 loss: 0.013294
六、調優模型
1） 在FC層增加dropout
1 self.fc = nn.Sequential( 2 nn.Linear(2304, 512), 3 nn.Dropout(0.3), 4 nn.ReLU(), 5 nn.Linear(512, 256), 6 nn.Dropout(0.3), 7 nn.Linear(256, 7) 8 )
效果沒有顯著提升，val acc大概還是0.6左右
[001/030] 11.63 sec(s) Train Acc: 0.307534 Loss: 0.013370 | Val Acc: 0.412928 loss: 0.012034 [002/030] 10.04 sec(s) Train Acc: 0.445191 Loss: 0.011217 | Val Acc: 0.495403 loss: 0.010660 [003/030] 11.71 sec(s) Train Acc: 0.490961 Loss: 0.010363 | Val Acc: 0.514349 loss: 0.010133 [004/030] 10.32 sec(s) Train Acc: 0.509805 Loss: 0.009945 | Val Acc: 0.544999 loss: 0.009528 [005/030] 11.72 sec(s) Train Acc: 0.536452 Loss: 0.009538 | Val Acc: 0.553636 loss: 0.009448 [006/030] 10.73 sec(s) Train Acc: 0.552823 Loss: 0.009249 | Val Acc: 0.553079 loss: 0.009476 [007/030] 11.18 sec(s) Train Acc: 0.563134 Loss: 0.009051 | Val Acc: 0.551407 loss: 0.009214 [008/030] 10.37 sec(s) Train Acc: 0.575882 Loss: 0.008729 | Val Acc: 0.559487 loss: 0.009178 [009/030] 10.68 sec(s) Train Acc: 0.592149 Loss: 0.008511 | Val Acc: 0.585678 loss: 0.008921 [010/030] 10.49 sec(s) Train Acc: 0.600683 Loss: 0.008264 | Val Acc: 0.570354 loss: 0.009281 [011/030] 10.57 sec(s) Train Acc: 0.611725 Loss: 0.008074 | Val Acc: 0.586236 loss: 0.008738 [012/030] 10.60 sec(s) Train Acc: 0.625727 Loss: 0.007824 | Val Acc: 0.597660 loss: 0.008503 [013/030] 10.58 sec(s) Train Acc: 0.635306 Loss: 0.007631 | Val Acc: 0.583171 loss: 0.008948 [014/030] 10.60 sec(s) Train Acc: 0.644606 Loss: 0.007423 | Val Acc: 0.583728 loss: 0.008670 [015/030] 11.75 sec(s) Train Acc: 0.655300 Loss: 0.007204 | Val Acc: 0.583449 loss: 0.009537 [016/030] 11.72 sec(s) Train Acc: 0.668536 Loss: 0.006962 | Val Acc: 0.613820 loss: 0.008576 [017/030] 11.78 sec(s) Train Acc: 0.677383 Loss: 0.006736 | Val Acc: 0.582056 loss: 0.010016 [018/030] 10.39 sec(s) Train Acc: 0.686753 Loss: 0.006568 | Val Acc: 0.604068 loss: 0.009147 [019/030] 10.35 sec(s) Train Acc: 0.696402 Loss: 0.006376 | Val Acc: 0.586514 loss: 0.009409 [020/030] 10.37 sec(s) Train Acc: 0.701870 Loss: 0.006202 | Val Acc: 0.602675 loss: 0.009220 [021/030] 10.39 sec(s) Train Acc: 0.714758 Loss: 0.005984 | Val Acc: 0.595152 loss: 0.009192 [022/030] 10.44 sec(s) Train Acc: 0.722178 Loss: 0.005780 | Val Acc: 0.601282 loss: 0.009065 [023/030] 10.65 sec(s) Train Acc: 0.735623 Loss: 0.005579 | Val Acc: 0.621064 loss: 0.009224 [024/030] 10.46 sec(s) Train Acc: 0.743809 Loss: 0.005383 | Val Acc: 0.618278 loss: 0.010290 [025/030] 10.44 sec(s) Train Acc: 0.752447 Loss: 0.005239 | Val Acc: 0.616606 loss: 0.010834 [026/030] 10.32 sec(s) Train Acc: 0.758403 Loss: 0.005108 | Val Acc: 0.589858 loss: 0.009424 [027/030] 10.32 sec(s) Train Acc: 0.766728 Loss: 0.004936 | Val Acc: 0.608526 loss: 0.010024 [028/030] 10.33 sec(s) Train Acc: 0.771814 Loss: 0.004792 | Val Acc: 0.609083 loss: 0.010309 [029/030] 10.29 sec(s) Train Acc: 0.780487 Loss: 0.004629 | Val Acc: 0.600167 loss: 0.011290 [030/030] 10.26 sec(s) Train Acc: 0.794106 Loss: 0.004395 | Val Acc: 0.606854 loss: 0.012611
2）在第一層的maxpool中stride設成1，並周圍補一圈0 --> 不縮小圖像
因為48*48這個圖還是比較馬賽克化的
這樣操作后，模型從最后一層conv后輸出3*3變成了6*6，模型具體結構如下：
name class_name input_shape output_shape nb_params 1 cnn.0 Conv2d (-1, 1, 48, 48) (-1, 64, 49, 49) tensor(320) 2 cnn.1 BatchNorm2d (-1, 64, 49, 49) (-1, 64, 49, 49) tensor(128) 3 cnn.2 ReLU (-1, 64, 49, 49) (-1, 64, 49, 49) 0 4 cnn.3 MaxPool2d (-1, 64, 49, 49) (-1, 64, 50, 50) 0 5 cnn.4 Conv2d (-1, 64, 50, 50) (-1, 128, 51, 51) tensor(32896) 6 cnn.5 BatchNorm2d (-1, 128, 51, 51) (-1, 128, 51, 51) tensor(256) 7 cnn.6 ReLU (-1, 128, 51, 51) (-1, 128, 51, 51) 0 8 cnn.7 MaxPool2d (-1, 128, 51, 51) (-1, 128, 25, 25) 0 9 cnn.8 Conv2d (-1, 128, 25, 25) (-1, 256, 25, 25) tensor(295168) 10 cnn.9 BatchNorm2d (-1, 256, 25, 25) (-1, 256, 25, 25) tensor(512) 11 cnn.10 ReLU (-1, 256, 25, 25) (-1, 256, 25, 25) 0 12 cnn.11 MaxPool2d (-1, 256, 25, 25) (-1, 256, 12, 12) 0 13 cnn.12 Conv2d (-1, 256, 12, 12) (-1, 256, 12, 12) tensor(590080) 14 cnn.13 BatchNorm2d (-1, 256, 12, 12) (-1, 256, 12, 12) tensor(512) 15 cnn.14 ReLU (-1, 256, 12, 12) (-1, 256, 12, 12) 0 16 cnn.15 MaxPool2d (-1, 256, 12, 12) (-1, 256, 6, 6) 0 17 fc.0 Linear (-1, 9216) (-1, 512) tensor(4719104) 18 fc.1 Dropout (-1, 512) (-1, 512) 0 19 fc.2 ReLU (-1, 512) (-1, 512) 0 20 fc.3 Linear (-1, 512) (-1, 256) tensor(131328) 21 fc.4 Dropout (-1, 256) (-1, 256) 0 22 fc.5 Linear (-1, 256) (-1, 7) tensor(1799)
但訓練結果沒有提升，反而看起來略有點下降了。可能因為這樣引入了更多噪音
[001/030] 15.70 sec(s) Train Acc: 0.254276 Loss: 0.014978 | Val Acc: 0.329061 loss: 0.013547 [002/030] 14.21 sec(s) Train Acc: 0.359678 Loss: 0.012589 | Val Acc: 0.418222 loss: 0.011816 [003/030] 14.17 sec(s) Train Acc: 0.417883 Loss: 0.011663 | Val Acc: 0.472276 loss: 0.011007 [004/030] 14.30 sec(s) Train Acc: 0.451844 Loss: 0.011097 | Val Acc: 0.479521 loss: 0.010857 [005/030] 14.30 sec(s) Train Acc: 0.461388 Loss: 0.010875 | Val Acc: 0.509334 loss: 0.010366 [006/030] 14.32 sec(s) Train Acc: 0.484726 Loss: 0.010492 | Val Acc: 0.522708 loss: 0.010089 [007/030] 14.32 sec(s) Train Acc: 0.500644 Loss: 0.010178 | Val Acc: 0.533296 loss: 0.009900 [008/030] 14.31 sec(s) Train Acc: 0.509979 Loss: 0.010009 | Val Acc: 0.547785 loss: 0.009626 [009/030] 14.32 sec(s) Train Acc: 0.522937 Loss: 0.009812 | Val Acc: 0.543327 loss: 0.009497 [010/030] 14.32 sec(s) Train Acc: 0.529416 Loss: 0.009635 | Val Acc: 0.565060 loss: 0.009770 [011/030] 14.31 sec(s) Train Acc: 0.537810 Loss: 0.009511 | Val Acc: 0.561995 loss: 0.009458 [012/030] 14.33 sec(s) Train Acc: 0.544115 Loss: 0.009311 | Val Acc: 0.538033 loss: 0.009728 [013/030] 14.32 sec(s) Train Acc: 0.552928 Loss: 0.009205 | Val Acc: 0.560323 loss: 0.009435 [014/030] 14.32 sec(s) Train Acc: 0.561218 Loss: 0.009048 | Val Acc: 0.573976 loss: 0.009330 [015/030] 14.32 sec(s) Train Acc: 0.568254 Loss: 0.008903 | Val Acc: 0.573976 loss: 0.009089 [016/030] 14.35 sec(s) Train Acc: 0.575743 Loss: 0.008777 | Val Acc: 0.571468 loss: 0.009189 [017/030] 14.34 sec(s) Train Acc: 0.581838 Loss: 0.008667 | Val Acc: 0.567846 loss: 0.009613 [018/030] 14.32 sec(s) Train Acc: 0.589954 Loss: 0.008496 | Val Acc: 0.578991 loss: 0.009191 [019/030] 14.32 sec(s) Train Acc: 0.593124 Loss: 0.008375 | Val Acc: 0.590415 loss: 0.009168 [020/030] 14.32 sec(s) Train Acc: 0.603191 Loss: 0.008238 | Val Acc: 0.598217 loss: 0.009239 [021/030] 14.32 sec(s) Train Acc: 0.609704 Loss: 0.008045 | Val Acc: 0.598774 loss: 0.008932 [022/030] 14.32 sec(s) Train Acc: 0.613327 Loss: 0.008028 | Val Acc: 0.598774 loss: 0.008821 [023/030] 14.32 sec(s) Train Acc: 0.623881 Loss: 0.007821 | Val Acc: 0.598495 loss: 0.008917 [024/030] 14.32 sec(s) Train Acc: 0.631056 Loss: 0.007649 | Val Acc: 0.599053 loss: 0.008865 [025/030] 14.34 sec(s) Train Acc: 0.637082 Loss: 0.007531 | Val Acc: 0.597660 loss: 0.008953 [026/030] 14.32 sec(s) Train Acc: 0.639834 Loss: 0.007439 | Val Acc: 0.606576 loss: 0.009263 [027/030] 14.31 sec(s) Train Acc: 0.652200 Loss: 0.007253 | Val Acc: 0.597381 loss: 0.008817 [028/030] 14.30 sec(s) Train Acc: 0.657076 Loss: 0.007143 | Val Acc: 0.586236 loss: 0.009791 [029/030] 14.31 sec(s) Train Acc: 0.662301 Loss: 0.007033 | Val Acc: 0.612984 loss: 0.009230 [030/030] 14.30 sec(s) Train Acc: 0.672577 Loss: 0.006849 | Val Acc: 0.598495 loss: 0.009119
七、訓練+驗證一起重新訓練一遍最優模型
這里還是用最開始的model
合並train，test
1 train_val_x = np.concatenate((train_x, val_x), axis=0) 2 train_val_y = np.concatenate((train_y, val_y), axis=0) 3 train_val_set = MicroExpreDataset(train_val_x, train_val_y, train_transform) 4 train_val_loader = DataLoader(train_val_set, batch_size=batch_size, shuffle=True)
用best_model進行訓練
1 model_best = Classifier().cuda() 2 loss = nn.CrossEntropyLoss() 3 optimizer = torch.optim.Adam(model_best.parameters(), lr=0.001) 4 num_epoch = 30 5 6 for epoch in range(num_epoch): 7 epoch_start_time = time.time() 8 train_acc = 0.0 9 train_loss = 0.0 10 11 model_best.train() 12 for i, data in enumerate(train_val_loader): 13 optimizer.zero_grad() 14 train_pred = model_best(data[0].cuda()) 15 batch_loss = loss(train_pred, data[1].cuda()) 16 batch_loss.backward() 17 optimizer.step() 18 19 train_acc += np.sum(np.argmax(train_pred.cpu().data.numpy(), axis=1) == data[1].numpy()) 20 train_loss+= batch_loss.item() 21 22 # print out the result 23 print('[%03d/%03d] %2.2f sec(s) Train Acc: %3.6f Loss: %3.6f' % \ 24 (epoch + 1, num_epoch, time.time()-epoch_start_time, \ 25 train_acc/train_val_set.__len__(), train_loss/train_val_set.__len__()))
訓練結果大概89%左右，但應該是有一點過擬合了
1 [001/030] 11.02 sec(s) Train Acc: 0.349867 Loss: 0.012808 2 [002/030] 10.87 sec(s) Train Acc: 0.494024 Loss: 0.010299 3 [003/030] 10.91 sec(s) Train Acc: 0.540529 Loss: 0.009427 4 [004/030] 10.97 sec(s) Train Acc: 0.573101 Loss: 0.008838 5 [005/030] 10.97 sec(s) Train Acc: 0.594774 Loss: 0.008422 6 [006/030] 10.97 sec(s) Train Acc: 0.610224 Loss: 0.008127 7 [007/030] 11.07 sec(s) Train Acc: 0.625735 Loss: 0.007769 8 [008/030] 11.06 sec(s) Train Acc: 0.643476 Loss: 0.007467 9 [009/030] 10.97 sec(s) Train Acc: 0.656604 Loss: 0.007177 10 [010/030] 10.99 sec(s) Train Acc: 0.675367 Loss: 0.006820 11 [011/030] 10.97 sec(s) Train Acc: 0.688928 Loss: 0.006606 12 [012/030] 10.96 sec(s) Train Acc: 0.708279 Loss: 0.006197 13 [013/030] 10.91 sec(s) Train Acc: 0.719549 Loss: 0.005955 14 [014/030] 10.98 sec(s) Train Acc: 0.726144 Loss: 0.005742 15 [015/030] 10.97 sec(s) Train Acc: 0.748746 Loss: 0.005355 16 [016/030] 10.96 sec(s) Train Acc: 0.768654 Loss: 0.004975 17 [017/030] 10.97 sec(s) Train Acc: 0.775714 Loss: 0.004779 18 [018/030] 10.99 sec(s) Train Acc: 0.795281 Loss: 0.004470 19 [019/030] 10.97 sec(s) Train Acc: 0.804694 Loss: 0.004222 20 [020/030] 10.98 sec(s) Train Acc: 0.818286 Loss: 0.003909 21 [021/030] 10.98 sec(s) Train Acc: 0.828720 Loss: 0.003722 22 [022/030] 10.97 sec(s) Train Acc: 0.837204 Loss: 0.003552 23 [023/030] 10.97 sec(s) Train Acc: 0.851167 Loss: 0.003269 24 [024/030] 10.98 sec(s) Train Acc: 0.857360 Loss: 0.003158 25 [025/030] 10.96 sec(s) Train Acc: 0.863057 Loss: 0.002980 26 [026/030] 10.99 sec(s) Train Acc: 0.874915 Loss: 0.002788 27 [027/030] 10.98 sec(s) Train Acc: 0.878166 Loss: 0.002646 28 [028/030] 10.98 sec(s) Train Acc: 0.886835 Loss: 0.002485 29 [029/030] 10.98 sec(s) Train Acc: 0.893771 Loss: 0.002362 30 [030/030] 10.98 sec(s) Train Acc: 0.896278 Loss: 0.002323
保存模型
torch.save(model_best, 'model_best.pkl')
注：
1）這里直接用全保存下來的model，讀取再預測時會有一些問題。根據官方建議只保存訓練參數，所以后來又存一下只有參數的模型供后面做demo用
torch.save(model_best.state_dict(),'model_best_para.pkl')
2）不論是保存的整個模型，還是只保存參數，最后再讀取模型/參數時，都仍需要提供原模型結構的代碼
八、測試
用測試集再算一遍看下acc到達多少
讀取模型
# load model test_model = torch.load('model_best.pkl')
1 test_set = MicroExpreDataset(test_x, test_y, test_transform) 2 test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False) 3 4 test_model.eval() 5 prediction = [] 6 with torch.no_grad(): 7 for i, data in enumerate(test_loader): 8 test_pred = test_model(data[0].cuda()) 9 test_label = np.argmax(test_pred.cpu().data.numpy(), axis=1) 10 for y in test_label: 11 prediction.append(y) 12 13 test_acc = np.sum(prediction == test_y) / test_set.__len__() 14 test_acc 15 16 # test_acc= 0.62245
最后test acc = 0.622
合並了訓練集和驗證集后效果略提升了一些。
分析：
（1）訓練數據集本身清晰度不夠，可能細微表情變換較難識別出來。另外某些表情有一定相似性，例如生氣、厭惡、恐懼等。
（2）微表情中某些類別樣本不均衡，進一步導致難以識別。例如快樂表情准確率更高，而恐懼就很低。
補充：在網上看了另外一個博主的工作，他采用的數據增強的方法如下：
4.2 數據增強
為了防止網絡過快地過擬合，可以人為的做一些圖像變換，例如翻轉，旋轉，切割等。上述操作稱為數據增強。數據操作還有另一大好處是擴大數據庫的數據量，使得訓練的網絡魯棒性更強。在本實驗中，在訓練階段，我們采用隨機切割44*44的圖像，並將圖像進行隨機鏡像，然后送入訓練。在測試階段，本文采用一種集成的方法來減少異常值。我們將圖片在左上角，左下角，右上角，右下角，中心進行切割和並做鏡像操作，這樣的操作使得數據庫擴大了10倍，再將這10張圖片送入模型。然后將得到的概率取平均，最大的輸出分類即為對應表情，這種方法有效地降低了分類錯誤。
其中圖像變換我們也做了。值得參考的是該作者測試時還采用了集成方法，即一張圖片操作成10張，讓模型判斷並求平均。
九、調用攝像頭，在線demo
步驟：
1）調用opencv自帶的人臉識別器
路徑（mac參考路徑） /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/sitepackages/cv2/data/
一般使用：
在這里我們用alt2來實現。
2）調用系統攝像頭拍攝出人臉圖片
3）對人臉圖片進行預處理
4）將處理完的圖片傳入模型
5）將模型計算出來的結果反饋至運行窗口界面
需注意：
1）模型中並沒有加入softmax層，最后輸出的是交叉熵損失。如果要在每幀圖片里顯示成每個類別的概率，需給輸出結果加一個softmax：p_result = F.softmax(result, dim=1)
2）在調用 cascade_classifier.detectMultiScale這個函數時，建議設為1.1，否則可能導致人臉識別不夠敏感
參考：
https://www.cnblogs.com/XDU-Lakers/p/10587894.html
https://blog.csdn.net/mathlxj/article/details/87920084
https://github.com/tgpcai/Microexpression_recognition