diff --git a/CASME2_3.py b/CASME2_3.py old mode 100644 new mode 100755 index f4579d8..f4cc4c0 --- a/CASME2_3.py +++ b/CASME2_3.py @@ -1,610 +1,616 @@ -import math -import numpy as np -import torchvision.models -import torch.utils.data as data -from torchvision import transforms -import cv2 -import torch.nn.functional as F -from torch.autograd import Variable -import pandas as pd -import os, torch -import torch.nn as nn -#import image_utils -import argparse, random -from functools import partial - -from CA_block import resnet18_pos_attention,resnet50 - -from PC_module import VisionTransformer_POS - -from torchvision.transforms import Resize -torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument('--raf_path', type=str, default='datasets/CASME2/', help='Raf-DB dataset path.') - parser.add_argument('--checkpoint', type=str, default=None, - help='Pytorch checkpoint file path') - parser.add_argument('--pretrained', type=str, default=None, - help='Pretrained weights') - parser.add_argument('--beta', type=float, default=0.7, help='Ratio of high importance group in one mini-batch.') - parser.add_argument('--relabel_epoch', type=int, default=1000, - help='Relabeling samples on each mini-batch after 10(Default) epochs.') - parser.add_argument('--batch_size', type=int, default=34, help='Batch size.') - parser.add_argument('--optimizer', type=str, default="adam", help='Optimizer, adam or sgd.') - parser.add_argument('--lr', type=float, default=0.0001, help='Initial learning rate for sgd.') - parser.add_argument('--momentum', default=0.9, type=float, help='Momentum for sgd') - parser.add_argument('--workers', default=0, type=int, help='Number of data loading workers (default: 4)') - parser.add_argument('--epochs', type=int, default=7000, help='Total training epochs.') - parser.add_argument('--drop_rate', type=float, default=0, help='Drop out rate.') - return parser.parse_args() - - - - - - -class RafDataSet(data.Dataset): - def __init__(self, raf_path, phase,num_loso, transform = None, basic_aug = False, transform_norm=None): - self.phase = phase - self.transform = transform - self.raf_path = raf_path - self.transform_norm = transform_norm - SUBJECT_COLUMN =0 - NAME_COLUMN = 1 - ONSET_COLUMN = 2 - APEX_COLUMN = 3 - OFF_COLUMN = 4 - LABEL_AU_COLUMN = 5 - LABEL_ALL_COLUMN = 6 - - - df = pd.read_excel(os.path.join(self.raf_path, 'CASME2-coding-20190701.xlsx'),usecols=[0,1,3,4,5,7,8]) - df['Subject'] = df['Subject'].apply(str) - - if phase == 'train': - dataset = df.loc[df['Subject']!=num_loso] - else: - dataset = df.loc[df['Subject'] == num_loso] - - Subject = dataset.iloc[:, SUBJECT_COLUMN].values - File_names = dataset.iloc[:, NAME_COLUMN].values - Label_all = dataset.iloc[:, LABEL_ALL_COLUMN].values # 0:Surprise, 1:Fear, 2:Disgust, 3:Happiness, 4:Sadness, 5:Anger, 6:Neutral - Onset_num = dataset.iloc[:, ONSET_COLUMN].values - Apex_num = dataset.iloc[:, APEX_COLUMN].values - Offset_num = dataset.iloc[:, OFF_COLUMN].values - Label_au = dataset.iloc[:, LABEL_AU_COLUMN].values - self.file_paths_on = [] - self.file_paths_off = [] - self.file_paths_apex = [] - self.label_all = [] - self.label_au = [] - self.sub= [] - self.file_names =[] - a=0 - b=0 - c=0 - d=0 - e=0 - # use aligned images for training/testing - for (f,sub,onset,apex,offset,label_all,label_au) in zip(File_names,Subject,Onset_num,Apex_num,Offset_num,Label_all,Label_au): - - - if label_all == 'happiness' or label_all == 'repression' or label_all == 'disgust' or label_all == 'surprise' or label_all == 'fear' or label_all == 'sadness': - - self.file_paths_on.append(onset) - self.file_paths_off.append(offset) - self.file_paths_apex.append(apex) - self.sub.append(sub) - self.file_names.append(f) - if label_all == 'happiness': - self.label_all.append(0) - a=a+1 - elif label_all == 'surprise': - self.label_all.append(1) - b=b+1 - else: - self.label_all.append(2) - c=c+1 - - # label_au =label_au.split("+") - if isinstance(label_au, int): - self.label_au.append([label_au]) - else: - label_au = label_au.split("+") - self.label_au.append(label_au) - - - - - - - ##label - - self.basic_aug = basic_aug - #self.aug_func = [image_utils.flip_image,image_utils.add_gaussian_noise] - - def __len__(self): - return len(self.file_paths_on) - - def __getitem__(self, idx): - ##sampling strategy for training set - if self.phase == 'train': - onset = self.file_paths_on[idx] - apex = self.file_paths_apex[idx] - offset =self.file_paths_off[idx] - on0 = str(random.randint(int(onset), int(onset + int(0.2* (apex - onset) / 4)))) - # on0 = str(int(onset)) - on1 = str( - random.randint(int(onset + int(0.9 * (apex - onset) / 4)), int(onset + int(1.1 * (apex - onset) / 4)))) - on2 = str( - random.randint(int(onset + int(1.8 * (apex - onset) / 4)), int(onset + int(2.2 * (apex - onset) / 4)))) - on3 = str(random.randint(int(onset + int(2.7 * (apex - onset) / 4)), onset + int(3.3 * (apex - onset) / 4))) - # apex0 = str(apex) - apex0 = str( - random.randint(int(apex - int(0.15* (apex - onset) / 4)), apex + int(0.15 * (offset - apex) / 4))) - off0 = str( - random.randint(int(apex + int(0.9 * (offset - apex) / 4)), int(apex + int(1.1 * (offset - apex) / 4)))) - off1 = str( - random.randint(int(apex + int(1.8 * (offset - apex) / 4)), int(apex + int(2.2 * (offset - apex) / 4)))) - off2 = str( - random.randint(int(apex + int(2.9 * (offset - apex) / 4)), int(apex + int(3.1 * (offset - apex) / 4)))) - off3 = str(random.randint(int(apex + int(3.8 * (offset - apex) / 4)), offset)) - - - - sub =str(self.sub[idx]) - f = str(self.file_names[idx]) - else:##sampling strategy for testing set - onset = self.file_paths_on[idx] - apex = self.file_paths_apex[idx] - offset = self.file_paths_off[idx] - - on0 = str(onset) - on1 = str(int(onset + int((apex - onset) / 4))) - on2 = str(int(onset + int(2 * (apex - onset) / 4))) - on3 = str(int(onset + int(3 * (apex - onset) / 4))) - apex0 = str(apex) - off0 = str(int(apex + int((offset - apex) / 4))) - off1 = str(int(apex + int(2 * (offset - apex) / 4))) - off2 = str(int(apex + int(3 * (offset - apex) / 4))) - off3 = str(offset) - - sub = str(self.sub[idx]) - f = str(self.file_names[idx]) - - - on0 ='reg_img'+on0+'.jpg' - on1 = 'reg_img' + on1 + '.jpg' - on2 = 'reg_img' + on2 + '.jpg' - on3 = 'reg_img' + on3 + '.jpg' - apex0 ='reg_img' + apex0 + '.jpg' - off0 ='reg_img' + off0 + '.jpg' - off1='reg_img' + off1 + '.jpg' - off2 ='reg_img' + off2 + '.jpg' - off3 = 'reg_img' + off3 + '.jpg' - path_on0 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, on0) - path_on1 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, on1) - path_on2 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, on2) - path_on3 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, on3) - path_apex0 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, apex0) - path_off0 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, off0) - path_off1 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, off1) - path_off2 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, off2) - path_off3 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, off3) - image_on0 = cv2.imread(path_on0) - image_on1= cv2.imread(path_on1) - image_on2 = cv2.imread(path_on2) - image_on3 = cv2.imread(path_on3) - image_apex0 = cv2.imread(path_apex0) - image_off0 = cv2.imread(path_off0) - image_off1 = cv2.imread(path_off1) - image_off2 = cv2.imread(path_off2) - image_off3 = cv2.imread(path_off3) - - image_on0 = image_on0[:, :, ::-1] # BGR to RGB - image_on1 = image_on1[:, :, ::-1] - image_on2 = image_on2[:, :, ::-1] - image_on3 = image_on3[:, :, ::-1] - image_off0 = image_off0[:, :, ::-1] - image_off1 = image_off1[:, :, ::-1] - image_off2 = image_off2[:, :, ::-1] - image_off3 = image_off3[:, :, ::-1] - image_apex0 = image_apex0[:, :, ::-1] - - label_all = self.label_all[idx] - label_au = self.label_au[idx] - - # normalization for testing and training - if self.transform is not None: - image_on0 = self.transform(image_on0) - image_on1 = self.transform(image_on1) - image_on2 = self.transform(image_on2) - image_on3 = self.transform(image_on3) - image_off0 = self.transform(image_off0) - image_off1 = self.transform(image_off1) - image_off2 = self.transform(image_off2) - image_off3 = self.transform(image_off3) - image_apex0 = self.transform(image_apex0) - ALL = torch.cat( - (image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, - image_off3), dim=0) - ## data augmentation for training only - if self.transform_norm is not None and self.phase == 'train': - ALL = self.transform_norm(ALL) - image_on0 = ALL[0:3, :, :] - image_on1 = ALL[3:6, :, :] - image_on2 = ALL[6:9, :, :] - image_on3 = ALL[9:12, :, :] - image_apex0 = ALL[12:15, :, :] - image_off0 = ALL[15:18, :, :] - image_off1 = ALL[18:21, :, :] - image_off2 = ALL[21:24, :, :] - image_off3 = ALL[24:27, :, :] - - - temp = torch.zeros(38) - for i in label_au: - temp[int(i) - 1] = 1 - - return image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, label_all, temp - - -def initialize_weight_goog(m, n=''): - if isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1.0) - m.bias.data.zero_() - elif isinstance(m, nn.Linear): - fan_out = m.weight.size(0) # fan-out - fan_in = 0 - if 'routing_fn' in n: - fan_in = m.weight.size(1) - init_range = 1.0 / math.sqrt(fan_in + fan_out) - m.weight.data.uniform_(-init_range, init_range) - m.bias.data.zero_() - - -def criterion2(y_pred, y_true): - y_pred = (1 - 2 * y_true) * y_pred - y_pred_neg = y_pred - y_true * 1e12 - y_pred_pos = y_pred - (1 - y_true) * 1e12 - zeros = torch.zeros_like(y_pred[..., :1]) - y_pred_neg = torch.cat((y_pred_neg, zeros), dim=-1) - y_pred_pos = torch.cat((y_pred_pos, zeros), dim=-1) - neg_loss = torch.logsumexp(y_pred_neg, dim=-1) - pos_loss = torch.logsumexp(y_pred_pos, dim=-1) - return torch.mean(neg_loss + pos_loss) - - -class MMNet(nn.Module): - def __init__(self): - super(MMNet, self).__init__() - - - self.conv_act = nn.Sequential( - nn.Conv2d(in_channels=3, out_channels=90*2, kernel_size=3, stride=2,padding=1, bias=False,groups=2), - nn.BatchNorm2d(180), - nn.ReLU(inplace=True), - ) - self.pos =nn.Sequential( - nn.Conv2d(in_channels=3, out_channels=512, kernel_size=1, stride=1, bias=False), - nn.BatchNorm2d(512), - nn.ReLU(inplace=True), - - ) - ##Position Calibration Module(subbranch) - self.vit_pos=VisionTransformer_POS(img_size=14, - patch_size=1, embed_dim=512, depth=3, num_heads=4, mlp_ratio=2, qkv_bias=True,norm_layer=partial(nn.LayerNorm, eps=1e-6),drop_path_rate=0.3) - self.resize=Resize([14,14]) - ##main branch consisting of CA blocks - self.main_branch =resnet18_pos_attention() - self.head1 = nn.Sequential( - nn.Dropout(p=0.5), - nn.Linear(1 * 112 *112, 38,bias=False), - - ) - - self.timeembed = nn.Parameter(torch.zeros(1, 4, 111, 111)) - - self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) - def forward(self, x1, x2, x3, x4, x5, x6, x7, x8, x9, if_shuffle): - ##onset:x1 apex:x5 - B = x1.shape[0] - - #Position Calibration Module (subbranch) - POS =self.vit_pos(self.resize(x1)).transpose(1,2).view(B,512,14,14) - act = x5 -x1 - act=self.conv_act(act) - #main branch and fusion - out,_=self.main_branch(act,POS) - - return out - - - - - -def run_training(): - - args = parse_args() - imagenet_pretrained = True - - if not imagenet_pretrained: - for m in res18.modules(): - initialize_weight_goog(m) - - if args.pretrained: - print("Loading pretrained weights...", args.pretrained) - pretrained = torch.load(args.pretrained) - pretrained_state_dict = pretrained['state_dict'] - model_state_dict = res18.state_dict() - loaded_keys = 0 - total_keys = 0 - for key in pretrained_state_dict: - if ((key == 'module.fc.weight') | (key == 'module.fc.bias')): - pass - else: - model_state_dict[key] = pretrained_state_dict[key] - total_keys += 1 - if key in model_state_dict: - loaded_keys += 1 - print("Loaded params num:", loaded_keys) - print("Total params num:", total_keys) - res18.load_state_dict(model_state_dict, strict=False) - ### data normalization for both training set - data_transforms = transforms.Compose([ - transforms.ToPILImage(), - transforms.Resize((224, 224)), - - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]), - - ]) - ### data augmentation for training set only - data_transforms_norm = transforms.Compose([ - - transforms.RandomHorizontalFlip(p=0.5), - transforms.RandomRotation(4), - transforms.RandomCrop(224, padding=4), - - - ]) - - - ### data normalization for both teating set - data_transforms_val = transforms.Compose([ - transforms.ToPILImage(), - transforms.Resize((224, 224)), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225])]) - - - - criterion = torch.nn.CrossEntropyLoss() - #leave one subject out protocal - LOSO = ['17', '26', '16', '9', '5', '24', '2', '13', '4', '23', '11', '12', '8', '14', '3', '19', '1', '10', - '20', '21', '22', '15', '6', '25', '7'] - - val_now = 0 - num_sum = 0 - pos_pred_ALL = torch.zeros(3) - pos_label_ALL = torch.zeros(3) - TP_ALL = torch.zeros(3) - - for subj in LOSO: - train_dataset = RafDataSet(args.raf_path, phase='train', num_loso=subj, transform=data_transforms, - basic_aug=True, transform_norm=data_transforms_norm) - val_dataset = RafDataSet(args.raf_path, phase='test', num_loso=subj, transform=data_transforms_val) - train_loader = torch.utils.data.DataLoader(train_dataset, - batch_size=24, - num_workers=args.workers, - shuffle=True, - pin_memory=True) - val_loader = torch.utils.data.DataLoader(val_dataset, - batch_size=24, - num_workers=args.workers, - shuffle=False, - pin_memory=True) - print('num_sub', subj) - print('Train set size:', train_dataset.__len__()) - print('Validation set size:', val_dataset.__len__()) - - max_corr = 0 - max_f1 = 0 - max_pos_pred = torch.zeros(3) - max_pos_label = torch.zeros(3) - max_TP = torch.zeros(3) - ##model initialization - net_all = MMNet() - - params_all = net_all.parameters() - - if args.optimizer == 'adam': - optimizer_all = torch.optim.AdamW(params_all, lr=0.0008, weight_decay=0.7) - ##optimizer for MMNet - - elif args.optimizer == 'sgd': - optimizer = torch.optim.SGD(params, args.lr, - momentum=args.momentum, - weight_decay=1e-4) - else: - raise ValueError("Optimizer not supported.") - ##lr_decay - scheduler_all = torch.optim.lr_scheduler.ExponentialLR(optimizer_all, gamma=0.987) - - net_all = net_all.cuda() - - for i in range(1, 100): - running_loss = 0.0 - correct_sum = 0 - running_loss_MASK = 0.0 - correct_sum_MASK = 0 - iter_cnt = 0 - - net_all.train() - - - for batch_i, ( - image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, - label_all, - label_au) in enumerate(train_loader): - batch_sz = image_on0.size(0) - b, c, h, w = image_on0.shape - iter_cnt += 1 - - image_on0 = image_on0.cuda() - image_on1 = image_on1.cuda() - image_on2 = image_on2.cuda() - image_on3 = image_on3.cuda() - image_apex0 = image_apex0.cuda() - image_off0 = image_off0.cuda() - image_off1 = image_off1.cuda() - image_off2 = image_off2.cuda() - image_off3 = image_off3.cuda() - label_all = label_all.cuda() - label_au = label_au.cuda() - - - ##train MMNet - ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, - image_off2, image_off3, False) - - loss_all = criterion(ALL, label_all) - - optimizer_all.zero_grad() - - loss_all.backward() - - optimizer_all.step() - running_loss += loss_all - _, predicts = torch.max(ALL, 1) - correct_num = torch.eq(predicts, label_all).sum() - correct_sum += correct_num - - - - - - - ## lr decay - if i <= 50: - - scheduler_all.step() - if i>=0: - acc = correct_sum.float() / float(train_dataset.__len__()) - - running_loss = running_loss / iter_cnt - - print('[Epoch %d] Training accuracy: %.4f. Loss: %.3f' % (i, acc, running_loss)) - - - pos_label = torch.zeros(3) - pos_pred = torch.zeros(3) - TP = torch.zeros(3) - ##test - with torch.no_grad(): - running_loss = 0.0 - iter_cnt = 0 - bingo_cnt = 0 - sample_cnt = 0 - pre_lab_all = [] - Y_test_all = [] - net_all.eval() - # net_au.eval() - for batch_i, ( - image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, - image_off3, label_all, - label_au) in enumerate(val_loader): - batch_sz = image_on0.size(0) - b, c, h, w = image_on0.shape - - image_on0 = image_on0.cuda() - image_on1 = image_on1.cuda() - image_on2 = image_on2.cuda() - image_on3 = image_on3.cuda() - image_apex0 = image_apex0.cuda() - image_off0 = image_off0.cuda() - image_off1 = image_off1.cuda() - image_off2 = image_off2.cuda() - image_off3 = image_off3.cuda() - label_all = label_all.cuda() - label_au = label_au.cuda() - - ##test - ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, False) - - - loss = criterion(ALL, label_all) - running_loss += loss - iter_cnt += 1 - _, predicts = torch.max(ALL, 1) - correct_num = torch.eq(predicts, label_all) - bingo_cnt += correct_num.sum().cpu() - sample_cnt += ALL.size(0) - - for cls in range(3): - - for element in predicts: - if element == cls: - pos_label[cls] = pos_label[cls] + 1 - for element in label_all: - if element == cls: - pos_pred[cls] = pos_pred[cls] + 1 - for elementp, elementl in zip(predicts, label_all): - if elementp == elementl and elementp == cls: - TP[cls] = TP[cls] + 1 - - count = 0 - SUM_F1 = 0 - for index in range(3): - if pos_label[index] != 0 or pos_pred[index] != 0: - count = count + 1 - SUM_F1 = SUM_F1 + 2 * TP[index] / (pos_pred[index] + pos_label[index]) - - AVG_F1 = SUM_F1 / count - - - running_loss = running_loss / iter_cnt - acc = bingo_cnt.float() / float(sample_cnt) - acc = np.around(acc.numpy(), 4) - if bingo_cnt > max_corr: - max_corr = bingo_cnt - if AVG_F1 >= max_f1: - max_f1 = AVG_F1 - max_pos_label = pos_label - max_pos_pred = pos_pred - max_TP = TP - print("[Epoch %d] Validation accuracy:%.4f. Loss:%.3f, F1-score:%.3f" % (i, acc, running_loss, AVG_F1)) - num_sum = num_sum + max_corr - pos_label_ALL = pos_label_ALL + max_pos_label - pos_pred_ALL = pos_pred_ALL + max_pos_pred - TP_ALL = TP_ALL + max_TP - count = 0 - SUM_F1 = 0 - for index in range(3): - if pos_label_ALL[index] != 0 or pos_pred_ALL[index] != 0: - count = count + 1 - SUM_F1 = SUM_F1 + 2 * TP_ALL[index] / (pos_pred_ALL[index] + pos_label_ALL[index]) - - F1_ALL = SUM_F1 / count - val_now = val_now + val_dataset.__len__() - print("[..........%s] correctnum:%d . zongshu:%d " % (subj, max_corr, val_dataset.__len__())) - print("[ALL_corr]: %d [ALL_val]: %d" % (num_sum, val_now)) - print("[F1_now]: %.4f [F1_ALL]: %.4f" % (max_f1, F1_ALL)) - - - - - - - -if __name__ == "__main__": - run_training() +import math +import torch +import numpy as np +import torchvision.models +import torch.utils.data as data +from torchvision import transforms +import cv2 +import torch.nn.functional as F +from torch.autograd import Variable +import pandas as pd +import os +import torch +import torch.nn as nn +#import image_utils +import argparse +import random +from functools import partial + +from CA_block import resnet18_pos_attention + +from PC_module import VisionTransformer_POS + +from torchvision.transforms import Resize +#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--raf_path', type=str, + default='Cropped', help='Raf-DB dataset path.') + parser.add_argument('--checkpoint', type=str, default=None, + help='Pytorch checkpoint file path') + parser.add_argument('--pretrained', type=str, default=None, + help='Pretrained weights') + parser.add_argument('--beta', type=float, default=0.7, + help='Ratio of high importance group in one mini-batch.') + parser.add_argument('--relabel_epoch', type=int, default=1000, + help='Relabeling samples on each mini-batch after 10(Default) epochs.') + parser.add_argument('--batch_size', type=int, + default=34, help='Batch size.') + parser.add_argument('--optimizer', type=str, + default="adam", help='Optimizer, adam or sgd.') + parser.add_argument('--lr', type=float, default=0.0001, + help='Initial learning rate for sgd.') + parser.add_argument('--momentum', default=0.9, + type=float, help='Momentum for sgd') + parser.add_argument('--workers', default=0, type=int, + help='Number of data loading workers (default: 4)') + parser.add_argument('--epochs', type=int, default=7000, + help='Total training epochs.') + parser.add_argument('--drop_rate', type=float, + default=0, help='Drop out rate.') + return parser.parse_args() + + +class RafDataSet(data.Dataset): + def __init__(self, raf_path, phase, num_loso, transform=None, basic_aug=False, transform_norm=None): + self.phase = phase + self.transform = transform + self.raf_path = raf_path + self.transform_norm = transform_norm + SUBJECT_COLUMN = 0 + NAME_COLUMN = 1 + ONSET_COLUMN = 2 + APEX_COLUMN = 3 + OFF_COLUMN = 4 + LABEL_AU_COLUMN = 5 + LABEL_ALL_COLUMN = 6 + + df = pd.read_excel(os.path.join( + self.raf_path, 'CASME2-coding-20140508.xlsx'), usecols=[0, 1, 3, 4, 5, 7, 8]) + df['Subject'] = df['Subject'].apply(str) + + if phase == 'train': + dataset = df.loc[df['Subject'] != num_loso] + else: + dataset = df.loc[df['Subject'] == num_loso] + Subject = dataset.iloc[:, SUBJECT_COLUMN].values + File_names = dataset.iloc[:, NAME_COLUMN].values + # 0:Surprise, 1:Fear, 2:Disgust, 3:Happiness, 4:Sadness, 5:Anger, 6:Neutral + Label_all = dataset.iloc[:, LABEL_ALL_COLUMN].values + Onset_num = dataset.iloc[:, ONSET_COLUMN].values + Apex_num = dataset.iloc[:, APEX_COLUMN].values + Offset_num = dataset.iloc[:, OFF_COLUMN].values + Label_au = dataset.iloc[:, LABEL_AU_COLUMN].values + self.file_paths_on = [] + self.file_paths_off = [] + self.file_paths_apex = [] + self.label_all = [] + self.label_au = [] + self.sub = [] + self.file_names = [] + a = 0 + b = 0 + c = 0 + d = 0 + e = 0 + # use aligned images for training/testing + for (f, sub, onset, apex, offset, label_all, label_au) in zip(File_names, Subject, Onset_num, Apex_num, Offset_num, Label_all, Label_au): + + if label_all == 'happiness' or label_all == 'repression' or label_all == 'disgust' or label_all == 'surprise' or label_all == 'fear' or label_all == 'sadness': + + self.file_paths_on.append(onset) + self.file_paths_off.append(offset) + self.file_paths_apex.append(apex) + self.sub.append(sub) + self.file_names.append(f) + if label_all == 'happiness': + self.label_all.append(0) + a = a+1 + elif label_all == 'surprise': + self.label_all.append(1) + b = b+1 + else: + self.label_all.append(2) + c = c+1 + + # label_au =label_au.split("+") + if isinstance(label_au, int): + self.label_au.append([label_au]) + else: + label_au = label_au.split("+") + self.label_au.append(label_au) + + # label + + self.basic_aug = basic_aug + #self.aug_func = [image_utils.flip_image,image_utils.add_gaussian_noise] + + def __len__(self): + return len(self.file_paths_on) + + def __getitem__(self, idx): + # sampling strategy for training set + if self.phase == 'train': + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset = self.file_paths_off[idx] + on0 = str(random.randint(int(onset), int( + onset + int(0.2 * (apex - onset) / 4)))) + # on0 = str(int(onset)) + on1 = str( + random.randint(int(onset + int(0.9 * (apex - onset) / 4)), int(onset + int(1.1 * (apex - onset) / 4)))) + on2 = str( + random.randint(int(onset + int(1.8 * (apex - onset) / 4)), int(onset + int(2.2 * (apex - onset) / 4)))) + on3 = str(random.randint(int( + onset + int(2.7 * (apex - onset) / 4)), onset + int(3.3 * (apex - onset) / 4))) + # apex0 = str(apex) + apex0 = str( + random.randint(int(apex - int(0.15 * (apex - onset) / 4)), apex + int(0.15 * (offset - apex) / 4))) + off0 = str( + random.randint(int(apex + int(0.9 * (offset - apex) / 4)), int(apex + int(1.1 * (offset - apex) / 4)))) + off1 = str( + random.randint(int(apex + int(1.8 * (offset - apex) / 4)), int(apex + int(2.2 * (offset - apex) / 4)))) + off2 = str( + random.randint(int(apex + int(2.9 * (offset - apex) / 4)), int(apex + int(3.1 * (offset - apex) / 4)))) + off3 = str(random.randint( + int(apex + int(3.8 * (offset - apex) / 4)), offset)) + + sub = 'sub'+str(self.sub[idx].zfill(2)) + f = str(self.file_names[idx]) + else: # sampling strategy for testing set + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset = self.file_paths_off[idx] + + on0 = str(onset) + on1 = str(int(onset + int((apex - onset) / 4))) + on2 = str(int(onset + int(2 * (apex - onset) / 4))) + on3 = str(int(onset + int(3 * (apex - onset) / 4))) + apex0 = str(apex) + off0 = str(int(apex + int((offset - apex) / 4))) + off1 = str(int(apex + int(2 * (offset - apex) / 4))) + off2 = str(int(apex + int(3 * (offset - apex) / 4))) + off3 = str(offset) + + sub = 'sub'+str(self.sub[idx].zfill(2)) + f = str(self.file_names[idx]) + + on0 = 'reg_img'+on0+'.jpg' + on1 = 'reg_img' + on1 + '.jpg' + on2 = 'reg_img' + on2 + '.jpg' + on3 = 'reg_img' + on3 + '.jpg' + apex0 = 'reg_img' + apex0 + '.jpg' + off0 = 'reg_img' + off0 + '.jpg' + off1 = 'reg_img' + off1 + '.jpg' + off2 = 'reg_img' + off2 + '.jpg' + off3 = 'reg_img' + off3 + '.jpg' + + path_on0 = os.path.join( + self.raf_path, sub, f, on0) + path_on1 = os.path.join( + self.raf_path, sub, f, on1) + path_on2 = os.path.join( + self.raf_path, sub, f, on2) + path_on3 = os.path.join( + self.raf_path, sub, f, on3) + path_apex0 = os.path.join( + self.raf_path, sub, f, apex0) + path_off0 = os.path.join( + self.raf_path, sub, f, off0) + path_off1 = os.path.join( + self.raf_path, sub, f, off1) + path_off2 = os.path.join( + self.raf_path, sub, f, off2) + path_off3 = os.path.join( + self.raf_path, sub, f, off3) + + image_on0 = cv2.imread(path_on0) + image_on1 = cv2.imread(path_on1) + image_on2 = cv2.imread(path_on2) + image_on3 = cv2.imread(path_on3) + image_apex0 = cv2.imread(path_apex0) + image_off0 = cv2.imread(path_off0) + image_off1 = cv2.imread(path_off1) + image_off2 = cv2.imread(path_off2) + image_off3 = cv2.imread(path_off3) + + image_on0 = image_on0[:, :, ::-1] # BGR to RGB + image_on1 = image_on1[:, :, ::-1] + image_on2 = image_on2[:, :, ::-1] + image_on3 = image_on3[:, :, ::-1] + image_off0 = image_off0[:, :, ::-1] + image_off1 = image_off1[:, :, ::-1] + image_off2 = image_off2[:, :, ::-1] + image_off3 = image_off3[:, :, ::-1] + image_apex0 = image_apex0[:, :, ::-1] + + label_all = self.label_all[idx] + label_au = self.label_au[idx] + + # normalization for testing and training + if self.transform is not None: + image_on0 = self.transform(image_on0) + image_on1 = self.transform(image_on1) + image_on2 = self.transform(image_on2) + image_on3 = self.transform(image_on3) + image_off0 = self.transform(image_off0) + image_off1 = self.transform(image_off1) + image_off2 = self.transform(image_off2) + image_off3 = self.transform(image_off3) + image_apex0 = self.transform(image_apex0) + ALL = torch.cat( + (image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3), dim=0) + # data augmentation for training only + if self.transform_norm is not None and self.phase == 'train': + ALL = self.transform_norm(ALL) + image_on0 = ALL[0:3, :, :] + image_on1 = ALL[3:6, :, :] + image_on2 = ALL[6:9, :, :] + image_on3 = ALL[9:12, :, :] + image_apex0 = ALL[12:15, :, :] + image_off0 = ALL[15:18, :, :] + image_off1 = ALL[18:21, :, :] + image_off2 = ALL[21:24, :, :] + image_off3 = ALL[24:27, :, :] + + temp = torch.zeros(38) + for i in label_au: + temp[int(i) - 1] = 1 + + return image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, label_all, temp + + +def initialize_weight_goog(m, n=''): + if isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1.0) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + fan_out = m.weight.size(0) # fan-out + fan_in = 0 + if 'routing_fn' in n: + fan_in = m.weight.size(1) + init_range = 1.0 / math.sqrt(fan_in + fan_out) + m.weight.data.uniform_(-init_range, init_range) + m.bias.data.zero_() + + +def criterion2(y_pred, y_true): + y_pred = (1 - 2 * y_true) * y_pred + y_pred_neg = y_pred - y_true * 1e12 + y_pred_pos = y_pred - (1 - y_true) * 1e12 + zeros = torch.zeros_like(y_pred[..., :1]) + y_pred_neg = torch.cat((y_pred_neg, zeros), dim=-1) + y_pred_pos = torch.cat((y_pred_pos, zeros), dim=-1) + neg_loss = torch.logsumexp(y_pred_neg, dim=-1) + pos_loss = torch.logsumexp(y_pred_pos, dim=-1) + return torch.mean(neg_loss + pos_loss) + + +class MMNet(nn.Module): + def __init__(self): + super(MMNet, self).__init__() + + self.conv_act = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=90*2, kernel_size=3, + stride=2, padding=1, bias=False, groups=1), # original 2(not sure the effec) + nn.BatchNorm2d(180), + nn.ReLU(inplace=True), + ) + self.pos = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=512, + kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(512), + nn.ReLU(inplace=True), + + ) + # Position Calibration Module(subbranch) + self.vit_pos = VisionTransformer_POS(img_size=14, + patch_size=1, embed_dim=512, depth=3, num_heads=4, mlp_ratio=2, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), drop_path_rate=0.3) + self.resize = Resize([14, 14]) + # main branch consisting of CA blocks + self.main_branch = resnet18_pos_attention() + self.head1 = nn.Sequential( + nn.Dropout(p=0.5), + nn.Linear(1 * 112 * 112, 38, bias=False), + + ) + + self.timeembed = nn.Parameter(torch.zeros(1, 4, 111, 111)) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + + def forward(self, x1, x2, x3, x4, x5, x6, x7, x8, x9, if_shuffle): + # onset:x1 apex:x5 + B = x1.shape[0] + + # Position Calibration Module (subbranch) + POS = self.vit_pos(self.resize(x1)).transpose( + 1, 2).view(B, 512, 14, 14) + act = x5 - x1 + act = self.conv_act(act) + # main branch and fusion + out, _ = self.main_branch(act, POS) + + return out + + +def run_training(): + + args = parse_args() + imagenet_pretrained = True + + if not imagenet_pretrained: + for m in res18.modules(): + initialize_weight_goog(m) + + if args.pretrained: + print("Loading pretrained weights...", args.pretrained) + pretrained = torch.load(args.pretrained) + pretrained_state_dict = pretrained['state_dict'] + model_state_dict = res18.state_dict() + loaded_keys = 0 + total_keys = 0 + for key in pretrained_state_dict: + if ((key == 'module.fc.weight') | (key == 'module.fc.bias')): + pass + else: + model_state_dict[key] = pretrained_state_dict[key] + total_keys += 1 + if key in model_state_dict: + loaded_keys += 1 + print("Loaded params num:", loaded_keys) + print("Total params num:", total_keys) + res18.load_state_dict(model_state_dict, strict=False) + # data normalization for both training set + data_transforms = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + + ]) + # data augmentation for training set only + data_transforms_norm = transforms.Compose([ + + transforms.RandomHorizontalFlip(p=0.5), + transforms.RandomRotation(4), + transforms.RandomCrop(224, padding=4), + + + ]) + + # data normalization for both teating set + data_transforms_val = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) + + criterion = torch.nn.CrossEntropyLoss() + # leave one subject out protocal + LOSO = ['8', '17', '26', '16', '9', '5', '24', '2', '13', '4', '23', '11', '12', '14', '3', '19', '1', '10', + '20', '21', '22', '15', '6', '25', '7'] + + val_now = 0 + num_sum = 0 + pos_pred_ALL = torch.zeros(3) + pos_label_ALL = torch.zeros(3) + TP_ALL = torch.zeros(3) + + for subj in LOSO: + train_dataset = RafDataSet(args.raf_path, phase='train', num_loso=subj, transform=data_transforms, + basic_aug=True, transform_norm=data_transforms_norm) + val_dataset = RafDataSet( + args.raf_path, phase='test', num_loso=subj, transform=data_transforms_val) + train_loader = torch.utils.data.DataLoader(train_dataset, + batch_size=128, + num_workers=args.workers, + shuffle=True, + pin_memory=True) + val_loader = torch.utils.data.DataLoader(val_dataset, + batch_size=128, + num_workers=args.workers, + shuffle=False, + pin_memory=True) + print('num_sub', subj) + print('Train set size:', train_dataset.__len__()) + print('Validation set size:', val_dataset.__len__()) + + max_corr = 0 + max_f1 = 0 + max_pos_pred = torch.zeros(3) + max_pos_label = torch.zeros(3) + max_TP = torch.zeros(3) + # model initialization + net_all = MMNet() + # net_all = net_all.cuda() + params_all = net_all.parameters() + + if args.optimizer == 'adam': + optimizer_all = torch.optim.AdamW( + params_all, lr=0.0008, weight_decay=0.7) + # optimizer for MMNet + + elif args.optimizer == 'sgd': + optimizer = torch.optim.SGD(params, args.lr, + momentum=args.momentum, + weight_decay=1e-4) + else: + raise ValueError("Optimizer not supported.") + # lr_decay + scheduler_all = torch.optim.lr_scheduler.ExponentialLR( + optimizer_all, gamma=0.987) + net_all = net_all.cuda() + for i in range(1, 100): + running_loss = 0.0 + correct_sum = 0 + running_loss_MASK = 0.0 + correct_sum_MASK = 0 + iter_cnt = 0 + + net_all.train() + + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, + label_all, + label_au) in enumerate(train_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + iter_cnt += 1 + + image_on0 = image_on0.cuda() + image_on1 = image_on1.cuda() + image_on2 = image_on2.cuda() + image_on3 = image_on3.cuda() + image_apex0 = image_apex0.cuda() + image_off0 = image_off0.cuda() + image_off1 = image_off1.cuda() + image_off2 = image_off2.cuda() + image_off3 = image_off3.cuda() + label_all = label_all.cuda() + label_au = label_au.cuda() + + # train MMNet + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, + image_off2, image_off3, False) + + loss_all = criterion(ALL, label_all) + + optimizer_all.zero_grad() + + loss_all.backward() + + optimizer_all.step() + running_loss += loss_all + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all).sum() + correct_sum += correct_num + + # lr decay + if i <= 50: + + scheduler_all.step() + if i >= 0: + acc = correct_sum.float() / float(train_dataset.__len__()) + + running_loss = running_loss / iter_cnt + + print('[Epoch %d] Training accuracy: %.4f. Loss: %.3f' % + (i, acc, running_loss)) + + pos_label = torch.zeros(3) + pos_pred = torch.zeros(3) + TP = torch.zeros(3) + # test + with torch.no_grad(): + running_loss = 0.0 + iter_cnt = 0 + bingo_cnt = 0 + sample_cnt = 0 + pre_lab_all = [] + Y_test_all = [] + net_all.eval() + # net_au.eval() + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3, label_all, + label_au) in enumerate(val_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + + image_on0 = image_on0.cuda() + image_on1 = image_on1.cuda() + image_on2 = image_on2.cuda() + image_on3 = image_on3.cuda() + image_apex0 = image_apex0.cuda() + image_off0 = image_off0.cuda() + image_off1 = image_off1.cuda() + image_off2 = image_off2.cuda() + image_off3 = image_off3.cuda() + label_all = label_all.cuda() + label_au = label_au.cuda() + + # test + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, + image_off0, image_off1, image_off2, image_off3, False) + + loss = criterion(ALL, label_all) + running_loss += loss + iter_cnt += 1 + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all) + bingo_cnt += correct_num.sum().cpu() + #bingo_cnt += correct_num.sum() + sample_cnt += ALL.size(0) + + for cls in range(3): + + for element in predicts: + if element == cls: + pos_label[cls] = pos_label[cls] + 1 + for element in label_all: + if element == cls: + pos_pred[cls] = pos_pred[cls] + 1 + for elementp, elementl in zip(predicts, label_all): + if elementp == elementl and elementp == cls: + TP[cls] = TP[cls] + 1 + + count = 0 + SUM_F1 = 0 + for index in range(3): + if pos_label[index] != 0 or pos_pred[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * \ + TP[index] / (pos_pred[index] + + pos_label[index]) + + AVG_F1 = SUM_F1 / count + + running_loss = running_loss / iter_cnt + acc = bingo_cnt.float() / float(sample_cnt) + acc = acc.cpu() + acc = np.around(acc.numpy(), 4) + if bingo_cnt > max_corr: + max_corr = bingo_cnt + if AVG_F1 >= max_f1: + max_f1 = AVG_F1 + max_pos_label = pos_label + max_pos_pred = pos_pred + max_TP = TP + print("[Epoch %d] Validation accuracy:%.4f. Loss:%.3f, F1-score:%.3f" % + (i, acc, running_loss, AVG_F1)) + num_sum = num_sum + max_corr + pos_label_ALL = pos_label_ALL + max_pos_label + pos_pred_ALL = pos_pred_ALL + max_pos_pred + TP_ALL = TP_ALL + max_TP + count = 0 + SUM_F1 = 0 + for index in range(3): + if pos_label_ALL[index] != 0 or pos_pred_ALL[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * \ + TP_ALL[index] / (pos_pred_ALL[index] + + pos_label_ALL[index]) + + F1_ALL = SUM_F1 / count + val_now = val_now + val_dataset.__len__() + print("[..........%s] correctnum:%d . zongshu:%d " % + (subj, max_corr, val_dataset.__len__())) + print("[ALL_corr]: %d [ALL_val]: %d" % (num_sum, val_now)) + print("[F1_now]: %.4f [F1_ALL]: %.4f" % (max_f1, F1_ALL)) + + +if __name__ == "__main__": + run_training() diff --git a/CASME2_5.py b/CASME2_5.py old mode 100644 new mode 100755 index 1be5efb..eec0dff --- a/CASME2_5.py +++ b/CASME2_5.py @@ -1,620 +1,636 @@ -import math -import numpy as np -import torchvision.models -import torch.utils.data as data -from torchvision import transforms -import cv2 -import torch.nn.functional as F -from torch.autograd import Variable -import pandas as pd -import os, torch -import torch.nn as nn -#import image_utils -import argparse, random -from functools import partial -from opencv_flow import OpticFlow -from resmodel import resnet18 -from CA_block import resnet18_pos_attention,resnet50 -from PC_module import VisionTransformer_POS -from timm.models import create_model -from einops import rearrange, repeat -from einops.layers.torch import Rearrange -from torch.utils.data import ConcatDataset -from gradcam import show_cam_on_image, GradCam, preprocess_image -import confusion_matrix -from torchvision.transforms import Resize -torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument('--raf_path', type=str, default='datasets/CASME2/', help='Raf-DB dataset path.') - parser.add_argument('--checkpoint', type=str, default=None, - help='Pytorch checkpoint file path') - parser.add_argument('--pretrained', type=str, default=None, - help='Pretrained weights') - parser.add_argument('--beta', type=float, default=0.7, help='Ratio of high importance group in one mini-batch.') - parser.add_argument('--relabel_epoch', type=int, default=1000, - help='Relabeling samples on each mini-batch after 10(Default) epochs.') - parser.add_argument('--batch_size', type=int, default=34, help='Batch size.') - parser.add_argument('--optimizer', type=str, default="adam", help='Optimizer, adam or sgd.') - parser.add_argument('--lr', type=float, default=0.0001, help='Initial learning rate for sgd.') - parser.add_argument('--momentum', default=0.9, type=float, help='Momentum for sgd') - parser.add_argument('--workers', default=0, type=int, help='Number of data loading workers (default: 4)') - parser.add_argument('--epochs', type=int, default=7000, help='Total training epochs.') - parser.add_argument('--drop_rate', type=float, default=0, help='Drop out rate.') - return parser.parse_args() - - - - - - -class RafDataSet(data.Dataset): - def __init__(self, raf_path, phase,num_loso, transform = None, basic_aug = False, transform_norm=None): - self.phase = phase - self.transform = transform - self.raf_path = raf_path - self.transform_norm = transform_norm - SUBJECT_COLUMN =0 - NAME_COLUMN = 1 - ONSET_COLUMN = 2 - APEX_COLUMN = 3 - OFF_COLUMN = 4 - LABEL_AU_COLUMN = 5 - LABEL_ALL_COLUMN = 6 - - - df = pd.read_excel(os.path.join(self.raf_path, 'CASME2-coding-20190701.xlsx'),usecols=[0,1,3,4,5,7,8]) - df['Subject'] = df['Subject'].apply(str) - - if phase == 'train': - dataset = df.loc[df['Subject']!=num_loso] - else: - dataset = df.loc[df['Subject'] == num_loso] - - Subject = dataset.iloc[:, SUBJECT_COLUMN].values - File_names = dataset.iloc[:, NAME_COLUMN].values - Label_all = dataset.iloc[:, LABEL_ALL_COLUMN].values # 0:Surprise, 1:Fear, 2:Disgust, 3:Happiness, 4:Sadness, 5:Anger, 6:Neutral - Onset_num = dataset.iloc[:, ONSET_COLUMN].values - Apex_num = dataset.iloc[:, APEX_COLUMN].values - Offset_num = dataset.iloc[:, OFF_COLUMN].values - Label_au = dataset.iloc[:, LABEL_AU_COLUMN].values - self.file_paths_on = [] - self.file_paths_off = [] - self.file_paths_apex = [] - self.label_all = [] - self.label_au = [] - self.sub= [] - self.file_names =[] - a=0 - b=0 - c=0 - d=0 - e=0 - # use aligned images for training/testing - for (f,sub,onset,apex,offset,label_all,label_au) in zip(File_names,Subject,Onset_num,Apex_num,Offset_num,Label_all,Label_au): - - - if label_all == 'happiness' or label_all == 'repression' or label_all == 'disgust' or label_all == 'surprise' or label_all == 'others': - - self.file_paths_on.append(onset) - self.file_paths_off.append(offset) - self.file_paths_apex.append(apex) - self.sub.append(sub) - self.file_names.append(f) - if label_all == 'happiness': - self.label_all.append(0) - a=a+1 - elif label_all == 'repression': - self.label_all.append(1) - b=b+1 - elif label_all == 'disgust': - self.label_all.append(2) - c=c+1 - elif label_all == 'surprise': - self.label_all.append(3) - d=d+1 - else: - self.label_all.append(4) - e=e+1 - - # label_au =label_au.split("+") - if isinstance(label_au, int): - self.label_au.append([label_au]) - else: - label_au = label_au.split("+") - self.label_au.append(label_au) - - - - - - - ##label - - self.basic_aug = basic_aug - #self.aug_func = [image_utils.flip_image,image_utils.add_gaussian_noise] - - def __len__(self): - return len(self.file_paths_on) - - def __getitem__(self, idx): - ##sampling strategy for training set - if self.phase == 'train': - onset = self.file_paths_on[idx] - apex = self.file_paths_apex[idx] - offset =self.file_paths_off[idx] - on0 = str(random.randint(int(onset), int(onset + int(0.15* (apex - onset) / 4)))) - # on0 = str(int(onset)) - on1 = str( - random.randint(int(onset + int(0.9 * (apex - onset) / 4)), int(onset + int(1.1 * (apex - onset) / 4)))) - on2 = str( - random.randint(int(onset + int(1.8 * (apex - onset) / 4)), int(onset + int(2.2 * (apex - onset) / 4)))) - on3 = str(random.randint(int(onset + int(2.7 * (apex - onset) / 4)), onset + int(3.3 * (apex - onset) / 4))) - # apex0 = str(apex) - apex0 = str( - random.randint(int(apex - int(0.15* (apex - onset) / 4)), apex)) - off0 = str( - random.randint(int(apex + int(0.9 * (offset - apex) / 4)), int(apex + int(1.1 * (offset - apex) / 4)))) - off1 = str( - random.randint(int(apex + int(1.8 * (offset - apex) / 4)), int(apex + int(2.2 * (offset - apex) / 4)))) - off2 = str( - random.randint(int(apex + int(2.9 * (offset - apex) / 4)), int(apex + int(3.1 * (offset - apex) / 4)))) - off3 = str(random.randint(int(apex + int(3.8 * (offset - apex) / 4)), offset)) - - - - sub =str(self.sub[idx]) - f = str(self.file_names[idx]) - else:##sampling strategy for testing set - onset = self.file_paths_on[idx] - apex = self.file_paths_apex[idx] - offset = self.file_paths_off[idx] - - on0 = str(onset) - on1 = str(int(onset + int((apex - onset) / 4))) - on2 = str(int(onset + int(2 * (apex - onset) / 4))) - on3 = str(int(onset + int(3 * (apex - onset) / 4))) - apex0 = str(apex) - off0 = str(int(apex + int((offset - apex) / 4))) - off1 = str(int(apex + int(2 * (offset - apex) / 4))) - off2 = str(int(apex + int(3 * (offset - apex) / 4))) - off3 = str(offset) - - sub = str(self.sub[idx]) - f = str(self.file_names[idx]) - - - on0 ='reg_img'+on0+'.jpg' - on1 = 'reg_img' + on1 + '.jpg' - on2 = 'reg_img' + on2 + '.jpg' - on3 = 'reg_img' + on3 + '.jpg' - apex0 ='reg_img' + apex0 + '.jpg' - off0 ='reg_img' + off0 + '.jpg' - off1='reg_img' + off1 + '.jpg' - off2 ='reg_img' + off2 + '.jpg' - off3 = 'reg_img' + off3 + '.jpg' - path_on0 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, on0) - path_on1 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, on1) - path_on2 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, on2) - path_on3 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, on3) - path_apex0 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, apex0) - path_off0 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, off0) - path_off1 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, off1) - path_off2 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, off2) - path_off3 = os.path.join(self.raf_path, 'Cropped-updated/Cropped/', sub, f, off3) - image_on0 = cv2.imread(path_on0) - image_on1= cv2.imread(path_on1) - image_on2 = cv2.imread(path_on2) - image_on3 = cv2.imread(path_on3) - image_apex0 = cv2.imread(path_apex0) - image_off0 = cv2.imread(path_off0) - image_off1 = cv2.imread(path_off1) - image_off2 = cv2.imread(path_off2) - image_off3 = cv2.imread(path_off3) - - image_on0 = image_on0[:, :, ::-1] # BGR to RGB - image_on1 = image_on1[:, :, ::-1] - image_on2 = image_on2[:, :, ::-1] - image_on3 = image_on3[:, :, ::-1] - image_off0 = image_off0[:, :, ::-1] - image_off1 = image_off1[:, :, ::-1] - image_off2 = image_off2[:, :, ::-1] - image_off3 = image_off3[:, :, ::-1] - image_apex0 = image_apex0[:, :, ::-1] - - label_all = self.label_all[idx] - label_au = self.label_au[idx] - - # normalization for testing and training - if self.transform is not None: - image_on0 = self.transform(image_on0) - image_on1 = self.transform(image_on1) - image_on2 = self.transform(image_on2) - image_on3 = self.transform(image_on3) - image_off0 = self.transform(image_off0) - image_off1 = self.transform(image_off1) - image_off2 = self.transform(image_off2) - image_off3 = self.transform(image_off3) - image_apex0 = self.transform(image_apex0) - ALL = torch.cat( - (image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, - image_off3), dim=0) - ## data augmentation for training only - if self.transform_norm is not None and self.phase == 'train': - ALL = self.transform_norm(ALL) - image_on0 = ALL[0:3, :, :] - image_on1 = ALL[3:6, :, :] - image_on2 = ALL[6:9, :, :] - image_on3 = ALL[9:12, :, :] - image_apex0 = ALL[12:15, :, :] - image_off0 = ALL[15:18, :, :] - image_off1 = ALL[18:21, :, :] - image_off2 = ALL[21:24, :, :] - image_off3 = ALL[24:27, :, :] - - - temp = torch.zeros(38) - for i in label_au: - temp[int(i) - 1] = 1 - - return image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, label_all, temp - - -def initialize_weight_goog(m, n=''): - if isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1.0) - m.bias.data.zero_() - elif isinstance(m, nn.Linear): - fan_out = m.weight.size(0) # fan-out - fan_in = 0 - if 'routing_fn' in n: - fan_in = m.weight.size(1) - init_range = 1.0 / math.sqrt(fan_in + fan_out) - m.weight.data.uniform_(-init_range, init_range) - m.bias.data.zero_() - - -def criterion2(y_pred, y_true): - y_pred = (1 - 2 * y_true) * y_pred - y_pred_neg = y_pred - y_true * 1e12 - y_pred_pos = y_pred - (1 - y_true) * 1e12 - zeros = torch.zeros_like(y_pred[..., :1]) - y_pred_neg = torch.cat((y_pred_neg, zeros), dim=-1) - y_pred_pos = torch.cat((y_pred_pos, zeros), dim=-1) - neg_loss = torch.logsumexp(y_pred_neg, dim=-1) - pos_loss = torch.logsumexp(y_pred_pos, dim=-1) - return torch.mean(neg_loss + pos_loss) - - -class MMNet(nn.Module): - def __init__(self): - super(MMNet, self).__init__() - - - self.conv_act = nn.Sequential( - nn.Conv2d(in_channels=3, out_channels=90*2, kernel_size=3, stride=2,padding=1, bias=False,groups=1), - nn.BatchNorm2d(180), - nn.ReLU(inplace=True), - - ) - self.pos =nn.Sequential( - nn.Conv2d(in_channels=3, out_channels=512, kernel_size=1, stride=1, bias=False), - nn.BatchNorm2d(512), - nn.ReLU(inplace=True), - - ) - ##Position Calibration Module(subbranch) - self.vit_pos=VisionTransformer_POS(img_size=14, - patch_size=1, embed_dim=512, depth=2, num_heads=4, mlp_ratio=4, qkv_bias=True,norm_layer=partial(nn.LayerNorm, eps=1e-6),drop_path_rate=0.) - self.resize=Resize([14,14]) - ##main branch consisting of CA blocks - self.main_branch =resnet18_pos_attention() - - self.head = nn.Sequential( - nn.Dropout(p=0.5), - nn.Linear(1 * 112 *112, 38,bias=False), - - ) - - self.timeembed = nn.Parameter(torch.zeros(1, 4, 111, 111)) - - self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) - def forward(self, x1, x2, x3, x4, x5, x6, x7, x8, x9, if_shuffle): - ##onset:x1 apex:x5 - B = x1.shape[0] - # Position Calibration Module (subbranch) - POS =self.vit_pos(self.resize(x1)).transpose(1,2).view(B,512,14,14) - - act =x5 -x1 - act=self.conv_act(act) - # main branch and fusion - out,_=self.main_branch(act,POS) - - return out - - - - - -def run_training(): - - - args = parse_args() - imagenet_pretrained = True - - if not imagenet_pretrained: - for m in res18.modules(): - initialize_weight_goog(m) - - if args.pretrained: - print("Loading pretrained weights...", args.pretrained) - pretrained = torch.load(args.pretrained) - pretrained_state_dict = pretrained['state_dict'] - model_state_dict = res18.state_dict() - loaded_keys = 0 - total_keys = 0 - for key in pretrained_state_dict: - if ((key == 'module.fc.weight') | (key == 'module.fc.bias')): - pass - else: - model_state_dict[key] = pretrained_state_dict[key] - total_keys += 1 - if key in model_state_dict: - loaded_keys += 1 - print("Loaded params num:", loaded_keys) - print("Total params num:", total_keys) - res18.load_state_dict(model_state_dict, strict=False) - ##data normalization for both training set - data_transforms = transforms.Compose([ - transforms.ToPILImage(), - transforms.Resize((224, 224)), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]), - - ]) - ### data augmentation for training set only - data_transforms_norm = transforms.Compose([ - - transforms.RandomHorizontalFlip(p=0.5), - transforms.RandomRotation(3), - transforms.RandomCrop(224, padding=15), - - ]) - - ### data normalization for both teating set - data_transforms_val = transforms.Compose([ - transforms.ToPILImage(), - transforms.Resize((224, 224)), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225])]) - - - - - criterion = torch.nn.CrossEntropyLoss() - # leave one subject out protocal - LOSO = ['17', '26', '16', '9', '5', '24', '2', '13', '4', '23', '11', '12', '8', '14', '3', '19', '1', '18', '10', - '20', '21', '22', '15', '6', '25', '7'] - - val_now = 0 - num_sum = 0 - pos_pred_ALL = torch.zeros(5) - pos_label_ALL = torch.zeros(5) - TP_ALL = torch.zeros(5) - - for subj in LOSO: - train_dataset = RafDataSet(args.raf_path, phase='train', num_loso=subj, transform=data_transforms, - basic_aug=True, transform_norm=data_transforms_norm) - val_dataset = RafDataSet(args.raf_path, phase='test', num_loso=subj, transform=data_transforms_val) - train_loader = torch.utils.data.DataLoader(train_dataset, - batch_size=32, - num_workers=args.workers, - shuffle=True, - pin_memory=True) - val_loader = torch.utils.data.DataLoader(val_dataset, - batch_size=32, - num_workers=args.workers, - shuffle=False, - pin_memory=True) - print('num_sub', subj) - print('Train set size:', train_dataset.__len__()) - print('Validation set size:', val_dataset.__len__()) - - max_corr = 0 - max_f1 = 0 - max_pos_pred = torch.zeros(5) - max_pos_label = torch.zeros(5) - max_TP = torch.zeros(5) - ##model initialization - net_all = MMNet() - - params_all = net_all.parameters() - - if args.optimizer == 'adam': - optimizer_all = torch.optim.AdamW(params_all, lr=0.0008, weight_decay=0.6) - ##optimizer for MMNet - - elif args.optimizer == 'sgd': - optimizer = torch.optim.SGD(params, args.lr, - momentum=args.momentum, - weight_decay=1e-4) - else: - raise ValueError("Optimizer not supported.") - ##lr_decay - scheduler_all = torch.optim.lr_scheduler.ExponentialLR(optimizer_all, gamma=0.987) - - net_all = net_all.cuda() - - for i in range(1, 75): - running_loss = 0.0 - correct_sum = 0 - running_loss_MASK = 0.0 - correct_sum_MASK = 0 - iter_cnt = 0 - - net_all.train() - - - for batch_i, ( - image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, - label_all, - label_au) in enumerate(train_loader): - batch_sz = image_on0.size(0) - b, c, h, w = image_on0.shape - iter_cnt += 1 - - image_on0 = image_on0.cuda() - image_on1 = image_on1.cuda() - image_on2 = image_on2.cuda() - image_on3 = image_on3.cuda() - image_apex0 = image_apex0.cuda() - image_off0 = image_off0.cuda() - image_off1 = image_off1.cuda() - image_off2 = image_off2.cuda() - image_off3 = image_off3.cuda() - label_all = label_all.cuda() - label_au = label_au.cuda() - - ##train MMNet - ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, - image_off2, image_off3, False) - - loss_all = criterion(ALL, label_all) - - optimizer_all.zero_grad() - - loss_all.backward() - - optimizer_all.step() - running_loss += loss_all - _, predicts = torch.max(ALL, 1) - correct_num = torch.eq(predicts, label_all).sum() - correct_sum += correct_num - - - - - - - ## lr decay - if i <= 50: - - scheduler_all.step() - if i>=0: - acc = correct_sum.float() / float(train_dataset.__len__()) - - running_loss = running_loss / iter_cnt - - print('[Epoch %d] Training accuracy: %.4f. Loss: %.3f' % (i, acc, running_loss)) - - - pos_label = torch.zeros(5) - pos_pred = torch.zeros(5) - TP = torch.zeros(5) - - with torch.no_grad(): - running_loss = 0.0 - iter_cnt = 0 - bingo_cnt = 0 - sample_cnt = 0 - pre_lab_all = [] - Y_test_all = [] - net_all.eval() - # net_au.eval() - for batch_i, ( - image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, - image_off3, label_all, - label_au) in enumerate(val_loader): - batch_sz = image_on0.size(0) - b, c, h, w = image_on0.shape - - image_on0 = image_on0.cuda() - image_on1 = image_on1.cuda() - image_on2 = image_on2.cuda() - image_on3 = image_on3.cuda() - image_apex0 = image_apex0.cuda() - image_off0 = image_off0.cuda() - image_off1 = image_off1.cuda() - image_off2 = image_off2.cuda() - image_off3 = image_off3.cuda() - label_all = label_all.cuda() - label_au = label_au.cuda() - - ##test - ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, False) - - - loss = criterion(ALL, label_all) - running_loss += loss - iter_cnt += 1 - _, predicts = torch.max(ALL, 1) - correct_num = torch.eq(predicts, label_all) - bingo_cnt += correct_num.sum().cpu() - sample_cnt += ALL.size(0) - - for cls in range(5): - - for element in predicts: - if element == cls: - pos_label[cls] = pos_label[cls] + 1 - for element in label_all: - if element == cls: - pos_pred[cls] = pos_pred[cls] + 1 - for elementp, elementl in zip(predicts, label_all): - if elementp == elementl and elementp == cls: - TP[cls] = TP[cls] + 1 - # if pos_label != 0 or pos_pred != 0: - # f1 = 2 * TP / (pos_pred + pos_label) - # F1.append(f1) - count = 0 - SUM_F1 = 0 - for index in range(5): - if pos_label[index] != 0 or pos_pred[index] != 0: - count = count + 1 - SUM_F1 = SUM_F1 + 2 * TP[index] / (pos_pred[index] + pos_label[index]) - - AVG_F1 = SUM_F1 / count - - - running_loss = running_loss / iter_cnt - acc = bingo_cnt.float() / float(sample_cnt) - acc = np.around(acc.numpy(), 4) - if bingo_cnt > max_corr: - max_corr = bingo_cnt - if AVG_F1 >= max_f1: - max_f1 = AVG_F1 - max_pos_label = pos_label - max_pos_pred = pos_pred - max_TP = TP - print("[Epoch %d] Validation accuracy:%.4f. Loss:%.3f, F1-score:%.3f" % (i, acc, running_loss, AVG_F1)) - num_sum = num_sum + max_corr - pos_label_ALL = pos_label_ALL + max_pos_label - pos_pred_ALL = pos_pred_ALL + max_pos_pred - TP_ALL = TP_ALL + max_TP - count = 0 - SUM_F1 = 0 - for index in range(5): - if pos_label_ALL[index] != 0 or pos_pred_ALL[index] != 0: - count = count + 1 - SUM_F1 = SUM_F1 + 2 * TP_ALL[index] / (pos_pred_ALL[index] + pos_label_ALL[index]) - - F1_ALL = SUM_F1 / count - val_now = val_now + val_dataset.__len__() - print("[..........%s] correctnum:%d . zongshu:%d " % (subj, max_corr, val_dataset.__len__())) - print("[ALL_corr]: %d [ALL_val]: %d" % (num_sum, val_now)) - print("[F1_now]: %.4f [F1_ALL]: %.4f" % (max_f1, F1_ALL)) - - - - -if __name__ == "__main__": - run_training() +import math +import numpy as np +import torchvision.models +import torch.utils.data as data +from torchvision import transforms +import cv2 +import torch.nn.functional as F +from torch.autograd import Variable +import pandas as pd +import os +import torch +import torch.nn as nn +#import image_utils +import argparse +import random +from torch.utils.data.dataloader import default_collate +from functools import partial +# from opencv_flow import OpticFlow +# from resmodel import resnet18 +# from resmodel_attn_group import resnet18_pos_attention, resnet50 +from CA_block import resnet18_pos_attention +from timm.models import create_model +from einops import rearrange, repeat +from einops.layers.torch import Rearrange +from PC_module import VisionTransformer_POS +from torch.utils.data import ConcatDataset + +# from gradcam import show_cam_on_image, GradCam, preprocess_image +# import confusion_matrix +from torchvision.transforms import Resize +torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--raf_path', type=str, + default='Cropped', help='Raf-DB dataset path.') + parser.add_argument('--checkpoint', type=str, default=None, + help='Pytorch checkpoint file path') + parser.add_argument('--pretrained', type=str, default=None, + help='Pretrained weights') + parser.add_argument('--beta', type=float, default=0.7, + help='Ratio of high importance group in one mini-batch.') + parser.add_argument('--relabel_epoch', type=int, default=1000, + help='Relabeling samples on each mini-batch after 10(Default) epochs.') + parser.add_argument('--batch_size', type=int, + default=30, help='Batch size.') + parser.add_argument('--optimizer', type=str, + default="adam", help='Optimizer, adam or sgd.') + parser.add_argument('--lr', type=float, default=0.0001, + help='Initial learning rate for sgd.') + parser.add_argument('--momentum', default=0.9, + type=float, help='Momentum for sgd') + parser.add_argument('--workers', default=4, type=int, + help='Number of data loading workers (default: 4)') + parser.add_argument('--epochs', type=int, default=7000, + help='Total training epochs.') + parser.add_argument('--drop_rate', type=float, + default=0, help='Drop out rate.') + return parser.parse_args() + + +class RafDataSet(data.Dataset): + def __init__(self, raf_path, phase, num_loso, transform=None, basic_aug=False, transform_norm=None): + self.phase = phase + self.transform = transform + self.raf_path = raf_path + self.transform_norm = transform_norm + SUBJECT_COLUMN = 0 + NAME_COLUMN = 1 + ONSET_COLUMN = 2 + APEX_COLUMN = 3 + OFF_COLUMN = 4 + LABEL_AU_COLUMN = 5 + LABEL_ALL_COLUMN = 6 + + df = pd.read_excel(os.path.join( + self.raf_path, 'CASME2-coding-20230315.xlsx'), usecols=[0, 1, 3, 4, 5, 7, 8]) + df['Subject'] = df['Subject'].apply(str) + + if phase == 'train': + dataset = df.loc[df['Subject'] != num_loso] + else: + dataset = df.loc[df['Subject'] == num_loso] + + Subject = dataset.iloc[:, SUBJECT_COLUMN].values + File_names = dataset.iloc[:, NAME_COLUMN].values + # 0:Surprise, 1:Fear, 2:Disgust, 3:Happiness, 4:Sadness, 5:Anger, 6:Neutral + Label_all = dataset.iloc[:, LABEL_ALL_COLUMN].values + Onset_num = dataset.iloc[:, ONSET_COLUMN].values + Apex_num = dataset.iloc[:, APEX_COLUMN].values + Offset_num = dataset.iloc[:, OFF_COLUMN].values + Label_au = dataset.iloc[:, LABEL_AU_COLUMN].values + self.file_paths_on = [] + self.file_paths_off = [] + self.file_paths_apex = [] + self.label_all = [] + self.label_au = [] + self.sub = [] + self.file_names = [] + a = 0 + b = 0 + c = 0 + d = 0 + e = 0 + # use aligned images for training/testing + for (f, sub, onset, apex, offset, label_all, label_au) in zip(File_names, Subject, Onset_num, Apex_num, Offset_num, Label_all, Label_au): + + if label_all == 'happiness' or label_all == 'repression' or label_all == 'disgust' or label_all == 'surprise' or label_all == 'others': + + self.file_paths_on.append(onset) + self.file_paths_off.append(offset) + self.file_paths_apex.append(apex) + self.sub.append(sub) + self.file_names.append(f) + if label_all == 'happiness': + self.label_all.append(0) + a = a+1 + elif label_all == 'repression': + self.label_all.append(1) + b = b+1 + elif label_all == 'disgust': + self.label_all.append(2) + c = c+1 + elif label_all == 'surprise': + self.label_all.append(3) + d = d+1 + else: + self.label_all.append(4) + e = e+1 + + # label_au =label_au.split("+") + if isinstance(label_au, int): + self.label_au.append([label_au]) + else: + label_au = label_au.split("+") + self.label_au.append(label_au) + # label + + self.basic_aug = basic_aug + #self.aug_func = [image_utils.flip_image,image_utils.add_gaussian_noise] + + def __len__(self): + return len(self.file_paths_on) + + def __getitem__(self, idx): + # sampling strategy for training set + if self.phase == 'train': + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset = self.file_paths_off[idx] + on0 = str(random.randint(int(onset), int( + onset + int(0.15 * (apex - onset) / 4)))) + # on0 = str(int(onset)) + on1 = str( + random.randint(int(onset + int(0.9 * (apex - onset) / 4)), int(onset + int(1.1 * (apex - onset) / 4)))) + on2 = str( + random.randint(int(onset + int(1.8 * (apex - onset) / 4)), int(onset + int(2.2 * (apex - onset) / 4)))) + on3 = str(random.randint(int( + onset + int(2.7 * (apex - onset) / 4)), onset + int(3.3 * (apex - onset) / 4))) + # apex0 = str(apex) + apex0 = str( + random.randint(int(apex - int(0.15 * (apex - onset) / 4)), apex)) + off0 = str( + random.randint(int(apex + int(0.9 * (offset - apex) / 4)), int(apex + int(1.1 * (offset - apex) / 4)))) + off1 = str( + random.randint(int(apex + int(1.8 * (offset - apex) / 4)), int(apex + int(2.2 * (offset - apex) / 4)))) + off2 = str( + random.randint(int(apex + int(2.9 * (offset - apex) / 4)), int(apex + int(3.1 * (offset - apex) / 4)))) + off3 = str(random.randint( + int(apex + int(3.8 * (offset - apex) / 4)), offset)) + + sub = 'sub'+str(self.sub[idx].zfill(2)) + f = str(self.file_names[idx]) + else: # sampling strategy for testing set + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset = self.file_paths_off[idx] + + on0 = str(onset) + on1 = str(int(onset + int((apex - onset) / 4))) + on2 = str(int(onset + int(2 * (apex - onset) / 4))) + on3 = str(int(onset + int(3 * (apex - onset) / 4))) + apex0 = str(apex) + off0 = str(int(apex + int((offset - apex) / 4))) + off1 = str(int(apex + int(2 * (offset - apex) / 4))) + off2 = str(int(apex + int(3 * (offset - apex) / 4))) + off3 = str(offset) + + sub = 'sub'+str(self.sub[idx].zfill(2)) + f = str(self.file_names[idx]) + + on0 = 'reg_img'+on0+'.jpg' + on1 = 'reg_img' + on1 + '.jpg' + on2 = 'reg_img' + on2 + '.jpg' + on3 = 'reg_img' + on3 + '.jpg' + apex0 = 'reg_img' + apex0 + '.jpg' + off0 = 'reg_img' + off0 + '.jpg' + off1 = 'reg_img' + off1 + '.jpg' + off2 = 'reg_img' + off2 + '.jpg' + off3 = 'reg_img' + off3 + '.jpg' + path_on0 = os.path.join( + self.raf_path, sub, f, on0) + path_on1 = os.path.join( + self.raf_path, sub, f, on1) + path_on2 = os.path.join( + self.raf_path, sub, f, on2) + path_on3 = os.path.join( + self.raf_path, sub, f, on3) + path_apex0 = os.path.join( + self.raf_path, sub, f, apex0) + path_off0 = os.path.join( + self.raf_path, sub, f, off0) + path_off1 = os.path.join( + self.raf_path, sub, f, off1) + path_off2 = os.path.join( + self.raf_path, sub, f, off2) + path_off3 = os.path.join( + self.raf_path, sub, f, off3) + image_on0 = cv2.imread(path_on0) + image_on1 = cv2.imread(path_on1) + image_on2 = cv2.imread(path_on2) + image_on3 = cv2.imread(path_on3) + image_apex0 = cv2.imread(path_apex0) + image_off0 = cv2.imread(path_off0) + image_off1 = cv2.imread(path_off1) + image_off2 = cv2.imread(path_off2) + image_off3 = cv2.imread(path_off3) + + image_on0 = image_on0[:, :, ::-1] # BGR to RGB + image_on1 = image_on1[:, :, ::-1] + image_on2 = image_on2[:, :, ::-1] + image_on3 = image_on3[:, :, ::-1] + image_off0 = image_off0[:, :, ::-1] + image_off1 = image_off1[:, :, ::-1] + image_off2 = image_off2[:, :, ::-1] + image_off3 = image_off3[:, :, ::-1] + image_apex0 = image_apex0[:, :, ::-1] + + label_all = self.label_all[idx] + label_au = self.label_au[idx] + + # normalization for testing and training + if self.transform is not None: + image_on0 = self.transform(image_on0) + image_on1 = self.transform(image_on1) + image_on2 = self.transform(image_on2) + image_on3 = self.transform(image_on3) + image_off0 = self.transform(image_off0) + image_off1 = self.transform(image_off1) + image_off2 = self.transform(image_off2) + image_off3 = self.transform(image_off3) + image_apex0 = self.transform(image_apex0) + ALL = torch.cat( + (image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3), dim=0) + # data augmentation for training only + if self.transform_norm is not None and self.phase == 'train': + ALL = self.transform_norm(ALL) + image_on0 = ALL[0:3, :, :] + image_on1 = ALL[3:6, :, :] + image_on2 = ALL[6:9, :, :] + image_on3 = ALL[9:12, :, :] + image_apex0 = ALL[12:15, :, :] + image_off0 = ALL[15:18, :, :] + image_off1 = ALL[18:21, :, :] + image_off2 = ALL[21:24, :, :] + image_off3 = ALL[24:27, :, :] + + temp = torch.zeros(38) + for i in label_au: + temp[int(i) - 1] = 1 + + return image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, label_all, temp + + +def initialize_weight_goog(m, n=''): + if isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1.0) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + fan_out = m.weight.size(0) # fan-out + fan_in = 0 + if 'routing_fn' in n: + fan_in = m.weight.size(1) + init_range = 1.0 / math.sqrt(fan_in + fan_out) + m.weight.data.uniform_(-init_range, init_range) + m.bias.data.zero_() + + +def criterion2(y_pred, y_true): + y_pred = (1 - 2 * y_true) * y_pred + y_pred_neg = y_pred - y_true * 1e12 + y_pred_pos = y_pred - (1 - y_true) * 1e12 + zeros = torch.zeros_like(y_pred[..., :1]) + y_pred_neg = torch.cat((y_pred_neg, zeros), dim=-1) + y_pred_pos = torch.cat((y_pred_pos, zeros), dim=-1) + neg_loss = torch.logsumexp(y_pred_neg, dim=-1) + pos_loss = torch.logsumexp(y_pred_pos, dim=-1) + return torch.mean(neg_loss + pos_loss) + + +class MMNet(nn.Module): + def __init__(self): + super(MMNet, self).__init__() + + self.conv_act = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=90*2, kernel_size=3, + stride=2, padding=1, bias=False, groups=1), + nn.BatchNorm2d(180), + nn.ReLU(inplace=True), + + ) + self.pos = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=512, + kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(512), + nn.ReLU(inplace=True), + + ) + # Position Calibration Module(subbranch) + self.vit_pos = VisionTransformer_POS(img_size=14, + patch_size=1, embed_dim=512, depth=2, num_heads=4, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), drop_path_rate=0.) + self.resize = Resize([14, 14]) + # main branch consisting of CA blocks + self.main_branch = resnet18_pos_attention() + + self.head = nn.Sequential( + nn.Dropout(p=0.5), + nn.Linear(1 * 112 * 112, 38, bias=False), + + ) + + self.timeembed = nn.Parameter(torch.zeros(1, 4, 111, 111)) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + + def forward(self, x1, x2, x3, x4, x5, x6, x7, x8, x9, if_shuffle): + # onset:x1 apex:x5 + B = x1.shape[0] + # Position Calibration Module (subbranch) + POS = self.vit_pos(self.resize(x1)).transpose( + 1, 2).view(B, 512, 14, 14) + + act = x5 - x1 + act = self.conv_act(act) + # main branch and fusion + out, _ = self.main_branch(act, POS) + + return out + + +def run_training(): + + args = parse_args() + imagenet_pretrained = True + + if not imagenet_pretrained: + for m in res18.modules(): + initialize_weight_goog(m) + + if args.pretrained: + print("Loading pretrained weights...", args.pretrained) + pretrained = torch.load(args.pretrained) + pretrained_state_dict = pretrained['state_dict'] + model_state_dict = res18.state_dict() + loaded_keys = 0 + total_keys = 0 + for key in pretrained_state_dict: + if ((key == 'module.fc.weight') | (key == 'module.fc.bias')): + pass + else: + model_state_dict[key] = pretrained_state_dict[key] + total_keys += 1 + if key in model_state_dict: + loaded_keys += 1 + print("Loaded params num:", loaded_keys) + print("Total params num:", total_keys) + res18.load_state_dict(model_state_dict, strict=False) + # data normalization for both training set + data_transforms = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + + ]) + # data augmentation for training set only + data_transforms_norm = transforms.Compose([ + + transforms.RandomHorizontalFlip(p=0.5), + transforms.RandomRotation(3), + transforms.RandomCrop(224, padding=15), + + ]) + + # data normalization for both teating set + data_transforms_val = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) + + criterion = torch.nn.CrossEntropyLoss() + # leave one subject out protocal + LOSO = ['17', '26', '16', '9', '5', '24', '2', '13', '4', '23', '11', '12', '8', '14', '3', '19', '1', '18', '10', + '20', '21', '22', '15', '6', '25', '7'] + + val_now = 0 + num_sum = 0 + pos_pred_ALL = torch.zeros(5) + pos_label_ALL = torch.zeros(5) + TP_ALL = torch.zeros(5) + + for subj in LOSO: + train_dataset = RafDataSet(args.raf_path, phase='train', num_loso=subj, transform=data_transforms, + basic_aug=True, transform_norm=data_transforms_norm) + val_dataset = RafDataSet( + args.raf_path, phase='test', num_loso=subj, transform=data_transforms_val) + + train_loader = torch.utils.data.DataLoader(train_dataset, + batch_size=20, # 32 + # collate_fn=lambda x: default_collate( + # x).to(device), + num_workers=args.workers, + shuffle=True, + pin_memory=True, + prefetch_factor=2, + persistent_workers=True) + val_loader = torch.utils.data.DataLoader(val_dataset, + batch_size=20, # 32 + # collate_fn=lambda x: default_collate( + # x).to(device), + num_workers=args.workers, + shuffle=False, + pin_memory=True, + prefetch_factor=2, + persistent_workers=True) + # let train_loader and val_loader become cuda tensor + + print('num_sub', subj) + print('Train set size:', train_dataset.__len__()) + print('Validation set size:', val_dataset.__len__()) + + max_corr = 0 + max_f1 = 0 + max_pos_pred = torch.zeros(5) + max_pos_label = torch.zeros(5) + max_TP = torch.zeros(5) + # model initialization + net_all = MMNet() + + net_all = net_all.cuda() + params_all = net_all.parameters() + if args.optimizer == 'adam': + optimizer_all = torch.optim.AdamW( + params_all, lr=0.0008, weight_decay=0.6) + # optimizer for MMNet + + elif args.optimizer == 'sgd': + optimizer = torch.optim.SGD(params, args.lr, + momentum=args.momentum, + weight_decay=1e-4) + else: + raise ValueError("Optimizer not supported.") + # lr_decay + scheduler_all = torch.optim.lr_scheduler.ExponentialLR( + optimizer_all, gamma=0.987) + # let all data in train_loader become cuda tensor + + for i in range(1, 75): + running_loss = 0.0 + correct_sum = 0 + running_loss_MASK = 0.0 + correct_sum_MASK = 0 + iter_cnt = 0 + net_all.train() + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, + label_all, + label_au) in enumerate(train_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + iter_cnt += 1 + image_on0 = image_on0.cuda() + image_on1 = image_on1.cuda() + image_on2 = image_on2.cuda() + image_on3 = image_on3.cuda() + image_apex0 = image_apex0.cuda() + image_off0 = image_off0.cuda() + image_off1 = image_off1.cuda() + image_off2 = image_off2.cuda() + image_off3 = image_off3.cuda() + label_all = label_all.cuda() + label_au = label_au.cuda() + # train MMNet + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, + image_off2, image_off3, False) + + loss_all = criterion(ALL, label_all) + + optimizer_all.zero_grad() + + loss_all.backward() + + optimizer_all.step() + running_loss += loss_all + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all).sum() + correct_sum += correct_num + + # lr decay + if i <= 50: + + scheduler_all.step() + if i >= 0: + acc = correct_sum.float() / float(train_dataset.__len__()) + + running_loss = running_loss / iter_cnt + + print('[Epoch %d] Training accuracy: %.4f. Loss: %.3f' % + (i, acc, running_loss)) + + pos_label = torch.zeros(5) + pos_pred = torch.zeros(5) + TP = torch.zeros(5) + + with torch.no_grad(): + running_loss = 0.0 + iter_cnt = 0 + bingo_cnt = 0 + sample_cnt = 0 + test_cnt = 0 + pre_lab_all = [] + Y_test_all = [] + net_all.eval() + # net_au.eval() + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3, label_all, + label_au) in enumerate(val_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + + image_on0 = image_on0.cuda() + image_on1 = image_on1.cuda() + image_on2 = image_on2.cuda() + image_on3 = image_on3.cuda() + image_apex0 = image_apex0.cuda() + image_off0 = image_off0.cuda() + image_off1 = image_off1.cuda() + image_off2 = image_off2.cuda() + image_off3 = image_off3.cuda() + label_all = label_all.cuda() + label_au = label_au.cuda() + # test + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, + image_off0, image_off1, image_off2, image_off3, False) + + loss = criterion(ALL, label_all) + running_loss += loss + iter_cnt += 1 + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all) + bingo_cnt += correct_num.sum().cpu() + sample_cnt += ALL.size(0) + + for cls in range(5): + + for element in predicts: + if element == cls: + pos_label[cls] = pos_label[cls] + 1 + for element in label_all: + if element == cls: + pos_pred[cls] = pos_pred[cls] + 1 + for elementp, elementl in zip(predicts, label_all): + if elementp == elementl and elementp == cls: + TP[cls] = TP[cls] + 1 + # if pos_label != 0 or pos_pred != 0: + # f1 = 2 * TP / (pos_pred + pos_label) + # F1.append(f1) + count = 0 + SUM_F1 = 0 + for index in range(5): + if pos_label[index] != 0 or pos_pred[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * \ + TP[index] / (pos_pred[index] + + pos_label[index]) + + AVG_F1 = SUM_F1 / count + + running_loss = running_loss / iter_cnt + acc = bingo_cnt.float() / float(sample_cnt) + acc = np.around(acc.numpy(), 4) + if bingo_cnt > max_corr: + max_corr = bingo_cnt + if AVG_F1 >= max_f1: + max_f1 = AVG_F1 + max_pos_label = pos_label + max_pos_pred = pos_pred + max_TP = TP + print("[Epoch %d] Validation accuracy:%.4f. Loss:%.3f, F1-score:%.3f" % + (i, acc, running_loss, AVG_F1)) + num_sum = num_sum + max_corr + pos_label_ALL = pos_label_ALL + max_pos_label + pos_pred_ALL = pos_pred_ALL + max_pos_pred + TP_ALL = TP_ALL + max_TP + count = 0 + SUM_F1 = 0 + for index in range(5): + if pos_label_ALL[index] != 0 or pos_pred_ALL[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * \ + TP_ALL[index] / (pos_pred_ALL[index] + + pos_label_ALL[index]) + + F1_ALL = SUM_F1 / count + val_now = val_now + val_dataset.__len__() + print("[..........%s] correctnum:%d . zongshu:%d " % + (subj, max_corr, val_dataset.__len__())) + print("[ALL_corr]: %d [ALL_val]: %d" % (num_sum, val_now)) + print("[F1_now]: %.4f [F1_ALL]: %.4f" % (max_f1, F1_ALL)) + torch.save(net_all.state_dict(), 'model_{}.pth'.format(subj)) + + +if __name__ == "__main__": + run_training() diff --git a/CASME3_7.py b/CASME3_7.py new file mode 100644 index 0000000..cfbdbd6 --- /dev/null +++ b/CASME3_7.py @@ -0,0 +1,619 @@ +import math +import numpy as np +import torch.utils.data as data +from torchvision import transforms +import cv2 +import pandas as pd +import os, torch +import torch.nn as nn +#import image_utils +import argparse, random +from functools import partial +from CA_block import resnet18_pos_attention +from PC_module import VisionTransformer_POS +from torchvision.transforms import Resize +from torch.utils.tensorboard import SummaryWriter # to print to tensorboard +import warnings +# Suppress the warning message related to the antialias parameter +warnings.filterwarnings("ignore", message="The default value of the antialias parameter of all the resizing transforms") +torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--raf_path', type=str, default='/home/drink36/Desktop/apex_frame_take/casme3_result_2', help='Raf-DB dataset path.') + parser.add_argument('--checkpoint', type=str, default=None, + help='Pytorch checkpoint file path') + parser.add_argument('--pretrained', type=str, default=None, + help='Pretrained weights') + parser.add_argument('--beta', type=float, default=0.7, help='Ratio of high importance group in one mini-batch.') + parser.add_argument('--relabel_epoch', type=int, default=1000, + help='Relabeling samples on each mini-batch after 10(Default) epochs.') + parser.add_argument('--batch_size', type=int, default=64, help='Batch size.') + parser.add_argument('--optimizer', type=str, default="adam", help='Optimizer, adam or sgd.') + parser.add_argument('--lr', type=float, default=0.0001, help='Initial learning rate for sgd.') + parser.add_argument('--momentum', default=0.9, type=float, help='Momentum for sgd') + parser.add_argument('--workers', default=4, type=int, help='Number of data loading workers (default: 4)') + parser.add_argument('--epochs', type=int, default=7000, help='Total training epochs.') + parser.add_argument('--drop_rate', type=float, default=0, help='Drop out rate.') + return parser.parse_args() + + + + + + +class RafDataSet(data.Dataset): + def __init__(self, raf_path, phase,num_loso, transform = None, basic_aug = False, transform_norm=None): + self.phase = phase + self.transform = transform + self.raf_path = raf_path + self.transform_norm = transform_norm + SUBJECT_COLUMN =0 + NAME_COLUMN = 1 + ONSET_COLUMN = 2 + APEX_COLUMN = 3 + OFF_COLUMN = 4 + LABEL_AU_COLUMN = 5 + LABEL_ALL_COLUMN = 6 + + + df = pd.read_csv("result_3.csv",usecols=[0,1,2,3,4,5,7]) + df['Subject'] = df['Subject'].apply(str) + + if phase == 'train': + dataset = df.loc[df['Subject']!=num_loso] + else: + dataset = df.loc[df['Subject'] == num_loso] + + Subject = dataset.iloc[:, SUBJECT_COLUMN].values + File_names = dataset.iloc[:, NAME_COLUMN].values + Label_all = dataset.iloc[:, LABEL_ALL_COLUMN].values # 0:Surprise, 1:Fear, 2:Disgust, 3:Happiness, 4:Sadness, 5:Anger, 6:Neutral + Onset_num = dataset.iloc[:, ONSET_COLUMN].values + Apex_num = dataset.iloc[:, APEX_COLUMN].values + Offset_num = dataset.iloc[:, OFF_COLUMN].values + Label_au = dataset.iloc[:, LABEL_AU_COLUMN].values + self.file_paths_on = [] + self.file_paths_off = [] + self.file_paths_apex = [] + self.label_all = [] + self.label_au = [] + self.sub= [] + self.file_names =[] + # a=0 + # b=0 + # c=0 + # d=0 + # e=0 + # use aligned images for training/testing + for (f,sub,onset,apex,offset,label_all,label_au) in zip(File_names,Subject,Onset_num,Apex_num,Offset_num,Label_all,Label_au): + + + if label_all == 'happy' or label_all == 'disgust' or label_all == 'surprise' or label_all == 'others' or label_all == 'Others' or label_all=='anger' or label_all=='fear' or label_all=='sad': + self.file_paths_on.append(onset) + self.file_paths_off.append(offset) + self.file_paths_apex.append(apex) + self.sub.append(sub) + self.file_names.append(f) + if label_all == 'happy': + self.label_all.append(0) + elif label_all == 'fear': + self.label_all.append(1) + elif label_all == 'sad': + self.label_all.append(2) + elif label_all == 'disgust': + self.label_all.append(3) + elif label_all == 'anger': + self.label_all.append(4) + elif label_all == 'surprise': + self.label_all.append(5) + else: + self.label_all.append(6) + + # label_au =label_au.split("+") + if isinstance(label_au, int): + self.label_au.append([label_au]) + else: + label_au = label_au.split("+") + self.label_au.append(label_au) + + + ##label + + self.basic_aug = basic_aug + #self.aug_func = [image_utils.flip_image,image_utils.add_gaussian_noise] + + def __len__(self): + return len(self.file_paths_on) + + def __getitem__(self, idx): + ##sampling strategy for training set + if self.phase == 'train': + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset =self.file_paths_off[idx] + on0 = str(random.randint(int(onset), int(onset + int(0.15* (apex - onset) / 4)))) + # on0 = str(int(onset)) + on1 = str( + random.randint(int(onset + int(0.9 * (apex - onset) / 4)), int(onset + int(1.1 * (apex - onset) / 4)))) + on2 = str( + random.randint(int(onset + int(1.8 * (apex - onset) / 4)), int(onset + int(2.2 * (apex - onset) / 4)))) + on3 = str(random.randint(int(onset + int(2.7 * (apex - onset) / 4)), onset + int(3.3 * (apex - onset) / 4))) + # apex0 = str(apex) + apex0 = str( + random.randint(int(apex - int(0.15* (apex - onset) / 4)), apex)) + off0 = str( + random.randint(int(apex + int(0.9 * (offset - apex) / 4)), int(apex + int(1.1 * (offset - apex) / 4)))) + off1 = str( + random.randint(int(apex + int(1.8 * (offset - apex) / 4)), int(apex + int(2.2 * (offset - apex) / 4)))) + off2 = str( + random.randint(int(apex + int(2.9 * (offset - apex) / 4)), int(apex + int(3.1 * (offset - apex) / 4)))) + off3 = str(random.randint(int(apex + int(3.8 * (offset - apex) / 4)), offset)) + + + + sub =str(self.sub[idx]) + f = str(self.file_names[idx]) + else:##sampling strategy for testing set + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset = self.file_paths_off[idx] + + on0 = str(onset) + on1 = str(int(onset + int((apex - onset) / 4))) + on2 = str(int(onset + int(2 * (apex - onset) / 4))) + on3 = str(int(onset + int(3 * (apex - onset) / 4))) + apex0 = str(apex) + off0 = str(int(apex + int((offset - apex) / 4))) + off1 = str(int(apex + int(2 * (offset - apex) / 4))) + off2 = str(int(apex + int(3 * (offset - apex) / 4))) + off3 = str(offset) + + sub = str(self.sub[idx]) + f = str(self.file_names[idx]) + + + on0 = on0+'.jpg' + on1 = on1 + '.jpg' + on2 = on2 + '.jpg' + on3 = on3 + '.jpg' + apex0 = apex0 + '.jpg' + off0 = off0 + '.jpg' + off1= off1 + '.jpg' + off2 = off2 + '.jpg' + off3 = off3 + '.jpg' + path_on0 = os.path.join(self.raf_path, sub, f, on0) + path_on1 = os.path.join(self.raf_path, sub, f, on1) + path_on2 = os.path.join(self.raf_path, sub, f, on2) + path_on3 = os.path.join(self.raf_path, sub, f, on3) + path_apex0 = os.path.join(self.raf_path, sub, f, apex0) + path_off0 = os.path.join(self.raf_path, sub, f, off0) + path_off1 = os.path.join(self.raf_path, sub, f, off1) + path_off2 = os.path.join(self.raf_path, sub, f, off2) + path_off3 = os.path.join(self.raf_path, sub, f, off3) + image_on0 = cv2.imread(path_on0) + image_on1= cv2.imread(path_on1) + image_on2 = cv2.imread(path_on2) + image_on3 = cv2.imread(path_on3) + image_apex0 = cv2.imread(path_apex0) + image_off0 = cv2.imread(path_off0) + image_off1 = cv2.imread(path_off1) + image_off2 = cv2.imread(path_off2) + image_off3 = cv2.imread(path_off3) + + image_on0 = image_on0[:, :, ::-1] # BGR to RGB + image_on1 = image_on1[:, :, ::-1] + image_on2 = image_on2[:, :, ::-1] + image_on3 = image_on3[:, :, ::-1] + image_off0 = image_off0[:, :, ::-1] + image_off1 = image_off1[:, :, ::-1] + image_off2 = image_off2[:, :, ::-1] + image_off3 = image_off3[:, :, ::-1] + image_apex0 = image_apex0[:, :, ::-1] + + label_all = self.label_all[idx] + label_au = self.label_au[idx] + + # normalization for testing and training + if self.transform is not None: + image_on0 = self.transform(image_on0) + image_on1 = self.transform(image_on1) + image_on2 = self.transform(image_on2) + image_on3 = self.transform(image_on3) + image_off0 = self.transform(image_off0) + image_off1 = self.transform(image_off1) + image_off2 = self.transform(image_off2) + image_off3 = self.transform(image_off3) + image_apex0 = self.transform(image_apex0) + ALL = torch.cat( + (image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3), dim=0) + ## data augmentation for training only + if self.transform_norm is not None and self.phase == 'train': + ALL = self.transform_norm(ALL) + image_on0 = ALL[0:3, :, :] + image_on1 = ALL[3:6, :, :] + image_on2 = ALL[6:9, :, :] + image_on3 = ALL[9:12, :, :] + image_apex0 = ALL[12:15, :, :] + image_off0 = ALL[15:18, :, :] + image_off1 = ALL[18:21, :, :] + image_off2 = ALL[21:24, :, :] + image_off3 = ALL[24:27, :, :] + + + temp = torch.zeros(65) + for i in label_au: + temp[int(i) - 1] = 1 + # # trans image to cuda + # image_on0 = image_on0.to(device) + # image_on1 = image_on1.to(device) + # image_on2 = image_on2.to(device) + # image_on3 = image_on3.to(device) + # image_apex0 = image_apex0.to(device) + # image_off0 = image_off0.to(device) + # image_off1 = image_off1.to(device) + # image_off2 = image_off2.to(device) + # image_off3 = image_off3.to(device) + # label_all = label_all.to(device) + # temp = temp.to(device) + + return image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, label_all, temp + + +def initialize_weight_goog(m, n=''): + if isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1.0) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + fan_out = m.weight.size(0) # fan-out + fan_in = 0 + if 'routing_fn' in n: + fan_in = m.weight.size(1) + init_range = 1.0 / math.sqrt(fan_in + fan_out) + m.weight.data.uniform_(-init_range, init_range) + m.bias.data.zero_() + + +def criterion2(y_pred, y_true): + y_pred = (1 - 2 * y_true) * y_pred + y_pred_neg = y_pred - y_true * 1e12 + y_pred_pos = y_pred - (1 - y_true) * 1e12 + zeros = torch.zeros_like(y_pred[..., :1]) + y_pred_neg = torch.cat((y_pred_neg, zeros), dim=-1) + y_pred_pos = torch.cat((y_pred_pos, zeros), dim=-1) + neg_loss = torch.logsumexp(y_pred_neg, dim=-1) + pos_loss = torch.logsumexp(y_pred_pos, dim=-1) + return torch.mean(neg_loss + pos_loss) + + +class MMNet(nn.Module): + def __init__(self): + super(MMNet, self).__init__() + + + self.conv_act = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=90*2, kernel_size=3, stride=2,padding=1, bias=False,groups=1), + nn.BatchNorm2d(180), + nn.ReLU(inplace=True), + + ) + self.pos =nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=512, kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(512), + nn.ReLU(inplace=True), + + ) + ##Position Calibration Module(subbranch) + self.vit_pos=VisionTransformer_POS(img_size=14, + patch_size=1, embed_dim=512, depth=2, num_heads=4, mlp_ratio=2, qkv_bias=True,norm_layer=partial(nn.LayerNorm, eps=1e-6),drop_path_rate=0.) + self.resize=Resize([14,14]) + ##main branch consisting of CA blocks + self.main_branch =resnet18_pos_attention() + + self.head = nn.Sequential( + nn.Dropout(p=0.5), + nn.Linear(1 * 112 *112, 65,bias=False), + + ) + + self.timeembed = nn.Parameter(torch.zeros(1, 4, 111, 111)) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + def forward(self, x1, x2, x3, x4, x5, x6, x7, x8, x9, if_shuffle): + ##onset:x1 apex:x5 + B = x1.shape[0] + # Position Calibration Module (subbranch) + POS =self.vit_pos(self.resize(x1)).transpose(1,2).view(B,512,14,14) + + act =x5 -x1 + act=self.conv_act(act) + # main branch and fusion + out,_=self.main_branch(act,POS) + + return out + + + + + +def run_training(): + args = parse_args() + imagenet_pretrained = True + + if not imagenet_pretrained: + for m in res18.modules(): + initialize_weight_goog(m) + + if args.pretrained: + print("Loading pretrained weights...", args.pretrained) + pretrained = torch.load(args.pretrained) + pretrained_state_dict = pretrained['state_dict'] + model_state_dict = res18.state_dict() + loaded_keys = 0 + total_keys = 0 + for key in pretrained_state_dict: + if ((key == 'module.fc.weight') | (key == 'module.fc.bias')): + pass + else: + model_state_dict[key] = pretrained_state_dict[key] + total_keys += 1 + if key in model_state_dict: + loaded_keys += 1 + print("Loaded params num:", loaded_keys) + print("Total params num:", total_keys) + res18.load_state_dict(model_state_dict, strict=False) + ##data normalization for both training set + data_transforms = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + + ]) + ### data augmentation for training set only + data_transforms_norm = transforms.Compose([ + + transforms.RandomHorizontalFlip(p=0.5), + transforms.RandomRotation(3), + transforms.RandomCrop(224, padding=15), + + ]) + + ### data normalization for both teating set + data_transforms_val = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) + + + + + criterion = torch.nn.CrossEntropyLoss() + # leave one subject out protocal + # 'spNO.1', 'spNO.2', 'spNO.3', 'spNO.5', 'spNO.6', 'spNO.7', 'spNO.8', 'spNO.9', 'spNO.10', 'spNO.11', 'spNO.12', 'spNO.13', 'spNO.14', 'spNO.15', 'spNO.17', 'spNO.39', 'spNO.40', 'spNO.41', 'spNO.42', 'spNO.77', 'spNO.138', 'spNO.139', 'spNO.142', 'spNO.143', 'spNO.144', 'spNO.145', 'spNO.146', 'spNO.147', 'spNO.148', 'spNO.149', 'spNO.150', 'spNO.152', 'spNO.153', 'spNO.154', 'spNO.155', 'spNO.157', 'spNO.159', 'spNO.160', 'spNO.161', 'spNO.162', 'spNO.163', 'spNO.165', 'spNO.166', 'spNO.167', 'spNO.168', 'spNO.169', 'spNO.170', 'spNO.171', 'spNO.172', 'spNO.173', 'spNO.174', 'spNO.175', 'spNO.176', 'spNO.177', 'spNO.178', 'spNO.179', 'spNO.180', 'spNO.181', 'spNO.182', 'spNO.183', 'spNO.184', 'spNO.185', 'spNO.186', 'spNO.187', 'spNO.188', 'spNO.189', 'spNO.190', 'spNO.192', 'spNO.194', 'spNO.195', 'spNO.196', 'spNO.197', 'spNO.198', 'spNO.200', 'spNO.201', 'spNO.202', 'spNO.203', 'spNO.204', 'spNO.206', 'spNO.207', 'spNO.208', 'spNO.209', 'spNO.210', 'spNO.211', 'spNO.212', 'spNO.213', 'spNO.214', 'spNO.216', 'spNO.217' + LOSO = ['spNO.206'] + print(len(LOSO)) + val_now = 0 + num_sum = 0 + pos_pred_ALL = torch.zeros(7) + pos_label_ALL = torch.zeros(7) + TP_ALL = torch.zeros(7) + + for subj in LOSO: + writer = SummaryWriter('./log/casme3') + train_dataset = RafDataSet(args.raf_path, phase='train', num_loso=subj, transform=data_transforms, + basic_aug=True, transform_norm=data_transforms_norm) + val_dataset = RafDataSet(args.raf_path, phase='test', num_loso=subj, transform=data_transforms_val) + train_loader = torch.utils.data.DataLoader(train_dataset, + batch_size=64, + num_workers=args.workers, + shuffle=True, + pin_memory=True, + prefetch_factor=2, + persistent_workers=True) + val_loader = torch.utils.data.DataLoader(val_dataset, + batch_size=64, + num_workers=args.workers, + shuffle=False, + pin_memory=True, + prefetch_factor=2, + persistent_workers=True) + print('num_sub', subj) + print('Train set size:', train_dataset.__len__()) + print('Validation set size:', val_dataset.__len__()) + if(val_dataset.__len__()==0): + continue + max_acc = 0 + max_corr = 0 + max_f1 = 0 + max_pos_pred = torch.zeros(7) + max_pos_label = torch.zeros(7) + max_TP = torch.zeros(7) + ##model initialization + net_all = MMNet() + net_all=net_all.to(device) + params_all = net_all.parameters() + + if args.optimizer == 'adam': + optimizer_all = torch.optim.AdamW(params_all, lr=0.0008, weight_decay=0.6) + ##optimizer for MMNet + + elif args.optimizer == 'sgd': + optimizer = torch.optim.SGD(params, args.lr, + momentum=args.momentum, + weight_decay=1e-4) + else: + raise ValueError("Optimizer not supported.") + ##lr_decay + scheduler_all = torch.optim.lr_scheduler.ExponentialLR(optimizer_all, gamma=0.987) + + + for i in range(1, 75): + running_loss = 0.0 + correct_sum = 0 + running_loss_MASK = 0.0 + correct_sum_MASK = 0 + iter_cnt = 0 + + net_all.train() + + + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, + label_all, + label_au) in enumerate(train_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + iter_cnt += 1 + + image_on0 = image_on0.to(device) + image_on1 = image_on1.to(device) + image_on2 = image_on2.to(device) + image_on3 = image_on3.to(device) + image_apex0 = image_apex0.to(device) + image_off0 = image_off0.to(device) + image_off1 = image_off1.to(device) + image_off2 = image_off2.to(device) + image_off3 = image_off3.to(device) + label_all = label_all.to(device) + label_au = label_au.to(device) + + ##train MMNet + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, + image_off2, image_off3, False) + + loss_all = criterion(ALL, label_all) + optimizer_all.zero_grad() + loss_all.backward() + optimizer_all.step() + running_loss += loss_all + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all).sum() + correct_sum += correct_num + writer.add_scalar('Train/Loss', running_loss / iter_cnt, i) + writer.add_scalar('Train/Acc', correct_sum.float() / float(train_dataset.__len__()), i) + ## lr decay + if i <= 50: + + scheduler_all.step() + if i>=0: + acc = correct_sum.float() / float(train_dataset.__len__()) + + running_loss = running_loss / iter_cnt + + print('[Epoch %d] Training accuracy: %.4f. Loss: %.3f' % (i, acc, running_loss)) + if(acc>=max_acc): + max_acc=acc + torch.save(net_all.state_dict(), + 'model_{}.pth'.format(subj)) + print('model saved') + pos_label = torch.zeros(7) + pos_pred = torch.zeros(7) + TP = torch.zeros(7) + + with torch.no_grad(): + running_loss = 0.0 + iter_cnt = 0 + bingo_cnt = 0 + sample_cnt = 0 + pre_lab_all = [] + Y_test_all = [] + net_all.eval() + # net_au.eval() + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3, label_all, + label_au) in enumerate(val_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + + image_on0 = image_on0.to(device) + image_on1 = image_on1.to(device) + image_on2 = image_on2.to(device) + image_on3 = image_on3.to(device) + image_apex0 = image_apex0.to(device) + image_off0 = image_off0.to(device) + image_off1 = image_off1.to(device) + image_off2 = image_off2.to(device) + image_off3 = image_off3.to(device) + label_all = label_all.to(device) + label_au = label_au.to(device) + + ##test + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, False) + + + loss = criterion(ALL, label_all) + running_loss += loss + iter_cnt += 1 + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all) + bingo_cnt += correct_num.sum().cpu() + sample_cnt += ALL.size(0) + + for cls in range(7): + + for element in predicts: + if element == cls: + pos_label[cls] = pos_label[cls] + 1 + for element in label_all: + if element == cls: + pos_pred[cls] = pos_pred[cls] + 1 + for elementp, elementl in zip(predicts, label_all): + if elementp == elementl and elementp == cls: + TP[cls] = TP[cls] + 1 + # if pos_label != 0 or pos_pred != 0: + # f1 = 2 * TP / (pos_pred + pos_label) + # F1.append(f1) + count = 0 + SUM_F1 = 0 + for index in range(7): + if pos_label[index] != 0 or pos_pred[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * TP[index] / (pos_pred[index] + pos_label[index]) + + AVG_F1 = SUM_F1 / count + + + running_loss = running_loss / iter_cnt + acc = bingo_cnt.float() / float(sample_cnt) + acc = np.around(acc.numpy(), 4) + if bingo_cnt > max_corr: + max_corr = bingo_cnt + if AVG_F1 >= max_f1: + max_f1 = AVG_F1 + max_pos_label = pos_label + max_pos_pred = pos_pred + max_TP = TP + writer.add_scalar('Val/Loss', running_loss, i) + writer.add_scalar('Val/Acc', acc, i) + print("[Epoch %d] Validation accuracy:%.4f. Loss:%.3f, F1-score:%.3f" % (i, acc, running_loss, AVG_F1)) + num_sum = num_sum + max_corr + pos_label_ALL = pos_label_ALL + max_pos_label + pos_pred_ALL = pos_pred_ALL + max_pos_pred + TP_ALL = TP_ALL + max_TP + count = 0 + SUM_F1 = 0 + for index in range(7): + if pos_label_ALL[index] != 0 or pos_pred_ALL[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * TP_ALL[index] / (pos_pred_ALL[index] + pos_label_ALL[index]) + F1_ALL = SUM_F1 / count + val_now = val_now + val_dataset.__len__() + print("[..........%s] correctnum:%d . zongshu:%d " % (subj, max_corr, val_dataset.__len__())) + print("[ALL_corr]: %d [ALL_val]: %d" % (num_sum, val_now)) + print("[F1_now]: %.4f [F1_ALL]: %.4f" % (max_f1, F1_ALL)) + + + +if __name__ == "__main__": + run_training() diff --git a/CA_block.py b/CA_block.py old mode 100644 new mode 100755 index 700df1b..38aba59 --- a/CA_block.py +++ b/CA_block.py @@ -29,9 +29,11 @@ def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): def conv1x1(in_planes, out_planes, stride=1, groups=1): """1x1 convolution""" - return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False,groups=groups) + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False, groups=groups) + +# CA BLOCK + -##CA BLOCK class CABlock(nn.Module): expansion = 1 @@ -43,24 +45,26 @@ def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, # if groups != 1 or base_width != 64: # raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: - raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + raise NotImplementedError( + "Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv3x3(inplanes, planes, stride,groups=groups) + self.conv1 = conv3x3(inplanes, planes, stride, groups=groups) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) - self.conv2 = conv1x1(planes, planes,groups=groups) + self.conv2 = conv1x1(planes, planes, groups=groups) self.bn2 = norm_layer(planes) self.attn = nn.Sequential( - nn.Conv2d(2, 1, kernel_size=1, stride=1,bias=False), # 32*33*33 + nn.Conv2d(2, 1, kernel_size=1, stride=1, bias=False), # 32*33*33 nn.BatchNorm2d(1), nn.Sigmoid(), ) self.downsample = downsample self.stride = stride - self.planes=planes + self.planes = planes def forward(self, x): - x, attn_last,if_attn =x##attn_last: downsampled attention maps from last layer as a prior knowledge + # attn_last: downsampled attention maps from last layer as a prior knowledge + x, attn_last, if_attn = x identity = x out = self.conv1(x) @@ -83,13 +87,9 @@ def forward(self, x): attn = attn.repeat(1, self.planes, 1, 1) if if_attn: - out = out *attn - - - return out,attn[:, 0, :, :].unsqueeze(1),True - - + out = out * attn + return out, attn[:, 0, :, :].unsqueeze(1), True class ResNet(nn.Module): @@ -113,35 +113,32 @@ def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group - self.conv1 = nn.Conv2d(90*2, self.inplanes, kernel_size=3, stride=1,padding=1, - bias=False,groups=1) + self.conv1 = nn.Conv2d(90*2, self.inplanes, kernel_size=3, stride=1, padding=1, + bias=False, groups=1) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2,padding=1) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2) - self.layer1 = self._make_layer(block, 128, layers[0],groups=1) + self.layer1 = self._make_layer(block, 128, layers[0], groups=1) self.inplanes = int(self.inplanes*1) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, - dilate=replace_stride_with_dilation[0],groups=1) + dilate=replace_stride_with_dilation[0], groups=1) self.inplanes = int(self.inplanes * 1) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, - dilate=replace_stride_with_dilation[1],groups=1) + dilate=replace_stride_with_dilation[1], groups=1) self.inplanes = int(self.inplanes * 1) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, - dilate=replace_stride_with_dilation[2],groups=1) + dilate=replace_stride_with_dilation[2], groups=1) self.inplanes = int(self.inplanes * 1) - - - - - self.fc = nn.Linear(512* block.expansion*196, 5) + self.fc = nn.Linear(512 * block.expansion*196, 7) self.drop = nn.Dropout(p=0.1) for m in self.modules(): if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + nn.init.kaiming_normal_( + m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) @@ -151,12 +148,12 @@ def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): - if isinstance(m, Bottleneck): + if isinstance(m, block.Bottleneck): nn.init.constant_(m.bn3.weight, 0) - elif isinstance(m, BasicBlock): + elif isinstance(m, block.BasicBlock): nn.init.constant_(m.bn2.weight, 0) - def _make_layer(self, block, planes, blocks, stride=1, dilate=False,groups=1): + def _make_layer(self, block, planes, blocks, stride=1, dilate=False, groups=1): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation @@ -180,47 +177,49 @@ def _make_layer(self, block, planes, blocks, stride=1, dilate=False,groups=1): return nn.Sequential(*layers) - def _forward_impl(self, x,POS):##x->input of main branch; POS->position embeddings generated by sub branch + # x->input of main branch; POS->position embeddings generated by sub branch + def _forward_impl(self, x, POS): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) - ##main branch - x,attn1,_ = self.layer1((x,None,True)) + # main branch + x, attn1, _ = self.layer1((x, None, True)) temp = attn1 attn1 = self.maxpool(attn1) - x ,attn2,_= self.layer2((x,attn1,True)) + x, attn2, _ = self.layer2((x, attn1, True)) + attn2 = self.maxpool(attn2) - attn2=self.maxpool(attn2) - - x ,attn3,_= self.layer3((x,attn2,True)) + x, attn3, _ = self.layer3((x, attn2, True)) # attn3 = self.maxpool(attn3) - x,attn4,_ = self.layer4((x,attn3,True)) + x, attn4, _ = self.layer4((x, attn3, True)) - x=x+POS#fusion of motion pattern feature and position embeddings + x = x+POS # fusion of motion pattern feature and position embeddings x = torch.flatten(x, 1) x = self.fc(x) - return x,temp.view(x.size(0),-1) + return x, temp.view(x.size(0), -1) - def forward(self, x,POS): - return self._forward_impl(x,POS) + def forward(self, x, POS): + return self._forward_impl(x, POS) def _resnet(arch, block, layers, pretrained, progress, **kwargs): model = ResNet(block, layers, **kwargs) if pretrained: - state_dict = load_state_dict_from_url(model_urls[arch], - progress=progress) + state_dict = torch.hub.load_state_dict_from_url(model_urls[arch], + progress=progress) model.load_state_dict(state_dict) return model -##main branch consisting of CA blocks +# main branch consisting of CA blocks + + def resnet18_pos_attention(pretrained=False, progress=True, **kwargs): r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" `_ @@ -231,6 +230,3 @@ def resnet18_pos_attention(pretrained=False, progress=True, **kwargs): """ return _resnet('resnet18', CABlock, [1, 1, 1, 1], pretrained, progress, **kwargs) - - - diff --git a/Concat.py b/Concat.py new file mode 100755 index 0000000..55525a9 --- /dev/null +++ b/Concat.py @@ -0,0 +1,22 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import os +import sys +path = "temp.csv" +path2 = "SMIC_all_cropped\concat.csv" +df = pd.read_csv(path) +df2 = pd.read_csv(path2) +# change the column name of df +df.columns = ['Subject', 'File_name', + 'Apex_predicted', 'ons', 'offs', 'Emotion'] +# append new column TYPE with value casme to df +df['TYPE'] = 'casme' +# Let TYPE become column 0 +cols = df.columns.tolist() +cols = cols[-1:] + cols[:-1] +df = df[cols] +df_new = pd.concat([df, df2], axis=0) +print(df_new) +df_new.to_csv("temp2.csv", index=False) diff --git a/FERV39K.py b/FERV39K.py new file mode 100644 index 0000000..907c57d --- /dev/null +++ b/FERV39K.py @@ -0,0 +1,565 @@ +import math +import numpy as np +import torch.utils.data as data +from torchvision import transforms +import cv2 +import pandas as pd +import os, torch +import torch.nn as nn +#import image_utils +import argparse, random +from functools import partial +from CA_block import resnet18_pos_attention +from PC_module import VisionTransformer_POS +from torchvision.transforms import Resize +from torch.utils.tensorboard import SummaryWriter # to print to tensorboard +import warnings +# Suppress the warning message related to the antialias parameter +warnings.filterwarnings("ignore", message="The default value of the antialias parameter of all the resizing transforms") +torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--raf_path', type=str, default='', help='Raf-DB dataset path.') + parser.add_argument('--checkpoint', type=str, default=None, + help='Pytorch checkpoint file path') + parser.add_argument('--pretrained', type=str, default=None, + help='Pretrained weights') + parser.add_argument('--beta', type=float, default=0.7, help='Ratio of high importance group in one mini-batch.') + parser.add_argument('--relabel_epoch', type=int, default=1000, + help='Relabeling samples on each mini-batch after 10(Default) epochs.') + parser.add_argument('--batch_size', type=int, default=64, help='Batch size.') + parser.add_argument('--optimizer', type=str, default="adam", help='Optimizer, adam or sgd.') + parser.add_argument('--lr', type=float, default=0.0001, help='Initial learning rate for sgd.') + parser.add_argument('--momentum', default=0.9, type=float, help='Momentum for sgd') + parser.add_argument('--workers', default=4, type=int, help='Number of data loading workers (default: 4)') + parser.add_argument('--epochs', type=int, default=7000, help='Total training epochs.') + parser.add_argument('--drop_rate', type=float, default=0, help='Drop out rate.') + return parser.parse_args() + + + + + + +class RafDataSet(data.Dataset): + def __init__(self, raf_path, phase, transform = None, basic_aug = False, transform_norm=None): + self.phase = phase + self.transform = transform + self.raf_path = raf_path + self.transform_norm = transform_norm + NAME_COLUMN = 0 + APEX_COLUMN = 1 + OFF_COLUMN = 2 + LABEL_ALL_COLUMN = 3 + + if phase == 'train': + dataset = pd.read_csv("result_ferv39k.csv",usecols=[0,1,2,3]) + else: + dataset = pd.read_csv("t_result_ferv39k.csv",usecols=[0,1,2,3]) + + File_names = dataset.iloc[:, NAME_COLUMN].values + Label_all = dataset.iloc[:, LABEL_ALL_COLUMN].values # 0:Happy, 1:Sad, 2:Neutral, 3:Anger, 4:Surprise, 5:Disgust, 6:fear + Apex_num = dataset.iloc[:, APEX_COLUMN].values + Offset_num = dataset.iloc[:, OFF_COLUMN].values + self.file_paths_on = [] + self.file_paths_off = [] + self.file_paths_apex = [] + self.label_all = [] + self.file_names =[] + print(Label_all) + # a=0 + # b=0 + # c=0 + # d=0 + # e=0 + # use aligned images for training/testing + for (f,apex,offset,label_all) in zip(File_names,Apex_num,Offset_num,Label_all): + if label_all < 7: + self.file_paths_on.append(0) + self.file_paths_off.append(offset-1) + self.file_paths_apex.append(apex) + self.file_names.append(f) + if label_all == 0: + self.label_all.append(0) + elif label_all == 6: + self.label_all.append(1) + elif label_all == 1: + self.label_all.append(2) + elif label_all == 5: + self.label_all.append(3) + elif label_all == 3: + self.label_all.append(4) + elif label_all == 4: + self.label_all.append(5) + else: + self.label_all.append(6) + + + + ##label + + self.basic_aug = basic_aug + #self.aug_func = [image_utils.flip_image,image_utils.add_gaussian_noise] + + def __len__(self): + return len(self.file_paths_on) + + def __getitem__(self, idx): + ##sampling strategy for training set + if self.phase == 'train': + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset =self.file_paths_off[idx] + on0 = random.randint(int(onset), int(onset + int(0.15* (apex - onset) / 4))) + # on0 = str(int(onset)) + on1 = random.randint(int(onset + int(0.9 * (apex - onset) / 4)), int(onset + int(1.1 * (apex - onset) / 4))) + on2 = random.randint(int(onset + int(1.8 * (apex - onset) / 4)), int(onset + int(2.2 * (apex - onset) / 4))) + on3 = random.randint(int(onset + int(2.7 * (apex - onset) / 4)), onset + int(3.3 * (apex - onset) / 4)) + # apex0 = str(apex) + apex0 = random.randint(int(apex - int(0.15* (apex - onset) / 4)), apex) + off0 = random.randint(int(apex + int(0.9 * (offset - apex) / 4)), int(apex + int(1.1 * (offset - apex) / 4))) + off1 = random.randint(int(apex + int(1.8 * (offset - apex) / 4)), int(apex + int(2.2 * (offset - apex) / 4))) + off2 = random.randint(int(apex + int(2.9 * (offset - apex) / 4)), int(apex + int(3.1 * (offset - apex) / 4))) + off3 = random.randint(int(apex + int(3.8 * (offset - apex) / 4)), offset) + f = str(self.file_names[idx]) + else:##sampling strategy for testing set + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset = self.file_paths_off[idx] + + on0 = (onset) + on1 = (int(onset + int((apex - onset) / 4))) + on2 = (int(onset + int(2 * (apex - onset) / 4))) + on3 = (int(onset + int(3 * (apex - onset) / 4))) + apex0 = (apex) + off0 = (int(apex + int((offset - apex) / 4))) + off1 = (int(apex + int(2 * (offset - apex) / 4))) + off2 = (int(apex + int(3 * (offset - apex) / 4))) + off3 = (offset) + + f = str(self.file_names[idx]) + + img_list=os.listdir(os.path.join(f)) + # sort only without the last 4 characters + img_list.sort(key=lambda f: int(''.join(filter(str.isdigit, f[:-4])))) + path_on0 = os.path.join(f, img_list[on0]) + path_on1 = os.path.join(f, img_list[on1]) + path_on2 = os.path.join(f, img_list[on2]) + path_on3 = os.path.join(f, img_list[on3]) + path_apex0 = os.path.join(f, img_list[apex0]) + path_off0 = os.path.join(f, img_list[off0]) + path_off1 = os.path.join(f, img_list[off1]) + path_off2 = os.path.join(f, img_list[off2]) + path_off3 = os.path.join(f, img_list[off3]) + image_on0 = cv2.imread(path_on0) + image_on1= cv2.imread(path_on1) + image_on2 = cv2.imread(path_on2) + image_on3 = cv2.imread(path_on3) + image_apex0 = cv2.imread(path_apex0) + image_off0 = cv2.imread(path_off0) + image_off1 = cv2.imread(path_off1) + image_off2 = cv2.imread(path_off2) + image_off3 = cv2.imread(path_off3) + + image_on0 = image_on0[:, :, ::-1] # BGR to RGB + image_on1 = image_on1[:, :, ::-1] + image_on2 = image_on2[:, :, ::-1] + image_on3 = image_on3[:, :, ::-1] + image_off0 = image_off0[:, :, ::-1] + image_off1 = image_off1[:, :, ::-1] + image_off2 = image_off2[:, :, ::-1] + image_off3 = image_off3[:, :, ::-1] + image_apex0 = image_apex0[:, :, ::-1] + + label_all = self.label_all[idx] + + # normalization for testing and training + if self.transform is not None: + image_on0 = self.transform(image_on0) + image_on1 = self.transform(image_on1) + image_on2 = self.transform(image_on2) + image_on3 = self.transform(image_on3) + image_off0 = self.transform(image_off0) + image_off1 = self.transform(image_off1) + image_off2 = self.transform(image_off2) + image_off3 = self.transform(image_off3) + image_apex0 = self.transform(image_apex0) + ALL = torch.cat( + (image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3), dim=0) + ## data augmentation for training only + if self.transform_norm is not None and self.phase == 'train': + ALL = self.transform_norm(ALL) + image_on0 = ALL[0:3, :, :] + image_on1 = ALL[3:6, :, :] + image_on2 = ALL[6:9, :, :] + image_on3 = ALL[9:12, :, :] + image_apex0 = ALL[12:15, :, :] + image_off0 = ALL[15:18, :, :] + image_off1 = ALL[18:21, :, :] + image_off2 = ALL[21:24, :, :] + image_off3 = ALL[24:27, :, :] + + + temp = torch.zeros(65) + # # trans image to cuda + # image_on0 = image_on0.to(device) + # image_on1 = image_on1.to(device) + # image_on2 = image_on2.to(device) + # image_on3 = image_on3.to(device) + # image_apex0 = image_apex0.to(device) + # image_off0 = image_off0.to(device) + # image_off1 = image_off1.to(device) + # image_off2 = image_off2.to(device) + # image_off3 = image_off3.to(device) + # label_all = label_all.to(device) + # temp = temp.to(device) + + return image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, label_all, temp + + +def initialize_weight_goog(m, n=''): + if isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1.0) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + fan_out = m.weight.size(0) # fan-out + fan_in = 0 + if 'routing_fn' in n: + fan_in = m.weight.size(1) + init_range = 1.0 / math.sqrt(fan_in + fan_out) + m.weight.data.uniform_(-init_range, init_range) + m.bias.data.zero_() + + +def criterion2(y_pred, y_true): + y_pred = (1 - 2 * y_true) * y_pred + y_pred_neg = y_pred - y_true * 1e12 + y_pred_pos = y_pred - (1 - y_true) * 1e12 + zeros = torch.zeros_like(y_pred[..., :1]) + y_pred_neg = torch.cat((y_pred_neg, zeros), dim=-1) + y_pred_pos = torch.cat((y_pred_pos, zeros), dim=-1) + neg_loss = torch.logsumexp(y_pred_neg, dim=-1) + pos_loss = torch.logsumexp(y_pred_pos, dim=-1) + return torch.mean(neg_loss + pos_loss) + + +class MMNet(nn.Module): + def __init__(self): + super(MMNet, self).__init__() + + + self.conv_act = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=90*2, kernel_size=3, stride=2,padding=1, bias=False,groups=1), + nn.BatchNorm2d(180), + nn.ReLU(inplace=True), + + ) + self.pos =nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=512, kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(512), + nn.ReLU(inplace=True), + + ) + ##Position Calibration Module(subbranch) + self.vit_pos=VisionTransformer_POS(img_size=14, + patch_size=1, embed_dim=512, depth=2, num_heads=4, mlp_ratio=2, qkv_bias=True,norm_layer=partial(nn.LayerNorm, eps=1e-6),drop_path_rate=0.) + self.resize=Resize([14,14]) + ##main branch consisting of CA blocks + self.main_branch =resnet18_pos_attention() + + self.head = nn.Sequential( + nn.Dropout(p=0.5), + nn.Linear(1 * 112 *112, 65,bias=False), + + ) + + self.timeembed = nn.Parameter(torch.zeros(1, 4, 111, 111)) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + def forward(self, x1, x2, x3, x4, x5, x6, x7, x8, x9, if_shuffle): + ##onset:x1 apex:x5 + B = x1.shape[0] + # Position Calibration Module (subbranch) + POS =self.vit_pos(self.resize(x1)).transpose(1,2).view(B,512,14,14) + + act =x5 -x1 + act=self.conv_act(act) + # main branch and fusion + out,_=self.main_branch(act,POS) + + return out + + + + + +def run_training(): + args = parse_args() + imagenet_pretrained = True + + if not imagenet_pretrained: + for m in res18.modules(): + initialize_weight_goog(m) + + if args.pretrained: + print("Loading pretrained weights...", args.pretrained) + pretrained = torch.load(args.pretrained) + pretrained_state_dict = pretrained['state_dict'] + model_state_dict = res18.state_dict() + loaded_keys = 0 + total_keys = 0 + for key in pretrained_state_dict: + if ((key == 'module.fc.weight') | (key == 'module.fc.bias')): + pass + else: + model_state_dict[key] = pretrained_state_dict[key] + total_keys += 1 + if key in model_state_dict: + loaded_keys += 1 + print("Loaded params num:", loaded_keys) + print("Total params num:", total_keys) + res18.load_state_dict(model_state_dict, strict=False) + ##data normalization for both training set + data_transforms = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + + ]) + ### data augmentation for training set only + data_transforms_norm = transforms.Compose([ + + transforms.RandomHorizontalFlip(p=0.5), + transforms.RandomRotation(3), + transforms.RandomCrop(224, padding=15), + + ]) + + ### data normalization for both teating set + data_transforms_val = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) + + + + + criterion = torch.nn.CrossEntropyLoss() + # leave one subject out protocal + # 'spNO.1', 'spNO.2', 'spNO.3', 'spNO.5', 'spNO.6', 'spNO.7', 'spNO.8', 'spNO.9', 'spNO.10', 'spNO.11', 'spNO.12', 'spNO.13', 'spNO.14', 'spNO.15', 'spNO.17', 'spNO.39', 'spNO.40', 'spNO.41', 'spNO.42', 'spNO.77', 'spNO.138', 'spNO.139', 'spNO.142', 'spNO.143', 'spNO.144', 'spNO.145', 'spNO.146', 'spNO.147', 'spNO.148', 'spNO.149', 'spNO.150', 'spNO.152', 'spNO.153', 'spNO.154', 'spNO.155', 'spNO.157', 'spNO.159', 'spNO.160', 'spNO.161', 'spNO.162', 'spNO.163', 'spNO.165', 'spNO.166', 'spNO.167', 'spNO.168', 'spNO.169', 'spNO.170', 'spNO.171', 'spNO.172', 'spNO.173', 'spNO.174', 'spNO.175', 'spNO.176', 'spNO.177', 'spNO.178', 'spNO.179', 'spNO.180', 'spNO.181', 'spNO.182', 'spNO.183', 'spNO.184', 'spNO.185', 'spNO.186', 'spNO.187', 'spNO.188', 'spNO.189', 'spNO.190', 'spNO.192', 'spNO.194', 'spNO.195', 'spNO.196', 'spNO.197', 'spNO.198', 'spNO.200', 'spNO.201', 'spNO.202', 'spNO.203', 'spNO.204', 'spNO.206', 'spNO.207', 'spNO.208', 'spNO.209', 'spNO.210', 'spNO.211', 'spNO.212', 'spNO.213', 'spNO.214', 'spNO.216', 'spNO.217' + val_now = 0 + num_sum = 0 + pos_pred_ALL = torch.zeros(7) + pos_label_ALL = torch.zeros(7) + TP_ALL = torch.zeros(7) + + + writer = SummaryWriter('./log/casme3') + train_dataset = RafDataSet(args.raf_path, phase='train', transform=data_transforms, basic_aug=True, transform_norm=data_transforms_norm) + val_dataset = RafDataSet(args.raf_path, phase='test', transform=data_transforms_val) + train_loader = torch.utils.data.DataLoader(train_dataset, + batch_size=64, + num_workers=args.workers, + shuffle=True, + pin_memory=True, + prefetch_factor=2, + persistent_workers=True) + val_loader = torch.utils.data.DataLoader(val_dataset, + batch_size=64, + num_workers=args.workers, + shuffle=False, + pin_memory=True, + prefetch_factor=2, + persistent_workers=True) + + print('Train set size:', train_dataset.__len__()) + print('Validation set size:', val_dataset.__len__()) + max_acc = 0 + max_corr = 0 + max_f1 = 0 + max_pos_pred = torch.zeros(7) + max_pos_label = torch.zeros(7) + max_TP = torch.zeros(7) + ##model initialization + net_all = MMNet() + net_all=net_all.to(device) + params_all = net_all.parameters() + + if args.optimizer == 'adam': + optimizer_all = torch.optim.AdamW(params_all, lr=0.0008, weight_decay=0.6) + ##optimizer for MMNet + + elif args.optimizer == 'sgd': + optimizer = torch.optim.SGD(params, args.lr, + momentum=args.momentum, + weight_decay=1e-4) + else: + raise ValueError("Optimizer not supported.") + ##lr_decay + scheduler_all = torch.optim.lr_scheduler.ExponentialLR(optimizer_all, gamma=0.987) + + + for i in range(1, 75): + running_loss = 0.0 + correct_sum = 0 + running_loss_MASK = 0. + correct_sum_MASK = 0 + iter_cnt = 0 + net_all.train() + + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, + label_all, + label_au) in enumerate(train_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + iter_cnt += 1 + + image_on0 = image_on0.to(device) + image_on1 = image_on1.to(device) + image_on2 = image_on2.to(device) + image_on3 = image_on3.to(device) + image_apex0 = image_apex0.to(device) + image_off0 = image_off0.to(device) + image_off1 = image_off1.to(device) + image_off2 = image_off2.to(device) + image_off3 = image_off3.to(device) + label_all = label_all.to(device) + label_au = label_au.to(device) + + ##train MMNet + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, + image_off2, image_off3, False) + + loss_all = criterion(ALL, label_all) + optimizer_all.zero_grad() + loss_all.backward() + optimizer_all.step() + running_loss += loss_all + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all).sum() + correct_sum += correct_num + writer.add_scalar('Train/Loss', running_loss / iter_cnt, i) + writer.add_scalar('Train/Acc', correct_sum.float() / float(train_dataset.__len__()), i) + ## lr decay + if i <= 50: + scheduler_all.step() + if i>=0: + acc = correct_sum.float() / float(train_dataset.__len__()) + running_loss = running_loss / iter_cnt + + print('[Epoch %d] Training accuracy: %.4f. Loss: %.3f' % (i, acc, running_loss)) + if(acc>=max_acc): + max_acc=acc + torch.save(net_all.state_dict(), + 'model_FERV39K.pth') + print('model saved') + pos_label = torch.zeros(7) + pos_pred = torch.zeros(7) + TP = torch.zeros(7) + with torch.no_grad(): + running_loss = 0.0 + iter_cnt = 0 + bingo_cnt = 0 + sample_cnt = 0 + pre_lab_all = [] + Y_test_all = [] + net_all.eval() + # net_au.eval() + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3, label_all, + label_au) in enumerate(val_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + image_on0 = image_on0.to(device) + image_on1 = image_on1.to(device) + image_on2 = image_on2.to(device) + image_on3 = image_on3.to(device) + image_apex0 = image_apex0.to(device) + image_off0 = image_off0.to(device) + image_off1 = image_off1.to(device) + image_off2 = image_off2.to(device) + image_off3 = image_off3.to(device) + label_all = label_all.to(device) + label_au = label_au.to(device) + ##test + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, False) + + + loss = criterion(ALL, label_all) + running_loss += loss + iter_cnt += 1 + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all) + bingo_cnt += correct_num.sum().cpu() + sample_cnt += ALL.size(0) + + for cls in range(7): + for element in predicts: + if element == cls: + pos_label[cls] = pos_label[cls] + 1 + for element in label_all: + if element == cls: + pos_pred[cls] = pos_pred[cls] + 1 + for elementp, elementl in zip(predicts, label_all): + if elementp == elementl and elementp == cls: + TP[cls] = TP[cls] + 1 + # if pos_label != 0 or pos_pred != 0: + # f1 = 2 * TP / (pos_pred + pos_label) + # F1.append(f1) + count = 0 + SUM_F1 = 0 + for index in range(7): + if pos_label[index] != 0 or pos_pred[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * TP[index] / (pos_pred[index] + pos_label[index]) + + AVG_F1 = SUM_F1 / count + + + running_loss = running_loss / iter_cnt + acc = bingo_cnt.float() / float(sample_cnt) + acc = np.around(acc.numpy(), 4) + if bingo_cnt > max_corr: + max_corr = bingo_cnt + if AVG_F1 >= max_f1: + max_f1 = AVG_F1 + max_pos_label = pos_label + max_pos_pred = pos_pred + max_TP = TP + writer.add_scalar('Val/Loss', running_loss, i) + writer.add_scalar('Val/Acc', acc, i) + print("[Epoch %d] Validation accuracy:%.4f. Loss:%.3f, F1-score:%.3f" % (i, acc, running_loss, AVG_F1)) + num_sum = num_sum + max_corr + pos_label_ALL = pos_label_ALL + max_pos_label + pos_pred_ALL = pos_pred_ALL + max_pos_pred + TP_ALL = TP_ALL + max_TP + count = 0 + SUM_F1 = 0 + for index in range(7): + if pos_label_ALL[index] != 0 or pos_pred_ALL[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * TP_ALL[index] / (pos_pred_ALL[index] + pos_label_ALL[index]) + F1_ALL = SUM_F1 / count + val_now = val_now + val_dataset.__len__() + print("[..........%s] correctnum:%d . zongshu:%d " % (subj, max_corr, val_dataset.__len__())) + print("[ALL_corr]: %d [ALL_val]: %d" % (num_sum, val_now)) + print("[F1_now]: %.4f [F1_ALL]: %.4f" % (max_f1, F1_ALL)) + + + +if __name__ == "__main__": + run_training() diff --git a/Figure_1.png b/Figure_1.png new file mode 100644 index 0000000..b06e085 Binary files /dev/null and b/Figure_1.png differ diff --git a/Hybird.py b/Hybird.py new file mode 100755 index 0000000..27cd55c --- /dev/null +++ b/Hybird.py @@ -0,0 +1,660 @@ +import math +import numpy as np +import torchvision.models +import torch.utils.data as data +from torchvision import transforms +import cv2 +import torch.nn.functional as F +from torch.autograd import Variable +import pandas as pd +import os +import torch +import torch.nn as nn +#import image_utils +import argparse +import random +from functools import partial + +from CA_block import resnet18_pos_attention + +from PC_module import VisionTransformer_POS + +from torchvision.transforms import Resize +#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--raf_path', type=str, + default='dataset', help='Raf-DB dataset path.') + parser.add_argument('--checkpoint', type=str, default=None, + help='Pytorch checkpoint file path') + parser.add_argument('--pretrained', type=str, default=None, + help='Pretrained weights') + parser.add_argument('--beta', type=float, default=0.7, + help='Ratio of high importance group in one mini-batch.') + parser.add_argument('--relabel_epoch', type=int, default=1000, + help='Relabeling samples on each mini-batch after 10(Default) epochs.') + parser.add_argument('--batch_size', type=int, + default=128, help='Batch size.') + parser.add_argument('--optimizer', type=str, + default="adam", help='Optimizer, adam or sgd.') + parser.add_argument('--lr', type=float, default=0.0001, + help='Initial learning rate for sgd.') + parser.add_argument('--momentum', default=0.9, + type=float, help='Momentum for sgd') + parser.add_argument('--workers', default=4, type=int, + help='Number of data loading workers (default: 4)') + parser.add_argument('--epochs', type=int, default=7000, + help='Total training epochs.') + parser.add_argument('--drop_rate', type=float, + default=0, help='Drop out rate.') + return parser.parse_args() + + +class RafDataSet(data.Dataset): + def __init__(self, raf_path, phase, num_loso, transform=None, basic_aug=False, transform_norm=None): + self.phase = phase + self.transform = transform + self.raf_path = raf_path + self.transform_norm = transform_norm + TYPE_COLUMN = 0 + SUBJECT_COLUMN = 1 + NAME_COLUMN = 2 + ONSET_COLUMN = 4 + APEX_COLUMN = 3 + OFF_COLUMN = 5 + LABEL_ALL_COLUMN = 6 + df_csv = pd.read_csv(os.path.join( + self.raf_path, 'data.csv'), usecols=[0, 1, 2, 3, 4, 5, 6]) + # df = pd.read_excel(os.path.join( + # self.raf_path, 'CASME2-coding-20140508.xlsx'), usecols=[0, 1, 3, 4, 5, 7, 8]) + df = df_csv + df['Subject'] = df['Subject'].apply(str) + + if phase == 'train': + dataset = df.loc[df['Subject'] != num_loso] + else: + dataset = df.loc[df['Subject'] == num_loso] + Type = dataset.iloc[:, TYPE_COLUMN].values + Subject = dataset.iloc[:, SUBJECT_COLUMN].values + File_names = dataset.iloc[:, NAME_COLUMN].values + # 0:Surprise, 1:Fear, 2:Disgust, 3:Happiness, 4:Sadness, 5:Anger, 6:Neutral + Label_all = dataset.iloc[:, LABEL_ALL_COLUMN].values + Onset_num = dataset.iloc[:, ONSET_COLUMN].values + Apex_num = dataset.iloc[:, APEX_COLUMN].values + Offset_num = dataset.iloc[:, OFF_COLUMN].values + self.file_paths_on = [] + self.file_paths_off = [] + self.file_paths_apex = [] + self.label_all = [] + self.sub = [] + self.typ = [] + self.file_names = [] + a = 0 + b = 0 + c = 0 + d = 0 + e = 0 + # use aligned images for training/testing + for (t, f, sub, onset, apex, offset, label_all) in zip(Type, File_names, Subject, Onset_num, Apex_num, Offset_num, Label_all): + + if label_all == 'negative' or label_all == 'positive' or label_all == 'surprise' or label_all == 'happiness' or label_all == 'disgust' or label_all == 'repression': + if(t == 'HS'): + self.file_paths_on.append(str(onset).zfill(6)) + self.file_paths_off.append(str(offset).zfill(6)) + self.file_paths_apex.append(str(apex).zfill(6)) + elif(t != 'casme'): + self.file_paths_on.append(str(onset).zfill(5)) + self.file_paths_off.append(str(offset).zfill(5)) + self.file_paths_apex.append(str(apex).zfill(5)) + else: + self.file_paths_on.append(onset) + self.file_paths_off.append(offset) + self.file_paths_apex.append(apex) + self.typ.append(t) + self.sub.append(sub) + self.file_names.append(f) + if label_all == 'negative' or label_all == 'disgust' or label_all == 'repression': + self.label_all.append(0) + a = a+1 + elif label_all == 'positive' or label_all == 'happiness': + self.label_all.append(1) + b = b+1 + else: + self.label_all.append(2) + c = c+1 + + # label_au =label_au.split("+") + + # label + #self.aug_func = [image_utils.flip_image,image_utils.add_gaussian_noise] + + def __len__(self): + return len(self.file_paths_on) + + def __getitem__(self, idx): + # sampling strategy for training set + if self.phase == 'train': + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset = self.file_paths_off[idx] + # on0 = str(random.randint(int(onset), int( + # onset + int(0.2 * (apex - onset) / 3)))) + on0 = str(onset) + on1 = on0 + on2 = on0 + on3 = on0 + # on1 = str( + # random.randint(int(onset + int(0.9 * (apex - onset) / 4)), int(onset + int(1.1 * (apex - onset) / 4)))) + # on2 = str( + # random.randint(int(onset + int(1.8 * (apex - onset) / 4)), int(onset + int(2.2 * (apex - onset) / 4)))) + # on3 = str(random.randint(int( + # onset + int(2.7 * (apex - onset) / 4)), onset + int(3.3 * (apex - onset) / 4))) + apex0 = str(apex) + # apex0 = str( + # random.randint(int(apex - int(0.15 * (apex - onset) / 4)), apex + int(0.15 * (offset - apex) / 4))) + # off0 = str( + # random.randint(int(apex + int(0.9 * (offset - apex) / 4)), int(apex + int(1.1 * (offset - apex) / 4)))) + off0 = str(offset) + off1 = off0 + off2 = off0 + off3 = off0 + + # off1 = str( + # random.randint(int(apex + int(1.8 * (offset - apex) / 4)), int(apex + int(2.2 * (offset - apex) / 4)))) + # off2 = str( + # random.randint(int(apex + int(2.9 * (offset - apex) / 4)), int(apex + int(3.1 * (offset - apex) / 4)))) + # off3 = str(random.randint( + # int(apex + int(3.8 * (offset - apex) / 4)), offset)) + t = self.typ[idx] + sub = ('s'+str(self.sub[idx])if t != + 'casme' else 'sub' + str(self.sub[idx]).zfill(2)) + f = str(self.file_names[idx]) + else: # sampling strategy for testing set + onset = self.file_paths_on[idx] + apex = self.file_paths_apex[idx] + offset = self.file_paths_off[idx] + + on0 = str(onset) + on1 = on0 + on2 = on0 + on3 = on0 + # on1 = str(int(onset + int((apex - onset) / 4))) + # on2 = str(int(onset + int(2 * (apex - onset) / 4))) + # on3 = str(int(onset + int(3 * (apex - onset) / 4))) + apex0 = str(apex) + off0 = str(offset) + off1 = off0 + off2 = off0 + off3 = off0 + # off0 = str(int(apex + int((offset - apex) / 4))) + # off1 = str(int(apex + int(2 * (offset - apex) / 4))) + # off2 = str(int(apex + int(3 * (offset - apex) / 4))) + # off3 = str(offset) + t = self.typ[idx] + sub = ('s'+str(self.sub[idx])if t != + 'casme' else 'sub' + str(self.sub[idx]).zfill(2)) + f = str(self.file_names[idx]) + if(t == 'HS'): + on0 = 'reg_image'+on0+'.bmp' + on1 = 'reg_image' + on1 + '.bmp' + on2 = 'reg_image' + on2 + '.bmp' + on3 = 'reg_image' + on3 + '.bmp' + apex0 = 'reg_image' + apex0 + '.bmp' + off0 = 'reg_image' + off0 + '.bmp' + off1 = 'reg_image' + off1 + '.bmp' + off2 = 'reg_image' + off2 + '.bmp' + off3 = 'reg_image' + off3 + '.bmp' + elif(t == 'casme'): + on0 = 'reg_img'+on0+'.jpg' + on1 = 'reg_img' + on1 + '.jpg' + on2 = 'reg_img' + on2 + '.jpg' + on3 = 'reg_img' + on3 + '.jpg' + apex0 = 'reg_img' + apex0 + '.jpg' + off0 = 'reg_img' + off0 + '.jpg' + off1 = 'reg_img' + off1 + '.jpg' + off2 = 'reg_img' + off2 + '.jpg' + off3 = 'reg_img' + off3 + '.jpg' + else: + on0 = 'reg_'+on0+'.bmp' + on1 = 'reg_' + on1 + '.bmp' + on2 = 'reg_' + on2 + '.bmp' + on3 = 'reg_' + on3 + '.bmp' + apex0 = 'reg_' + apex0 + '.bmp' + off0 = 'reg_' + off0 + '.bmp' + off1 = 'reg_' + off1 + '.bmp' + off2 = 'reg_' + off2 + '.bmp' + off3 = 'reg_' + off3 + '.bmp' + path_on0 = os.path.join( + self.raf_path, t, sub, f, on0) + path_on1 = os.path.join( + self.raf_path, t, sub, f, on1) + path_on2 = os.path.join( + self.raf_path, t, sub, f, on2) + path_on3 = os.path.join( + self.raf_path, t, sub, f, on3) + path_apex0 = os.path.join( + self.raf_path, t, sub, f, apex0) + path_off0 = os.path.join( + self.raf_path, t, sub, f, off0) + path_off1 = os.path.join( + self.raf_path, t, sub, f, off1) + path_off2 = os.path.join( + self.raf_path, t, sub, f, off2) + path_off3 = os.path.join( + self.raf_path, t, sub, f, off3) + + image_on0 = cv2.imread(path_on0) + image_on1 = cv2.imread(path_on1) + image_on2 = cv2.imread(path_on2) + image_on3 = cv2.imread(path_on3) + image_apex0 = cv2.imread(path_apex0) + image_off0 = cv2.imread(path_off0) + image_off1 = cv2.imread(path_off1) + image_off2 = cv2.imread(path_off2) + image_off3 = cv2.imread(path_off3) + + image_on0 = image_on0[:, :, ::-1] # BGR to RGB + image_on1 = image_on1[:, :, ::-1] + image_on2 = image_on2[:, :, ::-1] + image_on3 = image_on3[:, :, ::-1] + image_off0 = image_off0[:, :, ::-1] + image_off1 = image_off1[:, :, ::-1] + image_off2 = image_off2[:, :, ::-1] + image_off3 = image_off3[:, :, ::-1] + image_apex0 = image_apex0[:, :, ::-1] + + label_all = self.label_all[idx] + + # normalization for testing and training + if self.transform is not None: + image_on0 = self.transform(image_on0) + image_on1 = self.transform(image_on1) + image_on2 = self.transform(image_on2) + image_on3 = self.transform(image_on3) + image_off0 = self.transform(image_off0) + image_off1 = self.transform(image_off1) + image_off2 = self.transform(image_off2) + image_off3 = self.transform(image_off3) + image_apex0 = self.transform(image_apex0) + ALL = torch.cat( + (image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3), dim=0) + # data augmentation for training only + if self.transform_norm is not None and self.phase == 'train': + ALL = self.transform_norm(ALL) + image_on0 = ALL[0:3, :, :] + image_on1 = ALL[3:6, :, :] + image_on2 = ALL[6:9, :, :] + image_on3 = ALL[9:12, :, :] + image_apex0 = ALL[12:15, :, :] + image_off0 = ALL[15:18, :, :] + image_off1 = ALL[18:21, :, :] + image_off2 = ALL[21:24, :, :] + image_off3 = ALL[24:27, :, :] + + return image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, label_all + + +def initialize_weight_goog(m, n=''): + if isinstance(m, nn.Conv2d): + fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1.0) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + fan_out = m.weight.size(0) # fan-out + fan_in = 0 + if 'routing_fn' in n: + fan_in = m.weight.size(1) + init_range = 1.0 / math.sqrt(fan_in + fan_out) + m.weight.data.uniform_(-init_range, init_range) + m.bias.data.zero_() + + +def criterion2(y_pred, y_true): + y_pred = (1 - 2 * y_true) * y_pred + y_pred_neg = y_pred - y_true * 1e12 + y_pred_pos = y_pred - (1 - y_true) * 1e12 + zeros = torch.zeros_like(y_pred[..., :1]) + y_pred_neg = torch.cat((y_pred_neg, zeros), dim=-1) + y_pred_pos = torch.cat((y_pred_pos, zeros), dim=-1) + neg_loss = torch.logsumexp(y_pred_neg, dim=-1) + pos_loss = torch.logsumexp(y_pred_pos, dim=-1) + return torch.mean(neg_loss + pos_loss) + + +class MMNet(nn.Module): + def __init__(self): + super(MMNet, self).__init__() + + self.conv_act = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=90*2, kernel_size=3, + stride=2, padding=1, bias=False, groups=1), # original 2(not sure the effec) + nn.BatchNorm2d(180), + nn.ReLU(inplace=True), + ) + self.pos = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=512, + kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(512), + nn.ReLU(inplace=True), + + ) + # Position Calibration Module(subbranch) + self.vit_pos = VisionTransformer_POS(img_size=14, + patch_size=1, embed_dim=512, depth=3, num_heads=4, mlp_ratio=2, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), drop_path_rate=0.3) + self.resize = Resize([14, 14]) + # main branch consisting of CA blocks + self.main_branch = resnet18_pos_attention() + self.head1 = nn.Sequential( + nn.Dropout(p=0.5), + nn.Linear(1 * 112 * 112, 38, bias=False), + + ) + + self.timeembed = nn.Parameter(torch.zeros(1, 4, 111, 111)) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + + def forward(self, x1, x2, x3, x4, x5, x6, x7, x8, x9, if_shuffle): + # onset:x1 apex:x5 + B = x1.shape[0] + + # Position Calibration Module (subbranch) + POS = self.vit_pos(self.resize(x1)).transpose( + 1, 2).view(B, 512, 14, 14) + act = x5 - x1 + act = self.conv_act(act) + # main branch and fusion + out, _ = self.main_branch(act, POS) + + return out + + +def run_training(): + + args = parse_args() + imagenet_pretrained = True + + if not imagenet_pretrained: + for m in res18.modules(): + initialize_weight_goog(m) + + if args.pretrained: + print("Loading pretrained weights...", args.pretrained) + pretrained = torch.load(args.pretrained) + pretrained_state_dict = pretrained['state_dict'] + model_state_dict = res18.state_dict() + loaded_keys = 0 + total_keys = 0 + for key in pretrained_state_dict: + if ((key == 'module.fc.weight') | (key == 'module.fc.bias')): + pass + else: + model_state_dict[key] = pretrained_state_dict[key] + total_keys += 1 + if key in model_state_dict: + loaded_keys += 1 + print("Loaded params num:", loaded_keys) + print("Total params num:", total_keys) + res18.load_state_dict(model_state_dict, strict=False) + # data normalization for both training set + data_transforms = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + + ]) + # data augmentation for training set only + data_transforms_norm = transforms.Compose([ + + transforms.RandomHorizontalFlip(p=0.5), + transforms.RandomRotation(4), + transforms.RandomCrop(224, padding=4), + + + ]) + + # data normalization for both teating set + data_transforms_val = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) + + criterion = torch.nn.CrossEntropyLoss() + # leave one subject out protocal + # + # '17', '26', '20', '16', '9','5','24', '2', '13', '4', '23','11', '12', '8', '14', '3', '19', + LOSO = ['20', '17', '12', ] + #'1', '18', '21', '22', '15', '6', '25', '7', + + val_now = 0 + num_sum = 0 + pos_pred_ALL = torch.zeros(3) + pos_label_ALL = torch.zeros(3) + TP_ALL = torch.zeros(3) + + for subj in LOSO: + train_dataset = RafDataSet(args.raf_path, phase='train', num_loso=subj, transform=data_transforms, + basic_aug=True, transform_norm=data_transforms_norm) + val_dataset = RafDataSet( + args.raf_path, phase='test', num_loso=subj, transform=data_transforms_val) + train_loader = torch.utils.data.DataLoader(train_dataset, + batch_size=64, # 32 + # collate_fn=lambda x: default_collate( + # x).to(device), + num_workers=args.workers, + shuffle=True, + pin_memory=True, + prefetch_factor=2, + persistent_workers=True) + val_loader = torch.utils.data.DataLoader(val_dataset, + batch_size=64, # 32 + # collate_fn=lambda x: default_collate( + # x).to(device), + num_workers=args.workers, + shuffle=False, + pin_memory=True, + prefetch_factor=2, + persistent_workers=True) + print('num_sub', subj) + print('Train set size:', train_dataset.__len__()) + print('Validation set size:', val_dataset.__len__()) + + max_corr = 0 + max_f1 = 0 + max_pos_pred = torch.zeros(3) + max_pos_label = torch.zeros(3) + max_TP = torch.zeros(3) + # model initialization + net_all = MMNet() + # net_all = net_all.cuda() + params_all = net_all.parameters() + + if args.optimizer == 'adam': + optimizer_all = torch.optim.AdamW( + params_all, lr=0.0008, weight_decay=0.7) + # optimizer for MMNet + + elif args.optimizer == 'sgd': + optimizer = torch.optim.SGD(params, args.lr, + momentum=args.momentum, + weight_decay=1e-4) + else: + raise ValueError("Optimizer not supported.") + # lr_decay + scheduler_all = torch.optim.lr_scheduler.ExponentialLR( + optimizer_all, gamma=0.987) + net_all = net_all.cuda() + for i in range(1, 80): + running_loss = 0.0 + correct_sum = 0 + running_loss_MASK = 0.0 + correct_sum_MASK = 0 + iter_cnt = 0 + + net_all.train() + + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, image_off3, + label_all, + ) in enumerate(train_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + iter_cnt += 1 + + image_on0 = image_on0.cuda() + image_on1 = image_on1.cuda() + image_on2 = image_on2.cuda() + image_on3 = image_on3.cuda() + image_apex0 = image_apex0.cuda() + image_off0 = image_off0.cuda() + image_off1 = image_off1.cuda() + image_off2 = image_off2.cuda() + image_off3 = image_off3.cuda() + label_all = label_all.cuda() + + # train MMNet + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, + image_off2, image_off3, False) + + loss_all = criterion(ALL, label_all) + + optimizer_all.zero_grad() + + loss_all.backward() + + optimizer_all.step() + running_loss += loss_all + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all).sum() + correct_sum += correct_num + + # lr decay + if i <= 50: + + scheduler_all.step() + if i >= 0: + acc = correct_sum.float() / float(train_dataset.__len__()) + + running_loss = running_loss / iter_cnt + + print('[Epoch %d] Training accuracy: %.4f. Loss: %.3f' % + (i, acc, running_loss)) + + pos_label = torch.zeros(3) + pos_pred = torch.zeros(3) + TP = torch.zeros(3) + # test + with torch.no_grad(): + running_loss = 0.0 + iter_cnt = 0 + bingo_cnt = 0 + sample_cnt = 0 + pre_lab_all = [] + Y_test_all = [] + net_all.eval() + # net_au.eval() + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3, label_all, + ) in enumerate(val_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + + image_on0 = image_on0.cuda() + image_on1 = image_on1.cuda() + image_on2 = image_on2.cuda() + image_on3 = image_on3.cuda() + image_apex0 = image_apex0.cuda() + image_off0 = image_off0.cuda() + image_off1 = image_off1.cuda() + image_off2 = image_off2.cuda() + image_off3 = image_off3.cuda() + label_all = label_all.cuda() + + # test + ALL = net_all(image_on0, image_on1, image_on2, image_on3, image_apex0, + image_off0, image_off1, image_off2, image_off3, False) + + loss = criterion(ALL, label_all) + running_loss += loss + iter_cnt += 1 + _, predicts = torch.max(ALL, 1) + correct_num = torch.eq(predicts, label_all) + bingo_cnt += correct_num.sum().cpu() + #bingo_cnt += correct_num.sum() + sample_cnt += ALL.size(0) + + for cls in range(3): + + for element in predicts: + if element == cls: + pos_label[cls] = pos_label[cls] + 1 + for element in label_all: + if element == cls: + pos_pred[cls] = pos_pred[cls] + 1 + for elementp, elementl in zip(predicts, label_all): + if elementp == elementl and elementp == cls: + TP[cls] = TP[cls] + 1 + + count = 0 + SUM_F1 = 0 + for index in range(3): + if pos_label[index] != 0 or pos_pred[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * \ + TP[index] / (pos_pred[index] + + pos_label[index]) + + AVG_F1 = SUM_F1 / count + + running_loss = running_loss / iter_cnt + acc = bingo_cnt.float() / float(sample_cnt) + acc = acc.cpu() + acc = np.around(acc.numpy(), 4) + if bingo_cnt > max_corr: + max_corr = bingo_cnt + torch.save(net_all.state_dict(), + 'model_{}.pth'.format(subj)) + if AVG_F1 >= max_f1: + max_f1 = AVG_F1 + max_pos_label = pos_label + max_pos_pred = pos_pred + max_TP = TP + print("[Epoch %d] Validation accuracy:%.4f. Loss:%.3f, F1-score:%.3f" % + (i, acc, running_loss, AVG_F1)) + num_sum = num_sum + max_corr + pos_label_ALL = pos_label_ALL + max_pos_label + pos_pred_ALL = pos_pred_ALL + max_pos_pred + TP_ALL = TP_ALL + max_TP + count = 0 + SUM_F1 = 0 + for index in range(3): + if pos_label_ALL[index] != 0 or pos_pred_ALL[index] != 0: + count = count + 1 + SUM_F1 = SUM_F1 + 2 * \ + TP_ALL[index] / (pos_pred_ALL[index] + + pos_label_ALL[index]) + + F1_ALL = SUM_F1 / count + val_now = val_now + val_dataset.__len__() + print("[..........%s] correctnum:%d . zongshu:%d " % + (subj, max_corr, val_dataset.__len__())) + print("[ALL_corr]: %d [ALL_val]: %d" % (num_sum, val_now)) + print("[F1_now]: %.4f [F1_ALL]: %.4f" % (max_f1, F1_ALL)) + + +if __name__ == "__main__": + run_training() diff --git a/Model_load.py b/Model_load.py new file mode 100755 index 0000000..a06c35a --- /dev/null +++ b/Model_load.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from torch.autograd import Variable +import torchvision +import torchvision.transforms as transforms +import torchvision.datasets as datasets +import torchvision.models as models +import numpy as np +import matplotlib.pyplot as plt +import os +import CASME3_7 +import seaborn as sns +import time +import pandas as pd +torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) +criterion = torch.nn.CrossEntropyLoss() +path = 'Model/casmeiii7' +path_save = 'Model/casmeiii7_all' +if(os.path.exists(path_save) == False): + os.mkdir(path_save) +modellist = os.listdir(path) +# sort the model list as the order of epoch +modellist.sort(key=lambda x: int(x[11:-4])) +TP_C_total = [0, 0, 0,0,0,0,0] +TP_total = [0, 0, 0,0,0,0,0] +running_loss_t = [] +result_file = pd.DataFrame(columns=['model', 'loss', 'acc', 'TP_C', 'TP']) +loso=['spNO.1', 'spNO.2', 'spNO.3', 'spNO.5', 'spNO.6', 'spNO.7', 'spNO.8', 'spNO.9', 'spNO.10', 'spNO.11', 'spNO.12', 'spNO.13', 'spNO.14', 'spNO.15', 'spNO.17', 'spNO.39', 'spNO.40', 'spNO.41', 'spNO.42', 'spNO.77', 'spNO.138', 'spNO.139', 'spNO.142', 'spNO.143', 'spNO.144', 'spNO.145', 'spNO.146', 'spNO.147', 'spNO.148', 'spNO.149', 'spNO.150', 'spNO.152', 'spNO.153', 'spNO.154', 'spNO.155', 'spNO.157', 'spNO.159', 'spNO.160', 'spNO.161', 'spNO.162', 'spNO.163', 'spNO.165', 'spNO.166', 'spNO.167', 'spNO.168', 'spNO.169', 'spNO.170', 'spNO.171', 'spNO.172', 'spNO.173', 'spNO.174', 'spNO.175', 'spNO.176', 'spNO.177', 'spNO.178', 'spNO.179', 'spNO.180', 'spNO.181', 'spNO.182', 'spNO.183', 'spNO.184', 'spNO.185', 'spNO.186', 'spNO.187', 'spNO.188', 'spNO.189', 'spNO.190', 'spNO.192', 'spNO.194', 'spNO.195', 'spNO.196', 'spNO.197', 'spNO.198', 'spNO.200', 'spNO.201', 'spNO.202', 'spNO.203', 'spNO.204', 'spNO.206', 'spNO.207', 'spNO.208', 'spNO.209', 'spNO.210', 'spNO.211', 'spNO.212', 'spNO.213', 'spNO.214', 'spNO.216', 'spNO.217'] +for i in range(len(modellist)): + test = CASME3_7.RafDataSet( + '/home/drink36/Desktop/apex_frame_take/casme3_result_2', phase='test', num_loso=loso[i], + transform=transforms.Compose([transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])])) + test_loader = torch.utils.data.DataLoader(test, + batch_size=1, # 32 + # collate_fn=lambda x: default_collate( + # x).to(device), + shuffle=False, + pin_memory=True, + ) + model = CASME3_7.MMNet() + # model = torch.load(os.path.join(path_save, modellist[i])) + model.load_state_dict(torch.load(os.path.join(path, modellist[i]))) + model = model.cuda() + model.eval() + with torch.no_grad(): + running_loss = 0.0 + iter_cnt = 0 + bingo_cnt = 0 + sample_cnt = 0 + pos_label = [] + pos_pred = [] + TP = [0, 0, 0,0,0,0,0] + TP_C = [0, 0, 0,0,0,0,0] + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3, label_all,label_au + ) in enumerate(test_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + image_on0 = image_on0.cuda() + image_on1 = image_on1.cuda() + image_on2 = image_on2.cuda() + image_on3 = image_on3.cuda() + image_apex0 = image_apex0.cuda() + image_off0 = image_off0.cuda() + image_off1 = image_off1.cuda() + image_off2 = image_off2.cuda() + image_off3 = image_off3.cuda() + label_all = label_all.cuda() + label_au = label_au.cuda() + time1 = time.time() + ALL = model(image_on0, image_on1, image_on2, image_on3, image_apex0, + image_off0, image_off1, image_off2, image_off3, False) + loss = criterion(ALL, label_all) + running_loss += loss.item() + iter_cnt += 1 + _, pred = torch.max(ALL, 1) + corret_num = torch.eq(pred, label_all) + bingo_cnt += corret_num.sum().cpu() + sample_cnt += batch_sz + # 0:negative, 1:positive, 2:surprise + for cls in range(7): + for element in pred: + if element == cls: + pos_pred.append(cls) + for element in label_all: + if element == cls: + pos_label.append(cls) + TP_C[cls] = TP_C[cls] + 1 + for elementp, elementl in zip(pred, label_all): + if elementp == elementl and elementp == cls: + TP[cls] = TP[cls] + 1 + running_loss = running_loss / iter_cnt + acc = bingo_cnt.float() / float(sample_cnt) + acc = acc.cpu() + acc = np.around(acc.numpy(), 4) + # save the model + torch.save(model, os.path.join(path_save, modellist[i])) + # save the result to dataframe + result_file.loc[i] = [modellist[i], running_loss, acc, TP_C, TP] + print('model:', modellist[i]) + print('Test Loss: {:.4f}, Test Acc: {:.4f}'.format(running_loss, acc)) + running_loss_t.append(running_loss) + print('TP_C:', TP_C) + print('TP:', TP) + print('------------------------------------') + TP_C_total = [x + y for x, y in zip(TP_C_total, TP_C)] + TP_total = [x + y for x, y in zip(TP_total, TP)] +print('TP_C_total:', TP_C_total) +print('TP_total:', TP_total) +print('TP/TP_C:', [float(x) / float(y) + for x, y in zip(TP_total, TP_C_total)]) +print(sum(TP_total) / sum(TP_C_total)) +# print('running_loss_t:', running_loss_t) +# sort the model list as the order of loss and keep the top 5 +modellist = [x for _, x in sorted(zip(running_loss_t, modellist))] +# average the running_loss +running_loss_t = [sum(running_loss_t) / len(running_loss_t)] +# add new row to dataframe +result_file.loc[len(modellist)] = ["ALL",running_loss_t ,sum(TP_total) / sum(TP_C_total), TP_C_total, TP_total] +# save the result to csv +result_file.to_csv('result_file.csv', index=False) diff --git a/Model_load_FERV39K.py b/Model_load_FERV39K.py new file mode 100644 index 0000000..eebeabd --- /dev/null +++ b/Model_load_FERV39K.py @@ -0,0 +1,179 @@ +import torch +import torchvision.transforms as transforms +import numpy as np +import os +import FERV39K +import time +import matplotlib.pyplot as plt +import pandas as pd +from sklearn.metrics import confusion_matrix +import warnings + +# Suppress the warning message related to the antialias parameter +warnings.filterwarnings("ignore", message="The default value of the antialias parameter of all the resizing transforms") +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) +criterion = torch.nn.CrossEntropyLoss() +path = 'model_FERV39K.pth' +path_save = 'Model/FERV39K_7' +if(os.path.exists(path_save) == False): + os.mkdir(path_save) +# sort the model list as the order of epoch +true_label = [] +pred_label = [] +TP_C_total = [0, 0, 0,0,0,0,0] +TP_total = [0, 0, 0,0,0,0,0] +running_loss_t = [] +result_file = pd.DataFrame(columns=['model', 'loss', 'acc', 'TP_C', 'TP']) +classes=['happy','fear','sad','disgust','anger','surprise','Others'] +for i in range(1): + test = FERV39K.RafDataSet( + '', phase='test', + transform=transforms.Compose([transforms.ToPILImage(), + transforms.Resize((224, 224),antialias=True), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]),) + + test_loader = torch.utils.data.DataLoader(test, + batch_size=128, # 32 + # collate_fn=lambda x: default_collate( + # x).to(device), + shuffle=False, + pin_memory=True, + ) + print('Test set size:', test_loader.__len__()) + + model = FERV39K.MMNet() + # model = torch.load(os.path.join(path_save, modellist[i])) + model.load_state_dict(torch.load(path)) + model = model.to(device) + model.eval() + with torch.no_grad(): + running_loss = 0.0 + iter_cnt = 0 + bingo_cnt = 0 + sample_cnt = 0 + pos_label = [] + pos_pred = [] + TP = [0, 0, 0,0,0,0,0] + TP_C = [0, 0, 0,0,0,0,0] + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3, label_all,label_au + ) in enumerate(test_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + image_on0 = image_on0.to(device) + image_on1 = image_on1.to(device) + image_on2 = image_on2.to(device) + image_on3 = image_on3.to(device) + image_apex0 = image_apex0.to(device) + image_off0 = image_off0.to(device) + image_off1 = image_off1.to(device) + image_off2 = image_off2.to(device) + image_off3 = image_off3.to(device) + label_all = label_all.to(device) + label_au = label_au.to(device) + time1 = time.time() + ALL = model(image_on0, image_on1, image_on2, image_on3, image_apex0, + image_off0, image_off1, image_off2, image_off3, False) + loss = criterion(ALL, label_all) + running_loss += loss.item() + iter_cnt += 1 + _, pred = torch.max(ALL, 1) + true_label+=label_all.cpu().numpy().tolist() + pred_label+=pred.cpu().numpy().tolist() + batch_correct = torch.eq(pred, label_all).sum().float().item() + bingo_cnt += batch_correct + sample_cnt += batch_sz + # 0:negative, 1:positive, 2:surprise + for cls in range(7): + for element in pred: + if element == cls: + pos_pred.append(cls) + for element in label_all: + if element == cls: + pos_label.append(cls) + TP_C[cls] = TP_C[cls] + 1 + for elementp, elementl in zip(pred, label_all): + if elementp == elementl and elementp == cls: + TP[cls] = TP[cls] + 1 + print('Test Loss: {:.4f}, Test Acc: {:.4f}'.format(running_loss / iter_cnt, bingo_cnt / float(sample_cnt))) + running_loss = running_loss / iter_cnt + acc = bingo_cnt / float(sample_cnt) + acc = np.around(acc, 4) + # save the result to dataframe + print('Test Loss: {:.4f}, Test Acc: {:.4f}'.format(running_loss, acc)) + running_loss_t.append(running_loss) + print('TP_C:', TP_C) + print('TP:', TP) + print('------------------------------------') + TP_C_total = [x + y for x, y in zip(TP_C_total, TP_C)] + TP_total = [x + y for x, y in zip(TP_total, TP)] +print('TP_C_total:', TP_C_total) +print('TP_total:', TP_total) +# print('TP/TP_C:', [float(x) / float(y) +# for x, y in zip(TP_total, TP_C_total)]) +print("UAR:",sum([float(x) / float(y) + for x, y in zip(TP_total, TP_C_total)])/7) + +print("WAR:",sum(TP_total) / sum(TP_C_total)) +confusion_mat=confusion_matrix(true_label,pred_label) +# calculate the column sum of confusion matrix +col_sum = confusion_mat.sum(axis=0) +# print('running_loss_t:', running_loss_t) +# sort the model list as the order of loss and keep the top 5 +# modellist = [x for _, x in sorted(zip(running_loss_t, modellist))] +# # average the running_loss +# running_loss_t = [sum(running_loss_t) / len(running_loss_t)] +# # add new row to dataframe +# result_file.loc[len(modellist)] = ["ALL",running_loss_t ,sum(TP_total) / sum(TP_C_total), TP_C_total, TP_total] +# # save the result to csv +# result_file.to_csv('result1_file.csv', index=False) + +# ... +# Print the confusion matrix to the console +print(confusion_mat) +# convert to percentage with 2 decimal and 100% +confusion_mat = np.around(confusion_mat*100 / np.sum(confusion_mat, axis=1)[:, np.newaxis], decimals=2) +print(confusion_mat) +# Visualize the confusion matrix as a heatmap and trans color to more clear +fig, ax = plt.subplots(figsize=(10, 10)) +im = ax.imshow(confusion_mat, cmap=plt.cm.Blues) +# Add axis labels and tick marks +ax.set_xticks(np.arange(len(classes))) +ax.set_yticks(np.arange(len(classes))) +# delete the number 0 in TP_C_total +TP_C_total = [x for x in TP_C_total if x != 0] +# add total numnber of each classes to classes name +classes_pred = classes +# true +classes = [x + '\n' + str(y) for x, y in zip(classes, TP_C_total)] +ax.set_yticklabels(classes) +# predicted +classes_pred = [x + '\n' + str(y) for x, y in zip(classes_pred, col_sum)] +ax.set_xticklabels(classes_pred) +plt.setp(ax.get_xticklabels(), rotation=45, ha="right", + rotation_mode="anchor") + +# Add colorbar +cbar = ax.figure.colorbar(im, ax=ax) + +# Add title and axis labels +ax.set_title("Confusion Matrix") +ax.set_xlabel("Predicted Label") +ax.set_ylabel("True Label") +# add total numnber of each classes + +# Loop over data to add text annotations +for i in range(len(classes)): + for j in range(len(classes)): + text = ax.text(j, i, confusion_mat[i, j], + ha="center", va="center", color="black") + +# Show the plot +plt.show() + +# save the model +torch.save(model, os.path.join(path_save, 'model_FERV39K.pth')) \ No newline at end of file diff --git a/Model_load_new.py b/Model_load_new.py new file mode 100644 index 0000000..4a56d09 --- /dev/null +++ b/Model_load_new.py @@ -0,0 +1,187 @@ +import torch +import torchvision.transforms as transforms +import numpy as np +import os +import CASME3_7 +import time +import matplotlib.pyplot as plt +import pandas as pd +from sklearn.metrics import confusion_matrix +import warnings + +# Suppress the warning message related to the antialias parameter +warnings.filterwarnings("ignore", message="The default value of the antialias parameter of all the resizing transforms") +device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +torch.set_printoptions(precision=3, edgeitems=14, linewidth=350) +criterion = torch.nn.CrossEntropyLoss() +path = 'Model/casmeiii_5' +path_save = 'Model/casmeiii_5_all' +if(os.path.exists(path_save) == False): + os.mkdir(path_save) +modellist = os.listdir(path) +# sort the model list as the order of epoch +modellist.sort(key=lambda x: int(x[11:-4])) +true_label = [] +pred_label = [] +TP_C_total = [0, 0, 0,0,0,0,0] +TP_total = [0, 0, 0,0,0,0,0] +running_loss_t = [] +result_file = pd.DataFrame(columns=['model', 'loss', 'acc', 'TP_C', 'TP']) +loso=['spNO.1', 'spNO.2', 'spNO.3', 'spNO.5', 'spNO.6', 'spNO.7', 'spNO.8', 'spNO.9', 'spNO.10', 'spNO.11', 'spNO.12', 'spNO.13', 'spNO.14', 'spNO.15', 'spNO.17', 'spNO.39', 'spNO.40', 'spNO.41', 'spNO.42', 'spNO.77', 'spNO.138', 'spNO.139', 'spNO.142', 'spNO.143', 'spNO.144', 'spNO.145', 'spNO.146', 'spNO.147', 'spNO.148', 'spNO.149', 'spNO.150', 'spNO.152', 'spNO.153', 'spNO.154', 'spNO.155', 'spNO.157', 'spNO.159', 'spNO.160', 'spNO.161', 'spNO.162', 'spNO.163', 'spNO.165', 'spNO.166', 'spNO.167', 'spNO.168', 'spNO.169', 'spNO.170', 'spNO.171', 'spNO.172', 'spNO.173', 'spNO.174', 'spNO.175', 'spNO.176', 'spNO.177', 'spNO.178', 'spNO.179', 'spNO.180', 'spNO.181', 'spNO.182', 'spNO.183', 'spNO.184', 'spNO.185', 'spNO.186', 'spNO.187', 'spNO.188', 'spNO.189', 'spNO.190', 'spNO.192', 'spNO.194', 'spNO.195', 'spNO.196', 'spNO.197', 'spNO.198', 'spNO.200', 'spNO.201', 'spNO.202', 'spNO.203', 'spNO.204', 'spNO.206', 'spNO.207', 'spNO.208', 'spNO.209', 'spNO.210', 'spNO.211', 'spNO.212', 'spNO.213', 'spNO.214', 'spNO.216', 'spNO.217'] +classes=['happy','fear','disgust','surprise','Others'] +for i in range(len(modellist)): + test = CASME3_7.RafDataSet( + '/home/drink36/Desktop/apex_frame_take/casme3_result_2', phase='test', num_loso=loso[i], + transform=transforms.Compose([transforms.ToPILImage(), + transforms.Resize((224, 224),antialias=True), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]),) + + test_loader = torch.utils.data.DataLoader(test, + batch_size=1, # 32 + # collate_fn=lambda x: default_collate( + # x).to(device), + shuffle=False, + pin_memory=True, + ) + if(test_loader.__len__() == 0): + continue + model = CASME3_7.MMNet() + # model = torch.load(os.path.join(path_save, modellist[i])) + model.load_state_dict(torch.load(os.path.join(path, modellist[i]))) + model = model.to(device) + model.eval() + with torch.no_grad(): + running_loss = 0.0 + iter_cnt = 0 + bingo_cnt = 0 + sample_cnt = 0 + pos_label = [] + pos_pred = [] + TP = [0, 0, 0,0,0,0,0] + TP_C = [0, 0, 0,0,0,0,0] + for batch_i, ( + image_on0, image_on1, image_on2, image_on3, image_apex0, image_off0, image_off1, image_off2, + image_off3, label_all,label_au + ) in enumerate(test_loader): + batch_sz = image_on0.size(0) + b, c, h, w = image_on0.shape + image_on0 = image_on0.to(device) + image_on1 = image_on1.to(device) + image_on2 = image_on2.to(device) + image_on3 = image_on3.to(device) + image_apex0 = image_apex0.to(device) + image_off0 = image_off0.to(device) + image_off1 = image_off1.to(device) + image_off2 = image_off2.to(device) + image_off3 = image_off3.to(device) + label_all = label_all.to(device) + label_au = label_au.to(device) + time1 = time.time() + ALL = model(image_on0, image_on1, image_on2, image_on3, image_apex0, + image_off0, image_off1, image_off2, image_off3, False) + loss = criterion(ALL, label_all) + running_loss += loss.item() + iter_cnt += 1 + _, pred = torch.max(ALL, 1) + true_label+=label_all.cpu().numpy().tolist() + pred_label+=pred.cpu().numpy().tolist() + batch_correct = torch.eq(pred, label_all).sum().float().item() + bingo_cnt += batch_correct + sample_cnt += batch_sz + # 0:negative, 1:positive, 2:surprise + for cls in range(7): + for element in pred: + if element == cls: + pos_pred.append(cls) + for element in label_all: + if element == cls: + pos_label.append(cls) + TP_C[cls] = TP_C[cls] + 1 + for elementp, elementl in zip(pred, label_all): + if elementp == elementl and elementp == cls: + TP[cls] = TP[cls] + 1 + running_loss = running_loss / iter_cnt + acc = bingo_cnt / float(sample_cnt) + acc = np.around(acc, 4) + # save the model + torch.save(model, os.path.join(path_save, modellist[i])) + # save the result to dataframe + result_file.loc[i] = [modellist[i], running_loss, acc, TP_C, TP] + print('model:', modellist[i]) + print('Test Loss: {:.4f}, Test Acc: {:.4f}'.format(running_loss, acc)) + running_loss_t.append(running_loss) + print('TP_C:', TP_C) + print('TP:', TP) + print('------------------------------------') + TP_C_total = [x + y for x, y in zip(TP_C_total, TP_C)] + TP_total = [x + y for x, y in zip(TP_total, TP)] +print('TP_C_total:', TP_C_total) +print('TP_total:', TP_total) +# print('TP/TP_C:', [float(x) / float(y) +# for x, y in zip(TP_total, TP_C_total)]) +# UAR (Unweighted Average Recall) +TP_C_total = [x for x in TP_C_total if x != 0] +TP_total = [x for x in TP_total if x != 0] +print("UAR:",sum([float(x) / float(y) + for x, y in zip(TP_total, TP_C_total)])/5) +# WAR +print("WAR:",sum(TP_total) / sum(TP_C_total)) +confusion_mat=confusion_matrix(true_label,pred_label) +col_sum = confusion_mat.sum(axis=0) +# print('running_loss_t:', running_loss_t) +# sort the model list as the order of loss and keep the top 5 +# modellist = [x for _, x in sorted(zip(running_loss_t, modellist))] +# # average the running_loss +# running_loss_t = [sum(running_loss_t) / len(running_loss_t)] +# # add new row to dataframe +# result_file.loc[len(modellist)] = ["ALL",running_loss_t ,sum(TP_total) / sum(TP_C_total), TP_C_total, TP_total] +# # save the result to csv +# result_file.to_csv('result1_file.csv', index=False) + +# ... +# Print the confusion matrix to the console +print(confusion_mat) +# convert to percentage with 2 decimal and 100% +confusion_mat = np.around(confusion_mat*100 / np.sum(confusion_mat, axis=1)[:, np.newaxis], decimals=2) + +print(confusion_mat) +# Visualize the confusion matrix as a heatmap and trans color to more clear +fig, ax = plt.subplots(figsize=(10, 10)) +im = ax.imshow(confusion_mat, cmap=plt.cm.Blues) + +# Add axis labels and tick marks +ax.set_xticks(np.arange(len(classes))) +ax.set_yticks(np.arange(len(classes))) +# delete the number 0 in TP_C_total +TP_C_total = [x for x in TP_C_total if x != 0] +# add total numnber of each classes to classes name +classes_pred = classes +# true +classes = [x + '\n' + str(y) for x, y in zip(classes, TP_C_total)] +ax.set_yticklabels(classes) +# predicted +classes_pred = [x + '\n' + str(y) for x, y in zip(classes_pred, col_sum)] +ax.set_xticklabels(classes_pred) + +plt.setp(ax.get_xticklabels(), rotation=45, ha="right", + rotation_mode="anchor") + +# Add colorbar +cbar = ax.figure.colorbar(im, ax=ax) + +# Add title and axis labels +ax.set_title("Confusion Matrix") +ax.set_xlabel("Predicted Label") +ax.set_ylabel("True Label") +# add total numnber of each classes + +# Loop over data to add text annotations +for i in range(len(classes)): + for j in range(len(classes)): + text = ax.text(j, i, confusion_mat[i, j], + ha="center", va="center", color="black") + +# Show the plot +plt.show() \ No newline at end of file diff --git a/PC_module.py b/PC_module.py old mode 100644 new mode 100755 index 6d37fe2..260aa08 --- a/PC_module.py +++ b/PC_module.py @@ -1,326 +1,326 @@ -# Copyright (c) 2015-present, Facebook, Inc. -# All rights reserved. -import torch -import torch.nn as nn -from functools import partial - -from timm.models.vision_transformer import _cfg -from timm.models.registry import register_model -from timm.models.layers import trunc_normal_ -import math -import logging -from functools import partial -from collections import OrderedDict - -import torch -import torch.nn as nn -import torch.nn.functional as F -from itertools import repeat -from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -import collections.abc -def drop_path(x, drop_prob: float = 0., training: bool = False): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, - the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... - See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for - changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use - 'survival rate' as the argument. - - """ - if drop_prob == 0. or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) - random_tensor.floor_() # binarize - output = x.div(keep_prob) * random_tensor - return output - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - """ - def __init__(self, drop_prob=None): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training) -def _ntuple(n): - def parse(x): - if isinstance(x, collections.abc.Iterable): - return x - return tuple(repeat(x, n)) - return parse - - -to_1tuple = _ntuple(1) -to_2tuple = _ntuple(2) -to_3tuple = _ntuple(3) -to_4tuple = _ntuple(4) -to_ntuple = _ntuple -__all__ = [ - 'deit_tiny_patch16_224', 'deit_small_patch16_224', 'deit_base_patch16_224', - 'deit_tiny_distilled_patch16_224', 'deit_small_distilled_patch16_224', - 'deit_base_distilled_patch16_224', 'deit_base_patch16_384', - 'deit_base_distilled_patch16_384', -] - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class Attention(nn.Module): - def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights - self.scale = qk_scale or head_dim ** -0.5 - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x): - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - varq = torch.var(q, dim=2).sum(dim=2).sum()/B/N - vark = torch.var(k, dim=2).sum(dim=2).sum()/B/N - varv = torch.var(v, dim=2).sum(dim=2).sum()/B/N - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class Block(nn.Module): - - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - def forward(self, x): - x = x + self.drop_path(self.attn(self.norm1(x))) - x = x + self.drop_path(self.mlp(self.norm2(x))) - return x - - -class PatchEmbed(nn.Module): - """ Image to Patch Embedding - """ - def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) - self.img_size = img_size - self.patch_size = patch_size - self.num_patches = num_patches - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) - - def forward(self, x): - B, C, H, W = x.shape - # FIXME look at relaxing size constraints - assert H == self.img_size[0] and W == self.img_size[1], \ - f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." - x = self.proj(x).flatten(2).transpose(1, 2) - return x - - -class HybridEmbed(nn.Module): - """ CNN Feature Map Embedding - Extract feature map from CNN, flatten, project to embedding dim. - """ - def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768): - super().__init__() - assert isinstance(backbone, nn.Module) - img_size = to_2tuple(img_size) - self.img_size = img_size - self.backbone = backbone - if feature_size is None: - with torch.no_grad(): - # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature - # map for all networks, the feature metadata has reliable channel and stride info, but using - # stride to calc feature dim requires info about padding of each stage that isn't captured. - training = backbone.training - if training: - backbone.eval() - o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1])) - if isinstance(o, (list, tuple)): - o = o[-1] # last feature if backbone outputs list/tuple of features - feature_size = o.shape[-2:] - feature_dim = o.shape[1] - backbone.train(training) - else: - feature_size = to_2tuple(feature_size) - if hasattr(self.backbone, 'feature_info'): - feature_dim = self.backbone.feature_info.channels()[-1] - else: - feature_dim = self.backbone.num_features - self.num_patches = feature_size[0] * feature_size[1] - self.proj = nn.Conv2d(feature_dim, embed_dim, 1) - - def forward(self, x): - x = self.backbone(x) - if isinstance(x, (list, tuple)): - x = x[-1] # last feature if backbone outputs list/tuple of features - x = self.proj(x).flatten(2).transpose(1, 2) - return x - - -###Position Calibration Module -class VisionTransformer_POS(nn.Module): - """ Vision Transformer - - A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - - https://arxiv.org/abs/2010.11929 - """ - def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, - num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0.15, hybrid_backbone=None, norm_layer=None): - """ - Args: - img_size (int, tuple): input image size - patch_size (int, tuple): patch size - in_chans (int): number of input channels - num_classes (int): number of classes for classification head - embed_dim (int): embedding dimension - depth (int): depth of transformer - num_heads (int): number of attention heads - mlp_ratio (int): ratio of mlp hidden dim to embedding dim - qkv_bias (bool): enable bias for qkv if True - qk_scale (float): override default qk scale of head_dim ** -0.5 if set - representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set - drop_rate (float): dropout rate - attn_drop_rate (float): attention dropout rate - drop_path_rate (float): stochastic depth rate - hybrid_backbone (nn.Module): CNN backbone to use in-place of PatchEmbed module - norm_layer: (nn.Module): normalization layer - """ - super().__init__() - norm_layer=partial(nn.LayerNorm, eps=1e-6) - self.num_classes = num_classes - self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models - norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) - - if hybrid_backbone is not None: - self.patch_embed = HybridEmbed( - hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim) - else: - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) - num_patches = self.patch_embed.num_patches - - self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) - self.pos_embed = nn.Parameter(torch.zeros(1, 196, embed_dim)) - self.pos_drop = nn.Dropout(p=drop_rate) - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule - self.blocks = nn.ModuleList([ - Block( - dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) - for i in range(depth)]) - self.norm = norm_layer(embed_dim) - - # Representation layer - if representation_size: - self.num_features = representation_size - self.pre_logits = nn.Sequential(OrderedDict([ - ('fc', nn.Linear(embed_dim, representation_size)), - ('act', nn.Tanh()) - ])) - else: - self.pre_logits = nn.Identity() - - # Classifier head - self.head = nn.Linear(self.num_features, 5) if num_classes > 0 else nn.Identity() - # self.to_Mask = nn.Sequential(nn.Conv2d(in_channels=self.num_features,out_channels=1,kernel_size=3,padding=1), - # nn.Hardsigmoid(), - # ) - # self.to_Mask = nn.Linear(self.num_features,1) - self.to_Mask = nn.Sequential(nn.Linear(self.num_features,1), - nn.Sigmoid(), - ) - trunc_normal_(self.pos_embed, std=.02) - trunc_normal_(self.cls_token, std=.02) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'pos_embed', 'cls_token'} - - def get_classifier(self): - return self.head - - def reset_classifier(self, num_classes, global_pool=''): - self.num_classes = num_classes - self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() - - def forward_features(self, x): - B = x.shape[0] - x = self.patch_embed(x) - - - x = x + self.pos_embed - x = self.pos_drop(x) - - - for blk in self.blocks: - x = blk(x) - - - x = self.norm(x) - x = self.pre_logits(x) - return x - - def forward(self, x): - x = self.forward_features(x) - - return x - - - - - +# Copyright (c) 2015-present, Facebook, Inc. +# All rights reserved. +import torch +import torch.nn as nn +from functools import partial + +from timm.models.vision_transformer import _cfg +from timm.models.registry import register_model +from timm.models.layers import trunc_normal_ +import math +import logging +from functools import partial +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from itertools import repeat +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +import collections.abc +def drop_path(x, drop_prob: float = 0., training: bool = False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple +__all__ = [ + 'deit_tiny_patch16_224', 'deit_small_patch16_224', 'deit_base_patch16_224', + 'deit_tiny_distilled_patch16_224', 'deit_small_distilled_patch16_224', + 'deit_base_distilled_patch16_224', 'deit_base_patch16_384', + 'deit_base_distilled_patch16_384', +] + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + varq = torch.var(q, dim=2).sum(dim=2).sum()/B/N + vark = torch.var(k, dim=2).sum(dim=2).sum()/B/N + varv = torch.var(v, dim=2).sum(dim=2).sum()/B/N + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x): + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class HybridEmbed(nn.Module): + """ CNN Feature Map Embedding + Extract feature map from CNN, flatten, project to embedding dim. + """ + def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768): + super().__init__() + assert isinstance(backbone, nn.Module) + img_size = to_2tuple(img_size) + self.img_size = img_size + self.backbone = backbone + if feature_size is None: + with torch.no_grad(): + # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature + # map for all networks, the feature metadata has reliable channel and stride info, but using + # stride to calc feature dim requires info about padding of each stage that isn't captured. + training = backbone.training + if training: + backbone.eval() + o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1])) + if isinstance(o, (list, tuple)): + o = o[-1] # last feature if backbone outputs list/tuple of features + feature_size = o.shape[-2:] + feature_dim = o.shape[1] + backbone.train(training) + else: + feature_size = to_2tuple(feature_size) + if hasattr(self.backbone, 'feature_info'): + feature_dim = self.backbone.feature_info.channels()[-1] + else: + feature_dim = self.backbone.num_features + self.num_patches = feature_size[0] * feature_size[1] + self.proj = nn.Conv2d(feature_dim, embed_dim, 1) + + def forward(self, x): + x = self.backbone(x) + if isinstance(x, (list, tuple)): + x = x[-1] # last feature if backbone outputs list/tuple of features + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +###Position Calibration Module +class VisionTransformer_POS(nn.Module): + """ Vision Transformer + + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - + https://arxiv.org/abs/2010.11929 + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.15, hybrid_backbone=None, norm_layer=None): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + qk_scale (float): override default qk scale of head_dim ** -0.5 if set + representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + hybrid_backbone (nn.Module): CNN backbone to use in-place of PatchEmbed module + norm_layer: (nn.Module): normalization layer + """ + super().__init__() + norm_layer=partial(nn.LayerNorm, eps=1e-6) + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + + if hybrid_backbone is not None: + self.patch_embed = HybridEmbed( + hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim) + else: + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, 196, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + # Representation layer + if representation_size: + self.num_features = representation_size + self.pre_logits = nn.Sequential(OrderedDict([ + ('fc', nn.Linear(embed_dim, representation_size)), + ('act', nn.Tanh()) + ])) + else: + self.pre_logits = nn.Identity() + + # Classifier head + self.head = nn.Linear(self.num_features, 5) if num_classes > 0 else nn.Identity() + # self.to_Mask = nn.Sequential(nn.Conv2d(in_channels=self.num_features,out_channels=1,kernel_size=3,padding=1), + # nn.Hardsigmoid(), + # ) + # self.to_Mask = nn.Linear(self.num_features,1) + self.to_Mask = nn.Sequential(nn.Linear(self.num_features,1), + nn.Sigmoid(), + ) + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + B = x.shape[0] + x = self.patch_embed(x) + + + x = x + self.pos_embed + x = self.pos_drop(x) + + + for blk in self.blocks: + x = blk(x) + + + x = self.norm(x) + x = self.pre_logits(x) + return x + + def forward(self, x): + x = self.forward_features(x) + + return x + + + + + diff --git a/README.md b/README.md old mode 100644 new mode 100755 index d38aa84..8980541 --- a/README.md +++ b/README.md @@ -1,20 +1,22 @@ -# MMNet -A novel two-branch （micro-expression recognition）MER paradigm -![](./pipline.png) -# Training on CASME II dataset -```bash -# 3-class MER -python CASME2_3.py -# 5-class MER -python CASME2_5.py -``` -# Citation -If you find our work useful, please consider citing our paper: -```bash -@inproceedings{li2022mmnet, - title={MMNet: Muscle motion-guided network for micro-expression recognition}, - author={Li, Hanting and Sui, Mingzhe and Zhu, Zhaoqing and Zhao, Feng}, - booktitle={Proceedings of the 31th International Joint Conference on Artificial Intelligence}, - year={2022} -} -``` +# MMNet +A novel two-branch （micro-expression recognition）MER paradigm +![](./pipline.png) +# Training on CASME II dataset +```bash +# 3-class MER +python CASME2_3.py +# 5-class MER +python CASME2_5.py +``` +# Citation +# also train on CASME3_7,CASME3_5,SMIC,FERV39K by drink36 +# We change this paper's full connect layer to 7 classes, You can find our model here https://drive.google.com/drive/folders/1U1f4GEfxPxGxPaUFaFvXOarc4m5xtPWy?usp=sharing +If you find our work useful, please consider citing our paper: +```bash +@inproceedings{li2022mmnet, + title={MMNet: Muscle motion-guided network for micro-expression recognition}, + author={Li, Hanting and Sui, Mingzhe and Zhu, Zhaoqing and Zhao, Feng}, + booktitle={Proceedings of the 31th International Joint Conference on Artificial Intelligence}, + year={2022} +} +``` diff --git a/analyze.py b/analyze.py new file mode 100644 index 0000000..1329a96 --- /dev/null +++ b/analyze.py @@ -0,0 +1,46 @@ +import pandas as pd +import os +import numpy as np +import csv +path='result_file.csv' +# read ['TP_C'] and ['TP'] as float list +df=pd.read_csv(path) +all=df.iloc[89] +all=all.tolist() +#let all['acc'] be float +for i in range(3,5): + all[i]=all[i].replace('[','') + all[i]=all[i].replace(']','') + all[i]=all[i].replace(' ','') + all[i]=all[i].split(',') + all[i]=[float(k) for k in all[i]] +all[3]=np.array(all[3]) +all[4]=np.array(all[4]) +dataframe=pd.DataFrame(columns = ['model_name','happy','fear','sad','disgust','anger','surprise','others','all_acc']) +# add model name to all[4]/all[3]'s result +temp=np.append(['ALL'],all[4]/all[3]) +temp=np.append(temp,all[2]) +dataframe.loc[(0,)]=temp +print(dataframe) +dataframe.to_csv('result_file_analyze.csv',index=False) +# for i in range(89): +# all=df.iloc[i].tolist() +# for j in range(3,5): +# all[j]=all[j].replace('[','') +# all[j]=all[j].replace(']','') +# all[j]=all[j].replace(' ','') +# all[j]=all[j].split(',') +# all[j]=[float(k) for k in all[j]] +# all[3]=np.array(all[3]) +# all[4]=np.array(all[4]) +# temp=np.append(all[0],all[4]/all[3]) +# temp=np.append(temp,all[2]) +# dataframe.loc[(i+1,)]=temp +# # set nan to 0 to every cell +# for i in range(1,6): +# dataframe.iloc[:,i]=dataframe.iloc[:,i].fillna(0) +# print(dataframe.iloc[:,i]) +# print(dataframe) +# save to csv +# dataframe.to_csv('result_file_analyze.csv',index=False) + diff --git a/cas(me)3_part_A_ME_label_JpgIndex_v2.xlsx b/cas(me)3_part_A_ME_label_JpgIndex_v2.xlsx new file mode 100755 index 0000000..5fc9c92 Binary files /dev/null and b/cas(me)3_part_A_ME_label_JpgIndex_v2.xlsx differ diff --git a/model_FERV39K.pth b/model_FERV39K.pth new file mode 100644 index 0000000..545b51b Binary files /dev/null and b/model_FERV39K.pth differ diff --git a/pre_processing.py b/pre_processing.py new file mode 100644 index 0000000..4409db0 --- /dev/null +++ b/pre_processing.py @@ -0,0 +1,23 @@ +import csv +import os +import sys +import numpy as np +import pandas as pd +path='/home/drink36/Desktop/apex_frame_take/casme3_result_2' + +df=pd.read_csv('result_2.csv') +# delete element which emotion equals to 'anger','surprise' +class_list=['happy','fear','sad','disgust','anger','surprise','others'] +# show emotion number +for i in range(7): + print(df[df['emotion']==class_list[i]].shape[0]) +# delete anger and surprise + + +df=df.drop(df[(df['emotion']=='anger')].index) +df=df.drop(df[(df['emotion']=='sad')].index) +print(df) +print(len(df)) +df.to_csv('result_3.csv',index=False) +# if subject list is not in list1, then delete it +# sorted only by number \ No newline at end of file diff --git a/result.csv b/result.csv new file mode 100644 index 0000000..7e642f9 --- /dev/null +++ b/result.csv @@ -0,0 +1,809 @@ 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a/result_file_analyze.csv b/result_file_analyze.csv new file mode 100644 index 0000000..e0b30ff --- /dev/null +++ b/result_file_analyze.csv @@ -0,0 +1,4 @@ +model_name,happy ,fear ,sad ,disgust,anger,surprise ,others ,all_acc +ALL, 0.16666666666666666,0.11538461538461539,0.057692307692307696,0.48 ,0.05 ,0.5428571428571428,0.3111111111111111,0.3453145057766367 +"[54, 78, 52, 225, 60, 175, 135]" +"[ 9, 9 , 3 , 108, 3 , 95 , 42]" \ No newline at end of file diff --git a/t_result_ferv39k.csv b/t_result_ferv39k.csv new file mode 100644 index 0000000..fd7d479 --- /dev/null +++ b/t_result_ferv39k.csv @@ -0,0 +1,5617 @@ +File_name , Apex_predicted,Total_frame,Emotion +/home/drink36/Desktop/Dataset/39K/Face/Action/Happy/0059,15,24,0 +/home/drink36/Desktop/Dataset/39K/Face/Action/Happy/0005,9,36,0 +/home/drink36/Desktop/Dataset/39K/Face/Action/Happy/0021,13,29,0 +/home/drink36/Desktop/Dataset/39K/Face/Action/Happy/0025,7,16,0 +/home/drink36/Desktop/Dataset/39K/Face/Action/Happy/0058,6,32,0 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+26,EP18_50,138,78,161,disgust +26,EP18_51,62,21,81,disgust diff --git a/test_Model.py b/test_Model.py new file mode 100755 index 0000000..153ed06 --- /dev/null +++ b/test_Model.py @@ -0,0 +1,104 @@ +import torch +from torch import nn +from torchvision import models +from torchvision import transforms +import numpy as np +import matplotlib.pyplot as plt +import os +import cv2 +import time +# print(torch.cuda.is_available()) # true 查看GPU是否可用 +# print(torch.cuda.device_count()) # GPU数量， 1 +# print(torch.cuda.current_device()) # 当前GPU的索引， 0 +# print(torch.cuda.get_device_name(0)) # 输出GPU名称 +face_label = ['negative', 'positive', 'surprise'] +modelpath = 'Model/Hybird_all/model_12.pth' +testpath = 'test_dataset' +model = torch.load(modelpath) +model = model.cuda() +model.eval() +img_name = os.listdir(testpath) +img_path = [os.path.join(testpath, img_name[i]) for i in range(len(img_name))] +# define the transform +transformer = transforms.Compose([transforms.ToPILImage(), + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225])]) +time1 = time.time() + +# read img frome test_dataset + +img_on0 = cv2.imread(img_path[0]) +img_apex0 = cv2.imread(img_path[1]) +img_off0 = cv2.imread(img_path[2]) +print(img_on0.shape) +print(img_apex0.shape) +print(img_off0.shape) +img_on0 = img_on0[:, :, ::-1] +img_apex0 = img_apex0[:, :, ::-1] +img_off0 = img_off0[:, :, ::-1] + +# transform the img +img_on0 = transformer(img_on0) +img_apex0 = transformer(img_apex0) +img_off0 = transformer(img_off0) +img_on1 = img_on0 +img_on2 = img_on0 +img_on3 = img_on0 +img_off1 = img_off0 +img_off2 = img_off0 +img_off3 = img_off0 +ALL = torch.cat((img_on0, img_on1, img_on2, img_on3, img_apex0, + img_off0, img_off1, img_off2, img_off3), dim=0) + +img_on0 = ALL[0:3, :, :] +img_on1 = ALL[3:6, :, :] +img_on2 = ALL[6:9, :, :] +img_on3 = ALL[9:12, :, :] +img_apex0 = ALL[12:15, :, :] +img_off0 = ALL[15:18, :, :] +img_off1 = ALL[18:21, :, :] +img_off2 = ALL[21:24, :, :] +img_off3 = ALL[24:27, :, :] +# let the img in the cuda +# add the batch dimension +img_on0 = img_on0.unsqueeze(0) +img_on1 = img_on1.unsqueeze(0) +img_on2 = img_on2.unsqueeze(0) +img_on3 = img_on3.unsqueeze(0) +img_apex0 = img_apex0.unsqueeze(0) +img_off0 = img_off0.unsqueeze(0) +img_off1 = img_off1.unsqueeze(0) +img_off2 = img_off2.unsqueeze(0) +img_off3 = img_off3.unsqueeze(0) +img_on0 = img_on0.cuda() +img_on1 = img_on1.cuda() +img_on2 = img_on2.cuda() +img_on3 = img_on3.cuda() +img_apex0 = img_apex0.cuda() +img_off0 = img_off0.cuda() +img_off1 = img_off1.cuda() +img_off2 = img_off2.cuda() +img_off3 = img_off3.cuda() +# let face_label in the cuda +# predict the img +print('time to preprocess: ', time.time() - time1, 's') +preprocess_time = time.time() - time1 + +total_time = 0 +for i in range(100): + time2 = time.time() + with torch.no_grad(): + output = model(img_on0, img_on1, img_on2, img_on3, img_apex0, + img_off0, img_off1, img_off2, img_off3, False) + print(time.time() - time2, 's') + total_time += time.time() - time2 + _, pred = torch.max(output, 1) + #print('time to predict: ', time.time() - time2, 's') + # change the tensor to numpy + pred = (pred.cpu().numpy())[0] + print(face_label[pred]) + +print('total time: ', time.time() - time1, 's') +print('average time: ', total_time/100, 's')