From b429f9807da158bc9921011ec8df3e1ec0674a7e Mon Sep 17 00:00:00 2001 From: Guanhua Zhang Date: Tue, 7 May 2024 17:01:12 +0200 Subject: [PATCH] init --- CRT.py | 438 +++++++++++++++++++++++++++++++++++++++++++ README.md | 69 +++++++ base_models.py | 274 +++++++++++++++++++++++++++ env.yaml | 276 +++++++++++++++++++++++++++ main.py | 77 ++++++++ pretrained_model.pkl | Bin 0 -> 12799711 bytes utils.py | 52 +++++ 7 files changed, 1186 insertions(+) create mode 100644 CRT.py create mode 100644 README.md create mode 100644 base_models.py create mode 100644 env.yaml create mode 100644 main.py create mode 100644 pretrained_model.pkl create mode 100644 utils.py diff --git a/CRT.py b/CRT.py new file mode 100644 index 0000000..a11a4d6 --- /dev/null +++ b/CRT.py @@ -0,0 +1,438 @@ +import pdb, copy +import numpy as np +import torch +from torch import nn +import torch.nn.functional as F +import torch.optim as optim +from torch.utils.data import DataLoader + +from einops import rearrange, repeat +from einops.layers.torch import Rearrange + +from base_models import MLP, resnet1d18, SSLDataSet +from sklearn.metrics import f1_score + +class cnn_extractor(nn.Module): + def __init__(self, dim, input_plane): + super(cnn_extractor, self).__init__() + self.cnn = resnet1d18(input_channels=dim, inplanes=input_plane) + + def forward(self, x): + x = self.cnn(x) + return x + + +class PreNorm(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x, **kwargs): + return self.fn(self.norm(x), **kwargs) + + +class FeedForward(nn.Module): + def __init__(self, dim, hidden_dim, dropout=0.): + super().__init__() + self.net = nn.Sequential( + nn.Linear(dim, hidden_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, dim), + nn.Dropout(dropout) + ) + + def forward(self, x): + return self.net(x) + + +class Attention(nn.Module): + def __init__(self, dim, heads=8, dim_head=64, dropout=0.): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head ** -0.5 + + self.attend = nn.Softmax(dim=-1) + self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, dim), + nn.Dropout(dropout) + ) if project_out else nn.Identity() + + def forward(self, x): + qkv = self.to_qkv(x).chunk(3, dim=-1) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), qkv) + + dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale + + attn = self.attend(dots) + + out = torch.matmul(attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + return self.to_out(out) + + +class Transformer(nn.Module): + def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.): + super().__init__() + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append(nn.ModuleList([ + PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)), + PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout)) + ])) + + def forward(self, x): + for attn, ff in self.layers: + x = attn(x) + x + x = ff(x) + x + return x + + +class TFR(nn.Module): + def __init__(self, seq_len, patch_len, dim, depth, heads, mlp_dim, channels=12, + dim_head=64, dropout=0., emb_dropout=0.): + ''' + The encoder of CRT + ''' + super().__init__() + self.patch_len = patch_len + self.seq_len = seq_len + + assert seq_len % (4 * patch_len) == 0, \ + 'The seq_len should be 4 * n * patch_len, or there must be patch with both magnitude and phase data.' + num_patches = seq_len // patch_len + patch_dim = channels * patch_len + + self.to_patch = nn.Sequential(Rearrange('b c (n p1) -> b n c p1', p1=patch_len), + Rearrange('b n c p1 -> (b n) c p1')) + self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 3, dim)) + self.modal_embedding = nn.Parameter(torch.randn(3, 1, dim)) + self.cls_token = nn.Parameter(torch.randn(1, 3, dim)) + self.dropout = nn.Dropout(emb_dropout) + self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout) + + self.cnn1 = cnn_extractor(dim=channels, input_plane=dim // 8) # For temporal data + self.cnn2 = cnn_extractor(dim=channels, input_plane=dim // 8) # For magnitude data + self.cnn3 = cnn_extractor(dim=channels, input_plane=dim // 8) # For phase data + + def forward(self, x, opt): + batch, _, time_steps = x.shape + t, m, p = x[:, :, :time_steps // 2], x[:, :, time_steps // 2: time_steps * 3 // 4], x[:, :, -time_steps // 4:] + assert t.shape[-1] % opt.patch_len == 0 == m.shape[-1] % opt.patch_len == p.shape[-1] % opt.patch_len + + t, m, p = self.to_patch(t), self.to_patch(m), self.to_patch(p) + patch2seq = nn.Sequential(nn.AdaptiveAvgPool1d(1), + Rearrange('(b n) c 1 -> b n c', b=batch)) + + cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=batch) + x = torch.cat((cls_tokens[:, 0:1, :], patch2seq(self.cnn1(t)), + cls_tokens[:, 1:2, :], patch2seq(self.cnn2(m)), + cls_tokens[:, 2:3, :], patch2seq(self.cnn3(p))), dim=1) + + b, t, c = x.shape + time_steps = t - 3 + t_token_idx, m_token_idx, p_token_idx = 0, time_steps // 2 + 1, time_steps * 3 // 4 + 2 + x[:m_token_idx] += self.modal_embedding[:1] + x[m_token_idx: p_token_idx] += self.modal_embedding[1:2] + x[p_token_idx: ] += self.modal_embedding[2:] + x += self.pos_embedding[:, : t] + x = self.dropout(x) + x = self.transformer(x) + t_token, m_token, p_token = x[:, t_token_idx], x[:, m_token_idx], x[:, p_token_idx] + avg = (t_token + m_token + p_token) / 3 + return avg + + +def TFR_Encoder(seq_len, patch_len, dim, in_dim, depth): + vit = TFR(seq_len=seq_len, + patch_len=patch_len, + #num_classes=num_class, + dim=dim, + depth=depth, + heads=8, + mlp_dim=dim, + dropout=0.2, + emb_dropout=0.1, + channels=in_dim) + return vit + +class CRT(nn.Module): + def __init__( + self, + encoder, + decoder_dim, + decoder_depth=2, + decoder_heads=8, + decoder_dim_head=64, + patch_len = 20, + in_dim=12 + ): + super().__init__() + self.encoder = encoder + num_patches, encoder_dim = encoder.pos_embedding.shape[-2:] + self.to_patch = encoder.to_patch + pixel_values_per_patch = in_dim * patch_len + + # decoder parameters + self.modal_embedding = self.encoder.modal_embedding + self.mask_token = nn.Parameter(torch.randn(3, decoder_dim)) + self.decoder = Transformer(dim=decoder_dim, + depth=decoder_depth, + heads=decoder_heads, + dim_head=decoder_dim_head, + mlp_dim=decoder_dim) + self.decoder_pos_emb = nn.Embedding(num_patches, decoder_dim) + self.to_pixels = nn.ModuleList([nn.Linear(decoder_dim, pixel_values_per_patch) for i in range(3)]) + self.projs = nn.ModuleList([nn.Linear(decoder_dim, decoder_dim) for i in range(2)]) + + self.to_clicks = nn.Linear(decoder_dim, 2 * patch_len) + + def IDC_loss(self, tokens, encoded_tokens): + B, T, D = tokens.shape + tokens, encoded_tokens = F.normalize(tokens, dim=-1), F.normalize(encoded_tokens, dim=-1) + encoded_tokens = encoded_tokens.transpose(2, 1) + cross_mul = torch.exp(torch.matmul(tokens, encoded_tokens)) + mask = (1 - torch.eye(T)).unsqueeze(0).to(tokens.device) + cross_mul = cross_mul * mask + return torch.log(cross_mul.sum(-1).sum(-1)).mean(-1) + + def forward(self, x, clicks, mask_ratio=0.75, beta = 1e-4, beta2 = 1e-4): + device = x.device + patches = self.to_patch[0](x) + batch, num_patches, c, length = patches.shape + clickpatches = self.to_patch[0](clicks.unsqueeze(1).float()) + + num_masked = int(mask_ratio * num_patches) + rand_indices1 = torch.randperm(num_patches // 2, device=device) + masked_indices1 = rand_indices1[: num_masked // 2].sort()[0] + unmasked_indices1 = rand_indices1[num_masked // 2:].sort()[0] + rand_indices2 = torch.randperm(num_patches // 4, device=device) + masked_indices2, unmasked_indices2 = rand_indices2[: num_masked // 4].sort()[0], rand_indices2[num_masked // 4:].sort()[0] + rand_indices = torch.cat((masked_indices1, unmasked_indices1, + masked_indices2 + num_patches // 2, unmasked_indices2 + num_patches // 2, + masked_indices2 + num_patches // 4 * 3, unmasked_indices2 + num_patches // 4 * 3)) + + masked_num_t, masked_num_f = masked_indices1.shape[0], 2 * masked_indices2.shape[0] + unmasked_num_t, unmasked_num_f = unmasked_indices1.shape[0], 2 * unmasked_indices2.shape[0] + + tpatches = patches[:, : num_patches // 2, :, :] + mpatches, ppatches = patches[:, num_patches // 2: num_patches * 3 // 4, :, :], patches[:, -num_patches // 4:, :, :] + + unmasked_tpatches = tpatches[:, unmasked_indices1, :, :] + unmasked_mpatches, unmasked_ppatches = mpatches[:, unmasked_indices2, :, :], ppatches[:, unmasked_indices2, :, :] + t_tokens, m_tokens, p_tokens = self.to_patch[1](unmasked_tpatches), self.to_patch[1](unmasked_mpatches), self.to_patch[1](unmasked_ppatches) + t_tokens, m_tokens, p_tokens = self.encoder.cnn1(t_tokens), self.encoder.cnn2(m_tokens), self.encoder.cnn3(p_tokens) + Flat = nn.Sequential(nn.AdaptiveAvgPool1d(1), + Rearrange('(b n) c 1 -> b n c', b=batch)) + t_tokens, m_tokens, p_tokens = Flat(t_tokens), Flat(m_tokens), Flat(p_tokens) + ori_tokens = torch.cat((t_tokens, m_tokens, p_tokens), 1).clone() + + cls_tokens = repeat(self.encoder.cls_token, '() n d -> b n d', b=batch) + tokens = torch.cat((cls_tokens[:, 0:1, :], t_tokens, + cls_tokens[:, 1:2, :], m_tokens, + cls_tokens[:, 2:3, :], p_tokens), dim=1) + + t_idx, m_idx, p_idx = num_patches // 2 - 1, num_patches * 3 // 4 - 1, num_patches - 1 + pos_embedding = torch.cat((self.encoder.pos_embedding[:, 0:1, :], self.encoder.pos_embedding[:, unmasked_indices1 + 1, :], + self.encoder.pos_embedding[:, t_idx + 2: t_idx + 3], + self.encoder.pos_embedding[:, unmasked_indices2 + t_idx + 3, :], + self.encoder.pos_embedding[:, m_idx + 3: m_idx + 4], + self.encoder.pos_embedding[:, unmasked_indices2 + m_idx + 4, :]), dim=1) + + modal_embedding = torch.cat((repeat(self.modal_embedding[0], '1 d -> 1 n d', n=unmasked_num_t + 1), + repeat(self.modal_embedding[1], '1 d -> 1 n d', n=unmasked_num_f // 2 + 1), + repeat(self.modal_embedding[2], '1 d -> 1 n d', n=unmasked_num_f // 2 + 1)), dim=1) + + tokens = tokens + pos_embedding + modal_embedding + + encoded_tokens = self.encoder.transformer(tokens) # tokens: unmasked tokens + CLS tokens + + t_idx, m_idx, p_idx = unmasked_num_t, unmasked_num_f // 2 + unmasked_num_t + 1, -1 + + idc_loss = self.IDC_loss(self.projs[0](ori_tokens), self.projs[1](torch.cat(([encoded_tokens[:, 1: t_idx+1], encoded_tokens[:, t_idx+2: m_idx+1], encoded_tokens[:, m_idx+2: ]]), dim=1))) + + decoder_tokens = encoded_tokens + + mask_tokens1 = repeat(self.mask_token[0], 'd -> b n d', b=batch, n=masked_num_t) + mask_tokens2 = repeat(self.mask_token[1], 'd -> b n d', b=batch, n=masked_num_f // 2) + mask_tokens3 = repeat(self.mask_token[2], 'd -> b n d', b=batch, n=masked_num_f // 2) + mask_tokens = torch.cat((mask_tokens1, mask_tokens2, mask_tokens3), dim=1) + + decoder_pos_emb = self.decoder_pos_emb(torch.cat( + (masked_indices1, masked_indices2 + num_patches // 2, masked_indices2 + num_patches * 3 // 4))) + + mask_tokens = mask_tokens + decoder_pos_emb + decoder_tokens = torch.cat((decoder_tokens, mask_tokens), dim=1) + decoded_tokens = self.decoder(decoder_tokens) + + mask_tokens = decoded_tokens[:, -mask_tokens.shape[1]:] + + pred_pixel_values_t = self.to_pixels[0](torch.cat((decoder_tokens[:, 1: t_idx + 1], mask_tokens[:, : masked_num_t]), 1)) + pred_pixel_values_m = self.to_pixels[1](torch.cat((decoder_tokens[:, t_idx+2: m_idx+1], mask_tokens[:, masked_num_t: masked_num_f // 2 + masked_num_t]), 1)) + pred_pixel_values_p = self.to_pixels[2](torch.cat((decoder_tokens[:, m_idx+2: -mask_tokens.shape[1]], mask_tokens[:, -masked_num_f // 2:]), 1)) + pred_pixel_values = torch.cat((pred_pixel_values_t, pred_pixel_values_m, pred_pixel_values_p), dim=1) + + recon_loss = F.mse_loss(pred_pixel_values, rearrange(patches[:,rand_indices], 'b n c p -> b n (c p)')) + + rmvcls_embedding = torch.cat((decoder_tokens[:, 1: t_idx + 1], mask_tokens[:, : masked_num_t]), 1) + click_pred = self.to_clicks(rmvcls_embedding) + click_pred = rearrange(click_pred, 'b n (c p) -> b n c p', p=2) + click_pred = rearrange(click_pred, 'b n c p -> (b n c) p') + clicksGT = clickpatches[:, rand_indices1].squeeze() + clicksGT = rearrange(clicksGT, 'b n c -> (b n c)') + clicksOH = F.one_hot(clicksGT.to(torch.int64), num_classes=2) + pos_weight = (clicks==0).sum()/(clicks==1).sum() + click_pred_loss = nn.BCEWithLogitsLoss(pos_weight=pos_weight)(click_pred, clicksOH.float()) + + return recon_loss + beta * idc_loss + beta2 * click_pred_loss + +class Model(nn.Module): + def __init__(self, opt): + super(Model, self).__init__() + self.opt = opt + self.encoder = TFR_Encoder(seq_len=opt.seq_len, + patch_len=opt.patch_len, + dim=opt.dim, + in_dim=opt.in_dim, + depth=opt.depth) + self.crt = CRT(encoder=self.encoder, + decoder_dim=opt.dim, + in_dim=opt.in_dim, + patch_len=opt.patch_len) + + def forward(self, x, clicks, ratio = 0.5): + return self.crt(x, clicks, mask_ratio=ratio, beta2=self.opt.beta2) + +def self_supervised_learning(model, X, clicks, opt, modelfile, min_ratio=0.3, max_ratio=0.8): + optimizer = optim.Adam(model.parameters(), opt.AElearningRate) + scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.9, patience=20) + + model.to(opt.device) + model.train() + + dataset = SSLDataSet(X, clicks) + dataloader = DataLoader(dataset, batch_size=opt.AEbatchSize, shuffle=True) + + losses = [] + for _ in range(opt.AEepochs): + for idx, batch in enumerate(dataloader): + x, clicks = tuple(t.to(opt.device) for t in batch) + loss = model.to(opt.device)(x, clicks, ratio=max(min_ratio, min(max_ratio, _ / opt.AEepochs))) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses.append(float(loss)) + if idx % 20 == 0: + print(idx,'/',len(dataloader), sum(losses) / len(losses)) + scheduler.step(_) + torch.save(model.to('cpu'), modelfile) + +class encoder_classifier(nn.Module): + def __init__(self, AE, opt): + super().__init__() + self.opt = opt + hidden_channels = 64 + self.inputdim = opt.dim + self.encoder = copy.deepcopy(AE.encoder) + self.classifier = nn.Sequential( + nn.Linear(self.inputdim, hidden_channels), + nn.ReLU(), + nn.Dropout(p = opt.Headdropout), + nn.Linear(hidden_channels, hidden_channels), + nn.ReLU(), + nn.Dropout(p = opt.Headdropout), + nn.Linear(hidden_channels, opt.num_class) + ) + # initialize the classifier + for m in self.classifier.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + nn.init.constant_(m.bias, 0) + def forward(self, x): + features = self.encoder(x, self.opt) + out = self.classifier(features) + return out + +def weight_init_layer(m): + if hasattr(m, 'reset_parameters'): + m.reset_parameters() +def weight_init_whole(m): + children = list(m.children()) + if len(children)==0: # No more children layers + m.apply(weight_init_layer) + else: # Get children layers + for c in children: + weight_init_whole(c) + +def finetuning(AE, train_set, valid_set, opt, modelfile, multi_label=True): + stage_info = {0: 'Train the classifier only', 1: 'Finetune the whole model'} + print('Stage %d: '%(opt.stage), stage_info[opt.stage]) + + train_loader = DataLoader(train_set, batch_size=opt.HeadbatchSize, shuffle=True) + model = encoder_classifier(AE, opt) + model.train() + + best_res = 0 + step = 0 + if opt.stage == 0: + optimizer = optim.Adam(model.classifier.parameters(), lr=opt.HeadlearningRate) + else: + lr = opt.HeadlearningRate/2 + optimizer = optim.Adam(model.parameters(), lr=lr) + scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.9, patience=20) + for epoch in range(opt.Headepochs): + for batch_idx, batch in enumerate(train_loader): + step += 1 + x, y, clicks = tuple(t.to(opt.device) for t in batch) + pred = model.to(opt.device)(x) + if not multi_label: + pos_weight = (y==0.).sum()/y.sum() + y = F.one_hot(y, num_classes=2) + #loss = nn.BCEWithLogitsLoss(pos_weight=pos_weight)(pred, y.squeeze().float()) + loss = nn.BCEWithLogitsLoss()(pred, y.squeeze().float()) + else: + if pred.shape[0]==1: + loss = nn.CrossEntropyLoss()(pred, y.long()) + else: + loss = nn.CrossEntropyLoss()(pred, y.squeeze().long()) + optimizer.zero_grad() + loss.backward() + optimizer.step() + if step % 10 == 0: + [trainacc, trainf1] = test(model, train_set, opt.HeadbatchSize, multi_label) + if trainf1 > best_res: + best_res = trainf1 + scheduler.step(best_res) + +def test(model, dataset, batch_size, multi_label): + def topAcc(GTlabel, pred_proba, top): + count = 0 + for i in range(GTlabel.shape[0]): + if (GTlabel[i]) in np.argsort(-pred_proba[i])[:top]: + count+=1.0 + return count/GTlabel.shape[0] + + model.eval() + model.to(model.opt.device) + testloader = DataLoader(dataset, batch_size=batch_size) + pred_prob = None + with torch.no_grad(): + for batch in testloader: + x, y, clicks = tuple(t.to(model.opt.device) for t in batch) + pred = model(x) + if pred_prob is None: + pred_prob = pred.cpu().detach().numpy() + else: + pred_prob = np.concatenate([pred_prob, pred.cpu().detach().numpy()], axis=0) + acc = topAcc(dataset.label.squeeze(), pred_prob, 1) + pred = np.argmax(pred_prob, axis=1) + f1 = f1_score(dataset.label.squeeze(), pred, average='macro') + + model.train() + return [acc, f1] \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..ec03ce6 --- /dev/null +++ b/README.md @@ -0,0 +1,69 @@ +
+

Mouse2Vec: Learning Reusable Semantic Representations of Mouse Behaviour

+ +**[Guanhua Zhang][4],   [Zhiming Hu][5],   [Mihai Bâce][3],   [Andreas Bulling][6]**
+**ACM CHI 2024**, Honolulu, Hawaii
+**[[Project][2]]** **[[Paper][7]]**
+---------------- + +# Setup +We recommend to setup a virtual environment using Anaconda.
+1. Create a conda environment and install dependencies + ```shell + conda env create --name mouse2vec --file=env.yaml + conda activate mouse2vec + ``` +2. Clone our repository to download our code and a pretrained model + ```shell + git clone this_repo.git + ``` + +# Run the code +Our code supports training using GPUs or CPUs. It will prioritise GPUs if available (line 45 in main.py). You can also assign a particular card via CUDA_VISIBLE_DEVICES (e.g., the following commands use GPU card no.3). +## Train Mouse2Vec Autoencoder +
+ +Execute +```shell +CUDA_VISIBLE_DEVICES=3 python main.py --ssl True +``` +## Use Mouse2Vec for Downstream Tasks + +We enable two ways to use Mouse2Vec on your datasets for downstream tasks. +1. Use the (frozen) pretrained model and only train a MLP-based classifier
+ +Execute +```shell +CUDA_VISIBLE_DEVICES=3 python main.py --ssl True --load True --stage 0 --testDataset [Your Dataset] +``` + +2. Finetune both Mouse2Vec and the classifier
+ +Execute +```shell +CUDA_VISIBLE_DEVICES=3 python main.py --ssl True --load True --stage 1 --testDataset [Your Dataset] +``` + +# Citation +If you find our code useful or use it in your own projects, please cite our paper: +``` +@inproceedings{zhang24_chi, + title = {Mouse2Vec: Learning Reusable Semantic Representations of Mouse Behaviour}, + author = {Zhang, Guanhua and Hu, Zhiming and B{\^a}ce, Mihai and Bulling, Andreas}, + year = {2024}, + pages = {1--17}, + booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)}, + doi = {10.1145/3613904.3642141} +} +``` + +# Acknowledgements +Our work relied on the codebase of [Cross Reconstruction Transformer][1]. Thanks to the authors for sharing their code. + +[1]: https://github.com/BobZwr/Cross-Reconstruction-Transformer +[2]: https://perceptualui.org/publications/zhang24_chi/ +[3]: https://scholar.google.com/citations?user=ku-t0MMAAAAJ&hl=en&oi=ao +[4]: https://scholar.google.com/citations?user=NqkK0GwAAAAJ&hl=en +[5]: https://scholar.google.com/citations?hl=en&user=OLB_xBEAAAAJ +[6]: https://www.perceptualui.org/people/bulling/ +[7]: https://perceptualui.org/publications/zhang24_chi.pdf \ No newline at end of file diff --git a/base_models.py b/base_models.py new file mode 100644 index 0000000..3ce6a05 --- /dev/null +++ b/base_models.py @@ -0,0 +1,274 @@ +import numpy as np, pdb +import functools + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import Dataset + + +class SSLDataSet(Dataset): + def __init__(self, data, clicks): + super(SSLDataSet, self).__init__() + self.data = data + self.clicks = clicks + + def __getitem__(self, idx): + return torch.tensor(self.data[idx], dtype=torch.float), torch.tensor(self.clicks[idx], dtype=torch.float) + + def __len__(self): + return self.data.shape[0] + +class FTDataSet(Dataset): + def __init__(self, data, label, clicks, actions=None, multi_label=False): + super(FTDataSet, self).__init__() + self.data = data + self.label = label + self.clicks = clicks + self.multi_label = multi_label + self.actions = actions + + def __getitem__(self, index): + if self.multi_label: + if self.actions is None: + return (torch.tensor(self.data[index], dtype=torch.float), torch.tensor(self.label[index], dtype=torch.float), torch.tensor(self.clicks[index], dtype=torch.float)) + else: + return (torch.tensor(self.data[index], dtype=torch.float), torch.tensor(self.label[index], dtype=torch.float), torch.tensor(self.clicks[index], dtype=torch.float), torch.tensor(self.actions[index], dtype=torch.float)) + else: + return (torch.tensor(self.data[index], dtype=torch.float), torch.tensor(self.label[index], dtype=torch.long), torch.tensor(self.clicks[index], dtype=torch.float)) + + def __len__(self): + return self.data.shape[0] + +# Resnet 1d + +def conv(in_planes, out_planes, stride=1, kernel_size=3): + "convolution with padding 自动使用zeros进行padding" + return nn.Conv1d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, + padding=(kernel_size - 1) // 2, bias=False) + +class ZeroPad1d(nn.Module): + def __init__(self, pad_left, pad_right): + super().__init__() + self.pad_left = pad_left + self.pad_right = pad_right + + def forward(self, x): + return F.pad(x, (self.pad_left, self.pad_right)) + + +class BasicBlock1d(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super().__init__() + + self.conv1 = conv(inplanes, planes, stride=stride, kernel_size=3) + self.bn1 = nn.BatchNorm1d(planes) + self.relu = nn.ReLU(inplace=True) + + self.conv2 = conv(planes, planes, kernel_size=3) + self.bn2 = nn.BatchNorm1d(planes) + + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck1d(nn.Module): + """Bottleneck for ResNet52 ...""" + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super().__init__() + kernel_size = 3 + self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm1d(planes) + self.conv2 = nn.Conv1d(planes, planes, kernel_size=kernel_size, stride=stride, + padding=(kernel_size - 1) // 2, bias=False) + self.bn2 = nn.BatchNorm1d(planes) + self.conv3 = nn.Conv1d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm1d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + +class ResNet1d(nn.Module): + '''1d adaptation of the torchvision resnet''' + + def __init__(self, block, layers, kernel_size=3, input_channels=12, inplanes=64, + fix_feature_dim=False, kernel_size_stem=None, stride_stem=2, pooling_stem=True, + stride=2): + super(ResNet1d, self).__init__() + + self.inplanes = inplanes + layers_tmp = [] + if kernel_size_stem is None: + kernel_size_stem = kernel_size[0] if isinstance(kernel_size, list) else kernel_size + + # conv-bn-relu (basic feature extraction) + layers_tmp.append(nn.Conv1d(input_channels, inplanes, + kernel_size=kernel_size_stem, + stride=stride_stem, + padding=(kernel_size_stem - 1) // 2, bias=False)) + layers_tmp.append(nn.BatchNorm1d(inplanes)) + layers_tmp.append(nn.ReLU(inplace=True)) + + if pooling_stem is True: + layers_tmp.append(nn.MaxPool1d(kernel_size=3, stride=2, padding=1)) + + for i, l in enumerate(layers): + if i == 0: + layers_tmp.append(self._make_block(block, inplanes, layers[0])) + else: + layers_tmp.append( + self._make_block(block, inplanes if fix_feature_dim else (2 ** i) * inplanes, layers[i], + stride=stride)) + + self.feature_extractor = nn.Sequential(*layers_tmp) + + def _make_block(self, block, planes, blocks, stride=1, kernel_size=3): + down_sample = None + + # 注定会进行下采样 + if stride != 1 or self.inplanes != planes * block.expansion: + down_sample = nn.Sequential( + nn.Conv1d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm1d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, down_sample)) + self.inplanes = planes * block.expansion + + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + return self.feature_extractor(x) + +def resnet1d14(inplanes, input_channels): + return ResNet1d(BasicBlock1d, [2,2,2], inplanes=inplanes, input_channels=input_channels) + +def resnet1d18(**kwargs): + return ResNet1d(BasicBlock1d, [2, 2, 2, 2], **kwargs) + +# MLP +class MLP(nn.Module): + def __init__(self, in_channels, hidden_channels, n_classes, bn = True): + super(MLP, self).__init__() + self.in_channels = in_channels + self.n_classes = n_classes + self.hidden_channels = hidden_channels + self.fc1 = nn.Linear(self.in_channels, self.hidden_channels) + self.fc2 = nn.Linear(self.hidden_channels, self.n_classes) + self.ac = nn.ReLU() + self.bn = nn.BatchNorm1d(hidden_channels) + self.ln = nn.LayerNorm(hidden_channels) + self.fc3 = nn.Linear(self.hidden_channels, self.hidden_channels) + + def forward(self, x): + hidden = self.fc1(x) + hidden = self.bn(hidden) + hidden = self.ac(hidden) + + '''hidden = self.fc3(hidden) + hidden = self.ln2(hidden) + hidden = self.ac(hidden)''' + + out = self.fc2(hidden) + + return out + +# Time-steps features -> aggregated features +class Flatten(nn.Module): + def __init__(self): + super(Flatten, self).__init__() + + def forward(self, tensor): + b = tensor.size(0) + return tensor.reshape(b, -1) + +class AdaptiveConcatPool1d(nn.Module): + "Layer that concats `AdaptiveAvgPool1d` and `AdaptiveMaxPool1d`." + + def __init__(self, sz=None): + "Output will be 2*sz or 2 if sz is None" + super().__init__() + sz = sz or 1 + self.ap, self.mp = nn.AdaptiveAvgPool1d(sz), nn.AdaptiveMaxPool1d(sz) + + def forward(self, x): + """x is shaped of B, C, T""" + return torch.cat([self.mp(x), self.ap(x), x[..., -1:]], 1) + +def bn_drop_lin(n_in, n_out, bn, p, actn): + "`n_in`->bn->dropout->linear(`n_in`,`n_out`)->`actn`" + layers = list() + + if bn: + layers.append(nn.BatchNorm1d(n_in)) + + if p > 0.: + layers.append(nn.Dropout(p=p)) + + layers.append(nn.Linear(n_in, n_out)) + + if actn is not None: + layers.append(actn) + + return layers + +def create_head1d(nf: int, nc: int, lin_ftrs=[512, ], dropout=0.5, bn: bool = True, act="relu"): + lin_ftrs = [3 * nf] + lin_ftrs + [nc] + + activations = [nn.ReLU(inplace=True) if act == "relu" else nn.ELU(inplace=True)] * (len(lin_ftrs) - 2) + [None] + layers = [AdaptiveConcatPool1d(), Flatten()] + + for ni, no, actn in zip(lin_ftrs[:-1], lin_ftrs[1:], activations): + layers += bn_drop_lin(ni, no, bn, dropout, actn) + + layers += [nn.Sigmoid()] + + return nn.Sequential(*layers) diff --git a/env.yaml b/env.yaml new file mode 100644 index 0000000..dc9ba84 --- /dev/null +++ b/env.yaml @@ -0,0 +1,276 @@ +channels: + - pytorch + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=4.5=1_gnu + - alembic=1.8.1=py39h06a4308_0 + - argon2-cffi=21.3.0=pyhd3eb1b0_0 + - argon2-cffi-bindings=21.2.0=py39h7f8727e_0 + - asttokens=2.0.5=pyhd3eb1b0_0 + - attrs=22.1.0=py39h06a4308_0 + - autopage=0.5.0=pyhd8ed1ab_0 + - backcall=0.2.0=pyhd3eb1b0_0 + - blas=1.0=mkl + - bleach=4.1.0=pyhd3eb1b0_0 + - bzip2=1.0.8=h7b6447c_0 + - ca-certificates=2023.01.10=h06a4308_0 + - certifi=2022.12.7=py39h06a4308_0 + - cffi=1.14.6=py39h400218f_0 + - cliff=3.10.0=pyhd8ed1ab_0 + - cmaes=0.8.2=pyh44b312d_0 + - cmd2=2.3.3=py39hf3d152e_1 + - colorlog=5.0.1=py39h06a4308_1 + - comm=0.1.3=pyhd8ed1ab_0 + - cudatoolkit=10.2.89=hfd86e86_1 + - decorator=5.1.1=pyhd3eb1b0_0 + - defusedxml=0.7.1=pyhd3eb1b0_0 + - entrypoints=0.4=py39h06a4308_0 + - executing=0.8.3=pyhd3eb1b0_0 + - ffmpeg=4.3=hf484d3e_0 + - freetype=2.11.0=h70c0345_0 + - giflib=5.2.1=h7b6447c_0 + - gmp=6.2.1=h2531618_2 + - gnutls=3.6.15=he1e5248_0 + - intel-openmp=2021.4.0=h06a4308_3561 + - ipython=8.10.0=py39h06a4308_0 + - ipython_genutils=0.2.0=pyhd3eb1b0_1 + - jedi=0.18.1=py39h06a4308_1 + - jinja2=3.1.2=py39h06a4308_0 + - jpeg=9d=h7f8727e_0 + - jsonschema=4.17.3=py39h06a4308_0 + - jupyter_client=7.1.2=pyhd3eb1b0_0 + - jupyter_core=5.3.0=py39h06a4308_0 + - lame=3.100=h7b6447c_0 + - lcms2=2.12=h3be6417_0 + - ld_impl_linux-64=2.35.1=h7274673_9 + - libblas=3.9.0=12_linux64_mkl + - libcblas=3.9.0=12_linux64_mkl + - libffi=3.3=he6710b0_2 + - libgcc-ng=9.3.0=h5101ec6_17 + - libgfortran-ng=7.5.0=ha8ba4b0_17 + - libgfortran4=7.5.0=ha8ba4b0_17 + - libgomp=9.3.0=h5101ec6_17 + - libiconv=1.15=h63c8f33_5 + - libidn2=2.3.2=h7f8727e_0 + - liblapack=3.9.0=12_linux64_mkl + - libllvm10=10.0.1=hbcb73fb_5 + - libpng=1.6.37=hbc83047_0 + - libsodium=1.0.18=h7b6447c_0 + - libstdcxx-ng=9.3.0=hd4cf53a_17 + - libtasn1=4.16.0=h27cfd23_0 + - libtiff=4.2.0=h85742a9_0 + - libunistring=0.9.10=h27cfd23_0 + - libuv=1.40.0=h7b6447c_0 + - libwebp=1.2.0=h89dd481_0 + - libwebp-base=1.2.0=h27cfd23_0 + - llvmlite=0.36.0=py39h612dafd_4 + - lz4-c=1.9.3=h295c915_1 + - mako=1.2.3=py39h06a4308_0 + - markupsafe=2.1.1=py39h7f8727e_0 + - matplotlib-inline=0.1.6=py39h06a4308_0 + - mistune=0.8.4=py39h27cfd23_1000 + - mkl=2021.4.0=h06a4308_640 + - mkl-service=2.4.0=py39h7f8727e_0 + - mkl_fft=1.3.1=py39hd3c417c_0 + - mkl_random=1.2.2=py39h51133e4_0 + - nbconvert=5.5.0=py_0 + - nbformat=5.7.0=py39h06a4308_0 + - ncurses=6.3=h7f8727e_2 + - nest-asyncio=1.5.6=py39h06a4308_0 + - nettle=3.7.3=hbbd107a_1 + - notebook=6.4.6=pyha770c72_0 + - numba=0.53.1=py39ha9443f7_0 + - numpy=1.21.2=py39h20f2e39_0 + - numpy-base=1.21.2=py39h79a1101_0 + - olefile=0.46=pyhd3eb1b0_0 + - openh264=2.1.0=hd408876_0 + - openssl=1.1.1t=h7f8727e_0 + - optuna=2.10.0=pyhd8ed1ab_0 + - packaging=23.0=py39h06a4308_0 + - pandoc=2.12=h06a4308_3 + - pandocfilters=1.5.0=pyhd3eb1b0_0 + - parso=0.8.3=pyhd3eb1b0_0 + - pbr=5.6.0=pyhd3eb1b0_0 + - pexpect=4.8.0=pyhd3eb1b0_3 + - pickleshare=0.7.5=pyhd3eb1b0_1003 + - pillow=8.4.0=py39h5aabda8_0 + - pip=21.2.4=py39h06a4308_0 + - platformdirs=2.5.2=py39h06a4308_0 + - plotly=4.14.3=pyhd3eb1b0_0 + - prometheus_client=0.14.1=py39h06a4308_0 + - prompt-toolkit=3.0.36=py39h06a4308_0 + - psutil=5.8.0=py39h3811e60_1 + - ptyprocess=0.7.0=pyhd3eb1b0_2 + - pure_eval=0.2.2=pyhd3eb1b0_0 + - pycparser=2.21=pyhd3eb1b0_0 + - pygments=2.11.2=pyhd3eb1b0_0 + - pyparsing=3.0.9=py39h06a4308_0 + - pyperclip=1.8.2=pyhd8ed1ab_2 + - pyrsistent=0.18.0=py39heee7806_0 + - python=3.9.7=h12debd9_1 + - python-dateutil=2.8.2=pyhd3eb1b0_0 + - python-fastjsonschema=2.16.2=py39h06a4308_0 + - python_abi=3.9=2_cp39 + - pytorch=1.10.1=py3.9_cuda10.2_cudnn7.6.5_0 + - pytorch-mutex=1.0=cuda + - pyts=0.12.0=pyh6c4a22f_0 + - readline=8.1=h27cfd23_0 + - retrying=1.3.3=pyhd3eb1b0_2 + - send2trash=1.8.0=pyhd3eb1b0_1 + - setuptools=58.0.4=py39h06a4308_0 + - six=1.16.0=pyhd3eb1b0_0 + - sqlalchemy=1.3.23=py39h3811e60_0 + - sqlite=3.36.0=hc218d9a_0 + - stack_data=0.2.0=pyhd3eb1b0_0 + - stevedore=3.5.0=py39hf3d152e_2 + - tbb=2020.3=hfd86e86_0 + - terminado=0.17.1=py39h06a4308_0 + - testpath=0.6.0=py39h06a4308_0 + - tk=8.6.11=h1ccaba5_0 + - torchaudio=0.10.1=py39_cu102 + - torchvision=0.11.2=py39_cu102 + - tornado=6.1=py39h27cfd23_0 + - tqdm=4.65.0=py39hb070fc8_0 + - traitlets=5.7.1=py39h06a4308_0 + - typing-extensions=3.10.0.2=hd3eb1b0_0 + - typing_extensions=3.10.0.2=pyh06a4308_0 + - wcwidth=0.2.5=pyhd3eb1b0_0 + - webencodings=0.5.1=py39h06a4308_1 + - wheel=0.37.0=pyhd3eb1b0_1 + - xz=5.2.5=h7b6447c_0 + - yaml=0.2.5=h7b6447c_0 + - zeromq=4.3.4=h2531618_0 + - zlib=1.2.11=h7f8727e_4 + - zstd=1.4.9=haebb681_0 + - pip: + - absl-py==1.0.0 + - aiohttp==3.8.1 + - aiosignal==1.2.0 + - altair==4.1.0 + - appdirs==1.4.4 + - astor==0.8.1 + - async-timeout==4.0.2 + - base58==2.1.1 + - blinker==1.4 + - cachetools==4.2.4 + - charset-normalizer==2.0.9 + - click==7.1.2 + - configparser==5.2.0 + - cycler==0.11.0 + - cython==0.29.35 + - datasets==1.17.0 + - debugpy==1.5.1 + - deprecated==1.2.13 + - dictset==0.3.1.2 + - dill==0.3.4 + - docker-pycreds==0.4.0 + - dtaidistance==2.3.10 + - einops==0.6.0 + - fastdtw==0.3.4 + - filelock==3.4.0 + - fonttools==4.28.5 + - frozenlist==1.2.0 + - fsspec==2021.11.1 + - gitdb==4.0.9 + - gitpython==3.1.24 + - google-auth==2.3.3 + - google-auth-oauthlib==0.4.6 + - grpcio==1.43.0 + - h5py==3.6.0 + - hdbscan==0.8.29 + - hmmlearn==0.2.8 + - huggingface-hub==0.2.1 + - idna==3.3 + - imbalanced-learn==0.9.0 + - import-ipynb==0.1.3 + - importlib-metadata==6.6.0 + - install-jdk==0.3.0 + - ipykernel==6.6.0 + - ipynb==0.5.1 + - ipywidgets==7.6.5 + - javabridge==1.0.16 + - joblib==1.1.0 + - jupyterlab-widgets==1.0.2 + - kiwisolver==1.3.2 + - markdown==3.3.6 + - matplotlib==3.5.1 + - msgpack==1.0.3 + - multidict==5.2.0 + - multiprocess==0.70.12.2 + - nltk==3.7 + - numexpr==2.8.1 + - oauthlib==3.1.1 + - opencv-python==4.5.5.62 + - pandas==1.5.3 + - pathtools==0.1.2 + - patsy==0.5.2 + - promise==2.3 + - protobuf==3.19.1 + - pyarrow==6.0.1 + - pyasn1==0.4.8 + - pyasn1-modules==0.2.8 + - pydeck==0.7.1 + - pympler==1.0 + - pynndescent==0.5.10 + - pystaggrelite3==0.1.3 + - python-crfsuite==0.9.9 + - python-weka-wrapper==0.3.18 + - pytorch-ignite==0.4.8 + - pytorch-warmup==0.1.0 + - pytz==2021.3 + - pytz-deprecation-shim==0.1.0.post0 + - pyyaml==6.0 + - pyzmq==22.3.0 + - ray==1.9.2 + - redis==4.1.1 + - regex==2021.11.10 + - requests==2.26.0 + - requests-oauthlib==1.3.0 + - rsa==4.8 + - sacremoses==0.0.46 + - scikit-learn==1.0.1 + - scipy==1.10.1 + - seaborn==0.11.2 + - sentencepiece==0.1.96 + - sentry-sdk==1.5.1 + - seqeval==1.2.2 + - setproctitle==1.3.2 + - shortuuid==1.0.8 + - simpletransformers==0.63.3 + - sklearn-crfsuite==0.3.6 + - smmap==5.0.0 + - statsmodels==0.13.2 + - streamlit==1.3.0 + - subprocess32==3.5.4 + - tables==3.7.0 + - tabulate==0.8.9 + - tensorboard==2.11.2 + - tensorboard-data-server==0.6.1 + - tensorboard-plugin-wit==1.8.1 + - termcolor==1.1.0 + - textdistance==4.2.2 + - threadpoolctl==3.0.0 + - tokenizers==0.10.3 + - toml==0.10.2 + - toolz==0.11.2 + - torch-summary==1.4.5 + - torch-tb-profiler==0.4.1 + - torchinfo==1.7.0 + - torchtext==0.11.1 + - transformers==4.14.1 + - tzdata==2021.5 + - tzlocal==4.1 + - umap-learn==0.5.3 + - urllib3==1.26.7 + - validators==0.18.2 + - watchdog==2.1.6 + - weka==2.0.2 + - werkzeug==2.0.2 + - widgetsnbextension==3.5.2 + - wrapt==1.13.3 + - xxhash==2.0.2 + - yarl==1.7.2 + - yaspin==2.1.0 + - zipp==3.15.0 \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..e85e8a9 --- /dev/null +++ b/main.py @@ -0,0 +1,77 @@ +import numpy as np +import random, pdb, os, copy +import argparse +import pandas as pd +import pickle as pkl +import torch + +from utils import MakeDir, set_seed, str2bool +from CRT import Model, self_supervised_learning, finetuning +from base_models import FTDataSet + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument("--ssl", type=str2bool, default=False, help='Self-supervised learning (pretrain the autoencoder)') + parser.add_argument("--sl", type=str2bool, default=True, help='Supervised learning (downstream tasks)') + parser.add_argument("--load", type=str2bool, default=True) + + parser.add_argument("--patch_len", type=int, default=5) + parser.add_argument("--dim", type=int, default=128) + parser.add_argument("--depth", type=int, default=6) + parser.add_argument("--dropout", type=float, default=0.1) + parser.add_argument("--Headdropout", type=float, default=0.1) + parser.add_argument("--beta2", type=float, default=1) + + parser.add_argument("--AElearningRate", type=float, default=1e-4) + parser.add_argument("--HeadlearningRate", type=float, default=1e-3) + parser.add_argument("--AEbatchSize", type=int, default=512) + parser.add_argument("--HeadbatchSize", type=int, default=32) + parser.add_argument("--AEepochs", type=int, default=100) + parser.add_argument("--Headepochs", type=int, default=50) + + parser.add_argument("--AEwin", type=int, default=5) + parser.add_argument("--Headwin", type=int, default=5) + parser.add_argument("--slid", type=int, default=1) + parser.add_argument("--timeWinFreq", type=int, default=20) + + parser.add_argument("--pretrainDataset", type=str, default='Buffalo_EMAKI') + parser.add_argument("--testDataset", type=str) + + parser.add_argument("--stage", type=int, choices=[0,1], default=0, help='0: Train the classifier only; 1: Finetune the whole model') + parser.add_argument("--seed", type=int, default=0) + opt = parser.parse_args() + opt.in_dim = 2 # x,y + opt.seq_len = opt.AEwin * opt.timeWinFreq *2 + opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + set_seed(opt.seed) + aemodelfile = 'pretrained_model.pkl' + + if opt.ssl: + ''' + Load dataset for pretraining the Autoencoder + taskdata: on-screen coordinates (x,y), their magnitude and phase + ''' + taskdata, taskclicks = loadPretrainDataset(opt) + model = Model(opt).to(opt.device) + self_supervised_learning(model, taskdata, taskclicks, opt, modelfile=aemodelfile) + + if opt.load: + model = torch.load(aemodelfile, map_location=opt.device) + else: + model = Model(opt).to(opt.device) + pdb.set_trace() + + if opt.sl: + ''' + Load dataset for downstream tasks + _X: on-screen coordinates (x,y), their magnitude and phase + _y: labels + _clicks: clicks + ''' + train_X, train_y, train_clicks, valid_X, valid_y, valid_clicks = loadDownstreamDataset(opt) + + TrainSet = FTDataSet(train_X, train_y, train_clicks, multi_label=True) # Binary or multi-class + ValidSet = FTDataSet(valid_X, valid_y, valid_clicks, multi_label=True) + + finetuning(model, TrainSet, ValidSet, opt, aemodelfile.split('.pkl')[0]+'downstream.pkl', multi_label=True) \ No newline at end of file diff --git a/pretrained_model.pkl b/pretrained_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e9ecc9e0f79a0ce5c1db49ae15a6de7cb71a8290 GIT binary patch literal 12799711 zcmbT9b-div^S@i1;_gtqcwKO}qQQ!kQn_$@sqJ)&FBDl8cXxMpcX!th?(S~C=Y8JU zbZ@)m`{H;1m^?Eld(LDsndIbVvs?6^a;gp;rkk$AB!9DZn7zZ`7}`2~kCv5&4K5E} zam?-`)?a((4m-5_8$ZR+b+WBHZ8~~b%ZN^;4$B-^>R2gF(Pe--EXUyCqlS;#tu*Cf 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