Reformulate Q-Transformer
This commit is contained in:
D-X-Y
@@ -14,10 +14,10 @@ from typing import Optional
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import logging
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from qlib.utils import (
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unpack_archive_with_buffer,
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save_multiple_parts_file,
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create_save_path,
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drop_nan_by_y_index,
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unpack_archive_with_buffer,
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save_multiple_parts_file,
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create_save_path,
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drop_nan_by_y_index,
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)
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from qlib.log import get_module_logger, TimeInspector
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@@ -25,6 +25,7 @@ import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torch.utils.data as th_data
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import layers as xlayers
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from utils import count_parameters
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@@ -34,409 +35,399 @@ from qlib.data.dataset import DatasetH
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from qlib.data.dataset.handler import DataHandlerLP
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default_net_config = dict(d_feat=6, hidden_size=48, depth=5, pos_drop=0.1)
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default_opt_config = dict(epochs=200, lr=0.001, batch_size=2000, early_stop=20, loss="mse", optimizer="adam")
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class QuantTransformer(Model):
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"""Transformer-based Quant Model
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"""Transformer-based Quant Model"""
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"""
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def __init__(self, net_config=None, opt_config=None, metric="", GPU=0, seed=None, **kwargs):
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# Set logger.
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self.logger = get_module_logger("QuantTransformer")
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self.logger.info("QuantTransformer pytorch version...")
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def __init__(
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self,
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d_feat=6,
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hidden_size=48,
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depth=5,
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pos_dropout=0.1,
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n_epochs=200,
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lr=0.001,
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metric="",
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batch_size=2000,
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early_stop=20,
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loss="mse",
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optimizer="adam",
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GPU=0,
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seed=None,
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**kwargs
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):
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# Set logger.
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self.logger = get_module_logger("QuantTransformer")
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self.logger.info("QuantTransformer pytorch version...")
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# set hyper-parameters.
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self.net_config = net_config or default_net_config
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self.opt_config = opt_config or default_opt_config
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self.metric = metric
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self.device = torch.device("cuda:{:}".format(GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
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self.seed = seed
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# set hyper-parameters.
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self.d_feat = d_feat
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self.hidden_size = hidden_size
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self.depth = depth
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self.pos_dropout = pos_dropout
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self.n_epochs = n_epochs
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self.lr = lr
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self.metric = metric
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self.batch_size = batch_size
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self.early_stop = early_stop
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self.optimizer = optimizer.lower()
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self.loss = loss
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self.device = torch.device("cuda:{:}".format(GPU) if torch.cuda.is_available() else "cpu")
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self.use_gpu = torch.cuda.is_available()
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self.seed = seed
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self.logger.info(
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"Transformer parameters setting:"
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"\nnet_config : {:}"
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"\nopt_config : {:}"
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"\nmetric : {:}"
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"\ndevice : {:}"
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"\nseed : {:}".format(
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self.net_config,
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self.opt_config,
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self.metric,
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self.device,
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self.seed,
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)
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)
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self.logger.info(
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"Transformer parameters setting:"
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"\nd_feat : {}"
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"\nhidden_size : {}"
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"\ndepth : {}"
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"\ndropout : {}"
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"\nn_epochs : {}"
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"\nlr : {}"
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"\nmetric : {}"
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"\nbatch_size : {}"
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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hidden_size,
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depth,
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pos_dropout,
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n_epochs,
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lr,
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metric,
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batch_size,
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early_stop,
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optimizer.lower(),
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loss,
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GPU,
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self.use_gpu,
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seed,
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)
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)
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if self.seed is not None:
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np.random.seed(self.seed)
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torch.manual_seed(self.seed)
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if self.seed is not None:
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np.random.seed(self.seed)
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torch.manual_seed(self.seed)
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self.model = TransformerModel(
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d_feat=self.net_config["d_feat"],
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embed_dim=self.net_config["hidden_size"],
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depth=self.net_config["depth"],
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pos_drop=self.net_config["pos_drop"],
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)
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self.logger.info("model: {:}".format(self.model))
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self.logger.info("model size: {:.3f} MB".format(count_parameters(self.model)))
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self.model = TransformerModel(d_feat=self.d_feat,
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embed_dim=self.hidden_size,
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depth=self.depth,
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pos_dropout=pos_dropout)
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self.logger.info('model: {:}'.format(self.model))
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self.logger.info('model size: {:.3f} MB'.format(count_parameters(self.model)))
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr)
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else:
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raise NotImplementedError("optimizer {:} is not supported!".format(optimizer))
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self.fitted = False
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self.model.to(self.device)
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def loss_fn(self, pred, label):
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mask = ~torch.isnan(label)
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if self.loss == "mse":
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return F.mse_loss(pred[mask], label[mask])
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else:
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raise ValueError("unknown loss `{:}`".format(self.loss))
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def metric_fn(self, pred, label):
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mask = torch.isfinite(label)
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if self.metric == "" or self.metric == "loss":
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return -self.loss_fn(pred[mask], label[mask])
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else:
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raise ValueError("unknown metric `{:}`".format(self.metric))
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def train_epoch(self, x_train, y_train):
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x_train_values = x_train.values
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y_train_values = np.squeeze(y_train.values)
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self.model.train()
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indices = np.arange(len(x_train_values))
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np.random.shuffle(indices)
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for i in range(len(indices))[:: self.batch_size]:
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if len(indices) - i < self.batch_size:
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break
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feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
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label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
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pred = self.model(feature)
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loss = self.loss_fn(pred, label)
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self.train_optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
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self.train_optimizer.step()
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def test_epoch(self, data_x, data_y):
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# prepare training data
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x_values = data_x.values
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y_values = np.squeeze(data_y.values)
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self.model.eval()
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scores = []
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losses = []
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indices = np.arange(len(x_values))
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for i in range(len(indices))[:: self.batch_size]:
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if len(indices) - i < self.batch_size:
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break
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feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
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label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
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pred = self.model(feature)
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loss = self.loss_fn(pred, label)
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losses.append(loss.item())
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score = self.metric_fn(pred, label)
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scores.append(score.item())
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return np.mean(losses), np.mean(scores)
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def fit(
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self,
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dataset: DatasetH,
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evals_result=dict(),
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verbose=True,
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save_path=None,
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):
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df_train, df_valid, df_test = dataset.prepare(
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["train", "valid", "test"],
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col_set=["feature", "label"],
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data_key=DataHandlerLP.DK_L,
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)
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x_train, y_train = df_train["feature"], df_train["label"]
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x_valid, y_valid = df_valid["feature"], df_valid["label"]
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if save_path == None:
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save_path = create_save_path(save_path)
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stop_steps = 0
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train_loss = 0
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best_score = -np.inf
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best_epoch = 0
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evals_result["train"] = []
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evals_result["valid"] = []
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# train
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self.logger.info("training...")
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self.fitted = True
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for step in range(self.n_epochs):
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self.logger.info("Epoch%d:", step)
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self.logger.info("training...")
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self.train_epoch(x_train, y_train)
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self.logger.info("evaluating...")
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train_loss, train_score = self.test_epoch(x_train, y_train)
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val_loss, val_score = self.test_epoch(x_valid, y_valid)
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self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
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evals_result["train"].append(train_score)
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evals_result["valid"].append(val_score)
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if val_score > best_score:
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best_score = val_score
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stop_steps = 0
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best_epoch = step
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best_param = copy.deepcopy(self.model.state_dict())
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else:
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stop_steps += 1
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if stop_steps >= self.early_stop:
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self.logger.info("early stop")
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break
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self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
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self.model.load_state_dict(best_param)
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torch.save(best_param, save_path)
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if self.use_gpu:
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torch.cuda.empty_cache()
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def predict(self, dataset):
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if not self.fitted:
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raise ValueError("model is not fitted yet!")
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x_test = dataset.prepare("test", col_set="feature")
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index = x_test.index
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self.model.eval()
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x_values = x_test.values
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sample_num = x_values.shape[0]
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preds = []
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for begin in range(sample_num)[:: self.batch_size]:
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if sample_num - begin < self.batch_size:
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end = sample_num
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else:
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end = begin + self.batch_size
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x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
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with torch.no_grad():
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if self.use_gpu:
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pred = self.model(x_batch).detach().cpu().numpy()
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if self.opt_config["optimizer"] == "adam":
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self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.opt_config["lr"])
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elif self.opt_config["optimizer"] == "adam":
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self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.opt_config["lr"])
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else:
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pred = self.model(x_batch).detach().numpy()
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raise NotImplementedError("optimizer {:} is not supported!".format(optimizer))
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preds.append(pred)
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self.fitted = False
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self.model.to(self.device)
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return pd.Series(np.concatenate(preds), index=index)
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@property
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def use_gpu(self):
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self.device == torch.device("cpu")
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def loss_fn(self, pred, label):
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mask = ~torch.isnan(label)
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if self.opt_config["loss"] == "mse":
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return F.mse_loss(pred[mask], label[mask])
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else:
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raise ValueError("unknown loss `{:}`".format(self.loss))
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def metric_fn(self, pred, label):
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mask = torch.isfinite(label)
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if self.metric == "" or self.metric == "loss":
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return -self.loss_fn(pred[mask], label[mask])
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else:
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raise ValueError("unknown metric `{:}`".format(self.metric))
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def train_epoch(self, xloader, model, loss_fn, optimizer):
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model.train()
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scores, losses = [], []
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for ibatch, (feats, labels) in enumerate(xloader):
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feats = feats.to(self.device, non_blocking=True)
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labels = labels.to(self.device, non_blocking=True)
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# forward the network
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preds = model(feats)
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loss = loss_fn(preds, labels)
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with torch.no_grad():
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score = self.metric_fn(preds, labels)
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losses.append(loss.item())
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scores.append(loss.item())
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# optimize the network
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optimizer.zero_grad()
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loss.backward()
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torch.nn.utils.clip_grad_value_(model.parameters(), 3.0)
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optimizer.step()
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return np.mean(losses), np.mean(scores)
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def test_epoch(self, xloader, model, loss_fn, metric_fn):
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model.eval()
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scores, losses = [], []
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with torch.no_grad():
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for ibatch, (feats, labels) in enumerate(xloader):
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feats = feats.to(self.device, non_blocking=True)
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labels = labels.to(self.device, non_blocking=True)
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# forward the network
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preds = model(feats)
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loss = loss_fn(preds, labels)
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score = self.metric_fn(preds, labels)
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losses.append(loss.item())
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scores.append(loss.item())
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return np.mean(losses), np.mean(scores)
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def fit(
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self,
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dataset: DatasetH,
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evals_result=dict(),
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verbose=True,
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save_path=None,
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):
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def _prepare_dataset(df_data):
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return th_data.TensorDataset(
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torch.from_numpy(df_data["feature"].values).float(),
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torch.from_numpy(df_data["label"].values).squeeze().float(),
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)
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df_train, df_valid, df_test = dataset.prepare(
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["train", "valid", "test"],
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col_set=["feature", "label"],
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data_key=DataHandlerLP.DK_L,
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)
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train_dataset, valid_dataset, test_dataset = (
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_prepare_dataset(df_train),
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_prepare_dataset(df_valid),
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_prepare_dataset(df_test),
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)
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train_loader = th_data.DataLoader(
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train_dataset, batch_size=self.opt_config["batch_size"], shuffle=True, drop_last=False, pin_memory=True
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)
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valid_loader = th_data.DataLoader(
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valid_dataset, batch_size=self.opt_config["batch_size"], shuffle=False, drop_last=False, pin_memory=True
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)
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test_loader = th_data.DataLoader(
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test_dataset, batch_size=self.opt_config["batch_size"], shuffle=False, drop_last=False, pin_memory=True
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)
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if save_path == None:
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save_path = create_save_path(save_path)
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stop_steps, best_score, best_epoch = 0, -np.inf, 0
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train_loss = 0
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evals_result["train"] = []
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evals_result["valid"] = []
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# train
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self.logger.info("Fit procedure for [{:}] with save path={:}".format(self.__class__.__name__, save_path))
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def _internal_test():
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train_loss, train_score = self.test_epoch(train_loader, self.model, self.loss_fn, self.metric_fn)
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valid_loss, valid_score = self.test_epoch(valid_loader, self.model, self.loss_fn, self.metric_fn)
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test_loss, test_score = self.test_epoch(test_loader, self.model, self.loss_fn, self.metric_fn)
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xstr = "train-score={:.6f}, valid-score={:.6f}, test-score={:.6f}".format(
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train_score, valid_score, test_score
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)
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return dict(train=train_score, valid=valid_score, test=test_score), xstr
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_, eval_str = _internal_test()
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self.logger.info("Before Training: {:}".format(eval_str))
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for iepoch in range(self.opt_config["epochs"]):
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self.logger.info("Epoch={:03d}/{:03d} ::==>>".format(iepoch, self.opt_config["epochs"]))
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train_loss, train_score = self.train_epoch(train_loader, self.model, self.loss_fn, self.train_optimizer)
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self.logger.info("Training :: loss={:.6f}, score={:.6f}".format(train_loss, train_score))
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eval_score_dict, eval_str = _internal_test()
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self.logger.info("Evaluating :: {:}".format(eval_str))
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evals_result["train"].append(eval_score_dict["train"])
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evals_result["valid"].append(eval_score_dict["valid"])
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if eval_score_dict["valid"] > best_score:
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stop_steps, best_epoch, best_score = 0, iepoch, eval_score_dict["valid"]
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best_param = copy.deepcopy(self.model.state_dict())
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else:
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stop_steps += 1
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if stop_steps >= self.opt_config["early_stop"]:
|
||||
self.logger.info("early stop at {:}-th epoch, where the best is @{:}".format(iepoch, best_epoch))
|
||||
break
|
||||
|
||||
self.logger.info("The best score: {:.6f} @ {:02d}-th epoch".format(best_score, best_epoch))
|
||||
self.model.load_state_dict(best_param)
|
||||
torch.save(best_param, save_path)
|
||||
|
||||
if self.use_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
self.fitted = True
|
||||
|
||||
def predict(self, dataset):
|
||||
|
||||
if not self.fitted:
|
||||
raise ValueError("model is not fitted yet!")
|
||||
|
||||
x_test = dataset.prepare("test", col_set="feature")
|
||||
index = x_test.index
|
||||
self.model.eval()
|
||||
x_values = x_test.values
|
||||
sample_num = x_values.shape[0]
|
||||
preds = []
|
||||
|
||||
for begin in range(sample_num)[:: self.batch_size]:
|
||||
|
||||
if sample_num - begin < self.batch_size:
|
||||
end = sample_num
|
||||
else:
|
||||
end = begin + self.batch_size
|
||||
|
||||
x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
if self.use_gpu:
|
||||
pred = self.model(x_batch).detach().cpu().numpy()
|
||||
else:
|
||||
pred = self.model(x_batch).detach().numpy()
|
||||
|
||||
preds.append(pred)
|
||||
|
||||
return pd.Series(np.concatenate(preds), index=index)
|
||||
|
||||
|
||||
# Real Model
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
|
||||
super(Attention, self).__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 math.sqrt(head_dim)
|
||||
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
super(Attention, self).__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 math.sqrt(head_dim)
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
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
|
||||
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=4.0,
|
||||
qkv_bias=False,
|
||||
qk_scale=None,
|
||||
attn_drop=0.0,
|
||||
mlp_drop=0.0,
|
||||
drop_path=0.0,
|
||||
act_layer=nn.GELU,
|
||||
norm_layer=nn.LayerNorm,
|
||||
):
|
||||
super(Block, self).__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=mlp_drop
|
||||
)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = xlayers.DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = xlayers.MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=mlp_drop)
|
||||
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None,
|
||||
attn_drop=0., mlp_drop=0., drop_path=0.,
|
||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super(Block, self).__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=mlp_drop)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = xlayers.DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = xlayers.MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=mlp_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
|
||||
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 SimpleEmbed(nn.Module):
|
||||
def __init__(self, d_feat, embed_dim):
|
||||
super(SimpleEmbed, self).__init__()
|
||||
self.d_feat = d_feat
|
||||
self.embed_dim = embed_dim
|
||||
self.proj = nn.Linear(d_feat, embed_dim)
|
||||
|
||||
def __init__(self, d_feat, embed_dim):
|
||||
super(SimpleEmbed, self).__init__()
|
||||
self.d_feat = d_feat
|
||||
self.embed_dim = embed_dim
|
||||
self.proj = nn.Linear(d_feat, embed_dim)
|
||||
|
||||
def forward(self, x):
|
||||
x = x.reshape(len(x), self.d_feat, -1) # [N, F*T] -> [N, F, T]
|
||||
x = x.permute(0, 2, 1) # [N, F, T] -> [N, T, F]
|
||||
out = self.proj(x) * math.sqrt(self.embed_dim)
|
||||
return out
|
||||
def forward(self, x):
|
||||
x = x.reshape(len(x), self.d_feat, -1) # [N, F*T] -> [N, F, T]
|
||||
x = x.permute(0, 2, 1) # [N, F, T] -> [N, T, F]
|
||||
out = self.proj(x) * math.sqrt(self.embed_dim)
|
||||
return out
|
||||
|
||||
|
||||
class TransformerModel(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_feat: int,
|
||||
embed_dim: int = 64,
|
||||
depth: int = 4,
|
||||
num_heads: int = 4,
|
||||
mlp_ratio: float = 4.0,
|
||||
qkv_bias: bool = True,
|
||||
qk_scale: Optional[float] = None,
|
||||
pos_drop=0.0,
|
||||
mlp_drop_rate=0.0,
|
||||
attn_drop_rate=0.0,
|
||||
drop_path_rate=0.0,
|
||||
norm_layer=None,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
d_feat (int, tuple): input image size
|
||||
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
|
||||
pos_drop (float): dropout rate for the positional embedding
|
||||
mlp_drop_rate (float): the dropout rate for MLP layers in a block
|
||||
attn_drop_rate (float): attention dropout rate
|
||||
drop_path_rate (float): stochastic depth rate
|
||||
norm_layer: (nn.Module): normalization layer
|
||||
"""
|
||||
super(TransformerModel, self).__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.num_features = embed_dim
|
||||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||||
|
||||
def __init__(self,
|
||||
d_feat: int,
|
||||
embed_dim: int = 64,
|
||||
depth: int = 4,
|
||||
num_heads: int = 4,
|
||||
mlp_ratio: float = 4.,
|
||||
qkv_bias: bool = True,
|
||||
qk_scale: Optional[float] = None,
|
||||
pos_dropout=0., mlp_drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None):
|
||||
"""
|
||||
Args:
|
||||
d_feat (int, tuple): input image size
|
||||
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
|
||||
pos_dropout (float): dropout rate for the positional embedding
|
||||
mlp_drop_rate (float): the dropout rate for MLP layers in a block
|
||||
attn_drop_rate (float): attention dropout rate
|
||||
drop_path_rate (float): stochastic depth rate
|
||||
norm_layer: (nn.Module): normalization layer
|
||||
"""
|
||||
super(TransformerModel, self).__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.num_features = embed_dim
|
||||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||||
self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
|
||||
|
||||
self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)
|
||||
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
||||
self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65, dropout=pos_drop)
|
||||
|
||||
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
||||
self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65, dropout=pos_dropout)
|
||||
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,
|
||||
attn_drop=attn_drop_rate,
|
||||
mlp_drop=mlp_drop_rate,
|
||||
drop_path=dpr[i],
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
for i in range(depth)
|
||||
]
|
||||
)
|
||||
self.norm = norm_layer(embed_dim)
|
||||
|
||||
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,
|
||||
attn_drop=attn_drop_rate, mlp_drop=mlp_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
|
||||
for i in range(depth)])
|
||||
self.norm = norm_layer(embed_dim)
|
||||
# regression head
|
||||
self.head = nn.Linear(self.num_features, 1)
|
||||
|
||||
# regression head
|
||||
self.head = nn.Linear(self.num_features, 1)
|
||||
xlayers.trunc_normal_(self.cls_token, std=0.02)
|
||||
self.apply(self._init_weights)
|
||||
|
||||
xlayers.trunc_normal_(self.cls_token, std=.02)
|
||||
self.apply(self._init_weights)
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
xlayers.trunc_normal_(m.weight, std=0.02)
|
||||
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)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
xlayers.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)
|
||||
def forward_features(self, x):
|
||||
batch, flatten_size = x.shape
|
||||
feats = self.input_embed(x) # batch * 60 * 64
|
||||
|
||||
def forward_features(self, x):
|
||||
batch, flatten_size = x.shape
|
||||
feats = self.input_embed(x) # batch * 60 * 64
|
||||
cls_tokens = self.cls_token.expand(batch, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
||||
feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
|
||||
feats_w_tp = self.pos_embed(feats_w_ct)
|
||||
|
||||
cls_tokens = self.cls_token.expand(batch, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
||||
feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
|
||||
feats_w_tp = self.pos_embed(feats_w_ct)
|
||||
xfeats = feats_w_tp
|
||||
for block in self.blocks:
|
||||
xfeats = block(xfeats)
|
||||
|
||||
xfeats = feats_w_tp
|
||||
for block in self.blocks:
|
||||
xfeats = block(xfeats)
|
||||
xfeats = self.norm(xfeats)[:, 0]
|
||||
return xfeats
|
||||
|
||||
xfeats = self.norm(xfeats)[:, 0]
|
||||
return xfeats
|
||||
|
||||
def forward(self, x):
|
||||
feats = self.forward_features(x)
|
||||
predicts = self.head(feats).squeeze(-1)
|
||||
return predicts
|
||||
def forward(self, x):
|
||||
feats = self.forward_features(x)
|
||||
predicts = self.head(feats).squeeze(-1)
|
||||
return predicts
|
||||
|
||||
Reference in New Issue
Block a user