import os
import tqdm
import time
import logging
import numpy as np
from collections import defaultdict
import torch
import torch.nn as nn
import torch.nn.functional as F
from metrics import ClassificationMetrics
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class GRUCell(nn.Module):
"""
GRU Cell
:param h_dim: Hidden state vector dimension
:type h_dim: int
:param inp_x_dim: Input vector dimension
:type inp_x_dim: int
:param out_x_dim: Output vector dimension
:type out_x_dim: int
"""
def __init__(self, h_dim, inp_x_dim, out_x_dim):
super(GRUCell, self).__init__()
self.zh_dense = nn.Linear(h_dim, h_dim)
self.zx_dense = nn.Linear(inp_x_dim, h_dim)
self.rh_dense = nn.Linear(h_dim, h_dim)
self.rx_dense = nn.Linear(inp_x_dim, h_dim)
self.uh_dense = nn.Linear(h_dim, h_dim)
self.ux_dense = nn.Linear(inp_x_dim, h_dim)
self.xh_dense = nn.Linear(h_dim, out_x_dim)
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def forward(self, ht_1, xt):
"""
Forward propogation
:param ht_1: Hidden state vector
:type ht_1: torch.Tensor (batch_size, h_dim)
:param xt: Input vector
:type xt: torch.Tensor (batch_size, embed_dim)
:return: New hidden states, output
:rtype: tuple (torch.Tensor [batch_size, h_dim], torch.Tensor [batch_size, out_dim])
"""
zt = nn.Sigmoid()(self.zh_dense(ht_1) + self.zx_dense(xt))
rt = nn.Sigmoid()(self.rh_dense(ht_1) + self.rx_dense(xt))
ht_ = nn.Tanh()(self.uh_dense(rt * ht_1) + self.ux_dense(xt))
ht = (1 - zt) * ht_1 + zt * ht_
yt = self.xh_dense(ht)
return ht, yt
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class GRUModel(nn.Module):
"""
GRU Architecture
:param config_dict: Config Params Dictionary
:type config_dict: dict
"""
def __init__(self, config_dict):
super(GRUModel, self).__init__()
self.seq_len = config_dict["dataset"]["seq_len"]
self.h_dim = config_dict["model"]["h_dim"][0]
self.x_dim = config_dict["model"]["x_dim"][0]
num_vocab = config_dict["dataset"]["num_vocab"]
embed_dim = config_dict["model"]["embed_dim"]
num_classes = config_dict["dataset"]["num_classes"]
self.embed_layer = nn.Embedding(num_vocab, embed_dim)
self.gru_cell = GRUCell(self.h_dim, embed_dim, self.x_dim)
self.classifier_layer = nn.Linear(self.x_dim, num_classes)
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def forward(self, X):
"""
Forward Propogation
:param X: Input tokens
:type X: torch.Tensor (batch_size, seq_len)
:return: Predicted labels
:rtype: toch.Tensor (batch_size, seq_len, num_classes)
"""
x_embed = self.embed_layer(X.to(torch.long))
self.num_samples = X.size(0)
ht = self.init_hidden()
yprobs = []
for i in range(self.seq_len):
ht, yt = self.gru_cell(ht, x_embed[:, i, :])
# yprob = nn.Softmax()(self.classifier_layer(yt))
yprob = self.classifier_layer(yt)
yprobs.append(yprob[:, None, :])
return torch.concat(yprobs, dim=1)
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def init_hidden(self):
"""
Initialized hidden state
:return: Hidden state
:rtype: torch.Tensor (num_samples, h_dim)
"""
ht = nn.init.kaiming_uniform_(torch.empty(self.num_samples, self.h_dim))
return ht
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class GRUTrainer(nn.Module):
"""
GRU Trainer
:param model: GRU model
:type model: torch.nn.Module
:param optimizer: Optimizer
:type optimizer: torch.optim
:param config_dict: Config Params Dictionary
:type config_dict: dict
"""
def __init__(self, model, optimizer, config_dict):
super(GRUTrainer, self).__init__()
self.logger = logging.getLogger(__name__)
self.model = model
self.optim = optimizer
self.config_dict = config_dict
self.metric_cls = ClassificationMetrics(config_dict)
self.eval_metric = config_dict["train"]["eval_metric"]
self.target_names = list(config_dict["dataset"]["labels"])
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def train_one_epoch(self, data_loader, epoch):
"""
Train step
:param data_loader: Train Data Loader
:type data_loader: torch.utils.data.Dataloader
:param epoch: Epoch number
:type epoch: int
:return: Train Losse, Train Metrics
:rtype: tuple (torch.float32, dict)
"""
self.model.train()
total_loss, num_instances = 0, 0
y_true, y_pred = [], []
self.logger.info(
f"-----------Epoch {epoch}/{self.config_dict['train']['epochs']}-----------"
)
pbar = tqdm.tqdm(
enumerate(data_loader), total=len(data_loader), desc="Training"
)
for batch_id, (X, y) in pbar:
y_hat = self.model(X)
loss = self.calc_loss(y_hat, y)
loss.backward()
self.optim.step()
self.optim.zero_grad()
total_loss += loss
num_instances += y.size(0)
y_true.append(y.cpu().detach().numpy().argmax(-1).flatten())
y_pred.append(y_hat.cpu().detach().numpy().reshape(-1, y_hat.shape[-1]))
train_loss = total_loss / num_instances
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
train_metrics = self.metric_cls.get_metrics(y_true, y_pred, self.target_names)
return train_loss, train_metrics
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@torch.no_grad()
def val_one_epoch(self, data_loader):
"""
Validation step
:param data_loader: Validation Data Loader
:type data_loader: torch.utils.data.Dataloader
:return: Validation Losse, Validation Metrics
:rtype: tuple (torch.float32, dict)
"""
self.model.eval()
total_loss, num_instances = 0, 0
y_true, y_pred = [], []
pbar = tqdm.tqdm(
enumerate(data_loader), total=len(data_loader), desc="Validation"
)
for batch_id, (X, y) in pbar:
y_hat = self.model(X)
loss = self.calc_loss(y_hat, y)
total_loss += loss
num_instances += y.size(0)
y_true.append(y.cpu().detach().numpy().argmax(-1).flatten())
y_pred.append(y_hat.cpu().detach().numpy().reshape(-1, y_hat.shape[-1]))
val_loss = total_loss / num_instances
y_true = np.concatenate(y_true, axis=0)
y_pred = np.concatenate(y_pred, axis=0)
val_metrics = self.metric_cls.get_metrics(y_true, y_pred, self.target_names)
return val_loss, val_metrics
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@torch.no_grad()
def predict(self, data_loader):
"""
Runs inference to predict a translation of soruce sentence
:param data_loader: Infer Data loader
:type data_loader: torch.utils.data.DataLoader
:return: Predicted labels
:rtype: numpy.ndarray (num_samples, seq_len)
"""
self.model.eval()
y_pred = []
pbar = tqdm.tqdm(
enumerate(data_loader), total=len(data_loader), desc="Inference"
)
for batch_id, X in pbar:
tokens_hat = self.model(X[0])
y_pred.append(tokens_hat.cpu().detach().numpy().argmax(-1).flatten())
y_pred = np.concatenate(y_pred, axis=0)
return y_pred
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def fit(self, train_loader, val_loader):
"""
Fits the model on dataset. Runs training and Validation steps for given epochs and saves best model based on the evaluation metric
:param train_loader: Train Data loader
:type train_loader: torch.utils.data.DataLoader
:param val_loader: Validaion Data Loader
:type val_loader: torch.utils.data.DataLoader
:return: Training History
:rtype: dict
"""
num_epochs = self.config_dict["train"]["epochs"]
output_folder = self.config_dict["paths"]["output_folder"]
best_val_metric = -np.inf
history = defaultdict(list)
start = time.time()
for epoch in range(1, num_epochs + 1):
train_loss, train_metrics = self.train_one_epoch(train_loader, epoch)
val_loss, val_metrics = self.val_one_epoch(val_loader)
history["train_loss"].append(float(train_loss.detach().numpy()))
history["val_loss"].append(float(val_loss.detach().numpy()))
for key in train_metrics.keys():
history[f"train_{key}"].append(train_metrics[key])
history[f"val_{key}"].append(val_metrics[key])
self.logger.info(f"Train Loss : {train_loss} - Val Loss : {val_loss}")
for key in train_metrics.keys():
self.logger.info(
f"Train {key} : {train_metrics[key]} - Val {key} : {val_metrics[key]}"
)
if val_metrics[self.eval_metric] >= best_val_metric:
self.logger.info(
f"Validation {self.eval_metric} score improved from {best_val_metric} to {val_metrics[self.eval_metric]}"
)
best_val_metric = val_metrics[self.eval_metric]
torch.save(
self.model.state_dict(),
os.path.join(output_folder, "best_model.pt"),
)
else:
self.logger.info(
f"Validation {self.eval_metric} score didn't improve from {best_val_metric}"
)
end = time.time()
time_taken = end - start
self.logger.info(
"Training completed in {:.0f}h {:.0f}m {:.0f}s".format(
time_taken // 3600, (time_taken % 3600) // 60, (time_taken % 3600) % 60
)
)
self.logger.info(f"Best Val {self.eval_metric} score: {best_val_metric}")
return history
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def calc_loss(self, y_pred, y_true):
"""
Crossentropy loss for predicted tokens
:param y_pred: Predicted tokens
:type y_pred: torch.Tensor (batch_size, seq_len, num_vocab)
:param y_true: True tokens
:type y_true: torch.Tensor (batch_size, seq_len)
:return: BCE Loss
:rtype: torch.float32
"""
y_pred = torch.reshape(y_pred, (-1, 2))
y_true = torch.reshape(y_true, (-1, 2))
loss_fn = nn.CrossEntropyLoss(reduction="sum")
return loss_fn(y_pred, y_true)