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main.py
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main.py
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import data.data as d
import models.baseline2d as baseline2d
import models.fc2_20_2_dense as richoux
import models.dscript_like as dscript_like
import models.attention as attention
import torch
import torch.nn as nn
import torch.utils.data as data
import wandb
import argparse
import random
import numpy as np
import traceback
def confmat(y_true, y_pred):
true_positive = torch.sum((y_true == 1) & (y_pred == 1)).item()
false_positive = torch.sum((y_true == 0) & (y_pred == 1)).item()
true_negative = torch.sum((y_true == 0) & (y_pred == 0)).item()
false_negative = torch.sum((y_true == 1) & (y_pred == 0)).item()
return true_positive, false_positive, true_negative, false_negative
def metrics(true_positive, false_positive, true_negative, false_negative):
N = true_positive + false_positive + true_negative + false_negative
pred_positive = true_positive + false_positive
real_positive = true_positive + false_negative
if real_positive == 0:
accuracy = precision = recall = f1_score = 0
else:
accuracy = (true_positive + true_negative) / N
precision = true_positive / pred_positive if pred_positive > 0 else 0
recall = true_positive / real_positive if real_positive > 0 else 0
f1_score = (2 * (precision * recall)) / (precision + recall) if (precision + recall) > 0 else 0
return accuracy, precision, recall, f1_score
sweep_config = {
'crossattention':{
"name": "CrossAttention_sweep_bayesian_val",
"method": "bayes",
"metric": {"goal": "maximize", "name": "val_accuracy"},
"parameters": {
"data_name": {"value": "gold_stand"},
"num_epochs": {"value": 20},
"subset": {"value": True},
"subset_size": {"value": 0.5},
"use_embeddings": {"value": True},
"mean_embedding": {"value": False},
"embedding_dim": {"value": 1280},
"use_wandb": {"value": True},
"early_stopping": {"value": 8},
"model_name": {"value": "crossattention"},
"run_name": {"value": ''},
"batch_size": {"value": 16},
"max_seq_len": {"value": 1000},
"learning_rate": {
"distribution": "uniform",
"min": 0.0001,
"max": 0.01
},
"dropout": {
"distribution": "uniform",
"min": 0.1,
"max": 0.5
},
"num_heads": {
"values": [2, 4, 8, 16]
},
"attention_dim": {
"distribution": "q_uniform",
"min": 16,
"max": 1024,
"q": 16
},
"ff_dim":{
"values": [64, 128, 256, 512, 1024]
},
"pooling":{
"values": ['avg', 'max']
},
"kernel_size":{
"values": [1, 2, 3, 4, 5, 6]
}
}
},
'TUnA':{
"name": "TUnA_sweep_bayesian_val",
"method": "bayes",
"metric": {"goal": "maximize", "name": "val_accuracy"},
"parameters": {
"data_name": {"value": "gold_stand"},
"num_epochs": {"value": 20},
"subset": {"value": True},
"subset_size": {"value": 0.5},
"use_embeddings": {"value": True},
"mean_embedding": {"value": False},
"embedding_dim": {"value": 1280},
"use_wandb": {"value": True},
"early_stopping": {"value": 8},
"model_name": {"value": "TUnA"},
"run_name": {"value": ''},
"batch_size": {"value": 16},
"max_seq_len": {"value": 1000},
"learning_rate": {
"distribution": "uniform",
"min": 0.0001,
"max": 0.01
},
"dropout": {
"distribution": "uniform",
"min": 0.1,
"max": 0.5
},
"num_heads": {
"values": [2, 4, 8, 16]
},
"attention_dim": {
"distribution": "q_uniform",
"min": 16,
"max": 512,
"q": 16
},
"ff_dim":{
"values": [64, 128, 256, 512, 1024]
},
"rffs":{
"values": [64, 128, 256, 512, 1024, 2048]
},
'cross':{
"values": [True, False]
}
}
},
'richoux':{
"name": "Richoux_sweep_bayesian_val",
"method": "bayes",
"metric": {"goal": "maximize", "name": "val_accuracy"},
"parameters": {
"data_name": {"value": "gold_stand"},
"num_epochs": {"value": 20},
"subset": {"value": True},
"subset_size": {"value": 0.5},
"use_embeddings": {"value": True},
"mean_embedding": {"value": True},
"use_wandb": {"value": True},
"early_stopping": {"value": 8},
"model_name": {"value": "richoux"},
"run_name": {"value": ''},
"batch_size": {"value": 512},
"max_seq_len": {"value": 10000},
"embedding_dim": {"values": [1280, 2560, 5120]},
"learning_rate": {
"distribution": "uniform",
"min": 0.0001,
"max": 0.01
},
"ff_dim1":{
"distribution": "int_uniform",
"min": 10,
"max": 1000
},
"ff_dim2":{
"distribution": "int_uniform",
"min": 10,
"max": 500
},
"ff_dim3":{
"distribution": "int_uniform",
"min": 10,
"max": 500
},
"spec_norm":{
"values": [True, False]
}
}
},
'dscript_like':{
"name": "dscript_like_sweep_bayesian_val",
'method': 'bayes',
"metric": {"goal": "maximize", "name": "val_accuracy"},
'parameters': {
"data_name": {"value": "gold_stand"},
"num_epochs": {"value": 20},
"subset": {"value": True},
"subset_size": {"value": 0.5},
"use_embeddings": {"value": True},
"mean_embedding": {"value": False},
"use_wandb": {"value": True},
"early_stopping": {"value": 8},
"model_name": {"value": "dscript_like"},
"run_name": {"value": ''},
"batch_size": {"value": 16},
"max_seq_len": {"value": 1000},
"embedding_dim": {"values": [1280, 2560, 5120]},
"learning_rate": {
"distribution": "uniform",
"min": 0.0001,
"max": 0.01
},
'd': {
'distribution': 'int_uniform',
'min': 20,
'max': 300
},
'w': {
'distribution': 'int_uniform',
'min': 1,
'max': 10
},
'h': {
'distribution': 'int_uniform',
'min': 10,
'max': 150
},
'x0': {
'distribution': 'uniform',
'min': -1.0,
'max': 1.0
},
'k': {
'distribution': 'uniform',
'min': 1,
'max': 50
},
'pool_size': {
'distribution': 'int_uniform',
'min': 1,
'max': 10
},
'do_pool': {
'values': [True, False]
},
'do_w': {
'values': [True, False]
},
'theta_init': {
'distribution': 'uniform',
'min': 0.0,
'max': 1.0
},
'lambda_init': {
'distribution': 'uniform',
'min': 0.0,
'max': 1.0
},
'gamma_init': {
'distribution': 'uniform',
'min': 0.0,
'max': 1.0
},
}
},
'selfattention':{
"name": "SelfAttention_sweep_bayesian_val",
"method": "bayes",
"metric": {"goal": "maximize", "name": "val_accuracy"},
"parameters": {
"data_name": {"value": "gold_stand"},
"num_epochs": {"value": 20},
"subset": {"value": True},
"subset_size": {"value": 0.5},
"use_embeddings": {"value": True},
"mean_embedding": {"value": False},
"embedding_dim": {"value": 1280},
"use_wandb": {"value": True},
"early_stopping": {"value": 8},
"model_name": {"value": "selfattention"},
"run_name": {"value": ''},
"batch_size": {"value": 16},
"max_seq_len": {"value": 1000},
"learning_rate": {
"distribution": "uniform",
"min": 0.0001,
"max": 0.01
},
"dropout": {
"distribution": "uniform",
"min": 0.1,
"max": 0.5
},
"num_heads": {
"values": [2, 4, 8, 16]
},
"attention_dim": {
"distribution": "q_uniform",
"min": 16,
"max": 1024,
"q": 16
},
"ff_dim":{
"values": [64, 128, 256, 512, 1024]
},
"pooling":{
"values": ['avg', 'max']
},
"kernel_size":{
"values": [1, 2, 3, 4, 5, 6]
}
}
}
}
best_configs = {
"crossattention": {
"seed": 3292779418,
"save_confpred": True,
"save_model": True,
"test": True,
"data_name": "gold_stand",
"num_epochs": 25,
"subset": True,
"subset_size": 0.5,
"use_embeddings": True,
"mean_embedding": False,
"embedding_dim": 1280,
"use_wandb": True,
"early_stopping": 8,
"model_name": "crossattention",
"run_name": 'best_crossattention',
"batch_size": 16,
"max_seq_len": 1000,
"learning_rate": 0.0001,
"dropout": 0.1,
"num_heads": 8,
"attention_dim": 64,
"ff_dim": 256,
"pooling": 'avg',
"kernel_size": 3
},
"dscript_like": {
"seed": 3292779418,
"save_confpred": True,
"save_model": True,
"test": True,
"data_name": "gold_stand",
"num_epochs": 20,
"subset": True,
"subset_size": 0.5,
"use_embeddings": True,
"mean_embedding": False,
"use_wandb": True,
"early_stopping": 8,
"model_name": "dscript_like",
"run_name": "best_dscript_like",
"batch_size": 16,
"max_seq_len": 1000,
"embedding_dim": 1280,
"learning_rate": 0.008056061488991427,
"d": 112,
"w": 4,
"h": 44,
"x0": 0.4989645451223388,
"k": 20.296019995569164,
"pool_size": 8,
"do_pool": False,
"do_w": True,
"theta_init": 0.9537458250320732,
"lambda_init": 0.42347718679986945,
"gamma_init": 0.22024583140255627
},
"selfattention": {
"seed": 3292779418,
"save_confpred": True,
"save_model": True,
"test": True,
"data_name": "gold_stand",
"num_epochs": 25,
"subset": True,
"subset_size": 0.5,
"use_embeddings": True,
"mean_embedding": False,
"embedding_dim": 1280,
"use_wandb": True,
"early_stopping": 8,
"model_name": "selfattention",
"run_name": 'best_selfattention',
"batch_size": 16,
"max_seq_len": 1000,
"learning_rate": 0.0001,
"dropout": 0.1,
"num_heads": 8,
"attention_dim": 64,
"ff_dim": 256,
"pooling": 'max',
"kernel_size": 3
},
"richoux": {
"seed": 3292779418,
"save_confpred": True,
"save_model": True,
"test": True,
"data_name": "gold_stand",
"num_epochs": 25,
"subset": True,
"subset_size": 0.5,
"use_embeddings": True,
"mean_embedding": True,
"use_wandb": True,
"early_stopping": 8,
"model_name": "richoux",
"run_name": 'best_rich',
"batch_size": 1024,
"max_seq_len": 10000,
"embedding_dim": 2560,
"learning_rate": 0.001,
"ff_dim1": 20,
"ff_dim2": 20,
"ff_dim3": 20,
"spec_norm": True
},
"TUnA": {
"seed": 3292779418,
"save_confpred": True,
"save_model": True,
"test": True,
"data_name": "gold_stand",
"num_epochs": 25,
"subset": True,
"subset_size": 0.5,
"use_embeddings": True,
"mean_embedding": False,
"embedding_dim": 1280,
"use_wandb": True,
"early_stopping": 8,
"model_name": "TUnA",
"run_name": 'best_TUnA',
"batch_size": 32,
"max_seq_len": 1000,
"learning_rate": 0.0005,
"dropout": 0.15,
"num_heads": 8,
"attention_dim": 64,
"ff_dim": 256,
"rffs": 1028,
'cross': False
},
"baseline2d": {
"seed": None,
"save_confpred": True,
"save_model": True,
"test": True,
"data_name": "gold_stand",
"num_epochs": 25,
"subset": True,
"subset_size": 0.5,
"use_embeddings": True,
"mean_embedding": False,
"embedding_dim": 1280,
"use_wandb": True,
"early_stopping": 8,
"model_name": "baseline2d",
"run_name": 'best_baseline2d',
"batch_size": 40,
"max_seq_len": 1000,
"learning_rate": 0.001,
"h3": 64,
"kernel_size": 2,
"pooling": 'avg'
}
}
def train(config=None, sweep=False):
try:
seed = None
if config is not None:
seed = config.get('seed', None)
if seed is None:
seed = random.randint(0, 2**32 - 1)
print(f"Random seed: {seed}")
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
############################################################__Parameters__#################################################################
if config is None:
wandb.init(config=config, project="hypertune")
config = wandb.config
config['seed'] = seed
print(config)
#general params
test = config.get('test', False)
data_name = config.get('data_name')
model_name = config.get('model_name')
learning_rate = config.get('learning_rate')
num_epochs = config.get('num_epochs')
bs = config.get('batch_size')
max = config.get('max_seq_len')
subset = config.get('subset')
subset_size = config.get('subset_size')
use_embeddings = config.get('use_embeddings')
mean_embedding = config.get('mean_embedding')
embedding_dim = config.get('embedding_dim')
use_wandb = config.get('use_wandb')
early_stopping = config.get('early_stopping', 0)
save_confpred = config.get('save_confpred', False)
save_model = config.get('save_model', False)
#params for attention models
dropout = config.get('dropout', 0.2) #for tuna #for cross
num_heads = config.get('num_heads', 8) #for tuna #for cross
run_name = config.get('run_name')
h3 = config.get('attention_dim', 64) #for tuna #for cross
ff_dim = config.get('ff_dim', 256) #for cross
pooling = config.get('pooling', 'avg') #for cross
kernel_size = config.get('kernel_size', 2) #for cross
rffs = config.get('rffs', 1028) #for tuna
cross = config.get('cross', False) #for tuna
#params for richoux
ff_dim1 = config.get('ff_dim1', 20) #for rich
ff_dim2 = config.get('ff_dim2', 20) #for rich
ff_dim3 = config.get('ff_dim3', 20) #for rich
spec_norm = config.get('spec_norm', False) #for rich
#params for dscript_like
d_ = config.get('d',128)
w = config.get('w', 7)
h = config.get('h', 50)
x0 = config.get('x0', 0.5)
k = config.get('k', 20)
pool_size = config.get('pool_size', 9)
do_pool = config.get('do_pool', False)
do_w = config.get('do_w', True)
theta_init = config.get('theta_init', 1.0)
lambda_init = config.get('lambda_init', 0.0)
gamma_init = config.get('gamma_init', 0.0)
per_tok_models = ["baseline2d", "dscript_like", "selfattention", "crossattention",
"ICAN_cross", "AttDscript", "Rich-ATT", "TUnA"]
mean_models = ["richoux"]
padded_models = ["richoux", "TUnA"]
# some cases, could be done better or more elegantly
if model_name in per_tok_models:
use_embeddings = True
use_2d_data = True
elif model_name in mean_models:
use_embeddings = True
use_2d_data = False
else:
raise ValueError(f"The model name {model_name} does not exist. Please check for spelling errors.")
if model_name in padded_models:
use_2d_data = False
train_data = "/nfs/home/students/t.reim/bachelor/pytorchtest/data/" + data_name + "/" + data_name + "_train_all_seq.csv"
test_data = "/nfs/home/students/t.reim/bachelor/pytorchtest/data/" + data_name + "/" + data_name + "_test_all_seq.csv"
val_data = "/nfs/home/students/t.reim/bachelor/pytorchtest/data/" + data_name + "/" + data_name + "_val_all_seq.csv"
if embedding_dim == 2560:
emb_name = 'esm2_t36_3B'
layer = 36
elif embedding_dim == 1280:
emb_name = 'esm2_t33_650'
layer = 33
elif embedding_dim == 5120:
emb_name = 'esm2_t48_15B'
layer = 48
if mean_embedding:
emb_type = 'mean'
else:
emb_type = 'per_tok'
if use_embeddings:
embedding_dir = "/nfs/scratch/t.reim/embeddings/" + emb_name + "/" + emb_type + "/"
if run_name is None:
run_name = f"{model_name}_{emb_name}_{subset_size}"
if not sweep:
if use_wandb == True:
wandb.init(project="bachelor",
name=run_name,
config=config)
print("Parameters:")
for key, value in config.items():
print(f"{key}: {value}")
############################################################__Data__and_Models__#################################################################
if use_2d_data:
dataset = d.dataset2d(train_data, layer, max, embedding_dir)
else:
dataset = d.MyDataset(train_data, layer, max, use_embeddings, mean_embedding, embedding_dir)
if subset:
sampler = data.RandomSampler(dataset, num_samples=int(len(dataset)*subset_size), replacement=True)
dataloader = data.DataLoader(dataset, batch_size=bs, sampler=sampler)
print("Using Subset")
print("Subset Size: ", int(len(dataset)*subset_size))
else:
dataloader = data.DataLoader(dataset, batch_size=bs, shuffle=True)
if use_2d_data:
vdataset = d.dataset2d(val_data, layer, max, embedding_dir)
else:
vdataset = d.MyDataset(val_data, layer, max, use_embeddings, mean_embedding, embedding_dir)
if subset:
sampler = data.RandomSampler(vdataset, num_samples=int(len(vdataset)*subset_size), replacement=True)
vdataloader = data.DataLoader(vdataset, batch_size=bs, sampler=sampler)
print("Val Subset Size: ", int(len(vdataset)*subset_size))
else:
vdataloader = data.DataLoader(vdataset, batch_size=bs, shuffle=True)
if use_2d_data:
test_dataset = d.dataset2d(test_data, layer, max, embedding_dir)
else:
test_dataset = d.MyDataset(test_data, layer, max, use_embeddings, mean_embedding, embedding_dir)
test_dataloader = data.DataLoader(test_dataset, batch_size=bs, shuffle=True)
print("Test Data Size: ", int(len(test_dataset)))
if use_embeddings:
print("Using Embeddings: ", emb_name, " Mean: ", mean_embedding)
insize = embedding_dim
else:
print("Not Using Embeddings")
print("Max Sequence Length: ", dataset.__max__())
insize = dataset.__max__()*24
model_mapping = {
"dscript_like": dscript_like.DScriptLike(embed_dim=insize, d=d_, w=w, h=h, x0=x0, k=k, pool_size=pool_size, do_pool=do_pool,
do_w=do_w, theta_init=theta_init, lambda_init=lambda_init, gamma_init=gamma_init),
"richoux": richoux.FC2_20_2Dense(embed_dim=insize, ff_dim1=ff_dim1, ff_dim2=ff_dim2, ff_dim3=ff_dim3, spec_norm=spec_norm),
"baseline2d": baseline2d.baseline2d(embed_dim=insize, h3=h3, kernel_size=kernel_size, pooling=pooling),
"selfattention": attention.SelfAttInteraction(embed_dim=insize, num_heads=num_heads, h3=h3, dropout=dropout,
ff_dim=ff_dim, pooling=pooling, kernel_size=kernel_size),
"crossattention": attention.CrossAttInteraction(embed_dim=insize, num_heads=num_heads, h3=h3, dropout=dropout,
ff_dim=ff_dim, pooling=pooling, kernel_size=kernel_size),
"ICAN_cross": attention.ICAN_cross(embed_dim=insize, num_heads=num_heads, cnn_drop=dropout, transformer_drop=dropout, hid_dim=h3, ff_dim=ff_dim),
"AttDscript": attention.AttentionDscript(embed_dim=insize, num_heads=num_heads, dropout=dropout, d=h3, w=w, h=h, x0=x0, k=k,
pool_size=pool_size, do_pool=do_pool, do_w=do_w, theta_init=theta_init,
lambda_init=lambda_init, gamma_init=gamma_init),
"Rich-ATT": attention.AttentionRichoux(embed_dim=insize, num_heads=num_heads, dropout=dropout),
"TUnA": attention.TUnA(embed_dim=insize, num_heads=num_heads, dropout=dropout, rffs=rffs, cross=cross, hid_dim=h3)
}
model = model_mapping.get(model_name)
if model is None:
raise ValueError(f"Invalid model_name: {model_name}")
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr= learning_rate)
if torch.cuda.is_available():
print("Using CUDA")
model = model.cuda()
criterion = criterion.cuda()
device = torch.device("cuda")
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
else:
print("Using CPU")
device = torch.device("cpu")
############################################################__Training__#################################################################
best_val_acc = 0.0
best_model = None
es = early_stopping != 0
patience = early_stopping
counter = 0
confident_train_predictions = []
confident_val_predictions = []
confident_test_predictions = []
threshold = 0.9
for epoch in range(num_epochs):
epoch_loss= 0.0
tp, fp, tn, fn = 0, 0, 0, 0
model.train()
for batch in dataloader:
optimizer.zero_grad()
if use_2d_data:
outputs = model.batch_iterate(batch, device, layer, embedding_dir)
else:
tensor = batch['tensor'].to(device)
outputs = model(tensor)
if save_confpred:
names1 = batch['name1']
names2 = batch['name2']
output_values = outputs.detach().cpu().numpy()
predicted_labels = torch.round(outputs.float()).detach().cpu().numpy()
labels = batch['interaction'].numpy()
for value, predicted_label, label, id1, id2 in zip(output_values, predicted_labels, labels, names1, names2):
if value > threshold and predicted_label == 1 and label == 1:
confident_train_predictions.append((id1, id2))
labels = batch['interaction']
labels = labels.unsqueeze(1).float()
predicted_labels = torch.round(outputs.float())
loss = criterion(outputs, labels.to(device))
loss.backward()
optimizer.step()
epoch_loss += loss.item()
met = confmat(labels.to(device), predicted_labels)
tp, fp, tn, fn = tp + met[0], fp + met[1], tn + met[2], fn + met[3]
acc, prec, rec, f1 = metrics(tp, fp, tn, fn)
avg_loss = epoch_loss / len(dataloader)
if use_wandb == True:
wandb.log({
"epoch_loss": avg_loss,
"epoch_accuracy": acc,
"epoch_precision": prec,
"epoch_recall": rec,
"epoch_f1_score": f1
})
print(f"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss}, Accuracy: {acc}, Precision: {prec}, Recall: {rec}, F1 Score: {f1}")
print(f"TP: {tp}, FP: {fp}, TN: {tn}, FN: {fn}")
############################################################__Validation__#################################################################
model.eval()
val_loss = 0.0
tp, fp, tn, fn = 0, 0, 0, 0
with torch.no_grad():
for val_batch in vdataloader:
if use_2d_data:
val_outputs = model.batch_iterate(val_batch, device, layer, embedding_dir)
else:
val_inputs = val_batch['tensor'].to(device)
val_outputs = model(val_inputs)
if save_confpred:
names1 = val_batch['name1']
names2 = val_batch['name2']
output_values = val_outputs.detach().cpu().numpy()
interactions = torch.round(val_outputs).detach().cpu().numpy()
labels = batch['interaction'].numpy()
for value, interaction, label, name1, name2 in zip(output_values, interactions, labels, names1, names2):
if value > threshold and interaction == 1 and label == 1:
confident_val_predictions.append((name1, name2))
val_labels = val_batch['interaction']
val_labels = val_labels.unsqueeze(1).float()
predicted_labels = torch.round(val_outputs.float())
met = confmat(val_labels.to(device), predicted_labels)
val_loss += criterion(val_outputs, val_labels.to(device))
tp, fp, tn, fn = tp + met[0], fp + met[1], tn + met[2], fn + met[3]
avg_loss = val_loss / len(vdataloader)
acc, prec, rec, f1 = metrics(tp, fp, tn, fn)
if use_wandb == True:
wandb.log({
"val_loss": avg_loss,
"val_accuracy": acc,
"val_precision": prec,
"val_recall": rec,
"val_f1_score": f1
})
print(f"Epoch {epoch+1}/{num_epochs}, Average Val Loss: {avg_loss}, Val Accuracy: {acc}, Val Precision: {prec}, Val Recall: {rec}, Val F1 Score: {f1}")
print(f"TP: {tp}, FP: {fp}, TN: {tn}, FN: {fn}")
if test:
test_loss = 0.0
tp, fp, tn, fn = 0, 0, 0, 0
with torch.no_grad():
for test_batch in test_dataloader:
if use_2d_data:
test_outputs = model.batch_iterate(test_batch, device, layer, embedding_dir)
else:
test_inputs = test_batch['tensor'].to(device)
test_outputs = model(test_inputs)
if save_confpred:
names1 = test_batch['name1']
names2 = test_batch['name2']
output_values = test_outputs.detach().cpu().numpy()
interactions = torch.round(test_outputs).detach().cpu().numpy()
labels = batch['interaction'].numpy()
for value, interaction, label, name1, name2 in zip(output_values, interactions, labels, names1, names2):
if value > threshold and interaction == 1 and label == 1:
confident_test_predictions.append((name1, name2))
test_labels = test_batch['interaction']
test_labels = test_labels.unsqueeze(1).float()
predicted_labels = torch.round(test_outputs.float())
met = confmat(test_labels.to(device), predicted_labels)
test_loss += criterion(test_outputs, test_labels.to(device))
tp, fp, tn, fn = tp + met[0], fp + met[1], tn + met[2], fn + met[3]
avg_loss = test_loss / len(test_dataloader)
acc, prec, rec, f1 = metrics(tp, fp, tn, fn)
if use_wandb == True:
wandb.log({
"test_loss": avg_loss,
"test_accuracy": acc,
"test_precision": prec,
"test_recall": rec,
"test_f1_score": f1
})
print(f"Epoch {epoch+1}/{num_epochs}, Average test Loss: {avg_loss}, Test Accuracy: {acc}, Test Precision: {prec}, Test Recall: {rec}, Test F1 Score: {f1}")
print(f"TP: {tp}, FP: {fp}, TN: {tn}, FN: {fn}")
if es:
if acc > best_val_acc:
best_val_acc = acc
best_model = model.state_dict()
counter = 0
else:
counter += 1
if counter >= patience:
print("Early stopping triggered. No improvement in validation accuracy. Best epoch: " + str(epoch+1 - patience))
break
else:
best_model = model.state_dict()
if save_confpred:
with open(f"/nfs/home/students/t.reim/bachelor/pytorchtest/data/gold_stand/confident_train_pred_{model_name}.csv", 'w') as f:
for name1, name2 in confident_train_predictions:
f.write(f'{name1}\t{name2}\n')
with open(f"/nfs/home/students/t.reim/bachelor/pytorchtest/data/gold_stand/confident_val_pred_{model_name}.csv", 'w') as f:
for name1, name2 in confident_val_predictions:
f.write(f'{name1}\t{name2}\n')
with open(f"/nfs/home/students/t.reim/bachelor/pytorchtest/data/gold_stand/confident_test_pred_{model_name}.csv", 'w') as f:
for name1, name2 in confident_test_predictions:
f.write(f'{name1}\t{name2}\n')
if save_model:
if test:
torch.save(best_model, f"/nfs/home/students/t.reim/bachelor/pytorchtest/models/pretrained/{model_name}_{emb_name}_test.pt")
else:
torch.save(best_model, f"/nfs/home/students/t.reim/bachelor/pytorchtest/models/pretrained/{model_name}_{emb_name}_.pt")
del model
torch.cuda.empty_cache()
except Exception as e:
print(e)
tb = traceback.format_exc()
print(tb)
del model
torch.cuda.empty_cache()
raise e
def main():
debug = False
if debug:
args = argparse.Namespace(
data_name="gold_stand",
model_name="TUnA",
learning_rate=0.001,
num_epochs=25,
batch_size=2,
max_seq_len=1000,
subset=False,
subset_size=0.5,
use_embeddings=True,
mean_embedding=False,
embedding_dim=1280,
use_wandb=False,
early_stopping=True,
dropout=0.3,
num_heads=8,
run_name=None,
attention_dim=64
)
params = vars(args)
train(params)
else:
parser = argparse.ArgumentParser(description='PyTorch Training')
parser.add_argument('-data', '--data_name', type=str, default='gold_stand', help='name of dataset')
parser.add_argument('-model', '--model_name', type=str, default='dscript_like', help='name of model')
parser.add_argument('-lr', '--learning_rate', type=float, default=0.001, help='learning rate')
parser.add_argument('-epoch', '--num_epochs', type=int, default=25, help='number of epochs')
parser.add_argument('-batch', '--batch_size', type=int, default=1024, help='batch size')
parser.add_argument('-max', '--max_seq_len', type=int, default=10000, help='max sequence length')
parser.add_argument('-sub', '--subset', action='store_true', help='use subset')
parser.add_argument('-subsize', '--subset_size', type=float, default=0.5, help='subset size')
parser.add_argument('-emb', '--use_embeddings', action='store_true', help='use embeddings')
parser.add_argument('-mean', '--mean_embedding', action='store_true', help='use mean embedding')
parser.add_argument('-emb_dim', '--embedding_dim', type=int, default=2560, help='embedding dimension')
parser.add_argument('-wandb', '--use_wandb', action='store_true', help='use wandb')
parser.add_argument('-es', '--early_stopping', type=int, default=0, help='early stopping patience')
parser.add_argument('-dropout', '--dropout', type=float, default=0.2, help='dropout value between 0 and 1')
parser.add_argument('-heads', '--num_heads', type=int, default=8, help='number of heads for attention')
parser.add_argument('-run', '--run_name', type=str, default=None, help='name of the run to appear in wandb')
parser.add_argument('-h3', '--attention_dim', type=int, default=64, help='embedding dimension before attention')
parser.add_argument('-sweep', '--sweep', action='store_true', help='use wandb sweep/hyperparam_tuning')
parser.add_argument('-save_confpred', '--save_confpred', action='store_true', help='save confident predictions')
parser.add_argument('-pool', '--pooling', type=str, default='avg', help='pooling method')
parser.add_argument('-tb', '--test_best', action='store_true', help='test best config')
args = parser.parse_args()
if args.sweep:
model = args.model_name
sweep_id = wandb.sweep(sweep_config[model], project="hypertune")
print("Sweep ID: ", sweep_id)
#wandb.agent(sweep_id, function=train(sweep=True), count=1)
if args.test_best:
model = args.model_name
train(best_configs[model])
else:
params = vars(args)
train(params)
if __name__ == "__main__":
main()