merging main

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.9 (LC-MS-RT-prediction)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (LC-MS-RT-prediction)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (LC-MS-RT-prediction)" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file

main_intensity.py

-import os
-import wandb as wdb
-import torch.nn as nn
-import torch.optim as optim
-import torch
-from dataloader import load_data, load_split_intensity, Intentsity_Dataset, load_intensity_from_files
-from model import RT_pred_model, Intensity_pred_model_multi_head, RT_pred_model_self_attention
-from config import load_args
-from loss import masked_cos_sim, masked_pearson_correlation_distance
-
-
-def train(model, data_train, epoch, optimizer, criterion, cuda=False):
-    losses = 0.
-    distance = 0.
-    for data1, data2, data3, target in data_train:
-        if torch.cuda.is_available():
-            data1, data2, data3, target = data1.cuda(), data2.cuda(), data3.cuda(), target.cuda()
-        pred_rt = model.forward(data1, data2, data3)
-        target.float()
-        loss = criterion(pred_rt, target)
-        dist = torch.mean(torch.abs(pred_rt - target))
-        distance += dist.item()
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-        losses += loss.item()
-
-    # wdb.log({"train loss": losses / len(data_train), "train mean distance": distance / len(data_train)})
-
-    print('epoch : ', epoch, ',train losses : ', losses / len(data_train), " ,mean distance : ",
-          distance / len(data_train))
-
-
-def eval(model, data_test, epoch, criterion=masked_cos_sim, cuda=False):
-    losses = 0.
-    distance = 0.
-    for data, target in data_test:
-        if torch.cuda.is_available():
-            data, target = data.cuda(), target.cuda()
-        pred_rt = model(data)
-        loss = criterion(pred_rt, target)
-        losses += loss.item()
-        dist = torch.mean(torch.abs(pred_rt - target))
-        distance += dist.item()
-    # wdb.log({"eval loss": losses / len(data_test), "eval mean distance": distance / len(data_test)})
-    print('epoch : ', epoch, ',eval losses : ', losses / len(data_test), " ,eval mean distance: :",
-          distance / len(data_test))
-
-
-def save(model, optimizer, epoch, checkpoint_name):
-    print('\nModel Saving...')
-    model_state_dict = model.state_dict()
-    os.makedirs('checkpoints', exist_ok=True)
-    torch.save({
-        'model_state_dict': model_state_dict,
-        'global_epoch': epoch,
-        'optimizer_state_dict': optimizer.state_dict(),
-    }, os.path.join('checkpoints', checkpoint_name))
-
-
-def run(epochs, eval_inter, save_inter, model, data_train, data_test, optimizer, criterion=masked_cos_sim,
-        cuda=False):
-    for e in range(1, epochs + 1):
-        train(model, data_train, e, optimizer, criterion, cuda=cuda)
-        if e % eval_inter == 0:
-            eval(model, data_test, e, cuda=cuda)
-        if e % save_inter == 0:
-            save(model, optimizer, epochs, 'model_self_attention_' + str(e) + '.pt')
-
-
-def main(args):
-
-    os.environ["WANDB_API_KEY"] = 'b4a27ac6b6145e1a5d0ee7f9e2e8c20bd101dccd'
-    os.environ["WANDB_MODE"] = "offline"
-    os.environ["WANDB_DIR"] = os.path.abspath("./wandb_run")
-
-    wdb.init(project="RT prediction", dir='./wandb_run')
-    print(torch.cuda.is_available())
-
-    sources_train = ('data/intensity/sequence_train.npy',
-                     'data/intensity/intensity_train.npy',
-                     'data/intensity/collision_energy_train.npy',
-                     'data/intensity/precursor_charge_train.npy')
-
-    sources_test = ('data/intensity/sequence_test.npy',
-                    'data/intensity/intensity_test.npy',
-                    'data/intensity/collision_energy_test.npy',
-                    'data/intensity/precursor_charge_test.npy')
-
-    data_train = load_intensity_from_files(sources_train[0], sources_train[1], sources_train[2], sources_train[3], args.batch_size)
-    data_test = load_intensity_from_files(sources_test[0], sources_test[1], sources_test[2], sources_test[3], args.batch_size)
-
-    print('\nData loaded')
-    model = Intensity_pred_model_multi_head()
-    if torch.cuda.is_available():
-        model = model.cuda()
-    optimizer = optim.Adam(model.parameters(), lr=0.001)
-    print('\nModel initialised')
-    run(args.epochs, args.eval_inter, args.save_inter, model, data_train, data_test, optimizer=optimizer, cuda=True)
-
-    wdb.finish()
-
-if __name__ == "__main__":
-    args = load_args()
-    print(args)
-    main(args)