From 2fc337022d0293e962203d76b15ba6c0b172ddbd Mon Sep 17 00:00:00 2001
From: schne <leo.schneider@ecl19.ec-lyon.fr>
Date: Wed, 31 Jan 2024 10:38:12 +0100
Subject: [PATCH] merging main
---
.idea/misc.xml | 2 +-
main_intensity.py | 106 ----------------------------------------------
2 files changed, 1 insertion(+), 107 deletions(-)
delete mode 100644 main_intensity.py
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 791742a..1b5f6f7 100644
--- a/.idea/misc.xml
+++ b/.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
diff --git a/main_intensity.py b/main_intensity.py
deleted file mode 100644
index 04df4ed..0000000
--- a/main_intensity.py
+++ /dev/null
@@ -1,106 +0,0 @@
-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)
--
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