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import torch
import torch.optim as optim
import wandb as wdb
import common_dataset
import dataloader
from config_common import load_args
from common_dataset import load_data
from dataloader import load_data
from loss import masked_cos_sim, distance, masked_spectral_angle
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from model import RT_pred_model_self_attention_multi
def train(model, data_train, epoch, optimizer, criterion_rt, criterion_intensity, metric_rt, metric_intensity, forward,
wandb=None):
losses_rt = 0.
losses_int = 0.
dist_rt_acc = 0.
dist_int_acc = 0.
model.train()
for param in model.parameters():
param.requires_grad = True
if forward == 'both':
for seq, charge, rt, intensity in data_train:
rt, intensity = rt.float(), intensity.float()
if torch.cuda.is_available():
seq, charge, rt, intensity = seq.cuda(), charge.cuda(), rt.cuda(), intensity.cuda()
pred_rt, pred_int = model.forward(seq, charge)
loss_rt = criterion_rt(rt, pred_rt)
loss_int = criterion_intensity(intensity, pred_int)
loss = loss_rt + loss_int
dist_rt = metric_rt(rt, pred_rt)
dist_int = metric_intensity(intensity, pred_int)
dist_rt_acc += dist_rt.item()
dist_int_acc += dist_int.item()
losses_rt += loss_rt.item()
losses_int += 5.*loss_int.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if wandb is not None:
wdb.log({"train rt loss": losses_rt / len(data_train), "train int loss": losses_int / len(data_train),
"train rt mean metric": dist_rt_acc / len(data_train),
"train int mean metric": dist_int_acc / len(data_train),
'train epoch': epoch})
print('epoch : ', epoch, 'train rt loss', losses_rt / len(data_train), 'train int loss',
losses_int / len(data_train), "train rt mean metric : ", dist_rt_acc / len(data_train),
"train int mean metric",
dist_int_acc / len(data_train))
if forward == 'rt':
for seq, rt in data_train:
rt = rt.float()
if torch.cuda.is_available():
seq, rt = seq.cuda(), rt.cuda()
pred_rt = model.forward_rt(seq)
loss_rt = criterion_rt(rt, pred_rt)
loss = loss_rt
dist_rt = metric_rt(rt, pred_rt)
dist_rt_acc += dist_rt.item()
losses_rt += loss_rt.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if wandb is not None:
wdb.log({"train rt loss": losses_rt / len(data_train),
"train rt mean metric": dist_rt_acc / len(data_train),
'train epoch': epoch})
print('epoch : ', epoch, 'train rt loss', losses_rt / len(data_train), "train rt mean metric : ",
dist_rt_acc / len(data_train))
if forward == 'int':
for seq, charge, intensity in data_train:
intensity = intensity.float()
if torch.cuda.is_available():
seq, charge, intensity = seq.cuda(), charge.cuda(), intensity.cuda()
pred_int = model.forward_int(seq, charge)
loss_int = criterion_intensity(intensity, pred_int)
loss = loss_int
dist_int = metric_intensity(intensity, pred_int)
dist_int_acc += dist_int.item()
losses_int += loss_int.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if wandb is not None:
wdb.log({"train int loss": losses_int / len(data_train),
"train int mean metric": dist_int_acc / len(data_train),
'train epoch': epoch})
print('epoch : ', epoch, 'train int loss',
losses_int / len(data_train),
"train int mean metric",
dist_int_acc / len(data_train))
def eval(model, data_val, epoch, criterion_rt, criterion_intensity, metric_rt, metric_intensity, forward, wandb=None):
losses_rt = 0.
losses_int = 0.
dist_rt_acc = 0.
dist_int_acc = 0.
for param in model.parameters():
param.requires_grad = False
if forward == 'both':
for seq, charge, rt, intensity in data_val:
rt, intensity = rt.float(), intensity.float()
if torch.cuda.is_available():
seq, charge, rt, intensity = seq.cuda(), charge.cuda(), rt.cuda(), intensity.cuda()
pred_rt, pred_int = model.forward(seq, charge)
loss_rt = criterion_rt(rt, pred_rt)
loss_int = criterion_intensity(intensity, pred_int)
losses_rt += loss_rt.item()
losses_int += loss_int.item()
dist_rt = metric_rt(rt, pred_rt)
dist_int = metric_intensity(intensity, pred_int)
dist_rt_acc += dist_rt.item()
dist_int_acc += dist_int.item()
if wandb is not None:
wdb.log({"val rt loss": losses_rt / len(data_val), "val int loss": losses_int / len(data_val),
"val rt mean metric": dist_rt_acc / len(data_val),
"val int mean metric": dist_int_acc / len(data_val),
'val epoch': epoch})
print('epoch : ', epoch, 'val rt loss', losses_rt / len(data_val), 'val int loss', losses_int / len(data_val),
"val rt mean metric : ",
dist_rt_acc / len(data_val), "val int mean metric", dist_int_acc / len(data_val))
if forward == 'rt': #adapted to prosit dataset format
for seq, rt in data_val:
rt = rt.float()
if torch.cuda.is_available():
seq, rt = seq.cuda(), rt.cuda()
pred_rt = model.forward_rt(seq)
loss_rt = criterion_rt(rt, pred_rt)
losses_rt += loss_rt.item()
dist_rt = metric_rt(rt, pred_rt)
dist_rt_acc += dist_rt.item()
if wandb is not None:
wdb.log({"val rt loss": losses_rt / len(data_val),
"val rt mean metric": dist_rt_acc / len(data_val),
'val epoch': epoch})
print('epoch : ', epoch, 'val rt loss', losses_rt / len(data_val),
"val rt mean metric : ",
dist_rt_acc / len(data_val))
if forward == 'int': #adapted to prosit dataset format
for seq, charge, _, intensity in data_val:
intensity = intensity.float()
if torch.cuda.is_available():
seq, charge, intensity = seq.cuda(), charge.cuda(), intensity.cuda()
pred_int = model.forward_int(seq, charge)
loss_int = criterion_intensity(intensity, pred_int)
losses_int += loss_int.item()
dist_int = metric_intensity(intensity, pred_int)
dist_int_acc += dist_int.item()
if wandb is not None:
wdb.log({"val int loss": losses_int / len(data_val),
"val int mean metric": dist_int_acc / len(data_val),
'val epoch': epoch})
print('epoch : ', epoch, 'val int loss', losses_int / len(data_val), "val int mean metric",
dist_int_acc / len(data_val))
def run(epochs, eval_inter, save_inter, model, data_train, data_val, data_test, optimizer, criterion_rt,
criterion_intensity, metric_rt, metric_intensity, forward, wandb=None, output='output/out.csv'):
if forward =='transfer' :
for e in range(1, epochs + 1):
train(model, data_train, e, optimizer, criterion_rt, criterion_intensity, metric_rt, metric_intensity, 'rt',
wandb=wandb)
if e % eval_inter == 0:
eval(model, data_val, e, criterion_rt, criterion_intensity, metric_rt, metric_intensity, 'both',
wandb=wandb)
if e % save_inter == 0:
save(model, 'model_common_' + str(e) + '.pt')
save_pred(model, data_val, 'rt', output)
else :
for e in range(1, epochs + 1):
train(model, data_train, e, optimizer, criterion_rt, criterion_intensity, metric_rt, metric_intensity, forward,
wandb=wandb)
if e % eval_inter == 0:
eval(model, data_val, e, criterion_rt, criterion_intensity, metric_rt, metric_intensity, forward,
wandb=wandb)
if e % save_inter == 0:
save(model, 'model_common_' + str(e) + '.pt')
save_pred(model, data_val, forward, output)
def main(args):
if args.wandb is not None:
os.environ["WANDB_API_KEY"] = 'b4a27ac6b6145e1a5d0ee7f9e2e8c20bd101dccd'
os.environ["WANDB_MODE"] = "offline"
os.environ["WANDB_DIR"] = os.path.abspath("./wandb_run")
wdb.init(project="Common prediction", dir='./wandb_run', name=args.wandb)
print(args)
print('Cuda : ', torch.cuda.is_available())
if args.forward == 'both':
data_train, data_val, data_test = common_dataset.load_data(path_train=args.dataset_train,
path_val=args.dataset_val,
path_test=args.dataset_test,
batch_size=args.batch_size, length=25, pad = True, convert=True, vocab='iapuc')
data_train, _, _ = dataloader.load_data(data_sources=[args.dataset_train,args.dataset_val,args.dataset_test],
batch_size=args.batch_size, length=25)
elif args.forward == 'transfer':
data_train, _, _ = dataloader.load_data(data_sources=[args.dataset_train,'database/data_holdout.csv','database/data_holdout.csv'],
_, data_val, data_test = common_dataset.load_data(path_train=args.dataset_val,
path_val=args.dataset_val,
path_test=args.dataset_test,
batch_size=args.batch_size, length=25, pad = True, convert=True, vocab='iapuc')
model = Model_Common_Transformer(encoder_ff=args.encoder_ff, decoder_rt_ff=args.decoder_rt_ff,
decoder_int_ff=args.decoder_int_ff
, n_head=args.n_head, encoder_num_layer=args.encoder_num_layer,
decoder_int_num_layer=args.decoder_int_num_layer,
decoder_rt_num_layer=args.decoder_rt_num_layer, drop_rate=args.drop_rate,
embedding_dim=args.embedding_dim, acti=args.activation, norm=args.norm_first)
if torch.cuda.is_available():
model = model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
print('\nModel initialised')
run(epochs=args.epochs, eval_inter=args.eval_inter, save_inter=args.save_inter, model=model, data_train=data_train,
data_val=data_val, data_test=data_test, optimizer=optimizer, criterion_rt=torch.nn.MSELoss(),
criterion_intensity=masked_cos_sim, metric_rt=distance, metric_intensity=masked_spectral_angle,
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if args.wandb is not None:
wdb.finish()
def save(model, checkpoint_name):
print('\nModel Saving...')
os.makedirs('checkpoints', exist_ok=True)
torch.save(model, os.path.join('checkpoints', checkpoint_name))
def load(path):
model = torch.load(os.path.join('checkpoints', path))
return model
def get_n_params(model):
pp = 0
for n, p in list(model.named_parameters()):
nn = 1
for s in list(p.size()):
nn = nn * s
print(n, nn)
pp += nn
return pp
def save_pred(model, data_val, forward, output_path):
data_frame = pd.DataFrame()
for param in model.parameters():
param.requires_grad = False
if forward == 'both':
pred_rt, pred_int, seqs, charges, true_rt, true_int = [], [], [], [], [], []
for seq, charge, rt, intensity in data_val:
rt, intensity = rt.float(), intensity.float()
if torch.cuda.is_available():
seq, charge, rt, intensity = seq.cuda(), charge.cuda(), rt.cuda(), intensity.cuda()
pr_rt, pr_intensity = model.forward(seq, charge)
pred_rt.extend(pr_rt.data.cpu().tolist())
pred_int.extend(pr_intensity.data.cpu().tolist())
seqs.extend(seq.data.cpu().tolist())
charges.extend(charge.data.cpu().tolist())
true_rt.extend(rt.data.cpu().tolist())
true_int.extend(intensity.data.cpu().tolist())
data_frame['rt pred'] = pred_rt
data_frame['seq'] = seqs
data_frame['pred int'] = pred_int
data_frame['true rt'] = true_rt
data_frame['true int'] = true_int
data_frame['charge'] = charges
if forward == 'rt': #adapted to prosit dataset format
pred_rt, seqs, true_rt = [], [], []
for seq, rt in data_val:
rt = rt.float()
if torch.cuda.is_available():
seq, rt = seq.cuda(), rt.cuda()
pr_rt = model.forward_rt(seq)
pred_rt.extend(pr_rt.data.cpu().tolist())
seqs.extend(seq.data.cpu().tolist())
true_rt.extend(rt.data.cpu().tolist())
data_frame['rt pred'] = pred_rt
data_frame['seq'] = seqs
data_frame['true rt'] = true_rt
if forward == 'int': #adapted to prosit dataset format
pred_int, seqs, charges, true_int = [], [], [], []
for seq, charge, _, intensity in data_val:
intensity = intensity.float()
if torch.cuda.is_available():
seq, charge, intensity = seq.cuda(), charge.cuda(), intensity.cuda()
pred_int = model.forward_int(seq, charge)
seqs.extend(seq.data.cpu().tolist())
charges.extend(charge.data.cpu().tolist())
true_int.extend(intensity.data.cpu().tolist())
data_frame['seq'] = seqs
data_frame['pred int'] = pred_int
data_frame['true int'] = true_int
data_frame['charge'] = charges
if __name__ == "__main__":
args = load_args()
main(args)