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train_zsl_nus.py
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train_zsl_nus.py
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# import json
import os
import argparse
# from subprocess import call
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torchvision.transforms as transforms
from torch.optim import lr_scheduler
from src_files.helper_functions.helper_functions import mAP, f1, CutoutPIL, ModelEma, \
add_weight_decay, get_datasets_from_csv, update_wordvecs
from src_files.models import create_model
from src_files.loss_functions.losses import AsymmetricLoss, CLIPLoss
from randaugment import RandAugment
from torch.cuda.amp import GradScaler, autocast
import pickle
import torch.multiprocessing as tmul
import numpy as np
from sklearn.metrics import f1_score, recall_score
import wandb
from src_files.helper_functions.helper_functions import mAP, CocoDetection, AverageMeter
import time
import clip
parser = argparse.ArgumentParser(description='PyTorch MS_COCO Training')
parser.add_argument('--data', type=str, default='/home/muhammad.ali/Desktop/Research/MLDECODER/ML_Decoder/')
parser.add_argument('--lr', default=1e-4, type=float)
parser.add_argument('--model-name', default='tresnet_m')
parser.add_argument('--model-path', default='/home/muhammad.ali/Desktop/Research/MLDECODER/tresnet_m21k.pth', type=str)
parser.add_argument('--num-classes', default=925)
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers')
parser.add_argument('--image_size', default=224, type=int,
metavar='N', help='input image size (default: 224)')
parser.add_argument('--batch-size', default=56, type=int,
metavar='N', help='mini-batch size')
parser.add_argument('--thr', default=0.75, type=float,
metavar='N', help='threshold value')
parser.add_argument('--print-freq', '-p', default=32, type=int,
metavar='N', help='print frequency (default: 64)')
# ML-Decoder
parser.add_argument('--use-ml-decoder', default=1, type=int)
parser.add_argument('--num-of-groups', default=-1, type=int) # full-decoding
parser.add_argument('--decoder-embedding', default=768, type=int)
# CLIP
parser.add_argument('--replace-image-encoder-with-clip',default= 0,type=int, help='if set to True, the image encoder is replaced with clip image encoder')
parser.add_argument('--text-embeddings', default='wordvec', type=str, help='the text embedings to load, options=["wordvec","clip"]')
parser.add_argument('--add-clip-loss', default=0, type=int)
parser.add_argument('--clip-loss-temp', default=0.1, type=float)
parser.add_argument('--clip-loss-weight', default=1, type=float)
parser.add_argument('--classif-loss-weight', default=1.0, type=float)
parser.add_argument('--gzsl', default=0, type=int)
parser.add_argument('--resume_training', default=0, type=int)
parser.add_argument('--exp_name', default = 'test',type= str)
parser.add_argument('--validate_only', default=0, type=int)
parser.add_argument('--add-clip-head', default=0, type=int)
def main():
args = parser.parse_args()
wandb.init(config=args)
# wandb.define_metric('mAP', summary='max')
#NUS-WIDE defaults
args.zsl = 1
args.num_of_groups = 925
args.use_ml_decoder = 1
args.num_classes = 925
# Setup model
print('creating model {}...'.format(args.model_name))
model = create_model(args).cuda()
print('done')
#NUS-WIDE Data loading
#json_path = os.path.join(args.data, '/home/muhammad.ali/Desktop/Research/MLDECODER/benchmark_81_v0.json')
#json_path = os.path.join(args.data, '/home/muhammad.ali/Desktop/Research/MLDECODER/data.csv')
json_path = os.path.join(args.data, 'benchmark_81_v0.json')
wordvec_array = torch.load(os.path.join(args.data, args.text_embeddings+ '_array.pth'))
clip_model = None
clip_criterion = None
if args.add_clip_loss:
device = "cuda" if torch.cuda.is_available() else "cpu"
clip_model, _ = clip.load('RN50', device)
clip_criterion = CLIPLoss(args.clip_loss_temp, args.decoder_embedding, 1024, 512, device)
train_transform = transforms.Compose([
transforms.Resize((args.image_size, args.image_size)),
CutoutPIL(cutout_factor=0.5),
RandAugment(),
transforms.ToTensor(),
# normalize,
])
val_transform = transforms.Compose([
transforms.Resize((args.image_size, args.image_size)),
transforms.ToTensor(),
# normalize, # no need, toTensor does normalization
])
train_dataset, val_dataset, train_cls_ids, test_cls_ids = get_datasets_from_csv(args, args.data,
args.data,
train_transform, val_transform,
json_path)
# wordvec_array += torch.randn_like(wordvec_array)
train_wordvecs = wordvec_array[..., train_cls_ids].float()
# noise = np.random.normal(0,1,300*925).reshape(300,925)
# x = torch.zeros(300, 925, 100, dtype=torch.float64) # augmentations as well as random augmentations
# x = x + (0.1**0.5)*torch.randn(300, 925, 100)
# train_wordvecs = train_wordvecs + noise
test_wordvecs = wordvec_array[..., test_cls_ids].float()
# if args.gzsl:
# test_wordvecs = torch.cat((test_wordvecs, train_wordvecs), axis=1)
print('classes {}'.format(len(train_dataset.classes)))
print('train_cls_ids {} test_cls_ids {} '.format(train_cls_ids.shape, test_cls_ids.shape))
print("len(val_dataset)): ", len(val_dataset))
print("len(train_dataset)): ", len(train_dataset))
# Pytorch Data loader
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=False)
tmul.set_sharing_strategy('file_system')
# Actuall Training
train_multi_label_zsl(args, model, clip_model, clip_criterion, train_loader, val_loader, args.lr, train_wordvecs, test_wordvecs)
def train_multi_label_zsl(args, model, clip_model, clip_criterion, train_loader, val_loader, lr, train_wordvecs=None,
test_wordvecs=None):
ema = ModelEma(model, 0.9997) # 0.9997^641=0.82
# set optimizer
Epochs = 40
weight_decay = 1e-2 # 5e-3
criterion = AsymmetricLoss(gamma_neg=4, gamma_pos=0, clip=0.05, disable_torch_grad_focal_loss=True)
parameters = add_weight_decay(model, weight_decay)
optimizer = torch.optim.Adam(params=parameters, lr=lr, weight_decay=0) # true wd, filter_bias_and_bn
if args.add_clip_loss:
optimizer.add_param_group({'params': list(clip_model.parameters()) + list(clip_criterion.parameters())})
# clip_optimizer = torch.optim.Adam(params=list(clip_model.parameters()) + list(clip_criterion.parameters()), lr=lr, weight_decay=0)
steps_per_epoch = len(train_loader)
scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr, steps_per_epoch=steps_per_epoch, epochs=Epochs,
pct_start=0.2)
highest_mAP = 0
trainInfoList = []
scaler = GradScaler()
for epoch in range(Epochs):
if not args.validate_only:
update_wordvecs(model, train_wordvecs)
for i, input in enumerate(train_loader):
inputData = input['image'].cuda()
target = input['target'].cuda() # (batch,3,num_classes)
with autocast(): # mixed precision
if args.add_clip_loss:
clip_tokens = input['clip_tokens'].cuda()
text_features = clip_model.encode_text(clip_tokens)
output, image_embeddings = model(inputData, text_features) # sigmoid will be done in loss !
output = output.float()
if args.add_clip_loss:
clip_loss, _, _ = clip_criterion(image_embeddings, text_features)
loss = criterion(output, target) * args.classif_loss_weight
if args.add_clip_loss:
loss += clip_loss * args.clip_loss_weight
# clip_loss.backward()
# clip_optimizer.step()
# clip_optimizer.zero_grad()
model.zero_grad()
scaler.scale(loss).backward()
# loss.backward()
scaler.step(optimizer)
scaler.update()
# optimizer.step()
scheduler.step()
ema.update(model)
# store information
if i % 100 == 0:
trainInfoList.append([epoch, i, loss.item()])
print('Epoch [{}/{}], Step [{}/{}], LR {:.1e}, Loss: {:.1f}'
.format(epoch, Epochs, str(i).zfill(3), str(steps_per_epoch).zfill(3),
scheduler.get_last_lr()[0], \
loss.item()))
wandb.log({"loss": loss})
if args.add_clip_loss:
wandb.log({"clip_loss": clip_loss})
try:
torch.save(model.state_dict(), os.path.join(
'models',args.exp_name, 'model-{}-{}.ckpt'.format(epoch + 1, i + 1)))
except:
pass
model.eval()
update_wordvecs(model, test_wordvecs=test_wordvecs)
update_wordvecs(ema.module, test_wordvecs=test_wordvecs)
mAP_score, f1_3, p_3, r_3, f1_5, p_5, r_5 = validate_multi(args, val_loader, model, ema)
model.train()
if mAP_score > highest_mAP:
highest_mAP = mAP_score
f1_3_at_highest_mAP = f1_3
p_3_at_highest_mAP = p_3
r_3_at_highest_mAP = r_3
f1_5_at_highest_mAP = f1_5
p_5_at_highest_mAP = p_5
r_5_at_highest_mAP = r_5
try:
torch.save(model.state_dict(), os.path.join(
'models', args.exp_name,'model-highest-'+str(epoch)+'.ckpt'))
except:
pass
print('current_mAP = {:.2f}, highest_mAP = {:.2f}\n'.format(mAP_score, highest_mAP))
print('current_f1_k3 = {:.2f}, f1_k3_at_highest_mAP = {:.2f}\n'.format(f1_3, f1_3_at_highest_mAP))
print('current_p_k3 = {:.2f}, p_k3_at_highest_mAP = {:.2f}\n'.format(p_3, p_3_at_highest_mAP))
print('current_r_k3 = {:.2f}, r_k3_at_highest_mAP = {:.2f}\n'.format(r_3, r_3_at_highest_mAP))
print('current_f1_k5 = {:.2f}, f1_k5_at_highest_mAP = {:.2f}\n'.format(f1_5, f1_5_at_highest_mAP))
print('current_p_k5 = {:.2f}, p_k5_at_highest_mAP = {:.2f}\n'.format(p_5, p_5_at_highest_mAP))
print('current_r_k5 = {:.2f}, r_k5_at_highest_mAP = {:.2f}\n'.format(r_5, r_5_at_highest_mAP))
if args.validate_only:
break
wandb.log({
"highest_mAP": highest_mAP,
"f1_3_at_highest_mAP": f1_3_at_highest_mAP,
"p_3_at_highest_mAP": p_3_at_highest_mAP,
"r_3_at_highest_mAP": r_3_at_highest_mAP,
"f1_5_at_highest_mAP": f1_5_at_highest_mAP,
"p_5_at_highest_mAP": p_5_at_highest_mAP,
"r_5_at_highest_mAP": r_5_at_highest_mAP
})
def validate_multi(args, val_loader, model, ema_model):
print("starting validation")
batch_time = AverageMeter()
prec = AverageMeter()
rec = AverageMeter()
mAP_meter = AverageMeter()
end = time.time()
tp, fp, fn, tn, count = 0, 0, 0, 0, 0
Sig = torch.nn.Sigmoid()
preds_regular = []
preds_ema = []
targets = []
for i, data_dict in enumerate(val_loader):
input = data_dict['image']
target = data_dict['target']
# compute output
with torch.no_grad():
with autocast():
output_regular = Sig(model(input.cuda())).cpu()
output_ema = Sig(ema_model.module(input.cuda())).cpu()
# for mAP calculation
preds_regular.append(output_regular.cpu().detach())
preds_ema.append(output_ema.cpu().detach())
targets.append(target.cpu().detach())
# measure accuracy and record loss
pred = output_regular.data.gt(args.thr).long()
tp += (pred + target).eq(2).sum(dim=0)
fp += (pred - target).eq(1).sum(dim=0)
fn += (pred - target).eq(-1).sum(dim=0)
tn += (pred + target).eq(0).sum(dim=0)
count += input.size(0)
this_tp = (pred + target).eq(2).sum()
this_fp = (pred - target).eq(1).sum()
this_fn = (pred - target).eq(-1).sum()
this_tn = (pred + target).eq(0).sum()
this_prec = this_tp.float() / (
this_tp + this_fp).float() * 100.0 if this_tp + this_fp != 0 else 0.0
this_rec = this_tp.float() / (
this_tp + this_fn).float() * 100.0 if this_tp + this_fn != 0 else 0.0
prec.update(float(this_prec), input.size(0))
rec.update(float(this_rec), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
p_c = [float(tp[i].float() / (tp[i] + fp[i]).float()) * 100.0 if tp[
i] > 0 else 0.0
for i in range(len(tp))]
r_c = [float(tp[i].float() / (tp[i] + fn[i]).float()) * 100.0 if tp[
i] > 0 else 0.0
for i in range(len(tp))]
f_c = [2 * p_c[i] * r_c[i] / (p_c[i] + r_c[i]) if tp[i] > 0 else 0.0 for
i in range(len(tp))]
mean_p_c = sum(p_c) / len(p_c)
mean_r_c = sum(r_c) / len(r_c)
mean_f_c = sum(f_c) / len(f_c)
p_o = tp.sum().float() / (tp + fp).sum().float() * 100.0
r_o = tp.sum().float() / (tp + fn).sum().float() * 100.0
f_o = 2 * p_o * r_o / (p_o + r_o)
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Precision {prec.val:.2f} ({prec.avg:.2f})\t'
'Recall {rec.val:.2f} ({rec.avg:.2f})'.format(
i, len(val_loader), batch_time=batch_time,
prec=prec, rec=rec))
print(
'P_C {:.2f} R_C {:.2f} F_C {:.2f} P_O {:.2f} R_O {:.2f} F_O {:.2f}'
.format(mean_p_c, mean_r_c, mean_f_c, p_o, r_o, f_o))
print(
'--------------------------------------------------------------------')
print(' * P_C {:.2f} R_C {:.2f} F_C {:.2f} P_O {:.2f} R_O {:.2f} F_O {:.2f}'
.format(mean_p_c, mean_r_c, mean_f_c, p_o, r_o, f_o))
mAP_score_regular = mAP(torch.cat(targets).numpy(), torch.cat(preds_regular).numpy())
mAP_score_ema = mAP(torch.cat(targets).numpy(), torch.cat(preds_ema).numpy())
# rc_3, rc_5 = recall(torch.cat(preds_regular), torch.cat(targets), k_val=3), recall(torch.cat(preds_regular), torch.cat(targets), k_val=5)
print("mAP score regular {:.2f}, mAP score EMA {:.2f}".format(mAP_score_regular, mAP_score_ema))
# print("recall score regular {:.2f}, rc_3 {:.2f},rc_5 {:.2f}".format(rc_3, rc_5))
f1_3, p_3, r_3 = f1(torch.cat(preds_regular), torch.cat(targets), 'overall', k_val=3)
f1_5, p_5, r_5 = f1(torch.cat(preds_regular), torch.cat(targets), 'overall', k_val=5)
print("fi score k=3: {:.2f}. f1 score k=5: {:.2f}".format(f1_3, f1_5))
return max(mAP_score_regular, mAP_score_ema), f1_3, p_3, r_3, f1_5, p_5, r_5
if __name__ == '__main__':
main()