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train_unfrozen.py
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from models import model_unfrozen
import clip
import torch
import torch.nn as nn
import torch.nn.functional as F
# from pascal_voc_loader import pascalVOCLoader
from pascal_5i_loader import Pascal5iLoader
from config import config
from torch.utils.data import DataLoader
from tqdm import tqdm
from torch.utils.tensorboard import SummaryWriter
import time
import os,sys
import subprocess # for uploading tensorboard
from torch.profiler import profile, record_function, ProfilerActivity
import matplotlib.pyplot as plt
from models.model import intersectionAndUnionGPU
previous_runs = os.listdir('fewshotruns')
if len(previous_runs) == 0:
run_number = 1
else:
run_number = max([int(s.split('run_')[1]) for s in previous_runs]) + 1
logdir = 'run_%02d' % run_number
writer = SummaryWriter('fewshotruns/'+logdir)
layout = {
"Loss and mIOU for train and val": {
"loss": ["Multiline", ["loss/train", "loss/val"]],
"miou": ["Multiline", ["miou/train", "miou/val"]],
},
}
writer.add_custom_scalars(layout)
def norm_im(im):
x_min, x_max = im.min(), im.max()
ims = (im - x_min) / (x_max-x_min)
return ims
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Running on', device, 'logging in', logdir)
segclip, preproc, preproc_lbl = model_unfrozen.load_custom_clip('RN50', device=device, img_size=224)
segclip.to(device) # redundant
if len(sys.argv)>1:
config['fold'] = int(sys.argv[1])
# dataset = pascalVOCLoader(config['pascal_root'], preproc, preproc_lbl, split='train', img_size=224, is_transform=True)
dataset = Pascal5iLoader(config['pascal_root'], fold=config['fold'], preproc=preproc, preproc_lbl=preproc_lbl)
trainloader = DataLoader(dataset, batch_size=config['batch_size'], pin_memory=True, num_workers=config['num_workers'])
# valset = pascalVOCLoader(config['pascal_root'], preproc, preproc_lbl, split='val', img_size=224, is_transform=True)
valset = Pascal5iLoader(config['pascal_root'], fold=config['fold'], preproc=preproc, preproc_lbl=preproc_lbl, train=False)
valloader = DataLoader(valset, batch_size=config['batch_size'], pin_memory=True, num_workers=config['num_workers'])
loss_fn = nn.CrossEntropyLoss()
optimiser = torch.optim.Adam(segclip.parameters(), lr=config['lr'], weight_decay=config['weight_decay'])
pascal_labels = [
'aeroplane',
'bicycle',
'bird',
'boat',
'bottle',
'bus',
'car',
'cat',
'chair',
'cow',
'dog',
'horse',
'motorbike',
'person',
'sheep',
'sofa',
'diningtable',
'pottedplant',
'train',
'tvmonitor',
]
template = 'a photo of a '
pascal_labels = [template+x for x in pascal_labels]
pascal_labels.insert(0, '')
pascal_labels_train = [pascal_labels[x] for x in dataset.label_set]
# pascal_labels_val = [pascal_labels[x] for x in valset.label_set]
pascal_labels_val = pascal_labels
pascal_labels_train.insert(0, '')
# pascal_labels_val.insert(0, '')
text_tokens_train = clip.tokenize(pascal_labels_train).to(device)
text_tokens_val = clip.tokenize(pascal_labels_val).to(device)
print(text_tokens_train.shape)
print(text_tokens_val.shape)
final_loss = 0
final_miou_t = 0
final_miou_v = 0
start = time.time()
# with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
# with record_function("one epoch total"):
for epoch in tqdm(range(config['num_epochs'])):
# tqdm.write(f'Epoch {epoch} started.')
epoch_loss_t = 0
epoch_miou_t = 0
pbar = tqdm(enumerate(trainloader), total=len(trainloader), leave=False)
segclip.train()
for i, (batch_img, batch_lbl) in pbar:
# times.append(t_diff)
batch_img, batch_lbl = batch_img.to(device), batch_lbl.to(device)
# if i==0:
# writer.add_graph(segclip, (batch_img, text_tokens))
output = segclip(batch_img, text_tokens_train)
# print(output.min(), output.max())
loss = loss_fn(output, batch_lbl)
loss.backward()
optimiser.step()
batch_pred = F.softmax(output, dim=1).argmax(dim=1)
# batch_pred[batch_pred > config['fold']*5] += 5
inter,union,_ = intersectionAndUnionGPU(batch_pred, batch_lbl, output.shape[1])
batch_miou = (inter.sum()/union.sum()).item()
# tqdm.write(str(i)+str(u))
epoch_miou_t += batch_miou
# tqdm.write(str(batch_miou))
# tqdm.write(str((i/u).mean()))
pbar.set_description_str(f'train loss: {loss.item():.4f}, iou: {batch_miou:.4f}')
epoch_loss_t += loss.item()
# if i==len(trainloader)-1:
# pred = torch.stack([dataset.decode_segmap(x).permute(2,0,1) for x in batch_pred]).to(device)
# lbl = torch.stack([dataset.decode_segmap(x).permute(2,0,1) for x in batch_lbl]).to(device)
# writer.add_images('img + GT', (norm_im(batch_img)*255).int() | lbl.int(), epoch)
# writer.add_images('img + pred', (norm_im(batch_img)*255).int() | pred.int(), epoch)
# writer.add_images('img', norm_im(batch_img), epoch)
# writer.add_images('GT', lbl, epoch)
# writer.add_images('pred', pred, epoch)
epoch_loss_t /= len(trainloader)
epoch_miou_t /= len(trainloader)
segclip.eval()
epoch_miou_v = 0
epoch_loss_v = 0
pbar2 = tqdm(enumerate(valloader), total=len(valloader), leave=False)
for i, (batch_img, batch_lbl) in pbar2:
# times.append(t_diff)
batch_img, batch_lbl = batch_img.to(device), batch_lbl.to(device)
# if i==0:
# writer.add_graph(segclip, (batch_img, text_tokens))
output = segclip(batch_img, text_tokens_val)
# print(output.min(), output.max())
loss = loss_fn(output, batch_lbl)
batch_pred = F.softmax(output, dim=1).argmax(dim=1)
# batch_pred[batch_pred > 0] += config['fold']*5
inter,union,_ = intersectionAndUnionGPU(batch_pred, batch_lbl, output.shape[1])
batch_miou = (inter.sum()/union.sum()).item()
# tqdm.write(str(i)+str(u))
epoch_miou_v += batch_miou
# tqdm.write(str(batch_miou))
# tqdm.write(str((i/u).mean()))
pbar2.set_description_str(f'val loss: {loss.item():.4f}, iou: {batch_miou:.4f}')
epoch_loss_v += loss.item()
if i==0 and epoch%5==4:
pred = torch.stack([dataset.decode_segmap(x).permute(2,0,1) for x in batch_pred]).to(device)
lbl = torch.stack([dataset.decode_segmap(x).permute(2,0,1) for x in batch_lbl]).to(device)
writer.add_images('img vs pred vs GT', torch.cat([norm_im(batch_img), norm_im(pred), norm_im(lbl)], dim=2), epoch)
# writer.add_images('img + GT', (norm_im(batch_img)*255).int() | lbl.int(), epoch)
# writer.add_images('img + pred', (norm_im(batch_img)*255).int() | pred.int(), epoch)
# writer.add_images('img', norm_im(batch_img), epoch)
# writer.add_images('GT', lbl, epoch)
# writer.add_images('pred', pred, epoch)
epoch_loss_v /= len(valloader)
epoch_miou_v /= len(valloader)
tqdm.write(f'(train/val) Epoch {epoch} loss: {epoch_loss_t:.4f}/{epoch_loss_v:.4f}, mean mIOU: {epoch_miou_t:.4f}/{epoch_miou_v:.4f}')
writer.add_scalar('loss/train', epoch_loss_t, epoch)
writer.add_scalar('loss/val', epoch_loss_v, epoch)
writer.add_scalar('miou/train', epoch_miou_t, epoch)
writer.add_scalar('miou/val', epoch_miou_v, epoch)
final_miou_t += epoch_miou_t
final_miou_v += epoch_miou_v
final_loss = epoch_loss_v
# f = open('profile.txt','w')
# f.write(prof.key_averages().table(sort_by="cpu_time_total"))
end = time.time()
t_diff = end - start
final_miou_t /= config['num_epochs']
final_miou_v /= config['num_epochs']
# writer.add_scalar(f'Epoch time', t_diff, epoch)
print('End')
print()
writer.add_hparams(config, {'mean train mIOU':final_miou_t, 'mean val mIOU':final_miou_v, 'final loss': final_loss, 'total time': t_diff}, run_name='.')
writer.close()
torch.save(segclip.state_dict(), f'fewshotruns/{logdir}/model.pt')
# subprocess.run(['tensorboard', 'dev', 'upload', '--logdir', 'runs/'])