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Train_model_subpixel.py
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Train_model_subpixel.py
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"""script for subpixel experiment (not tested)
"""
import numpy as np
import paddle
import logging
from tqdm import tqdm
from pathlib import Path
import paddle.optimizer
import paddle.io
from utils.loader import dataLoader
from utils.loader import modelLoader
from utils.loader import pretrainedLoader
from utils.tools import dict_update
from utils.utils import labels2Dto3D
from utils.utils import flattenDetection
from utils.utils import labels2Dto3D_flattened
from utils.utils import pltImshow
from utils.utils import saveImg
from utils.utils import precisionRecall_torch
from utils.utils import save_checkpoint
from Train_model_frontend import Train_model_frontend
class Train_model_subpixel(Train_model_frontend):
default_config = {'train_iter': 170000,
'save_interval': 2000,
'tensorboard_interval': 200,
'model': {'subpixel': {'enable': False}}}
def __init__(self, config, save_path=Path('.'), device='gpu', verbose=False):
print('using: Train_model_subpixel')
self.config = self.default_config
self.config = dict_update(self.config, config)
self.device = device
self.save_path = save_path
self.cell_size = 8
self.max_iter = config['train_iter']
self._train = True
self._eval = True
pass
def print(self):
print('hello')
def loadModel(self):
model = self.config['model']['name']
params = self.config['model']['params']
print('model: ', model)
net = modelLoader(model=model, **params)
logging.info('=> setting adam solver')
optimizer = self.adamOptim(net, lr=self.config['model']['learning_rate'])
n_iter = 0
if self.config['retrain'] == True:
logging.info('New model')
pass
else:
path = self.config['pretrained']
mode = '' if path[:-3] == '.pdiparams' else 'full'
logging.info('load pretrained model from: %s', path)
net, optimizer, n_iter = pretrainedLoader(net, optimizer, n_iter, path, mode=mode, full_path=True)
logging.info('successfully load pretrained model from: %s', path)
def setIter(n_iter):
if self.config['reset_iter']:
logging.info('reset iterations to 0')
n_iter = 0
return n_iter
self.net = net
self.optimizer = optimizer
self.n_iter = setIter(n_iter)
pass
def train_val_sample(self, sample, n_iter=0, train=False):
task = 'train' if train else 'val'
tb_interval = self.config['tensorboard_interval']
losses, tb_imgs, tb_hist = {}, {}, {}
img, labels_2D, mask_2D = sample['image'], sample['labels_2D'], sample['valid_mask']
labels_res = sample['labels_res']
batch_size, H, W = img.shape[0], img.shape[2], img.shape[3]
self.batch_size = batch_size
Hc = H // self.cell_size
Wc = W // self.cell_size
self.optimizer.zero_grad()
label_idx = labels_2D[...].nonzero()
from utils.losses import extract_patches
patch_size = self.config['model']['params']['patch_size']
patches = extract_patches(label_idx
img,
patch_size=patch_size)
patch_channels = self.config['model']['params'].get('subpixel_channel', 1)
if patch_channels == 2:
patch_heat = extract_patches(label_idx,
img,
patch_size=patch_size)
def label_to_points(labels_res, points):
labels_res = labels_res.transpose(1, 2).transpose(2, 3).unsqueeze(1)
points_res = labels_res[points[:, (0)], points[:, (1)], points[:, (2)], points[:, (3)], :]
return points_res
points_res = label_to_points(labels_res, label_idx)
num_patches_max = 500
pred_res = self.net(patches[:num_patches_max, ...])
def get_loss(points_res, pred_res):
loss = points_res - pred_res
loss = paddle.norm(loss, p=2, axis=-1).mean()
return loss
loss = get_loss(points_res[:num_patches_max, ...],
pred_res)
self.loss = loss
losses.update({'loss': loss})
tb_hist.update({'points_res_0': points_res[:, 0]})
tb_hist.update({'points_res_1': points_res[:, 1]})
tb_hist.update({'pred_res_0': pred_res[:, 0]})
tb_hist.update({'pred_res_1': pred_res[:, 1]})
tb_imgs.update({'patches': patches[:, ...].unsqueeze(1)})
tb_imgs.update({'img': img})
losses.update({'loss': loss})
if train:
loss.backward()
self.optimizer.step()
self.tb_scalar_dict(losses, task)
if n_iter % tb_interval == 0 or task == 'val':
logging.info('current iteration: %d, tensorboard_interval: %d',
n_iter, tb_interval)
self.tb_images_dict(task, tb_imgs, max_img=5)
self.tb_hist_dict(task, tb_hist)
return loss.item()
def tb_images_dict(self, task, tb_imgs, max_img=5):
for element in list(tb_imgs):
for idx in range(tb_imgs[element].shape[0]):
if idx >= max_img:
break
self.writer.add_image(task + '-' + element + '/%d' % idx,
tb_imgs[element][idx, ...], self.n_iter)
def tb_hist_dict(self, task, tb_dict):
for element in list(tb_dict):
self.writer.add_histogram(task + '-' + element, tb_dict[element], self.n_iter)
pass
if __name__ == '__main__':
filename = 'configs/magicpoint_shapes_subpix.yaml'
import yaml
device = paddle.device.set_device('gpu')
paddle.set_default_dtype('float32')
with open(filename, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
from utils.loader import dataLoader as dataLoader
task = config['data']['dataset']
data = dataLoader(config, dataset=task, warp_input=True)
train_loader, val_loader = data['train_loader'], data['val_loader']
train_agent = Train_model_subpixel(config, device=device)
train_agent.print()
from visualdl import LogWriter
writer = LogWriter()
train_agent.writer = writer
train_agent.train_loader = train_loader
train_agent.val_loader = val_loader
train_agent.loadModel()
train_agent.dataParallel()
try:
train_agent.train()
except KeyboardInterrupt:
print('press ctrl + c, save model!')
train_agent.saveModel()
pass