def Train_Dataset(self, root_dir, coco_dir, img_dir, set_dir, batch_size=8, image_size=512, use_gpu=True, num_workers=3):
'''
User function: Set training dataset parameters
Dataset Directory Structure
root_dir
|
|------coco_dir
| |
| |----img_dir
| |
| |------<set_dir_train> (set_dir) (Train)
| |
| |---------img1.jpg
| |---------img2.jpg
| |---------..........(and so on)
|
|
| |---annotations
| |----|
| |--------------------instances_Train.json (instances_<set_dir_train>.json)
| |--------------------classes.txt
- instances_Train.json -> In proper COCO format
- classes.txt -> A list of classes in alphabetical order
For TrainSet
- root_dir = "../sample_dataset";
- coco_dir = "kangaroo";
- img_dir = "images";
- set_dir = "Train";
Note: Annotation file name too coincides against the set_dir
Args:
root_dir (str): Path to root directory containing coco_dir
coco_dir (str): Name of coco_dir containing image folder and annotation folder
img_dir (str): Name of folder containing all training and validation folders
set_dir (str): Name of folder containing all training images
batch_size (int): Mini batch sampling size for training epochs
image_size (int): Either of [512, 300]
use_gpu (bool): If True use GPU else run on CPU
num_workers (int): Number of parallel processors for data loader
Returns:
None
'''
self.system_dict["dataset"]["train"]["root_dir"] = root_dir;
self.system_dict["dataset"]["train"]["coco_dir"] = coco_dir;
self.system_dict["dataset"]["train"]["img_dir"] = img_dir;
self.system_dict["dataset"]["train"]["set_dir"] = set_dir;
self.system_dict["params"]["batch_size"] = batch_size;
self.system_dict["params"]["image_size"] = image_size;
self.system_dict["params"]["use_gpu"] = use_gpu;
self.system_dict["params"]["num_workers"] = num_workers;
if(self.system_dict["params"]["use_gpu"]):
if torch.cuda.is_available():
self.system_dict["local"]["num_gpus"] = torch.cuda.device_count()
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
self.system_dict["local"]["training_params"] = {"batch_size": self.system_dict["params"]["batch_size"] * self.system_dict["local"]["num_gpus"],
"shuffle": True,
"drop_last": True,
"collate_fn": collater,
"num_workers": self.system_dict["params"]["num_workers"]}
self.system_dict["local"]["training_set"] = CocoDataset(root_dir=self.system_dict["dataset"]["train"]["root_dir"] + "/" + self.system_dict["dataset"]["train"]["coco_dir"],
img_dir = self.system_dict["dataset"]["train"]["img_dir"],
set_dir = self.system_dict["dataset"]["train"]["set_dir"],
transform = transforms.Compose([Normalizer(), Resizer()])) #Augmenter(),
self.system_dict["local"]["training_generator"] = DataLoader(self.system_dict["local"]["training_set"],
**self.system_dict["local"]["training_params"]);
def train(opt):
num_gpus = 1
if torch.cuda.is_available():
num_gpus = torch.cuda.device_count()
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
training_params = {
"batch_size": opt.batch_size * num_gpus,
"shuffle": True,
"drop_last": True,
"collate_fn": collater,
"num_workers": 2
} ####################I CHANGED FOR TESTING (original was 12)
test_params = {
"batch_size": opt.batch_size,
"shuffle": False,
"drop_last": False,
"collate_fn": collater,
"num_workers": 2
} ####################I CHANGED TO 0 FOR TESTING (original was 12)
training_set = CocoDataset(root_dir=opt.data_path,
set="train2017",
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
training_generator = DataLoader(training_set, **training_params)
test_set = CocoDataset(root_dir=opt.data_path,
set="val2017",
transform=transforms.Compose(
[Normalizer(), Resizer()]))
test_generator = DataLoader(test_set, **test_params)
#load model to resume training from checkpoint
#if resume checkpoint is specified
# if opt.resume:
# if os.path.isfile(opt.resume):
# print("=> loading checkpoint '{}'".format(opt.resume))
# # Load model
# checkpoint = torch.load(opt.resume)
# start_epoch = checkpoint['epoch'] + 1
# else:
# start_epoch = 1 #otherwise if training from scratch, sets the starting epoch to 1
#create models
if opt.resume: #load pretrained model if provided and resumes
model = torch.load(opt.resume).module
else: #otherwise create fresh one
model = EfficientDet(num_classes=training_set.num_classes())
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
if not os.path.isdir(opt.saved_path):
os.makedirs(opt.saved_path)
writer = SummaryWriter(opt.log_path)
if torch.cuda.is_available():
model = model.cuda()
model = nn.DataParallel(model) #wrap with dataparallel
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
best_loss = 1e5
best_epoch = 0
model.train() #puts model in training mode
num_iter_per_epoch = len(training_generator)
for epoch in range(opt.start_epoch, opt.num_epochs + 1): #for each epoch
model.train()
# if torch.cuda.is_available():
# model.module.freeze_bn()
# else:
# model.freeze_bn()
epoch_loss = []
progress_bar = tqdm(training_generator)
for iter, data in enumerate(progress_bar):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
cls_loss, reg_loss = model(
[data['img'].cuda().float(), data['annot'].cuda()])
else:
cls_loss, reg_loss = model(
[data['img'].float(), data['annot']])
cls_loss = cls_loss.mean()
reg_loss = reg_loss.mean()
loss = cls_loss + reg_loss
if loss == 0:
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
optimizer.step()
epoch_loss.append(float(loss))
total_loss = np.mean(epoch_loss)
progress_bar.set_description(
'Epoch: {}/{}. Iteration: {}/{}. Cls loss: {:.5f}. Reg loss: {:.5f}. Batch loss: {:.5f} Total loss: {:.5f}'
.format(epoch, opt.num_epochs, iter + 1,
num_iter_per_epoch, cls_loss, reg_loss, loss,
total_loss))
writer.add_scalar('Train/Total_loss', total_loss,
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Regression_loss', reg_loss,
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Classfication_loss (focal loss)',
cls_loss, epoch * num_iter_per_epoch + iter)
except Exception as e:
print(e)
continue
scheduler.step(np.mean(epoch_loss))
if epoch % opt.test_interval == 0:
model.eval()
loss_regression_ls = []
loss_classification_ls = []
for iter, data in enumerate(test_generator):
with torch.no_grad():
if torch.cuda.is_available():
cls_loss, reg_loss = model(
[data['img'].cuda().float(), data['annot'].cuda()])
else:
cls_loss, reg_loss = model(
[data['img'].float(), data['annot']])
cls_loss = cls_loss.mean()
reg_loss = reg_loss.mean()
loss_classification_ls.append(float(cls_loss))
loss_regression_ls.append(float(reg_loss))
cls_loss = np.mean(loss_classification_ls)
reg_loss = np.mean(loss_regression_ls)
loss = cls_loss + reg_loss
print(
'Epoch: {}/{}. Classification loss: {:1.5f}. Regression loss: {:1.5f}. Total loss: {:1.5f}'
.format(epoch, opt.num_epochs, cls_loss, reg_loss,
np.mean(loss)))
writer.add_scalar('Test/Total_loss', loss, epoch)
writer.add_scalar('Test/Regression_loss', reg_loss, epoch)
writer.add_scalar('Test/Classfication_loss (focal loss)', cls_loss,
epoch)
#save model
torch.save(
model, os.path.join(opt.saved_path,
"edet_{}.pth".format(epoch)))
if loss + opt.es_min_delta < best_loss:
best_loss = loss
best_epoch = epoch
dummy_input = torch.rand(opt.batch_size, 3, 512, 512)
if torch.cuda.is_available():
dummy_input = dummy_input.cuda()
if isinstance(model, nn.DataParallel):
model.module.backbone_net.model.set_swish(
memory_efficient=False)
# torch.onnx.export(model.module, dummy_input,
# os.path.join(opt.saved_path, "signatrix_efficientdet_coco.onnx"),
# verbose=False)
model.module.backbone_net.model.set_swish(
memory_efficient=True)
else:
model.backbone_net.model.set_swish(memory_efficient=False)
# torch.onnx.export(model, dummy_input,
# os.path.join(opt.saved_path, "signatrix_efficientdet_coco.onnx"),
# verbose=False)
model.backbone_net.model.set_swish(memory_efficient=True)
# Early stopping
if epoch - best_epoch > opt.es_patience > 0:
print(
"Stop training at epoch {}. The lowest loss achieved is {}"
.format(epoch, loss))
break
writer.close()
def Val_Dataset(self, root_dir, coco_dir, img_dir, set_dir):
'''
User function: Set training dataset parameters
Dataset Directory Structure
root_dir
|
|------coco_dir
| |
| |----img_dir
| |
| |------<set_dir_val> (set_dir) (Validation)
| |
| |---------img1.jpg
| |---------img2.jpg
| |---------..........(and so on)
|
|
| |---annotations
| |----|
| |--------------------instances_Val.json (instances_<set_dir_val>.json)
| |--------------------classes.txt
- instances_Train.json -> In proper COCO format
- classes.txt -> A list of classes in alphabetical order
For ValSet
- root_dir = "..sample_dataset";
- coco_dir = "kangaroo";
- img_dir = "images";
- set_dir = "Val";
Note: Annotation file name too coincides against the set_dir
Args:
root_dir (str): Path to root directory containing coco_dir
coco_dir (str): Name of coco_dir containing image folder and annotation folder
img_dir (str): Name of folder containing all training and validation folders
set_dir (str): Name of folder containing all validation images
Returns:
None
'''
self.system_dict["dataset"]["val"]["status"] = True;
self.system_dict["dataset"]["val"]["root_dir"] = root_dir;
self.system_dict["dataset"]["val"]["coco_dir"] = coco_dir;
self.system_dict["dataset"]["val"]["img_dir"] = img_dir;
self.system_dict["dataset"]["val"]["set_dir"] = set_dir;
self.system_dict["local"]["val_params"] = {"batch_size": self.system_dict["params"]["batch_size"],
"shuffle": False,
"drop_last": False,
"collate_fn": collater,
"num_workers": self.system_dict["params"]["num_workers"]}
self.system_dict["local"]["val_set"] = CocoDataset(root_dir=self.system_dict["dataset"]["val"]["root_dir"] + "/" + self.system_dict["dataset"]["val"]["coco_dir"],
img_dir = self.system_dict["dataset"]["val"]["img_dir"],
set_dir = self.system_dict["dataset"]["val"]["set_dir"],
transform=transforms.Compose([Normalizer(), Resizer()]))
self.system_dict["local"]["test_generator"] = DataLoader(self.system_dict["local"]["val_set"],
**self.system_dict["local"]["val_params"])
if not len(results):
return
# write output
json.dump(results,
open('{}_bbox_results.json'.format(dataset.set_name), 'w'),
indent=4)
# load results in COCO evaluation tool
coco_true = dataset.coco
coco_pred = coco_true.loadRes('{}_bbox_results.json'.format(
dataset.set_name))
# run COCO evaluation
coco_eval = COCOeval(coco_true, coco_pred, 'bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
if __name__ == '__main__':
efficientdet = torch.load(
"trained_models/signatrix_efficientdet_coco.pth").module
efficientdet.cuda()
dataset_val = CocoDataset("/disk4t/data/coco/data/coco",
set='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
evaluate_coco(dataset_val, efficientdet)
def train(opt):
num_gpus = 1
if torch.cuda.is_available():
num_gpus = torch.cuda.device_count()
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
training_params = {"batch_size": opt.batch_size * num_gpus,
"shuffle": True,
"drop_last": True,
"collate_fn": collater,
"num_workers": 12}
test_params = {"batch_size": opt.batch_size,
"shuffle": False,
"drop_last": False,
"collate_fn": collater,
"num_workers": 12}
training_set = CocoDataset("/content/gdrive/My Drive/findShip/train.pickle",#root_dir=opt.data_path, set="train2017",
transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]))
training_generator = DataLoader(training_set, **training_params)
test_set = CocoDataset("/content/gdrive/My Drive/findShip/test.pickle",#root_dir=opt.data_path, set="val2017",
transform=transforms.Compose([Normalizer(), Resizer()]))
test_generator = DataLoader(test_set, **test_params)
model = EfficientDet(num_classes=training_set.num_classes())
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
if not os.path.isdir(opt.saved_path):
os.makedirs(opt.saved_path)
writer = SummaryWriter(opt.log_path)
if torch.cuda.is_available():
model = model.cuda()
model = nn.DataParallel(model)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)
best_loss = 1e5
best_epoch = 0
model.train()
num_iter_per_epoch = len(training_generator)
for epoch in range(opt.num_epochs):
model.train()
# if torch.cuda.is_available():
# model.module.freeze_bn()
# else:
# model.freeze_bn()
epoch_loss = []
progress_bar = tqdm(training_generator)
for iter, data in enumerate(progress_bar):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
cls_loss, reg_loss = model([data['img'].cuda().float(), data['annot'].cuda()])
else:
cls_loss, reg_loss = model([data['img'].float(), data['annot']])
cls_loss = cls_loss.mean()
reg_loss = reg_loss.mean()
loss = cls_loss + reg_loss
if loss == 0:
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
optimizer.step()
epoch_loss.append(float(loss))
total_loss = np.mean(epoch_loss)
progress_bar.set_description(
'Epoch: {}/{}. Iteration: {}/{}. Cls loss: {:.5f}. Reg loss: {:.5f}. Batch loss: {:.5f} Total loss: {:.5f}'.format(
epoch + 1, opt.num_epochs, iter + 1, num_iter_per_epoch, cls_loss, reg_loss, loss,
total_loss))
writer.add_scalar('Train/Total_loss', total_loss, epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Regression_loss', reg_loss, epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Classfication_loss (focal loss)', cls_loss, epoch * num_iter_per_epoch + iter)
except Exception as e:
print(e)
continue
scheduler.step(np.mean(epoch_loss))
if epoch % opt.test_interval == 0:
model.eval()
loss_regression_ls = []
loss_classification_ls = []
for iter, data in enumerate(test_generator):
with torch.no_grad():
if torch.cuda.is_available():
cls_loss, reg_loss = model([data['img'].cuda().float(), data['annot'].cuda()])
else:
cls_loss, reg_loss = model([data['img'].float(), data['annot']])
cls_loss = cls_loss.mean()
reg_loss = reg_loss.mean()
loss_classification_ls.append(float(cls_loss))
loss_regression_ls.append(float(reg_loss))
cls_loss = np.mean(loss_classification_ls)
reg_loss = np.mean(loss_regression_ls)
loss = cls_loss + reg_loss
print(
'Epoch: {}/{}. Classification loss: {:1.5f}. Regression loss: {:1.5f}. Total loss: {:1.5f}'.format(
epoch + 1, opt.num_epochs, cls_loss, reg_loss,
np.mean(loss)))
writer.add_scalar('Test/Total_loss', loss, epoch)
writer.add_scalar('Test/Regression_loss', reg_loss, epoch)
writer.add_scalar('Test/Classfication_loss (focal loss)', cls_loss, epoch)
if loss + opt.es_min_delta < best_loss:
best_loss = loss
best_epoch = epoch
torch.save(model, os.path.join(opt.saved_path, "signatrix_efficientdet_coco.pth"))
dummy_input = torch.rand(opt.batch_size, 3, 512, 512)
if torch.cuda.is_available():
dummy_input = dummy_input.cuda()
if isinstance(model, nn.DataParallel):
model.module.backbone_net.model.set_swish(memory_efficient=False)
torch.onnx.export(model.module, dummy_input,
os.path.join(opt.saved_path, "signatrix_efficientdet_coco.onnx"),
verbose=False)
model.module.backbone_net.model.set_swish(memory_efficient=True)
else:
model.backbone_net.model.set_swish(memory_efficient=False)
torch.onnx.export(model, dummy_input,
os.path.join(opt.saved_path, "signatrix_efficientdet_coco.onnx"),
verbose=False)
model.backbone_net.model.set_swish(memory_efficient=True)
# Early stopping
if epoch - best_epoch > opt.es_patience > 0:
print("Stop training at epoch {}. The lowest loss achieved is {}".format(epoch, loss))
break
writer.close()
return
# write output
json.dump(results,
open('{}_bbox_results.json'.format(dataset.set_name), 'w'),
indent=4)
# load results in COCO evaluation tool
coco_true = dataset.coco
coco_pred = coco_true.loadRes('{}_bbox_results.json'.format(
dataset.set_name))
# run COCO evaluation
coco_eval = COCOeval(coco_true, coco_pred, 'bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
if __name__ == '__main__':
#load model
efficientdet = torch.load(args.model).module
efficientdet.cuda()
dataset_val = CocoDataset(args.dataset,
set='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
evaluate_coco(dataset_val, efficientdet)
def train(opt):
num_gpus = 1
if torch.cuda.is_available():
num_gpus = torch.cuda.device_count()
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
training_params = {
"batch_size": opt.batch_size * num_gpus,
"shuffle": True,
"drop_last": True,
"collate_fn": collater,
"num_workers": 12
}
test_params = {
"batch_size": opt.batch_size,
"shuffle": False,
"drop_last": False,
"collate_fn": collater,
"num_workers": 12
}
training_set = CocoDataset(root_dir=opt.data_path,
set="train2017",
transform=transforms.Compose(
[Normalizer(),
Augmenter(),
Resizer()]))
training_generator = DataLoader(training_set, **training_params)
test_set = CocoDataset(root_dir=opt.data_path,
set="val2017",
transform=transforms.Compose(
[Normalizer(), Resizer()]))
test_generator = DataLoader(test_set, **test_params)
channels_map = {
'efficientnet-b0': [40, 80, 192],
'efficientnet-b1': [40, 80, 192],
'efficientnet-b2': [48, 88, 208],
'efficientnet-b3': [48, 96, 232],
'efficientnet-b4': [56, 112, 272],
'efficientnet-b5': [64, 128, 304],
'efficientnet-b6': [72, 144, 344],
'efficientnet-b7': [80, 160, 384],
'efficientnet-b8': [80, 160, 384]
}
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
if not os.path.isdir(opt.saved_path):
os.makedirs(opt.saved_path)
writer = SummaryWriter(opt.log_path)
if opt.resume:
resume_path = os.path.join(opt.saved_path,
'signatrix_efficientdet_coco_latest.pth')
model = torch.load(resume_path).module
print("model loaded from {}".format(resume_path))
else:
model = EfficientDet(
num_classes=training_set.num_classes(),
network=opt.backbone_network,
remote_loading=opt.remote_loading,
advprop=opt.advprop,
conv_in_channels=channels_map[opt.backbone_network])
print("model created with backbone {}, advprop {}".format(
opt.backbone_network, opt.advprop))
if torch.cuda.is_available():
model = model.cuda()
model = nn.DataParallel(model)
if opt.resume:
m = round(opt.start_epoch / 100)
opt.lr = opt.lr * (0.1**m)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=3,
verbose=True)
best_loss = 1e5
best_epoch = 0
model.train()
num_iter_per_epoch = len(training_generator)
start_epoch = 0
if opt.resume:
start_epoch = opt.start_epoch
for epoch in range(start_epoch, opt.num_epochs):
model.train()
# if torch.cuda.is_available():
# model.module.freeze_bn()
# else:
# model.freeze_bn()
epoch_loss = []
progress_bar = tqdm(training_generator)
for iter, data in enumerate(progress_bar):
try:
optimizer.zero_grad()
if torch.cuda.is_available():
cls_loss, reg_loss = model(
[data['img'].cuda().float(), data['annot'].cuda()])
else:
cls_loss, reg_loss = model(
[data['img'].float(), data['annot']])
cls_loss = cls_loss.mean()
reg_loss = reg_loss.mean()
loss = cls_loss + reg_loss
if loss == 0:
continue
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
optimizer.step()
epoch_loss.append(float(loss))
total_loss = np.mean(epoch_loss)
progress_bar.set_description(
'{} Epoch: {}/{}. Iteration: {}/{}. Cls loss: {:.5f}. Reg loss: {:.5f}. Batch loss: {:.5f} Total loss: {:.5f}'
.format(datetime.now(), epoch + 1, opt.num_epochs,
iter + 1, num_iter_per_epoch, cls_loss, reg_loss,
loss, total_loss))
writer.add_scalar('Train/Total_loss', total_loss,
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Regression_loss', reg_loss,
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Classfication_loss (focal loss)',
cls_loss, epoch * num_iter_per_epoch + iter)
except Exception as e:
print(e)
continue
scheduler.step(np.mean(epoch_loss))
if epoch % opt.test_interval == 0:
model.eval()
loss_regression_ls = []
loss_classification_ls = []
for iter, data in enumerate(test_generator):
with torch.no_grad():
if torch.cuda.is_available():
cls_loss, reg_loss = model(
[data['img'].cuda().float(), data['annot'].cuda()])
else:
cls_loss, reg_loss = model(
[data['img'].float(), data['annot']])
cls_loss = cls_loss.mean()
reg_loss = reg_loss.mean()
loss_classification_ls.append(float(cls_loss))
loss_regression_ls.append(float(reg_loss))
cls_loss = np.mean(loss_classification_ls)
reg_loss = np.mean(loss_regression_ls)
loss = cls_loss + reg_loss
print(
'{} Epoch: {}/{}. Classification loss: {:1.5f}. Regression loss: {:1.5f}. Total loss: {:1.5f}'
.format(datetime.now(), epoch + 1, opt.num_epochs, cls_loss,
reg_loss, np.mean(loss)))
writer.add_scalar('Test/Total_loss', loss, epoch)
writer.add_scalar('Test/Regression_loss', reg_loss, epoch)
writer.add_scalar('Test/Classfication_loss (focal loss)', cls_loss,
epoch)
if loss + opt.es_min_delta < best_loss:
best_loss = loss
best_epoch = epoch
torch.save(
model,
os.path.join(
opt.saved_path,
"signatrix_efficientdet_coco_best_epoch{}.pth".format(
epoch)))
'''
dummy_input = torch.rand(opt.batch_size, 3, 512, 512)
if torch.cuda.is_available():
dummy_input = dummy_input.cuda()
if isinstance(model, nn.DataParallel):
model.module.backbone_net.model.set_swish(memory_efficient=False)
torch.onnx.export(model.module, dummy_input,
os.path.join(opt.saved_path, "signatrix_efficientdet_coco.onnx"),
verbose=False)
model.module.backbone_net.model.set_swish(memory_efficient=True)
else:
model.backbone_net.model.set_swish(memory_efficient=False)
torch.onnx.export(model, dummy_input,
os.path.join(opt.saved_path, "signatrix_efficientdet_coco.onnx"),
verbose=False)
model.backbone_net.model.set_swish(memory_efficient=True)
'''
print("epoch:", epoch, "best_epoch:", best_epoch,
"epoch - best_epoch=", epoch - best_epoch)
# Early stopping
if epoch - best_epoch > opt.es_patience > 0:
print(
"Stop training at epoch {}. The lowest loss achieved is {}"
.format(epoch, loss))
break
if epoch % opt.save_interval == 0:
torch.save(
model,
os.path.join(opt.saved_path,
"signatrix_efficientdet_coco_latest.pth"))
writer.close()
indent=4)
# load results in COCO evaluation tool
coco_true = dataset.coco
coco_pred = coco_true.loadRes('{}_bbox_results.json'.format(
dataset.set_name))
# run COCO evaluation
coco_eval = COCOeval(coco_true, coco_pred, 'bbox')
coco_eval.params.imgIds = image_ids
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
#modified.
from contextlib import redirect_stdout
with open(os.path.join("/content/", "map_log.txt"), 'a') as txt:
with redirect_stdout(txt):
coco_eval.summarize()
if __name__ == '__main__':
efficientdet = torch.load(
"trained_models/signatrix_efficientdet_coco.pth").module
efficientdet.cuda()
dataset_val = CocoDataset("data/COCO",
set='val2017',
transform=transforms.Compose(
[Normalizer(), Resizer()]))
evaluate_coco(dataset_val, efficientdet)