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rock_train_ntl_rgbd3.py
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rock_train_ntl_rgbd3.py
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"""
eureka_train.py
Zhiang Chen, Dec 25 2019
train mask rcnn with eureka data
"""
import transforms as T
from engine import train_one_epoch, evaluate
import utils
import torch
from rock import Dataset
from model import get_rock_model_instance_segmentation
import numpy as np
torch.manual_seed(0)
class ToTensor(object):
def __call__(self, image, target):
# image = F.to_tensor(image).float()
image = torch.from_numpy(image / 255.0).float()
image = image.permute((2, 0, 1))
return image, target
def get_transform(train):
transforms = []
transforms.append(ToTensor()) # torchvision.transforms.functional is a garbage, sorry guys
return T.Compose(transforms)
def get_mean_std(input_channel, image_mean, image_std):
if input_channel == 8:
return image_mean, image_std
elif input_channel == 3:
return image_mean[:3], image_std[:3]
elif input_channel == 5:
return image_mean[:5], image_std[:5]
elif input_channel == 6:
return image_mean[:3] + image_mean[-3:], image_std[:3] + image_mean[-3:]
elif input_channel == 4:
return image_mean[:3] + [np.mean(image_mean[-3:]).tolist()], image_std[:3] + [np.mean(image_std[-3:]).tolist()]
elif input_channel == 'dem':
return image_mean[-3:], image_std[-3:]
if __name__ == '__main__':
# train on the GPU or on the CPU, if a GPU is not available
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
device = torch.device('cuda:1')
# our dataset has three classes only - background, non-damaged, and damaged
num_classes = 2
input_c = 6
# use our dataset and defined transformations
dataset = Dataset("./datasets/iros/bishop/aug/", transforms=get_transform(train=True), include_name=False, input_channel=input_c)
##dataset_test = Dataset("./datasets/Rock/data_test/", transforms=get_transform(train=False), include_name=False, input_channel=input_c)
#dataset_test = Dataset("./datasets/Rock_test/mult/", transforms=get_transform(train=False), include_name=False, input_channel=input_c)
dataset_test = Dataset("./datasets/iros/bishop_test/mult_masks/", transforms=get_transform(train=False), include_name=False, input_channel=input_c)
# image_mean, image_std, _, _ = dataset.imageStat()
image_mean = [0.2635908247051704, 0.2565450032962188, 0.24311759802366928, 0.3007502338171828, 0.35368477144149774, 0.35639093071269307, 0.5402165474345183, 0.24508291731782375]
image_std = [0.14736204788409055, 0.13722317885795837, 0.12990199087409235, 0.15134661805921518, 0.16149370275679834, 0.26121346688112296, 0.22545272450120832, 0.2513372955897915]
image_mean, image_std = get_mean_std(input_c, image_mean, image_std)
# split the dataset in train and test set
#indices = torch.randperm(len(dataset)).tolist()
#dataset = torch.utils.data.Subset(dataset, indices)
#indices_test = torch.randperm(len(dataset_test)).tolist()
#dataset_test = torch.utils.data.Subset(dataset_test, indices_test)
# define training and validation data loaders
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=4, shuffle=False, num_workers=8,
collate_fn=utils.collate_fn)
data_loader_test = torch.utils.data.DataLoader(
dataset_test, batch_size=1, shuffle=False, num_workers=4,
collate_fn=utils.collate_fn)
# get the model using our helper function
mask_rcnn = get_rock_model_instance_segmentation(num_classes, input_channel=input_c, image_mean=image_mean, image_std=image_std, pretrained=False)
read_param = False
if read_param:
mask_rcnn.load_state_dict(torch.load("trained_param_6/epoch_0050.param"))
print("Loaded weights")
# move model to the right device
mask_rcnn.to(device)
# construct an optimizer
params = [p for p in mask_rcnn.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.01, momentum=0.9, weight_decay=0.0005)
# and a learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=10,
gamma=0.5)
init_epoch = 0
num_epochs = 50
save_param = "trained_param_bishop_ntl_rgbd3/epoch_{:04d}.param".format(init_epoch)
torch.save(mask_rcnn.state_dict(), save_param)
#'''
for epoch in range(init_epoch, init_epoch + num_epochs):
save_param = "trained_param_bishop_ntl_rgbd3/epoch_{:04d}.param".format(epoch)
#torch.save(mask_rcnn.state_dict(), save_param)
# train for one epoch, printing every 10 iterations
print(save_param)
train_one_epoch(mask_rcnn, optimizer, data_loader, device, epoch, print_freq=100)
# update the learning rate
lr_scheduler.step()
# evaluate on the test dataset
#print('\n')
#print("trained_param_4/epoch_00%02d.param" % epoch)
#mask_rcnn.load_state_dict(torch.load("trained_param_4/epoch_00%02d.param" % epoch))
evaluate(mask_rcnn, data_loader_test, device=device)
#save_param = "trained_param_8_fresh/epoch_{:04d}.param".format(epoch)
torch.save(mask_rcnn.state_dict(), save_param)
'''
for epoch in range(init_epoch, init_epoch + num_epochs):
#save_param = "trained_param_3_fresh/epoch_{:04d}.param".format(epoch)
#torch.save(mask_rcnn.state_dict(), save_param)
# train for one epoch, printing every 10 iterations
#train_one_epoch(mask_rcnn, optimizer, data_loader, device, epoch, print_freq=100)
# update the learning rate
#lr_scheduler.step()
# evaluate on the test dataset
print('\n')
name = "trained_param_8/epoch_00%02d.param" % epoch
print(name)
mask_rcnn.load_state_dict(torch.load(name))
evaluate(mask_rcnn, data_loader_test, device=device)
#save_param = "trained_param_8_fresh/epoch_{:04d}.param".format(epoch)
#torch.save(mask_rcnn.state_dict(), save_param)
'''