def debug_me():
# Define network
net = Net()
print(net)
data_transform = transforms.Compose(
[Rescale(250), RandomCrop(224),
Normalize()])
aww_dataset = FacialKeypointsDataset(
csv_file='data/aww_frames_keypoints.csv',
root_dir='data/aww/',
transform=data_transform)
sample = aww_dataset[0]
print(sample['image'].shape, sample['keypoints'].shape)
print(np.max(sample['keypoints']))
aww_loader = DataLoader(aww_dataset,
batch_size=10,
shuffle=True,
num_workers=4)
aww_images, aww_outputs, gt_pts = net_sample_output(net, aww_loader)
visualize_output(aww_images, aww_outputs, gt_pts, 1)
'''
def transform_data(self, csv_file, root_dir):
# create the transformed dataset
transformed_dataset = FacialKeypointsDataset(
csv_file=csv_file,
root_dir=root_dir,
transform=self.data_transform)
return transformed_dataset
def main():
hyp_batch_size = 20
net = Net2()
model_dir = '../saved_models/'
model_name = 'keypoints_model_2.pt'
data_transform = transforms.Compose([
Rescale(256),
RandomCrop(224),
Normalize(),
ToTensor()
])
# retreive the saved model
net_state_dict = torch.load(model_dir+model_name)
net.load_state_dict(net_state_dict)
# load the test data
test_dataset = FacialKeypointsDataset(csv_file='../files/test_frames_keypoints.csv',
root_dir='../files/test/',
transform=data_transform)
# load test data in batches
batch_size = hyp_batch_size
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
test_images, test_outputs, gt_pts = net_sample_output(test_loader, net)
print(test_images.data.size())
print(test_outputs.data.size())
print(gt_pts.size())
# Get the weights in the first conv layer, "conv1"
# if necessary, change this to reflect the name of your first conv layer
weights1 = net.conv1.weight.data
w = weights1.numpy()
filter_index = 0
print(w[filter_index][0])
print(w[filter_index][0].shape)
# display the filter weights
plt.imshow(w[filter_index][0], cmap='gray')
#plt.show()
##TODO: load in and display any image from the transformed test dataset
i = 1
show_image(test_images, w, i)
def initialize_test_loader(transform):
test_dataset = FacialKeypointsDataset(
csv_file='./data/test_frames_keypoints.csv',
root_dir='./data/test/',
transform=transform)
print('Number of test images: ', len(test_dataset))
assert test_dataset[0]['image'].size(
) == desired_image_shape, "Wrong test image dimension"
assert test_dataset[0]['keypoints'].size(
) == desired_keypoints_shape, "Wrong test keypoints dimension"
test_loader = DataLoader(test_dataset,
batch_size=evaluate_batch_size,
shuffle=True,
num_workers=0)
return test_loader
def __init__(self, root_dir=None):
self.data_root_dir = root_dir or './data/'
self.training_csv = os.path.join(self.data_root_dir,
'training_frames_keypoints.csv')
self.training_data_dir = os.path.join(self.data_root_dir, 'training/')
self.test_csv = os.path.join(self.data_root_dir,
'test_frames_keypoints.csv')
self.test_data_dir = os.path.join(self.data_root_dir, 'test/')
self.key_pts_frame = pd.read_csv(self.training_csv)
self.face_dataset = FacialKeypointsDataset(
csv_file=self.training_csv, root_dir=self.training_data_dir)
self.face_dataset_len = len(self.face_dataset)
# define the data tranform
# order matters! i.e. rescaling should come before a smaller crop
self.data_transform = transforms.Compose(
[Rescale(250),
RandomCrop(224),
Normalize(),
ToTensor()])
self.transformed_training_data = self.transform_data(
self.training_csv, self.training_data_dir)
self.transformed_test_data = self.transform_data(
self.test_csv, self.test_data_dir)
from models import Net
net = Net()
net.load_state_dict(
torch.load(
r'C:\Users\Semanti Basu\Documents\OneDrive_2020-02-19\3D Ceaser dataset\Image and point generation\Image and point generation\frontaltrainedmodel_10epoch.pth'
))
## print out your net and prepare it for testing (uncomment the line below)
net.eval()
data_transform = transforms.Compose(
[Rescale(225), RandomCrop(224),
Normalize(), ToTensor()])
transformed_dataset = FacialKeypointsDataset(
csv_file=
r'C:\Users\Semanti Basu\Documents\OneDrive_2020-02-19\3D Ceaser dataset\Image and point generation\Image and point generation\frontalpoints.csv',
root_dir=
r'C:\Users\Semanti Basu\Documents\OneDrive_2020-02-19\3D Ceaser dataset\Image and point generation\Image and point generation\ceasar_mat',
transform=data_transform)
# load training data in batches
batch_size = 10
train_loader = DataLoader(transformed_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
def net_sample_output():
# iterate through the test dataset
for i, sample in enumerate(train_loader):
assert (data_transform is not None), 'Define a data_transform'
# In[ ]:
# In[136]:
# In[137]:
###########load the csv
# order matters! i.e. rescaling should come before a smaller crop
data_transform = transforms.Compose([
Rescale(224),
# RandomCrop(222),
Normalize(),
ToTensor()
])
# create the transformed dataset
transformed_dataset = FacialKeypointsDataset(
csv_file=('/home/jonathan/Project_Eye/data/try_harder_cleaned.csv'),
root_dir='/home/jonathan/Project_Eye/data/training/',
transform=data_transform)
print('Number of images: ', len(transformed_dataset))
# iterate through the transformed dataset and print some stats about the first few samples
for i in range(4):
sample = transformed_dataset[i]
print(i, sample['image'].size(), sample['keypoints'].size())
# ## Batching and loading data
#
# In[138]:
# load training data in batches
batch_size = 1
train_loader = DataLoader(transformed_dataset,
batch_size=batch_size,
shuffle=True,
package_dir = os.path.dirname(os.path.abspath(__file__))
net = Net()
# net.load_state_dict(torch.load(package_dir + '/saved_models/model3.pt'))
print(net)
data_transform = transforms.Compose([Rescale(250),
RandomCrop(224),
Normalize(),
ToTensor()])
##########LOAD TRAIN DATASET
transformed_dataset = FacialKeypointsDataset(csv_file= package_dir + '/data/training_frames_keypoints.csv',
root_dir= package_dir + '/data/training/',
transform=data_transform)
batch_size = 30
train_loader = DataLoader(transformed_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
##########LOAD TEST DATASET
test_dataset = FacialKeypointsDataset(csv_file=package_dir+'/data/test_frames_keypoints.csv',
root_dir=package_dir+'/data/test/',
transform=data_transform)
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
## TODO: define the data_transform using transforms.Compose([all tx's, . , .])
# order matters! i.e. rescaling should come before a smaller crop
data_transform = transforms.Compose(
[Rescale(227), RandomCrop(224),
Normalize(), ToTensor()])
# testing that you've defined a transform
assert (data_transform is not None), 'Define a data_transform'
# In[5]:
# create the transformed dataset
transformed_dataset = FacialKeypointsDataset(
csv_file=
'D:/Computer_vision_udacity/P1_Facial_Keypoints-master/data/training_frames_keypoints.csv',
root_dir=
'D:/Computer_vision_udacity/P1_Facial_Keypoints-master/data/training/',
transform=data_transform)
print('Number of images: ', len(transformed_dataset))
# iterate through the transformed dataset and print some stats about the first few samples
for i in range(4):
sample = transformed_dataset[i]
print(i, sample['image'].size(), sample['keypoints'].size(),
sample['keypoints'])
# ## Batching and loading data
#
# Next, having defined the transformed dataset, we can use PyTorch's DataLoader class to load the training data in batches of whatever size as well as to shuffle the data for training the model. You can read more about the parameters of the DataLoader, in [this documentation](http://pytorch.org/docs/master/data.html).
#
from data_load import Rescale, RandomCrop, Normalize, ToTensor
## TODO: define the data_transform using transforms.Compose([all tx's, . , .])
# order matters! i.e. rescaling should come before a smaller crop
# testing that you've defined a transform
data_transform = transforms.Compose(
[Rescale(250), RandomCrop(224),
Normalize(), ToTensor()])
assert (data_transform is not None), 'Define a data_transform'
# create the transformed dataset
transformed_dataset = FacialKeypointsDataset(
csv_file=
"D:\\Users\\Tsvetan\\FootDataset\\person_keypoints_train2017_foot_v1\\NEWOUT.csv",
root_dir=
"D:\\Users\\Tsvetan\\FootDataset\\person_keypoints_train2017_foot_v1\\out\\",
transform=data_transform)
print('Number of images: ', len(transformed_dataset))
# iterate through the transformed dataset and print some stats about the first few samples
for i in range(4):
sample = transformed_dataset[i]
print(i, sample['image'].size(), sample['keypoints'].size())
# load training data in batches
batch_size = 32
train_loader = DataLoader(transformed_dataset,
batch_size=batch_size,
def train(cfg):
# make run_dir with date
d = datetime.datetime.now()
run_dir = pjoin(cfg.out_dir, 'exp_{:%Y-%m-%d_%H-%M}'.format(d))
if(not os.path.exists(run_dir)):
os.makedirs(run_dir)
with open(pjoin(run_dir, 'cfg.yml'), 'w') as outfile:
yaml.dump(cfg.__dict__, stream=outfile, default_flow_style=False)
# generate tensorboard object
writer = SummaryWriter(run_dir)
net = UNet(cfg.out_channels,
in_channels=cfg.in_channels,
depth=cfg.depth,
merge_mode=cfg.merge_mode)
#criterion = nn.CrossEntropyLoss()
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=cfg.lr, momentum=cfg.momentum)
optimizer = optim.Adam(params=net.parameters(), lr=0.0025, weight_decay=0.00001)
#softm = nn.Softmax(dim=1)
softm = nn.Sigmoid() ################################
# make augmenter
transf = iaa.Sequential([
iaa.SomeOf(3, [
iaa.Affine(rotate=iap.Uniform(-cfg.aug_rotate, cfg.aug_rotate)),
iaa.Affine(shear=iap.Uniform(-cfg.aug_shear, cfg.aug_shear)),
iaa.Fliplr(1.),
iaa.Flipud(1.),
iaa.GaussianBlur(sigma=iap.Uniform(0.0, cfg.aug_gaussblur))
]),
iaa.Resize(cfg.in_shape), rescale_augmenter
])
base_loader = FacialKeypointsDataset(csv_file=cfg.csv_file,
root_dir=cfg.root_dir,
sig_kp=cfg.sig_kp,
transform=transf)
# build train, val and test sets randomly with given split ratios
idxs = np.arange(len(base_loader))
np.random.shuffle(idxs)
train_idxs = idxs[:int(len(base_loader) * cfg.train_split)]
others_idxs = idxs[int(len(base_loader) * cfg.train_split):]
val_idxs = others_idxs[:int(others_idxs.size * cfg.val_split)]
test_idxs = others_idxs[int(others_idxs.size * cfg.val_split):]
train_loader = torch.utils.data.Subset(base_loader, train_idxs)
val_loader = torch.utils.data.Subset(base_loader, val_idxs)
test_loader = torch.utils.data.Subset(base_loader, test_idxs)
loaders = {'train': DataLoader(train_loader,
batch_size=cfg.batch_size,
num_workers=cfg.n_workers),
'val': DataLoader(train_loader,
batch_size=cfg.batch_size,
num_workers=cfg.n_workers),
'test': DataLoader(train_loader,
batch_size=cfg.batch_size,
num_workers=cfg.n_workers)}
# convert batch to device
device = torch.device('cuda' if cfg.cuda else 'cpu')
net.to(device)
batch_to_device = lambda batch: {
k: v.to(device) if (isinstance(v, torch.Tensor)) else v
for k, v in batch.items()
}
for epoch in range(cfg.n_epochs): # loop over the dataset multiple times
for phase in loaders.keys():
if phase == 'train':
net.train()
else:
net.eval() # Set model to evaluate mode
running_loss = 0.0
# train on batches of data, assumes you already have train_loader
pbar = tqdm.tqdm(total=len(loaders[phase]))
for i, data in enumerate(loaders[phase]):
data = batch_to_device(data)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
#import pdb; pdb.set_trace()
out = softm(net(data['image'].float()))
#data_truth = softm(torch.squeeze(data['truth'])) ## here I am not sure, bc the entropy fct already has softmax
#data_truth = data_truth.long().reshape(4, 224*224)
#out = out.reshape(4, 1, 224*224)
# data_truth should have shape 4, 244,244
# input matrix is in the shape: (Minibatch, Classes, H, W)
# the target is in size (Minibatch, H, W)
loss = criterion(out, data['truth'].float())
#############################
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.cpu().detach().numpy()
loss_ = running_loss / ((i + 1) * cfg.batch_size)
pbar.set_description('[{}] loss: {:.4f}'.format(phase, loss_))
pbar.update(1)
pbar.close()
writer.add_scalar('{}/loss'.format(phase),
loss_,
epoch)
# make preview images
if phase == 'test':
data = next(iter(loaders[phase]))
data = batch_to_device(data)
pred_ = softm(net(data['image'].float())).cpu()
im_ = data['image'].float().cpu().detach()
im_ = [torch.cat(3*[im_[i, ...]]) for i in range(im_.shape[0])]
truth_ = data['truth'].float().cpu()
truth_ = [
torch.cat([truth_[i, ...]])
for i in range(truth_.shape[0])
]
# normalize prediction maps in-place
for b in range(pred_.shape[0]):
for n in range(pred_.shape[1]):
pred_[b, n, ...] = (pred_[b, n, ...] - pred_[b, n, ...].min())
pred_[b, n, ...] = pred_[b, n, ...] / pred_[b, n, ...].max()
# find max location on each channel of each batch element
pos = []
for b in range(pred_.shape[0]):
pos.append([])
for n in range(pred_.shape[1]):
idx_max = pred_[b, n, ...].argmax()
i, j = np.unravel_index(idx_max, pred_[b, n, ...].shape)
pos[-1].append((i, j))
# draw circle on image through numpy :(
rr, cc = draw.circle(i, j, 5, shape=im_[b][n, ...].shape)
im__ = np.rollaxis(im_[b].detach().numpy(), 0, 3)
im__[rr, cc, ...] = (1., 0., 0.)
im_[b] = torch.from_numpy(np.rollaxis(im__, -1, 0))
pred_ = [torch.cat([pred_[i, ...]]) for i in range(pred_.shape[0])]
#import pdb; pdb.set_trace()
all_ = [
tutls.make_grid([im_[i], truth_[i], pred_[i]],
nrow=len(pred_),
padding=10,
pad_value=1.)
for i in range(len(truth_))
]
all_ = torch.cat(all_, dim=1)
writer.add_image('test/img', all_, epoch)
# save checkpoint
if phase == 'val':
is_best = False
if (loss_ < best_loss):
is_best = True
best_loss = loss_
path = pjoin(run_dir, 'checkpoints')
save_checkpoint(
{
'epoch': epoch + 1,
'model': net,
'best_loss': best_loss,
'optimizer': optimizer.state_dict()
},
is_best,
path=path)
def main():
# Define network
net = Net()
print(net)
data_transform = transforms.Compose(
[Rescale(250), Normalize(), RandomCrop(224)])
train_dataset = FacialKeypointsDataset(
csv_file='data/training_frames_keypoints.csv',
root_dir='data/training/',
transform=data_transform)
# iterate through the transformed dataset and print some stats about the first few samples
for i in range(4):
sample = train_dataset[i]
print(i, sample['image'].size, sample['keypoints'].size)
train_loader = DataLoader(train_dataset,
batch_size=10,
shuffle=True,
num_workers=4)
test_dataset = FacialKeypointsDataset(
csv_file='data/test_frames_keypoints.csv',
root_dir='data/test/',
transform=data_transform)
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=True,
num_workers=4)
test_images, test_outputs, gt_pts = net_sample_output(net, test_loader)
# print out the dimensions of the data to see if they make sense
print(test_images.data.size())
print(test_outputs.data.size())
print(gt_pts.size())
# call it
visualize_output(test_images, test_outputs, gt_pts, 1)
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(),
lr=0.001,
betas=(0.9, 0.999),
eps=1e-8)
n_epochs = 2
train_net(net, criterion, optimizer, train_loader, n_epochs)
# get a sample of test data again
test_images, test_outputs, gt_pts = net_sample_output(net, test_loader)
print(test_images.data.size())
print(test_outputs.data.size())
print(gt_pts.size())
model_dir = 'saved_models/'
model_name = 'keypoints_model_1.pt'
# after training, save your model parameters in the dir 'saved_models'
torch.save(net.state_dict(), model_dir + model_name)
weights1 = net.conv1.weight.data
w = weights1.numpy()
filter_index = 0
print(w[filter_index][0])
print(w[filter_index][0].shape)
# display the filter weights
plt.imshow(w[filter_index][0], cmap='gray')
import matplotlib.pyplot as plt
import numpy as np
device = torch.device('cuda')
net = Net()
data_transform = transforms.Compose(
[Rescale(256),
RandomCrop(224),
Normalize(),
ToTensor()]
)
train_dataset = FacialKeypointsDataset(
"./data/training_frames_keypoints.csv",
"./data/training",
data_transform
)
test_dataset = FacialKeypointsDataset(
"./data/test_frames_keypoints.csv",
"./data/test",
data_transform
)
batch_size = 256
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True
)
import torch
import torch.optim as optim
import torch.nn as nn
from models import Net
from torch.utils.data import DataLoader
from torchvision import transforms
from data_load import FacialKeypointsDataset
from data_load import Rescale, RandomCrop, Normalize, ToTensor
# 载入数据
data_transform = transforms.Compose(
[Rescale(250), RandomCrop(224),
Normalize(), ToTensor()])
transformed_dataset = FacialKeypointsDataset(
csv_file=
'C:/Users/76785/Downloads/Facial-Key-Point-Detection-CNN-master/data/training_frames_keypoints.csv',
root_dir=
'C:/Users/76785/Downloads/Facial-Key-Point-Detection-CNN-master/data/training',
transform=data_transform)
batch_size = 10
train_loader = DataLoader(transformed_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
test_dataset = FacialKeypointsDataset(
csv_file=
'C:/Users/76785/Downloads/Facial-Key-Point-Detection-CNN-master/data/test_frames_keypoints.csv',
root_dir=
'C:/Users/76785/Downloads/Facial-Key-Point-Detection-CNN-master/data/test/',
transform=data_transform)
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
def main():
#------------------------------------------------------------------------------------------------------------------
# Hyperparameters
hyp_epochs = 5
hyp_batch_size = 20
#hyp_optim = "SGD"
hyp_optim = "Adam"
#hyp_net = Net1()
hyp_net = Net2()
print("Hyperparameters")
print("--------------")
print("Epochs = ", hyp_epochs)
print("Batch Size = ", hyp_batch_size)
print("Optimizer = ", hyp_optim)
print("--------------")
## TODO: Define the Net in models.py
net = hyp_net
print(net)
## TODO: define the data_transform using transforms.Compose([all tx's, . , .])
# order matters! i.e. rescaling should come before a smaller crop
data_transform = transforms.Compose(
[Rescale(256), RandomCrop(224),
Normalize(), ToTensor()])
# testing that you've defined a transform
assert (data_transform is not None), 'Define a data_transform'
# create the transformed dataset
transformed_dataset = FacialKeypointsDataset(
csv_file='../files/training_frames_keypoints.csv',
root_dir='../files/training/',
transform=data_transform)
print('Number of images: ', len(transformed_dataset))
# iterate through the transformed dataset and print some stats about the first few samples
for i in range(4):
sample = transformed_dataset[i]
print(i, sample['image'].size(), sample['keypoints'].size())
# load training data in batches
batch_size = hyp_batch_size
train_loader = DataLoader(transformed_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
# load in the test data, using the dataset class
# AND apply the data_transform you defined above
# create the test dataset
test_dataset = FacialKeypointsDataset(
csv_file='../files/test_frames_keypoints.csv',
root_dir='../files/test/',
transform=data_transform)
# load test data in batches
batch_size = hyp_batch_size
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
# test the model on a batch of test images
# call the above function
# returns: test images, test predicted keypoints, test ground truth keypoints
test_images, test_outputs, gt_pts = net_sample_output(test_loader, net)
# print out the dimensions of the data to see if they make sense
print(test_images.data.size())
print(test_outputs.data.size())
print(gt_pts.size())
# visualize the output
# by default this shows a batch of 10 images
# call it
_visualise = False
if _visualise == True:
visualize_output(test_images, test_outputs, gt_pts)
## TODO: Define the loss and optimization
import torch.optim as optim
criterion = nn.MSELoss()
hyp_optimizer = None
if hyp_optim == "Adam":
hyp_optimizer = optim.Adam(net.parameters(), lr=0.001)
if hyp_optim == "SGD":
hyp_optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
optimizer = hyp_optimizer
# train your network
n_epochs = hyp_epochs # start small, and increase when you've decided on your model structure and hyperparams
# this is a Workspaces-specific context manager to keep the connection
# alive while training your model, not part of pytorch
train_net(n_epochs, train_loader, net, criterion, optimizer)
# get a sample of test data again
test_images, test_outputs, gt_pts = net_sample_output(test_loader, net)
print(test_images.data.size())
print(test_outputs.data.size())
print(gt_pts.size())
## TODO: change the name to something uniqe for each new model
model_dir = '../saved_models/'
model_name = 'keypoints_model_2.pt'
# after training, save your model parameters in the dir 'saved_models'
torch.save(net.state_dict(), model_dir + model_name)
# --------------------------------------------------------------------
# To run the following code after retreiving an existing model,
# you can do so in the resume.py file
# --------------------------------------------------------------------
# Get the weights in the first conv layer, "conv1"
# if necessary, change this to reflect the name of your first conv layer
weights1 = net.conv1.weight.data
w = weights1.numpy()
filter_index = 0
print(w[filter_index][0])
print(w[filter_index][0].shape)
# display the filter weights
plt.imshow(w[filter_index][0], cmap='gray')
##TODO: load in and display any image from the transformed test dataset
i = 1
show_image(test_images, w, i)
# testing that you've defined a transform
assert(train_transform is not None and test_transform is not None), 'Define a data_transform'
# create the transformed dataset
if args["dataset"] == "Kaggle":
X, y = load_KagggleDataset(split=args['split'],train_30=args['train30'],train_8=args['train8'])
X_test, y_test = X[:300], y[:300]
X_train, y_train = X[300:], y[300:]
transformed_dataset = KagggleDataset(X_train, y_train, train_transform)
test_dataset = KagggleDataset(X_test, y_test, test_transform)
sub, div = 48., 48.
else:
transformed_dataset = FacialKeypointsDataset(csv_file='./data/training_frames_keypoints.csv',
root_dir='./data/training/',
transform=train_transform)
test_dataset = FacialKeypointsDataset(csv_file='./data/test_frames_keypoints.csv',
root_dir='./data/test/',
transform = test_transform)
sub, div = 100.,50.
print('Number of images: ', len(transformed_dataset))
# iterate through the transformed dataset and print some stats about the first few samples
for i in range(4):
sample = transformed_dataset[i]
print(i, sample['image'].shape, sample['keypoints'].size())
# load training data in batches
batch_size = args["batch_size"]
train_loader = DataLoader(transformed_dataset,
batch_size=batch_size,
def train(cfg):
# make run_dir with date
import pdb; pdb.set_trace() ## DEBUG ##
d = datetime.datetime.now()
run_dir = pjoin(cfg.out_dir, 'exp_{:%Y-%m-%d_%H-%M}'.format(d))
if(not os.path.exists(run_dir)):
os.makedirs(run_dir)
with open(pjoin(run_dir, 'cfg.yml'), 'w') as outfile:
yaml.dump(cfg.__dict__, stream=outfile, default_flow_style=False)
# generate tensorboard object
writer = SummaryWriter(run_dir)
net = UNet(cfg.out_channels,
in_channels=cfg.in_channels,
depth=cfg.depth,
merge_mode=cfg.merge_mode)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=cfg.lr, momentum=cfg.momentum)
# make augmenter
transf = iaa.Sequential([
iaa.SomeOf(3, [
iaa.Affine(rotate=iap.Uniform(-cfg.aug_rotate, cfg.aug_rotate)),
iaa.Affine(shear=iap.Uniform(-cfg.aug_shear, cfg.aug_shear)),
iaa.Fliplr(1.),
iaa.Flipud(1.),
iaa.GaussianBlur(sigma=iap.Uniform(0.0, cfg.aug_gaussblur))
]),
iaa.Resize(cfg.in_shape), rescale_augmenter
])
base_loader = FacialKeypointsDataset(csv_file=cfg.csv_file,
root_dir=cfg.root_dir,
sig_kp=cfg.sig_kp,
transform=transf)
# build train, val and test sets randomly with given split ratios
idxs = np.arange(len(base_loader))
np.random.shuffle(idxs)
train_idxs = idxs[:int(len(base_loader) * cfg.train_split)]
others_idxs = idxs[int(len(base_loader) * cfg.train_split):]
val_idxs = others_idxs[:int(others_idxs.size * cfg.val_split)]
test_idxs = others_idxs[int(others_idxs.size * cfg.val_split):]
train_loader = torch.utils.data.Subset(base_loader, train_idxs)
val_loader = torch.utils.data.Subset(base_loader, val_idxs)
test_loader = torch.utils.data.Subset(base_loader, test_idxs)
loaders = {'train': DataLoader(train_loader,
batch_size=cfg.batch_size,
num_workers=cfg.n_workers),
'val': DataLoader(train_loader,
batch_size=cfg.batch_size,
num_workers=cfg.n_workers),
'test': DataLoader(train_loader,
num_workers=cfg.n_workers)}
# convert batch to device
device = torch.device('cuda' if cfg.cuda else 'cpu')
net.to(device)
batch_to_device = lambda batch: {
k: v.to(device) if (isinstance(v, torch.Tensor)) else v
for k, v in batch.items()
}
for epoch in range(cfg.n_epochs): # loop over the dataset multiple times
for phase in loaders.keys():
if phase == 'train':
net.train()
else:
net.eval() # Set model to evaluate mode
running_loss = 0.0
# train on batches of data, assumes you already have train_loader
pbar = tqdm.tqdm(total=len(loaders[phase]))
for i, data in enumerate(loaders[phase]):
data = batch_to_device(data)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
out = net(data['image'])
loss = criterion(out,
data['truth'])
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.cpu().detach().numpy()
loss_ = running_loss / ((i + 1) * cfg.batch_size)
pbar.set_description('[{}] loss: {:.4f}'.format(phase, loss_))
pbar.update(1)
pbar.close()
writer.add_scalar('{}/loss'.format(phase),
loss_,
epoch)
# make preview images
if phase == 'prev':
data = next(iter(loaders[phase]))
data = batch_to_device(data)
pred_ = net(data['image']).cpu()
pred_ = [
pred_[i, ...].repeat(3, 1, 1)
for i in range(pred_.shape[0])
]
im_ = data['image'].cpu()
im_ = [im_[i, ...] for i in range(im_.shape[0])]
truth_ = data['truth'].cpu()
truth_ = [
truth_[i, ...].repeat(3, 1, 1)
for i in range(truth_.shape[0])
]
all_ = [
tutls.make_grid([im_[i], truth_[i], pred_[i]],
nrow=len(pred_),
padding=10,
pad_value=1.)
for i in range(len(truth_))
]
all_ = torch.cat(all_, dim=1)
writer.add_image('test/img', all_, epoch)
# save checkpoint
if phase == 'val':
is_best = False
if (loss_ < best_loss):
is_best = True
best_loss = loss_
path = pjoin(run_dir, 'checkpoints')
save_checkpoint(
{
'epoch': epoch + 1,
'model': net,
'best_loss': best_loss,
'optimizer': optimizer.state_dict()
},
is_best,
path=path)
torch.cuda.empty_cache()
offline = True
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_epochs = 20
print("AI Running on", device)
net = FCN().apply(initialize_weights_advance_).to(device)
print(net)
data_transform = transforms.Compose(
[Rescale(250), RandomCrop(224),
Normalize(), ToTensor()])
transformed_dataset = FacialKeypointsDataset(
csv_file='data/training_frames_keypoints.csv',
root_dir='data/training/',
transform=data_transform)
# load training data in batches
batch_size = 128
train_loader = DataLoader(transformed_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4)
criterion = nn.MSELoss()
optimizer = optim.Adam(params=net.parameters(), lr=0.001)
losses = train_net(n_epochs, net, train_loader, device, optimizer,
data_transform = transforms.Compose([Rescale(250),
RandomCrop(crop),
Normalize(crop),
ToTensor()])
# testing that you've defined a transform
assert(data_transform is not None), 'Define a data_transform'
# In[134]:
# create the transformed dataset
transformed_dataset = FacialKeypointsDataset(csv_file='training_frames_keypoints.csv',
root_dir='/home/tianbai/P1_Facial_Keypoints/data/training/',
transform=data_transform)
print('Number of images: ', len(transformed_dataset))
for i in range(4):
sample = transformed_dataset[i]
print(i, sample['image'].size(), sample['keypoints'].size())
print (sample['keypoints'][0:5])
# In[135]:
