import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
# %matplotlib inline
# Use a white background for matplotlib figures
matplotlib.rcParams['figure.facecolor'] = '#ffffff'
dataset = MNIST(root='data/', download=True, transform=ToTensor())
image, label = dataset[0]
print('image.shape:', image.shape)
plt.imshow(image.permute(1,2), cmap='gray')
print('Label:', label)
val_size = 10000
train_size = len(dataset) - val_size
train_ds, val_ds = random_split(dataset, [train_size, val_size])
len(train_ds), len(val_ds)
batch_size = 128
train_loader = DataLoader(train_ds, batch_size, shuffle=True, num_workers=4, pin_memory=True)
val_loader = DataLoader(val_ds, batch_size*2, num_workers=4, pin_memory=True)
import torch
from torch import nn
from torchvision import datasets
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader
device = 'cuda'
training_data = datasets.FashionMNIST(root='data',
train=True,
download=True,
transform=ToTensor())
test_data = datasets.FashionMNIST(root='data',
train=False,
download=True,
transform=ToTensor())
batch_size = 100
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)
class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
self.stack = nn.Sequential(
nn.Conv2d(1, 24, 3, 2, 1),
nn.ReLU(),
def train_hr_transform(crop_size):
return Compose([
RandomCrop(crop_size),
ToTensor(),
])
def display_transform():
return Compose([ToPILImage(), Resize(400), CenterCrop(400), ToTensor()])
def train_hr_transform(crop_size):
return Compose([
CenterCrop(crop_size),
# Resize((128,128), interpolation=Image.BICUBIC),
ToTensor()
])
help='test low resolution image name')
parser.add_argument('--model_path', type=str)
opt = parser.parse_args()
UPSCALE_FACTOR = opt.upscale_factor
TEST_MODE = True if opt.test_mode == 'GPU' else False
IMAGE_NAME = opt.image_name
model = Generator(UPSCALE_FACTOR).eval()
if TEST_MODE:
model.cuda()
model.load_state_dict(torch.load(f'{opt.model_path}'))
else:
model.load_state_dict(
torch.load(f'{opt.model_path}',
map_location=lambda storage, loc: storage))
image = Image.open(IMAGE_NAME)
image = Variable(ToTensor()(image), volatile=True).unsqueeze(0)
if TEST_MODE:
image = image.cuda()
start = time.clock()
out = model(image)
elapsed = (time.clock() - start)
print('cost' + str(elapsed) + 's')
out_img = ToPILImage()(out[0].data.cpu())
out_path = Path('test_outputs')
out_path.mkdir(exist_ok=True, parents=True)
out_img.save(str(out_path / Path(opt.image_name).name))
normal_factor = 1.
# setting to eval mode
i2d.eval()
#img = Variable(torch.FloatTensor(1), volatile=True)
#if args.cuda:
# img = img.cuda()
# https://discuss.pytorch.org/t/out-of-memory-error-during-evaluation-but-training-works-fine/12274/3
with torch.no_grad():
with open('D:/DataSets/RGB2Depth/20200602_112100/train_images.txt') as f:
for line in f:
line = line.rstrip('\n')
print('line: {}'.format(line))
img_in = ToTensor()( Image.open(line) ).to(device)
print('evaluating...')
#img = torch.from_numpy(img_in.transpose(2, 0, 1)).float().to(device)
img = img_in
img = torch.unsqueeze(img, 0)
print('img {}'.format(img.shape))
z_fake = i2d(img)
z_fake = F.interpolate(z_fake, size=(img.shape[2],img.shape[3]), mode='bilinear', align_corners=True) # resize new line to reduce the computation time
z_fake = torch.squeeze(z_fake, 0)
z_fake = torch.squeeze(z_fake, 0)
img = torch.squeeze(img, 0)
print(z_fake)
img_color = img.cpu().numpy().transpose(1, 2, 0)
root_folder = vars(args)['root']
model_url = r'Opacity_DN169_BCE_SGD\Opacity_DN169_BCE_SGD.pth'
if vars(args)['model_url'] is not None:
model_url = vars(args)['model_url']
csv_hm = r"C:\Users\maest\OneDrive\DTU\Semestre 4\Thesis\Code\CheXNet_aproach\Datase_stratification\PADChest_hm_LRUMDP_opacity.csv"
if vars(args)['hm_csv'] is not None:
csv_hm = vars(args)['hm_csv']
batch_size = 1
radiographic_findings_opacity = ['opacity']
transforms_test = transforms.Compose([
Resize(512),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
hm_dataset = PadChestDataset(csv_hm,
radiographic_findings_opacity,
root_folder,
transform=transforms_test)
hm_loader = torch.utils.data.DataLoader(hm_dataset, batch_size=1)
unorm = UnNormalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
for enum, data in enumerate(hm_loader, 0):
print(enum)
images, labels = data
image_hm = images
images = images.cuda()
def main():
parser = argparse.ArgumentParser(description='Dataloader test')
parser.add_argument('--gpu', default='0', help='gpu id')
parser.add_argument('--workers',
default=16,
type=int,
help='num workers for data loading')
parser.add_argument('--nb_epoch',
default=100,
type=int,
help='training epoch')
parser.add_argument('--lr', default=1e-4, type=float, help='learning rate')
parser.add_argument('--power',
default=0,
type=float,
help='lr poly power; 0 indicates step decay by half')
parser.add_argument('--batch_size', default=8, type=int, help='batch size')
parser.add_argument('--size', default=256, type=int, help='image size')
parser.add_argument(
'--anchor_imsize',
default=416,
type=int,
help='scale used to calculate anchors defined in model cfg file')
parser.add_argument('--data_root',
type=str,
default='./ln_data/DMS/',
help='path to ReferIt splits data folder')
parser.add_argument('--split_root',
type=str,
default='data',
help='location of pre-parsed dataset info')
parser.add_argument('--dataset',
default='referit',
type=str,
help='referit/flickr/unc/unc+/gref')
parser.add_argument('--time',
default=20,
type=int,
help='maximum time steps (lang length) per batch')
parser.add_argument('--emb_size',
default=512,
type=int,
help='fusion module embedding dimensions')
parser.add_argument('--resume',
default='',
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument(
'--pretrain',
default='',
type=str,
metavar='PATH',
help=
'pretrain support load state_dict that are not identical, while have no loss saved as resume'
)
parser.add_argument('--print_freq',
'-p',
default=2000,
type=int,
metavar='N',
help='print frequency (default: 1e3)')
parser.add_argument('--savename',
default='default',
type=str,
help='Name head for saved model')
parser.add_argument('--seed', default=13, type=int, help='random seed')
parser.add_argument('--bert_model',
default='bert-base-uncased',
type=str,
help='bert model')
parser.add_argument('--test',
dest='test',
default=False,
action='store_true',
help='test')
parser.add_argument('--nflim', default=3, type=int, help='nflim')
parser.add_argument('--mstage',
dest='mstage',
default=False,
action='store_true',
help='if mstage')
parser.add_argument('--mstack',
dest='mstack',
default=False,
action='store_true',
help='if mstack')
parser.add_argument('--w_div',
default=0.125,
type=float,
help='weight of the diverge loss')
parser.add_argument('--fusion', default='prod', type=str, help='prod/cat')
parser.add_argument('--tunebert',
dest='tunebert',
default=False,
action='store_true',
help='if tunebert')
parser.add_argument('--large',
dest='large',
default=False,
action='store_true',
help='if large mode: fpn16, convlstm out, size 512')
global args, anchors_full
args = parser.parse_args()
if args.large:
args.gsize = 16
args.size = 512
else:
args.gsize = 8
print(
'----------------------------------------------------------------------'
)
print(sys.argv[0])
print(args)
print(
'----------------------------------------------------------------------'
)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
## fix seed
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(args.seed)
np.random.seed(args.seed + 1)
torch.manual_seed(args.seed + 2)
torch.cuda.manual_seed_all(args.seed + 3)
eps = 1e-10
## following anchor sizes calculated by kmeans under args.anchor_imsize=416
## An typo of 'refeit' in original experiments, thus removing the anchor for referit.
## Detailed discussion in "https://github.com/zyang-ur/ReSC/issues/5"
# if args.dataset=='refeit':
# anchors = '30,36, 78,46, 48,86, 149,79, 82,148, 331,93, 156,207, 381,163, 329,285'
# elif args.dataset=='flickr':
if args.dataset == 'flickr':
anchors = '29,26, 55,58, 137,71, 82,121, 124,205, 204,132, 209,263, 369,169, 352,294'
else:
anchors = '10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326'
anchors = [float(x) for x in anchors.split(',')]
anchors_full = [(anchors[i], anchors[i + 1])
for i in range(0, len(anchors), 2)][::-1]
## save logs
if args.savename == 'default':
args.savename = 'filmconv_nofpn32_%s_batch%d' % (args.dataset,
args.batch_size)
if not os.path.exists('./logs'):
os.mkdir('logs')
logging.basicConfig(level=logging.INFO,
filename="./logs/%s" % args.savename,
filemode="a+",
format="%(asctime)-15s %(levelname)-8s %(message)s")
logging.info(str(sys.argv))
logging.info(str(args))
input_transform = Compose([
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_dataset = ReferDataset(data_root=args.data_root,
split_root=args.split_root,
dataset=args.dataset,
split='train',
imsize=args.size,
transform=input_transform,
max_query_len=args.time,
augment=True)
val_dataset = ReferDataset(data_root=args.data_root,
split_root=args.split_root,
dataset=args.dataset,
split='val',
imsize=args.size,
transform=input_transform,
max_query_len=args.time)
## note certain dataset does not have 'test' set:
## 'unc': {'train', 'val', 'trainval', 'testA', 'testB'}
test_dataset = ReferDataset(data_root=args.data_root,
split_root=args.split_root,
dataset=args.dataset,
testmode=True,
split='val',
imsize=args.size,
transform=input_transform,
max_query_len=args.time)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
drop_last=True,
num_workers=args.workers)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
drop_last=True,
num_workers=args.workers)
test_loader = DataLoader(test_dataset,
batch_size=1,
shuffle=False,
pin_memory=True,
drop_last=True,
num_workers=0)
## Model
model = grounding_model_multihop(NFilm=args.nflim, fusion=args.fusion, intmd=args.mstack, mstage=args.mstage, \
emb_size=args.emb_size, coordmap=True, convlstm=args.large, \
bert_model=args.bert_model, dataset=args.dataset, tunebert=args.tunebert)
model = torch.nn.DataParallel(model).cuda()
if args.pretrain:
model = load_pretrain(model, args, logging)
if args.resume:
model = load_resume(model, args, logging)
print('Num of parameters:',
sum([param.nelement() for param in model.parameters()]))
logging.info('Num of parameters:%d' %
int(sum([param.nelement() for param in model.parameters()])))
if args.tunebert:
visu_param = model.module.visumodel.parameters()
text_param = model.module.textmodel.parameters()
rest_param = [
param for param in model.parameters()
if ((param not in visu_param) and (param not in text_param))
]
visu_param = list(model.module.visumodel.parameters())
text_param = list(model.module.textmodel.parameters())
sum_visu = sum([param.nelement() for param in visu_param])
sum_text = sum([param.nelement() for param in text_param])
sum_fusion = sum([param.nelement() for param in rest_param])
print('visu, text, fusion module parameters:', sum_visu, sum_text,
sum_fusion)
else:
visu_param = model.module.visumodel.parameters()
rest_param = [
param for param in model.parameters() if param not in visu_param
]
visu_param = list(model.module.visumodel.parameters())
sum_visu = sum([param.nelement() for param in visu_param])
sum_text = sum([
param.nelement() for param in model.module.textmodel.parameters()
])
sum_fusion = sum([param.nelement() for param in rest_param]) - sum_text
print('visu, text, fusion module parameters:', sum_visu, sum_text,
sum_fusion)
## optimizer; rmsprop default
if args.tunebert:
optimizer = torch.optim.RMSprop([{
'params': rest_param
}, {
'params': visu_param,
'lr': args.lr / 10.
}, {
'params': text_param,
'lr': args.lr / 10.
}],
lr=args.lr,
weight_decay=0.0005)
else:
optimizer = torch.optim.RMSprop([{
'params': rest_param
}, {
'params': visu_param,
'lr': args.lr / 10.
}],
lr=args.lr,
weight_decay=0.0005)
## training and testing
best_accu = -float('Inf')
if args.test:
_ = test_epoch(test_loader, model)
else:
for epoch in range(args.nb_epoch):
adjust_learning_rate(args, optimizer, epoch)
train_epoch(train_loader, model, optimizer, epoch)
accu_new = validate_epoch(val_loader, model)
## remember best accu and save checkpoint
is_best = accu_new > best_accu
best_accu = max(accu_new, best_accu)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_loss': accu_new,
'optimizer': optimizer.state_dict(),
},
is_best,
args,
filename=args.savename)
print('\nBest Accu: %f\n' % best_accu)
logging.info('\nBest Accu: %f\n' % best_accu)
def main():
parser = argparse.ArgumentParser(description='Visual Question Answering')
# Experiment params
parser.add_argument('--mode',
type=str,
help='train or test mode',
required=True,
choices=['train', 'test'])
parser.add_argument('--expt_dir',
type=str,
help='root directory to save model & summaries',
required=True)
parser.add_argument('--expt_name',
type=str,
help='expt_dir/expt_name: organize experiments',
required=True)
parser.add_argument(
'--run_name',
type=str,
help='expt_dir/expt_name/run_name: organize training runs',
required=True)
parser.add_argument('--model',
type=str,
help='VQA model',
choices=['baseline', 'attention', 'bert'],
required=True)
# Data params
parser.add_argument('--train_img',
type=str,
help='path to training images directory',
required=True)
parser.add_argument('--train_file',
type=str,
help='training dataset file',
required=True)
parser.add_argument('--val_img',
type=str,
help='path to validation images directory')
parser.add_argument('--val_file', type=str, help='validation dataset file')
parser.add_argument('--num_cls',
'-K',
type=int_min_two,
help='top K answers (labels); min=2',
default=1000)
# Vocab params
parser.add_argument(
'--vocab_file',
type=str,
help='vocabulary pickle file (gen. by prepare_data.py)')
# Training params
parser.add_argument('--batch_size',
'-bs',
type=int,
help='batch size',
default=8)
parser.add_argument('--num_epochs',
'-ep',
type=int,
help='number of epochs',
default=50)
parser.add_argument('--learning_rate',
'-lr',
type=float,
help='initial learning rate',
default=1e-4)
parser.add_argument('--log_interval',
type=int,
help='interval size for logging training summaries',
default=100)
parser.add_argument('--save_interval',
type=int,
help='save model after `n` weight update steps',
default=3000)
parser.add_argument('--val_size',
type=int,
help='validation set size for evaluating accuracy',
default=10000)
# Evaluation params
parser.add_argument('--K_eval',
type=int,
help='top-K labels during evaluation/inference',
default=1000)
# Model params
parser.add_argument(
'--model_ckpt',
type=str,
help='resume training/perform inference; e.g. model_1000.pth')
parser.add_argument('--vgg_wts_path',
type=str,
help='VGG-11 (bn) pre-trained weights (.pth) file')
parser.add_argument('--vgg_train',
type=str2bool,
help='whether to train the VGG encoder',
default='false')
# parser.add_argument('--model_config', type=str, help='model config file - specifies model architecture')
# GPU params
# parser.add_argument('--num_gpus', type=int, help='number of GPUs to use for training', default=1)
parser.add_argument('--gpu_id',
type=int,
help='cuda:gpu_id (0,1,2,..) if num_gpus = 1',
default=0)
parser.add_argument('--opt_lvl',
type=int,
help='Automatic-Mixed Precision: opt-level (O_)',
default=1,
choices=[0, 1, 2, 3])
# Misc params
parser.add_argument('--num_workers',
type=int,
help='number of worker threads for Dataloader',
default=1)
args = parser.parse_args()
device = torch.device(
'cuda:{}'.format(args.gpu_id) if torch.cuda.is_available() else 'cpu')
print('Selected Device: {}'.format(device))
# torch.cuda.get_device_properties(device).total_memory # in Bytes
# Train params
n_epochs = args.num_epochs
batch_size = args.batch_size
lr = args.learning_rate
# Load vocab (.pickle) file
vocab = load_vocab(args.vocab_file)
print('Vocabulary loaded from {}'.format(args.vocab_file))
# Unpack vocab
word2idx, idx2word, label2idx, idx2label, max_seq_length = [
v for k, v in vocab.items()
]
vocab_size = len(word2idx)
# Model Config
model_config = setup_model_configs(args, vocab_size)
image_size = model_config['image_size']
# TODO: Multi-GPU PyTorch Implementation
# if args.num_gpus > 1 and torch.cuda.device_count() > 1:
# print("Using {} GPUs!".format(torch.cuda.device_count()))
# model = nn.DataParallel(model, device_ids=[0, 1])
# model.to(device)
# Train
if args.mode == 'train':
# Setup train log directory
log_dir = os.path.join(args.expt_dir, args.expt_name, args.run_name)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
print('Training Log Directory: {}\n'.format(log_dir))
# TensorBoard summaries setup --> /expt_dir/expt_name/run_name/
writer = SummaryWriter(log_dir)
# Train log file
log_file = setup_logs_file(parser, log_dir)
# Dataset & Dataloader
train_dataset = VQADataset(args.train_file,
args.train_img,
word2idx,
label2idx,
max_seq_length,
transform=Compose([
Resize(image_size),
ToTensor(),
Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size,
shuffle=True,
drop_last=True,
num_workers=args.num_workers)
print('Question Vocabulary Size: {} \n\n'.format(vocab_size))
print('Train Data Size: {}'.format(train_dataset.__len__()))
# Plot data (image, question, answer) for sanity check
# plot_data(train_loader, idx2word, idx2label, num_plots=10)
# sys.exit()
if args.val_file:
# Use the same word-index dicts as that obtained for the training set
val_dataset = VQADataset(args.val_file,
args.val_img,
word2idx,
label2idx,
max_seq_length,
transform=Compose([
Resize(image_size),
ToTensor(),
Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
]))
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size,
shuffle=True,
drop_last=True,
num_workers=args.num_workers)
log_msg = 'Validation Data Size: {}\n'.format(
val_dataset.__len__())
log_msg += 'Validation Accuracy is computed using {} samples. See --val_size\n'.format(
args.val_size)
print_and_log(log_msg, log_file)
# Num of classes = K + 1 (for UNKNOWN)
num_classes = args.num_cls + 1
# Setup model params
question_encoder_params = model_config['question_params']
image_encoder_params = model_config['image_params']
# Define model & load to device
VQANet = model_config['model']
model = VQANet(question_encoder_params,
image_encoder_params,
K=num_classes)
model.to(device)
# Load model checkpoint file (if specified) from `log_dir`
if args.model_ckpt:
model_ckpt_path = os.path.join(log_dir, args.model_ckpt)
checkpoint = torch.load(model_ckpt_path)
model.load_state_dict(checkpoint)
log_msg = 'Model successfully loaded from {}'.format(
model_ckpt_path) + '\nResuming Training...'
print_and_log(log_msg, log_file)
# Loss & Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr)
# TODO: StepLR Scheduler
# scheduler = StepLR(optimizer, step_size=1, gamma=0.1)
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O{}".format(args.opt_lvl))
steps_per_epoch = len(train_loader)
start_time = time()
curr_step = 0
# TODO: Save model with best validation accuracy
best_val_acc = 0.0
for epoch in range(n_epochs):
for batch_data in train_loader:
# Load batch data
image = batch_data['image']
question = batch_data['question']
ques_len = batch_data['ques_len']
label = batch_data['label']
# Sort batch based on sequence length
image, question, label, ques_len = sort_batch(
image, question, label, ques_len)
# Load data onto the available device
image = image.to(device) # [B, C, H, W]
question = question.to(device) # [B, L]
ques_len = ques_len.to(device) # [B]
label = label.to(device) # [B]
# Forward Pass
label_predict = model(image, question, ques_len)
# Compute Loss
loss = criterion(label_predict, label)
# Backward Pass
optimizer.zero_grad()
# loss.backward()
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
optimizer.step()
# Print Results - Loss value & Validation Accuracy
if (curr_step + 1) % args.log_interval == 0 or curr_step == 1:
# Validation set accuracy
if args.val_file:
validation_metrics = compute_validation_metrics(
model, val_loader, device, size=args.val_size)
# Reset the mode to training
model.train()
log_msg = 'Validation Accuracy: {:.2f} % || Validation Loss: {:.4f}'.format(
validation_metrics['accuracy'],
validation_metrics['loss'])
print_and_log(log_msg, log_file)
# If current model has the best accuracy on the validation set & >= training accuracy,
# save model to disk
# Add summaries to TensorBoard
writer.add_scalar('Val/Accuracy',
validation_metrics['accuracy'],
curr_step)
writer.add_scalar('Val/Loss',
validation_metrics['loss'],
curr_step)
# Add summaries to TensorBoard
writer.add_scalar('Train/Loss', loss.item(), curr_step)
# Compute elapsed & remaining time for training to complete
time_elapsed = (time() - start_time) / 3600
# total time = time_per_step * steps_per_epoch * total_epochs
total_time = (time_elapsed /
curr_step) * steps_per_epoch * n_epochs
time_left = total_time - time_elapsed
log_msg = 'Epoch [{}/{}], Step [{}/{}], Loss: {:.4f} | time elapsed: {:.2f}h | time left: {:.2f}h'.format(
epoch + 1, n_epochs, curr_step + 1, steps_per_epoch,
loss.item(), time_elapsed, time_left)
print_and_log(log_msg, log_file)
# Save the model
if (curr_step + 1) % args.save_interval == 0:
print('Saving the model at the {} step to directory:{}'.
format(curr_step + 1, log_dir))
save_path = os.path.join(
log_dir, 'model_' + str(curr_step + 1) + '.pth')
torch.save(model.state_dict(), save_path)
curr_step += 1
# Validation set accuracy on the entire set
if args.val_file:
# Total validation set size
total_validation_size = val_dataset.__len__()
validation_metrics = compute_validation_metrics(
model, val_loader, device, total_validation_size)
log_msg = '\nAfter {} epoch:\n'.format(epoch + 1)
log_msg += 'Validation Accuracy: {:.2f} % || Validation Loss: {:.4f}\n'.format(
validation_metrics['accuracy'], validation_metrics['loss'])
print_and_log(log_msg, log_file)
# Reset the mode to training
model.train()
writer.close()
log_file.close()
# TODO: Test/Inference
elif args.mode == 'test':
raise NotImplementedError('TODO: test mode')
downsample=opt.downsample)
elif opt.model_type == 'rbf' or opt.model_type == 'nerf':
model = modules.SingleBVPNet(type='relu',
mode=opt.model_type,
out_features=img_dataset.img_channels,
sidelength=image_resolution,
downsample=opt.downsample)
else:
raise NotImplementedError
model.cuda()
root_path = os.path.join(opt.logging_root, opt.experiment_name)
if opt.mask_path:
mask = Image.open(opt.mask_path)
mask = ToTensor()(mask)
mask = mask.float().cuda()
percentage = torch.sum(mask).cpu().numpy() / np.prod(mask.shape)
print("mask sparsity %f" % (percentage))
else:
mask = torch.rand(image_resolution) < opt.sparsity
mask = mask.float().cuda()
# Define the loss
if opt.prior is None:
loss_fn = partial(loss_functions.image_mse, mask.view(-1, 1))
elif opt.prior == 'TV':
loss_fn = partial(loss_functions.image_mse_TV_prior, mask.view(-1, 1),
opt.k1, model)
elif opt.prior == 'FH':
loss_fn = partial(loss_functions.image_mse_FH_prior, mask.view(-1, 1),
def main():
# ---------- LOAD DATASET AND FILE SELECTION ----------------------------------------------------------------------
start = time.time()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.cuda.empty_cache()
print(device)
# file_name_extension = 'Rotation_centered_im1'
file_name_extension = 'Rotation_Translation_im1'
# file_name_extension = 'Translation_im3' # choose the corresponding database to use
cubes_file = 'Npydatabase/wrist_{}.npy'.format(file_name_extension)
silhouettes_file = 'Npydatabase/sils_{}.npy'.format(file_name_extension)
parameters_file = 'Npydatabase/params_{}.npy'.format(file_name_extension)
wrist = np.load(cubes_file)
sils = np.load(silhouettes_file)
params = np.load(parameters_file)
train_im = wrist # 90% training
train_sil = sils
train_param = params
normalize = Normalize(mean=[0.5], std=[0.5])
transforms = Compose([ToTensor(), normalize])
train_dataset = CubeDataset(train_im, train_sil, train_param, transforms)
train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=1)
# # check to iterate inside the test dataloader
# for image, sil, param in train_dataloader:
#
# # print(image[2])
# print(image.size(), param.size()) #torch.Size([batch, 3, 512, 512]) torch.Size([batch, 6])
# im =0
# print(param[im]) # parameter in form tensor([2.5508, 0.0000, 0.0000, 0.0000, 0.0000, 5.0000])
#
# image2show = image[im] # indexing random one image
# print(image2show.size()) #torch.Size([3, 512, 512])
# plt.imshow((image2show * 0.5 + 0.5).numpy().transpose(1, 2, 0))
# plt.show()
# break # break here just to show 1 batch of data
count = 0
losses = []
a = []
b = []
c = []
tx = []
ty = []
tz = []
#ground value to be plotted on the graph as line
alpha_GT = np.array( m.degrees(params[0,0]))
beta_GT = np.array(m.degrees(params[0,1]))
gamma_GT = np.array(m.degrees(params[0,2]))#angle in degrer
tx_GT = np.array(params[0,3])
ty_GT = np.array(params[0,4])
tz_GT = np.array(params[0,5])
iterations = 100
# ---------- MODEL CREATION ----------------------------------------------------------------------
parser = argparse.ArgumentParser()
parser.add_argument('-io', '--filename_obj', type=str, default=os.path.join(data_dir, 'wrist.obj'))
parser.add_argument('-or', '--filename_output', type=str, default=os.path.join(result_dir, '{}_regression_animation_6params.gif'.format(file_name_extension)))
parser.add_argument('-mr', '--make_reference_image', type=int, default=0)
parser.add_argument('-g', '--gpu', type=int, default=0)
args = parser.parse_args()
# resnet50 = models.resnet50(pretrained=True)
model = Myresnet50(filename_obj=args.filename_obj)
# model = Model(args.filename_obj, args.filename_ref)
model.to(device)
model.train(True)
bool_first = True
Lr_start = 0.001
decreaseat = 40
lr = Lr_start
loop = tqdm.tqdm(range(iterations))
for i in loop:
for image, silhouette, parameter in train_dataloader:
image = image.to(device)
imgGT = image
parameter = parameter.to(device)
print(parameter)
silhouette = silhouette.to(device)
params = model(image)
print(params)
model.t = params[0,3:6]
model.R = R2Rmat(params[0,0:3]) #angle from resnet are in radian
bool_first = True
# first_
# print(model.t)
# print(model.R)
# regression between computed and ground truth
image = model.renderer(model.vertices, model.faces, R=model.R, t= model.t, mode='silhouettes')
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
loss = nn.MSELoss()(params, parameter).to(device)
if (i % decreaseat == 0 and i > 2):
lr = lr / 10
print('update lr, is now {}'.format(lr))
print('loss is {}'.format(loss))
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.detach().cpu().numpy())
# print(((model.K).detach().cpu().numpy()))
cp_x = ((model.t).detach().cpu().numpy())[0]
cp_y = ((model.t).detach().cpu().numpy())[1]
cp_z = ((model.t).detach().cpu().numpy())[2]
cp_rotMat = (model.R) #cp_rotMat = (model.R).detach().cpu().numpy()
r = Rot.from_dcm(cp_rotMat.detach().cpu().numpy())
r_euler = r.as_euler('xyz', degrees=True)
a.append(r_euler[0, 0]) # a.append(abs(r_euler[0,0] ))
b.append(r_euler[0, 1])
c.append(r_euler[0, 2])
cp_a = r_euler[0, 0]
cp_b = r_euler[0, 1]
cp_c = r_euler[0, 2]
tx.append(cp_x)
ty.append(cp_y)
tz.append(cp_z) #z axis value
images, _, _ = model.renderer(model.vertices, model.faces, torch.tanh(model.textures), R=model.R, t=model.t)
img = images.detach().cpu().numpy()[0].transpose(1, 2, 0)
if (i == iterations - 1):
imgGT = imgGT.squeeze() # float32 from 0-1
imgGT = imgGT.detach().cpu()
imgGT = (imgGT * 0.5 + 0.5).numpy().transpose(1, 2, 0)
# imgGT = (imgGT * 255).astype(np.uint8) # cast from float32 255.0 to 255 uint8
f = plt.subplot(1, 2, 1)
plt.imshow(imgGT)
f.set_title('Ground truth \n alpha {:.3f}° tx {}\n'
'beta {:.3f}° ty {}\n '
'gamma {:.3f}° tz {}'.format(alpha_GT, tx_GT, beta_GT, ty_GT, gamma_GT, tz_GT))
plt.xticks([0, 512])
plt.yticks([])
f = plt.subplot(1, 2, 2)
plt.imshow(img)
f.set_title('Regression \n alpha {:.3f}° tx {:.3f}\n'
'beta {:.3f}° ty {:.3f}\n'
'gamma {:.3f}° tz {:.3f}'.format(cp_a, cp_x, cp_b, cp_y, cp_c, cp_z))
plt.xticks([0, 512])
plt.yticks([])
plt.savefig('results/3_6params_regression/Final_regression_6params_{}iterations_{}.png'.format(iterations, file_name_extension),
bbox_inches='tight', pad_inches=0.05)
imsave('/tmp/_tmp_%04d.png' % i, img)
loop.set_description('Optimizing (loss %.4f)' % loss.data)
count = count + 1
end = time.time()
exectime = round((end - start), 2) # format in minute
print('time elapsed is: {} sec'.format(exectime))
#----------PLOT SECTION ------------------------------------------------------------------------
make_gif(args.filename_output)
fig, (p1, p2, p3) = plt.subplots(3, figsize=(15,10)) #largeur hauteur
fig.suptitle("Regression for 1 image, {} epochs in {} sec, rotation and translation, 6 parameters \n lr={} and decrease each {} iterations".format(iterations,exectime, Lr_start, decreaseat), fontsize=14)
p1.plot(np.arange(count), losses, label="Global Loss")
p1.set( ylabel='BCE Loss')
p1.set_yscale('log')
p1.set_ylim([0, 1])
p1.set(xlabel='Iterations')
# Place a legend to the right of this smaller subplot.
p1.legend()
p2.plot(np.arange(count), tx, label="x values", color = 'g' )
p2.axhline(y=tx_GT, color = 'g', linestyle= '--' )
p2.plot(np.arange(count), ty, label="y values", color = 'y')
p2.axhline(y=ty_GT, color = 'y', linestyle= '--' )
p2.plot(np.arange(count), tz, label="z values", color = 'b')
p2.axhline(y=tz_GT, color = 'b', linestyle= '--' )
p2.set(ylabel='Translation value')
p2.set_ylim([-5, 10])
p2.set(xlabel='Iterations')
p2.legend()
p3.plot(np.arange(count), a, label="alpha values", color = 'g')
p3.axhline(y=alpha_GT, color = 'g', linestyle= '--' )
p3.plot(np.arange(count), b, label="beta values", color = 'y')
p3.axhline(y=beta_GT, color = 'y', linestyle= '--')
p3.plot(np.arange(count), c, label="gamma values", color = 'b')
p3.axhline(y=gamma_GT, color = 'b', linestyle= '--' )
p3.set(xlabel='iterations', ylabel='Rotation value')
p3.set_ylim([-180, 180])
p3.legend()
fig.savefig('results/3_6params_regression/regression_1image_6params_{}.pdf'.format(file_name_extension), bbox_inches = 'tight', pad_inches = 0.05)
fig.savefig('results/3_6params_regression/regression_1image_6params_{}.png'.format(file_name_extension), bbox_inches = 'tight', pad_inches = 0.05)
matplotlib2tikz.save("results/3_6params_regression/regression_1image_6params_{}.tex".format(file_name_extension),figureheight='5.5cm', figurewidth='15cm')
plt.show()
def __init__(self,
root=expanduser("~") + "/.avalanche/data/core50/",
train=True,
transform=ToTensor(),
target_transform=None,
loader=pil_loader,
download=True,
object_level=True):
"""
:param root: root for the datasets data.
:param train: train or test split.
:param transform: eventual transformations to be applied.
:param target_transform: eventual transformation to be applied to the
targets.
:param loader: data loader method from disk.
:param download: boolean to automatically download data. Default to
True.
:param object_level: if the classification is objects based or
category based: 50 or 10 way classification problem. Default to True
(50-way object classification problem)
"""
self.train = train # training set or test set
self.transform = transform
self.target_transform = target_transform
self.root = root
self.loader = loader
self.object_level = object_level
self.log = logging.getLogger("avalanche")
# any scenario and run is good here since we want just to load the
# train images and targets with no particular order
scen = 'ni'
run = 0
nbatch = 8
if download:
self.core_data = CORE50_DATA(data_folder=root)
self.log.info("Loading paths...")
with open(os.path.join(root, 'paths.pkl'), 'rb') as f:
self.train_test_paths = pkl.load(f)
self.log.info("Loading labels...")
with open(os.path.join(root, 'labels.pkl'), 'rb') as f:
self.all_targets = pkl.load(f)
self.train_test_targets = []
for i in range(nbatch + 1):
self.train_test_targets += self.all_targets[scen][run][i]
self.log.info("Loading LUP...")
with open(os.path.join(root, 'LUP.pkl'), 'rb') as f:
self.LUP = pkl.load(f)
self.log.info("Loading labels names...")
with open(os.path.join(root, 'labels2names.pkl'), 'rb') as f:
self.labels2names = pkl.load(f)
self.idx_list = []
if train:
for i in range(nbatch + 1):
self.idx_list += self.LUP[scen][run][i]
else:
self.idx_list = self.LUP[scen][run][-1]
self.paths = []
self.targets = []
for idx in self.idx_list:
self.paths.append(self.train_test_paths[idx])
div = 1
if not self.object_level:
div = 5
self.targets.append(self.train_test_targets[idx] // div)
def resize(x, target_shape):
x = ToPILImage()(x.cpu().to(torch.float32))
x = Resize(target_shape)(x)
x = ToTensor()(x)
return x.cuda()
import torch
from PIL.Image import Image
from torch import Tensor
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor, ToPILImage, Compose, Normalize, \
RandomRotation
import numpy as np
from avalanche.benchmarks import NCScenario, nc_benchmark
from avalanche.benchmarks.classic.classic_benchmarks_utils import \
check_vision_benchmark
from avalanche.benchmarks.datasets import default_dataset_location
from avalanche.benchmarks.utils import AvalancheDataset
_default_mnist_train_transform = Compose(
[ToTensor(), Normalize((0.1307, ), (0.3081, ))])
_default_mnist_eval_transform = Compose(
[ToTensor(), Normalize((0.1307, ), (0.3081, ))])
class PixelsPermutation(object):
"""
Apply a fixed permutation to the pixels of the given image.
Works with both Tensors and PIL images. Returns an object of the same type
of the input element.
"""
def __init__(self, index_permutation: Sequence[int]):
self.permutation = index_permutation
self._to_tensor = ToTensor()
else:
torch.nn.utils.clip_grad_norm_(params, 5.0)
if __name__ == "__main__":
opt = parse_options(
"CIFAR10 EBM using RSM in flex.",
path="/g/korbel/mjendrusch/runs/experimental/cifar10-rsm-exp-14-VP",
device="cuda:0",
batch_size=128,
max_epochs=1000,
report_interval=1000,
checkpoint_interval=50000,
)
cifar10 = CIFAR10("examples/", download=False, transform=ToTensor())
data = CIFAR10Dataset(cifar10)
data = DataDistribution(data, batch_size=opt.batch_size, device=opt.device)
energy = AdaptedUNetEnergy().to(opt.device)
training = relaxed_score_matching_training(
energy,
data,
optimizer=torch.optim.Adam,
optimizer_kwargs=dict(lr=2e-4),
level_weight=scale_level,
level_distribution=VPNormalNoise(lambda t: 1e-3 + t * (1.0 - 1e-3)),
noise_distribution=VPNormalNoise(
lambda t: 1e-2 * (1e-3 + t * (1.0 - 1e-3))
), # TruncatedNormalNoise(lambda t: 0.01 * torch.ones_like(t))
def __init__(self, index_permutation: Sequence[int]):
self.permutation = index_permutation
self._to_tensor = ToTensor()
self._to_image = ToPILImage()
def train(args):
writer = SummaryWriter(log_dir=args.logdir)
# Datasets
dataset_tr = CUBDataset(
root=args.datapath,
train=True,
transforms=Compose([
Resize(256),
RandomCrop((224, 224), pad_if_needed=True),
RandomHorizontalFlip(),
ToTensor()
])
)
data_loader_tr = DataLoader(
dataset_tr,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.number_workers)
dataset_val = CUBDataset(
root=args.datapath,
train=False,
transforms=Compose([
CenterCrop(224),
ToTensor()
])
)
data_loader_val = DataLoader(
dataset_val,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.number_workers)
# Model
model = BirdNet(num_classes=20).to(args.device)
# Optimizer
optimizer = Adam(
params=model.classifier.parameters(), # Optimize only the classifier layer
lr=args.learning_rate,
weight_decay=args.weight_decay)
# Meters
meter_loss = AverageMeter()
meter_accuracy = AverageMeter()
train_accuracy, train_loss, val_accuracy, val_loss = 0,0,0,0
epoch_bar = tqdm.trange(args.number_epochs, desc='Epoch')
for epoch in epoch_bar:
epoch_start_time = time()
# Training
model.train()
torch.set_grad_enabled(True)
batch_bar = tqdm.tqdm(data_loader_tr, desc='Batch')
meter_loss.reset()
meter_accuracy.reset()
for batch in batch_bar:
input_batch = batch[0].to(args.device)
target = batch[1].to(args.device)
logits = model(input_batch)
number_samples = target.shape[0]
predictions = logits.argmax(dim=1)
accuracy = (predictions == target).float().sum()/number_samples
loss = F.cross_entropy(logits, target)
meter_accuracy.update(accuracy, number_samples)
meter_loss.update(loss, number_samples)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# batch_bar.set_postfix({'loss': loss.item()})
train_accuracy, train_loss = meter_accuracy.get_average(), meter_loss.get_average()
epoch_bar.set_postfix({"loss": train_loss,
"accuracy": train_accuracy})
writer.add_scalar("/train/loss", train_loss, epoch)
writer.add_scalar("/train/accuracy", train_accuracy, epoch)
# Validation
model.eval()
torch.set_grad_enabled(False)
batch_bar = tqdm.tqdm(data_loader_val, desc='Batch')
meter_loss.reset()
meter_accuracy.reset()
for batch in batch_bar:
input_batch = batch[0].to(args.device)
target = batch[1].to(args.device)
logits = model(input_batch)
number_samples = target.shape[0]
predictions = logits.argmax(dim=1)
accuracy = (predictions == target).float().sum()/number_samples
loss = F.cross_entropy(logits, target)
meter_accuracy.update(accuracy, number_samples)
meter_loss.update(loss, number_samples)
val_accuracy, val_loss = meter_accuracy.get_average(), meter_loss.get_average()
epoch_time = time()-epoch_start_time
epoch_bar.set_postfix({"loss": val_loss,
"accuracy": val_accuracy})
writer.add_scalar("/validation/loss", val_loss, epoch)
writer.add_scalar("/validation/accuracy", val_accuracy, epoch)
writer.add_scalar("time_per_epoch", epoch_time, epoch)
torch.save(model.classifier.state_dict(), str(args.logdir / "final_model.pt"))
return {"train": {"accuracy": train_accuracy, "loss": train_loss},
"validation": {"accuracy": val_accuracy, "loss": val_loss}}
def target_transform(crop_size):
return Compose([
CenterCrop(crop_size),
ToTensor(),
])
def __init__(self,
mode,
roidb_file=VG_SGG_FN,
dict_file=VG_SGG_DICT_FN,
image_file=IM_DATA_FN,
filter_empty_rels=True,
num_im=-1,
num_val_im=5000,
filter_duplicate_rels=True,
filter_non_overlap=True,
use_proposals=False):
"""
Torch dataset for VisualGenome
:param mode: Must be train, test, or val
:param roidb_file: HDF5 containing the GT boxes, classes, and relationships
:param dict_file: JSON Contains mapping of classes/relationships to words
:param image_file: HDF5 containing image filenames
:param filter_empty_rels: True if we filter out images without relationships between
boxes. One might want to set this to false if training a detector.
:param filter_duplicate_rels: Whenever we see a duplicate relationship we'll sample instead
:param num_im: Number of images in the entire dataset. -1 for all images.
:param num_val_im: Number of images in the validation set (must be less than num_im
unless num_im is -1.)
:param proposal_file: If None, we don't provide proposals. Otherwise file for where we get RPN
proposals
"""
if mode not in ('test', 'train', 'val'):
raise ValueError(
"Mode must be in test, train, or val. Supplied {}".format(
mode))
self.mode = mode
# Initialize
self.roidb_file = roidb_file
self.dict_file = dict_file
self.image_file = image_file
self.filter_non_overlap = filter_non_overlap
self.filter_duplicate_rels = filter_duplicate_rels and self.mode == 'train'
self.split_mask, self.gt_boxes, self.gt_classes, self.relationships = load_graphs_one_shot(
self.roidb_file,
self.mode,
num_im,
num_val_im=num_val_im,
filter_empty_rels=filter_empty_rels,
filter_non_overlap=self.filter_non_overlap and self.is_train,
)
self.filenames = load_image_filenames(image_file)
self.filenames = [
self.filenames[i] for i in np.where(self.split_mask)[0]
]
self.ind_to_classes, self.ind_to_predicates = load_info(dict_file)
if use_proposals:
print("Loading proposals", flush=True)
p_h5 = h5py.File(PROPOSAL_FN, 'r')
rpn_rois = p_h5['rpn_rois']
rpn_scores = p_h5['rpn_scores']
rpn_im_to_roi_idx = np.array(
p_h5['im_to_roi_idx'][self.split_mask])
rpn_num_rois = np.array(p_h5['num_rois'][self.split_mask])
self.rpn_rois = []
for i in range(len(self.filenames)):
rpn_i = np.column_stack((
rpn_scores[rpn_im_to_roi_idx[i]:rpn_im_to_roi_idx[i] +
rpn_num_rois[i]],
rpn_rois[rpn_im_to_roi_idx[i]:rpn_im_to_roi_idx[i] +
rpn_num_rois[i]],
))
self.rpn_rois.append(rpn_i)
else:
self.rpn_rois = None
# You could add data augmentation here. But we didn't.
# tform = []
# if self.is_train:
# tform.append(RandomOrder([
# Grayscale(),
# Brightness(),
# Contrast(),
# Sharpness(),
# Hue(),
# ]))
tform = [
SquarePad(),
Resize(IM_SCALE),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
self.transform_pipeline = Compose(tform)
def load_data():
train_dataset = FashionMNIST(root='./cache', download=True, train=True, transform=ToTensor())
eval_dataset = FashionMNIST(root='./cache', download=False, train=False, transform=ToTensor())
train_loader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)
eval_loader = DataLoader(dataset=eval_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=4)
return train_loader, eval_loader
from hbconfig import Config
from torch.autograd import Variable
from torchvision import transforms
from torchvision.transforms import ToTensor
from torchsummary import summary
import utils
from AutoAugment.autoaugment import ImageNetPolicy
from basic_utils import saving_config
from logger import Logger
from miniimagenet_loader import read_dataset_test, _sample_mini_dataset, _mini_batches, _split_train_test, _mini_batches_with_augmentation, AutoEncoder
from one_shot_aug.module import PretrainedClassifier, MiniImageNetModel
from utils import mkdir_p
meta_step_size = 1. # stepsize of outer optimization, i.e., meta-optimization
meta_step_size_final = 0.
tensor = ToTensor()
def augments_dataset(batch, k=5):
images = []
# labels=[]
for _ in range(k):
for img_, label in batch:
policy = ImageNetPolicy()
transformed = policy(img_)
# f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,6))
# ax1.imshow(img_)
# ax2.imshow(transformed[0])
# plt.show()
tensor = ToTensor()
if isinstance(transformed, (list, )):
if __name__ == '__main__':
P_TRAIN = 0.8 # proportion of examples to use for training
BATCH_SIZE = 32
NUM_WORKERS = 6
NUM_EPOCHS = 50
learning_rate = 0.001
DECAY_RATE = 5 # number of epochs after which to decay the learning rate
LR_DECAY = 0.5 # amount to decrease the learning rate every 'DECAY_RATE' epochs
CHECKPOINT_RATE = 5 # number of epochs after which to checkpoint the model
IMAGE_DIR = '/home/mchobanyan/data/emotion/images/imagenet/'
MODEL_DIR = '/home/mchobanyan/data/emotion/models/emotion_detect/imagenet/'
dataset = ColorAndGrayImages(image_dir=IMAGE_DIR,
colored_transform=Compose([
ToTensor(),
Normalize(IMAGENET_MEANS, IMAGENET_STDVS)
]),
gray_transform=ToTensor())
print(f'Number of images: {len(dataset)}')
train_size = int(len(dataset) * P_TRAIN)
val_size = len(dataset) - train_size
train_data, val_data = random_split(dataset, [train_size, val_size])
train_loader = DataLoader(train_data,
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS,
shuffle=True)
val_loader = DataLoader(val_data,
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS)
def train_lr_transform(crop_size, upscale_factor):
return Compose([
ToPILImage(),
Resize(crop_size // upscale_factor, interpolation=Image.BICUBIC),
ToTensor()
])
#!/usr/bin/env python3
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda, Compose
import matplotlib.pyplot as plt
training_data = datasets.FashionMNIST(
root="data",
train=True,
download=True,
transform=ToTensor(),
)
test_data = datasets.FashionMNIST(
root="data",
train=False,
download=True,
transform=ToTensor(),
)
batch_size = 64
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)
for X, y in test_dataloader:
print("Shape of X [N, C, H, W]: ", X.shape)
print("Shape of y: ", y.shape, y.dtype)
break
precision = correct / (correct + incorrect)
return precision
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 128
num_classes = 10
fully_supervised = False
reload = True
# image size 3, 32, 32
# batch size must be an even number
# shuffle must be True
ds = CIFAR10(r'c:\data\tv', download=True, transform=ToTensor())
len_train = len(ds) // 10 * 9
len_test = len(ds) - len_train
train, test = random_split(ds, [len_train, len_test])
train_l = DataLoader(train,
batch_size=batch_size,
shuffle=True,
drop_last=True)
test_l = DataLoader(test,
batch_size=batch_size,
shuffle=True,
drop_last=True)
if fully_supervised:
classifier = nn.Sequential(models.Encoder(),
models.Classifier()).to(device)
import torch
import torch.nn as nn
from torch.utils.data import random_split
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from poutyne import Experiment
# Instanciate the MNIST dataset
train_valid_dataset = MNIST('./datasets', train=True, download=True, transform=ToTensor())
test_dataset = MNIST('./datasets', train=False, download=True, transform=ToTensor())
train_dataset, valid_dataset = random_split(
train_valid_dataset, [50_000, 10_000], generator=torch.Generator().manual_seed(42)
)
# Select CUDA device if available
cuda_device = 0
device = torch.device('cuda:%d' % cuda_device if torch.cuda.is_available() else 'cpu')
# Define the network
network = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 100), nn.ReLU(), nn.Linear(100, 10))
epochs = 5
# Define the Experiment and train
experiment = Experiment(
'./simple_model', # Where to log
network,
optimizer='sgd',
loss_function='cross_entropy',
device=device,
)
experiment.train_dataset(train_dataset, valid_dataset, epochs=epochs)
def fake_data(size=100, image_size=(1, 4, 4), train=False):
return FakeData(size=size, image_size=image_size, transform=ToTensor())
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--epochs', type=int, default=50)
args = parser.parse_args()
batch_size = args.batch_size
epochs = args.epochs
lr = 0.01
weight_decay = 1e-5
print('dataset:', args.dataset)
print('epochs:', epochs)
print('batch size:', batch_size)
transform = transforms.Compose([RandomRotation(20), RandomResizedCrop(size=32, scale=(0.8, 1.1)), ToTensor()])
train_loader = DataLoader(datasets.CIFAR10('../data', train=True, download=True,
transform=transform),
batch_size=batch_size, shuffle=True)
test_loader = DataLoader(datasets.CIFAR10('../data', train=False,
transform=transforms.Compose([ToTensor()])),
batch_size=batch_size, shuffle=True)
model = BasicCNN()
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=weight_decay)
#optimizer = optim.Adam(model.parameters(), lr=lr)
# load the image file
lena = Image.open(file_name)
lena.resize((5,5))
print lena.mode
print lena.getpixel((0,0))
lena_ycbcr =lena.convert("YCbCr")
print lena_ycbcr.mode
print lena_ycbcr.getpixel((0,0))
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision.transforms import ToTensor
rgb = ToTensor()(lena)
rgb = rgb.view(1, rgb.size(0), rgb.size(1), rgb.size(2))
rgb = Variable(rgb)
rgb2ycbcr = Variable(torch.FloatTensor([[0.299, 0.587, 0.114], [-0.169, -0.331, 0.5], [0.5, -0.419, -0.081]]).resize_(3,3,1,1))
print rgb2ycbcr
print "---- rgb -----"
print rgb
ycbcr = F.conv2d(rgb, weight=rgb2ycbcr)
print "first pixel:", rgb.data[0,0,0,0]*255
print lena.getpixel((0,0))
print "---- ycbcr -----"
print ycbcr
def image_to_tensor(path: str) -> torch.Tensor:
image = Image.open(path)
image = ToTensor()(image)
return image
