def __init__(self):
self.model = DenseNet(growth_rate=8,
block_config=(2, 2, 2),
bn_size=4,
drop_rate=0,
num_init_features=8 * 2,
small_inputs=True,
efficient=True)
self.model.eval()
self.model.load_state_dict(
torch.load("save/param_best.pth",
map_location=lambda storage, loc: storage))
summary(self.model, input_size=(3, 480, 640))
def print_and_save_arguments(arguments, save_dir):
table = BeautifulTable()
for a in arguments:
table.append_row([a, arguments[a]])
## TODO: Remove this ugly bit from the code
# Get densenet configuration
depth = arguments['depth']
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
model = DenseNet(
growth_rate=arguments['growth_rate'],
block_config=block_config,
num_classes=10,
small_inputs=True,
efficient=False,
)
table.append_row(
["Param_per_model (M)",
count_parameters(model) / 1000000.0])
table.append_row(["CUDA", torch.cuda.is_available()])
print(table)
with open(os.path.join(save_dir, "config.txt"), 'a') as f:
f.write(str(table))
return
def run(args, use_cuda, output_dir):
trial_list = list(range(args.n_trials))
np.random.shuffle(trial_list)
for trial_i in trial_list:
trial_dir = os.path.join(output_dir, 'trial_{}'.format(trial_i))
os.makedirs(trial_dir, exist_ok=True)
loaders, params = get_dataloaders(args.batch_size,
trial_i,
args.dataset,
args.augment_data,
early_stop=args.early_stop)
if args.network_type == 'fc':
model = DenseModel(input_dim=np.prod(params['input_shape']),
output_dim=params['output_dim'],
hidden_nodes=args.hidden_nodes,
num_modules=args.n_modules,
activation=args.activation)
elif args.network_type == 'conv':
model = ConvModel(input_shape=params['input_shape'],
output_dim=params['output_dim'],
num_filters=args.filters,
kernel_sizes=args.kernels,
strides=args.strides,
dilations=args.dilations,
num_modules=args.n_modules,
activation=args.activation,
final_layer=args.conv_final_layer)
elif args.network_type == 'densenet':
model = DenseNet(input_shape=params['input_shape'],
output_dim=params['output_dim'],
growth_rate=args.densenet_k,
depth=args.densenet_depth,
reduction=args.densenet_reduction,
bottleneck=args.densenet_bottleneck,
num_modules=args.n_modules)
logging.debug(args)
logging.debug('Parameters: {}'.format(model.n_parameters()))
device = torch.device("cuda" if use_cuda else "cpu")
model = model.to(device)
model.reset_parameters()
weight_path = os.path.join(trial_dir, 'initial_weights.pt')
torch.save(model.state_dict(), weight_path)
for lambda_i, (lambda_, learning_rate) in enumerate(
zip(args.lambda_values, args.learning_rates)):
model.load_state_dict(torch.load(weight_path))
lambda_dir = os.path.join(trial_dir, str(lambda_))
os.makedirs(lambda_dir, exist_ok=True)
do_lambda_value(model, lambda_, learning_rate, args, loaders,
params['distribution'], device, lambda_dir)
def demo(save, depth=100, growth_rate=12, efficient=True, valid_size=5000,
n_epochs=300, batch_size=64, seed=None):
"""
A demo to show off training of efficient DenseNets.
Trains and evaluates a DenseNet-BC on CIFAR-10.
Args:
save (str) - path to save the model to (default /tmp)
depth (int) - depth of the network (number of convolution layers) (default 40)
growth_rate (int) - number of features added per DenseNet layer (default 12)
efficient (bool) - use the memory efficient implementation? (default True)
valid_size (int) - size of validation set
n_epochs (int) - number of epochs for training (default 300)
batch_size (int) - size of minibatch (default 256)
seed (int) - manually set the random seed (default None)
"""
# Get densenet configuration
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
tr_dt, tr_lb, te_dt, te_lb = genconfig()
tr_set = dataset(tr_dt, tr_lb)
te_set = dataset(te_dt, te_lb)
if valid_size:
indices = torch.randperm(len(tr_set))
train_indices = indices[:len(indices) - valid_size]
valid_indices = indices[len(indices) - valid_size:]
train_set = torch.utils.data.Subset(tr_set, train_indices)
valid_set = torch.utils.data.Subset(tr_set, valid_indices)
else:
train_set = tr_set
valid_set = None
# Models
model = DenseNet(
growth_rate=growth_rate,
block_config=block_config,
num_classes=4,
small_inputs=True,
efficient=efficient,
)
print(model)
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Train the model
train(model=model,
train_set=train_set, valid_set=valid_set, test_set=te_set,
save=save, n_epochs=n_epochs, batch_size=batch_size, seed=seed)
print('Done!')
def load_CC_ResNet(input_shape=(512, 512, 3)):
return DenseNet.DenseNet(nb_dense_block=4,
growth_rate=16,
nb_filter=32,
reduction=0.5,
dropout_rate=0.0,
weight_decay=0,
classes=1000,
weights_path=None)
def get_model(args):
if args.model == 'mlp':
model = MLP(num_classes=args.n_classes)
elif args.model == 'resnet':
model = ResNet(20, num_classes=args.n_classes)
elif args.model == 'densenet':
model = DenseNet(40, num_classes=args.n_classes)
return model
def densenet_regression(x,
y,
hidden_dims,
loss_fn=nn.MSELoss(),
lr=1e-2,
weight_decay=1e-4,
num_iters=1000,
print_every=100,
device=torch.device('cuda'),
verbose=True,
plot=True):
"""Use DenseNet with linear layers for regression
Returns:
model: nn.Module, learned regression model
"""
in_dim = x.size(-1)
out_dim = y.size(-1)
hidden_dims = hidden_dims + [out_dim]
model = DenseNet(in_dim, hidden_dims, dense=True,
residual=False).to(device)
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
model.parameters()),
lr=lr,
weight_decay=weight_decay,
amsgrad=True)
for i in range(num_iters):
y_pred = model(x)
loss = loss_fn(y_pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if verbose and (i % print_every == 0):
print(i, loss.item())
if plot:
plt.plot(x.detach().cpu().numpy().reshape(-1),
y.detach().cpu().numpy().reshape(-1), 'ro',
x.detach().cpu().numpy().reshape(-1),
y_pred.detach().cpu().numpy().reshape(-1), 'g--')
plt.show()
return model
class LineFilter:
def __init__(self):
self.model = DenseNet(growth_rate=8,
block_config=(2, 2, 2),
bn_size=4,
drop_rate=0,
num_init_features=8 * 2,
small_inputs=True,
efficient=True)
self.model.eval()
self.model.load_state_dict(
torch.load("save/param_best.pth",
map_location=lambda storage, loc: storage))
summary(self.model, input_size=(3, 480, 640))
def predict(self, input_data):
output = self.model(input_data).squeeze()
output[output > 255] = 255
output[output < 150] = 0
output = output.detach().numpy()
return output.astype(dtype=np.uint8)
def count_densenNet_param(growth_rate, depth):
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
model = DenseNet(
growth_rate=growth_rate,
block_config=block_config,
num_classes=10,
small_inputs=True,
)
return count_parameters(model)
def _get_model_archtecture(self):
"""
通过配置文件得到网络的结构
:return:
"""
if self.config['type'] == 'DenseNet':
from models import DenseNet
model_object = DenseNet.DenseNet(self.config['model_config'])
if self.config['type'] == 'ResNet':
from models import ResNet
model_object = ResNet.ResNet(self.config['model_config'])
if self.config['type'] == 'MobilenetV2':
from models import MobileNet
model_object = MobileNet.mobilenetV2(self.config['model_config'])
self.model = model_object.constuct_model()
def main():
args = parse_args()
cfg = cfg_from_file(args.config)
print('using config: {}'.format(args.config))
data_cfg = cfg['data']
datalist = datalist_from_file(data_cfg['datalist_path'])
num_train_files = len(datalist) // 5 * 4
train_dataset = IMetDataset(data_cfg['dataset_path'],
datalist[:num_train_files],
transform=data_cfg['train_transform'])
test_dataset = IMetDataset(data_cfg['dataset_path'],
datalist[num_train_files:],
transform=data_cfg['test_transform'])
train_dataloader = data.DataLoader(train_dataset,
batch_size=data_cfg['batch_size'],
shuffle=True)
test_dataloader = data.DataLoader(test_dataset,
batch_size=data_cfg['batch_size'])
backbone_cfg = cfg['backbone'].copy()
backbone_type = backbone_cfg.pop('type')
if backbone_type == 'ResNet':
backbone = ResNet(**backbone_cfg)
elif backbone_type == 'ResNeXt':
backbone = ResNeXt(**backbone_cfg)
elif backbone_type == 'DenseNet':
backbone = DenseNet(**backbone_cfg)
classifier = Classifier(backbone, backbone.out_feat_dim).cuda()
train_cfg, log_cfg = cfg['train'], cfg['log']
criterion = FocalLoss()
optimizer = torch.optim.SGD(classifier.parameters(),
lr=train_cfg['lr'],
weight_decay=train_cfg['weight_decay'],
momentum=train_cfg['momentum'])
trainer = Trainer(model=classifier,
train_dataloader=train_dataloader,
val_dataloader=test_dataloader,
criterion=criterion,
optimizer=optimizer,
train_cfg=train_cfg,
log_cfg=log_cfg)
trainer.train()
def demo(depth=58, growth_rate=12, efficient=False):
# Get densenet configuration
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
model = DenseNet(
growth_rate=growth_rate,
block_config=block_config,
num_classes=4,
small_inputs=True,
efficient=efficient,
)
model.load_state_dict(torch.load(os.path.join('./ckpt2/model.dat')))
t = list(model.state_dict())
n = len(t)
w = []
for x in range(3):
w.append([])
for i in range(9):
w[x].append([])
for j in range(9):
w[x][i].append(0)
for name in model.state_dict():
if len(name) == 49 and name[37] == 'c':
x, i, j = int(name[19]), int(name[32]), int(name[34])
a = abs(model.state_dict()[name])
w[x - 1][j][i - 1] = a.sum()
for x in range(3):
for i in range(9):
mx = 0
for j in range(i, 9):
mx = max(mx, w[x][i][j])
for j in range(i, 9):
w[x][i][j] = w[x][i][j] / mx
mask = []
for i in range(9):
mask.append([])
for j in range(9):
mask[i].append(j > i)
ax = []
for x in range(3):
sns.set()
ax.append(sns.heatmap(w[x], vmin = 0, vmax = 1, cmap = 'jet', square = True, mask = mask))
ax[x].set_title('Dense Block %s' % (x + 1))
ax[x].set_xlabel('Target layer (l)', fontsize=15)
ax[x].set_ylabel('Source layer (s)', fontsize=15)
plt.show(ax[x])
def __init__(self, net_cfgs, test_dataloader, validate_thresh):
self.test_dataloader = test_dataloader
self.validate_thresh = validate_thresh
self.net_list = []
for cfg in net_cfgs:
backbone_cfg = cfg.copy()
backbone_type = backbone_cfg.pop('type')
checkpoint = backbone_cfg.pop('checkpoint')
if backbone_type == 'ResNet':
backbone = ResNet(**backbone_cfg)
elif backbone_type == 'ResNeXt':
backbone = ResNeXt(**backbone_cfg)
elif backbone_type == 'DenseNet':
backbone = DenseNet(**backbone_cfg)
classifier = Classifier(backbone, backbone.out_feat_dim).cuda()
assert os.path.exists(checkpoint)
state_dict = torch.load(checkpoint)
classifier.load_state_dict(state_dict['model_params'])
classifier.eval()
self.net_list.append(classifier)
def train(data,
save,
valid_size=5000,
seed=None,
depth=40,
growth_rate=12,
n_epochs=300,
batch_size=64,
lr=0.1,
wd=0.0001,
momentum=0.9):
"""
A function to train a DenseNet-BC on CIFAR-100.
Args:
data (str) - path to directory where data should be loaded from/downloaded
(default $DATA_DIR)
save (str) - path to save the model to (default /tmp)
valid_size (int) - size of validation set
seed (int) - manually set the random seed (default None)
depth (int) - depth of the network (number of convolution layers) (default 40)
growth_rate (int) - number of features added per DenseNet layer (default 12)
n_epochs (int) - number of epochs for training (default 300)
batch_size (int) - size of minibatch (default 256)
lr (float) - initial learning rate
wd (float) - weight decay
momentum (float) - momentum
"""
if seed is not None:
torch.manual_seed(seed)
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Get densenet configuration
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
# Data transforms
mean = [0.5071, 0.4867, 0.4408]
stdv = [0.2675, 0.2565, 0.2761]
train_transforms = tv.transforms.Compose([
tv.transforms.RandomCrop(32, padding=4),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
tv.transforms.Normalize(mean=mean, std=stdv),
])
test_transforms = tv.transforms.Compose([
tv.transforms.ToTensor(),
tv.transforms.Normalize(mean=mean, std=stdv),
])
# Split training into train and validation - needed for calibration
#
# IMPORTANT! We need to use the same validation set for temperature
# scaling, so we're going to save the indices for later
train_set = tv.datasets.CIFAR100(data,
train=True,
transform=train_transforms,
download=True)
valid_set = tv.datasets.CIFAR100(data,
train=True,
transform=test_transforms,
download=False)
indices = torch.randperm(len(train_set))
train_indices = indices[:len(indices) - valid_size]
valid_indices = indices[len(indices) - valid_size:] if valid_size else None
# Make dataloaders
train_loader = torch.utils.data.DataLoader(
train_set,
pin_memory=True,
batch_size=batch_size,
sampler=SubsetRandomSampler(train_indices))
valid_loader = torch.utils.data.DataLoader(
valid_set,
pin_memory=True,
batch_size=batch_size,
sampler=SubsetRandomSampler(valid_indices))
# Make model, criterion, and optimizer
model = DenseNet(growth_rate=growth_rate,
block_config=block_config,
num_classes=100)
# Wrap model if multiple gpus
if torch.cuda.device_count() > 1:
model_wrapper = torch.nn.DataParallel(model).cuda()
else:
model_wrapper = model.cuda()
print(model_wrapper)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model_wrapper.parameters(),
lr=lr,
momentum=momentum,
nesterov=True)
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[0.5 * n_epochs, 0.75 * n_epochs], gamma=0.1)
# Train model
best_error = 1
for epoch in range(1, n_epochs + 1):
scheduler.step()
run_epoch(
loader=train_loader,
model=model_wrapper,
criterion=criterion,
optimizer=optimizer,
epoch=epoch,
n_epochs=n_epochs,
train=True,
)
valid_results = run_epoch(
loader=valid_loader,
model=model_wrapper,
criterion=criterion,
optimizer=optimizer,
epoch=epoch,
n_epochs=n_epochs,
train=False,
)
# Determine if model is the best
_, _, valid_error = valid_results
if valid_error[0] < best_error:
best_error = valid_error[0]
print('New best error: %.4f' % best_error)
# When we save the model, we're also going to
# include the validation indices
torch.save(model.state_dict(), os.path.join(save, 'model.pth'))
torch.save(valid_indices, os.path.join(save, 'valid_indices.pth'))
print('Done!')
# include the validation indices
torch.save(model.state_dict(), os.path.join(save, 'model.pth'))
torch.save(valid_indices, os.path.join(save, 'valid_indices.pth'))
print('Done!')
if __name__ == '__main__':
"""
Train a 40-layer DenseNet-BC on CIFAR-100
Args:
--data (str) - path to directory where data should be loaded from/downloaded
(default $DATA_DIR)
--save (str) - path to save the model to (default /tmp)
--valid_size (int) - size of validation set
--seed (int) - manually set the random seed (default None)
"""
data = path + '\\data\\'
save = path + 'model\\'
block_config = [(40 - 4) // 6 for _ in range(3)]
model = DenseNet(growth_rate=12,
block_config=block_config,
num_classes=100)
params = torch.load(save + 'model.pth')
model.load_state_dict(params)
# fire.Fire(train)
U2OS_data = RecursionDataset(os.path.join(args.data_dir, 'rxrx1.csv'),
train_dir,
sirna_encoder,
'train',
'U2OS',
args=args)
U2OS_loader = DataLoader(U2OS_data, batch_size=2, shuffle=False)
loaders = [HEPG2_train_loader, HUVEC_train_loader, RPE_train_loader]
est_time = get_est_time()
net = None
if (args.model_type == "densenet"):
print("you picked densenet")
net = DenseNet(len(sirnas)).to('cuda')
elif (args.model_type == "kaggle"):
print("you picked kaggle")
net = ModelAndLoss(len(sirnas)).to('cuda')
elif (args.model_type == "multitask"):
print("you picked multitask")
net = MultitaskNet(len(sirnas)).to('cuda')
elif (args.model_type == "lr"):
print("you picked lr")
net = LogisticRegression(512 * 512 * 6, len(sirnas)).to('cuda')
elif (args.model_type == "cnn"):
print("you picked cnn")
net = CNN(len(sirnas)).to('cuda')
else:
print("invalid model type")
def get_epoch_number(depth,
growth_rate,
ensemble_size,
dataset,
batch_size=256,
num_epochs=120):
# get data set
train_set, _, small_inputs, num_classes, _ = data.get_dataset(
dataset, "./data/")
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=batch_size,
shuffle=True,
pin_memory=(torch.cuda.is_available()),
num_workers=0)
# single
single = [
DenseNet(growth_rate=growth_rate,
block_config=[(depth - 4) // 6 for _ in range(3)],
num_classes=num_classes,
small_inputs=True,
efficient=small_inputs,
compression=1.0)
]
# vertical
ensemble_depth, _ = get_ensemble_depth(depth, growth_rate, ensemble_size)
vertical = [
DenseNet(growth_rate=growth_rate,
block_config=[(ensemble_depth - 4) // 6 for _ in range(3)],
num_classes=num_classes,
small_inputs=small_inputs,
efficient=True,
compression=1.0)
] * ensemble_size
# horizontal
ensemble_growth_rate, _ = get_ensemble_growth_rate(depth, growth_rate,
ensemble_size)
horizontal = [
DenseNet(growth_rate=ensemble_growth_rate,
block_config=[(depth - 4) // 6 for _ in range(3)],
num_classes=num_classes,
small_inputs=small_inputs,
efficient=True,
compression=1.0)
] * ensemble_size
single_epoch_time = estimate_epoch_time(
single, train_loader) # ?just to warm the cache?
vertical_epoch_time = estimate_epoch_time(vertical, train_loader)
single_epoch_time = estimate_epoch_time(single, train_loader)
horizontal_epoch_time = estimate_epoch_time(horizontal, train_loader)
print("single: ", single_epoch_time)
print("vertical: ", vertical_epoch_time)
print("horizontal: ", horizontal_epoch_time)
max_epoch_time = max(single_epoch_time, vertical_epoch_time,
horizontal_epoch_time)
single_epochs = (max_epoch_time / single_epoch_time) * num_epochs
vertical_epochs = (max_epoch_time / vertical_epoch_time) * num_epochs
horizontal_epochs = (max_epoch_time / horizontal_epoch_time) * num_epochs
return single_epochs, vertical_epochs, horizontal_epochs
def main(args):
# Fix seeds
molgrid.set_random_seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
# Set CuDNN options for reproducibility
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Set up libmolgrid
e = molgrid.ExampleProvider(data_root=args.data_root,
balanced=True,
shuffle=True)
e.populate(args.train_file)
gmaker = molgrid.GridMaker()
dims = gmaker.grid_dimensions(e.num_types())
tensor_shape = (args.batch_size, ) + dims
# Construct input tensors
input_tensor = torch.zeros(tensor_shape,
dtype=torch.float32,
device='cuda')
float_labels = torch.zeros(args.batch_size, dtype=torch.float32)
# Initialise network - Two models currently available (see models.py for details)
if args.model == 'Ragoza':
model = Basic_CNN(dims).to('cuda')
elif args.model == 'Imrie':
model = DenseNet(dims, block_config=(4, 4, 4)).to('cuda')
else:
print("Please specify a valid architecture")
exit()
# Set weights for network
if args.weights:
model.load_state_dict(torch.load(args.weights))
print("Loaded model parameters")
else:
model.apply(weights_init)
print("Randomly initialised model parameters")
# Print number of parameters in model
print("Number of model params: %dK" %
(sum([x.nelement() for x in model.parameters()]) / 1000))
# Train network
# Construct optimizer
optimizer = optim.SGD(model.parameters(),
lr=args.base_lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = lr_scheduler.ExponentialLR(optimizer, args.anneal_rate)
print("Initial learning rate: %.6f" % scheduler.get_lr()[0])
# Train loop
losses = []
for it in range(1, args.iterations + 1):
# Load data
batch = e.next_batch(args.batch_size)
gmaker.forward(batch,
input_tensor,
random_rotation=args.rotate,
random_translation=args.translate)
batch.extract_label(0, float_labels)
labels = float_labels.long().to('cuda')
# Train
optimizer.zero_grad()
output = model(input_tensor)
loss = F.cross_entropy(output, labels)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), args.clip_gradients)
optimizer.step()
losses.append(float(loss))
# Anneal learning rate
if it % args.anneal_iter == 0:
scheduler.step()
print("Current iteration: %d, Annealing learning rate: %.6f" %
(it, scheduler.get_lr()[0]))
# Progress
if it % args.display_iter == 0:
print("Current iteration: %d, Loss: %.3f" %
(it, float(np.mean(losses[-args.display_iter:]))))
# Save model
if it % args.save_iter == 0:
print("Saving model after %d iterations." % it)
torch.save(
model.state_dict(),
args.save_dir + "/" + args.save_prefix + ".iter-" + str(it))
# Test model
if args.test_file != '' and it % args.test_iter == 0:
# Set to test mode
model.eval()
predictions = []
labs = []
e_test = molgrid.ExampleProvider(data_root=args.data_root,
balanced=False,
shuffle=False)
e_test.populate(args.test_file)
num_samples = e_test.size()
num_batches = -(-num_samples // args.batch_size)
for _ in range(num_batches):
# Load data
batch = e_test.next_batch(args.batch_size)
batch_predictions = []
batch.extract_label(0, float_labels)
labs.extend(list(float_labels.detach().cpu().numpy()))
for _ in range(args.num_rotate):
gmaker.forward(batch,
input_tensor,
random_rotation=args.rotate,
random_translation=0.0)
# Predict
output = F.softmax(model(input_tensor), dim=1)
batch_predictions.append(
list(output.detach().cpu().numpy()[:, 0]))
predictions.extend(list(np.mean(batch_predictions, axis=0)))
# Print performance
labs = labs[:num_samples]
predictions = predictions[:num_samples]
print("Current iter: %d, AUC: %.2f" %
(it, roc_auc_score(labs, predictions)),
flush=True)
# Set to train mode
model.train()
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
worker_init_fn=None,
**kwargs)
test_loader = torch.utils.data.DataLoader(globals()[args.dataset](
root=args.data, transform=test_transform, train=False),
batch_size=args.test_batch_size,
shuffle=False,
**kwargs)
num_classes = {"CIFAR10": 10, "CIFAR100": 100, "ImageNet": 1000}
input_size = args.dataset == 'ImageNet' and 224 or 32
model = DenseNet(num_init_features=args.num_init_features,
block_config=args.block_config,
compression=args.compression,
input_size=input_size,
bn_size=args.bn_size,
num_classes=num_classes[args.dataset],
efficient=True)
print(model)
if not os.path.isdir(args.checkpoints):
os.mkdir(args.checkpoints)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
if 'epoch' in checkpoint:
args.start_epoch = checkpoint['epoch'] + 1
def train_model(x_trn, x_val, config, num_classes, weights, device):
y_gr_val = x_val['grapheme_root']
y_vo_val = x_val['vowel_diacritic']
y_co_val = x_val['consonant_diacritic']
model_params = config.model_params
train_dataset = BengaliDataset(x_trn,
n_channels=model_params.n_channels,
img_size=config.img_size,
transforms=config.augmentation)
valid_dataset = BengaliDataset(x_val,
n_channels=model_params.n_channels,
img_size=config.img_size,
transforms=None)
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,
shuffle=True,
num_workers=3)
valid_loader = DataLoader(valid_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=3)
del train_dataset, valid_dataset
gc.collect()
if 'se_resnext' in model_params.model_name:
model = SeNet(model_name=model_params.model_name,
n_channels=model_params.n_channels,
n_classes=model_params.n_classes,
pretrained=model_params.pretrained).to(device)
elif 'resnetd' in model_params.model_name:
model = ResNetD(model_name=model_params.model_name,
n_channels=model_params.n_channels,
n_classes=model_params.n_classes).to(device)
elif 'resne' in model_params.model_name:
model = ResNet(model_name=model_params.model_name,
n_channels=model_params.n_channels,
n_classes=model_params.n_classes,
pretrained=model_params.pretrained).to(device)
elif 'densenet' in model_params.model_name:
model = DenseNet(model_name=model_params.model_name,
n_channels=model_params.n_channels,
n_classes=model_params.n_classes,
pretrained=model_params.pretrained).to(device)
elif 'efficient' in model_params.model_name:
model = ENet(model_name=model_params.model_name,
n_channels=model_params.n_channels,
n_classes=model_params.n_classes,
pretrained=model_params.pretrained).to(device)
if config.model_state_fname is not None:
model.load_state_dict(
torch.load(f'../logs/{config.model_state_fname}/weight_best.pt'))
# relu_replace(model)
# bn_replace(model)
weights_gr = torch.from_numpy(weights['grapheme_root']).cuda()
weights_vo = torch.from_numpy(weights['vowel_diacritic']).cuda()
weights_co = torch.from_numpy(weights['consonant_diacritic']).cuda()
if config.loss == 'CrossEntropyLoss':
# criterion_gr = nn.CrossEntropyLoss(weight=weights_gr)
# criterion_vo = nn.CrossEntropyLoss(weight=weights_vo)
# criterion_co = nn.CrossEntropyLoss(weight=weights_co)
criterion_gr = nn.CrossEntropyLoss()
criterion_vo = nn.CrossEntropyLoss()
criterion_co = nn.CrossEntropyLoss()
elif config.loss == 'SmoothCrossEntropyLoss':
criterion_gr = SmoothCrossEntropyLoss()
criterion_vo = SmoothCrossEntropyLoss()
criterion_co = SmoothCrossEntropyLoss()
elif config.loss == 'FocalLoss':
criterion_gr = FocalLoss()
criterion_vo = FocalLoss()
criterion_co = FocalLoss()
elif config.loss == 'ClassBalancedLoss':
criterion_gr = ClassBalancedLoss(samples_per_cls=weights_gr,
no_of_classes=num_classes[0],
loss_type='focal',
beta=0.999,
gamma=2.0)
criterion_vo = ClassBalancedLoss(samples_per_cls=weights_vo,
no_of_classes=num_classes[1],
loss_type='focal',
beta=0.999,
gamma=2.0)
criterion_co = ClassBalancedLoss(samples_per_cls=weights_co,
no_of_classes=num_classes[2],
loss_type='focal',
beta=0.999,
gamma=2.0)
elif config.loss == 'OhemLoss':
criterion_gr = OhemLoss(rate=1.0)
criterion_vo = OhemLoss(rate=1.0)
criterion_co = OhemLoss(rate=1.0)
if config.optimizer.type == 'Adam':
optimizer = Adam(params=model.parameters(),
lr=config.optimizer.lr,
amsgrad=False,
weight_decay=1e-4)
elif config.optimizer.type == 'SGD':
optimizer = SGD(params=model.parameters(),
lr=config.optimizer.lr,
momentum=0.9,
weight_decay=1e-4,
nesterov=True)
scheduler_flg = False
if config.scheduler.type == 'cosine':
scheduler_flg = True
scheduler = CosineAnnealingLR(optimizer,
T_max=config.scheduler.t_max,
eta_min=config.scheduler.eta_min)
elif config.scheduler.type == 'cosine-warmup':
scheduler_flg = True
scheduler = CosineAnnealingWarmUpRestarts(
optimizer,
T_0=config.scheduler.t_0,
T_mult=config.scheduler.t_mult,
eta_max=config.scheduler.eta_max,
T_up=config.scheduler.t_up,
gamma=config.scheduler.gamma)
elif config.scheduler.type == 'step':
scheduler_flg = True
scheduler = StepLR(optimizer,
step_size=config.scheduler.step_size,
gamma=config.scheduler.gamma)
elif config.scheduler.type == 'reduce':
scheduler_flg = True
scheduler = ReduceLROnPlateau(optimizer,
factor=config.scheduler.factor,
patience=config.scheduler.patience,
min_lr=config.scheduler.min_lr)
best_epoch = -1
best_val_score = -np.inf
mb = master_bar(range(config.epochs))
train_loss_list = []
val_loss_list = []
val_score_list = []
counter = 0
for epoch in mb:
start_time = time.time()
model.train()
avg_loss = 0.
for images, labels_gr, labels_vo, labels_co in progress_bar(
train_loader, parent=mb):
images = Variable(images).to(device)
labels_gr = Variable(labels_gr).to(device)
labels_vo = Variable(labels_vo).to(device)
labels_co = Variable(labels_co).to(device)
if config.loss == 'OhemLoss':
if epoch < config.epochs * 0.2:
new_rate = 1.0
elif epoch < config.epochs * 0.4:
new_rate = 0.8
elif epoch < config.epochs * 0.6:
new_rate = 0.75
elif epoch < config.epochs * 0.8:
new_rate = 0.7
else:
new_rate = 0.6
criterion_gr.update_rate(new_rate)
criterion_vo.update_rate(new_rate)
criterion_co.update_rate(new_rate)
r = np.random.rand()
mix_params = config.augmentation.mix_params
if r < mix_params.mixup:
images, targets = mixup(images, labels_gr, labels_vo,
labels_co, 1.0)
preds_gr, preds_vo, preds_co = model(images)
loss = mixup_criterion(preds_gr, preds_vo, preds_co, targets,
criterion_gr, criterion_vo,
criterion_co)
elif r < (mix_params.mixup + mix_params.cutmix):
images, targets = cutmix(images, labels_gr, labels_vo,
labels_co, 1.0)
preds_gr, preds_vo, preds_co = model(images)
loss = cutmix_criterion(preds_gr, preds_vo, preds_co, targets,
criterion_gr, criterion_vo,
criterion_co)
else:
preds_gr, preds_vo, preds_co = model(images.float())
loss = criterion_gr(preds_gr, labels_gr) \
+ criterion_vo(preds_vo, labels_vo) \
+ criterion_co(preds_co, labels_co)
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_loss += loss.item() / len(train_loader)
train_loss_list.append(avg_loss)
model.eval()
valid_gr_preds = np.zeros((len(valid_loader.dataset), num_classes[0]))
valid_vo_preds = np.zeros((len(valid_loader.dataset), num_classes[1]))
valid_co_preds = np.zeros((len(valid_loader.dataset), num_classes[2]))
avg_val_loss = 0.
for i, (images, labels_gr, labels_vo,
labels_co) in enumerate(valid_loader):
images = Variable(images).to(device)
labels_gr = Variable(labels_gr).to(device)
labels_vo = Variable(labels_vo).to(device)
labels_co = Variable(labels_co).to(device)
preds_gr, preds_vo, preds_co = model(images.float())
loss_gr = criterion_gr(preds_gr, labels_gr)
loss_vo = criterion_vo(preds_vo, labels_vo)
loss_co = criterion_co(preds_co, labels_co)
valid_gr_preds[i * config.batch_size:(
i + 1) * config.batch_size] = preds_gr.cpu().detach().numpy()
valid_vo_preds[i * config.batch_size:(
i + 1) * config.batch_size] = preds_vo.cpu().detach().numpy()
valid_co_preds[i * config.batch_size:(
i + 1) * config.batch_size] = preds_co.cpu().detach().numpy()
avg_val_loss += (loss_gr.item() + loss_vo.item() +
loss_co.item()) / len(valid_loader)
recall_gr = recall_score(y_gr_val,
np.argmax(valid_gr_preds, axis=1),
average='macro')
recall_vo = recall_score(y_vo_val,
np.argmax(valid_vo_preds, axis=1),
average='macro')
recall_co = recall_score(y_co_val,
np.argmax(valid_co_preds, axis=1),
average='macro')
val_score = np.average([recall_gr, recall_vo, recall_co],
weights=[2, 1, 1])
val_loss_list.append(avg_val_loss)
val_score_list.append(val_score)
if scheduler_flg and config.scheduler.type != 'reduce':
scheduler.step()
elif scheduler_flg and config.scheduler.type == 'reduce':
scheduler.step(avg_val_loss)
elapsed = time.time() - start_time
mb.write(
f'Epoch {epoch+1} - avg_train_loss: {avg_loss:.4f} avg_val_loss: {avg_val_loss:.4f} val_score: {val_score:.4f} val_gr_score: {recall_gr:.4f} val_vo_score: {recall_vo:.4f} val_co_score: {recall_co:.4f} time: {elapsed:.0f}s'
)
logging.debug(
f'Epoch {epoch+1} - avg_train_loss: {avg_loss:.4f} avg_val_loss: {avg_val_loss:.4f} val_score: {val_score:.4f} val_gr_score: {recall_gr:.4f} val_vo_score: {recall_vo:.4f} val_co_score: {recall_co:.4f} time: {elapsed:.0f}s'
)
if best_val_score < val_score:
best_epoch = epoch + 1
best_val_score = val_score
best_recall_gr = recall_gr
best_recall_vo = recall_vo
best_recall_co = recall_co
best_valid_gr_preds = valid_gr_preds
best_valid_vo_preds = valid_vo_preds
best_valid_co_preds = valid_co_preds
best_model = model.state_dict()
counter = 0
counter += 1
if counter == config.early_stopping:
break
print('\n\n===================================\n')
print(f'CV: {best_val_score}\n')
print(f'BEST EPOCH: {best_epoch}')
print(f'BEST RECALL GR: {best_recall_gr}')
print(f'BEST RECALL VO: {best_recall_vo}')
print(f'BEST RECALL CO: {best_recall_co}')
logging.debug(f'\n\nCV: {best_val_score}\n')
logging.debug(f'BEST EPOCH: {best_epoch}')
logging.debug(f'BEST RECALL GR: {best_recall_gr}')
logging.debug(f'BEST RECALL VO: {best_recall_vo}')
logging.debug(f'BEST RECALL CO: {best_recall_co}\n\n')
print('\n===================================\n\n')
return best_model, [
best_valid_gr_preds, best_valid_vo_preds, best_valid_co_preds
], best_val_score, train_loss_list, val_loss_list, val_score_list
def demo(data, save, depth=100, growth_rate=12, efficient=True, valid_size=5000,
n_epochs=300, batch_size=64, seed=None,
conv_type="SPECTRAL_PARAM"):
"""
A demo to show off training of efficient DenseNets.
Trains and evaluates a DenseNet-BC on CIFAR-10.
Args:
data (str) - path to directory where data should be loaded from/downloaded
(default $DATA_DIR)
save (str) - path to save the model to (default /tmp)
depth (int) - depth of the network (number of convolution layers) (default 100)
growth_rate (int) - number of features added per DenseNet layer (default 12)
efficient (bool) - use the memory efficient implementation? (default True)
valid_size (int) - size of validation set
n_epochs (int) - number of epochs for training (default 300)
batch_size (int) - size of minibatch (default 256)
seed (int) - manually set the random seed (default None)
conv_type (str) - the type of applied convolution: SPECTRAL_PARAM or
STANDARD
"""
conv_type = ConvType[conv_type]
# Get densenet configuration
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
# Data transforms
mean = [0.5071, 0.4867, 0.4408]
stdv = [0.2675, 0.2565, 0.2761]
train_transforms = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
# Datasets
train_set = datasets.CIFAR10(data, train=True, transform=train_transforms,
download=True)
test_set = datasets.CIFAR10(data, train=False, transform=test_transforms,
download=True)
# Models
model = DenseNet(
growth_rate=growth_rate,
block_config=block_config,
num_classes=10,
small_inputs=True,
efficient=efficient,
conv_type=conv_type
)
print(model)
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Train the model
train(model=model, train_set=train_set, test_set=test_set, save=save,
conv_type=conv_type, valid_size=valid_size, n_epochs=n_epochs,
batch_size=batch_size, seed=seed)
print('Done!')
# some default params dataset/architecture related
train_params = train_params_cifar
print("Params:")
for k, v in model_params.items():
print("\t%s: %s" % (k, v))
print("Train params:")
for k, v in train_params.items():
print("\t%s: %s" % (k, v))
model_params['use_Y'] = False
print("Prepare training data...")
data_provider = get_data_provider_by_name(model_params['dataset'], train_params)
print("Initialize the model..")
tf.reset_default_graph()
model = DenseNet(data_provider=data_provider, **model_params)
print("Loading trained model")
model.load_model()
print("Data provider test images: ", data_provider.test.num_examples)
print("Testing...")
loss, accuracy = model.test(data_provider.test, batch_size=30)
print("mean cross_entropy: %f, mean accuracy: %f" % (loss, accuracy))
def labels_to_one_hot(labels, n_classes=43+1):
"""Convert 1D array of labels to one hot representation
Args:
def demo(data='./mydata',
save='./save',
depth=100,
growth_rate=12,
efficient=True,
valid_size=None,
n_epochs=300,
batch_size=64,
seed=None):
"""
A demo to show off training of efficient DenseNets.
Trains and evaluates a DenseNet-BC on CIFAR-10.
Args:
data (str) - path to directory where data should be loaded from/downloaded
(default $DATA_DIR)
save (str) - path to save the model to (default /tmp)
depth (int) - depth of the network (number of convolution layers) (default 40)
growth_rate (int) - number of features added per DenseNet layer (default 12)
efficient (bool) - use the memory efficient implementation? (default True)
valid_size (int) - size of validation set
n_epochs (int) - number of epochs for training (default 300)
batch_size (int) - size of minibatch (default 256)
seed (int) - manually set the random seed (default None)
"""
train_txt_dir = os.path.join(data, 'train.txt')
valid_txt_dir = os.path.join(data, 'valid.txt')
# Get densenet configuration
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 8 for _ in range(3)]
# Data transforms
mean = [0.5071, 0.4867, 0.4408]
stdv = [0.2675, 0.2565, 0.2761]
train_transforms = transforms.Compose([
# transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
# Datasets
# train_set = datasets.CIFAR10(data, train=True, transform=train_transforms, download=True)
train_set = MyDataset(train_txt_dir, transform=train_transforms)
test_set = MyDataset(valid_txt_dir, transform=test_transforms)
if valid_size:
valid_set = datasets.CIFAR10(data,
train=True,
transform=test_transforms)
indices = torch.randperm(len(train_set))
train_indices = indices[:len(indices) - valid_size]
valid_indices = indices[len(indices) - valid_size:]
train_set = torch.utils.data.Subset(train_set, train_indices)
valid_set = torch.utils.data.Subset(valid_set, valid_indices)
else:
valid_set = None
# Models
model = DenseNet(
growth_rate=growth_rate,
block_config=block_config,
num_classes=65,
small_inputs=True,
efficient=efficient,
)
print(model)
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Train the model
train(model=model,
train_set=train_set,
valid_set=valid_set,
test_set=test_set,
save=save,
n_epochs=n_epochs,
batch_size=batch_size,
seed=seed)
print('Done!')
def demo(data, save, depth=100, growth_rate=12, efficient=True, valid_size=5000,
n_epochs=300, batch_size=64, seed=None):
"""
A demo to show off training of efficient DenseNets.
Trains and evaluates a DenseNet-BC on CIFAR-10.
Args:
data (str) - path to directory where data should be loaded from/downloaded
(default $DATA_DIR)
save (str) - path to save the model to (default /tmp)
depth (int) - depth of the network (number of convolution layers) (default 40)
growth_rate (int) - number of features added per DenseNet layer (default 12)
efficient (bool) - use the memory efficient implementation? (default True)
valid_size (int) - size of validation set
n_epochs (int) - number of epochs for training (default 300)
batch_size (int) - size of minibatch (default 256)
seed (int) - manually set the random seed (default None)
"""
# Get densenet configuration
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
# Data transforms
mean=[0.49139968 0.48215841 0.44653091]
stdv= [0.24703223 0.24348513 0.26158784]
train_transforms = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
test_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=stdv),
])
# Datasets
train_set = datasets.CIFAR10(data, train=True, transform=train_transforms, download=True)
test_set = datasets.CIFAR10(data, train=False, transform=test_transforms, download=False)
if valid_size:
valid_set = datasets.CIFAR10(data, train=True, transform=test_transforms)
indices = torch.randperm(len(train_set))
train_indices = indices[:len(indices) - valid_size]
valid_indices = indices[len(indices) - valid_size:]
train_set = torch.utils.data.Subset(train_set, train_indices)
valid_set = torch.utils.data.Subset(valid_set, valid_indices)
else:
valid_set = None
# Models
model = DenseNet(
growth_rate=growth_rate,
block_config=block_config,
num_init_features=growth_rate*2,
num_classes=10,
small_inputs=True,
efficient=efficient,
)
print(model)
# Print number of parameters
num_params = sum(p.numel() for p in model.parameters())
print("Total parameters: ", num_params)
# Make save directory
if not os.path.exists(save):
os.makedirs(save)
if not os.path.isdir(save):
raise Exception('%s is not a dir' % save)
# Train the model
train(model=model, train_set=train_set, valid_set=valid_set, test_set=test_set, save=save,
n_epochs=n_epochs, batch_size=batch_size, seed=seed)
print('Done!')
# -*- encoding:utf-8 -*-
from models import DenseNet
if __name__ == '__main__':
cnn = DenseNet(num_blocks=1, theta=0.5)
cnn.train()
# some default params dataset/architecture related
train_params = get_train_params_by_name(args.dataset)
model_params['data_augmentation'] = train_params['data_augmentation']
model_params['use_Y'] = train_params['use_Y']
print("Params:")
for k, v in model_params.items():
print("\t%s: %s" % (k, v))
print("Train params:")
for k, v in train_params.items():
print("\t%s: %s" % (k, v))
print("Prepare training data...")
data_provider = get_data_provider_by_name(args.dataset, train_params)
print("Initialize the model..")
model = DenseNet(data_provider=data_provider, **model_params)
import tensorflow as tf
with tf.Session() as sess:
# `sess.graph` provides access to the graph used in a `tf.Session`.
writer = tf.summary.FileWriter("/tmp/log/...", sess.graph)
writer.close()
if args.train:
print("Data provider train images: ", data_provider.train.num_examples)
model.train_all_epochs(train_params)
if args.test:
if not args.train:
model.load_model()
print("Data provider test images: ", data_provider.test.num_examples)
print("Testing...")
def demo(data, save, depth=40, growth_rate=12, batch_size=256):
"""
Applies temperature scaling to a trained model.
Takes a pretrained DenseNet-CIFAR100 model, and a validation set
(parameterized by indices on train set).
Applies temperature scaling, and saves a temperature scaled version.
NB: the "save" parameter references a DIRECTORY, not a file.
In that directory, there should be two files:
- model.pth (model state dict)
- valid_indices.pth (a list of indices corresponding to the validation set).
data (str) - path to directory where data should be loaded from/downloaded
save (str) - directory with necessary files (see above)
"""
# Load model state dict
model_filename = os.path.join(save, 'model.pth')
if not os.path.exists(model_filename):
raise RuntimeError('Cannot find file %s to load' % model_filename)
state_dict = torch.load(model_filename)
# Load validation indices
valid_indices_filename = os.path.join(save, 'valid_indices.pth')
if not os.path.exists(valid_indices_filename):
raise RuntimeError('Cannot find file %s to load' %
valid_indices_filename)
valid_indices = torch.load(valid_indices_filename)
# Regenerate validation set loader
mean = [0.5071, 0.4867, 0.4408]
stdv = [0.2675, 0.2565, 0.2761]
test_transforms = tv.transforms.Compose([
tv.transforms.ToTensor(),
tv.transforms.Normalize(mean=mean, std=stdv),
])
valid_set = tv.datasets.CIFAR100(data,
train=True,
transform=test_transforms,
download=True)
valid_loader = torch.utils.data.DataLoader(
valid_set,
pin_memory=True,
batch_size=batch_size,
sampler=SubsetRandomSampler(valid_indices))
# Load original model
if (depth - 4) % 3:
raise Exception('Invalid depth')
block_config = [(depth - 4) // 6 for _ in range(3)]
orig_model = DenseNet(growth_rate=growth_rate,
block_config=block_config,
num_classes=100).cuda()
orig_model.load_state_dict(state_dict)
# Now we're going to wrap the model with a decorator that adds temperature scaling
model = ModelWithTemperature(orig_model)
# Tune the model temperature, and save the results
model.set_temperature(valid_loader)
model_filename = os.path.join(save, 'model_with_temperature.pth')
torch.save(model.state_dict(), model_filename)
print('Temperature scaled model sved to %s' % model_filename)
print('Done!')
t_backward.append(t_bp)
# free memory
del model
return t_forward, t_backward
use_cuda = True
multigpus = True
# set cudnn backend to benchmark config
cudnn.benchmark = True
# instantiate the models
densenet = DenseNet(efficient=False)
densnet_effi = DenseNet(efficient=True)
# build dummy variables to input and output
x = torch.randn(128, 3, 32, 32)
y = torch.randn(128, 100)
if use_cuda:
densenet = densenet.cuda()
densnet_effi = densnet_effi.cuda()
x = x.cuda()
y = y.cuda()
if multigpus:
densenet = nn.DataParallel(densenet, device_ids=[0, 1])
densnet_effi = nn.DataParallel(densnet_effi, device_ids=[0, 1])
# build the dict to iterate over
architectures = {'densenet': densenet, 'densenet-effi': densnet_effi}
if (args.eval_set == 'holdout'):
print('Evaluation Selection: U2OS (holdout)\t\t\tSize:', len(U2OS_test_data))
test_loader = U2OS_test_loader
elif (args.eval_set == 'combined'):
print('Evaluation Selection: HEPG2, HUVEC, RPE\t\t\tSize:', len(combined_test_data))
test_loader = combined_test_loader
else:
print('Evaluation Selection: INVALID')
combined_test_loader = U2OS_test_loader
net = None
if (args.model_type == "densenet"):
print("Model: densenet")
net = DenseNet(74).to('cuda')
elif (args.model_type == "kaggle"):
print("Model: kaggle")
net = ModelAndLoss(74).to('cuda')
elif(args.model_type == "multitask"):
print("Model: multitask")
net = MultitaskNet(74).to('cuda')
elif(args.model_type == "lr"):
print("Model: lr")
net = LogisticRegression(512*512*6, 74).to('cuda')
elif(args.model_type == "cnn"):
print("Model: cnn")
net = CNN(74).to('cuda')
else:
print("invalid model type")
# some default params dataset/architecture related
train_params = train_params_cifar
print("Params:")
for k, v in model_params.items():
print("\t%s: %s" % (k, v))
print("Train params:")
for k, v in train_params.items():
print("\t%s: %s" % (k, v))
model_params['use_Y'] = True
print("Prepare training data...")
data_provider = get_data_provider_by_name(model_params['dataset'], train_params)
print("Initialize the model..")
tf.reset_default_graph()
model = DenseNet(data_provider=data_provider, **model_params)
print("Loading trained model")
model.load_model()
print("Data provider test images: ", data_provider.test.num_examples)
print("Testing...")
loss, accuracy = model.test(data_provider.test, batch_size=30)
print("mean cross_entropy: %f, mean accuracy: %f" % (loss, accuracy))
total_prediction, y_test = model.predictions_test(data_provider.test, batch_size=100)
# Plotting incorrect examples
incorrectlist = []
for i in range(len(total_prediction)):
#if not correctness(y_test[i],total_prediction[i]):
for j in range(len(y_test[i])):
