def main():
train_loader, val_loader = create_train_val_dataloaders(
args.train_data_fp, batch_size=args.batch_size)
model = DenseNet(drop_prob=0)
if use_cuda:
model = model.cuda()
train(model, train_loader, val_loader)
def get_model(name, config):
if name == "densenet121":
return DenseNet(config.num_classes, config.densenet_weights, config)
elif name == "ResNet50":
return ResNet50(config.num_classes, config.resnet_weights, config)
else:
raise NotImplementedError
def main():
# read csv files for driving log
lines = []
with open('./data/data/driving_log.csv') as csvfile:
reader = csv.reader(csvfile)
for line in reader:
lines.append(line)
correction = 0.2 # This is a parameter to tune
current_path = './data/data/'
# modify the image path
images = []
measurements = []
for line in lines[1:]:
img_centre = line[0]
img_left = line[1][1:] # get rid of the space at the beginning
img_right = line[2][1:] # get rid of the space at the beginning
# create adjusted steering meaurements for the side camera iamges
steering_centre = float(line[3])
for num, img in enumerate([img_centre, img_left, img_right]):
image = cv2.imread(current_path + img)
images.append(image)
images.append(cv2.flip(image, 1))
if num == 1:
steering = steering_centre + correction
elif num == 2:
steering = steering_centre - correction
elif num == 0:
steering = steering_centre
measurements.append(steering)
measurements.append(steering * (-1.0))
X_train = np.array(images)
y_train = np.array(measurements)
# Load the model
model = DenseNet(input_shape=(160, 320, 3),
depth=3 * 3 + 4,
nb_dense_block=3,
growth_rate=12,
bottleneck=True,
reduction=0.5,
weights=None,
cropping=((70, 25), (0, 0)),
norm_lambda=True,
activation=None,
classes=1)
model.compile(loss='mse', optimizer='adam')
model.fit(X_train, y_train, validation_split=0.2, shuffle=True, epochs=5)
model.save('./models/model_densenet.h5')
def main():
exp_name = f'baseline_{now()}'
device, log, result_dir = setup(exp_name, conf)
train_df = load_csv(conf.train_csv)
if conf.npy:
train_images = np.load(conf.train_images)
else:
train_images = pd.read_parquet(conf.train_images)
test_df = load_csv(conf.test_csv)
if conf.npy:
test_images = np.load(conf.test_images)
else:
test_images = pd.read_parquet(conf.test_images)
log.info('done')
for i in range(5):
if i != conf.fold:
continue
if "resnet" in conf.arch or "resnext" in conf.arch:
model_ft = ResNet(conf,
arch_name=conf.arch,
input_size=conf.image_size)
model_ft.load_state_dict(
torch.load("result/baseline_2020_03_21_13_01_08/model_0.pkl"))
elif "densenet" in conf.arch:
model_ft = DenseNet(conf,
arch_name=conf.arch,
input_size=conf.image_size)
elif "efficientnet" in conf.arch:
model_ft = EfficientNet(conf, arch_name=conf.arch)
criterion = [
nn.CrossEntropyLoss(reduction="none"),
nn.CrossEntropyLoss(reduction="none"),
nn.CrossEntropyLoss(reduction="none")
]
criterion = [c.to(device) for c in criterion]
model_ft, val_preds = train_model(train_df,
train_images,
test_df,
test_images,
model_ft,
criterion,
log,
device,
result_dir,
fold=i,
num_epoch=conf.num_epoch)
torch.save(model_ft.state_dict(), result_dir / f'model_{i}.pkl')
np.save(result_dir / f'val_preds_{i}.npy', val_preds)
def get_new_model(tmp_scale = True, num_classes = args.num_classes):
if args.model == 'resnet18':
return ResNet18(tmp_scale = tmp_scale, num_classes = num_classes)
elif args.model == 'resnet50':
return ResNet50(tmp_scale = tmp_scale, num_classes = num_classes)
elif args.model == 'resnet101':
return ResNet101(tmp_scale = tmp_scale, num_classes = num_classes)
elif args.model == 'inceptionv4':
return inceptionv4(tmp_scale = tmp_scale, num_classes = num_classes)
elif args.model == 'densenet':
return DenseNet(tmp_scale = tmp_scale)
def get_model(args):
if args.model == 'simplenet':
model = SimpleNet()
elif args.model == 'densenet':
model = DenseNet(block_lst=(4, 4, 4, 4))
else:
raise ValueError
if args.cuda:
model.cuda()
return model
def __init__(self, num_classes=10, combine_at='prepool', join_how='concat', multitask=True,
drop_view_prob=0.0, architecture='densenet121'):
super(MultiViewCNN, self).__init__()
self.multitask = multitask
self.drop_view_prob = [1 - drop_view_prob, drop_view_prob / 2., drop_view_prob / 2.]
if multitask:
# Never drop view when multitask
# Use curriculum learning on loss instead
self.drop_view_prob = [1., 0., 0.]
self.combine_at = combine_at
self.join_how = join_how
params = {'in_channels': 1, 'num_classes': num_classes, **get_densenet_params(architecture)}
self.frontal_model = DenseNet(**params)
self.lateral_model = DenseNet(**params)
self.joint_in_features = self.frontal_model.classifier.in_features
if join_how == 'concat':
self.joint_in_features *= 2
self.classifier = nn.Linear(in_features=self.joint_in_features, out_features=num_classes)
def tiny_densenet121(memory_efficient=False, **kwargs):
r"""Tiny-Densenet-121 model from
"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>
The tiny-version has been specifically designed for neuroimaging data. It is 10X smaller than DenseNet.
Args:
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
"""
return DenseNet(16, (6, 12, 16),
64,
memory_efficient=memory_efficient,
**kwargs)
def main():
config = Config()
create_dirs([config.summary_dir, config.checkpoint_dir])
sess = tf.Session()
train_data = Dataset(config.root,
config.train_image_file,
config.type,
transform=Augmentaton(size=config.resize,
mean=config.means[config.type],
std=config.stds[config.type]),
max_samples=None)
valid_data = Dataset(config.root,
config.valid_image_file,
config.type,
transform=Augmentaton(size=config.resize,
mean=config.means[config.type],
std=config.stds[config.type]),
max_samples=None)
train_data_loader = DataLoader(train_data)
valid_data_loader = DataLoader(valid_data)
model = DenseNet(config)
logger = Logger(sess, config)
trainer = DenseNetTrainer(sess, model, train_data_loader,
valid_data_loader, config, logger)
model.load(sess)
if config.phase == "train":
trainer.train()
elif config.phase == "test":
trainer.test("prediction.csv")
def evaluate():
config = Config()
valid_data = Dataset(config.root,
valid_image_paths,
config.type,
transform=Augmentaton(size=config.resize,
mean=config.means[config.type],
std=config.stds[config.type]),
max_samples=10)
valid_data_loader = DataLoader(valid_data)
sess = tf.Session()
model = DenseNet(config)
logger = Logger(sess, config)
trainer = DenseNetTrainer(sess, model, valid_data_loader,
valid_data_loader, config, logger)
model.load(sess)
if config.phase == "train":
trainer.train()
elif config.phase == "test":
trainer.test(output_prediction_path)
def main():
dataset = Cifar10()
# dataset = Cifar100()
# dataset = Mnist()
# model = AlexNet()
# model = VGG()
# model = GoogLeNet()
# model = ResNet(model_type="101")
# model = InceptionV3()
model = DenseNet(model_type="201")
# training
trainer = ClfTrainer(model, dataset)
trainer.run_training(epochs, batch_size, learning_rate, './cifar10-ckpt')
def __init__(self, pretrained=False):
super(FECNet, self).__init__()
growth_rate = 64
depth = 100
block_config = [5]
efficient = True
self.Inc = InceptionResnetV1(pretrained='vggface2', device='cuda').eval()
for param in self.Inc.parameters():
param.requires_grad = False
self.dense = DenseNet(growth_rate=growth_rate,
block_config=block_config,
num_classes=16,
small_inputs=True,
efficient=efficient,
num_init_features=512).cuda()
if (pretrained):
load_weights(self)
def get_model(args):
if args.model == 'simplenet':
model = SimpleNet()
elif args.model == 'densenet':
model = DenseNet(block_lst=(4, 4, 4, 4))
else:
raise ValueError
model_path = os.path.join(args.model_path, args.model + '/')
os.makedirs(model_path, exist_ok=True)
lossf = nn.BCEWithLogitsLoss(size_average=False)
optimizer = optim.Adam(model.param_options(), weight_decay=args.l2)
if args.model == 'simplenet':
lrsched = LRSchedNone(optimizer.param_groups, 0.5e-3)
elif args.model == 'densenet':
lrsched = LRSchedNone(optimizer.param_groups, 1e-4)
if args.cuda:
model.cuda()
lossf.cuda()
return model, lossf, optimizer, lrsched, model_path
if model_config['use_depth_sep_conv']:
args.path += '#depth_conv'
else:
args.path += '#groups3x3=%d' % model_config['groups_3x3']
args.path += '#' + os.uname()[1]
# print configurations
print('Run config:')
for k, v in run_config.get_config().items():
print('\t%s: %s' % (k, v))
print('Network config:')
for k, v in model_config.items():
print('\t%s: %s' % (k, v))
model = DenseNet.set_standard_net(data_shape=data_shape,
n_classes=n_classes,
**model_config)
run_manager = RunManger(args.path,
model,
run_config,
out_log=True,
resume=True)
run_manager.save_config()
if args.train:
run_manager.train()
loss, acc = run_manager.validate()
test_log = 'test_loss: %f\t test_acc: %f' % (loss, acc)
run_manager.write_log(test_log, prefix='test')
json.dump({
patience = 50
base_path = '../trained_models/emotion_models/'
# data generator
data_generator = ImageDataGenerator(featurewise_center=False,
featurewise_std_normalization=False,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=.1,
horizontal_flip=True)
# model parameters/compilation
model = DenseNet(classes=num_classes,
input_shape=(64, 64, 1),
depth=40,
growth_rate=12,
bottleneck=True,
reduction=0.5)
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
datasets = ['fer2013']
for dataset_name in datasets:
print('Training dataset:', dataset_name)
# callbacks
log_file_path = base_path + dataset_name + '_emotion_training.log'
csv_logger = CSVLogger(log_file_path, append=False)
early_stop = EarlyStopping('val_loss', patience=patience)
def main(GPU=True,
epochs=20,
lr=0.0001,
momentum=0.9,
weight_decay=1e-4,
batch_size=32,
start_epochs=0,
model_name="msrn",
optimizer="adam",
naive=False):
assert model_name.lower() in [
"msrn", "densenet", "densenet64"
], "Model not available only support msrn and densenet"
discriminator = None
model_name = model_name.lower()
dense = True if model_name == "densenet" else False
# loading all of the data
training_data_loader = DataLoader(dataset=DataSets(dense=dense),
batch_size=batch_size)
testing_data_loader = DataLoader(dataset=DataSets(dense=dense,
dataset='test'),
batch_size=5)
# validation_data_loader = DataLoader(
# dataset=DataSets(dataset='val'),
# batch_size=5)
print("==================================================")
# checking for model type
print("Model: " + model_name + " with loss: ", end="")
if model_name == "msrn":
model = MSRN()
criterion = nn.L1Loss(reduction='elementwise_mean')
print(" L1 loss")
elif model_name == "densenet64":
model = DenseNet()
discriminator = MSRN()
criterion = nn.functional.nll_loss
elif model_name == "densenet":
model = DenseNet()
# this can be tested with cross entropy
# criterion = nn.CrossEntropyLoss
criterion = nn.functional.nll_loss
print(" negative Log loss")
else:
raise ValueError(
"Invalid model_name not support {}".format(model_name))
# check for GPU support
print("Using GPU: " + str(GPU))
if GPU:
model = nn.DataParallel(model).cuda()
if model_name == "msrn":
criterion = criterion.cuda()
else:
model = model.cpu()
print("Optimizer: " + optimizer + " with lr: " + str(lr))
# setting up optimizer
if optimizer == "adam":
optimizer = optim.Adam(model.parameters(),
lr=lr,
weight_decay=weight_decay)
elif optimizer == "sgd":
# TODO add momentum flag
optimizer = optim.SGD(model.parameters(),
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
elif optimizer == "rmsprop":
optimizer = optim.RMSprop(model.parameters(),
lr=lr,
weight_decay=weight_decay)
else:
raise ValueError("Not supported Loss function")
print("==================================================")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model_name == 'msrn':
MSRN().to(device).summary()
elif model_name == 'densenet64':
DenseNet(upscale=2).to(device).summary()
else:
DenseNet().to(device).summary()
print("==================================================")
log_folder = 'Logs/' + model_name
# Loggering the training loss
if not os.path.isdir(log_folder):
os.makedirs(log_folder)
model.name = model_name
append_write = 'w' if os.path.exists(log_folder) else 'a'
train_log = open(os.path.join(log_folder, 'train.csv'), append_write)
test_log = open(os.path.join(log_folder, 'test.csv'), append_write)
val_log = open(os.path.join(log_folder, 'val.csv'), append_write)
if start_epochs > 0:
model = load_model(model, model_name, start_epochs + 1)
# Training in numbers of epochs
for epoch in range(start_epochs, epochs):
train(training_data_loader, optimizer, model, criterion, epoch,
train_log, GPU, discriminator if discriminator else None,
naive) # if running on 64`
# densenet
print('testing the model')
if model_name == "densenet":
test_dense(testing_data_loader, optimizer, model, criterion, epoch,
test_log, GPU)
else:
test_msrn(testing_data_loader,
optimizer,
model,
criterion,
epoch,
test_log,
GPU,
discriminator=discriminator if discriminator else None)
save_checkpoint(model, epoch, model_name)
train_log.close()
test_log.close()
val_log.close()
helper.compute_rdp()
if helper.params['dataset'] == 'cifar10':
num_classes = 10
elif helper.params['dataset'] == 'cifar100':
num_classes = 100
elif helper.params['dataset'] == 'inat':
num_classes = len(helper.labels)
logger.info('num class: ', num_classes)
elif helper.params['dataset'] == 'dif':
num_classes = len(helper.labels)
else:
num_classes = 10
reseed(5)
if helper.params['model'] == 'densenet':
net = DenseNet(num_classes=num_classes,
depth=helper.params['densenet_depth'])
elif helper.params['model'] == 'resnet':
logger.info(f'Model size: {num_classes}')
net = models.resnet18(num_classes=num_classes)
elif helper.params['model'] == 'PretrainedRes':
net = models.resnet18(pretrained=True)
net.fc = nn.Linear(512, num_classes)
net = net.cuda()
elif helper.params['model'] == 'FlexiNet':
net = FlexiNet(3, num_classes)
elif helper.params['model'] == 'dif_inception':
net = inception_v3(pretrained=True, dif=True)
net.fc = nn.Linear(768, num_classes)
net.aux_logits = False
elif helper.params['model'] == 'inception':
net = inception_v3(pretrained=True)
class MultiViewCNN(nn.Module):
def __init__(self, num_classes=10, combine_at='prepool', join_how='concat', multitask=True,
drop_view_prob=0.0, architecture='densenet121'):
super(MultiViewCNN, self).__init__()
self.multitask = multitask
self.drop_view_prob = [1 - drop_view_prob, drop_view_prob / 2., drop_view_prob / 2.]
if multitask:
# Never drop view when multitask
# Use curriculum learning on loss instead
self.drop_view_prob = [1., 0., 0.]
self.combine_at = combine_at
self.join_how = join_how
params = {'in_channels': 1, 'num_classes': num_classes, **get_densenet_params(architecture)}
self.frontal_model = DenseNet(**params)
self.lateral_model = DenseNet(**params)
self.joint_in_features = self.frontal_model.classifier.in_features
if join_how == 'concat':
self.joint_in_features *= 2
self.classifier = nn.Linear(in_features=self.joint_in_features, out_features=num_classes)
def _combine_tensors(self, list_of_features, random_drop=1):
if self.join_how == 'mean' and random_drop == 1: # average
combined = torch.mean(torch.stack(list_of_features, dim=1), dim=1)
elif self.join_how == 'max' and random_drop == 1:
combined = torch.max(torch.stack(list_of_features, dim=1), dim=1)[0]
else: # average
combined = torch.cat(list_of_features, dim=1)
return combined
def _pool(self, features):
x = F.relu(features)
x = F.adaptive_avg_pool2d(x, output_size=(1, 1))
x = x.view(features.size(0), -1)
return x
def forward(self, images):
# Randomly drop a view while training
select = np.random.choice([1, 2, 3], p=self.drop_view_prob)
if select == 2 and self.training: # Frontal only
frontal_img = images[0]
lateral_img = torch.zeros_like(images[1])
elif select == 3 and self.training: # Lateral only
frontal_img = torch.zeros_like(images[0])
lateral_img = images[1]
else: # Keep both views
frontal_img, lateral_img = images
frontal_features = self.frontal_model.features(frontal_img)
lateral_features = self.lateral_model.features(lateral_img)
# Joint view
if self.combine_at == 'prepool':
joint = self._combine_tensors([frontal_features, lateral_features], random_drop=select)
joint = self._pool(joint)
else:
# Combine after pooling
pooled = []
for view in [frontal_features, lateral_features]:
pooled.append(self._pool(view))
joint = self._combine_tensors(pooled)
joint_logit = self.classifier(joint)
if self.multitask:
pooled_frontal_features = self._pool(frontal_features)
frontal_logit = self.frontal_model.classifier(pooled_frontal_features)
pooled_lateral_features = self._pool(lateral_features)
lateral_logit = self.lateral_model.classifier(pooled_lateral_features)
return joint_logit, frontal_logit, lateral_logit
else:
return joint_logit
def __init__(self,
p=None,
h=None,
use_word_embedding=True,
word_embedding_weights=None,
train_word_embeddings=False,
dropout_init_keep_rate=1.0,
dropout_decay_interval=10000,
dropout_decay_rate=0.977,
use_chars=False,
chars_per_word=16,
char_input_dim=100,
char_embedding_size=8,
char_conv_filters=100,
char_conv_kernel_size=5,
use_syntactical_features=False,
syntactical_feature_size=50,
use_exact_match=False,
first_scale_down_ratio=0.3,
nb_dense_blocks=3,
layers_per_dense_block=8,
nb_labels=3,
growth_rate=20,
transition_scale_down_ratio=0.5,
inputs=None,
outputs=None,
name="DIIN"):
"""Densely Interactive Inference Network(DIIN)
Model from paper `Natural Language Inference over Interaction Space`
(https://openreview.net/forum?id=r1dHXnH6-¬eId=r1dHXnH6-)
:param p: sequence length of premise
:param h: sequence length of hypothesis
:param use_word_embedding: whether or not to include word vectors in the model
:param use_chars: whether or not to include character embeddings in the model
:param use_syntactical_features: whether or not to include syntactical features (POS tags) in the model
:param use_exact_match: whether or not to include exact match features in the model
:param word_embedding_weights: matrix of weights for word embeddings(pre-trained vectors)
:param train_word_embeddings: whether or not to modify word embeddings while training
:param dropout_init_keep_rate: initial keep rate of dropout
:param dropout_decay_interval: the number of steps to wait for the next turn update, steps means single batch,
other than epoch
:param dropout_decay_rate: how much to change dropout at each interval
:param chars_per_word: how many chars are there per one word
:param char_input_dim: character unique numbers
:param char_embedding_size: output size of the character-embedding layer
:param char_conv_filters: filters of the kernel applied on character embeddings
:param char_conv_kernel_size: size of the kernel applied on character embeddings
:param syntactical_feature_size: size of the syntactical feature vector for each word
:param first_scale_down_ratio: scale ratio of map features as the input of first Densenet block
:param nb_dense_blocks: number of dense blocks in densenet
:param layers_per_dense_block: number of layers in one dense block
:param nb_labels: number of labels
:param growth_rate:growing rate in dense net
:param transition_scale_down_ratio: transition scale down ratio in dense net
:param inputs: inputs of keras models
:param outputs: outputs of keras models
:param name: models name
"""
if inputs or outputs:
super(DIINModel, self).__init__(inputs=inputs,
outputs=outputs,
name=name)
return
if use_word_embedding:
assert word_embedding_weights is not None, "Word embedding weights are needed"
inputs = []
premise_features = []
hypothesis_features = []
"""Embedding layer"""
# Input: word embedding
if use_word_embedding:
premise_word_input = Input(shape=(p, ),
dtype="int64",
name="premise_word_input")
hypothesis_word_input = Input(shape=(h, ),
dtype="int64",
name="hypothesis_word_input")
inputs.append(premise_word_input)
inputs.append(hypothesis_word_input)
word_embedding = Embedding(
input_dim=word_embedding_weights.shape[0],
output_dim=word_embedding_weights.shape[1],
weights=[word_embedding_weights],
trainable=train_word_embeddings,
name="word_embedding")
premise_word_embedding = word_embedding(premise_word_input)
hypothesis_word_embedding = word_embedding(hypothesis_word_input)
premise_word_embedding = DecayingDropout(
init_keep_rate=dropout_init_keep_rate,
decay_interval=dropout_decay_interval,
decay_rate=dropout_decay_rate,
name="premise_word_dropout")(premise_word_embedding)
hypothesis_word_embedding = DecayingDropout(
init_keep_rate=dropout_init_keep_rate,
decay_interval=dropout_decay_interval,
decay_rate=dropout_decay_rate,
name="hypothesis_word_dropout")(hypothesis_word_embedding)
premise_features.append(premise_word_embedding)
hypothesis_features.append(hypothesis_word_embedding)
# Input: character embedding
if use_chars:
premise_char_input = Input(shape=(p, chars_per_word),
dtype="int64",
name="premise_char_input")
hypothesis_char_input = Input(shape=(h, chars_per_word),
dtype="int64",
name="hypothesis_char_input")
inputs.append(premise_char_input)
inputs.append(hypothesis_char_input)
# Share weights of character-level embedding for premise and hypothesis
character_embedding = TimeDistributed(Sequential([
Embedding(input_dim=char_input_dim,
output_dim=char_embedding_size,
input_length=chars_per_word),
Conv1D(filters=char_conv_filters,
kernel_size=char_conv_kernel_size),
GlobalMaxPooling1D(),
]),
name="char_embedding")
character_embedding.build(
input_shape=(None, None, chars_per_word)) # Set input shape
premise_char_embedding = character_embedding(premise_char_input)
hypothesis_char_embedding = character_embedding(
hypothesis_char_input)
premise_features.append(premise_char_embedding)
hypothesis_features.append(hypothesis_char_embedding)
# Input: syntactical features
if use_syntactical_features:
premise_syntactical_input = Input(shape=(p,
syntactical_feature_size),
name="premise_syntactical_input")
hypothesis_syntactical_input = Input(
shape=(h, syntactical_feature_size),
name="hypothesis_syntactical_input")
inputs.append(premise_syntactical_input)
inputs.append(hypothesis_syntactical_input)
premise_features.append(premise_syntactical_input)
hypothesis_features.append(hypothesis_syntactical_input)
# Input: one-hot exact match feature
if use_exact_match:
premise_exact_match_input = Input(shape=(p, ),
name='premise_exact_match_input')
hypothesis_exact_match_input = Input(
shape=(h, ), name='hypothesis_exact_match_input')
inputs.append(premise_exact_match_input)
inputs.append(hypothesis_exact_match_input)
premise_exact_match = Reshape(
target_shape=(p, 1))(premise_exact_match_input)
hypothesis_exact_match = Reshape(
target_shape=(h, 1))(hypothesis_exact_match_input)
premise_features.append(premise_exact_match)
hypothesis_features.append(hypothesis_exact_match)
# Concatenate all features
if len(premise_features) > 1:
premise_embedding = Concatenate()(premise_features)
hypothesis_embedding = Concatenate()(hypothesis_features)
else:
premise_embedding = premise_features[0]
hypothesis_embedding = hypothesis_features[0]
d = K.int_shape(premise_embedding)[-1]
"""Encoding layer"""
premise_encoding = Encoding(name="premise_encoding")(premise_embedding)
hypothesis_encoding = Encoding(
name="hypothesis_encoding")(hypothesis_embedding)
"""Interaction layer"""
interaction = Interaction(name="interaction")(
[premise_encoding, hypothesis_encoding])
"""Feature extraction layer"""
feature_extractor_input = Conv2D(
filters=int(d * first_scale_down_ratio),
kernel_size=1,
activation=None,
name="bottleneck")(interaction) # Bottleneck layer
feature_extractor = DenseNet(
input_tensor=Input(shape=K.int_shape(feature_extractor_input)[1:]),
include_top=False,
nb_dense_block=nb_dense_blocks,
nb_layers_per_block=layers_per_dense_block,
growth_rate=growth_rate,
compression=transition_scale_down_ratio)(feature_extractor_input)
"""Output layer"""
features = DecayingDropout(init_keep_rate=dropout_init_keep_rate,
decay_interval=dropout_decay_interval,
decay_rate=dropout_decay_rate,
name="features")(feature_extractor)
if nb_labels == 2:
out = Dense(1, activation="sigmoid", name="output")(features)
else:
out = Dense(nb_labels, activation="softmax",
name="output")(features)
super(DIINModel, self).__init__(inputs=inputs, outputs=out, name=name)
test = list(test_loader)
test = list(zip(*test))
X_test = torch.cat(test[0], 0)
y_test = torch.cat(test[1], 0)
data = {
'X_train': X_train,
'y_train': y_train,
'X_val': X_val,
'y_val': y_val,
'X_test': X_test,
'y_test': y_test
}
model = DenseNet(input_param=(1, 64),
block_layers=(6, 4),
num_classes=10,
growth_rate=32,
bn_size=2,
dropout_rate=0,
transition_pool_param=(3, 1, 1))
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), weight_decay=1e-4)
solver = Solver(model, data, optimizer, loss_fn)
solver.train(num_epoch=2, file_prefix='mnist-')
solver.predict(file_prefix='mnist-')
def model_fn(features, labels, mode, params):
"""Model function for DenseNet classifier.
Args:
features: inputs.
labels: one hot encoded classes
mode: one of tf.estimator.ModeKeys.{TRAIN, INFER, EVAL}
params: a parameter dictionary with the following keys:
Returns:
ModelFnOps for Estimator API.
"""
number_classes = params.get('number_classes')
growth_rate = params.get('growth_rate')
dropout_keep_prob = params.get('dropout_keep_prob')
encoder_num_units = params.get('encoder_num_units')
decoder_num_units = params.get('decoder_num_units')
bottleneck_number_feature_maps = params.get(
'bottleneck_number_feature_maps')
tf.logging.info("features tensor {}".format(features))
features, image_ids, image_shapes = features['image'], features[
'image_id'], features['image_shape']
densenet = DenseNet(growth_rate=growth_rate,
dropout_keep_prob=dropout_keep_prob,
number_classes=number_classes,
is_training=(mode == tf.estimator.ModeKeys.TRAIN))
densenet.encode(
features=features,
num_units=encoder_num_units,
bottleneck_number_feature_maps=bottleneck_number_feature_maps)
logits = densenet.decode(decoder_num_units).output
probs = tf.nn.softmax(logits)
predictions = tf.argmax(probs, axis=-1)
if mode == tf.estimator.ModeKeys.PREDICT:
# resize the predictions back to original size.
# label_shape = tf.shape(features)[:2]
probs = tf.image.resize_bilinear(probs,
image_shapes,
name='resize_predictions')
predictions = tf.argmax(probs, axis=-1)
tf.logging.info("Starting to predict..")
predictions = {
'class_ids': predictions,
'probabilities': probs,
'logits': logits,
'image_ids': image_ids
}
tf.logging.info("prediction tensor {}".format(predictions))
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Add the loss.
# Calculate loss, which includes softmax cross entropy and L2 regularization.
# wraps the softmax_with_entropy fn. adds it to loss collection
tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=labels)
# include the regulization losses in the loss collection.
loss = tf.losses.get_total_loss()
if mode == tf.estimator.ModeKeys.EVAL:
tf.logging.info("Starting to evaluate..")
with tf.variable_scope('mean_iou_calc'):
prec = []
up_opts = []
for t in np.arange(0.5, 1.0, 0.05):
predicted_mask = tf.to_int32(probs > t)
score, up_opt = tf.metrics.mean_iou(labels, predicted_mask, 2)
up_opts.append(up_opt)
prec.append(score)
mean_iou = tf.reduce_mean(tf.stack(prec),
axis=0), tf.stack(up_opts)
eval_metrics = {'mean_iou': mean_iou}
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
eval_metric_ops=eval_metrics)
assert mode == tf.estimator.ModeKeys.TRAIN
tf.logging.info("Starting to train..")
global_step = tf.train.get_global_step()
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
optimizer = tf.train.AdagradOptimizer(learning_rate=1e-4)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss, global_step=global_step)
add_summaries(predictions, features, loss)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
elif args.model == 'resnet':
from models.resnet import ResNet
net = ResNet({
'input_shape': (1, 3, 32, 32),
'n_classes': num_classes,
'base_channels': 16,
'block_type': 'basic',
'depth': 20
})
elif args.model == 'densenet':
from models.densenet import DenseNet
net = DenseNet({
'input_shape': (1, 3, 32, 32),
'n_classes': num_classes,
"depth": 40,
"block_type": "bottleneck",
"growth_rate": 24,
"drop_rate": 0.0,
"compression_rate": 1
}) # 1 is turns compression off
start_epoch = 0
# Restore model
if args.load != '':
for i in range(1000 - 1, -1, -1):
model_name = os.path.join(args.load,
args.dataset + '_epoch_' + str(i) + '.pt')
if os.path.isfile(model_name):
net.load_state_dict(torch.load(model_name))
print('Model restored! Epoch:', i)
start_epoch = i + 1
def train_model(modname='alexnet', pm_ch='both', bs=16):
"""
Args:
modname (string): Name of the model. Has to be one of the values:
'alexnet', batch 64
'densenet'
'inception'
'resnet', batch 16
'squeezenet', batch 16
'vgg'
pm_ch (string): pixelmap channel -- 'time', 'charge', 'both', default to both
"""
# device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# hyper parameters
max_epochs = 10
learning_rate = 0.001
# determine number of input channels
nch = 2
if pm_ch != 'both':
nch = 1
ds = PixelMapDataset('training_file_list.txt', pm_ch)
# try out the data loader utility
dl = torch.utils.data.DataLoader(dataset=ds, batch_size=bs, shuffle=True)
# define model
model = None
if modname == 'alexnet':
model = alexnet(num_classes=3, in_ch=nch).to(device)
elif modname == 'densenet':
model = DenseNet(num_classes=3, in_ch=nch).to(device)
elif modname == 'inception':
model = inception_v3(num_classes=3, in_ch=nch).to(device)
elif modname == 'resnet':
model = resnet18(num_classes=3, in_ch=nch).to(device)
elif modname == 'squeezenet':
model = squeezenet1_1(num_classes=3, in_ch=nch).to(device)
elif modname == 'vgg':
model = vgg19_bn(in_ch=nch, num_classes=3).to(device)
else:
print('Model {} not defined.'.format(modname))
return
# loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# training process
total_step = len(dl)
for epoch in range(max_epochs):
for i, (view1, view2, local_labels) in enumerate(dl):
view1 = view1.float().to(device)
if modname == 'inception':
view1 = nn.ZeroPad2d((0, 192, 102, 101))(view1)
else:
view1 = nn.ZeroPad2d((0, 117, 64, 64))(view1)
local_labels = local_labels.to(device)
# forward pass
outputs = model(view1)
loss = criterion(outputs, local_labels)
# backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % bs == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(
epoch + 1, max_epochs, i + 1, total_step, loss.item()))
# save the model checkpoint
save_path = '../../../data/two_views/saved_models/{}/{}'.format(
modname, pm_ch)
os.makedirs(save_path, exist_ok=True)
torch.save(model.state_dict(), os.path.join(save_path, 'model.ckpt'))
def test_model(modname='alexnet', pm_ch='both', bs=16):
# hyperparameters
batch_size = bs
# device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# determine number of input channels
nch = 2
if pm_ch != 'both':
nch = 1
# restore model
model = None
if modname == 'alexnet':
model = alexnet(num_classes=3, in_ch=nch).to(device)
elif modname == 'densenet':
model = DenseNet(num_classes=3, in_ch=nch).to(device)
elif modname == 'inception':
model = inception_v3(num_classes=3, in_ch=nch).to(device)
elif modname == 'resnet':
model = resnet18(num_classes=3, in_ch=nch).to(device)
elif modname == 'squeezenet':
model = squeezenet1_1(num_classes=3, in_ch=nch).to(device)
elif modname == 'vgg':
model = vgg19_bn(in_ch=nch, num_classes=3).to(device)
else:
print('Model {} not defined.'.format(modname))
return
# retrieve trained model
# load path
load_path = '../../../data/two_views/saved_models/{}/{}'.format(
modname, pm_ch)
model_pathname = os.path.join(load_path, 'model.ckpt')
if not os.path.exists(model_pathname):
print('Trained model file {} does not exist. Abort.'.format(
model_pathname))
return
model.load_state_dict(torch.load(model_pathname))
# load test dataset
test_dataset = PixelMapDataset('test_file_list.txt', pm_ch)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
# test the model
model.eval(
) # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
with torch.no_grad():
correct = 0
total = 0
correct_cc_or_bkg = 0
ws_total = 0
ws_correct = 0
for view1, view2, labels in test_loader:
view1 = view1.float().to(device)
if modname == 'inception':
view1 = nn.ZeroPad2d((0, 192, 102, 101))(view1)
else:
view1 = nn.ZeroPad2d((0, 117, 64, 64))(view1)
labels = labels.to(device)
outputs = model(view1)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
for i in range(len(predicted)):
if (predicted[i] < 2
and labels[i] < 2) or (predicted[i] == 2
and labels[i] == 2):
correct_cc_or_bkg += 1
if labels[i] < 2:
ws_total += 1
if (predicted[i] == labels[i]):
ws_correct += 1
print('Model Performance:')
print('Model:', modname)
print('Channel:', pm_ch)
print(
'3-class Test Accuracy of the model on the test images: {}/{}, {:.2f} %'
.format(correct, total, 100 * correct / total))
print(
'2-class Test Accuracy of the model on the test images: {}/{}, {:.2f} %'
.format(correct_cc_or_bkg, total, 100 * correct_cc_or_bkg / total))
print(
'Wrong-sign Test Accuracy of the model on the test images: {}/{}, {:.2f} %'
.format(ws_correct, ws_total, 100 * ws_correct / ws_total))
def model_fn(features, labels, mode, params):
"""Model function for DenseNet classifier.
Args:
features: inputs.
labels: one hot encoded classes
mode: one of tf.estimator.ModeKeys.{TRAIN, INFER, EVAL}
params: a parameter dictionary with the following keys:
Returns:
ModelFnOps for Estimator API.
"""
number_classes = params.get('number_classes')
growth_rate = params.get('growth_rate')
dropout_keep_prob = params.get('dropout_keep_prob')
encoder_num_units = params.get('encoder_num_units')
decoder_num_units = params.get('decoder_num_units')
bottleneck_number_feature_maps = params.get(
'bottleneck_number_feature_maps')
densenet = DenseNet(growth_rate=growth_rate,
dropout_keep_prob=dropout_keep_prob,
number_classes=number_classes,
is_training=(mode == tf.estimator.ModeKeys.TRAIN))
densenet.encode(
features=features,
num_units=encoder_num_units,
bottleneck_number_feature_maps=bottleneck_number_feature_maps)
logits = densenet.decode(decoder_num_units).output
predictions = tf.argmax(logits, axis=1)
tf.summary.image('predictions', predictions)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class_ids': predictions,
'probabilities': tf.nn.softmax(logits),
'logits': logits,
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Add the loss.
# Calculate loss, which includes softmax cross entropy and L2 regularization.
# wraps the softmax_with_entropy fn. adds it to loss collection
tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=labels)
# include the regulization losses in the loss collection.
loss = tf.losses.get_total_loss()
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode=mode,
eval_metric_ops={
"accuracy":
tf.metrics.accuracy(
labels, predictions)
})
assert mode == tf.estimator.ModeKeys.TRAIN
global_step = tf.train.get_global_step()
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss, global_step=global_step)
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
train_loader = DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_worker)
test_loader = DataLoader(dataset_test,
batch_size=args.batch_size_test,
shuffle=False,
num_workers=args.num_worker)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
print('==> Making model..')
net = DenseNet(growth_rate=args.growth_rate,
theta=args.theta,
num_layers=[12, 12, 12],
num_classes=10)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
num_params = sum(p.numel() for p in net.parameters() if p.requires_grad)
print('The number of parameters of model is', num_params)
if args.resume is not None:
checkpoint = torch.load('./save_model/' + args.resume)
net.load_state_dict(checkpoint['net'])
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),