def create_model(ema=False):
if args.type == 1:
model = Classifier(num_classes=10)
else:
model = WideResNet(num_classes=10)
model = model.cuda()
if ema:
for param in model.parameters():
param.detach_()
return model
def __init__(self, model_name, vocab, lr, lr_decay, batch_size=64):
"""
PyTorch Lightning module that creates the overall model.
Inputs:
model_name - String denoting what encoder class to use. Either 'AWE', 'UniLSTM', 'BiLSTM', or 'BiLSTMMax'
vocab - Vocabulary from alignment between SNLI dataset and GloVe vectors
lr - Learning rate to use for the optimizer
lr_decay - Learning rate decay factor to use each epoch
batch_size - Size of the batches. Default is 64
"""
super().__init__()
self.save_hyperparameters()
# create an embedding layer for the vocabulary embeddings
self.glove_embeddings = nn.Embedding.from_pretrained(vocab.vectors)
# check which encoder model to use
if model_name == 'AWE':
self.encoder = AWEEncoder()
self.classifier = Classifier()
elif model_name == 'UniLSTM':
self.encoder = UniLSTM()
self.classifier = Classifier(input_dim=4 * 2048)
elif model_name == 'BiLSTM':
self.encoder = BiLSTM()
self.classifier = Classifier(input_dim=4 * 2 * 2048)
else:
self.encoder = BiLSTMMax()
self.classifier = Classifier(input_dim=4 * 2 * 2048)
# create the loss function
self.loss_function = nn.CrossEntropyLoss()
# create instance to save the last validation accuracy
self.last_val_acc = None
def create(self, classifier=Classifier()):
"""
(Classifier) -> (Classifier)
"""
classifierDB = ClassifierDB(classifier=classifier)
session = self.session_factory()
session.add(classifierDB)
session.flush()
session.refresh(classifierDB)
session.commit()
return Classifier(
classifierDB.id,
Plant(classifierDB.plant.id, classifierDB.plant.scientificName,
classifierDB.plant.commonName), classifierDB.tag,
classifierDB.path)
def train_classifier(x_data, y_data, auto_encoder):
model = Classifier(args).to(device)
x_data, y_data = Utils.SuffleData(x_data, y_data, len(xy))
x_train = x_data[:args.training_len]
y_train = y_data[:args.training_len]
x_train, y_train = Utils.generate_data(x_data, x_train, y_train, auto_encoder)
x_test = x_data[args.training_len:]
y_test = y_data[args.training_len:]
test_len = len(x_test)
model.learn(x_train, y_train, test_len, model, x_test, y_test, args.batch_size)
def __init__(self, keep_prob, \
batch_size=100, num_channels=1, learning_rate=0.001, fce=False, num_classes_per_set=5, \
num_samples_per_class=1, nClasses = 0, image_size = 28):
super(MatchingNetwork, self).__init__()
"""
Builds a matching network, the training and evaluation ops as well as data augmentation routines.
:param keep_prob: A tf placeholder of type tf.float32 denotes the amount of dropout to be used
:param batch_size: The batch size for the experiment
:param num_channels: Number of channels of the images
:param is_training: Flag indicating whether we are training or evaluating
:param rotate_flag: Flag indicating whether to rotate the images
:param fce: Flag indicating whether to use full context embeddings (i.e. apply an LSTM on the CNN embeddings)
:param num_classes_per_set: Integer indicating the number of classes per set
:param num_samples_per_class: Integer indicating the number of samples per class
:param nClasses: total number of classes. It changes the output size of the classifier g with a final FC layer.
:param image_input: size of the input image. It is needed in case we want to create the last FC classification
"""
self.batch_size = batch_size
self.fce = fce
self.g = Classifier(layer_size=64,
num_channels=num_channels,
nClasses=nClasses,
image_size=image_size)
if fce:
self.lstm = BidirectionalLSTM(layer_sizes=[32],
batch_size=self.batch_size,
vector_dim=self.g.outSize)
self.dn = DistanceNetwork()
self.classify = AttentionalClassify()
self.keep_prob = keep_prob
self.num_classes_per_set = num_classes_per_set
self.num_samples_per_class = num_samples_per_class
self.learning_rate = learning_rate
def create(self, analysis=Analysis()):
"""
(Analysis) -> (Analysis)
Add analysis to database
"""
analysisDB = AnalysisDB(analysis=analysis)
session = self.session_factory()
session.add(analysisDB)
session.flush()
session.refresh(analysisDB)
session.commit()
return Analysis(
analysisDB.id,
image=Image(
analysisDB.image.id,
Disease(
analysisDB.image.disease.id,
Plant(analysisDB.image.disease.plant.id,
analysisDB.image.disease.plant.scientificName,
analysisDB.image.disease.plant.commonName),
analysisDB.image.disease.scientificName,
analysisDB.image.disease.commonName), analysisDB.image.url,
analysisDB.image.description, analysisDB.image.source,
analysisDB.image.size),
classifier=Classifier(
analysisDB.classifier.id,
Plant(analysisDB.classifier.plant.id,
analysisDB.classifier.plant.scientificName,
analysisDB.classifier.plant.commonName),
analysisDB.classifier.tag, analysisDB.classifier.path),
user=User(id=analysisDB.user.id,
idType=analysisDB.user.idType,
email=analysisDB.user.email,
username=analysisDB.user.username))
def searchByID(self, id):
"""
(Int) -> (Classifier)
"""
session = self.session_factory()
classifierDB = session.query(ClassifierDB).get(id)
return Classifier(
classifierDB.id,
Plant(classifierDB.plant.id, classifierDB.plant.scientificName,
classifierDB.plant.commonName), classifierDB.tag,
classifierDB.path)
def search(self, classifier=Classifier(), pageSize=10, offset=0):
"""
(Classifier, pageSize, offset) -> {'total': int, 'content':[Classifier]}
"""
session = self.session_factory()
query = session.query(ClassifierDB).filter(
and_(ClassifierDB.tag.like('%' + classifier.tag + '%'),
ClassifierDB.path.like('%' + classifier.path + '%')))
content = query.slice(offset, pageSize).all()
total = query.count()
classifiers = []
for classifierDB in content:
classifiers.append(
Classifier(
classifierDB.id,
Plant(classifierDB.plant.id,
classifierDB.plant.scientificName,
classifierDB.plant.commonName), classifierDB.tag,
classifierDB.path))
dic = {'total': total, 'content': classifiers}
return dic
def __init__(self, model, ema_model, run_type=0, alpha=0.999):
self.model = model
self.ema_model = ema_model
self.alpha = alpha
if run_type == 1:
self.tmp_model = Classifier(num_classes=10).cuda()
else:
self.tmp_model = WideResNet(num_classes=10).cuda()
self.wd = 0.02 * args.lr
for param, ema_param in zip(self.model.parameters(),
self.ema_model.parameters()):
ema_param.data.copy_(param.data)
def create(self, analysisResult=AnalysisResult()):
"""
(AnalysisResult) -> (AnalysisResult)
Add analysis result to database
"""
analysisResultDB = AnalysisResultDB(analysisResult=analysisResult)
session = self.session_factory()
session.add(analysisResultDB)
session.flush()
session.commit()
session.refresh(analysisResultDB)
return AnalysisResult(
analysisResultDB.id,
Analysis(
analysisResultDB.analysis.id,
Image(
analysisResultDB.analysis.image.id,
Disease(
analysisResultDB.analysis.image.disease.id,
Plant(
analysisResultDB.analysis.image.disease.plant.id,
analysisResultDB.analysis.image.disease.plant.
scientificName, analysisResultDB.analysis.image.
disease.plant.commonName),
analysisResultDB.analysis.image.disease.scientificName,
analysisResultDB.analysis.image.disease.commonName),
analysisResultDB.analysis.image.url,
analysisResultDB.analysis.image.description,
analysisResultDB.analysis.image.source,
analysisResultDB.analysis.image.size),
Classifier(
analysisResultDB.analysis.classifier.id,
Plant(
analysisResultDB.analysis.classifier.plant.id,
analysisResultDB.analysis.classifier.plant.
scientificName,
analysisResultDB.analysis.classifier.plant.commonName),
analysisResultDB.analysis.classifier.tag,
analysisResultDB.analysis.classifier.path),
user=User(id=analysisResultDB.analysis.user.id,
idType=analysisResultDB.analysis.user.idType,
email=analysisResultDB.analysis.user.email,
username=analysisResultDB.analysis.user.username)),
Disease(
analysisResultDB.disease.id,
Plant(analysisResultDB.disease.plant.id,
analysisResultDB.disease.plant.scientificName,
analysisResultDB.disease.plant.commonName),
analysisResultDB.disease.scientificName,
analysisResultDB.disease.commonName), analysisResultDB.score,
analysisResultDB.frame)
def searchByID(self, id):
"""
(int) -> (AnalysisResult)
Search analysis result by ID
"""
session = self.session_factory()
analysisResultDB = session.query(AnalysisResultDB).get(id)
if (analysisResultDB is None):
raise Exception("AnalysisResults not found!")
return AnalysisResult(
analysisResultDB.id,
Analysis(
analysisResultDB.analysis.id,
Image(
analysisResultDB.analysis.image.id,
Disease(
analysisResultDB.analysis.image.disease.id,
Plant(
analysisResultDB.analysis.image.disease.plant.id,
analysisResultDB.analysis.image.disease.plant.
scientificName, analysisResultDB.analysis.image.
disease.plant.commonName),
analysisResultDB.analysis.image.disease.scientificName,
analysisResultDB.analysis.image.disease.commonName),
analysisResultDB.analysis.image.url,
analysisResultDB.analysis.image.description,
analysisResultDB.analysis.image.source,
analysisResultDB.analysis.image.size),
Classifier(
analysisResultDB.analysis.classifier.id,
Plant(
analysisResultDB.analysis.classifier.plant.id,
analysisResultDB.analysis.classifier.plant.
scientificName,
analysisResultDB.analysis.classifier.plant.commonName),
analysisResultDB.analysis.classifier.tag,
analysisResultDB.analysis.classifier.path),
user=User(id=analysisResultDB.analysis.user.id,
idType=analysisResultDB.analysis.user.idType,
email=analysisResultDB.analysis.user.email,
username=analysisResultDB.analysis.user.username)),
Disease(
analysisResultDB.disease.id,
Plant(analysisResultDB.disease.plant.id,
analysisResultDB.disease.plant.scientificName,
analysisResultDB.disease.plant.commonName),
analysisResultDB.disease.scientificName,
analysisResultDB.disease.commonName), analysisResultDB.score,
analysisResultDB.frame)
def update(self, classifier=Classifier()):
"""
(Classifier) -> (Classifier)
"""
session = self.session_factory()
classifierDB = session.query(ClassifierDB).filter_by(
id=classifier.id).first()
dic = {}
if (classifierDB.plant.id != classifier.plant.id):
dic['idPlant'] = classifier.plant.id
if (classifierDB.tag != classifier.tag):
dic['tag'] = classifier.tag
if (classifierDB.path != classifier.path):
dic['path'] = classifier.path
if (dic != {}):
session.query(ClassifierDB).filter_by(id=classifier.id).update(dic)
session.commit()
session.flush()
session.refresh(classifierDB)
return Classifier(
classifierDB.id,
Plant(classifierDB.plant.id, classifierDB.plant.scientificName,
classifierDB.plant.commonName), classifierDB.tag,
classifierDB.path)
def delete(self, classifier=Classifier()):
"""
(Plant) -> (Boolean)
"""
status = False
session = self.session_factory()
classifierDB = session.query(ClassifierDB).filter_by(
id=classifier.id).first()
session.delete(classifierDB)
session.commit()
session.flush()
if (not session.query(ClassifierDB).filter_by(
id=classifierDB.id).count()):
status = True
session.close()
return status
def searchByID(self, id):
"""
(int) -> (Analysis)
Search analysis by ID
"""
session = self.session_factory()
analysisDB = session.query(AnalysisDB).get(id)
if (analysisDB is None):
raise Exception("Analysis not found!")
results = []
for result in analysisDB.analysis_results:
results.append(
AnalysisResult(
id=result.id,
disease=Disease(
id=result.disease.id,
scientificName=result.disease.scientificName,
commonName=result.disease.commonName),
frame=result.frame,
score=result.score))
return Analysis(
id=analysisDB.id,
image=Image(
analysisDB.image.id,
Disease(
analysisDB.image.disease.id,
Plant(analysisDB.image.disease.plant.id,
analysisDB.image.disease.plant.scientificName,
analysisDB.image.disease.plant.commonName),
analysisDB.image.disease.scientificName,
analysisDB.image.disease.commonName), analysisDB.image.url,
analysisDB.image.description, analysisDB.image.source,
analysisDB.image.size),
classifier=Classifier(
analysisDB.classifier.id,
Plant(analysisDB.classifier.plant.id,
analysisDB.classifier.plant.scientificName,
analysisDB.classifier.plant.commonName),
analysisDB.classifier.tag, analysisDB.classifier.path),
analysis_results=results,
user=User(id=analysisDB.user.id,
idType=analysisDB.user.idType,
email=analysisDB.user.email,
username=analysisDB.user.username))
def build_model(config, num_classes):
# Build backbone
print("Initializing model: {}".format(config.MODEL.NAME))
if config.MODEL.NAME == 'resnet50':
model = ResNet50(res4_stride=config.MODEL.RES4_STRIDE)
else:
raise KeyError("Invalid model: '{}'".format(config.MODEL.NAME))
print("Model size: {:.5f}M".format(
sum(p.numel() for p in model.parameters()) / 1000000.0))
# Build classifier
if config.LOSS.CLA_LOSS in ['crossentropy', 'crossentropylabelsmooth']:
classifier = Classifier(feature_dim=config.MODEL.FEATURE_DIM,
num_classes=num_classes)
else:
classifier = NormalizedClassifier(feature_dim=config.MODEL.FEATURE_DIM,
num_classes=num_classes)
return model, classifier
def __init__(self,
id=0,
image=Image(),
classifier=Classifier(),
analysis_results=[],
user=User()):
"""Analisys model constructor
Args:
id: Integer number to identification
image: Image object that has been used for this Analysis
classifier: Classifier object that has been used for this Analysis
analysis_results: A list of AnalysisResult object with all results
user: User which perform this analysis
"""
self.id = id
self.image = image
self.classifier = classifier
self.analysis_results = analysis_results
self.user = user
def update(self, analysis=Analysis()):
"""
(Analysis) -> (Analysis)
update analysis table
"""
session = self.session_factory()
analysisDB = session.query(AnalysisDB).filter_by(
id=analysis.id).first()
dic = {}
if (analysisDB.idImage != analysis.image.id):
dic['idImage'] = analysis.image.id
if (analysisDB.idClassifier != analysis.classifier.id):
dic['idClassifier'] = analysis.classifier.id
if (dic != {}):
session.query(AnalysisDB).filter_by(id=analysis.id).update(dic)
session.commit()
session.flush()
session.refresh(analysisDB)
return Analysis(
analysisDB.id,
image=Image(
analysisDB.image.id,
Disease(
analysisDB.image.disease.id,
Plant(analysisDB.image.disease.plant.id,
analysisDB.image.disease.plant.scientificName,
analysisDB.image.disease.plant.commonName),
analysisDB.image.disease.scientificName,
analysisDB.image.disease.commonName), analysisDB.image.url,
analysisDB.image.description, analysisDB.image.source,
analysisDB.image.size),
classifier=Classifier(
analysisDB.classifier.id,
Plant(analysisDB.classifier.plant.id,
analysisDB.classifier.plant.scientificName,
analysisDB.classifier.plant.commonName),
analysisDB.classifier.tag, analysisDB.classifier.path),
user=User(id=analysisDB.user.id,
idType=analysisDB.user.idType,
email=analysisDB.user.email,
username=analysisDB.user.username))
def search(self, analysis=Analysis(), pageSize=10, offset=0):
"""
(Analysis, pageSize, offset) -> [Analysis]
search by analysis
"""
session = self.session_factory()
query = session.query(AnalysisDB).filter(
and_(AnalysisDB.idImage == analysis.image.id,
AnalysisDB.idClassifier == analysis.classifier.id))
content = query.slice(offset, pageSize).all()
total = query.count()
analyses = []
for analysisDB in content:
analyses.append(
Analysis(
analysisDB.id,
image=Image(
analysisDB.image.id,
Disease(
analysisDB.image.disease.id,
Plant(
analysisDB.image.disease.plant.id,
analysisDB.image.disease.plant.scientificName,
analysisDB.image.disease.plant.commonName),
analysisDB.image.disease.scientificName,
analysisDB.image.disease.commonName),
analysisDB.image.url, analysisDB.image.description,
analysisDB.image.source, analysisDB.image.size),
classifier=Classifier(
analysisDB.classifier.id,
Plant(analysisDB.classifier.plant.id,
analysisDB.classifier.plant.scientificName,
analysisDB.classifier.plant.commonName),
analysisDB.classifier.tag, analysisDB.classifier.path),
user=User(id=analysisDB.user.id,
idType=analysisDB.user.idType,
email=analysisDB.user.email,
username=analysisDB.user.username)))
return {'total': total, 'content': analyses}
def update(self, analysisResult=AnalysisResult()):
"""
(AnalysisResult) -> (AnalysisResult)
update analysis_result table
"""
session = self.session_factory()
analysisResultDB = session.query(AnalysisResultDB).filter_by(
id=analysisResult.id).first()
dic = {}
if (analysisResultDB.idAnalysis != analysisResult.analysis.id):
dic['idAnalysis'] = analysisResult.analysis.id
if (analysisResultDB.idDisease != analysisResult.disease.id):
dic['idDisease'] = analysisResult.disease.id
if (analysisResultDB.score != analysisResult.score):
dic['score'] = analysisResult.score
if (analysisResultDB.frame != analysisResult.frame):
dic['frame'] = analysisResult.frame
if (dic != {}):
session.query(AnalysisResultDB).filter_by(
id=analysisResult.id).update(dic)
session.commit()
session.flush()
session.refresh(analysisResultDB)
return AnalysisResult(
analysisResultDB.id,
Analysis(
analysisResultDB.analysis.id,
Image(
analysisResultDB.analysis.image.id,
Disease(
analysisResultDB.analysis.image.disease.id,
Plant(
analysisResultDB.analysis.image.disease.plant.id,
analysisResultDB.analysis.image.disease.plant.
scientificName, analysisResultDB.analysis.image.
disease.plant.commonName),
analysisResultDB.analysis.image.disease.scientificName,
analysisResultDB.analysis.image.disease.commonName),
analysisResultDB.analysis.image.url,
analysisResultDB.analysis.image.description,
analysisResultDB.analysis.image.source,
analysisResultDB.analysis.image.size),
Classifier(
analysisResultDB.analysis.classifier.id,
Plant(
analysisResultDB.analysis.classifier.plant.id,
analysisResultDB.analysis.classifier.plant.
scientificName,
analysisResultDB.analysis.classifier.plant.commonName),
analysisResultDB.analysis.classifier.tag,
analysisResultDB.analysis.classifier.path),
user=User(id=analysisResultDB.analysis.user.id,
idType=analysisResultDB.analysis.user.idType,
email=analysisResultDB.analysis.user.email,
username=analysisResultDB.analysis.user.username)),
Disease(
analysisResultDB.disease.id,
Plant(analysisResultDB.disease.plant.id,
analysisResultDB.disease.plant.scientificName,
analysisResultDB.disease.plant.commonName),
analysisResultDB.disease.scientificName,
analysisResultDB.disease.commonName), analysisResultDB.score,
analysisResultDB.frame)
def main_worker(args):
best_acc1 = 0
best_acc5 = 0
best_epoch_acc1 = 0
best_epoch_acc5 = 0
logs_dir = args.logdir
if not os.path.exists(logs_dir):
os.makedirs(logs_dir)
checkpoint_savedir = args.savedir
if not os.path.exists(checkpoint_savedir):
os.makedirs(checkpoint_savedir)
logfile = os.path.join(logs_dir, 'training_{}_b_{}_lrfreq_{}.log'.format(
args.arch, args.batch_size, args.learning_rate_adjust_frequency))
summaryfile = os.path.join(logs_dir, 'summary_file.txt')
checkpoint_savefile = os.path.join(checkpoint_savedir, '{}_batchsize_{}_lrfreq_{}.pth.tar'.format(
args.arch, args.batch_size, args.learning_rate_adjust_frequency))
best_checkpoint_savefile = os.path.join(checkpoint_savedir,'{}_batchsize_{}_lrfreq_{}_best.pth.tar'.format(
args.arch, args.batch_size, args.learning_rate_adjust_frequency))
writer = SummaryWriter(logs_dir)
# Data loading code
###########################################################################################
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(traindir, transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
val_dataset = datasets.ImageFolder(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
# can use a subset of all classes (specified in a file or randomly chosen)
if args.nb_classes < 1000:
train_indices = list(np.load('utils_sampling/imagenet_train_class_indices.npy'))
val_indices = list(np.load('utils_sampling/imagenet_val_class_indices.npy'))
classes_names = torch.load('utils_sampling/labels_dict')
if args.class_indices is not None:
class_indices = torch.load(args.class_indices)
else:
perm = torch.randperm(1000)
class_indices = perm[:args.nb_classes].tolist()
train_indices_full = [x for i in range(len(class_indices)) for x in range(train_indices[class_indices[i]],
train_indices[class_indices[i] + 1])]
val_indices_full = [x for i in range(len(class_indices)) for x in range(val_indices[class_indices[i]],
val_indices[class_indices[i] + 1])]
classes_indices_file = os.path.join(logs_dir, 'classes_indices_selected')
selected_classes_names = [classes_names[i] for i in class_indices]
torch.save(class_indices, classes_indices_file)
print_and_write('Selected {} classes indices: {}'.format(args.nb_classes, class_indices), logfile,
summaryfile)
print_and_write('Selected {} classes names: {}'.format(args.nb_classes, selected_classes_names), logfile,
summaryfile)
if args.random_seed is not None:
print_and_write('Random seed used {}'.format(args.random_seed), logfile, summaryfile)
train_dataset = torch.utils.data.Subset(train_dataset, train_indices_full)
val_dataset = torch.utils.data.Subset(val_dataset, val_indices_full)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
###########################################################################################
# Model creation
###########################################################################################
if args.arch in model_names:
n_space = 224
nb_channels_in = 3
# create scattering
J = args.scattering_J
L_ang = args.scat_angles
max_order = 2 if args.scattering_order2 else 1
if args.scattering_wph:
A = args.scattering_nphases
scattering = ScatteringTorch2D_wph(J=J, shape=(224, 224), L=L_ang, A=A, max_order=max_order,
backend=args.backend)
else:
scattering = Scattering2D(J=J, shape=(224, 224), L=L_ang, max_order=max_order,
backend=args.backend)
# Flatten scattering
scattering = nn.Sequential(scattering, nn.Flatten(1, 2))
if args.scattering_wph:
nb_channels_in += 3 * A * L_ang * J
else:
nb_channels_in += 3 * L_ang * J
if max_order == 2:
nb_channels_in += 3 * (L_ang ** 2) * J * (J - 1) // 2
n_space = n_space // (2 ** J)
###########################################################################################
# create linear proj
# Standardization (can also be performed with BatchNorm2d(affine=False))
if not os.path.exists('standardization'):
os.makedirs('standardization')
std_file = 'standardization/ImageNet2012_scattering_J{}_order{}_wph_{}_nphases_{}_nb_classes_{}.pth.tar'.format(
args.scattering_J, 2 if args.scattering_order2 else 1, args.scattering_wph,
args.scattering_nphases if args.scattering_wph else 0, args.nb_classes)
if os.path.isfile(std_file):
print_and_write("=> loading scattering mean and std '{}'".format(std_file), logfile)
std_dict = torch.load(std_file)
mean_std = std_dict['mean']
stding_mat = std_dict['matrix']
else:
mean_std, stding_mat, std = compute_stding_matrix(train_loader, scattering, logfile)
print_and_write("=> saving scattering mean and std '{}'".format(std_file), logfile)
std_dict = {'mean': mean_std, 'std': std, 'matrix': stding_mat}
torch.save(std_dict, std_file)
standardization = Rescaling(mean_std, stding_mat)
# standardization = nn.BatchNorm2d(nb_channels_in, affine=False)
if args.arch in ['sparsescatnet', 'sparsescatnetw']:
proj = nn.Conv2d(nb_channels_in, args.L_proj_size, kernel_size=args.L_kernel_size, stride=1,
padding=0, bias=False)
nb_channels_in = args.L_proj_size
linear_proj = LinearProj(standardization, proj, args.L_kernel_size)
else: # scatnet
proj = nn.Identity()
linear_proj = LinearProj(standardization, proj, 0)
###########################################################################################
# Create ISTC (when applicable)
if args.arch in ['sparsescatnet', 'sparsescatnetw']:
###########################################################################################
if args.arch == 'sparsescatnet':
arch_log = "=> creating model SparseScatNet with phase scattering {}, linear projection " \
"(projection dimension {}), ISTC with {} iterations, dictionary size {}, classifier {} " \
"pipeline".format(args.scattering_wph, args.L_proj_size, args.n_iterations,
args.dictionary_size, args.classifier_type)
istc = ISTC(nb_channels_in, dictionary_size=args.dictionary_size, n_iterations=args.n_iterations,
lambda_0=args.lambda_0, lambda_star=args.lambda_star, lambda_star_lb=args.lambda_star_lb,
grad_lambda_star=args.grad_lambda_star, epsilon_lambda_0=args.epsilon_lambda_0,
output_rec=args.output_rec)
elif args.arch == 'sparsescatnetw':
arch_log = "=> creating model SparseScatNetW with phase scattering {}, linear projection " \
"(projection dimension {}), ISTCW with {} iterations, dictionary size {}, classifier {} " \
"pipeline".format(args.scattering_wph, args.L_proj_size, args.n_iterations,
args.dictionary_size, args.classifier_type)
istc = ISTC(nb_channels_in, dictionary_size=args.dictionary_size, n_iterations=args.n_iterations,
lambda_0=args.lambda_0, lambda_star=args.lambda_star, lambda_star_lb=args.lambda_star_lb,
grad_lambda_star=args.grad_lambda_star, epsilon_lambda_0=args.epsilon_lambda_0,
output_rec=args.output_rec, use_W=True)
if not args.output_rec:
nb_channels_in = args.dictionary_size
elif args.arch == 'scatnet':
arch_log = "=> creating model ScatNet with phase scattering {} and classifier {}".\
format(args.scattering_wph, args.classifier_type)
# Create classifier
###########################################################################################
classifier = Classifier(n_space, nb_channels_in, classifier_type=args.classifier_type,
nb_classes=1000, nb_hidden_units=args.nb_hidden_units, nb_l_mlp=args.nb_l_mlp,
dropout_p_mlp=args.dropout_p_mlp, avg_ker_size=args.avg_ker_size)
# Create model
###########################################################################################
if args.arch in ['sparsescatnet', 'sparsescatnetw']:
model = SparseScatNet(scattering, linear_proj, istc, classifier, return_full_inf=True) # print model info
elif args.arch == 'scatnet':
model = nn.Sequential(scattering, linear_proj, classifier)
else:
print_and_write("Unknown model", logfile, summaryfile)
return
print_and_write(arch_log, logfile, summaryfile)
print_and_write('Number of epochs {}, learning rate decay epochs {}'.format(args.epochs,
args.learning_rate_adjust_frequency),
logfile, summaryfile)
# DataParallel will divide and allocate batch_size to all available GPUs
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print_and_write("=> loading checkpoint '{}'".format(args.resume), logfile, summaryfile)
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print_and_write("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']), logfile,
summaryfile)
else:
print_and_write("=> no checkpoint found at '{}'".format(args.resume), logfile, summaryfile)
cudnn.benchmark = True
if args.arch in ['sparsescatnet', 'sparsescatnetw']:
lambda_0_max = compute_lambda_0(train_loader, model).item()
if args.l0_inf_init:
with torch.no_grad():
model.module.istc.lambda_0.data.fill_(lambda_0_max)
model.module.istc.log_lambda_0.data = torch.log(model.module.istc.lambda_0.data)
model.module.istc.gamma.data.fill_(np.power(args.lambda_star / lambda_0_max, 1. / args.n_iterations))
model.module.istc.log_gamma.data = torch.log(model.module.istc.gamma.data)
for i in range(args.n_iterations - 1):
model.module.istc.lambdas.data[i] = lambda_0_max * (model.module.istc.gamma.data ** (i + 1))
model.module.istc.log_lambdas.data[i] = torch.log(model.module.istc.lambdas.data[i])
print_and_write('Lambda star lower bound {:.3f}'.format(args.lambda_star_lb), logfile, summaryfile)
print_and_write('epsilon lambda_0 {}'.format(args.epsilon_lambda_0), logfile, summaryfile)
with torch.no_grad():
with np.printoptions(precision=2, suppress=True):
lambda_0_init = model.module.istc.lambda_0.data.cpu().item()
print_and_write('Lambda_0 init {:.2f}'.format(lambda_0_init), logfile, summaryfile)
lambdas_init = model.module.istc.lambdas.data.cpu().numpy()
print_and_write('Lambdas init {}'.format(lambdas_init), logfile, summaryfile)
print_and_write('Lambda_star init {:.2f}'.format(args.lambda_star), logfile, summaryfile)
gamma_init = model.module.istc.gamma.data.cpu().item()
print_and_write('Gamma init {:.2f}'.format(gamma_init), logfile, summaryfile)
count = 0
for i in range(args.dictionary_size):
if model.module.istc.dictionary_weight.data[:, i].norm(p=2) < 0.99 or \
model.module.istc.dictionary_weight.data[:, i].norm(p=2) > 1.01:
count += 1
if count == 0:
print_and_write("Dictionary atoms initially well normalized", logfile,summaryfile)
else:
print_and_write("{} dictionary atoms not initially well normalized".format(count), logfile, summaryfile)
gram = torch.matmul(model.module.istc.w_weight.data[..., 0, 0].t(),
model.module.istc.dictionary_weight.data[..., 0, 0]).cpu().numpy()
if args.arch == 'sparsescatnetw':
count = 0
for i in range(args.dictionary_size):
if gram[i, i] < 0.99 or gram[i, i] > 1.01:
count += 1
if count == 0:
print_and_write("W^T D diagonal elements well equal to 1", logfile, summaryfile)
else:
print_and_write("{} W^T D diagonal elements not equal to 1".format(count),
logfile, summaryfile)
gram = np.abs(gram)
for i in range(args.dictionary_size):
gram[i, i] = 0
print_and_write("Initial max coherence {:.3f}, median coherence {:.3f}".
format(np.max(gram), np.median(gram)), logfile, summaryfile)
if args.evaluate:
print_and_write("Evaluating model at epoch {}...".format(args.start_epoch), logfile)
validate(val_loader, model, criterion, args.start_epoch, args, logfile, summaryfile, writer)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args, logfile, writer)
# evaluate on validation set
acc1, acc5 = validate(val_loader, model, criterion, epoch, args, logfile, summaryfile, writer)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
if is_best:
best_epoch_acc1 = epoch
if acc5 > best_acc5:
best_acc5 = acc5
best_epoch_acc5 = epoch
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
}, is_best, checkpoint_filename=checkpoint_savefile, best_checkpoint_filename=best_checkpoint_savefile)
if args.arch in ['sparsescatnet', 'sparsescatnetw']:
with torch.no_grad():
with np.printoptions(precision=2, suppress=True):
lambda_0_final = model.module.istc.lambda_0.data.cpu().item()
print_and_write('Lambda_0 final {:.2f}'.format(lambda_0_final), logfile, summaryfile)
lambdas_final = model.module.istc.lambdas.data.cpu().numpy()
print_and_write('Lambdas final {}'.format(lambdas_final), logfile, summaryfile)
lambda_star_final = model.module.istc.lambda_star.data.cpu().item()
print_and_write('Lambda_star final {:.2f}'.format(lambda_star_final), logfile, summaryfile)
gamma_final = model.module.istc.gamma.data.cpu().item()
print_and_write('Gamma final {:.2f}'.format(gamma_final), logfile, summaryfile)
count = 0
for i in range(args.dictionary_size):
if model.module.istc.dictionary_weight.data[:, i].norm(p=2) < 0.99 or \
model.module.istc.dictionary_weight.data[:, i].norm(p=2) > 1.01:
count += 1
if count == 0:
print_and_write("Dictionary atoms finally well normalized", logfile, summaryfile)
else:
print_and_write("{} dictionary atoms not finally well normalized".format(count), logfile, summaryfile)
gram = torch.matmul(model.module.istc.w_weight.data[..., 0, 0].t(),
model.module.istc.dictionary_weight.data[..., 0, 0]).cpu().numpy()
if args.arch == 'sparsescatnetw':
count = 0
for i in range(args.dictionary_size):
if gram[i, i] < 0.99 or gram[i, i] > 1.01:
count += 1
if count == 0:
print_and_write("W^T D diagonal elements well equal to 1", logfile, summaryfile)
else:
print_and_write("{} W^T D diagonal elements not equal to 1".format(count),
logfile, summaryfile)
gram = np.abs(gram)
for i in range(args.dictionary_size):
gram[i, i] = 0
print_and_write("Final max coherence {:.3f}, median coherence {:.3f}".
format(np.max(gram), np.median(gram)), logfile, summaryfile)
print_and_write(
"Best top 1 accuracy {:.2f} at epoch {}, best top 5 accuracy {:.2f} at epoch {}".format(best_acc1,
best_epoch_acc1,
best_acc5,
best_epoch_acc5),
logfile, summaryfile)
def search(self, analysisResult=AnalysisResult(), pageSize=10, offset=0):
"""
(AnalysisResult, pageSize, offset) -> [AnalysisResult]
search by analysisResult
"""
session = self.session_factory()
query = session.query(AnalysisResultDB).filter(
or_(AnalysisResultDB.idAnalysis == analysisResult.analysis.id,
AnalysisResultDB.idDisease == analysisResult.disease.id,
AnalysisResultDB.score == analysisResult.score,
AnalysisResultDB.frame == analysisResult.frame))
content = query.slice(offset, pageSize).all()
total = query.count()
analysisResults = []
for analysisResultDB in content:
analysisResults.append(
AnalysisResult(
analysisResultDB.id,
Analysis(
analysisResultDB.analysis.id,
Image(
analysisResultDB.analysis.image.id,
Disease(
analysisResultDB.analysis.image.disease.id,
Plant(
analysisResultDB.analysis.image.disease.
plant.id, analysisResultDB.analysis.image.
disease.plant.scientificName,
analysisResultDB.analysis.image.disease.
plant.commonName), analysisResultDB.
analysis.image.disease.scientificName,
analysisResultDB.analysis.image.disease.
commonName),
analysisResultDB.analysis.image.url,
analysisResultDB.analysis.image.description,
analysisResultDB.analysis.image.source,
analysisResultDB.analysis.image.size),
Classifier(
analysisResultDB.analysis.classifier.id,
Plant(
analysisResultDB.analysis.classifier.plant.id,
analysisResultDB.analysis.classifier.plant.
scientificName, analysisResultDB.analysis.
classifier.plant.commonName),
analysisResultDB.analysis.classifier.tag,
analysisResultDB.analysis.classifier.path),
user=User(
id=analysisResultDB.analysis.user.id,
idType=analysisResultDB.analysis.user.idType,
email=analysisResultDB.analysis.user.email,
username=analysisResultDB.analysis.user.username)),
Disease(
analysisResultDB.disease.id,
Plant(analysisResultDB.disease.plant.id,
analysisResultDB.disease.plant.scientificName,
analysisResultDB.disease.plant.commonName),
analysisResultDB.disease.scientificName,
analysisResultDB.disease.commonName),
analysisResultDB.score, analysisResultDB.frame))
return {'total': total, 'content': analysisResults}
class FullModel(pl.LightningModule):
def __init__(self, model_name, vocab, lr, lr_decay, batch_size=64):
"""
PyTorch Lightning module that creates the overall model.
Inputs:
model_name - String denoting what encoder class to use. Either 'AWE', 'UniLSTM', 'BiLSTM', or 'BiLSTMMax'
vocab - Vocabulary from alignment between SNLI dataset and GloVe vectors
lr - Learning rate to use for the optimizer
lr_decay - Learning rate decay factor to use each epoch
batch_size - Size of the batches. Default is 64
"""
super().__init__()
self.save_hyperparameters()
# create an embedding layer for the vocabulary embeddings
self.glove_embeddings = nn.Embedding.from_pretrained(vocab.vectors)
# check which encoder model to use
if model_name == 'AWE':
self.encoder = AWEEncoder()
self.classifier = Classifier()
elif model_name == 'UniLSTM':
self.encoder = UniLSTM()
self.classifier = Classifier(input_dim=4 * 2048)
elif model_name == 'BiLSTM':
self.encoder = BiLSTM()
self.classifier = Classifier(input_dim=4 * 2 * 2048)
else:
self.encoder = BiLSTMMax()
self.classifier = Classifier(input_dim=4 * 2 * 2048)
# create the loss function
self.loss_function = nn.CrossEntropyLoss()
# create instance to save the last validation accuracy
self.last_val_acc = None
def forward(self, sentences):
"""
The forward function calculates the loss for a given batch of sentences.
Inputs:
sentences - Batch of sentences with (premise, hypothesis, label) pairs
Ouptuts:
loss - Cross entropy loss of the predictions
accuracy - Accuracy of the predictions
"""
# get the sentence lengths of the batch
lengths_premises = torch.tensor(
[x[x != 1].shape[0] for x in sentences.premise],
device=self.device)
lengths_hypothesis = torch.tensor(
[x[x != 1].shape[0] for x in sentences.hypothesis],
device=self.device)
# pass premises and hypothesis through the embeddings
premises = self.glove_embeddings(sentences.premise)
hypothesis = self.glove_embeddings(sentences.hypothesis)
# forward the premises and hypothesis through the Encoder
premises = self.encoder(premises, lengths_premises)
hypothesis = self.encoder(hypothesis, lengths_hypothesis)
# calculate the difference and multiplication
difference = torch.abs(premises - hypothesis)
multiplication = premises * hypothesis
# create the sentence representations
sentence_representations = torch.cat(
[premises, hypothesis, difference, multiplication], dim=1)
# pass through the classifier
predictions = self.classifier(sentence_representations)
# calculate the loss and accuracy
loss = self.loss_function(predictions, sentences.label)
predicted_labels = torch.argmax(predictions, dim=1)
accuracy = torch.true_divide(
torch.sum(predicted_labels == sentences.label),
torch.tensor(sentences.label.shape[0],
device=sentences.label.device))
# return the loss and accuracy
return loss, accuracy
# function that configures the optimizer for the model
def configure_optimizers(self):
# create optimizer
optimizer = torch.optim.SGD([{
'params': self.encoder.parameters()
}, {
'params': self.classifier.parameters()
}],
lr=self.hparams.lr)
# freeze the embeddings
self.glove_embeddings.weight.requires_grad = False
# create learning rate decay
lr_scheduler = {
'scheduler':
StepLR(optimizer=optimizer,
step_size=1,
gamma=self.hparams.lr_decay),
'name':
'learning_rate'
}
# return the scheduler and optimizer
return [optimizer], [lr_scheduler]
# function that performs a training step
def training_step(self, batch, batch_idx):
# forward the batch through the model
train_loss, train_acc = self.forward(batch)
# log the training loss and accuracy
self.log("train_loss", train_loss, on_step=False, on_epoch=True)
self.log("train_acc", train_acc, on_step=False, on_epoch=True)
# return the training loss
return train_loss
# function that performs a validation step
def validation_step(self, batch, batch_idx):
# forward the batch through the model
val_loss, val_acc = self.forward(batch)
# log the validation loss and accuracy
self.log("val_loss", val_loss)
self.log("val_acc", val_acc)
# save the validation accuracy
self.last_val_acc = val_acc
# function that performs a test step
def test_step(self, batch, batch_idx):
# forward the batch through the model
test_loss, test_acc = self.forward(batch)
# log the test loss and accuracy
self.log("test_loss", test_loss)
self.log("test_acc", test_acc)
def compile(self):
if self.compiled:
print('Model already compiled.')
return
self.compiled = True
# Placeholders.
self.X = tf.placeholder(tf.float32, shape=(None, 32, 32, 1), name='X')
self.Y = tf.placeholder(tf.float32, shape=(None, 32, 32, 2), name='Y')
self.labels = tf.placeholder(tf.float32, shape=(None, 10), name='labels')
# Generator.
generator = Generator(self.seed)
# Discriminator.
discriminator = Discriminator(self.seed)
# Classifier.
classifier = Classifier(self.seed)
self.gen_out = generator.forward(self.X)
disc_out_real = discriminator.forward(tf.concat([self.X, self.Y], 3))
disc_out_fake = discriminator.forward(tf.concat([self.X, self.gen_out], 3), reuse_vars=True)
# VAC-GAN classifier losses.
classifier_real = classifier.forward(tf.concat([self.X, self.Y], 3))
classfier_fake = classifier.forward(tf.concat([self.X, self.gen_out], 3), reuse_vars=True)
classifier_l_real = tf.nn.softmax_cross_entropy_with_logits_v2(logits=classifier_real, labels=self.labels)
classifier_l_fake = tf.nn.softmax_cross_entropy_with_logits_v2(logits=classfier_fake, labels=self.labels)
self.classifier_loss_real = tf.reduce_mean(classifier_l_real)
self.classifier_loss_fake = tf.reduce_mean(classifier_l_fake)
self.classifier_loss = tf.reduce_mean(classifier_l_fake + classifier_l_real)
# Generator loss.
self.gen_loss_gan = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_out_fake, labels=tf.ones_like(disc_out_fake)))
self.gen_loss_l1 = tf.reduce_mean(tf.abs(self.Y - self.gen_out)) * self.l1_weight
self.gen_loss = self.gen_loss_gan + self.gen_loss_l1 + self.VAC_weight * self.classifier_loss
# Discriminator losses.
disc_l_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_out_fake, labels=tf.zeros_like(disc_out_fake))
disc_l_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_out_real, labels=tf.ones_like(disc_out_real)*self.label_smoothing)
self.disc_loss_fake = tf.reduce_mean(disc_l_fake)
self.disc_loss_real = tf.reduce_mean(disc_l_real)
self.disc_loss = tf.reduce_mean(disc_l_fake + disc_l_real)
# Global step.
self.global_step = tf.Variable(0, name='global_step', trainable=False)
# Learning rate.
if self.learning_rate_decay:
self.lr = tf.maximum(1e-6, tf.train.exponential_decay(
learning_rate=self.learning_rate,
global_step=self.global_step,
decay_steps=self.learning_rate_decay_steps,
decay_rate=self.learning_rate_decay_rate))
else:
self.lr = tf.constant(self.learning_rate)
# Optimizers.
self.gen_optimizer = tf.train.AdamOptimizer(learning_rate=self.lr).minimize(self.gen_loss, var_list=generator.variables)
self.disc_optimizer = tf.train.AdamOptimizer(learning_rate=self.lr/10).minimize(self.disc_loss, var_list=discriminator.variables)
self.classifier_optimizer = tf.train.AdamOptimizer(learning_rate=self.lr/10).minimize(self.classifier_loss, var_list=classifier.variables, global_step=self.global_step)
# Sampler.
gen_sample = Generator(self.seed, is_training=False)
self.sampler = gen_sample.forward(self.X, reuse_vars=True)
self.MAE = tf.reduce_mean(tf.abs(self.Y - self.sampler))
self.saver = tf.train.Saver()
