def execute_similarity_search(img_path, num_algs=32):
# this part can be refactored into a "setup model" func
# the func can have a flag for initing the hook or not.
path = "../saved_models/cifar_model_300_epochs.pth"
model = SimpleNet(10)
model.load_state_dict(torch.load(path))
# Init forward hook for exposing dense layer vectors
sf = SaveFeatureVectors(model.conv13[2])
model.eval()
is_correct = 0.0
path_list = []
for inputs, labels, paths in _get_single_image_batch(img_path):
outputs = model(inputs.float())
_, pred = outputs.max(1)
is_correct += (pred == labels.long()).sum().item()
print(f"Num correct: {is_correct}")
path_list.extend(paths)
# Generate Hash: todo-try stuff here.
m = MinHash(num_perm=num_algs)
m.update(sf.features.flatten())
def execute_inference(create_hashes=False,
num_algs=32,
hash_index="lsh-demo-3"):
path = "../saved_models/cifar_model_300_epochs.pth"
model = SimpleNet(10)
model.load_state_dict(torch.load(path))
# Init forward hook for exposing dense layer vectors
sf = SaveFeatureVectors(model.conv13[2])
model.eval()
is_correct = 0.0
path_list = []
for inputs, labels, paths in _get_test_batch():
outputs = model(inputs.float())
_, pred = outputs.max(1)
is_correct += (pred == labels.long()).sum().item()
print(f"Num correct: {is_correct}")
path_list.extend(paths)
print("Extracting Vectors")
if create_hashes:
generate_hash_tables(path_list, sf.features)
class PneumoniaTrainer:
def __init__(
self, model_name: str, epochs: int = 100, config: Optional[dict] = None
):
self.output_model = model_name + ".pth"
self.train_set = PneumoniaDataset(SetType.train)
self.train_loader = DataLoader(
PneumoniaDataset(SetType.train), batch_size=16, shuffle=True, num_workers=8
)
self.val_loader = DataLoader(
PneumoniaDataset(SetType.val, shuffle=False),
batch_size=16,
shuffle=False,
num_workers=8,
)
self.test_loader = DataLoader(
PneumoniaDataset(SetType.test, shuffle=False),
batch_size=16,
shuffle=False,
num_workers=8,
)
self.config = {
"pos_weight_bias": 0.5,
"starting_lr": 1e-2,
"momentum": 0.9,
"decay": 5e-4,
"lr_adjustment_factor": 0.3,
"scheduler_patience": 15,
"print_cadence": 100,
"comment": "Added large dense layer.",
"pos_weight": 1341 / 3875, # Number of negatives / positives.
}
self.epochs = epochs
self.device = torch.device("cuda:0")
self.writer = SummaryWriter(comment=self.config["comment"])
self.net = SimpleNet(1).to(self.device)
self.criterion = nn.BCEWithLogitsLoss(
pos_weight=torch.tensor(self.config["pos_weight"])
)
self.optimizer = optim.SGD(
self.net.parameters(),
lr=self.config["starting_lr"], # type: ignore
momentum=self.config["momentum"], # type: ignore
weight_decay=self.config["decay"], # type: ignore
)
self.scheduler = ReduceLROnPlateau(
self.optimizer,
factor=self.config["lr_adjustment_factor"], # type: ignore
mode="max",
verbose=True,
patience=self.config["scheduler_patience"], # type: ignore
)
print("Trainer Initialized.")
for dataset in [self.train_loader, self.test_loader, self.val_loader]:
print(f"Size of set: {len(dataset)}")
def train(self):
training_pass = 0
for epoch in range(self.epochs):
running_loss = 0.0
for i, (inputs, labels, metadata) in enumerate(self.train_loader):
self.net.train()
self.optimizer.zero_grad()
outputs = self.net(inputs.float().to(self.device))
loss = self.criterion(
outputs, labels.unsqueeze(1).float().to(self.device)
)
loss.backward()
self.optimizer.step()
running_loss += loss.item()
if i > 0 and i % self.config["print_cadence"] == 0:
mean_loss = running_loss / self.config["print_cadence"]
print(
f'Epoch: {epoch}\tBatch: {i}\tLoss: {mean_loss}'
)
self.writer.add_scalar(
"Train/RunningLoss",
mean_loss,
training_pass,
)
running_loss = 0.0
training_pass += 1
train_accuracy = self.log_training_metrics(epoch)
self.log_validation_metrics(epoch)
self.scheduler.step(train_accuracy)
accuracy, metrics = self.calculate_accuracy(self.test_loader)
self.writer.add_text("Test/Accuracy", f"{accuracy}")
for key, val in metrics.items():
self.writer.add_text(f"Test/{key}", f"{val}")
self.save_model()
def log_training_metrics(self, epoch: int):
accuracy, metrics = self.calculate_accuracy(self.train_loader)
self.writer.add_scalar(f"Train/Accuracy", accuracy, epoch)
for key, val in metrics.items():
self.writer.add_scalar(f"Train/{key}", val, epoch)
return accuracy
def log_validation_metrics(self, epoch: int):
accuracy, metrics = self.calculate_accuracy(self.val_loader)
self.writer.add_scalar("Validation/Accuracy", accuracy, epoch)
for key, val in metrics.items():
self.writer.add_scalar(f"Validation/{key}", val, epoch)
return accuracy
def calculate_accuracy(self, loader: DataLoader):
truth_list: list = []
pred_list: list = []
with torch.no_grad():
self.net.eval()
correct = 0.0
total = 0.0
for inputs, labels, metadata in loader:
outputs = self.net(inputs.float().to(self.device))
sigmoid = torch.nn.Sigmoid()
preds = sigmoid(outputs)
preds = np.round(preds.detach().cpu().squeeze(1))
pred_list.extend(preds) # type: ignore
truth_list.extend(labels)
total += labels.size(0)
correct += preds.eq(labels.float()).sum().item()
print(f"Correct:\t{correct}, Incorrect:\t{total-correct}")
tn, fp, fn, tp = confusion_matrix(truth_list, pred_list).ravel()
metrics = {
"Recall": tp / (tp + fn),
"Precision": tp / (tp + fp),
"FalseNegativeRate": fn / (tn + fn),
"FalsePositiveRate": fp / (tp + fp),
}
return correct / total, metrics
def save_model(self):
print("saving...")
torch.save(self.net.state_dict(), self.output_model)