def eval_fidelity(tree: ProtoTree,
test_loader: DataLoader,
device,
log: Log = None,
progress_prefix: str = 'Fidelity') -> dict:
tree = tree.to(device)
# Keep an info dict about the procedure
info = dict()
# Make sure the model is in evaluation mode
tree.eval()
# Show progress on progress bar
test_iter = tqdm(enumerate(test_loader),
total=len(test_loader),
desc=progress_prefix,
ncols=0)
distr_samplemax_fidelity = 0
distr_greedy_fidelity = 0
# Iterate through the test set
for i, (xs, ys) in test_iter:
xs, ys = xs.to(device), ys.to(device)
# Use the model to classify this batch of input data, with 3 types of routing
out_distr, _ = tree.forward(xs, 'distributed')
ys_pred_distr = torch.argmax(out_distr, dim=1)
out_samplemax, _ = tree.forward(xs, 'sample_max')
ys_pred_samplemax = torch.argmax(out_samplemax, dim=1)
out_greedy, _ = tree.forward(xs, 'greedy')
ys_pred_greedy = torch.argmax(out_greedy, dim=1)
# Calculate fidelity
distr_samplemax_fidelity += torch.sum(
torch.eq(ys_pred_samplemax, ys_pred_distr)).item()
distr_greedy_fidelity += torch.sum(
torch.eq(ys_pred_greedy, ys_pred_distr)).item()
# Update the progress bar
test_iter.set_postfix_str(f'Batch [{i + 1}/{len(test_iter)}]')
del out_distr
del out_samplemax
del out_greedy
distr_samplemax_fidelity = distr_samplemax_fidelity / float(
len(test_loader.dataset))
distr_greedy_fidelity = distr_greedy_fidelity / float(
len(test_loader.dataset))
info['distr_samplemax_fidelity'] = distr_samplemax_fidelity
info['distr_greedy_fidelity'] = distr_greedy_fidelity
log.log_message(
"Fidelity between standard distributed routing and sample_max routing: "
+ str(distr_samplemax_fidelity))
log.log_message(
"Fidelity between standard distributed routing and greedy routing: " +
str(distr_greedy_fidelity))
return info
def eval(tree: ProtoTree,
test_loader: DataLoader,
epoch,
device,
log: Log = None,
sampling_strategy: str = 'distributed',
log_prefix: str = 'log_eval_epochs',
progress_prefix: str = 'Eval Epoch') -> dict:
tree = tree.to(device)
# Keep an info dict about the procedure
info = dict()
if sampling_strategy != 'distributed':
info['out_leaf_ix'] = []
# Build a confusion matrix
cm = np.zeros((tree._num_classes, tree._num_classes), dtype=int)
# Make sure the model is in evaluation mode
tree.eval()
# Show progress on progress bar
test_iter = tqdm(enumerate(test_loader),
total=len(test_loader),
desc=progress_prefix + ' %s' % epoch,
ncols=0)
# Iterate through the test set
for i, (xs, ys) in test_iter:
xs, ys = xs.to(device), ys.to(device)
# Use the model to classify this batch of input data
out, test_info = tree.forward(xs, sampling_strategy)
ys_pred = torch.argmax(out, dim=1)
# Update the confusion matrix
cm_batch = np.zeros((tree._num_classes, tree._num_classes), dtype=int)
for y_pred, y_true in zip(ys_pred, ys):
cm[y_true][y_pred] += 1
cm_batch[y_true][y_pred] += 1
acc = acc_from_cm(cm_batch)
test_iter.set_postfix_str(
f'Batch [{i + 1}/{len(test_iter)}], Acc: {acc:.3f}')
# keep list of leaf indices where test sample ends up when deterministic routing is used.
if sampling_strategy != 'distributed':
info['out_leaf_ix'] += test_info['out_leaf_ix']
del out
del ys_pred
del test_info
info['confusion_matrix'] = cm
info['test_accuracy'] = acc_from_cm(cm)
log.log_message("\nEpoch %s - Test accuracy with %s routing: " %
(epoch, sampling_strategy) + str(info['test_accuracy']))
return info
def train_leaves_epoch(tree: ProtoTree,
train_loader: DataLoader,
epoch: int,
device,
progress_prefix: str = 'Train Leafs Epoch'
) -> dict:
#Make sure the tree is in eval mode for updating leafs
tree.eval()
with torch.no_grad():
_old_dist_params = dict()
for leaf in tree.leaves:
_old_dist_params[leaf] = leaf._dist_params.detach().clone()
# Optimize class distributions in leafs
eye = torch.eye(tree._num_classes).to(device)
# Show progress on progress bar
train_iter = tqdm(enumerate(train_loader),
total=len(train_loader),
desc=progress_prefix+' %s'%epoch,
ncols=0)
# Iterate through the data set
update_sum = dict()
# Create empty tensor for each leaf that will be filled with new values
for leaf in tree.leaves:
update_sum[leaf] = torch.zeros_like(leaf._dist_params)
for i, (xs, ys) in train_iter:
xs, ys = xs.to(device), ys.to(device)
#Train leafs without gradient descent
out, info = tree.forward(xs)
target = eye[ys] #shape (batchsize, num_classes)
for leaf in tree.leaves:
if tree._log_probabilities:
# log version
update = torch.exp(torch.logsumexp(info['pa_tensor'][leaf.index] + leaf.distribution() + torch.log(target) - out, dim=0))
else:
update = torch.sum((info['pa_tensor'][leaf.index] * leaf.distribution() * target)/out, dim=0)
update_sum[leaf] += update
for leaf in tree.leaves:
leaf._dist_params -= leaf._dist_params #set current dist params to zero
leaf._dist_params += update_sum[leaf] #give dist params new value
def gen_pred_vis(
tree: ProtoTree,
sample: torch.Tensor,
sample_dir: str,
folder_name: str,
args: argparse.Namespace,
classes: tuple,
pred_kwargs: dict = None,
):
pred_kwargs = pred_kwargs or dict() # TODO -- assert deterministic routing
# Create dir to store visualization
img_name = sample_dir.split('/')[-1].split(".")[-2]
if not os.path.exists(os.path.join(args.log_dir, folder_name)):
os.makedirs(os.path.join(args.log_dir, folder_name))
destination_folder = os.path.join(os.path.join(args.log_dir, folder_name),
img_name)
if not os.path.isdir(destination_folder):
os.mkdir(destination_folder)
if not os.path.isdir(destination_folder + '/node_vis'):
os.mkdir(destination_folder + '/node_vis')
# Get references to where source files are stored
upsample_path = os.path.join(
os.path.join(args.log_dir, args.dir_for_saving_images),
'pruned_and_projected')
nodevis_path = os.path.join(args.log_dir, 'pruned_and_projected/node_vis')
local_upsample_path = os.path.join(destination_folder,
args.dir_for_saving_images)
# Get the model prediction
with torch.no_grad():
pred, pred_info = tree.forward(sample,
sampling_strategy='greedy',
**pred_kwargs)
probs = pred_info['ps']
label_ix = torch.argmax(pred, dim=1)[0].item()
assert 'out_leaf_ix' in pred_info.keys()
# Save input image
sample_path = destination_folder + '/node_vis/sample.jpg'
# save_image(sample, sample_path)
Image.open(sample_dir).save(sample_path)
# Save an image containing the model output
output_path = destination_folder + '/node_vis/output.jpg'
leaf_ix = pred_info['out_leaf_ix'][0]
leaf = tree.nodes_by_index[leaf_ix]
decision_path = tree.path_to(leaf)
upsample_local(tree, sample, sample_dir, folder_name, img_name,
decision_path, args)
# Prediction graph is visualized using Graphviz
# Build dot string
s = 'digraph T {margin=0;rankdir=LR\n'
# s += "subgraph {"
s += 'node [shape=plaintext, label=""];\n'
s += 'edge [penwidth="0.5"];\n'
# Create a node for the sample image
s += f'sample[image="{sample_path}"];\n'
# Create nodes for all decisions/branches
# Starting from the leaf
for i, node in enumerate(decision_path[:-1]):
node_ix = node.index
prob = probs[node_ix].item()
s += f'node_{i+1}[image="{upsample_path}/{node_ix}_nearest_patch_of_image.png" group="{"g"+str(i)}"];\n'
if prob > 0.5:
s += f'node_{i+1}_original[image="{local_upsample_path}/{node_ix}_bounding_box_nearest_patch_of_image.png" imagescale=width group="{"g"+str(i)}"];\n'
label = "Present \nSimilarity %.4f " % prob
s += f'node_{i+1}->node_{i+1}_original [label="{label}" fontsize=10 fontname=Helvetica];\n'
else:
s += f'node_{i+1}_original[image="{sample_path}" group="{"g"+str(i)}"];\n'
label = "Absent \nSimilarity %.4f " % prob
s += f'node_{i+1}->node_{i+1}_original [label="{label}" fontsize=10 fontname=Helvetica];\n'
# s += f'node_{i+1}_original->node_{i+1} [label="{label}" fontsize=10 fontname=Helvetica];\n'
s += f'node_{i+1}->node_{i+2};\n'
s += "{rank = same; " f'node_{i+1}_original' + "; " + f'node_{i+1}' + "};"
# Create a node for the model output
s += f'node_{len(decision_path)}[imagepos="tc" imagescale=height image="{nodevis_path}/node_{leaf_ix}_vis.jpg" label="{classes[label_ix]}" labelloc=b fontsize=10 penwidth=0 fontname=Helvetica];\n'
# Connect the input image to the first decision node
s += 'sample->node_1;\n'
s += '}\n'
with open(os.path.join(destination_folder, 'predvis.dot'), 'w') as f:
f.write(s)
from_p = os.path.join(destination_folder, 'predvis.dot')
to_pdf = os.path.join(destination_folder, 'predvis.pdf')
check_call('dot -Tpdf -Gmargin=0 %s -o %s' % (from_p, to_pdf), shell=True)
def train_epoch(tree: ProtoTree,
train_loader: DataLoader,
optimizer: torch.optim.Optimizer,
epoch: int,
disable_derivative_free_leaf_optim: bool,
device,
log: Log = None,
log_prefix: str = 'log_train_epochs',
progress_prefix: str = 'Train Epoch'
) -> dict:
tree = tree.to(device)
# Make sure the model is in eval mode
tree.eval()
# Store info about the procedure
train_info = dict()
total_loss = 0.
total_acc = 0.
# Create a log if required
log_loss = f'{log_prefix}_losses'
nr_batches = float(len(train_loader))
with torch.no_grad():
_old_dist_params = dict()
for leaf in tree.leaves:
_old_dist_params[leaf] = leaf._dist_params.detach().clone()
# Optimize class distributions in leafs
eye = torch.eye(tree._num_classes).to(device)
# Show progress on progress bar
train_iter = tqdm(enumerate(train_loader),
total=len(train_loader),
desc=progress_prefix+' %s'%epoch,
ncols=0)
# Iterate through the data set to update leaves, prototypes and network
for i, (xs, ys) in train_iter:
# Make sure the model is in train mode
tree.train()
# Reset the gradients
optimizer.zero_grad()
xs, ys = xs.to(device), ys.to(device)
# Perform a forward pass through the network
ys_pred, info = tree.forward(xs)
# Learn prototypes and network with gradient descent.
# If disable_derivative_free_leaf_optim, leaves are optimized with gradient descent as well.
# Compute the loss
if tree._log_probabilities:
loss = F.nll_loss(ys_pred, ys)
else:
loss = F.nll_loss(torch.log(ys_pred), ys)
# Compute the gradient
loss.backward()
# Update model parameters
optimizer.step()
if not disable_derivative_free_leaf_optim:
#Update leaves with derivate-free algorithm
#Make sure the tree is in eval mode
tree.eval()
with torch.no_grad():
target = eye[ys] #shape (batchsize, num_classes)
for leaf in tree.leaves:
if tree._log_probabilities:
# log version
update = torch.exp(torch.logsumexp(info['pa_tensor'][leaf.index] + leaf.distribution() + torch.log(target) - ys_pred, dim=0))
else:
update = torch.sum((info['pa_tensor'][leaf.index] * leaf.distribution() * target)/ys_pred, dim=0)
leaf._dist_params -= (_old_dist_params[leaf]/nr_batches)
F.relu_(leaf._dist_params) #dist_params values can get slightly negative because of floating point issues. therefore, set to zero.
leaf._dist_params += update
# Count the number of correct classifications
ys_pred_max = torch.argmax(ys_pred, dim=1)
correct = torch.sum(torch.eq(ys_pred_max, ys))
acc = correct.item() / float(len(xs))
train_iter.set_postfix_str(
f'Batch [{i + 1}/{len(train_loader)}], Loss: {loss.item():.3f}, Acc: {acc:.3f}'
)
# Compute metrics over this batch
total_loss+=loss.item()
total_acc+=acc
if log is not None:
log.log_values(log_loss, epoch, i + 1, loss.item(), acc)
train_info['loss'] = total_loss/float(i+1)
train_info['train_accuracy'] = total_acc/float(i+1)
return train_info
def train_epoch_kontschieder(tree: ProtoTree,
train_loader: DataLoader,
optimizer: torch.optim.Optimizer,
epoch: int,
disable_derivative_free_leaf_optim: bool,
device,
log: Log = None,
log_prefix: str = 'log_train_epochs',
progress_prefix: str = 'Train Epoch'
) -> dict:
tree = tree.to(device)
# Store info about the procedure
train_info = dict()
total_loss = 0.
total_acc = 0.
# Create a log if required
log_loss = f'{log_prefix}_losses'
if log is not None and epoch==1:
log.create_log(log_loss, 'epoch', 'batch', 'loss', 'batch_train_acc')
# Reset the gradients
optimizer.zero_grad()
if disable_derivative_free_leaf_optim:
print("WARNING: kontschieder arguments will be ignored when training leaves with gradient descent")
else:
if tree._kontschieder_normalization:
# Iterate over the dataset multiple times to learn leaves following Kontschieder's approach
for _ in range(10):
# Train leaves with derivative-free algorithm using normalization factor
train_leaves_epoch(tree, train_loader, epoch, device)
else:
# Train leaves with Kontschieder's derivative-free algorithm, but using softmax
train_leaves_epoch(tree, train_loader, epoch, device)
# Train prototypes and network.
# If disable_derivative_free_leaf_optim, leafs are optimized with gradient descent as well.
# Show progress on progress bar
train_iter = tqdm(enumerate(train_loader),
total=len(train_loader),
desc=progress_prefix+' %s'%epoch,
ncols=0)
# Make sure the model is in train mode
tree.train()
for i, (xs, ys) in train_iter:
xs, ys = xs.to(device), ys.to(device)
# Reset the gradients
optimizer.zero_grad()
# Perform a forward pass through the network
ys_pred, _ = tree.forward(xs)
# Compute the loss
if tree._log_probabilities:
loss = F.nll_loss(ys_pred, ys)
else:
loss = F.nll_loss(torch.log(ys_pred), ys)
# Compute the gradient
loss.backward()
# Update model parameters
optimizer.step()
# Count the number of correct classifications
ys_pred = torch.argmax(ys_pred, dim=1)
correct = torch.sum(torch.eq(ys_pred, ys))
acc = correct.item() / float(len(xs))
train_iter.set_postfix_str(
f'Batch [{i + 1}/{len(train_loader)}], Loss: {loss.item():.3f}, Acc: {acc:.3f}'
)
# Compute metrics over this batch
total_loss+=loss.item()
total_acc+=acc
if log is not None:
log.log_values(log_loss, epoch, i + 1, loss.item(), acc)
train_info['loss'] = total_loss/float(i+1)
train_info['train_accuracy'] = total_acc/float(i+1)
return train_info