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train_and_test.py
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train_and_test.py
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from typing import Tuple
import torch
import numpy as np
from cleverhans.torch.attacks.projected_gradient_descent import projected_gradient_descent
from adversarial_attacks.ppnet_wrapper import PPNetAdversarialWrapper
from helpers import list_of_distances
from settings import masking_random_prob, img_size
def mixup_data(x: torch.Tensor, y: torch.Tensor, alpha: float = 1.0) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, float]:
lam = np.random.beta(alpha, alpha) if alpha > 0 else 1.
index = torch.randperm(x.shape[0], dtype=x.dtype, device=x.device).to(torch.long)
mixed_x = lam * x + (1 - lam) * x[index, ...]
y_a, y_b = y, y[index]
return mixed_x, y_a, y_b, lam
def _train_or_test(model, dataloader, optimizer=None, class_specific=True, use_l1_mask=True,
coefs=None, log=print, masking_type='none', neptune_run=None,
quantized_mask=False, sim_diff_weight=0.0, sim_diff_function='l1',
mixup: bool = False, adversarial_training: bool = False):
'''
model: the multi-gpu model
dataloader:
optimizer: if None, will be test evaluation
'''
is_train = optimizer is not None
n_examples = 0
n_correct = 0
n_batches = 0
total_loss = 0.0
total_cross_entropy = 0
total_cluster_cost = 0
# separation cost is meaningful only for class_specific
total_separation_cost = 0
total_avg_separation_cost = 0
total_sim_diff_loss = 0.0
if adversarial_training:
adversarial_wrapper = PPNetAdversarialWrapper(model=model, focal_sim=False)
else:
adversarial_wrapper = None
for i, (image, label) in enumerate(dataloader):
input = image.cuda()
target = label.cuda()
if mixup:
input, targets_a, targets_b, lam = mixup_data(input, target, 0.5)
# torch.enable_grad() has no effect outside of no_grad()
grad_req = torch.enable_grad() if is_train else torch.no_grad()
with grad_req:
# nn.Module has implemented __call__() function
# so no need to call .forward
if masking_type == 'random_no_loss' or masking_type == 'high_act_aug' and is_train:
min_box_size = img_size // 8
max_box_size = img_size // 2
masking_prob = 0.5
max_num_boxes = 5
with torch.no_grad():
# TODO move this to the Dataset
for sample_i in range(input.shape[0]):
if np.random.random() < masking_prob:
continue
possible_modifications = [
torch.zeros_like(input[sample_i]),
torch.rand(input.shape[1:]),
input[sample_i] + torch.rand(input[sample_i].shape, device=input.device)
]
num_boxes = np.random.randint(1, max_num_boxes + 1)
for _ in range(num_boxes):
width = np.random.randint(min_box_size, max_box_size)
height = np.random.randint(min_box_size, max_box_size)
left = np.random.randint(0, img_size - width)
top = np.random.randint(0, img_size - height)
input[sample_i, top:top + height, left:left + width] = \
possible_modifications[np.random.randint(3)][top:top + height, left:left + width]
if adversarial_training:
input = projected_gradient_descent(
model_fn=adversarial_wrapper,
x=input,
eps=0.4,
eps_iter=0.01,
nb_iter=40,
norm=np.inf,
)
output, min_distances, all_similarities = model(input, return_all_similarities=True)
sim_diff_loss = 0.0
if class_specific:
# input.shape, output.shape,
# min_distances.shape, all_similarities.shape,
# model.module.prototype_class_identity.shape
#
# torch.Size([40, 3, 224, 224]) torch.Size([40, 200])
# torch.Size([40, 2000]) torch.Size([40, 2000, 7, 7])
# torch.Size([2000, 200])
if masking_type == 'random':
random_mask = (torch.cuda.FloatTensor(all_similarities.shape[0], 1, all_similarities.shape[-1],
all_similarities.shape[
-1]).uniform_() > masking_random_prob).float()
random_mask_img = torch.nn.functional.interpolate(random_mask,
size=(input.shape[-1], input.shape[-1])).long()
new_input = input * random_mask_img
output2, min_distances2, all_similarities2 = model(new_input, return_all_similarities=True)
sim_diff = (all_similarities - all_similarities2) ** 2
sim_diff_loss = torch.sum(sim_diff * random_mask) / torch.sum(random_mask)
elif masking_type == 'high_act' or masking_type == 'high_act_aug':
with torch.no_grad():
proto_sim = []
proto_nums = []
for sample_i, sample_label in enumerate(label):
label_protos = model.module.prototype_class_identity[:, sample_label].nonzero()[:, 0]
proto_num = np.random.choice(label_protos)
proto_nums.append(proto_num)
proto_sim.append(all_similarities[sample_i, proto_num])
proto_sim = torch.stack(proto_sim, dim=0).unsqueeze(1)
if quantized_mask:
all_sim_scaled = torch.nn.functional.interpolate(proto_sim,
size=(input.shape[-1], input.shape[-1]),
mode='bilinear')
q = np.random.uniform(0.5, 0.98)
quantile_mask = torch.quantile(all_sim_scaled.flatten(start_dim=-2), q=q, dim=-1)
quantile_mask = quantile_mask.unsqueeze(-1).unsqueeze(-1)
high_act_mask_img = (all_sim_scaled > quantile_mask).float()
high_act_mask_act = torch.nn.functional.interpolate(high_act_mask_img,
size=(all_similarities.shape[-1],
all_similarities.shape[-1]),
mode='bilinear')
else:
proto_sim_min = proto_sim.flatten(start_dim=1).min(-1)[0] \
.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
proto_sim_norm = proto_sim - proto_sim_min
proto_sim_max = proto_sim_norm.flatten(start_dim=1).max(-1)[0] \
.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
proto_sim_norm /= proto_sim_max
high_act_mask_act = proto_sim_norm
high_act_mask_img = torch.nn.functional.interpolate(high_act_mask_act,
size=(input.shape[-1], input.shape[-1]),
mode='bilinear')
new_input = input * high_act_mask_img
output2, min_distances2, all_similarities2 = model(new_input.detach(), return_all_similarities=True)
proto_sim2 = []
for sample_i, sample_label in enumerate(label):
proto_sim2.append(all_similarities2[sample_i, proto_nums[sample_i]])
proto_sim2 = torch.stack(proto_sim2, dim=0).unsqueeze(1)
if sim_diff_function == 'l2':
sim_diff = (proto_sim - proto_sim2) ** 2
elif sim_diff_function == 'l1':
sim_diff = torch.abs(proto_sim - proto_sim2)
else:
raise ValueError(f'Unknown sim_diff_function: ', sim_diff_function)
if quantized_mask:
sim_diff_loss = torch.sum(sim_diff * high_act_mask_act) / torch.sum(high_act_mask_act)
else:
sim_diff_loss = torch.mean(sim_diff)
max_dist = (model.module.prototype_shape[1]
* model.module.prototype_shape[2]
* model.module.prototype_shape[3])
# prototypes_of_correct_class is a tensor of shape batch_size * num_prototypes
# calculate cluster cost
prototypes_of_correct_class = torch.t(model.module.prototype_class_identity[:, label]).cuda()
inverted_distances, _ = torch.max((max_dist - min_distances) * prototypes_of_correct_class, dim=1)
cluster_cost = torch.mean(max_dist - inverted_distances)
# calculate separation cost
prototypes_of_wrong_class = 1 - prototypes_of_correct_class
inverted_distances_to_nontarget_prototypes, _ = \
torch.max((max_dist - min_distances) * prototypes_of_wrong_class, dim=1)
separation_cost = torch.mean(max_dist - inverted_distances_to_nontarget_prototypes)
# calculate avg cluster cost
avg_separation_cost = \
torch.sum(min_distances * prototypes_of_wrong_class, dim=1) / torch.sum(prototypes_of_wrong_class,
dim=1)
avg_separation_cost = torch.mean(avg_separation_cost)
if use_l1_mask:
l1_mask = 1 - torch.t(model.module.prototype_class_identity).cuda()
l1 = (model.module.last_layer.weight * l1_mask).norm(p=1)
else:
l1 = model.module.last_layer.weight.norm(p=1)
else:
min_distance, _ = torch.min(min_distances, dim=1)
cluster_cost = torch.mean(min_distance)
l1 = model.module.last_layer.weight.norm(p=1)
# compute loss
if mixup:
cross_entropy = lam * \
torch.nn.functional.cross_entropy(output, targets_a) + (1 - lam) * \
torch.nn.functional.cross_entropy(output, targets_b)
else:
cross_entropy = torch.nn.functional.cross_entropy(output, target)
# evaluation statistics
_, predicted = torch.max(output.data, 1)
n_examples += target.size(0)
n_correct += (predicted == target).sum().item()
n_batches += 1
total_cross_entropy += cross_entropy.item()
total_cluster_cost += cluster_cost.item()
total_separation_cost += separation_cost.item()
total_avg_separation_cost += avg_separation_cost.item()
total_sim_diff_loss += sim_diff_loss.item() if torch.is_tensor(sim_diff_loss) else 0
# compute gradient and do SGD step
if is_train:
if class_specific:
if coefs is not None:
loss = (coefs['crs_ent'] * cross_entropy
+ coefs['clst'] * cluster_cost
+ coefs['sep'] * separation_cost
+ sim_diff_weight * sim_diff_loss
+ coefs['l1'] * l1)
else:
loss = cross_entropy + 0.8 * cluster_cost - 0.08 * separation_cost + 1e-4 * l1 + 0.1 * sim_diff_loss
else:
if coefs is not None:
loss = (coefs['crs_ent'] * cross_entropy
+ coefs['clst'] * cluster_cost
+ coefs['l1'] * l1)
else:
loss = cross_entropy + 0.8 * cluster_cost + 1e-4 * l1
if neptune_run is not None:
neptune_run['train/batch/loss'].append(loss.item())
total_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
del input
del target
del output
del predicted
del min_distances
# log('\ttime: \t{0}'.format(end - start))
# if is_train:
# log('t\loss: \t{0}'.format(total_loss / n_batches))
# log('\tcross ent: \t{0}'.format(total_cross_entropy / n_batches))
# log('\tcluster: \t{0}'.format(total_cluster_cost / n_batches))
# if class_specific:
# log('\tseparation:\t{0}'.format(total_separation_cost / n_batches))
# log('\tavg separation:\t{0}'.format(total_avg_separation_cost / n_batches))
# log('\taccu: \t\t{0}%'.format(n_correct / n_examples * 100))
# l1 = model.module.last_layer.weight.norm(p=1).item()
# log('\tl1: \t\t{0}'.format(l1))
p = model.module.prototype_vectors.view(model.module.num_prototypes, -1).cpu()
with torch.no_grad():
p_avg_pair_dist = torch.mean(list_of_distances(p, p)).item()
# log('\tp dist pair: \t{0}'.format(p_avg_pair_dist))
# if isinstance(sim_diff_loss, torch.Tensor):
# sim_diff_loss = sim_diff_loss.item()
# log('\tsim diff: \t{0}'.format(sim_diff_loss))
converged = total_cluster_cost < total_separation_cost
metrics = {
'loss_cross_entropy': total_cross_entropy / n_batches,
'loss_cluster': total_cluster_cost / n_batches,
'loss_separation': total_separation_cost,
'avg_separation': total_avg_separation_cost,
'l1': l1,
'p_avg_pair_dist': p_avg_pair_dist,
'sim_diff_loss': total_sim_diff_loss / n_batches
}
if is_train:
metrics['loss'] = total_loss / n_batches
return n_correct / n_examples, converged, metrics
def train(model, dataloader, optimizer, class_specific=False, coefs=None, log=print, masking_type='none',
neptune_run=None, quantized_mask=False, sim_diff_weight=0.0, sim_diff_function='l1',
mixup: bool = True, adversarial_training: bool = False):
assert (optimizer is not None)
model.train()
return _train_or_test(model=model, dataloader=dataloader, optimizer=optimizer,
class_specific=class_specific, coefs=coefs, log=log, masking_type=masking_type,
neptune_run=neptune_run, quantized_mask=quantized_mask,
sim_diff_function=sim_diff_function, sim_diff_weight=sim_diff_weight, mixup=mixup,
adversarial_training=adversarial_training)
def test(model, dataloader, class_specific=False, log=print, masking_type='none', neptune_run=None,
quantized_mask=False, sim_diff_weight=0.0, sim_diff_function='l1'):
model.eval()
return _train_or_test(model=model, dataloader=dataloader, optimizer=None,
class_specific=class_specific, log=log, masking_type=masking_type, neptune_run=neptune_run,
quantized_mask=quantized_mask, sim_diff_weight=sim_diff_weight,
sim_diff_function=sim_diff_function)
def last_only(model, log=print):
for p in model.module.features.parameters():
p.requires_grad = False
for p in model.module.add_on_layers.parameters():
p.requires_grad = False
model.module.prototype_vectors.requires_grad = False
for p in model.module.last_layer.parameters():
p.requires_grad = True
log('\tlast layer')
def warm_only(model, log=print):
for p in model.module.features.parameters():
p.requires_grad = False
for p in model.module.add_on_layers.parameters():
p.requires_grad = True
model.module.prototype_vectors.requires_grad = True
for p in model.module.last_layer.parameters():
p.requires_grad = True
log('\twarm')
def joint(model, log=print):
for p in model.module.features.parameters():
p.requires_grad = True
for p in model.module.add_on_layers.parameters():
p.requires_grad = True
model.module.prototype_vectors.requires_grad = True
for p in model.module.last_layer.parameters():
p.requires_grad = True
log('\tjoint')