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train_and_test.py
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train_and_test.py
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import time
import torch
from helpers import list_of_distances, make_one_hot
from settings import img_size
import torch.nn.functional as F
torch.autograd.set_detect_anomaly(True)
def _train_or_test(model, dataloader, optimizer=None, class_specific=False, use_l1_mask=True,
coefs=None, log=print, is_attn=True, k=10, ppnet=None):
'''
model: the multi-gpu model
dataloader:
optimizer: if None, will be test evaluation
'''
is_train = optimizer is not None
start = time.time()
n_examples = 0
n_correct = 0
n_batches = 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
for i, (image, label) in enumerate(dataloader):
attn = image[:, 3, :, :].cuda()
input = image[:, :3, :, :].cuda()
target = label.cuda()
batch_size = attn.size()[0]
# 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
output, min_distances, activations, patterns = model(input)
# compute loss
cross_entropy = torch.nn.functional.cross_entropy(output, target)
if class_specific:
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 for class-specific visual words
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)
# 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()
if class_specific:
total_separation_cost += separation_cost.item()
total_avg_separation_cost += avg_separation_cost.item()
# 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
+ coefs['l1'] * l1
)
else:
loss = cross_entropy + 0.8 * cluster_cost - 0.08 * separation_cost + 1e-4 * l1
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 is_attn:
# calculate the attention coverage loss
largest = torch.topk(min_distances, k, dim=1, largest=False).indices
largest = largest.unsqueeze(-1).unsqueeze(-1).expand(largest.shape[0], k, patterns.shape[2], patterns.shape[3])
patterns = torch.gather(patterns, dim=1, index = largest)
patterns = F.interpolate(patterns, [img_size, img_size], mode='bilinear', align_corners=True)
patterns = torch.max(patterns, dim=1)[0]
patterns = patterns.view(batch_size, -1)
patterns = patterns / (patterns.max(dim=1, keepdim=True).values + 1e-5)
attn_loss_f = torch.nn.MSELoss(reduction='mean')
attn_loss = attn_loss_f(patterns, attn.view(batch_size, -1))
loss = loss + 10 * attn_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
del input
del target
del output
del predicted
del min_distances
end = time.time()
log('\ttime: \t{0}'.format(end - start))
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))
log('\tl1: \t\t{0}'.format(model.module.last_layer.weight.norm(p=1).item()))
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))
log('\tp dist pair: \t{0}'.format(p_avg_pair_dist.item()))
return n_correct / n_examples
def train(model, dataloader, optimizer, class_specific=True, coefs=None, log=print, train_attn=False, ppnet=None):
assert(optimizer is not None)
log('\ttrain')
model.train()
if train_attn:
print("True")
return _train_or_test(model=model, dataloader=dataloader, optimizer=optimizer,
class_specific=class_specific, coefs=coefs, log=log, is_attn=True, ppnet=ppnet)
else:
# print("False")
# return _train_or_test(model=model, dataloader=dataloader, optimizer=optimizer,
# class_specific=class_specific, coefs=coefs, log=log, is_attn=False)
print("False")
return _train_or_test(model=model, dataloader=dataloader, optimizer=optimizer,
class_specific=class_specific, coefs=coefs, log=log, is_attn=True)
def train_warm(model, dataloader, optimizer, class_specific=True, coefs=None, log=print):
assert(optimizer is not None)
log('\ttrain')
model.train()
print("False")
return _train_or_test(model=model, dataloader=dataloader, optimizer=optimizer,
class_specific=class_specific, coefs=coefs, log=log, is_attn=False)
def test(model, dataloader, class_specific=True, log=print):
log('\ttest')
model.eval()
return _train_or_test(model=model, dataloader=dataloader, optimizer=None,
class_specific=class_specific, log=log)
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')