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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 sklearn.metrics import roc_auc_score
import numpy as np
import pandas as pd
import csv
from helpers import list_of_distances, make_one_hot
def _train_or_test(model, dataloader, optimizer=None, class_specific=True, use_l1_mask=True,
coefs=None, log=print, save_logits=False, finer_loader=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_output = []
total_one_hot_label = []
confusion_matrix = [0,0,0,0]
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_fa_cost = 0
with_fa = False # intialization, see line 41
for i, (image, label, patient_id) in enumerate(dataloader):
# get one batch from finer datatloader
if finer_loader:
finer_image, finer_label, _ = next(iter(finer_loader))
# print(image.shape)
image = torch.cat((image, finer_image))
label = torch.cat((label, finer_label))
# print(image.shape)
if image.shape[1] == 4:
with_fa = True
fine_annotation = image[:, 3:4, :, :]
image = image[:, 0:3, :, :] #(no view, create slice)
elif image.shape[1] == 3:
fine_annotation = torch.zeros(size=(image.shape[0], 1, image.shape[2], image.shape[3])) #means everything can be relevant
image = image
fine_annotation = fine_annotation.cuda()
input = image.cuda()
target = label.cuda()
# 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, upsampled_activation = model(input)
# compute loss
cross_entropy = torch.nn.functional.cross_entropy(output, target)
# only save to csv on test
if not is_train and save_logits:
_output_scores = [",".join([str(score) for score in scores.cpu().numpy()]) for scores in output]
write_file = './logit_csvs/0218_training_3_class_margin_logits.csv'
with open(write_file, mode='a') as logit_file:
logit_writer = csv.writer(logit_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
for _index in range(len(patient_id)):
logit_writer.writerow([patient_id[_index], _output_scores[_index]])
log(f'Wrote to {write_file}.')
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)
# print("before change")
# 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)
# print("after change")
# 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)
#fine annotation loss
fine_annotation_cost = 0
if with_fa:
proto_num_per_class = model.module.num_prototypes // model.module.num_classes
all_white_mask = torch.ones(image.shape[2], image.shape[3]).cuda()
for index in range(image.shape[0]):
fine_annotation_cost += torch.norm(upsampled_activation[index, :label[index] * proto_num_per_class] * (1 * all_white_mask)) + \
torch.norm(upsampled_activation[index, label[index] * proto_num_per_class : (label[index] + 1) * proto_num_per_class] * (1 * fine_annotation[index])) + \
torch.norm(upsampled_activation[index, (label[index]+1) * proto_num_per_class:] * (1 * all_white_mask))
else:
min_distance, _ = torch.min(min_distances, dim=1)
# label=0 negative, label=1 positive, minimize cluster loss maximize separation loss
# all prototypes are positive
positive_sample_index = torch.flatten(torch.nonzero(label)).tolist()
negative_sample_index = torch.flatten(torch.nonzero(label == 0)).tolist()
if len(positive_sample_index) > 0:
positive_proto_distance = min_distance[positive_sample_index]
else:
positive_proto_distance = torch.zeros(1)
if len(negative_sample_index) > 0:
negative_proto_distance = min_distance[negative_sample_index]
else:
negative_proto_distance = torch.zeros(1)
cluster_cost = torch.mean(positive_proto_distance)
separation_cost = torch.mean(negative_proto_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()
# confusion matrix
for t_idx, t in enumerate(target):
if predicted[t_idx] == t and predicted[t_idx] == 1: # true positive
confusion_matrix[0] += 1
elif t == 0 and predicted[t_idx] == 1:
confusion_matrix[1] += 1 # false positives
elif t == 1 and predicted[t_idx] == 0:
confusion_matrix[2] += 1 # false negative
else:
confusion_matrix[3] += 1
# one hot label for AUC
one_hot_label = np.zeros(shape=(len(target), model.module.num_classes))
for k in range(len(target)):
one_hot_label[k][target[k].item()] = 1
prob = torch.nn.functional.softmax(output, dim=1)
total_output.extend(prob.data.cpu().numpy())
total_one_hot_label.extend(one_hot_label)
# one hot label for AUC
n_batches += 1
total_cross_entropy += cross_entropy.item()
total_cluster_cost += cluster_cost.item()
total_separation_cost += separation_cost.item()
total_fa_cost += fine_annotation_cost
if class_specific:
total_avg_separation_cost += avg_separation_cost.item()
# compute gradient and do SGD step
if is_train:
if coefs is not None:
loss = (coefs['crs_ent'] * cross_entropy
+ coefs['clst'] * cluster_cost
+ coefs['sep'] * separation_cost
+ coefs['l1'] * l1
+ coefs['fine'] * fine_annotation_cost)
else:
loss = cross_entropy + 0.8 * cluster_cost - 0.08 * separation_cost + 1e-4 * l1
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))
log('\tseparation:\t{0}'.format(total_separation_cost / n_batches))
log('\tfine annotation:\t{0}'.format(total_fa_cost / n_batches))
if class_specific:
log('\tavg separation:\t{0}'.format(total_avg_separation_cost / n_batches))
avg_auc = 0
for auc_idx in range(len(total_one_hot_label[0])):
avg_auc += roc_auc_score(np.array(total_one_hot_label)[:, auc_idx], np.array(total_output)[:, auc_idx]) / len(total_one_hot_label[0])
log("\tauc score for class {} is: \t\t{}".format(auc_idx,
roc_auc_score(np.array(total_one_hot_label)[:, auc_idx], np.array(total_output)[:, auc_idx])))
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()))
log('\tthe confusion matrix is: \t\t{0}'.format(confusion_matrix))
return avg_auc
def train(model, dataloader, optimizer, class_specific=False, coefs=None, log=print, finer_loader=None):
assert(optimizer is not None)
log('\ttrain')
model.train()
return _train_or_test(model=model, dataloader=dataloader, optimizer=optimizer,
class_specific=class_specific, coefs=coefs, log=log, finer_loader=finer_loader)
def test(model, dataloader, class_specific=False, log=print, save_logits=False):
log('\ttest')
model.eval()
return _train_or_test(model=model, dataloader=dataloader, optimizer=None,
class_specific=class_specific, log=log, save_logits=save_logits)
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')