def external_entry_point(hypo_to_use,
path_for_data_set=r"dataset\HC_Body_Temperature.txt",
num_of_runs=8,
length_of_round=100):
(data_set) = data_prep.get_data_set_from_path(
path_for_data_set) # read data set from path
for i in range(1, num_of_runs + 1):
list_of_acc_on_train = []
list_of_acc_on_test = []
for j in range(0, length_of_round):
(shuffled_train_set,
shuffled_test_set) = data_prep.shuffle_dataset(
data_set) # shuffle data every iteration
H_set_of_hypos = AdaBoost_Algo(shuffled_train_set, i,
hypo_to_use) # get i best hypos
train_acc = utils.calc_accuracy(
shuffled_train_set, H_set_of_hypos,
hypo_to_use) # calc train acc on this hypo group
list_of_acc_on_train.append(train_acc) # collect to calc avg
test_acc = utils.calc_accuracy(shuffled_test_set, H_set_of_hypos,
hypo_to_use)
list_of_acc_on_test.append(test_acc)
print("avg TRAIN acc for round: ", i, " is: ",
"%.3f" % utils.calc_avg(list_of_acc_on_train))
print("avg TEST acc for round: ", i, " is: ",
"%.3f" % utils.calc_avg(list_of_acc_on_test))
def log_confusion_matrices(self, print_matrix = True, mod_name = ''):
cm = generate_cm(self.best_model_preds)
if print_matrix:
print(cm)
self.experiment.log_confusion_matrix(labels=self.train_loader.dataset.dataset.classes, matrix=cm.tolist(), title = mod_name + "Confusion matrix, individual clips", file_name= mod_name + "individual_clips.json")
cm = confusion_matrix(*calc_accuracy(self.best_model_preds, method = 'sum_predictions', export_for_cm=True))
self.experiment.log_confusion_matrix(labels=self.train_loader.dataset.dataset.classes, matrix=cm, title = mod_name + "Confusion matrix, sum predictions", file_name= mod_name + "sum_predictions.json")
cm = confusion_matrix(*calc_accuracy(self.best_model_preds, method = 'majority_vote', export_for_cm=True))
self.experiment.log_confusion_matrix(labels=self.train_loader.dataset.dataset.classes, matrix=cm, title = mod_name + "Confusion matrix, majority vote", file_name= mod_name + "majority_vote.json")
def train(n_epochs=50,
lbcnn_depth=2,
learning_rate=1e-2,
momentum=0.9,
weight_decay=1e-4,
lr_scheduler_step=5):
start = time.time()
models_dir = os.path.dirname(MODEL_PATH)
if not os.path.exists(models_dir):
os.makedirs(models_dir)
train_loader = get_mnist_loader(train=True)
test_loader = get_mnist_loader(train=False)
model = Lbcnn(depth=lbcnn_depth)
use_cuda = torch.cuda.is_available()
if use_cuda:
model = model.cuda()
best_accuracy = 0.
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(filter(lambda param: param.requires_grad,
model.parameters()),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay,
nesterov=True)
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=lr_scheduler_step)
for epoch in range(n_epochs):
for batch_id, (inputs, labels) in enumerate(
tqdm(train_loader, desc="Epoch {}/{}".format(epoch,
n_epochs))):
if use_cuda:
inputs = inputs.cuda()
labels = labels.cuda()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
accuracy_train = calc_accuracy(model, loader=train_loader)
accuracy_test = calc_accuracy(model, loader=test_loader)
print("Epoch {} accuracy: train={:.3f}, test={:.3f}".format(
epoch, accuracy_train, accuracy_test))
if accuracy_train > best_accuracy:
best_accuracy = accuracy_train
torch.save((lbcnn_depth, model.state_dict()), MODEL_PATH)
scheduler.step(epoch=epoch)
train_duration_sec = int(time.time() - start)
print('Finished Training. Total training time: {} sec'.format(
train_duration_sec))
def log_cv_confusion_matrices(self, mod_name = ''):
cm_all_videos = np.empty((10,6,6))
cm_sum_predictions = np.empty((10,6,6))
cm_majority_vote = np.empty((10,6,6))
for i, preds in enumerate(self.best_folds_model_preds):
cm_all_videos[i] = generate_cm(preds)
cm_sum_predictions[i] = confusion_matrix(*calc_accuracy(preds, method = 'sum_predictions', export_for_cm=True))
cm_majority_vote[i] = confusion_matrix(*calc_accuracy(preds, method = 'majority_vote', export_for_cm=True))
cm = cm_all_videos.sum(axis=0)
self.experiment.log_confusion_matrix(labels=self.train_loader.dataset.dataset.classes, matrix=cm, title = mod_name + "Confusion matrix, individual clips", file_name= mod_name + "individual_clips.json")
cm = cm_sum_predictions.sum(axis=0)
self.experiment.log_confusion_matrix(labels=self.train_loader.dataset.dataset.classes, matrix=cm, title = mod_name + "Confusion matrix, sum predictions", file_name= mod_name + "sum_predictions.json")
cm = cm_majority_vote.sum(axis=0)
self.experiment.log_confusion_matrix(labels=self.train_loader.dataset.dataset.classes, matrix=cm, title = mod_name + "Confusion matrix, majority vote", file_name= mod_name + "majority_vote.json")
def validate(data_loader, model):
losses = AverageMeter()
accuracy = AverageMeter()
model.eval()
with torch.no_grad():
tq = tqdm(data_loader, desc="Val progress: ")
for idx, (input_seq, target, _) in enumerate(tq):
input_seq = input_seq.to(cuda)
target = target.to(cuda)
B = input_seq.size(0)
output, _ = model(input_seq)
[_, N, D] = output.size()
output = output.view(B * N, D)
target = target.repeat(1, N).view(-1)
loss = criterion(output, target)
acc = calc_accuracy(output, target)
losses.update(loss.item(), B)
accuracy.update(acc.item(), B)
tq.set_postfix({
'loss': losses.avg,
'acc': accuracy.avg,
})
print('Val - Loss {loss.avg:.4f}\t'
'Acc: {acc.avg:.4f} \t'.format(loss=losses, acc=accuracy))
return losses.avg, accuracy.avg
def train():
net.train()
losses = []
accuracies = []
for image, question, answer in tqdm(train_dataloader, desc='train'):
image, question, answer = image.cuda(), question.cuda(), answer.cuda()
pred, _ = net(image, question)
loss = criterion(pred, answer)
optimizer.zero_grad()
loss.backward()
optimizer.step()
tb.add_scalar('train_loss', loss)
tb.iter()
losses.append(loss.item())
accuracy = calc_accuracy(pred, answer)
accuracies += [accuracy] * answer.size(0)
return {
'loss': sum(losses) / len(losses),
'acc': sum(accuracies) / len(accuracies),
}
def train(epoch, run, mod_name=''):
total_train_loss = 0
total_train_correct = 0
incorrect_classifications_train = []
epoch_classifications_train = []
run.model.train()
for batch_number, (images, labels, paths) in enumerate(run.train_loader):
# for i, (image, label, path) in enumerate(zip(images, labels, paths)):
# save_plot_clip_frames(image, label, path, added_info_to_path = epoch)
if run.grayscale:
images = torch.unsqueeze(
images, 1).double() # added channel dimensions (grayscale)
else:
images = images.float().permute(0, 4, 1, 2, 3).float()
labels = labels.long()
if torch.cuda.is_available():
images, labels = images.cuda(), labels.cuda()
run.optimizer.zero_grad(
) # Whenever pytorch calculates gradients it always adds it to whatever it has, so we need to reset it each batch.
preds = run.model(images) # Pass Batch
loss = run.criterion(preds, labels) # Calculate Loss
total_train_loss += loss.item()
loss.backward(
) # Calculate Gradients - the gradient is the direction we need to move towards the loss function minimum (LR will tell us how far to step)
run.optimizer.step(
) # Update Weights - the optimizer is able to update the weights because we passed it the weights as an argument in line 4.
num_correct = get_num_correct(preds, labels)
total_train_correct += num_correct
run.experiment.log_metric(mod_name + "Train batch accuracy",
num_correct / len(labels) * 100,
step=run.log_number_train)
run.experiment.log_metric(mod_name + "Avg train batch loss",
loss.item(),
step=run.log_number_train)
run.log_number_train += 1
# print('Train: Batch number:', batch_number, 'Num correct:', num_correct, 'Accuracy:', "{:.2%}".format(num_correct/len(labels)), 'Loss:', loss.item())
incorrect_classifications_train.append(
get_mistakes(preds, labels, paths))
for prediction in zip(preds, labels, paths):
epoch_classifications_train.append(prediction)
epoch_accuracy = calc_accuracy(epoch_classifications_train)
run.experiment.log_metric(mod_name + "Train epoch accuracy",
epoch_accuracy,
step=epoch)
run.experiment.log_metric(mod_name + "Avg train epoch loss",
total_train_loss / batch_number,
step=epoch)
print('\nTrain: Epoch:', epoch, 'num correct:', total_train_correct,
'Accuracy:',
str(epoch_accuracy) + '%')
def evaluate_data(evaluation_type, test_labels, parameter_string, fold,
all_probs, sess, init_opt, ys):
logging.info('# {} evaluation'.format(evaluation_type))
test_probs = []
test_results = []
for _ in range(num_test_bathces):
tmp_probs, tmp_results, tmp_labels = sess.run(
[probs_test, pred_test, ys])
test_probs.extend(tmp_probs)
test_results.extend(tmp_results)
test_labels.extend(tmp_labels)
accuracy = calc_accuracy(test_results, test_labels)
logging.info('The {} accuracy of parameter {} fold {} is {}'.format(
evaluation_type, parameter_string, fold, accuracy))
if evaluation_type == 'Test':
if fold == 0:
all_probs = np.array(test_probs)
else:
all_probs += np.array(test_probs)
logging.info('Reset the test iteration')
if fold != cfg.fold_num - 1:
test_labels = []
if evaluation_type == 'Dev':
sess.run(init_opt)
else:
pass
return all_probs, test_labels, accuracy
def validate(data_loader, model):
losses = AverageMeter()
accuracy = AverageMeter()
model.eval()
with torch.no_grad():
for idx, (input_seq, target) in tqdm(enumerate(data_loader),
total=len(data_loader)):
input_seq = input_seq.to(cuda)
target = target.to(cuda)
B = input_seq.size(0)
output, _ = model(input_seq)
[_, N, D] = output.size()
output = output.view(B * N, D)
target = target.repeat(1, N).view(-1)
loss = criterion(output, target)
acc = calc_accuracy(output, target)
losses.update(loss.item(), B)
accuracy.update(acc.item(), B)
print('Loss {loss.avg:.4f}\t'
'Acc: {acc.avg:.4f} \t'.format(loss=losses, acc=accuracy))
return losses.avg, accuracy.avg
def train(data_loader, model, optimizer, epoch):
losses = AverageMeter()
accuracy = AverageMeter()
model.train()
global iteration
for idx, (input_seq, target) in enumerate(data_loader):
tic = time.time()
input_seq = input_seq.to(cuda)
target = target.to(cuda)
B = input_seq.size(0)
output, _ = model(input_seq)
# visualize
if (iteration == 0) or (iteration == args.print_freq):
if B > 2: input_seq = input_seq[0:2,:]
writer_train.add_image('input_seq',
de_normalize(vutils.make_grid(
input_seq.transpose(2,3).contiguous().view(-1,3,args.img_dim,args.img_dim),
nrow=args.num_seq*args.seq_len)),
iteration)
del input_seq
[_, N, D] = output.size()
output = output.view(B*N, D)
target = target.repeat(1, N).view(-1)
loss = criterion(output, target)
acc = calc_accuracy(output, target)
del target
losses.update(loss.item(), B)
accuracy.update(acc.item(), B)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if idx % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.local_avg:.4f})\t'
'Acc: {acc.val:.4f} ({acc.local_avg:.4f}) T:{3:.2f}\t'.format(
epoch, idx, len(data_loader), time.time()-tic,
loss=losses, acc=accuracy))
total_weight = 0.0
decay_weight = 0.0
for m in model.parameters():
if m.requires_grad: decay_weight += m.norm(2).data
total_weight += m.norm(2).data
print('Decay weight / Total weight: %.3f/%.3f' % (decay_weight, total_weight))
writer_train.add_scalar('local/loss', losses.val, iteration)
writer_train.add_scalar('local/accuracy', accuracy.val, iteration)
iteration += 1
return losses.local_avg, accuracy.local_avg
def main(argv=None):
all_data = pickle.load(open(FLAGS.CUB_data + 'cub_image_dict.pkl', 'r'))
attention_dict = pickle.load(
open(FLAGS.CUB_data + 'cub_attention_dict.pkl', 'r'))
knowledge_dict = pickle.load(
open(FLAGS.CUB_data + 'cub_knowledge_dict.pkl', 'r'))
train_files, train_labels, test_files, test_labels = load_data()
test_f = []
test_l = []
test_fine_f = []
test_fine_l = []
for i in xrange(len(test_labels)):
if 'layer_fine' in all_data[test_labels[i]].keys():
# continue
test_f.append(test_files[i])
test_l.append(test_labels[i])
test_fine_f.append(test_files[i] + 'f')
test_fine_l.append(test_labels[i])
else:
test_f.append(test_files[i])
test_l.append(test_labels[i])
train_data = Dataset(train_files, train_labels, 64, attention_dict,
knowledge_dict, all_data)
test_data = Dataset(test_f,
test_l,
64,
attention_dict,
knowledge_dict,
all_data,
is_train=False)
test_data_fine = Dataset(test_fine_f,
test_fine_l,
64,
attention_dict,
knowledge_dict,
all_data,
is_train=False)
the_model = Model(lr=0.002)
fine_predict = the_model.classifier.predict(
input_fn=test_data_fine.input_fn)
coarse_predict = the_model.classifier.predict(input_fn=test_data.input_fn)
accuracy = utils.calc_accuracy(test_l, fine_predict, coarse_predict,
all_data)
print(accuracy)
#the_model.classifier.evaluate(input_fn=test_data.input_fn)
'''
for i in xrange(FLAGS.epoch):
the_model.classifier.evaluate(input_fn=test_data_fine.input_fn)
fine_predict = the_model.classifier.predict(input_fn=test_data_fine.input_fn)
coarse_predict = the_model.classifier.predict(input_fn=test_data.input_fn)
the_model.classifier.train(input_fn=train_data.input_fn,
hooks=the_model.train_hooks,
steps=200)
the_model.classifier.evaluate(input_fn=test_data.input_fn)
'''
'''
def test(model=None):
if model is None:
assert os.path.exists(MODEL_PATH), "Train a model first"
lbcnn_depth, state_dict = torch.load(MODEL_PATH)
model = Lbcnn(depth=lbcnn_depth)
model.load_state_dict(state_dict)
loader = get_mnist_loader(train=False)
accuracy = calc_accuracy(model, loader=loader, verbose=True)
print("MNIST test accuracy: {:.3f}".format(accuracy))
def reduced_rule2(rules, training_corpus, predictions, accuracy):
reduced_rules = []
for temprule in rules.keys():
if (rules[temprule] > 500):
copypredictions = copy.deepcopy(predictions)
singlerule = []
singlerule.append(temprule)
new_predictions = apply_rule2(copypredictions, singlerule)
newaccuracy = utils.calc_accuracy(training_corpus, new_predictions)
if (newaccuracy > accuracy):
reduced_rules.append(temprule)
return reduced_rules
def reduced_rule1(rules, train_sents, predict_sents, accuracy):
reduced_rules = []
for temprule in rules.keys():
if (rules[temprule] > 500):
copypredict_sents = copy.deepcopy(predict_sents)
singlerule = []
singlerule.append(temprule)
newPrediction = apply_rule1(copypredict_sents, singlerule)
newaccuracy = utils.calc_accuracy(train_sents, newPrediction)
if (newaccuracy > accuracy):
reduced_rules.append(temprule)
return reduced_rules
def val_(dataloader):
net.eval()
losses = []
accuracies = []
with torch.no_grad():
for image, question, answer in tqdm(dataloader, desc='val'):
image, question, answer = image.cuda(), question.cuda(), answer.cuda()
pred, _ = net(image, question)
loss = criterion(pred, answer)
losses.append(loss.item())
accuracy = calc_accuracy(pred, answer)
accuracies += [accuracy] * answer.size(0)
return sum(losses) / len(losses), sum(accuracies) / len(accuracies)
def test_run(data_type, lower_and_remove_punctuation, remove_stop_words, distance_method):
"""
Performs a test run, according to the given parameters
:param data_type: Defines how to store the sentences, expects: 'boolean' / 'tf' / 'tfidf'
:param lower_and_remove_punctuation: bool, if true turns all words to lower case and removes punctuation
:param remove_stop_words: bool, if true removes all stop words
:param distance_method: defines how to calculate distance, expects: 'euclidean' / 'cosine'
:return: accuracy, the accuracy of the test run
"""
file_name = "./dataset/amazon_cells_labelled_full.txt"
train_file_name = "./dataset/amazon_cells_labelled_train.txt"
test_file_name = "./dataset/amazon_cells_labelled_test.txt"
data = FileReader(file_name, lower_and_remove_punctuation, remove_stop_words)
train_set, _ = data.build_set(data_type, train_file_name)
test_set, _ = data.build_set(data_type, test_file_name)
classifier = RocchioClassifier(train_set)
accuracy = calc_accuracy(test_set, classifier, distance_method)
return accuracy
def test_solver():
num_test_cases = 50
# Generate Test cases
generate_test_cases(100, 450, 3, num_test_cases)
# Obtain labels from online solver
obtain_labels(num_test_cases)
row_solution = []
time_taken_total = 0
# Loop through test cases
for i in range(1, num_test_cases + 1):
file = os.path.join(base_dir, f"test_case_{i}.cnf")
_, _, cnf = read_data(file)
start = time.perf_counter()
# Run our solver
sat, _ = CDCL(cnf, single_UIP, branching_heuristic)
time_taken = time.perf_counter() - start
time_taken_total += time_taken
# Store solution
row_solution.append([i, sat, time_taken])
print(time_taken)
# Save solution
with open(os.path.join(base_dir, 'predictions.csv'), 'w',
newline='') as csvfile:
spamwriter = csv.writer(csvfile,
delimiter=' ',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
for row in row_solution:
spamwriter.writerow(row)
# Print metrics
accuracy, average_time_slower = calc_accuracy(num_test_cases)
print(f"Accuracy = {accuracy * 100}%")
print(f"Average Time Taken: {time_taken_total / num_test_cases}")
print(f"Slower by {average_time_slower}s on average")
def calc_distinct(results_dict, config):
tf.reset_default_graph()
print('Calculating metrics for: Distinct')
# ============= Metrics Folder - Distinct =============
output_dir = os.path.join(config['log_dir'], config['name'], 'test', 'metrics', 'distinct')
logs_dir = os.path.join(output_dir, 'logs')
if not os.path.exists(logs_dir):
os.makedirs(logs_dir)
# ============= Experiment Parameters =============
BATCH_SIZE = config['metrics_batch_size']
EPOCHS = config['metrics_epochs']
TEST_RATIO = config['metrics_test_ratio']
NUM_BINS = config['num_bins']
if 'k_dim' in config.keys():
N_KNOBS = config['k_dim']
elif 'w_dim' in config.keys():
N_KNOBS = config['w_dim']
else:
print('Number of knobs not specified. Returning...')
return {}
TARGET_CLASS = config['target_class']
if N_KNOBS <= 1:
print('This model has only one dimension. Distinctness metrics are not applicable.')
return {}
channels = config['num_channel']
input_size = config['input_size']
dataset = config['dataset']
# ============= Data =============
data = _EMPTY_ARR
labels = _EMPTY_ARR
source_len = len(results_dict['real_imgs'])
for dim in range(N_KNOBS):
for bin_i in range(NUM_BINS):
data_dim_bin = np.append(results_dict['real_imgs'], results_dict['fake_t_imgs'][:, dim, bin_i], axis=-1)
# dimension dim has been switched
switched_dim = np.ones(source_len, dtype=int)*dim
# unless the real probability and fake target probability are the same,
# in which no dimension has been switched
fixed_indices = (np.around(results_dict['real_ps'][:, dim, bin_i, TARGET_CLASS], decimals=2) ==
results_dict['fake_target_ps'][:, dim, bin_i])
labels_dim_bin = np.eye(N_KNOBS)[switched_dim]
labels_dim_bin[fixed_indices] = 0
data = safe_append(data, data_dim_bin)
labels = safe_append(labels, labels_dim_bin)
data_len = len(data)
data_inds = np.array(range(data_len))
np.random.shuffle(data_inds)
train_inds = data_inds[int(data_len * TEST_RATIO):]
test_inds = data_inds[:int(data_len * TEST_RATIO)]
print('The size of the training set: ', train_inds.shape[0])
print('The size of the testing set: ', test_inds.shape[0])
# ============= placeholder =============
with tf.name_scope('input'):
x_ = tf.placeholder(tf.float32, [None, input_size, input_size, channels*2], name='x-input')
y = tf.placeholder(tf.float32, [None, N_KNOBS], name='y-input')
isTrain = tf.placeholder(tf.bool)
# ============= Model =============
logit, prediction = classifier_distinct_64(x_, num_dims=N_KNOBS, isTrain=isTrain)
classif_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=y, logits=logit)
acc = calc_accuracy(prediction=prediction, labels=y)
loss = tf.losses.get_total_loss()
# ============= Optimization functions =============
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss)
# ============= summary =============
cls_loss = tf.summary.scalar('distinct/cls_loss', classif_loss)
total_loss = tf.summary.scalar('distinct/loss', loss)
cls_acc = tf.summary.scalar('distinct/acc', acc)
summary_tf = tf.summary.merge([cls_loss, total_loss, cls_acc])
# ============= Variables =============
# Note that this list of variables only include the weights and biases in the model.
lst_vars = []
for v in tf.global_variables():
lst_vars.append(v)
# ============= Session =============
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(var_list=lst_vars)
writer = tf.summary.FileWriter(output_dir + '/train', sess.graph)
writer_test = tf.summary.FileWriter(output_dir + '/test', sess.graph)
# ============= Training =============
train_loss = []
itr_train = 0
for epoch in range(EPOCHS):
total_loss = 0.0
np.random.shuffle(train_inds)
num_batch = math.ceil(train_inds.shape[0] / BATCH_SIZE)
for i in range(0, num_batch):
start = i * BATCH_SIZE
xs = data[train_inds[start:start + BATCH_SIZE]]
ys = labels[train_inds[start:start + BATCH_SIZE]]
[_, _loss, summary_str] = sess.run([train_step, loss, summary_tf],
feed_dict={x_: xs, isTrain: True, y: ys})
writer.add_summary(summary_str, itr_train)
itr_train += 1
total_loss += _loss
total_loss /= num_batch
print("Epoch: " + str(epoch) + " loss: " + str(total_loss) + '\n')
train_loss.append(total_loss)
checkpoint_name = os.path.join(output_dir, 'cp_epoch_{}.ckpt'.format(epoch))
saver.save(sess, checkpoint_name)
np.save(os.path.join(output_dir, 'logs', 'train_loss.npy'), np.asarray(train_loss))
# ============= Testing =============
test_preds = _EMPTY_ARR
test_loss = []
itr_test = 0
total_test_loss = 0.0
num_batch = math.ceil(test_inds.shape[0] / BATCH_SIZE)
for i in range(0, num_batch):
start = i * BATCH_SIZE
xs = data[test_inds[start:start + BATCH_SIZE]]
ys = labels[test_inds[start:start + BATCH_SIZE]]
[_loss, summary_str, _pred] = sess.run([loss, summary_tf, prediction],
feed_dict={x_: xs, isTrain: False, y: ys})
writer_test.add_summary(summary_str, itr_test)
itr_test += 1
total_test_loss += _loss
test_preds = safe_append(test_preds, _pred, axis=0)
total_test_loss /= num_batch
print("Epoch: " + str(epoch) + " Test loss: " + str(total_loss) + '\n')
test_loss.append(total_test_loss)
np.save(os.path.join(output_dir, 'logs', 'test_loss.npy'), np.asarray(test_loss))
np.save(os.path.join(output_dir, 'logs', 'test_preds.npy'), np.asarray(test_preds))
np.save(os.path.join(output_dir, 'logs', 'test_ys.npy'), np.asarray(labels[test_inds]))
np.save(os.path.join(output_dir, 'logs', 'test_xs.npy'), np.asarray(data[test_inds]))
accuracy, precision_per_dim, recall_per_dim = calc_metrics_arr(np.round(test_preds), labels[test_inds], average=None)
_, precision_micro, recall_micro = calc_metrics_arr(np.round(test_preds), labels[test_inds],
average='micro')
_, precision_macro, recall_macro = calc_metrics_arr(np.round(test_preds), labels[test_inds],
average='macro')
print('Distinct - accuracy: {:.3f}, '
'precision: per dim: {}, micro: {:.3f}, macro: {:.3f}, '
'recall: per dim: {}, micro: {:.3f}, macro: {:.3f}'.format(
accuracy, precision_per_dim, precision_micro, precision_macro,
recall_per_dim, recall_micro, recall_macro))
metrics_dict = {}
for metric in ['accuracy', 'precision_per_dim', 'precision_micro', 'precision_macro',
'recall_per_dim', 'recall_micro', 'recall_macro']:
metrics_dict.update({'distinct_{}'.format(metric): [eval(metric)]})
print('Metrics successfully calculated: Distinct')
return metrics_dict
def calc_realistic(results_dict, config):
tf.reset_default_graph()
print('Calculating metrics for: Realistic')
# ============= Metrics Folder - Realistic =============
output_dir = os.path.join(config['log_dir'], config['name'], 'test', 'metrics', 'realistic')
logs_dir = os.path.join(output_dir, 'logs')
if not os.path.exists(logs_dir):
os.makedirs(logs_dir)
# ============= Experiment Parameters =============
BATCH_SIZE = config['metrics_batch_size']
EPOCHS = config['metrics_epochs']
TEST_RATIO = config['metrics_test_ratio']
channels = config['num_channel']
input_size = config['input_size']
NUM_BINS = config['num_bins']
if 'k_dim' in config.keys():
N_KNOBS = config['k_dim']
elif 'w_dim' in config.keys():
N_KNOBS = config['w_dim']
else:
print('Number of knobs not specified. Returning...')
return {}
# ============= Data =============
half_len = len(results_dict['real_imgs'])
data_real = results_dict['real_imgs']
fake_inds = np.arange(half_len)
fake_knob = np.random.randint(low=0, high=N_KNOBS, size=half_len)
# fake_bin = np.random.randint(low=0, high=NUM_BINS, size=half_len)
fake_bin = np.random.randint(low=0, high=2, size=half_len)
fake_bin = fake_bin * (NUM_BINS-1)
data_fake = results_dict['fake_t_imgs'][fake_inds, fake_knob, fake_bin]
data = np.append(data_real, data_fake, axis=0)
labels = np.append(np.ones(half_len), np.zeros(half_len), axis=0)
data_len = len(data)
data_inds = np.array(range(data_len))
np.random.shuffle(data_inds)
train_inds = data_inds[int(data_len * TEST_RATIO):]
test_inds = data_inds[:int(data_len * TEST_RATIO)]
print('The size of the training set: ', train_inds.shape[0])
print('The size of the testing set: ', test_inds.shape[0])
# ============= placeholder =============
with tf.name_scope('input'):
x_ = tf.placeholder(tf.float32, [None, input_size, input_size, channels], name='x-input')
y_ = tf.placeholder(tf.int64, [None], name='y-input')
isTrain = tf.placeholder(tf.bool)
# ============= Model =============
y = tf.one_hot(y_, 2, on_value=1.0, off_value=0.0, axis=-1)
logit, prediction = classifier_realistic_64(x_, n_label=2, isTrain=isTrain)
classif_loss = tf.losses.softmax_cross_entropy(onehot_labels=y, logits=logit)
acc = calc_accuracy(prediction=prediction, labels=y)
loss = tf.losses.get_total_loss()
# ============= Optimization functions =============
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss)
# ============= summary =============
cls_loss = tf.summary.scalar('realistic/cls_loss', classif_loss)
total_loss = tf.summary.scalar('realistic/loss', loss)
cls_acc = tf.summary.scalar('realistic/acc', acc)
summary_tf = tf.summary.merge([cls_loss, total_loss, cls_acc])
# ============= Variables =============
# Note that this list of variables only include the weights and biases in the model.
lst_vars = []
for v in tf.global_variables():
lst_vars.append(v)
# ============= Session =============
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(var_list=lst_vars)
writer = tf.summary.FileWriter(output_dir + '/train', sess.graph)
writer_test = tf.summary.FileWriter(output_dir + '/test', sess.graph)
# ============= Training =============
train_loss = []
itr_train = 0
for epoch in range(EPOCHS):
total_loss = 0.0
np.random.shuffle(train_inds)
num_batch = math.ceil(train_inds.shape[0] / BATCH_SIZE)
for i in range(0, num_batch):
start = i * BATCH_SIZE
xs = data[train_inds[start:start + BATCH_SIZE]]
ys = labels[train_inds[start:start + BATCH_SIZE]]
[_, _loss, summary_str] = sess.run([train_step, loss, summary_tf],
feed_dict={x_: xs, isTrain: True, y_: ys})
writer.add_summary(summary_str, itr_train)
itr_train += 1
total_loss += _loss
total_loss /= num_batch
print("Epoch: " + str(epoch) + " loss: " + str(total_loss) + '\n')
train_loss.append(total_loss)
checkpoint_name = os.path.join(output_dir, 'cp_epoch_{}.ckpt'.format(epoch))
saver.save(sess, checkpoint_name)
np.save(os.path.join(output_dir, 'logs', 'train_loss.npy'), np.asarray(train_loss))
# ============= Testing =============
test_preds = _EMPTY_ARR
test_loss = []
itr_test = 0
total_test_loss = 0.0
num_batch = math.ceil(test_inds.shape[0] / BATCH_SIZE)
for i in range(0, num_batch):
start = i * BATCH_SIZE
xs = data[test_inds[start:start + BATCH_SIZE]]
ys = labels[test_inds[start:start + BATCH_SIZE]]
[_loss, summary_str, _pred] = sess.run([loss, summary_tf, prediction],
feed_dict={x_: xs, isTrain: False, y_: ys})
writer_test.add_summary(summary_str, itr_test)
itr_test += 1
total_test_loss += _loss
test_preds = safe_append(test_preds, _pred, axis=0)
total_test_loss /= num_batch
print("Epoch: " + str(epoch) + " Test loss: " + str(total_loss) + '\n')
test_loss.append(total_test_loss)
np.save(os.path.join(output_dir, 'logs', 'test_loss.npy'), np.asarray(test_loss))
np.save(os.path.join(output_dir, 'logs', 'test_preds.npy'), np.asarray(test_preds))
np.save(os.path.join(output_dir, 'logs', 'test_ys.npy'), np.asarray(labels[test_inds]))
np.save(os.path.join(output_dir, 'logs', 'test_xs.npy'), np.asarray(data[test_inds]))
accuracy, precision, recall = calc_metrics_arr(np.argmax(test_preds, axis=1), labels[test_inds])
print('Realistic - accuracy: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(accuracy, precision, recall))
metrics_dict = {}
for metric in ['accuracy', 'precision', 'recall']:
metrics_dict.update({'realistic_{}'.format(metric): [eval(metric)]})
print('Metrics successfully calculated: Realistic')
return metrics_dict
def evaluate(epoch, run, mod_name=''):
incorrect_classifications_val = []
total_val_loss = 0
total_val_correct = 0
best_val_acc = 0
epoch_classifications_val = []
run.model.eval()
with torch.no_grad():
for batch_number, (images, labels, paths) in enumerate(run.val_loader):
if run.grayscale:
images = torch.unsqueeze(
images, 1).double() # added channel dimensions (grayscale)
else:
images = images.float().permute(0, 4, 1, 2, 3).float()
labels = labels.long()
if torch.cuda.is_available():
images, labels = images.cuda(), labels.cuda()
preds = run.model(images) # Pass Batch
loss = run.criterion(preds, labels) # Calculate Loss
total_val_loss += loss.item()
num_correct = get_num_correct(preds, labels)
total_val_correct += num_correct
run.experiment.log_metric(mod_name + "Val batch accuracy",
num_correct / len(labels) * 100,
step=run.log_number_val)
run.experiment.log_metric(mod_name + "Avg val batch loss",
loss.item(),
step=run.log_number_val)
run.log_number_val += 1
# print('Val: Batch number:', batch_number, 'Num correct:', num_correct, 'Accuracy:', "{:.2%}".format(num_correct / len(labels)), 'Loss:', loss.item())
# print_mistakes(preds, labels, paths)
incorrect_classifications_val.append(
get_mistakes(preds, labels, paths))
for prediction in zip(preds, labels, paths):
epoch_classifications_val.append(prediction)
epoch_accuracy = calc_accuracy(epoch_classifications_val)
run.experiment.log_metric(mod_name + "Val epoch accuracy",
epoch_accuracy,
step=epoch)
run.experiment.log_metric(mod_name + "Avg val epoch loss",
total_val_loss / batch_number,
step=epoch)
print('Val Epoch:', epoch, 'num correct:', total_val_correct,
'Accuracy:',
str(epoch_accuracy) + '%')
is_best = (epoch_accuracy > run.best_val_acc) | (
(epoch_accuracy >= run.best_val_acc) &
(total_val_loss / batch_number < run.best_val_loss))
if is_best:
print("Best run so far! updating params...")
run.best_val_acc = epoch_accuracy
run.best_val_loss = total_val_loss / batch_number
run.best_model_preds = epoch_classifications_val
run.best_model_mistakes = incorrect_classifications_val
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': run.model.state_dict(),
'best_acc1': run.best_val_acc,
'optimizer': run.optimizer.state_dict(),
}, is_best)
# Step lr_scheduler
run.lr_scheduler.step()
X, Y = get_classification_data(sd=10, m=50)
adaboost = AdaBoost(n_models=20)
adaboost.fit(X, Y)
print("This is the final prediction:", adaboost.final_prediction(X))
print("This is the original labels:", Y)
print(adaboost.final_prediction(X) == Y)
print("Shape:", adaboost.final_prediction(X).shape)
print("type:", type(adaboost.final_prediction(X)))
visualise_predictions(adaboost.final_prediction, X)
print(f'accuracy: {calc_accuracy(adaboost.final_prediction(X), Y)}')
show_data(X, Y)
print("Evaluate for a point: ", adaboost.final_prediction(np.array([[1, 1]])))
# %%
import sklearn.ensemble
adaBoost = sklearn.ensemble.AdaBoostClassifier()
adaBoost.fit(X, Y)
predictions = adaBoost.predict(X)
calc_accuracy(predictions, Y)
#visualise_predictions(adaBoost.predict, X,Y)
#show_data(X, Y)
print("Adaboosts sklearn predictions:", predictions)
print(predictions.shape)
print(type(predictions))
print(f'accuracy: {calc_accuracy(predictions, Y)}')
# %%
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.CRITICAL)
rules = []
training_file = args[0]
training_sents = utils.read_tokens(training_file)
test_file = args[1]
test_sents = utils.read_tokens(test_file)
model = create_model(training_sents)
sents = utils.read_tokens(training_file)
predictions = predict_tags(sents, model)
accuracy = utils.calc_accuracy(training_sents, predictions)
print "Accuracy in training before rules applied [%s sentences]: %s" % (
len(sents), accuracy)
rules = template1(training_sents, predictions)
reduced_rules = reduced_rule1(rules, training_sents, predictions, accuracy)
new_predictions = apply_rule1(predictions, reduced_rules)
accuracy = utils.calc_accuracy(training_sents, new_predictions)
print "Accuracy in training after rule1 applied [%s sentences]: %s" % (
len(sents), accuracy)
test_sents1 = utils.read_tokens(test_file)
def main():
X_train = np.loadtxt('../datasets/gisette/gisette_train_data.txt')
y_train = np.loadtxt('../datasets/gisette/gisette_train_labels.txt',
dtype=int)
X_test = np.loadtxt('../datasets/gisette/gisette_valid_data.txt')
y_test = np.loadtxt('../datasets/gisette/gisette_valid_labels.txt',
dtype=int)
y_train = trans(y_train)
y_test = trans(y_test)
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
X_train, y_train = X_train[:200], y_train[:200]
X_test, y_test = X_test[:100], y_test[:100]
n_train = X_train.shape[0]
n0_list = [1, 4, 16, 64]
M_list = [1, 4, 16, 64, 256]
r = 1
accuracy = -1
n0_best = -1
M_best = -1
n_folds = 3 # requirement: 10
classes = 2
# train:
# 10-fold CV
# n0 \in {1,4,16,64}
# M \in {1,4,16,64,256}
# get the best pair of (n, M)
print('------------------now begin--------------------------')
for n0 in n0_list:
for M in M_list:
cur_accuracy_list = []
skf = StratifiedKFold(n_splits=n_folds)
cv = [(t, v) for (t, v) in skf.split(range(n_train), y_train)]
for k in range(n_folds):
train_idx, val_idx = cv[k]
comp_rf = CompRF(n0, M, r, "Classification", classes)
time_start = time.time()
y_predict = comp_rf.train_then_predict(X_train[train_idx],
y_train[train_idx],
X_train[val_idx])
time_end = time.time()
cur_accuracy = calc_accuracy(y_train[val_idx], y_predict)
cur_accuracy_list.append(cur_accuracy)
print("(n0={0}, M={1}, fold={3}): {2:.2f}% [time={4:.2f}]".
format(n0, M, cur_accuracy * 100, k,
time_end - time_start))
cur_accuracy = sum(cur_accuracy_list) / len(cur_accuracy_list)
print("(n0={0}, M={1}, average): {2:.2f}%".format(
n0, M, cur_accuracy * 100))
if cur_accuracy > accuracy:
accuracy = cur_accuracy
n0_best = n0
M_best = M
print("best_accuracy={0}%, best_n_0={1}, best_M={2}".format(
accuracy * 100, n0_best, M_best))
# test:
comp_rf = CompRF(n0_best, M_best, r)
y_predict = comp_rf.train_then_predict(X_train, y_train, X_test)
accuracy = calc_accuracy(y_test, y_predict)
print("accuracy: ", accuracy)
def train(self,dataset_path,num_classes,batch_size,lr_base,lr_decay,step_size,\
max_iteration,pretrained_model=None):
'''
@description: 构建VGG-Net16网络结构，训练网络模型，输出训练过程中的logs，保存网络模型
@params:
- dataset_path: 训练样本集和验证样本集对应的txt文件所在的路径
- num_classes: 分类数目
- batch_size: 训练过程中的每次输入网络中的样本数
- lr_base: 初始学习率
- lr_decay: 学习率衰减系数
- step_size: 学习率衰减速度 lr = lr_base * lr_decay ^ (global_step / step_size)
- max_iteration: 迭代的最大次数
- pretrained_model: 预训练的模型所在的路径
@return: None
'''
train_file_name = dataset_path + 'train_list.txt'
valid_file_name = dataset_path + 'valid_list.txt'
log_dir = './log/vgg'
model_dir = './model/vgg'
vgg = VGG(weight_decay=0.0005, keep_prob=0.5, num_classes=num_classes)
train_summary_list = []
valid_summary_list = []
with tf.Graph().as_default(), tf.device('/gpu:0'):
with tf.name_scope('input'):
#队列读取训练数据
train_image,train_label = get_batch(train_file_name,self._image_H,\
self._image_W,batch_size)
valid_image,valid_label = get_batch(valid_file_name,self._image_H,\
self._image_W,250,is_train=False)
x = tf.placeholder(tf.float32,[None,self._image_H,self._image_W,\
self._image_channels],name='x')
y = tf.placeholder(tf.int64, [None], name='y')
#loss, accuracy, train_op
logits, _ = vgg.vgg16(x)
loss = utils.calc_loss(logits, y)
accuracy = utils.calc_accuracy(logits, y)
train_op, learning_rate, global_step = utils.optimizer(
lr_base, step_size, lr_decay, loss)
#summary
train_summary_list.append(tf.summary.scalar('train_loss', loss))
valid_summary_list.append(tf.summary.scalar('valid_loss', loss))
train_summary_list.append(
tf.summary.scalar('train_accuracy', accuracy))
valid_summary_list.append(
tf.summary.scalar('test_accuracy', accuracy))
train_summary_list.append(
tf.summary.scalar('learning rate', learning_rate))
valid_summary_list.append(
tf.summary.scalar('learning rate', learning_rate))
for var in tf.trainable_variables():
valid_summary_list.append(tf.summary.histogram(var.name, var))
train_summary = tf.summary.merge(train_summary_list)
valid_summary = tf.summary.merge(valid_summary_list)
#session
saver = tf.train.Saver(max_to_keep=50)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,\
log_device_placement=True)) as sess:
train_writer = tf.summary.FileWriter(log_dir + 'train',
sess.graph)
test_writer = tf.summary.FileWriter(log_dir + 'valid')
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
#启动多线程
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
#加载预训练的模型
if pretrained_model != None:
ckpt = tf.train.get_checkpoint_state(pretrained_model)
print('Restoring pretrained model: %s' %
ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
train_time = 0
for step in range(max_iteration):
#模型持久化操作
# graph_def = tf.get_default_graph().as_graph_def()
# output_graph_def = graph_util.convert_variables_to_constants(sess,graph_def,['input/x','deepid/Relu'])
# with tf.gfile.GFile(model_dir+'deepid_model.pb','wb') as file:
# file.write(output_graph_def.SerializeToString())
# break
start_time = time.time()
image, label = sess.run([train_image, train_label])
_, train_loss, summary_str, train_step = sess.run(
[train_op, loss, train_summary, global_step],
feed_dict={
x: image,
y: label
})
train_writer.add_summary(summary_str,
global_step=train_step)
train_writer.flush()
duration = time.time() - start_time
train_time += duration
#valid and save model
if step % 1000 == 0 or (step + 1) == max_iteration:
image, label = sess.run([valid_image, valid_label])
lr,summary_str,valid_loss,validation_accuracy,\
train_step = sess.run([learning_rate,
valid_summary,
loss,
accuracy,
global_step],
feed_dict={x:image,y:label})
test_writer.add_summary(summary_str,
global_step=train_step)
test_writer.flush()
print('Step %d: train loss = %.3f, valid loss = %.3f,valid accuracy = %.3f%%, lr = %.6f (%.3f sec)'%\
(train_step,train_loss,valid_loss,validation_accuracy,\
lr,train_time))
saver.save(sess,
model_dir + 'model.ckpt',
global_step=train_step)
with open(log_dir + 'valid_result.txt',
'at') as file_writer:
file_writer.write('%d\t%.3f%%\t%.5f\t%d\r\n' %
(train_step, validation_accuracy,
lr, train_time))
#退出多线程
coord.request_stop()
coord.join(threads)
def train(data_loader, model, optimizer, epoch):
losses = AverageMeter()
accuracy = AverageMeter()
model.train()
global iteration
tq = tqdm(data_loader, desc="Train progress: Ep {}".format(epoch))
for idx, (input_seq, target, _) in enumerate(tq):
tic = time.time()
input_seq = input_seq.to(cuda)
target = target.to(cuda)
B = input_seq.size(0)
output, _ = model(input_seq)
# visualize
if (iteration == 0) or (iteration == args.print_freq):
if B > 2: input_seq = input_seq[0:2, :]
writer_train.add_image(
'input_seq',
de_normalize(
vutils.make_grid(input_seq[:, :3, ...].transpose(
2, 3).contiguous().view(-1, 3, args.img_dim,
args.img_dim),
nrow=args.num_seq * args.seq_len)),
iteration)
del input_seq
[_, N, D] = output.size()
output = output.view(B * N, D)
target = target.repeat(1, N).view(-1)
loss = criterion(output, target)
acc = calc_accuracy(output, target)
del target
losses.update(loss.item(), B)
accuracy.update(acc.item(), B)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_weight = 0.0
decay_weight = 0.0
for m in model.parameters():
if m.requires_grad: decay_weight += m.norm(2).data
total_weight += m.norm(2).data
tq_stats = {
'loss': losses.local_avg,
'acc': accuracy.local_avg,
'decay_wt': decay_weight.item(),
'total_wt': total_weight.item(),
}
tq.set_postfix(tq_stats)
if idx % args.print_freq == 0:
writer_train.add_scalar('local/loss', losses.val, iteration)
writer_train.add_scalar('local/accuracy', accuracy.val, iteration)
iteration += 1
return losses.local_avg, accuracy.local_avg
gt_labels = np.array(gt_labels)
return logits, gt_labels
if __name__ == '__main__':
model = NaiveBayes()
tokenizer = stemmedTokenizer
model.create_dict(json_reader("col774_yelp_data/train.json"), tokenizer)
model.train(json_reader("col774_yelp_data/train.json"), tokenizer)
# outputs = model.predict(json_reader("col774_yelp_data/test.json"), tokenizer)
# f = open("outputs_stemmed_test.pickle","wb")
# pickle.dump(outputs, f)
# f.close()
logits, gt_labels = _load_object("outputs_stemmed_test.pickle")
conf_matrix = create_confusion_matrix(logits, gt_labels)
print(calc_accuracy(logits, gt_labels) * 100)
print(conf_matrix)
plot_confusion_matrix(conf_matrix, model.classes)
probs = logits_to_prob_vector(logits)
plot_roc_curve(logits, gt_labels)
logging.info("# dev evaluation")
sess.run(dev_init_opt)
dev_results = []
dev_labels = []
cnt=0
for _ in range(num_dev_batches):
# cnt+=1
# print(cnt)
tmp_pred,tmp_target = sess.run([pred_eval,ys])
dev_results.extend(tmp_pred)
dev_labels.extend(tmp_target)
# print('DEV3')
# print(len(dev_results))
# print(len(dev_labels))
accuracy = calc_accuracy(dev_results,dev_labels)
dev_history.append(accuracy)
if accuracy > dev_best:
dev_best = accuracy
stop_times = 0
else:
stop_times += 1
if stop_times > cfg.patience:
logging.info('The model did not improve after{} times, you have got an excellent'
+'enough model.')
break
logging.info('# The dev accuracy is:{}'.format(accuracy))
def train(data_loader, model, optimizer, epoch):
losses = AverageMeter()
model.train()
global iteration
dissimilarity_score_dict = {}
target_dict = {}
number_of_chunks_dict = {}
for idx, (video_seq, audio_seq, target, audiopath) in enumerate(data_loader):
tic = time.time()
video_seq = video_seq.to(cuda)
audio_seq = audio_seq.to(cuda)
target = target.to(cuda)
B = video_seq.size(0)
vid_out = model.module.forward_lip(video_seq)
aud_out = model.module.forward_aud(audio_seq)
vid_class = model.module.final_classification_lip(vid_out)
aud_class = model.module.final_classification_aud(aud_out)
del video_seq
del audio_seq
loss1 = calc_loss(vid_out, aud_out, target, args.hyper_param)
loss2 = criterion(vid_class, target.view(-1))
loss3 = criterion(aud_class, target.view(-1))
acc = calc_accuracy(vid_out, aud_out, target, args.threshold)
loss = loss1 + loss2 + loss3
losses.update(loss.item(), B)
optimizer.zero_grad()
loss.backward()
optimizer.step()
for batch in range(B):
vid_name = audiopath[batch].split('/')[-2]
dist = torch.dist(vid_out[batch, :].view(-1), aud_out[batch, :].view(-1), 2)
tar = target[batch, :].view(-1).item()
if (dissimilarity_score_dict.get(vid_name)):
dissimilarity_score_dict[vid_name] += dist
number_of_chunks_dict[vid_name] += 1
else:
dissimilarity_score_dict[vid_name] = dist
number_of_chunks_dict[vid_name] = 1
if (target_dict.get(vid_name)):
pass
else:
target_dict[vid_name] = tar
if idx % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.4f} ({loss.local_avg:.4f})\t'.format(
epoch, idx, len(data_loader), time.time() - tic,
loss=losses))
total_weight = 0.0
decay_weight = 0.0
for m in model.parameters():
if m.requires_grad: decay_weight += m.norm(2).data
total_weight += m.norm(2).data
print('Decay weight / Total weight: %.3f/%.3f' % (decay_weight, total_weight))
writer_train.add_scalar('local/loss', losses.val, iteration)
iteration += 1
avg_score_real, avg_score_fake = get_scores(dissimilarity_score_dict, number_of_chunks_dict, target_dict)
return losses.local_avg, avg_score_real, avg_score_fake
action="store_true",
help="turn on debug mode")
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error("Please provide required arguments")
if options.debug:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.CRITICAL)
training_file = args[0]
training_sents = utils.read_tokens(training_file)
test_file = args[1]
test_sents = utils.read_tokens(test_file)
model = create_model(training_sents)
## read sentences again because predict_tags(...) rewrites the tags
sents = utils.read_tokens(training_file)
predictions = predict_tags(sents, model)
accuracy = utils.calc_accuracy(training_sents, predictions)
print "Accuracy in training [%s sentences]: %s" % (len(sents), accuracy)
## read sentences again because predict_tags(...) rewrites the tags
sents = utils.read_tokens(test_file)
predictions = predict_tags(sents, model)
accuracy = utils.calc_accuracy(test_sents, predictions)
print "Accuracy in training [%s sentences]: %s" % (len(sents), accuracy)
def eval_full_spectrograms(dataset, model_id, predictions_path, pred_threshold=0.5, overlap_threshold=0.1, smooth=True,
in_seconds=False, use_call_bounds=False, min_call_length=10, visualize=False, hierarchical_model=True):
"""
After saving predictions for the test set of full spectrograms, we
now want to calculate evaluation metrics on each of these spectrograms.
First we load in the sigmoid (0, 1) predictions and use the defined
threshold and smoothing flag to convert the predictions into binary
0/1 predcitions for each time slice. Then, we convert time slice
predictions to full call (start, end) time predictions so that
we can calculate elephant call specific evaluation metrics. Bellow
we discuss the metrics that are calculated individually for each
full spectrogram, as well as accross all the test spectrograms
Metrics:
- Call Prediction True Positives
- Call Prediction False Positives
- Call Recall True Positives
- Call Recall False Negatives
- F-score
- Accuracy
- Old Call Precision --- To be implemented
- Old Call Recall --- To be implemented
"""
# Maps spectrogram ids to dictionary of results for each spect
# Additionally includes a key "summary" that computes aggregated
# statistics over the entire test set of spectrograms
results = {}
results['summary'] = {'true_pos': 0,
'false_pos': 0,
'true_pos_recall': 0,
'false_neg': 0,
'f_score': 0,
'accuracy': 0
}
# Used to track the number of total calls for averaging
# aggregated statistics
num_preds = 0
num_gt = 0
for data in dataset:
spectrogram = data[0]
labels = data[1]
gt_call_path = data[2]
# Get the spec id
tags = gt_call_path.split('/')
tags = tags[-1].split('_')
data_id = tags[0] + '_' + tags[1]
print ("Generating Prediction for:", data_id)
predictions = np.load(os.path.join(predictions_path, model_id, data_id + '.npy'))
binary_preds, smoothed_predictions = get_binary_predictions(predictions, threshold=pred_threshold, smooth=smooth)
# Process the predictions to get predicted elephant calls
# Figure out better way to try different combinations of this
# Note that processed_preds zeros out predictions that are not long
# enough to be an elephant call
predicted_calls, processed_preds = find_elephant_calls(binary_preds, min_call_length=min_call_length, in_seconds=in_seconds)
print ("Num predicted calls", len(predicted_calls))
# Use the calls as defined in the orginal hand labeled file.
# This looks to avoid issues of overlapping calls seeming like
# single very large calls in the gt labeling
if use_call_bounds:
print ("Using CSV file with ground truth call start and end times")
gt_calls = process_ground_truth(gt_call_path, in_seconds=in_seconds)
else:
print ("Using spectrogram labeling to generate GT calls")
# We should never compute this in seconds
# Also let us keep all the calls, i.e. set min_length = 0
gt_calls, _ = find_elephant_calls(labels, min_call_length=0)
print ("Number of ground truth calls", len(gt_calls))
# Visualize the predictions around the gt calls
if visualize: # This is not super important
visual_full_recall(spectrogram, smoothed_predictions, labels, processed_preds)
# Look at precision metrics
# Call Prediction True Positives
# Call Prediction False Positives
true_pos, false_pos = call_prec_recall(predicted_calls, gt_calls, threshold=overlap_threshold, is_truth=False,
spectrogram=spectrogram, preds=binary_preds, gt_labels=labels)
# Look at recall metrics
# Call Recall True Positives
# Call Recall False Negatives
true_pos_recall, false_neg = call_prec_recall(gt_calls, predicted_calls, threshold=overlap_threshold, is_truth=True)
f_score = f1_score(labels, binary_preds)
accuracy = calc_accuracy(binary_preds, labels)
results[data_id] = {'true_pos': true_pos,
'false_pos': false_pos,
'true_pos_recall': true_pos_recall,
'false_neg': false_neg,
'f_score': f_score,
'predictions': smoothed_predictions,
'binary_preds': processed_preds,
'accuracy': accuracy
}
# If doing hierarchical modeling, save the model_0 predictions
# specifically for visualization!
if hierarchical_model:
model_0_predictions = np.load(os.path.join(predictions_path, model_id, 'Model_0', data_id + '.npy'))
_, model_0_smoothed_predictions = get_binary_predictions(model_0_predictions, threshold=pred_threshold, smooth=smooth)
results[data_id]['model_0_predictions'] = model_0_smoothed_predictions
# Update summary stats
results['summary']['true_pos'] += len(true_pos)
results['summary']['false_pos'] += len(false_pos)
results['summary']['true_pos_recall'] += len(true_pos_recall)
results['summary']['false_neg'] += len(false_neg)
results['summary']['f_score'] += f_score
results['summary']['accuracy'] += accuracy
# Calculate averaged statistics
results['summary']['f_score'] /= len(dataset)
results['summary']['accuracy'] /= len(dataset)
return results
