def forward(self, pred, target):
pred_ = torch.ones_like(pred) - pred
pred = torch.cat((pred_, pred), dim=1)
target = torch.long()
return self.ce_loss(pred, target)
def get_confusion_matrix(model, n_classes, dataloaders, device):
confusion_matrix = t.zeros(n_classes, n_classes)
with t.no_grad():
for i, (inputs, classes) in enumerate(dataloaders):
inputs = inputs.to(device)
classes = classes.to(device)
outputs = model(inputs)
_, preds = t.max(outputs, 1)
for t, p in zip(classes.view(-1), preds.view(-1)):
confusion_matrix[t.long(), p.long()] += 1
print(confusion_matrix)
def create_data_loader(self, bs):
human = []
virus = []
interaction_idx = []
for i in range(self.N):
idx = self.test[i]
h_idx = int(self.interactions['node1'][idx])
v_idx = int(self.interactions['node2'][idx])
human.append(torch.from_numpy(self.human[h_idx, :]))
virus.append(torch.from_numpy(self.virus[v_idx, :]))
interaction_idx.append(torch.long(idx))
print(human, virus, interaction_idx)
def select_action(initial_state):
epsilon = max(epsilon - delta, 0)
initial_state_tensor = torch.tensor(initial_state)
sample = random.choice(100) / 100
print(epsilon)
if sample > epsilon:
with torch.no_grad():
return dqn.q_netork(initial_state_tensor).max(1)[1].view(1, 1)
else:
return torch.tensor(random.choice([0, 1, 2, 3]),
dtype=torch.long())
def calculate_mAP(det_boxes, det_labels, det_scores, true_boxes, true_labels, true_difficulties):
"""
Calculate the Mean Average Precision (mAP) of detected objects.
:param det_boxes: list of tensors, one tensor for each image containing detected objects' bounding boxes
:param det_labels: list of tensors, one tensor for each image containing detected objects' labels
:param det_scores: list of tensors, one tensor for each image containing detected objects' labels' scores
:param true_boxes: list of tensors, one tensor for each image containing actual objects' bounding boxes
:param true_labels: list of tensors, one tensor for each image containing actual objects' labels
:param true_difficulties: list of tensors, one tensor for each image containing actual objects' difficulty (0 or 1)
:return: list of average precisions for all classes, mean average precision (mAP)
"""
assert len(det_boxes) == len(det_labels) == len(det_scores) == len(true_boxes) == len(true_labels) == len(true_difficulties)
n_classes = len(label_map)
# Store all (true) objects in a single continuous tensor while keeping track of the image it is from
true_images = list()
for i in range(len(true_labels)):
true_images.extend([i] * true_labels[i].size(0))
true_images = torch.LongTensor(true_images).to(device) # (n_objects), n_objects is the total no. of objects across all images
true_boxes = torch.cat(true_boxes, dim=0) # (n_objects, 4)
true_labels = torch.cat(true_labels ,dim=0) # (n_objects)
true_difficulties = torch.cat(true_difficulties, dim=0) # (n_objects)
assert true_images.size(0) == true_boxes.size(0) == true_labels.size(0)
# Store all detections in a single continuous tensor while keeping track of the image it is from
det_images = list()
for i in range(len(det_labels)):
det_images.extend([i] * det_labels[i].size(0))
det_images = torch.LongTensor(det_images).to(device) # (n_detections)
det_boxes = torch.cat(det_boxes, dim=0) # (n_detections, 4)
det_labels = torch.cat(det_labels, dim=0) # (n_detections)
det_scores = torch.cat(det_scores, dim=0) # (n_detections)
assert det_images.size(0) == det_boxes.size(0) == det_labels.size(0) == det_scores.size(0)
# Calculate APs for each class (except background)
average_precisions = torch.zeros((n_classes - 1), dtype=torch.float) # (n_classes - 1)
for c in range(1, n_classes):
# Extract only objects with this class
true_class_images = true_images[true_labels == c] # (n_class_objects)
true_class_boxes = true_boxes[true_labels == c] # (n_class_objects, 4)
true_class_difficulties = true_difficulties[true_labels == c] # (n_class_objects)
n_easy_class_objects = (1 - true_class_difficulties).sum().item() # ignore difficult objects
# Keep track of which true objects with this class have already been 'detected'
true_class_boxes_detected = torch.zeros((true_class_difficulties.size(0)), dtype=torch.uint8).to(device) # (n_class_objects)
# Extrack only detections with this class
det_class_images = det_images[det_labels == c] # (n_class_detections)
det_class_boxes = det_boxes[det_labels == c] # (n_class_detections, 4)
det_class_scores = det_scores[det_labels == c] # (n_class_detections)
n_class_detections = det_class_boxes.size(0)
if n_class_detections == 0:
continue
# Sort detections in decreasing order of confidence/scores
det_class_scores, sort_ind = torch.sort(det_class_scores, dim=0, descending=True) # (n_class_detections)
det_class_images = det_class_images[sort_ind] # (n_class_detections)
det_class_boxes = det_class_boxes[sort_ind] # (n_class_detections, 4)
# In the order of decreasing scores, check if true of false positive
true_positives = torch.zeros((n_class_detections), dtype=torch.float).to(device) # (n_class_detections)
false_positives = torch.zeros((n_class_detections), dtype=torch.float).to(device)
for d in range(n_class_detections):
this_detection_box = det_class_boxes[d].unsqueeze(0) # (1, 4)
this_image = det_class_images[d] # (), scalar
# Find objects in the same image with this class, their difficulties, and whether they have been detected
object_boxes = true_class_boxes[true_class_images == this_image] # (n_class_objects_in_img)
object_difficulties = true_class_difficulties[true_class_images == this_image] # (n_class_objects_in_img)
# If no such object in this image, then the detection is a false positive
if object_boxes.size(0) == 0:
false_positives[d] = 1
continue
# Find maximum overlap of this detection with objects in this image of this class
overlaps = find_jaccard_overlap(this_detection_box, object_boxes) # (1, n_class_objects_in_img)
max_overlap, ind = torch.max(overlaps.squeeze(0), dim=0) # (), () - scalars
# 'ind' is the index of the object in these image-level tensors 'object_boxes', 'object_difficulties'
# In the original class-level tensors 'true_class_boxes', etc., 'ind' corresponds to object with index.
original_ind = torch.long(range(true_class_boxes.size(0)))[true_class_images == this_image][ind]
# We need 'original_ind' to update 'true_class_boxes_detected'
# If the maximum overlap is greater than the threshold of 0.5, it's a match
if max_overlap.item() > 0.5:
# If the object it matched with is 'difficult', ignore it
if object_difficulties[ind] == 0:
# If this object has already not been detected, it's a true positive
if true_class_boxes_detected[original_ind] == 0:
true_positives[d] = 1
true_class_boxes_detected[original_ind] = 1 # this object has now been detected/accounted for
# Otherwise, it's a false positive (since this object is already accounted for)
else:
false_positives[d] = 1
# Otherwise, the detection occurs in a different location than the actual object, and is a false positive
else:
false_positives[d] = 1
# Compute cumulative precision and recall at each detection in the order of decreasing scores
cumul_true_positives = torch.cumsum(true_positives, dim=0) # (n_class_detections)
cumul_false_positives = torch.cumsum(false_positives, dim=0) # (n_class_detections)
cumul_precision = cumul_true_positives / (cumul_true_positives + cumul_false_positives + 1e-10) # (n_class_detections)
cumul_recall = cumul_true_positives / n_easy_class_objects # (n_class_detections)
# Find the mean of the maximum of the precisions corresponding to recalls above the threshold 't'
recall_thresholds = torch.arange(start=0, end=1.1, step=.1).tolist() # (11)
precisions = torch.zeros((len(recall_thresholds)), dtype=torch.float).to(device) # (11)
for i, t in enumerate(recall_thresholds):
recalls_above_t = cumul_recall >= t
if recalls_above_t.any():
precisions[i] = cumul_precision[recalls_above_t].max()
else:
precisions[i] = 0.
average_precisions[c - 1] = precisions.mean() # c is in [1, n_classes - 1]
# Calculate Mean Average Precision (mAP)
mean_average_precision = average_precisions.mean().item()
# Keep class-wise average precisions in a dictionary
average_precisions = {rev_label_map[c + 1]: v for c, v in enumerate(average_precisions.tolist())}
return average_precisions, mean_average_precision
def log_sum_exp(vec):
# 注：这是pytorch学习教程上的代码
# TODO:还不太清楚这个函数的作用，待补充
max_score = vec[0, argmax(vec)]
max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
return max_score + torch.long(torch.sum(torch.exp(vec - max_score_broadcast)))