Module 4_efficientdet.lib.src.loss
Expand source code
import torch
import torch.nn as nn
def calc_iou(a, b):
area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1])
iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 0])
ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 1])
iw = torch.clamp(iw, min=0)
ih = torch.clamp(ih, min=0)
ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area - iw * ih
ua = torch.clamp(ua, min=1e-8)
intersection = iw * ih
IoU = intersection / ua
return IoU
class FocalLoss(nn.Module):
def __init__(self):
super(FocalLoss, self).__init__()
def forward(self, classifications, regressions, anchors, annotations):
alpha = 0.25
gamma = 2.0
batch_size = classifications.shape[0]
classification_losses = []
regression_losses = []
anchor = anchors[0, :, :]
anchor_widths = anchor[:, 2] - anchor[:, 0]
anchor_heights = anchor[:, 3] - anchor[:, 1]
anchor_ctr_x = anchor[:, 0] + 0.5 * anchor_widths
anchor_ctr_y = anchor[:, 1] + 0.5 * anchor_heights
for j in range(batch_size):
classification = classifications[j, :, :]
regression = regressions[j, :, :]
bbox_annotation = annotations[j, :, :]
bbox_annotation = bbox_annotation[bbox_annotation[:, 4] != -1]
if bbox_annotation.shape[0] == 0:
if torch.cuda.is_available():
regression_losses.append(torch.tensor(0).float().cuda())
classification_losses.append(torch.tensor(0).float().cuda())
else:
regression_losses.append(torch.tensor(0).float())
classification_losses.append(torch.tensor(0).float())
continue
classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4)
IoU = calc_iou(anchors[0, :, :], bbox_annotation[:, :4])
IoU_max, IoU_argmax = torch.max(IoU, dim=1)
# compute the loss for classification
targets = torch.ones(classification.shape) * -1
if torch.cuda.is_available():
targets = targets.cuda()
targets[torch.lt(IoU_max, 0.4), :] = 0
positive_indices = torch.ge(IoU_max, 0.5)
num_positive_anchors = positive_indices.sum()
assigned_annotations = bbox_annotation[IoU_argmax, :]
targets[positive_indices, :] = 0
targets[positive_indices, assigned_annotations[positive_indices, 4].long()] = 1
alpha_factor = torch.ones(targets.shape) * alpha
if torch.cuda.is_available():
alpha_factor = alpha_factor.cuda()
alpha_factor = torch.where(torch.eq(targets, 1.), alpha_factor, 1. - alpha_factor)
focal_weight = torch.where(torch.eq(targets, 1.), 1. - classification, classification)
focal_weight = alpha_factor * torch.pow(focal_weight, gamma)
bce = -(targets * torch.log(classification) + (1.0 - targets) * torch.log(1.0 - classification))
cls_loss = focal_weight * bce
zeros = torch.zeros(cls_loss.shape)
if torch.cuda.is_available():
zeros = zeros.cuda()
cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, zeros)
classification_losses.append(cls_loss.sum() / torch.clamp(num_positive_anchors.float(), min=1.0))
if positive_indices.sum() > 0:
assigned_annotations = assigned_annotations[positive_indices, :]
anchor_widths_pi = anchor_widths[positive_indices]
anchor_heights_pi = anchor_heights[positive_indices]
anchor_ctr_x_pi = anchor_ctr_x[positive_indices]
anchor_ctr_y_pi = anchor_ctr_y[positive_indices]
gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0]
gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1]
gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths
gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights
gt_widths = torch.clamp(gt_widths, min=1)
gt_heights = torch.clamp(gt_heights, min=1)
targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi
targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi
targets_dw = torch.log(gt_widths / anchor_widths_pi)
targets_dh = torch.log(gt_heights / anchor_heights_pi)
targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh))
targets = targets.t()
norm = torch.Tensor([[0.1, 0.1, 0.2, 0.2]])
if torch.cuda.is_available():
norm = norm.cuda()
targets = targets / norm
regression_diff = torch.abs(targets - regression[positive_indices, :])
regression_loss = torch.where(
torch.le(regression_diff, 1.0 / 9.0),
0.5 * 9.0 * torch.pow(regression_diff, 2),
regression_diff - 0.5 / 9.0
)
regression_losses.append(regression_loss.mean())
else:
if torch.cuda.is_available():
regression_losses.append(torch.tensor(0).float().cuda())
else:
regression_losses.append(torch.tensor(0).float())
return torch.stack(classification_losses).mean(dim=0, keepdim=True), torch.stack(regression_losses).mean(dim=0,
keepdim=True)Functions
def calc_iou(a, b)-
Expand source code
def calc_iou(a, b): area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1]) iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 0]) ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 1]) iw = torch.clamp(iw, min=0) ih = torch.clamp(ih, min=0) ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area - iw * ih ua = torch.clamp(ua, min=1e-8) intersection = iw * ih IoU = intersection / ua return IoU
Classes
class FocalLoss-
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x))Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:
to, etc.Initializes internal Module state, shared by both nn.Module and ScriptModule.
Expand source code
class FocalLoss(nn.Module): def __init__(self): super(FocalLoss, self).__init__() def forward(self, classifications, regressions, anchors, annotations): alpha = 0.25 gamma = 2.0 batch_size = classifications.shape[0] classification_losses = [] regression_losses = [] anchor = anchors[0, :, :] anchor_widths = anchor[:, 2] - anchor[:, 0] anchor_heights = anchor[:, 3] - anchor[:, 1] anchor_ctr_x = anchor[:, 0] + 0.5 * anchor_widths anchor_ctr_y = anchor[:, 1] + 0.5 * anchor_heights for j in range(batch_size): classification = classifications[j, :, :] regression = regressions[j, :, :] bbox_annotation = annotations[j, :, :] bbox_annotation = bbox_annotation[bbox_annotation[:, 4] != -1] if bbox_annotation.shape[0] == 0: if torch.cuda.is_available(): regression_losses.append(torch.tensor(0).float().cuda()) classification_losses.append(torch.tensor(0).float().cuda()) else: regression_losses.append(torch.tensor(0).float()) classification_losses.append(torch.tensor(0).float()) continue classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4) IoU = calc_iou(anchors[0, :, :], bbox_annotation[:, :4]) IoU_max, IoU_argmax = torch.max(IoU, dim=1) # compute the loss for classification targets = torch.ones(classification.shape) * -1 if torch.cuda.is_available(): targets = targets.cuda() targets[torch.lt(IoU_max, 0.4), :] = 0 positive_indices = torch.ge(IoU_max, 0.5) num_positive_anchors = positive_indices.sum() assigned_annotations = bbox_annotation[IoU_argmax, :] targets[positive_indices, :] = 0 targets[positive_indices, assigned_annotations[positive_indices, 4].long()] = 1 alpha_factor = torch.ones(targets.shape) * alpha if torch.cuda.is_available(): alpha_factor = alpha_factor.cuda() alpha_factor = torch.where(torch.eq(targets, 1.), alpha_factor, 1. - alpha_factor) focal_weight = torch.where(torch.eq(targets, 1.), 1. - classification, classification) focal_weight = alpha_factor * torch.pow(focal_weight, gamma) bce = -(targets * torch.log(classification) + (1.0 - targets) * torch.log(1.0 - classification)) cls_loss = focal_weight * bce zeros = torch.zeros(cls_loss.shape) if torch.cuda.is_available(): zeros = zeros.cuda() cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, zeros) classification_losses.append(cls_loss.sum() / torch.clamp(num_positive_anchors.float(), min=1.0)) if positive_indices.sum() > 0: assigned_annotations = assigned_annotations[positive_indices, :] anchor_widths_pi = anchor_widths[positive_indices] anchor_heights_pi = anchor_heights[positive_indices] anchor_ctr_x_pi = anchor_ctr_x[positive_indices] anchor_ctr_y_pi = anchor_ctr_y[positive_indices] gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0] gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1] gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights gt_widths = torch.clamp(gt_widths, min=1) gt_heights = torch.clamp(gt_heights, min=1) targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi targets_dw = torch.log(gt_widths / anchor_widths_pi) targets_dh = torch.log(gt_heights / anchor_heights_pi) targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh)) targets = targets.t() norm = torch.Tensor([[0.1, 0.1, 0.2, 0.2]]) if torch.cuda.is_available(): norm = norm.cuda() targets = targets / norm regression_diff = torch.abs(targets - regression[positive_indices, :]) regression_loss = torch.where( torch.le(regression_diff, 1.0 / 9.0), 0.5 * 9.0 * torch.pow(regression_diff, 2), regression_diff - 0.5 / 9.0 ) regression_losses.append(regression_loss.mean()) else: if torch.cuda.is_available(): regression_losses.append(torch.tensor(0).float().cuda()) else: regression_losses.append(torch.tensor(0).float()) return torch.stack(classification_losses).mean(dim=0, keepdim=True), torch.stack(regression_losses).mean(dim=0, keepdim=True)Ancestors
- torch.nn.modules.module.Module
Methods
def forward(self, classifications, regressions, anchors, annotations)-
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the :class:
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.Expand source code
def forward(self, classifications, regressions, anchors, annotations): alpha = 0.25 gamma = 2.0 batch_size = classifications.shape[0] classification_losses = [] regression_losses = [] anchor = anchors[0, :, :] anchor_widths = anchor[:, 2] - anchor[:, 0] anchor_heights = anchor[:, 3] - anchor[:, 1] anchor_ctr_x = anchor[:, 0] + 0.5 * anchor_widths anchor_ctr_y = anchor[:, 1] + 0.5 * anchor_heights for j in range(batch_size): classification = classifications[j, :, :] regression = regressions[j, :, :] bbox_annotation = annotations[j, :, :] bbox_annotation = bbox_annotation[bbox_annotation[:, 4] != -1] if bbox_annotation.shape[0] == 0: if torch.cuda.is_available(): regression_losses.append(torch.tensor(0).float().cuda()) classification_losses.append(torch.tensor(0).float().cuda()) else: regression_losses.append(torch.tensor(0).float()) classification_losses.append(torch.tensor(0).float()) continue classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4) IoU = calc_iou(anchors[0, :, :], bbox_annotation[:, :4]) IoU_max, IoU_argmax = torch.max(IoU, dim=1) # compute the loss for classification targets = torch.ones(classification.shape) * -1 if torch.cuda.is_available(): targets = targets.cuda() targets[torch.lt(IoU_max, 0.4), :] = 0 positive_indices = torch.ge(IoU_max, 0.5) num_positive_anchors = positive_indices.sum() assigned_annotations = bbox_annotation[IoU_argmax, :] targets[positive_indices, :] = 0 targets[positive_indices, assigned_annotations[positive_indices, 4].long()] = 1 alpha_factor = torch.ones(targets.shape) * alpha if torch.cuda.is_available(): alpha_factor = alpha_factor.cuda() alpha_factor = torch.where(torch.eq(targets, 1.), alpha_factor, 1. - alpha_factor) focal_weight = torch.where(torch.eq(targets, 1.), 1. - classification, classification) focal_weight = alpha_factor * torch.pow(focal_weight, gamma) bce = -(targets * torch.log(classification) + (1.0 - targets) * torch.log(1.0 - classification)) cls_loss = focal_weight * bce zeros = torch.zeros(cls_loss.shape) if torch.cuda.is_available(): zeros = zeros.cuda() cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, zeros) classification_losses.append(cls_loss.sum() / torch.clamp(num_positive_anchors.float(), min=1.0)) if positive_indices.sum() > 0: assigned_annotations = assigned_annotations[positive_indices, :] anchor_widths_pi = anchor_widths[positive_indices] anchor_heights_pi = anchor_heights[positive_indices] anchor_ctr_x_pi = anchor_ctr_x[positive_indices] anchor_ctr_y_pi = anchor_ctr_y[positive_indices] gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0] gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1] gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights gt_widths = torch.clamp(gt_widths, min=1) gt_heights = torch.clamp(gt_heights, min=1) targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi targets_dw = torch.log(gt_widths / anchor_widths_pi) targets_dh = torch.log(gt_heights / anchor_heights_pi) targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh)) targets = targets.t() norm = torch.Tensor([[0.1, 0.1, 0.2, 0.2]]) if torch.cuda.is_available(): norm = norm.cuda() targets = targets / norm regression_diff = torch.abs(targets - regression[positive_indices, :]) regression_loss = torch.where( torch.le(regression_diff, 1.0 / 9.0), 0.5 * 9.0 * torch.pow(regression_diff, 2), regression_diff - 0.5 / 9.0 ) regression_losses.append(regression_loss.mean()) else: if torch.cuda.is_available(): regression_losses.append(torch.tensor(0).float().cuda()) else: regression_losses.append(torch.tensor(0).float()) return torch.stack(classification_losses).mean(dim=0, keepdim=True), torch.stack(regression_losses).mean(dim=0, keepdim=True)