def forward(self, x: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Tensor]:
confidences = []
locations = []
start_layer_index = 0
header_index = 0
end_layer_index = 0
for end_layer_index in self.source_layer_indexes:
if isinstance(end_layer_index, GraphPath):
path = end_layer_index
end_layer_index = end_layer_index.s0
added_layer = None
elif isinstance(end_layer_index, tuple):
added_layer = end_layer_index[1]
end_layer_index = end_layer_index[0]
path = None
else:
added_layer = None
path = None
for layer in self.base_net[start_layer_index:end_layer_index]:
x = layer(x)
if added_layer:
y = added_layer(x)
else:
y = x
if path:
sub = getattr(self.base_net[end_layer_index], path.name)
for layer in sub[:path.s1]:
x = layer(x)
y = x
for layer in sub[path.s1:]:
x = layer(x)
end_layer_index += 1
start_layer_index = end_layer_index
confidence, location = self.compute_header(header_index, y)
header_index += 1
confidences.append(confidence)
locations.append(location)
for layer in self.base_net[end_layer_index:]:
x = layer(x)
for layer in self.extras:
x = layer(x)
confidence, location = self.compute_header(header_index, x)
header_index += 1
confidences.append(confidence)
locations.append(location)
confidences = paddle.concat(confidences, 1)
locations = paddle.concat(locations, 1)
if self.is_test:
confidences = F.softmax(confidences, 2)
boxes = box_utils.convert_locations_to_boxes(
locations, self.priors, self.config.center_variance,
self.config.size_variance)
boxes = box_utils.center_form_to_corner_form(boxes)
return confidences, boxes
else:
return confidences, locations
def test(loader, net, criterion, device, writer=None, epoch=0):
net.eval()
running_loss = 0.0
running_regression_loss = 0.0
running_classification_loss = 0.0
num = 0
ToAbs = ToAbsoluteCoords()
for _, data in enumerate(loader):
images, boxes, labels = data
images = images.to(device)
boxes = boxes.to(device)
labels = labels.to(device)
num += 1
with torch.no_grad():
confidence, locations = net(images)
if writer is not None and num % 2 == 1:
normalized_image = ((images[0] + 1.0) / 2).to("cpu")
image_locations = locations[0].to("cpu")
image_confidences = confidence[0].to("cpu")
# Warning: Hardcoding for mobilenet config for now
config = mobilenetv1_ssd_config
# Filter locations by confidences for class 1
# TODO(dga): This should be more general and should be a function
# shared with the live demo at least...
confident_boxes_mask = image_confidences[:, 1] > 0.2
display_boxes = box_utils.convert_locations_to_boxes(
image_locations, config.priors, config.center_variance,
config.size_variance)
display_boxes = display_boxes[confident_boxes_mask]
display_boxes = box_utils.center_form_to_corner_form(
display_boxes)
permuted_image = normalized_image.permute(1, 2,
0) # CHW to HWC
_, boxesAbs, _ = ToAbs(permuted_image, display_boxes)
writer.add_image_with_boxes('Test Image', normalized_image,
boxesAbs, epoch)
regression_loss, classification_loss = criterion(
confidence, locations, labels, boxes)
loss = regression_loss + classification_loss
if writer is not None:
# TODO: Move this to log an average instead of every box
writer.add_scalar('Test_Loss', loss, epoch)
writer.add_scalar('Test_Regression_Loss', regression_loss,
epoch)
writer.add_scalar('Test_Classification_Loss',
classification_loss, epoch)
running_loss += loss.item()
running_regression_loss += regression_loss.item()
running_classification_loss += classification_loss.item()
return running_loss / num, running_regression_loss / num, running_classification_loss / num
def train(loader,
net,
criterion,
optimizer,
device,
debug_steps=100,
epoch=-1,
writer=None,
target_transform=None):
net.train(True)
running_loss = 0.0
running_regression_loss = 0.0
running_classification_loss = 0.0
ToAbs = ToAbsoluteCoords()
for i, data in enumerate(loader):
images, boxes, labels = data
# if writer is not None:
# normalized_image = (images[0] + 1.0)/2
# # Warning: Hardcoding for mobilenet config for now
# config = mobilenetv1_ssd_config
# display_boxes = box_utils.convert_locations_to_boxes(
# boxes, config.priors, config.center_variance, config.size_variance
# )
# display_boxes = box_utils.center_form_to_corner_form(display_boxes)
# # print("display_boxes_rel: ",display_boxes[0])
# # print("Display boxes shape:", display_boxes.shape)
# permuted_image = images[0].permute(1, 2, 0) # CHW to HWC
# # print("Permuted images shape", permuted_image.shape)
# _, boxesAbs, _ = ToAbs(permuted_image, display_boxes[0])
# writer.add_image_with_boxes('Training Image', normalized_image, boxesAbs, i)
# # print("Boxes0: ", boxesAbs)
images = images.to(device)
boxes = boxes.to(device)
labels = labels.to(device)
optimizer.zero_grad()
confidence, locations = net(images)
regression_loss, classification_loss = criterion(
confidence, locations, labels, boxes) # TODO CHANGE BOXES
loss = regression_loss + classification_loss
loss.backward()
optimizer.step()
if writer is not None and i % 29 == 0:
images = images.to("cpu")
boxes = boxes.to("cpu") # This is a batch of boxes
labels = labels.to("cpu")
locations = locations.to("cpu")
# Extract the relevant bbox data for images[0], the one we will display in TB:
normalized_image = (images[0] + 1.0) / 2
image_confidence = confidence[0]
image_locations = locations[0]
image_boxes = boxes[0] # The boxes just for images[0]
# Warning: Hardcoding for mobilenet config for now
config = mobilenetv1_ssd_config
gt_display_boxes = box_utils.convert_locations_to_boxes(
image_boxes, config.priors, config.center_variance,
config.size_variance)
pred_display_boxes = box_utils.convert_locations_to_boxes(
image_locations, config.priors, config.center_variance,
config.size_variance)
gt_display_boxes = box_utils.center_form_to_corner_form(
gt_display_boxes)
confident_boxes_mask = image_confidence[:, 1] > 0.2
pred_display_boxes = box_utils.convert_locations_to_boxes(
image_locations, config.priors, config.center_variance,
config.size_variance)
pred_display_boxes = pred_display_boxes[confident_boxes_mask]
pred_display_boxes = box_utils.center_form_to_corner_form(
pred_display_boxes)
# print("display_boxes_rel: ",display_boxes[0])
# print("Display boxes shape:", display_boxes.shape)
permuted_image = normalized_image.permute(1, 2, 0) # CHW to HWC
# print("Permuted images shape", permuted_image.shape)
_, gt_boxesAbs, _ = ToAbs(permuted_image, gt_display_boxes)
_, pred_boxesAbs, _ = ToAbs(permuted_image, pred_display_boxes)
writer.add_image_with_boxes('Training Image', normalized_image,
gt_boxesAbs, epoch)
writer.add_image_with_boxes('Predicted Image', normalized_image,
pred_boxesAbs, epoch)
# print("Boxes0: ", boxesAbs)
running_loss += loss.item()
running_regression_loss += regression_loss.item()
running_classification_loss += classification_loss.item()
if i % debug_steps == 0:
avg_loss = running_loss / debug_steps
avg_reg_loss = running_regression_loss / debug_steps
avg_clf_loss = running_classification_loss / debug_steps
logging.info(f"Epoch: {epoch}, Step: {i}, " +
f"Average Loss: {avg_loss:.4f}, " +
f"Average Regression Loss {avg_reg_loss:.4f}, " +
f"Average Classification Loss: {avg_clf_loss:.4f}")
if writer is not None:
writer.add_scalar('Training_Loss', avg_loss, epoch)
writer.add_scalar('Training_Regression_Loss', avg_reg_loss,
epoch)
writer.add_scalar('Training_Classification_Loss', avg_clf_loss,
epoch)
running_loss = 0.0
running_regression_loss = 0.0
running_classification_loss = 0.0