def __init__(self, problem):
super().__init__()
from utils.augmentations import FastBaseTransform
self.FastBaseTransform = FastBaseTransform
import cv2
self.cv2 = cv2
import matplotlib.pyplot as plt
self.plt = plt
from layers.output_utils import postprocess, undo_image_transformation
self.postprocess = postprocess
self.undo_image_transformation = undo_image_transformation
from utils import timer
self.timer = timer
import sys
syspathsave = None
if not 'yolact' in sys.path[1]:
import copy
syspathsave = copy.copy(sys.path)
sys.path.insert(1, '../yolact/')
from yolact import Yolact
from train import MultiBoxLoss
import data as D
self.D = D
from collections import defaultdict
self.color_cache = defaultdict(lambda: {})
net = Yolact()
net.train()
net.init_weights(backbone_path='../yolact/weights/' +
D.cfg.backbone.path)
criterion = MultiBoxLoss(num_classes=D.cfg.num_classes,
pos_threshold=D.cfg.positive_iou_threshold,
neg_threshold=D.cfg.negative_iou_threshold,
negpos_ratio=D.cfg.ohem_negpos_ratio)
self.net = net
self.criterion = criterion
if syspathsave:
sys.path = syspathsave
img, gt, gt_masks, h, w, num_crowd = dataset.pull_item(i_img)
batch = img.unsqueeze(0).cuda()
print(type(batch), batch.size())
preds = net(batch)
img_numpy = local_prep_display(preds, img, h, w)
plt.imshow(img_numpy)
plt.pause(0.5)
#-----------------------
try:
datum = next(data_loader_iterator)
except StopIteration:
break
images, targets, masks, num_crowds = local_prepare_data(datum)
net.train()
predsT = net(images[0])
losses = criterion(net, predsT, targets[0], masks[0],
num_crowds[0])
loss = sum([losses[k] for k in losses])
print(loss)
# no_inf_mean removes some components from the loss, so make sure to backward through all of it
# all_loss = sum([v.mean() for v in losses.values()])
# Backprop
loss.backward(
) # Do this to free up vram even if loss is not finite
#-----------------------
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
# if mode == 'train':
