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custom_yolov2.py
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custom_yolov2.py
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# coding: utf-8
import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils
from chainer import Link, Chain, ChainList
import chainer.links as L
import chainer.functions as F
from lib.utils import multi_box_iou
from lib.utils import Box
from lib.utils import box_iou
from lib.functions import reorg
class YOLOv2(Chain):
def __init__(self, n_classes_fcn, n_classes_yolo, n_boxes):
super(YOLOv2, self).__init__(
conv1=L.Convolution2D(3, 64, 3, stride=1, pad=1, nobias=True),
bn1=L.BatchNormalization(64, use_beta=False, eps=2e-5),
bias1=L.Bias(shape=(64,)),
conv2=L.Convolution2D(None, 64, 3, stride=1, pad=1, nobias=True),
bn2=L.BatchNormalization(64, use_beta=False, eps=2e-5),
bias2=L.Bias(shape=(64,)),
conv3=L.Convolution2D(None, 128, 3, stride=1, pad=1, nobias=True),
bn3=L.BatchNormalization(128, use_beta=False, eps=2e-5),
bias3=L.Bias(shape=(128,)),
conv4=L.Convolution2D(None, 128, 3, stride=1, pad=1, nobias=True),
bn4=L.BatchNormalization(128, use_beta=False, eps=2e-5),
bias4=L.Bias(shape=(128,)),
conv5=L.Convolution2D(None, 256, 3, stride=1, pad=1, nobias=True),
bn5=L.BatchNormalization(256, use_beta=False, eps=2e-5),
bias5=L.Bias(shape=(256,)),
conv6=L.Convolution2D(None, 256, 3, stride=1, pad=1, nobias=True),
bn6=L.BatchNormalization(256, use_beta=False, eps=2e-5),
bias6=L.Bias(shape=(256,)),
conv7=L.Convolution2D(None, 256, 3, stride=1, pad=1, nobias=True),
bn7=L.BatchNormalization(256, use_beta=False, eps=2e-5),
bias7=L.Bias(shape=(256,)),
conv8=L.Convolution2D(None, 512, 3, stride=1, pad=1, nobias=True),
bn8=L.BatchNormalization(512, use_beta=False, eps=2e-5),
bias8=L.Bias(shape=(512,)),
conv9=L.Convolution2D(None, 512, 3, stride=1, pad=1, nobias=True),
bn9=L.BatchNormalization(512, use_beta=False, eps=2e-5),
bias9=L.Bias(shape=(512,)),
conv10=L.Convolution2D(None, 512, 3, stride=1, pad=1, nobias=True),
bn10=L.BatchNormalization(512, use_beta=False, eps=2e-5),
bias10=L.Bias(shape=(512,)),
conv11=L.Convolution2D(None, 512, 3, stride=1, pad=1, nobias=True),
bn11=L.BatchNormalization(512, use_beta=False, eps=2e-5),
bias11=L.Bias(shape=(512,)),
conv12=L.Convolution2D(None, 512, 3, stride=1, pad=1, nobias=True),
bn12=L.BatchNormalization(512, use_beta=False, eps=2e-5),
bias12=L.Bias(shape=(512,)),
conv13=L.Convolution2D(None, 512, 3, stride=1, pad=1, nobias=True),
bn13=L.BatchNormalization(512, use_beta=False, eps=2e-5),
bias13=L.Bias(shape=(512,)),
pool1=L.Convolution2D(None, n_classes_fcn, 1, stride=1, pad=0),
pool2=L.Convolution2D(None, n_classes_fcn, 1, stride=1, pad=0),
pool3=L.Convolution2D(None, n_classes_fcn, 1, stride=1, pad=0),
upsample1=L.Deconvolution2D(None, n_classes_fcn, ksize=4, stride=2, pad=1),
upsample2=L.Deconvolution2D(None, n_classes_fcn, ksize=8, stride=4, pad=2),
upsample3=L.Deconvolution2D(None, n_classes_fcn, ksize=16, stride=8, pad=4),
conv14=L.Convolution2D(None, 1024, 3, stride=1, pad=1, nobias=True),
bn14=L.BatchNormalization(1024, use_beta=False, eps=2e-5),
bias14=L.Bias(shape=(1024,)),
conv15=L.Convolution2D(None, n_boxes * (5 + n_classes_yolo), ksize=1, stride=1, pad=0),
)
self.n_boxes = n_boxes
self.n_classes_fcn = n_classes_fcn
self.n_classes_yolo = n_classes_yolo
self.finetune = False
def __call__(self, x, FCN=True, train=False):
chainer.using_config('train', train)
h = F.relu(self.bias1(self.bn1(self.conv1(x), finetune=self.finetune)))
h = F.relu(self.bias2(self.bn2(self.conv2(h), finetune=self.finetune)))
h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
h = F.relu(self.bias3(self.bn3(self.conv3(h), finetune=self.finetune)))
h = F.relu(self.bias4(self.bn4(self.conv4(h), finetune=self.finetune)))
h = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
h = F.relu(self.bias5(self.bn5(self.conv5(h), finetune=self.finetune)))
h = F.relu(self.bias6(self.bn6(self.conv6(h), finetune=self.finetune)))
h = F.relu(self.bias7(self.bn7(self.conv7(h), finetune=self.finetune)))
p3 = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
h = F.relu(self.bias8(self.bn8(self.conv8(p3), finetune=self.finetune)))
h = F.relu(self.bias9(self.bn9(self.conv9(h), finetune=self.finetune)))
h = F.relu(self.bias10(self.bn10(self.conv10(h), finetune=self.finetune)))
high_resolution_feature = reorg(h)
p4 = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
h = F.relu(self.bias11(self.bn11(self.conv11(p4), finetune=self.finetune)))
h = F.relu(self.bias12(self.bn12(self.conv12(h), finetune=self.finetune)))
h = F.relu(self.bias13(self.bn13(self.conv13(h), finetune=self.finetune)))
#p5 = F.max_pooling_2d(h, ksize=2, stride=2, pad=0)
#h.unchain.backward()
h = F.concat((high_resolution_feature, h), axis=1)
h = F.relu(self.bias14(self.bn14(self.conv14(h), finetune=self.finetune)))
o_yolo = self.conv15(h)
u3 = self.pool1(p3)
u4 = self.upsample1(self.pool2(p4))
#u5 = self.upsample2(self.pool3(p5))
h = u3 + u4# + u5
o_fcn = self.upsample3(h)
if FCN:
return o_fcn
else:
return o_yolo
class YOLOv2Predictor(Chain):
def __init__(self, predictor):
super(YOLOv2Predictor, self).__init__(predictor=predictor)
self.anchors = [[5.375, 5.03125], [5.40625, 4.6875], [2.96875, 2.53125], [2.59375, 2.78125], [1.9375, 3.25]]
self.thresh = 0.6
self.seen = 0
self.unstable_seen = 5000
def __call__(self, input_x, t, FCN=False, train=False):
output = self.predictor(input_x, FCN=FCN, train=train)
if FCN:
if train:
loss = F.softmax_cross_entropy(output, t)
return loss
else:
loss = F.softmax(output)
return loss
batch_size, _, grid_h, grid_w = output.shape
self.seen += batch_size
x, y, w, h, conf, prob = F.split_axis(F.reshape(output, (batch_size, self.predictor.n_boxes, self.predictor.n_classes_yolo+5, grid_h, grid_w)), (1, 2, 3, 4, 5), axis=2)
x = F.sigmoid(x) # xのactivation
y = F.sigmoid(y) # yのactivation
conf = F.sigmoid(conf) # confのactivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
prob = F.softmax(prob) # probablitiyのacitivation
# 教師データの用意
tw = np.zeros(w.shape, dtype=np.float32) # wとhが0になるように学習(e^wとe^hは1に近づく -> 担当するbboxの倍率1)
th = np.zeros(h.shape, dtype=np.float32)
tx = np.tile(0.5, x.shape).astype(np.float32) # 活性化後のxとyが0.5になるように学習()
ty = np.tile(0.5, y.shape).astype(np.float32)
if self.seen < self.unstable_seen: # centerの存在しないbbox誤差学習スケールは基本0.1
box_learning_scale = np.tile(0.1, x.shape).astype(np.float32)
else:
box_learning_scale = np.tile(0, x.shape).astype(np.float32)
tconf = np.zeros(conf.shape, dtype=np.float32) # confidenceのtruthは基本0、iouがthresh以上のものは学習しない、ただしobjectの存在するgridのbest_boxのみ真のIOUに近づかせる
conf_learning_scale = np.tile(0.1, conf.shape).astype(np.float32)
tprob = prob.data.copy() # best_anchor以外は学習させない(自身との二乗和誤差 = 0)
# 全bboxとtruthのiouを計算(batch単位で計算する)
x_shift = Variable(np.broadcast_to(np.arange(grid_w, dtype=np.float32), x.shape[1:]))
y_shift = Variable(np.broadcast_to(np.arange(grid_h, dtype=np.float32).reshape(grid_h, 1), y.shape[1:]))
w_anchor = Variable(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 0], (self.predictor.n_boxes, 1, 1, 1)), w.shape[1:]))
h_anchor = Variable(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 1], (self.predictor.n_boxes, 1, 1, 1)), h.shape[1:]))
x_shift.to_gpu(), y_shift.to_gpu(), w_anchor.to_gpu(), h_anchor.to_gpu()
best_ious = []
for batch in range(batch_size):
#n_truth_boxes = len(t[batch])
n_truth_boxes = int(sum( x[0] != 10.0 for x in t[batch])) # ??
box_x = (x[batch] + x_shift) / grid_w
box_y = (y[batch] + y_shift) / grid_h
box_w = F.exp(w[batch]) * w_anchor / grid_w
box_h = F.exp(h[batch]) * h_anchor / grid_h
ious = []
for truth_index in range(n_truth_boxes):
t = chainer.cuda.to_cpu(t) # ??
truth_box_x = Variable(np.broadcast_to(np.array(t[batch][truth_index][1], dtype=np.float32), box_x.shape))
truth_box_y = Variable(np.broadcast_to(np.array(t[batch][truth_index][2], dtype=np.float32), box_y.shape))
truth_box_w = Variable(np.broadcast_to(np.array(t[batch][truth_index][3], dtype=np.float32), box_w.shape))
truth_box_h = Variable(np.broadcast_to(np.array(t[batch][truth_index][4], dtype=np.float32), box_h.shape))
truth_box_x.to_gpu(), truth_box_y.to_gpu(), truth_box_w.to_gpu(), truth_box_h.to_gpu()
ious.append(multi_box_iou(Box(box_x, box_y, box_w, box_h), Box(truth_box_x, truth_box_y, truth_box_w, truth_box_h)).data.get())
if ious:
ious = np.array(ious)
best_ious.append(np.max(ious, axis=0))
else:
best_ious.append(0)
best_ious = np.array(best_ious)
# 一定以上のiouを持つanchorに対しては、confを0に下げないようにする(truthの周りのgridはconfをそのまま維持)。
tconf[best_ious > self.thresh] = conf.data.get()[best_ious > self.thresh]
conf_learning_scale[best_ious > self.thresh] = 0
# objectの存在するanchor boxのみ、x、y、w、h、conf、probを個別修正
abs_anchors = self.anchors / np.array([grid_w, grid_h])
for batch in range(batch_size):
for truth_box in t[batch]:
if truth_box[0] == 10.0: # ??
continue
truth_w = int(float(truth_box[1]) * grid_w)
truth_h = int(float(truth_box[2]) * grid_h)
truth_n = 0
best_iou = 0.0
for anchor_index, abs_anchor in enumerate(abs_anchors):
iou = box_iou(Box(0, 0, float(truth_box[3]), float(truth_box[4])), Box(0, 0, abs_anchor[0], abs_anchor[1]))
if best_iou < iou:
best_iou = iou
truth_n = anchor_index
# objectの存在するanchorについて、centerを0.5ではなく、真の座標に近づかせる。anchorのスケールを1ではなく真のスケールに近づかせる。学習スケールを1にする。
box_learning_scale[batch, truth_n, :, truth_h, truth_w] = 1.0
tx[batch, truth_n, :, truth_h, truth_w] = float(truth_box[1]) * grid_w - truth_w
ty[batch, truth_n, :, truth_h, truth_w] = float(truth_box[2]) * grid_h - truth_h
tw[batch, truth_n, :, truth_h, truth_w] = np.log(float(truth_box[3]) / abs_anchors[truth_n][0])
th[batch, truth_n, :, truth_h, truth_w] = np.log(float(truth_box[4]) / abs_anchors[truth_n][1])
tprob[batch, :, truth_n, truth_h, truth_w] = 0
tprob[batch, int(truth_box[0]), truth_n, truth_h, truth_w] = 1
# IOUの観測
full_truth_box = Box(float(truth_box[1]), float(truth_box[2]), float(truth_box[3]), float(truth_box[4]))
predicted_box = Box(
(x[batch][truth_n][0][truth_h][truth_w].data.get() + truth_w) / grid_w,
(y[batch][truth_n][0][truth_h][truth_w].data.get() + truth_h) / grid_h,
np.exp(w[batch][truth_n][0][truth_h][truth_w].data.get()) * abs_anchors[truth_n][0],
np.exp(h[batch][truth_n][0][truth_h][truth_w].data.get()) * abs_anchors[truth_n][1]
)
predicted_iou = box_iou(full_truth_box, predicted_box)
tconf[batch, truth_n, :, truth_h, truth_w] = predicted_iou
conf_learning_scale[batch, truth_n, :, truth_h, truth_w] = 10.0
# debug prints
maps = F.transpose(prob[batch], (2, 3, 1, 0)).data
print("seen = %d" % self.seen)
# loss計算
tx, ty, tw, th, tconf, tprob = Variable(tx), Variable(ty), Variable(tw), Variable(th), Variable(tconf), Variable(tprob)
box_learning_scale, conf_learning_scale = Variable(box_learning_scale), Variable(conf_learning_scale)
tx.to_gpu(), ty.to_gpu(), tw.to_gpu(), th.to_gpu(), tconf.to_gpu(), tprob.to_gpu()
box_learning_scale.to_gpu()
conf_learning_scale.to_gpu()
x_loss = F.sum((tx - x) ** 2 * box_learning_scale) / 2
y_loss = F.sum((ty - y) ** 2 * box_learning_scale) / 2
w_loss = F.sum((tw - w) ** 2 * box_learning_scale) / 2
h_loss = F.sum((th - h) ** 2 * box_learning_scale) / 2
c_loss = F.sum((tconf - conf) ** 2 * conf_learning_scale) / 2
p_loss = F.sum((tprob - prob) ** 2) / 2
print("x_loss: %f y_loss: %f w_loss: %f h_loss: %f c_loss: %f p_loss: %f" %
(F.sum(x_loss).data, F.sum(y_loss).data, F.sum(w_loss).data, F.sum(h_loss).data, F.sum(c_loss).data, F.sum(p_loss).data)
)
loss = x_loss + y_loss + w_loss + h_loss + c_loss + p_loss
return loss
def init_anchor(self, anchors):
self.anchors = anchors
def predict(self, input_x, FCN=False):
output = self.predictor(input_x, FCN=FCN, train=False)
if FCN:
loss = F.softmax(output)
return loss
else:
batch_size, input_channel, input_h, input_w = input_x.shape
batch_size, _, grid_h, grid_w = output.shape
x, y, w, h, conf, prob = F.split_axis(F.reshape(output, (batch_size, self.predictor.n_boxes, self.predictor.n_classes_yolo+5, grid_h, grid_w)), (1, 2, 3, 4, 5), axis=2)
x = F.sigmoid(x) # xのactivation
y = F.sigmoid(y) # yのactivation
conf = F.sigmoid(conf) # confのactivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
prob = F.softmax(prob) # probablitiyのacitivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
# x, y, w, hを絶対座標へ変換
x_shift = Variable(np.broadcast_to(np.arange(grid_w, dtype=np.float32), x.shape))
y_shift = Variable(np.broadcast_to(np.arange(grid_h, dtype=np.float32).reshape(grid_h, 1), y.shape))
w_anchor = Variable(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 0], (self.predictor.n_boxes, 1, 1, 1)), w.shape))
h_anchor = Variable(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 1], (self.predictor.n_boxes, 1, 1, 1)), h.shape))
x_shift.to_gpu(), y_shift.to_gpu(), w_anchor.to_gpu(), h_anchor.to_gpu() # !!!need to comment out when using cpu!!!
box_x = (x + x_shift) / grid_w
box_y = (y + y_shift) / grid_h
box_w = F.exp(w) * w_anchor / grid_w
box_h = F.exp(h) * h_anchor / grid_h
return box_x, box_y, box_w, box_h, conf, prob