def create_train_pconv_unet():
input_image = layers.Input((320, 240, 3))
input_mask = layers.Input((320, 240, 3))
input_groundtruth = layers.Input((320, 240, 3))
## U-Net
# Encoder
conv1, mask1 = conv_bn_relu(input_image,
input_mask,
filters=32,
kernel_size=3,
downsampling=1,
reps=2) # 320x240
conv2, mask2 = conv_bn_relu(conv1,
mask1,
filters=64,
kernel_size=7,
downsampling=5,
reps=1)
conv2, mask2 = conv_bn_relu(conv2,
mask2,
filters=64,
kernel_size=3,
downsampling=1,
reps=2) # 64x48
conv3, mask3 = conv_bn_relu(conv2,
mask2,
filters=128,
kernel_size=3,
downsampling=2,
reps=3) # 32x24
conv4, mask4 = conv_bn_relu(conv3,
mask3,
filters=256,
kernel_size=3,
downsampling=2,
reps=3) # 16x12
conv5, mask5 = conv_bn_relu(conv4,
mask4,
filters=512,
kernel_size=3,
downsampling=2,
reps=3) # 8x6
## Decoder
img, mask = conv_bn_relu(conv5,
mask5,
filters=256,
kernel_size=3,
upsampling=2,
reps=3,
concat_img=conv4,
concat_mask=mask4) # 16x12
img, mask = conv_bn_relu(img,
mask,
filters=128,
kernel_size=3,
upsampling=2,
reps=3,
concat_img=conv3,
concat_mask=mask3) # 32x24
img, mask = conv_bn_relu(img,
mask,
filters=64,
kernel_size=3,
upsampling=2,
reps=3,
concat_img=conv2,
concat_mask=mask2) # 64x48
img, mask = conv_bn_relu(img,
mask,
filters=32,
kernel_size=7,
upsampling=5,
reps=1,
concat_img=conv1,
concat_mask=mask1) # 320x240
img, mask = conv_bn_relu(img,
mask,
filters=32,
kernel_size=3,
upsampling=1,
reps=2)
img, mask = conv_bn_relu(img,
mask,
filters=3,
kernel_size=1,
reps=1,
act="custom_tanh") # 差分出力
# skip connection
img = layers.Add()([img, input_image]) # 収束が早くなるはず
img = layers.Lambda(lambda x: K.clip(x, -1.0, 1.0))(img)
## 損失関数
# マスクしていない部分の真の画像＋マスク部分の予測画像
y_comp = layers.Lambda(lambda inputs: inputs[0] * inputs[1] +
(1 - inputs[0]) * inputs[2])(
[input_mask, input_groundtruth, img])
# Caffeカラースケールに変換
vgg_in_pred = layers.Lambda(convert_caffe_color_space)(img)
vgg_in_groundtruth = layers.Lambda(convert_caffe_color_space)(
input_groundtruth)
vgg_in_comp = layers.Lambda(convert_caffe_color_space)(y_comp)
# vggの特徴量
vgg_pred_1, vgg_pred_2, vgg_pred_3 = extract_vgg_features(
vgg_in_pred, (320, 240, 3), 0)
vgg_true_1, vgg_true_2, vgg_true_3 = extract_vgg_features(
vgg_in_groundtruth, (320, 240, 3), 1)
vgg_comp_1, vgg_comp_2, vgg_comp_3 = extract_vgg_features(
vgg_in_comp, (320, 240, 3), 2)
# 画像＋損失
join = LossLayer()([
input_mask, img, input_groundtruth, y_comp, vgg_pred_1, vgg_pred_2,
vgg_pred_3, vgg_true_1, vgg_true_2, vgg_true_3, vgg_comp_1, vgg_comp_2,
vgg_comp_3
])
# lossやmetricsの表示がうまくいかないので出力は1つにする
model = Model([input_image, input_mask, input_groundtruth],
join) # このモデルは>100MBだが、推論用モデルは93MB
return model
def create_train_pconv_unet():
input_image = layers.Input((448, 336, 3))
input_mask = layers.Input((448, 336, 3))
input_groundtruth = layers.Input((448, 336, 3))
## U-Net
# Encoder
conv1, mask1 = conv_bn_relu(input_image,
input_mask,
filters=32,
kernel_size=3,
downsampling=1,
reps=2) # 448x336
conv2, mask2 = conv_bn_relu(conv1,
mask1,
filters=64,
kernel_size=11,
downsampling=7,
reps=1)
conv2, mask2 = conv_bn_relu(conv2,
mask2,
filters=64,
kernel_size=3,
downsampling=1,
reps=2) # 64x48
conv3, mask3 = conv_bn_relu(conv2,
mask2,
filters=128,
kernel_size=3,
downsampling=2,
reps=3) # 32x24
conv4, mask4 = conv_bn_relu(conv3,
mask3,
filters=256,
kernel_size=3,
downsampling=2,
reps=3) # 16x12
conv5, mask5 = conv_bn_relu(conv4,
mask4,
filters=512,
kernel_size=3,
downsampling=2,
reps=3) # 8x6
## Decoder
img, mask = conv_bn_relu(conv5,
mask5,
filters=256,
kernel_size=3,
upsampling=2,
reps=3,
concat_img=conv4,
concat_mask=mask4) # 16x12
img, mask = conv_bn_relu(img,
mask,
filters=128,
kernel_size=3,
upsampling=2,
reps=3,
concat_img=conv3,
concat_mask=mask3) # 32x24
img, mask = conv_bn_relu(img,
mask,
filters=64,
kernel_size=3,
upsampling=2,
reps=3,
concat_img=conv2,
concat_mask=mask2) # 64x48
img, mask = conv_bn_relu(img,
mask,
filters=32,
kernel_size=11,
upsampling=7,
reps=1,
concat_img=conv1,
concat_mask=mask1) # 448x336
img, mask = conv_bn_relu(img,
mask,
filters=32,
kernel_size=3,
upsampling=1,
reps=2)
img, mask = conv_bn_relu(img,
mask,
filters=3,
kernel_size=1,
reps=1,
act="output") # 出力
## 損失関数
# マスクしていない部分の真の画像＋マスク部分の予測画像
y_comp = layers.Lambda(lambda inputs: inputs[0] * inputs[1] +
(1 - inputs[0]) * inputs[2])(
[input_mask, input_groundtruth, img])
# 640x480のグラム行列を取るのは大変なのでPoolingを入れる（NaN対策）->448x336から？
#vgg_in_pred = layers.AveragePooling2D(7)(img)
#vgg_in_groundtruth = layers.AveragePooling2D(7)(input_groundtruth)
#vgg_in_comp = layers.AveragePooling2D(7)(y_comp)
vgg_in_pred = img
vgg_in_groundtruth = input_groundtruth
vgg_in_comp = y_comp
# vggの特徴量
vgg_pred_1, vgg_pred_2, vgg_pred_3 = extract_vgg_features(
vgg_in_pred, (448, 336, 3), 0)
vgg_true_1, vgg_true_2, vgg_true_3 = extract_vgg_features(
vgg_in_groundtruth, (448, 336, 3), 1)
vgg_comp_1, vgg_comp_2, vgg_comp_3 = extract_vgg_features(
vgg_in_comp, (448, 336, 3), 2)
# 画像＋損失
join = LossLayer()([
input_mask, img, input_groundtruth, y_comp, vgg_pred_1, vgg_pred_2,
vgg_pred_3, vgg_true_1, vgg_true_2, vgg_true_3, vgg_comp_1, vgg_comp_2,
vgg_comp_3
])
# lossやmetricsの表示がうまくいかないので出力は1つにする
model = Model([input_image, input_mask, input_groundtruth],
join) # このモデルは>100MBだが、推論用モデルは93MB
return model