我正在使用Python 3.7.7。和張量流 2.1.0。我有一個預訓練的 U-Net 網絡，我想獲取它的編碼器和解碼器。如下圖所示：您可以看到卷積編碼器-解碼器架構。我想要獲取編碼器部分，即出現在圖像左側的圖層：以及解碼器部分：我從這個函數中得到了 U-Net 模型：def get_unet_uncompiled(img_shape = (200,200,1)): inputs = Input(shape=img_shape) conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', data_format="channels_last", name='conv1_1')(inputs) conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', data_format="channels_last", name='conv1_2')(conv1) pool1 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool1')(conv1) conv2 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv2_1')(pool1) conv2 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv2_2')(conv2) pool2 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool2')(conv2) conv3 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv3_1')(pool2) conv3 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv3_2')(conv3) pool3 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool3')(conv3) conv4 = Conv2D(256, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv4_1')(pool3) conv4 = Conv2D(256, (4, 4), activation='relu', padding='same', data_format="channels_last", name='conv4_2')(conv4) pool4 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool4')(conv4)
1 回答
繁星淼淼
TA貢獻1775條經驗 獲得超11個贊
我的建議是定義編碼器和解碼器的結構（get_encoder，get_decoder）。在整個模型的訓練之后，我們的想法是創建一個新的解碼器架構（通過get_decoder），我們可以用解碼器訓練的權重來填充它
pythonic 來說你可以用這種方式做到這一點......
def get_crop_shape(target, refer):
? ??
? ? # width, the 3rd dimension
? ? cw = (target.get_shape()[2] - refer.get_shape()[2])
? ? assert (cw >= 0)
? ? if cw % 2 != 0:
? ? ? ? cw1, cw2 = cw // 2, cw // 2 + 1
? ? else:
? ? ? ? cw1, cw2 = cw // 2, cw // 2
? ? # height, the 2nd dimension
? ? ch = (target.get_shape()[1] - refer.get_shape()[1])
? ? assert (ch >= 0)
? ? if ch % 2 != 0:
? ? ? ? ch1, ch2 = ch // 2, ch // 2 + 1
? ? else:
? ? ? ? ch1, ch2 = ch // 2, ch // 2
? ? return (ch1, ch2), (cw1, cw2)
def get_encoder(img_shape):
? ??
? ? inp = Input(shape=img_shape)
? ? conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', data_format="channels_last", name='conv1_1')(inp)
? ? conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', data_format="channels_last", name='conv1_2')(conv1)
? ? pool1 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool1')(conv1)
? ? conv2 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv2_1')(pool1)
? ? conv2 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv2_2')(conv2)
? ? pool2 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool2')(conv2)
? ? conv3 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv3_1')(pool2)
? ? conv3 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv3_2')(conv3)
? ? pool3 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool3')(conv3)
? ? conv4 = Conv2D(256, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv4_1')(pool3)
? ? conv4 = Conv2D(256, (4, 4), activation='relu', padding='same', data_format="channels_last", name='conv4_2')(conv4)
? ? pool4 = MaxPooling2D(pool_size=(2, 2), data_format="channels_last", name='pool4')(conv4)
? ? conv5 = Conv2D(512, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv5_1')(pool4)
? ? conv5 = Conv2D(512, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv5_2')(conv5)
? ??
? ? return conv5,conv4,conv3,conv2,conv1,inp
def get_decoder(convs):
? ??
? ? conv5,conv4,conv3,conv2,conv1,inputs = convs
? ??
? ? up_conv5 = UpSampling2D(size=(2, 2), data_format="channels_last", name='up_conv5')(conv5)
? ? ch, cw = get_crop_shape(conv4, up_conv5)
? ? crop_conv4 = Cropping2D(cropping=(ch, cw), data_format="channels_last", name='crop_conv4')(conv4)
? ? up6 = concatenate([up_conv5, crop_conv4])
? ? conv6 = Conv2D(256, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv6_1')(up6)
? ? conv6 = Conv2D(256, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv6_2')(conv6)
? ? up_conv6 = UpSampling2D(size=(2, 2), data_format="channels_last", name='up_conv6')(conv6)
? ? ch, cw = get_crop_shape(conv3, up_conv6)
? ? crop_conv3 = Cropping2D(cropping=(ch, cw), data_format="channels_last", name='crop_conv3')(conv3)
? ? up7 = concatenate([up_conv6, crop_conv3])
? ? conv7 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv7_1')(up7)
? ? conv7 = Conv2D(128, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv7_2')(conv7)
? ? up_conv7 = UpSampling2D(size=(2, 2), data_format="channels_last", name='up_conv7')(conv7)
? ? ch, cw = get_crop_shape(conv2, up_conv7)
? ? crop_conv2 = Cropping2D(cropping=(ch, cw), data_format="channels_last", name='crop_conv2')(conv2)
? ? up8 = concatenate([up_conv7, crop_conv2])
? ? conv8 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv8_1')(up8)
? ? conv8 = Conv2D(96, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv8_2')(conv8)
? ? up_conv8 = UpSampling2D(size=(2, 2), data_format="channels_last", name='up_conv8')(conv8)
? ? ch, cw = get_crop_shape(conv1, up_conv8)
? ? crop_conv1 = Cropping2D(cropping=(ch, cw), data_format="channels_last", name='crop_conv1')(conv1)
? ? up9 = concatenate([up_conv8, crop_conv1])
? ? conv9 = Conv2D(64, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv9_1')(up9)
? ? conv9 = Conv2D(64, (3, 3), activation='relu', padding='same', data_format="channels_last", name='conv9_2')(conv9)
? ? ch, cw = get_crop_shape(inputs, conv9)
? ? conv9 = ZeroPadding2D(padding=(ch, cw), data_format="channels_last", name='conv9_3')(conv9)
? ? conv10 = Conv2D(1, (1, 1), activation='sigmoid', data_format="channels_last", name='conv10_1')(conv9)
? ??
? ? return conv10
? ??
def get_unet(img_shape = (200,200,1)):
? ? enc = get_encoder(img_shape)
? ??
? ? dec = get_decoder(enc)
? ??
? ? model = Model(inputs=enc[-1], outputs=dec)
? ? return model
創建整個模型并擬合
img_shape = (200,200,1)
old_model = get_unet(img_shape)
# old_model.compile(...)
# old_model.fit(...)
一如既往地提取編碼器
# extract encoder
first_encoder_layer = 0
last_encoder_layer = 14
encoder_output_layer = [14, 11, 8, 5, 2, 0]
encoder = Model(inputs=old_model.layers[first_encoder_layer].input,
? ? ? ? ? ? ? ? outputs=[old_model.layers[l].output for l in encoder_output_layer],
? ? ? ? ? ? ? ? name='encoder')
encoder.summary()
創建解碼器結構并分配訓練后的權重
# extract decoder fitted weights
restored_w = []
for w in old_model.layers[last_encoder_layer + 1:]:
? ? restored_w.extend(w.get_weights())
??
# reconstruct decoder architecture setting the fitted weights
new_inp = [Input(l.shape[1:]) for l in get_encoder(img_shape)]
new_dec = get_decoder(new_inp)
decoder = Model(new_inp, new_dec)
decoder.set_weights(restored_w)
decoder.summary()
返回預測
# generate random images
n_images = 20
X = np.random.uniform(0,1, (n_images,200,200,1)).astype('float32')
# get encoder predictions?
pred_encoder = encoder.predict(X)
print([p.shape for p in pred_encoder])
# get decoder predictions
pred_decoder = decoder.predict(pred_encoder)
print(pred_decoder.shape)
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