code

1_lambda2model.py

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+#! /usr/bin/env python
+# coding=utf-8
+#================================================================
+#
+#   Author      : miemie2013
+#   Created date: 2019-12-20 14:29:47
+#   Description : 将训练模型中yolov3的所有部分提取出来。
+#                 需要修改'ep000020-loss24.876-val_loss51.209.h5'为你最后训练得到的文件名。
+#
+#================================================================
+import keras
+from train import decode, loss_layer, bbox_iou, focal
+
+
+model = keras.models.load_model('ep000020-loss24.876-val_loss51.209.h5', custom_objects={'decode': decode, 'loss_layer': loss_layer, 'bbox_iou': bbox_iou, 'focal': focal, '<lambda>': keras.losses.mean_squared_error, })
+
+# 训练好的模型 传入模型的图片是 bgr格式
+# 传入模型的图片是否转换为rgb？本地评估的话选择False
+# translate2rgb = True
+translate2rgb = False
+
+saved_model_name = 'aaaa'
+
+
+name0, name1, name2, name3 = 'input_1', 'conv2d_59', 'conv2d_67', 'conv2d_75'
+i0, i1, i2, i3 = 0, 0, 0, 0
+for i in range(len(model.layers)):
+    ly = model.layers[i]
+    if ly.name == name0:
+        i0 = i
+    elif ly.name == name1:
+        i1 = i
+    elif ly.name == name2:
+        i2 = i
+    elif ly.name == name3:
+        i3 = i
+
+
+model2 = keras.models.Model(inputs=model.layers[i0].input, outputs=[model.layers[i1].output, model.layers[i2].output, model.layers[i3].output])
+
+
+if translate2rgb:
+    weights = model2.layers[1].get_weights()[0]
+    h, w, c, k = weights.shape
+    w2 = weights.copy()
+    w2[:, :, 0:1, :] = weights[:, :, 2:3, :]
+    w2[:, :, 2:3, :] = weights[:, :, 0:1, :]
+    model2.layers[1].set_weights([w2])
+
+# keras.utils.vis_utils.plot_model(model2, to_file='model2.png', show_shapes=True)
+model2.compile(loss=[keras.losses.mean_squared_error, keras.losses.mean_squared_error, keras.losses.mean_squared_error], loss_weights=[1., 1., 1.], optimizer=keras.optimizers.Adam(lr=0.001))
+
+
+if translate2rgb:
+    model2.save(saved_model_name+'_rgb.h5')
+else:
+    model2.save(saved_model_name+'_bgr.h5')
+
+