Create convert_tf_to_darknet.py

convert_tf_to_darknet.py

@@ -0,0 +1,87 @@
+import tensorflow as tf
+import re
+import numpy as np
+
+def write_bn_and_weights(weightfile,layer_name):
+    write_bn(weightfile,layer_name)
+    write_convweights(weightfile,layer_name)
+
+def write_bias_and_weights(weightfile,layer_name):
+    write_bias(weightfile,layer_name)
+    write_convweights(weightfile,layer_name)
+
+def write_bias(weightfile,layer_name):
+    weightfile.write(reader.get_tensor(layer_name + '/bias').tobytes())
+
+
+def write_bn(weightfile,layer_name):
+    weightfile.write(reader.get_tensor(layer_name + '/batch_normalization/beta').tobytes())
+    weightfile.write(reader.get_tensor(layer_name + '/batch_normalization/gamma').tobytes())
+    weightfile.write(reader.get_tensor(layer_name + '/batch_normalization/moving_mean').tobytes())
+    weightfile.write(reader.get_tensor(layer_name + '/batch_normalization/moving_variance').tobytes())
+
+def write_convweights(weightfile,layer_name):
+    # 需要将(height, width, in_dim, out_dim)转换成(out_dim, in_dim, height, width)
+    conv_weights = np.transpose(reader.get_tensor(layer_name + '/weight'),[3,2,0,1])
+    weights.write(conv_weights.tobytes())
+
+def write_residual(weightfile,layer_name):
+    write_bn_and_weights(weightfile,layer_name + '/conv1')
+    write_bn_and_weights(weightfile,layer_name + '/conv2')
+
+reader = tf.train.NewCheckpointReader('checkpoint/83/yolov3_test_loss=11.0151.ckpt-12')
+
+global_variables = reader.get_variable_to_shape_map()
+
+#result = open('readerResults.txt','w')
+weights = open('test3.weight','wb')
+#keys = list(global_variables.keys())
+#sortedkeys = sorted(keys)
+#for variable_name in sortedkeys:
+    #if str(variable_name).startswith('darknet/conv0'):
+#    result.write('{}:{}\n'.format(variable_name,global_variables[variable_name]))
+#result.write('darknet/residual1/conv1/weight:{}'.format(reader.get_tensor('darknet/residual1/conv1/weight')))
+#weights.write(reader.get_tensor('darknet/residual1/conv1/weight'))
+
+numpy_data = np.ndarray(shape=(3,),dtype='int32',buffer = np.array([0,2,0],dtype='int32'))
+weights.write(numpy_data.tobytes())
+weights.flush()
+numpy_data = np.ndarray(shape=(1,),
+                          dtype='int64',
+                          buffer=np.array([320000],dtype='int64'))
+weights.write(numpy_data.tobytes())
+weights.flush()
+
+
+write_bn_and_weights(weights,'darknet/conv0')
+write_bn_and_weights(weights,'darknet/conv1')
+write_residual(weights,'darknet/residual0')
+write_bn_and_weights(weights,'darknet/conv4')
+write_residual(weights,'darknet/residual1')
+write_residual(weights,'darknet/residual2')
+write_bn_and_weights(weights,'darknet/conv9')
+for i in range(8):
+    write_residual(weights,'darknet/residual' + str(3 + i))
+write_bn_and_weights(weights,'darknet/conv26')
+for i in range(8):
+    write_residual(weights,'darknet/residual' + str(11 + i))
+write_bn_and_weights(weights,'darknet/conv43')
+for i in range(4):
+    write_residual(weights,'darknet/residual' + str(19 + i))
+for i in range(5):
+    write_bn_and_weights(weights,'conv' + str(52 + i))
+write_bn_and_weights(weights,'yolo1')
+write_bias_and_weights(weights,'feature_map_1')
+write_bn_and_weights(weights,'conv57')
+################upsample and route here########################
+for i in range(5):
+    write_bn_and_weights(weights,'conv' + str(58 + i))
+write_bn_and_weights(weights,'yolo2')
+write_bias_and_weights(weights,'feature_map_2')
+write_bn_and_weights(weights,'conv63')
+################upsample and route here########################
+for i in range(5):
+    #print(i)
+    write_bn_and_weights(weights,'conv' + str(64 + i))
+write_bn_and_weights(weights,'yolo3')
+write_bias_and_weights(weights,'feature_map_3')