text:
Convert to h5 is fine, but failed to further convert to coreml model

By following the description of keras-yolo3, I converted yolov3.weights to keras model yolo.h5. However, the conversion from yolo.h5 to its CoreML model failed. I pasted the detailed command line output as below. The error message and my environment details can be found in the bottom of this post. Sorry for the long command line output.

convert yolov3.weights to keras model yolo.h5

$ python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
Using TensorFlow backend.
Loading weights.
Weights Header:  0 2 0 [32013312]
Parsing Darknet config.
Creating Keras model.
Parsing section net_0
Parsing section convolutional_0
conv2d bn leaky (3, 3, 3, 32)
2018-06-18 10:02:53.364525: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Parsing section convolutional_1
conv2d bn leaky (3, 3, 32, 64)
Parsing section convolutional_2
conv2d bn leaky (1, 1, 64, 32)
Parsing section convolutional_3
conv2d bn leaky (3, 3, 32, 64)
Parsing section shortcut_0
Parsing section convolutional_4
conv2d bn leaky (3, 3, 64, 128)
Parsing section convolutional_5
conv2d bn leaky (1, 1, 128, 64)
Parsing section convolutional_6
conv2d bn leaky (3, 3, 64, 128)
Parsing section shortcut_1
Parsing section convolutional_7
conv2d bn leaky (1, 1, 128, 64)
Parsing section convolutional_8
conv2d bn leaky (3, 3, 64, 128)
Parsing section shortcut_2
Parsing section convolutional_9
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_10
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_11
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_3
Parsing section convolutional_12
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_13
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_4
Parsing section convolutional_14
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_15
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_5
Parsing section convolutional_16
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_17
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_6
Parsing section convolutional_18
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_19
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_7
Parsing section convolutional_20
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_21
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_8
Parsing section convolutional_22
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_23
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_9
Parsing section convolutional_24
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_25
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_10
Parsing section convolutional_26
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_27
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_28
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_11
Parsing section convolutional_29
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_30
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_12
Parsing section convolutional_31
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_32
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_13
Parsing section convolutional_33
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_34
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_14
Parsing section convolutional_35
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_36
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_15
Parsing section convolutional_37
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_38
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_16
Parsing section convolutional_39
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_40
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_17
Parsing section convolutional_41
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_42
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_18
Parsing section convolutional_43
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_44
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_45
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_19
Parsing section convolutional_46
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_47
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_20
Parsing section convolutional_48
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_49
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_21
Parsing section convolutional_50
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_51
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_22
Parsing section convolutional_52
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_53
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_54
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_55
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_56
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_57
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_58
conv2d    linear (1, 1, 1024, 255)
Parsing section yolo_0
Parsing section route_0
Parsing section convolutional_59
conv2d bn leaky (1, 1, 512, 256)
Parsing section upsample_0
Parsing section route_1
Concatenating route layers: [<tf.Tensor 'up_sampling2d_1/ResizeNearestNeighbor:0' shape=(?, ?, ?, 256) dtype=float32>, <tf.Tensor 'add_19/add:0' shape=(?, ?, ?, 512) dtype=float32>]
Parsing section convolutional_60
conv2d bn leaky (1, 1, 768, 256)
Parsing section convolutional_61
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_62
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_63
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_64
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_65
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_66
conv2d    linear (1, 1, 512, 255)
Parsing section yolo_1
Parsing section route_2
Parsing section convolutional_67
conv2d bn leaky (1, 1, 256, 128)
Parsing section upsample_1
Parsing section route_3
Concatenating route layers: [<tf.Tensor 'up_sampling2d_2/ResizeNearestNeighbor:0' shape=(?, ?, ?, 128) dtype=float32>, <tf.Tensor 'add_11/add:0' shape=(?, ?, ?, 256) dtype=float32>]
Parsing section convolutional_68
conv2d bn leaky (1, 1, 384, 128)
Parsing section convolutional_69
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_70
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_71
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_72
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_73
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_74
conv2d    linear (1, 1, 256, 255)
Parsing section yolo_2
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input_1 (InputLayer)            (None, None, None, 3 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, None, 3 128         conv2d_1[0][0]
__________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU)       (None, None, None, 3 0           batch_normalization_1[0][0]
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, None, None, 3 0           leaky_re_lu_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, None, None, 6 18432       zero_padding2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, None, None, 6 256         conv2d_2[0][0]
__________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, None, None, 3 2048        leaky_re_lu_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, None, None, 3 128         conv2d_3[0][0]
__________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU)       (None, None, None, 3 0           batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, None, None, 6 18432       leaky_re_lu_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, None, None, 6 256         conv2d_4[0][0]
__________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_4[0][0]
__________________________________________________________________________________________________
add_1 (Add)                     (None, None, None, 6 0           leaky_re_lu_2[0][0]
                                                                 leaky_re_lu_4[0][0]
__________________________________________________________________________________________________
zero_padding2d_2 (ZeroPadding2D (None, None, None, 6 0           add_1[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, None, None, 1 73728       zero_padding2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, None, None, 1 512         conv2d_5[0][0]
__________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, None, None, 6 8192        leaky_re_lu_5[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, None, None, 6 256         conv2d_6[0][0]
__________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, None, None, 1 73728       leaky_re_lu_6[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, None, None, 1 512         conv2d_7[0][0]
__________________________________________________________________________________________________
leaky_re_lu_7 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_7[0][0]
__________________________________________________________________________________________________
add_2 (Add)                     (None, None, None, 1 0           leaky_re_lu_5[0][0]
                                                                 leaky_re_lu_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, None, None, 6 8192        add_2[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, None, None, 6 256         conv2d_8[0][0]
__________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, None, None, 1 73728       leaky_re_lu_8[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, None, None, 1 512         conv2d_9[0][0]
__________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_9[0][0]
__________________________________________________________________________________________________
add_3 (Add)                     (None, None, None, 1 0           add_2[0][0]
                                                                 leaky_re_lu_9[0][0]
__________________________________________________________________________________________________
zero_padding2d_3 (ZeroPadding2D (None, None, None, 1 0           add_3[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, None, None, 2 294912      zero_padding2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, None, None, 2 1024        conv2d_10[0][0]
__________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, None, None, 1 512         conv2d_11[0][0]
__________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_11[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, None, None, 2 1024        conv2d_12[0][0]
__________________________________________________________________________________________________
leaky_re_lu_12 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_12[0][0]
__________________________________________________________________________________________________
add_4 (Add)                     (None, None, None, 2 0           leaky_re_lu_10[0][0]
                                                                 leaky_re_lu_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, None, None, 1 32768       add_4[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, None, None, 1 512         conv2d_13[0][0]
__________________________________________________________________________________________________
leaky_re_lu_13 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_13[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_13[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, None, None, 2 1024        conv2d_14[0][0]
__________________________________________________________________________________________________
leaky_re_lu_14 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_14[0][0]
__________________________________________________________________________________________________
add_5 (Add)                     (None, None, None, 2 0           add_4[0][0]
                                                                 leaky_re_lu_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, None, None, 1 32768       add_5[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, None, None, 1 512         conv2d_15[0][0]
__________________________________________________________________________________________________
leaky_re_lu_15 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_15[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, None, None, 2 1024        conv2d_16[0][0]
__________________________________________________________________________________________________
leaky_re_lu_16 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_16[0][0]
__________________________________________________________________________________________________
add_6 (Add)                     (None, None, None, 2 0           add_5[0][0]
                                                                 leaky_re_lu_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, None, None, 1 32768       add_6[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, None, None, 1 512         conv2d_17[0][0]
__________________________________________________________________________________________________
leaky_re_lu_17 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, None, None, 2 1024        conv2d_18[0][0]
__________________________________________________________________________________________________
leaky_re_lu_18 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_18[0][0]
__________________________________________________________________________________________________
add_7 (Add)                     (None, None, None, 2 0           add_6[0][0]
                                                                 leaky_re_lu_18[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, None, None, 1 32768       add_7[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, None, None, 1 512         conv2d_19[0][0]
__________________________________________________________________________________________________
leaky_re_lu_19 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_19[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_19[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, None, None, 2 1024        conv2d_20[0][0]
__________________________________________________________________________________________________
leaky_re_lu_20 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_20[0][0]
__________________________________________________________________________________________________
add_8 (Add)                     (None, None, None, 2 0           add_7[0][0]
                                                                 leaky_re_lu_20[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, None, None, 1 32768       add_8[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, None, None, 1 512         conv2d_21[0][0]
__________________________________________________________________________________________________
leaky_re_lu_21 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_21[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_21[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, None, None, 2 1024        conv2d_22[0][0]
__________________________________________________________________________________________________
leaky_re_lu_22 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_22[0][0]
__________________________________________________________________________________________________
add_9 (Add)                     (None, None, None, 2 0           add_8[0][0]
                                                                 leaky_re_lu_22[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, None, None, 1 32768       add_9[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, None, None, 1 512         conv2d_23[0][0]
__________________________________________________________________________________________________
leaky_re_lu_23 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_23[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_23[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, None, None, 2 1024        conv2d_24[0][0]
__________________________________________________________________________________________________
leaky_re_lu_24 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_24[0][0]
__________________________________________________________________________________________________
add_10 (Add)                    (None, None, None, 2 0           add_9[0][0]
                                                                 leaky_re_lu_24[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, None, None, 1 32768       add_10[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, None, None, 1 512         conv2d_25[0][0]
__________________________________________________________________________________________________
leaky_re_lu_25 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_25[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_25[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, None, None, 2 1024        conv2d_26[0][0]
__________________________________________________________________________________________________
leaky_re_lu_26 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_26[0][0]
__________________________________________________________________________________________________
add_11 (Add)                    (None, None, None, 2 0           add_10[0][0]
                                                                 leaky_re_lu_26[0][0]
__________________________________________________________________________________________________
zero_padding2d_4 (ZeroPadding2D (None, None, None, 2 0           add_11[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, None, None, 5 1179648     zero_padding2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, None, None, 5 2048        conv2d_27[0][0]
__________________________________________________________________________________________________
leaky_re_lu_27 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_27[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_27[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, None, None, 2 1024        conv2d_28[0][0]
__________________________________________________________________________________________________
leaky_re_lu_28 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_28[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, None, None, 5 2048        conv2d_29[0][0]
__________________________________________________________________________________________________
leaky_re_lu_29 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_29[0][0]
__________________________________________________________________________________________________
add_12 (Add)                    (None, None, None, 5 0           leaky_re_lu_27[0][0]
                                                                 leaky_re_lu_29[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, None, None, 2 131072      add_12[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, None, None, 2 1024        conv2d_30[0][0]
__________________________________________________________________________________________________
leaky_re_lu_30 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_30[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_30[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, None, None, 5 2048        conv2d_31[0][0]
__________________________________________________________________________________________________
leaky_re_lu_31 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_31[0][0]
__________________________________________________________________________________________________
add_13 (Add)                    (None, None, None, 5 0           add_12[0][0]
                                                                 leaky_re_lu_31[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, None, None, 2 131072      add_13[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, None, None, 2 1024        conv2d_32[0][0]
__________________________________________________________________________________________________
leaky_re_lu_32 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_32[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_32[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, None, None, 5 2048        conv2d_33[0][0]
__________________________________________________________________________________________________
leaky_re_lu_33 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_33[0][0]
__________________________________________________________________________________________________
add_14 (Add)                    (None, None, None, 5 0           add_13[0][0]
                                                                 leaky_re_lu_33[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D)              (None, None, None, 2 131072      add_14[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, None, None, 2 1024        conv2d_34[0][0]
__________________________________________________________________________________________________
leaky_re_lu_34 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_34[0][0]
__________________________________________________________________________________________________
conv2d_35 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_34[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, None, None, 5 2048        conv2d_35[0][0]
__________________________________________________________________________________________________
leaky_re_lu_35 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_35[0][0]
__________________________________________________________________________________________________
add_15 (Add)                    (None, None, None, 5 0           add_14[0][0]
                                                                 leaky_re_lu_35[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, None, None, 2 131072      add_15[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, None, None, 2 1024        conv2d_36[0][0]
__________________________________________________________________________________________________
leaky_re_lu_36 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_36[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_36[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, None, None, 5 2048        conv2d_37[0][0]
__________________________________________________________________________________________________
leaky_re_lu_37 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_37[0][0]
__________________________________________________________________________________________________
add_16 (Add)                    (None, None, None, 5 0           add_15[0][0]
                                                                 leaky_re_lu_37[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D)              (None, None, None, 2 131072      add_16[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, None, None, 2 1024        conv2d_38[0][0]
__________________________________________________________________________________________________
leaky_re_lu_38 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_38[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_38[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, None, None, 5 2048        conv2d_39[0][0]
__________________________________________________________________________________________________
leaky_re_lu_39 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_39[0][0]
__________________________________________________________________________________________________
add_17 (Add)                    (None, None, None, 5 0           add_16[0][0]
                                                                 leaky_re_lu_39[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D)              (None, None, None, 2 131072      add_17[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, None, None, 2 1024        conv2d_40[0][0]
__________________________________________________________________________________________________
leaky_re_lu_40 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_40[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_40[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, None, None, 5 2048        conv2d_41[0][0]
__________________________________________________________________________________________________
leaky_re_lu_41 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_41[0][0]
__________________________________________________________________________________________________
add_18 (Add)                    (None, None, None, 5 0           add_17[0][0]
                                                                 leaky_re_lu_41[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D)              (None, None, None, 2 131072      add_18[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, None, None, 2 1024        conv2d_42[0][0]
__________________________________________________________________________________________________
leaky_re_lu_42 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_42[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_42[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, None, None, 5 2048        conv2d_43[0][0]
__________________________________________________________________________________________________
leaky_re_lu_43 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_43[0][0]
__________________________________________________________________________________________________
add_19 (Add)                    (None, None, None, 5 0           add_18[0][0]
                                                                 leaky_re_lu_43[0][0]
__________________________________________________________________________________________________
zero_padding2d_5 (ZeroPadding2D (None, None, None, 5 0           add_19[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D)              (None, None, None, 1 4718592     zero_padding2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, None, None, 1 4096        conv2d_44[0][0]
__________________________________________________________________________________________________
leaky_re_lu_44 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_44[0][0]
__________________________________________________________________________________________________
conv2d_45 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_44[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, None, None, 5 2048        conv2d_45[0][0]
__________________________________________________________________________________________________
leaky_re_lu_45 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_45[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_45[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, None, None, 1 4096        conv2d_46[0][0]
__________________________________________________________________________________________________
leaky_re_lu_46 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_46[0][0]
__________________________________________________________________________________________________
add_20 (Add)                    (None, None, None, 1 0           leaky_re_lu_44[0][0]
                                                                 leaky_re_lu_46[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D)              (None, None, None, 5 524288      add_20[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, None, None, 5 2048        conv2d_47[0][0]
__________________________________________________________________________________________________
leaky_re_lu_47 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_47[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_47[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, None, None, 1 4096        conv2d_48[0][0]
__________________________________________________________________________________________________
leaky_re_lu_48 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_48[0][0]
__________________________________________________________________________________________________
add_21 (Add)                    (None, None, None, 1 0           add_20[0][0]
                                                                 leaky_re_lu_48[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D)              (None, None, None, 5 524288      add_21[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, None, None, 5 2048        conv2d_49[0][0]
__________________________________________________________________________________________________
leaky_re_lu_49 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_49[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_49[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, None, None, 1 4096        conv2d_50[0][0]
__________________________________________________________________________________________________
leaky_re_lu_50 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_50[0][0]
__________________________________________________________________________________________________
add_22 (Add)                    (None, None, None, 1 0           add_21[0][0]
                                                                 leaky_re_lu_50[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D)              (None, None, None, 5 524288      add_22[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, None, None, 5 2048        conv2d_51[0][0]
__________________________________________________________________________________________________
leaky_re_lu_51 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_51[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_51[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, None, None, 1 4096        conv2d_52[0][0]
__________________________________________________________________________________________________
leaky_re_lu_52 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_52[0][0]
__________________________________________________________________________________________________
add_23 (Add)                    (None, None, None, 1 0           add_22[0][0]
                                                                 leaky_re_lu_52[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D)              (None, None, None, 5 524288      add_23[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, None, None, 5 2048        conv2d_53[0][0]
__________________________________________________________________________________________________
leaky_re_lu_53 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_53[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_53[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, None, None, 1 4096        conv2d_54[0][0]
__________________________________________________________________________________________________
leaky_re_lu_54 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_54[0][0]
__________________________________________________________________________________________________
conv2d_55 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_54[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, None, None, 5 2048        conv2d_55[0][0]
__________________________________________________________________________________________________
leaky_re_lu_55 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_55[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_55[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, None, None, 1 4096        conv2d_56[0][0]
__________________________________________________________________________________________________
leaky_re_lu_56 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_56[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_56[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, None, None, 5 2048        conv2d_57[0][0]
__________________________________________________________________________________________________
leaky_re_lu_57 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_57[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_57[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, None, None, 2 1024        conv2d_60[0][0]
__________________________________________________________________________________________________
leaky_re_lu_59 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_59[0][0]
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D)  (None, None, None, 2 0           leaky_re_lu_59[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, None, None, 7 0           up_sampling2d_1[0][0]
                                                                 add_19[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D)              (None, None, None, 2 196608      concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, None, None, 2 1024        conv2d_61[0][0]
__________________________________________________________________________________________________
leaky_re_lu_60 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_60[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_60[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, None, None, 5 2048        conv2d_62[0][0]
__________________________________________________________________________________________________
leaky_re_lu_61 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_61[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_61[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, None, None, 2 1024        conv2d_63[0][0]
__________________________________________________________________________________________________
leaky_re_lu_62 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_62[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_62[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, None, None, 5 2048        conv2d_64[0][0]
__________________________________________________________________________________________________
leaky_re_lu_63 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_63[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_63[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, None, None, 2 1024        conv2d_65[0][0]
__________________________________________________________________________________________________
leaky_re_lu_64 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_64[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_64[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, None, None, 1 512         conv2d_68[0][0]
__________________________________________________________________________________________________
leaky_re_lu_66 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_66[0][0]
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D)  (None, None, None, 1 0           leaky_re_lu_66[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, None, None, 3 0           up_sampling2d_2[0][0]
                                                                 add_11[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D)              (None, None, None, 1 49152       concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, None, None, 1 512         conv2d_69[0][0]
__________________________________________________________________________________________________
leaky_re_lu_67 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_67[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_67[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, None, None, 2 1024        conv2d_70[0][0]
__________________________________________________________________________________________________
leaky_re_lu_68 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_68[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_68[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, None, None, 1 512         conv2d_71[0][0]
__________________________________________________________________________________________________
leaky_re_lu_69 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_69[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_69[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, None, None, 2 1024        conv2d_72[0][0]
__________________________________________________________________________________________________
leaky_re_lu_70 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_70[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_70[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, None, None, 1 512         conv2d_73[0][0]
__________________________________________________________________________________________________
leaky_re_lu_71 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_71[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_57[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_64[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_71[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, None, None, 1 4096        conv2d_58[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, None, None, 5 2048        conv2d_66[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, None, None, 2 1024        conv2d_74[0][0]
__________________________________________________________________________________________________
leaky_re_lu_58 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_58[0][0]
__________________________________________________________________________________________________
leaky_re_lu_65 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_65[0][0]
__________________________________________________________________________________________________
leaky_re_lu_72 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_72[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D)              (None, None, None, 2 261375      leaky_re_lu_58[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D)              (None, None, None, 2 130815      leaky_re_lu_65[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D)              (None, None, None, 2 65535       leaky_re_lu_72[0][0]
==================================================================================================
Total params: 62,001,757
Trainable params: 61,949,149
Non-trainable params: 52,608
__________________________________________________________________________________________________
None
Saved Keras model to model_data/yolo.h5
Read 62001757 of 62001757.0 from Darknet weights.

convert it to coreml model. h5_coreml_full.py is the very same thing as your script https://github.com/Ma-Dan/YOLOv3-CoreML/blob/master/Convert/coreml.py, only with the input file moved to command argument.

$ python h5_coreml_full.py model_data/yolo.h5
WARNING:root:Keras version 2.1.5 detected. Last version known to be fully compatible of Keras is 2.1.3 .
WARNING:root:TensorFlow version 1.6.0 detected. Last version known to be fully compatible is 1.5.0 .
2018-06-18 10:04:56.709285: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/keras/models.py:255: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.
  warnings.warn('No training configuration found in save file: '
0 : input_1, <keras.engine.topology.InputLayer object at 0x7f98b1961b70>
1 : conv2d_1, <keras.layers.convolutional.Conv2D object at 0x7f98b1961be0>
2 : batch_normalization_1, <keras.layers.normalization.BatchNormalization object at 0x7f98b1961ef0>
3 : leaky_re_lu_1, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b1961eb8>
4 : zero_padding2d_1, <keras.layers.convolutional.ZeroPadding2D object at 0x7f98b18f9278>
5 : conv2d_2, <keras.layers.convolutional.Conv2D object at 0x7f98b18f92e8>
6 : batch_normalization_2, <keras.layers.normalization.BatchNormalization object at 0x7f98b18f9470>
7 : leaky_re_lu_2, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18f95c0>
8 : conv2d_3, <keras.layers.convolutional.Conv2D object at 0x7f98b18f95f8>
9 : batch_normalization_3, <keras.layers.normalization.BatchNormalization object at 0x7f98b18f9780>
10 : leaky_re_lu_3, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18f98d0>
11 : conv2d_4, <keras.layers.convolutional.Conv2D object at 0x7f98b18f9908>
12 : batch_normalization_4, <keras.layers.normalization.BatchNormalization object at 0x7f98b18f9a90>
13 : leaky_re_lu_4, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18f9be0>
14 : add_1, <keras.layers.merge.Add object at 0x7f98b18f9c18>
15 : zero_padding2d_2, <keras.layers.convolutional.ZeroPadding2D object at 0x7f98b18f9c50>
16 : conv2d_5, <keras.layers.convolutional.Conv2D object at 0x7f98b18f9cc0>
17 : batch_normalization_5, <keras.layers.normalization.BatchNormalization object at 0x7f98b18f9e48>
18 : leaky_re_lu_5, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18f9f98>
19 : conv2d_6, <keras.layers.convolutional.Conv2D object at 0x7f98b1961fd0>
20 : batch_normalization_6, <keras.layers.normalization.BatchNormalization object at 0x7f98b18ff198>
21 : leaky_re_lu_6, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18ff2e8>
22 : conv2d_7, <keras.layers.convolutional.Conv2D object at 0x7f98b18ff320>
23 : batch_normalization_7, <keras.layers.normalization.BatchNormalization object at 0x7f98b18ff4a8>
24 : leaky_re_lu_7, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18ff5f8>
25 : add_2, <keras.layers.merge.Add object at 0x7f98b18ff630>
26 : conv2d_8, <keras.layers.convolutional.Conv2D object at 0x7f98b18ff668>
27 : batch_normalization_8, <keras.layers.normalization.BatchNormalization object at 0x7f98b18ff7f0>
28 : leaky_re_lu_8, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18ff940>
29 : conv2d_9, <keras.layers.convolutional.Conv2D object at 0x7f98b18ff978>
30 : batch_normalization_9, <keras.layers.normalization.BatchNormalization object at 0x7f98b18ffb00>
31 : leaky_re_lu_9, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18ffc50>
32 : add_3, <keras.layers.merge.Add object at 0x7f98b18ffc88>
33 : zero_padding2d_3, <keras.layers.convolutional.ZeroPadding2D object at 0x7f98b18ffcc0>
34 : conv2d_10, <keras.layers.convolutional.Conv2D object at 0x7f98b18ffd30>
35 : batch_normalization_10, <keras.layers.normalization.BatchNormalization object at 0x7f98b18ffeb8>
36 : leaky_re_lu_10, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18f9fd0>
37 : conv2d_11, <keras.layers.convolutional.Conv2D object at 0x7f98b190d080>
38 : batch_normalization_11, <keras.layers.normalization.BatchNormalization object at 0x7f98b190d208>
39 : leaky_re_lu_11, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b190d358>
40 : conv2d_12, <keras.layers.convolutional.Conv2D object at 0x7f98b190d390>
41 : batch_normalization_12, <keras.layers.normalization.BatchNormalization object at 0x7f98b190d518>
42 : leaky_re_lu_12, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b190d668>
43 : add_4, <keras.layers.merge.Add object at 0x7f98b190d6a0>
44 : conv2d_13, <keras.layers.convolutional.Conv2D object at 0x7f98b190d6d8>
45 : batch_normalization_13, <keras.layers.normalization.BatchNormalization object at 0x7f98b190d860>
46 : leaky_re_lu_13, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b190d9b0>
47 : conv2d_14, <keras.layers.convolutional.Conv2D object at 0x7f98b190d9e8>
48 : batch_normalization_14, <keras.layers.normalization.BatchNormalization object at 0x7f98b190db70>
49 : leaky_re_lu_14, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b190dcc0>
50 : add_5, <keras.layers.merge.Add object at 0x7f98b190dcf8>
51 : conv2d_15, <keras.layers.convolutional.Conv2D object at 0x7f98b190dd30>
52 : batch_normalization_15, <keras.layers.normalization.BatchNormalization object at 0x7f98b190deb8>
53 : leaky_re_lu_15, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18fffd0>
54 : conv2d_16, <keras.layers.convolutional.Conv2D object at 0x7f98b1915080>
55 : batch_normalization_16, <keras.layers.normalization.BatchNormalization object at 0x7f98b1915208>
56 : leaky_re_lu_16, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b1915358>
57 : add_6, <keras.layers.merge.Add object at 0x7f98b1915390>
58 : conv2d_17, <keras.layers.convolutional.Conv2D object at 0x7f98b19153c8>
59 : batch_normalization_17, <keras.layers.normalization.BatchNormalization object at 0x7f98b1915550>
60 : leaky_re_lu_17, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b19156a0>
61 : conv2d_18, <keras.layers.convolutional.Conv2D object at 0x7f98b19156d8>
62 : batch_normalization_18, <keras.layers.normalization.BatchNormalization object at 0x7f98b1915860>
63 : leaky_re_lu_18, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b19159b0>
64 : add_7, <keras.layers.merge.Add object at 0x7f98b19159e8>
65 : conv2d_19, <keras.layers.convolutional.Conv2D object at 0x7f98b1915a20>
66 : batch_normalization_19, <keras.layers.normalization.BatchNormalization object at 0x7f98b1915ba8>
67 : leaky_re_lu_19, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b1915cf8>
68 : conv2d_20, <keras.layers.convolutional.Conv2D object at 0x7f98b1915d30>
69 : batch_normalization_20, <keras.layers.normalization.BatchNormalization object at 0x7f98b1915eb8>
70 : leaky_re_lu_20, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b190dfd0>
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85 : add_10, <keras.layers.merge.Add object at 0x7f98b191cd30>
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87 : batch_normalization_25, <keras.layers.normalization.BatchNormalization object at 0x7f98b191cef0>
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92 : add_11, <keras.layers.merge.Add object at 0x7f98b18a53c8>
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97 : conv2d_28, <keras.layers.convolutional.Conv2D object at 0x7f98b18a5780>
98 : batch_normalization_28, <keras.layers.normalization.BatchNormalization object at 0x7f98b18a5908>
99 : leaky_re_lu_28, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18a5a58>
100 : conv2d_29, <keras.layers.convolutional.Conv2D object at 0x7f98b18a5a90>
101 : batch_normalization_29, <keras.layers.normalization.BatchNormalization object at 0x7f98b18a5c18>
102 : leaky_re_lu_29, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18a5d68>
103 : add_12, <keras.layers.merge.Add object at 0x7f98b18a5da0>
104 : conv2d_30, <keras.layers.convolutional.Conv2D object at 0x7f98b18a5dd8>
105 : batch_normalization_30, <keras.layers.normalization.BatchNormalization object at 0x7f98b18a5f60>
106 : leaky_re_lu_30, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b191cfd0>
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112 : batch_normalization_32, <keras.layers.normalization.BatchNormalization object at 0x7f98b18ac5f8>
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115 : batch_normalization_33, <keras.layers.normalization.BatchNormalization object at 0x7f98b18ac908>
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119 : batch_normalization_34, <keras.layers.normalization.BatchNormalization object at 0x7f98b18acc50>
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129 : batch_normalization_37, <keras.layers.normalization.BatchNormalization object at 0x7f98b18b45f8>
130 : leaky_re_lu_37, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18b4748>
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132 : conv2d_38, <keras.layers.convolutional.Conv2D object at 0x7f98b18b47b8>
133 : batch_normalization_38, <keras.layers.normalization.BatchNormalization object at 0x7f98b18b4940>
134 : leaky_re_lu_38, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18b4a90>
135 : conv2d_39, <keras.layers.convolutional.Conv2D object at 0x7f98b18b4ac8>
136 : batch_normalization_39, <keras.layers.normalization.BatchNormalization object at 0x7f98b18b4c50>
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142 : conv2d_41, <keras.layers.convolutional.Conv2D object at 0x7f98b18bd160>
143 : batch_normalization_41, <keras.layers.normalization.BatchNormalization object at 0x7f98b18bd2e8>
144 : leaky_re_lu_41, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18bd438>
145 : add_18, <keras.layers.merge.Add object at 0x7f98b18bd470>
146 : conv2d_42, <keras.layers.convolutional.Conv2D object at 0x7f98b18bd4a8>
147 : batch_normalization_42, <keras.layers.normalization.BatchNormalization object at 0x7f98b18bd630>
148 : leaky_re_lu_42, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18bd780>
149 : conv2d_43, <keras.layers.convolutional.Conv2D object at 0x7f98b18bd7b8>
150 : batch_normalization_43, <keras.layers.normalization.BatchNormalization object at 0x7f98b18bd940>
151 : leaky_re_lu_43, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18bda90>
152 : add_19, <keras.layers.merge.Add object at 0x7f98b18bdac8>
153 : zero_padding2d_5, <keras.layers.convolutional.ZeroPadding2D object at 0x7f98b18bdb00>
154 : conv2d_44, <keras.layers.convolutional.Conv2D object at 0x7f98b18bdb70>
155 : batch_normalization_44, <keras.layers.normalization.BatchNormalization object at 0x7f98b18bdcf8>
156 : leaky_re_lu_44, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18bde48>
157 : conv2d_45, <keras.layers.convolutional.Conv2D object at 0x7f98b18bde80>
158 : batch_normalization_45, <keras.layers.normalization.BatchNormalization object at 0x7f98b18b4f98>
159 : leaky_re_lu_45, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18c4198>
160 : conv2d_46, <keras.layers.convolutional.Conv2D object at 0x7f98b18c41d0>
161 : batch_normalization_46, <keras.layers.normalization.BatchNormalization object at 0x7f98b18c4358>
162 : leaky_re_lu_46, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18c44a8>
163 : add_20, <keras.layers.merge.Add object at 0x7f98b18c44e0>
164 : conv2d_47, <keras.layers.convolutional.Conv2D object at 0x7f98b18c4518>
165 : batch_normalization_47, <keras.layers.normalization.BatchNormalization object at 0x7f98b18c46a0>
166 : leaky_re_lu_47, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18c47f0>
167 : conv2d_48, <keras.layers.convolutional.Conv2D object at 0x7f98b18c4828>
168 : batch_normalization_48, <keras.layers.normalization.BatchNormalization object at 0x7f98b18c49b0>
169 : leaky_re_lu_48, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18c4b00>
170 : add_21, <keras.layers.merge.Add object at 0x7f98b18c4b38>
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172 : batch_normalization_49, <keras.layers.normalization.BatchNormalization object at 0x7f98b18c4cf8>
173 : leaky_re_lu_49, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18c4e48>
174 : conv2d_50, <keras.layers.convolutional.Conv2D object at 0x7f98b18c4e80>
175 : batch_normalization_50, <keras.layers.normalization.BatchNormalization object at 0x7f98b18bdfd0>
176 : leaky_re_lu_50, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18cd198>
177 : add_22, <keras.layers.merge.Add object at 0x7f98b18cd1d0>
178 : conv2d_51, <keras.layers.convolutional.Conv2D object at 0x7f98b18cd208>
179 : batch_normalization_51, <keras.layers.normalization.BatchNormalization object at 0x7f98b18cd390>
180 : leaky_re_lu_51, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18cd4e0>
181 : conv2d_52, <keras.layers.convolutional.Conv2D object at 0x7f98b18cd518>
182 : batch_normalization_52, <keras.layers.normalization.BatchNormalization object at 0x7f98b18cd6a0>
183 : leaky_re_lu_52, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18cd7f0>
184 : add_23, <keras.layers.merge.Add object at 0x7f98b18cd828>
185 : conv2d_53, <keras.layers.convolutional.Conv2D object at 0x7f98b18cd860>
186 : batch_normalization_53, <keras.layers.normalization.BatchNormalization object at 0x7f98b18cd9e8>
187 : leaky_re_lu_53, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18cdb38>
188 : conv2d_54, <keras.layers.convolutional.Conv2D object at 0x7f98b18cdb70>
189 : batch_normalization_54, <keras.layers.normalization.BatchNormalization object at 0x7f98b18cdcf8>
190 : leaky_re_lu_54, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18cde48>
191 : conv2d_55, <keras.layers.convolutional.Conv2D object at 0x7f98b18cde80>
192 : batch_normalization_55, <keras.layers.normalization.BatchNormalization object at 0x7f98b18c4fd0>
193 : leaky_re_lu_55, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18d4198>
194 : conv2d_56, <keras.layers.convolutional.Conv2D object at 0x7f98b18d41d0>
195 : batch_normalization_56, <keras.layers.normalization.BatchNormalization object at 0x7f98b18d4358>
196 : leaky_re_lu_56, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18d44a8>
197 : conv2d_57, <keras.layers.convolutional.Conv2D object at 0x7f98b18d44e0>
198 : batch_normalization_57, <keras.layers.normalization.BatchNormalization object at 0x7f98b18d4668>
199 : leaky_re_lu_57, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18d47b8>
200 : conv2d_60, <keras.layers.convolutional.Conv2D object at 0x7f98b18d47f0>
201 : batch_normalization_59, <keras.layers.normalization.BatchNormalization object at 0x7f98b18d4978>
202 : leaky_re_lu_59, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18d4ac8>
203 : up_sampling2d_1, <keras.layers.convolutional.UpSampling2D object at 0x7f98b18d4b00>
204 : concatenate_1, <keras.layers.merge.Concatenate object at 0x7f98b18d4b70>
205 : conv2d_61, <keras.layers.convolutional.Conv2D object at 0x7f98b18d4ba8>
206 : batch_normalization_60, <keras.layers.normalization.BatchNormalization object at 0x7f98b18d4d30>
207 : leaky_re_lu_60, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18d4e80>
208 : conv2d_62, <keras.layers.convolutional.Conv2D object at 0x7f98b18d4eb8>
209 : batch_normalization_61, <keras.layers.normalization.BatchNormalization object at 0x7f98b18cdfd0>
210 : leaky_re_lu_61, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18dc1d0>
211 : conv2d_63, <keras.layers.convolutional.Conv2D object at 0x7f98b18dc208>
212 : batch_normalization_62, <keras.layers.normalization.BatchNormalization object at 0x7f98b18dc390>
213 : leaky_re_lu_62, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18dc4e0>
214 : conv2d_64, <keras.layers.convolutional.Conv2D object at 0x7f98b18dc518>
215 : batch_normalization_63, <keras.layers.normalization.BatchNormalization object at 0x7f98b18dc6a0>
216 : leaky_re_lu_63, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18dc7f0>
217 : conv2d_65, <keras.layers.convolutional.Conv2D object at 0x7f98b18dc828>
218 : batch_normalization_64, <keras.layers.normalization.BatchNormalization object at 0x7f98b18dc9b0>
219 : leaky_re_lu_64, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18dcb00>
220 : conv2d_68, <keras.layers.convolutional.Conv2D object at 0x7f98b18dcb38>
221 : batch_normalization_66, <keras.layers.normalization.BatchNormalization object at 0x7f98b18dccc0>
222 : leaky_re_lu_66, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b18dce10>
223 : up_sampling2d_2, <keras.layers.convolutional.UpSampling2D object at 0x7f98b18dce48>
224 : concatenate_2, <keras.layers.merge.Concatenate object at 0x7f98b18dceb8>
225 : conv2d_69, <keras.layers.convolutional.Conv2D object at 0x7f98b18dcef0>
226 : batch_normalization_67, <keras.layers.normalization.BatchNormalization object at 0x7f98b18d4f60>
227 : leaky_re_lu_67, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b1864208>
228 : conv2d_70, <keras.layers.convolutional.Conv2D object at 0x7f98b1864240>
229 : batch_normalization_68, <keras.layers.normalization.BatchNormalization object at 0x7f98b18643c8>
230 : leaky_re_lu_68, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b1864518>
231 : conv2d_71, <keras.layers.convolutional.Conv2D object at 0x7f98b1864550>
232 : batch_normalization_69, <keras.layers.normalization.BatchNormalization object at 0x7f98b18646d8>
233 : leaky_re_lu_69, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b1864828>
234 : conv2d_72, <keras.layers.convolutional.Conv2D object at 0x7f98b1864860>
235 : batch_normalization_70, <keras.layers.normalization.BatchNormalization object at 0x7f98b18649e8>
236 : leaky_re_lu_70, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b1864b38>
237 : conv2d_73, <keras.layers.convolutional.Conv2D object at 0x7f98b1864b70>
238 : batch_normalization_71, <keras.layers.normalization.BatchNormalization object at 0x7f98b1864cf8>
239 : leaky_re_lu_71, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b1864e48>
240 : conv2d_58, <keras.layers.convolutional.Conv2D object at 0x7f98b1864e80>
241 : conv2d_66, <keras.layers.convolutional.Conv2D object at 0x7f98b18dcf98>
242 : conv2d_74, <keras.layers.convolutional.Conv2D object at 0x7f98b186a208>
243 : batch_normalization_58, <keras.layers.normalization.BatchNormalization object at 0x7f98b186a3c8>
244 : batch_normalization_65, <keras.layers.normalization.BatchNormalization object at 0x7f98b186a518>
245 : batch_normalization_72, <keras.layers.normalization.BatchNormalization object at 0x7f98b186a630>
246 : leaky_re_lu_58, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b186a748>
247 : leaky_re_lu_65, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b186a780>
248 : leaky_re_lu_72, <keras.layers.advanced_activations.LeakyReLU object at 0x7f98b186a7b8>
249 : conv2d_59, <keras.layers.convolutional.Conv2D object at 0x7f98b186a7f0>
250 : conv2d_67, <keras.layers.convolutional.Conv2D object at 0x7f98b186a978>
251 : conv2d_75, <keras.layers.convolutional.Conv2D object at 0x7f98b186ab38>
Traceback (most recent call last):
  File "h5_coreml_full.py", line 5, in <module>
    image_input_names='input1', output_names=['output1', 'output2', 'output3'], image_scale=1/255.)
  File "/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/coremltools/converters/keras/_keras_converter.py", line 745, in convert
    custom_conversion_functions=custom_conversion_functions)
  File "/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/coremltools/converters/keras/_keras_converter.py", line 543, in convertToSpec
    custom_objects=custom_objects)
  File "/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/coremltools/converters/keras/_keras2_converter.py", line 350, in _convert
    image_scale = image_scale)
  File "/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/coremltools/models/neural_network.py", line 2542, in set_pre_processing_parameters
    channels, height, width = array_shape
ValueError: not enough values to unpack (expected 3, got 1)

Please help and let me know how to get this resolved.
my environment(which is tested/required in keras-yolo3):
virtualenv -p /usr/bin/python36 coremltools
source coremltools/bin/activate
pip install keras==2.1.5
tensorflow==1.6.0
pip install -U coremltools
pip install h5py

This might be of use. I had the unpack issue and was able to solve it by specifying a defined input shape
https://github.com/apple/coremltools/issues/203

Same thing happened to me. It was because the convert.py script didn't specify the dimensions.
So simply change line 88 of file (https://github.com/qqwweee/keras-yolo3/blob/master/convert.py#L88) from
input_layer = Input(shape=(None, None, 3))

to
input_layer = Input(shape=(416, 416, 3))

Hope this helps!

authors: keithZumper tobyglei xzhub


text:
Temporarily updated the quad_x mixer to include Channel 5 of the pixr…

…acer hooked up to the Tarot 650 sport landing gear controller.

@potaito JFYI that are the mixer changes that got the Tarot 650 sport landing gear to work...

source: gharchive/pull-request

repo: MaEtUgR/Firmware

license_type: permissive

license_source: github-api


text:
sem-sync

Hi MaJerle,

/* Create threads /
esp_sys_sem_wait(&esp.sem_sync, 0);         / Lock semaphore /
if (!esp_sys_thread_create(&esp.thread_produce, "esp_produce", esp_thread_producer, &esp.sem_sync, ESP_SYS_THREAD_SS, ESP_SYS_THREAD_PRIO)) {
esp_sys_sem_release(&esp.sem_sync);     / Release semaphore /
goto cleanup;
}
esp_sys_sem_wait(&esp.sem_sync, 0);         / Wait semaphore, should be unlocked in producer thread /
if (!esp_sys_thread_create(&esp.thread_process, "esp_process", esp_thread_process, &esp.sem_sync, ESP_SYS_THREAD_SS, ESP_SYS_THREAD_PRIO)) {
esp_sys_sem_release(&esp.sem_sync);     / Release semaphore /
goto cleanup;
}
esp_sys_sem_wait(&esp.sem_sync, 0);         / Wait semaphore, should be unlocked in producer thread /
esp_sys_sem_release(&esp.sem_sync);         / Release semaphore            ① */

Why not put
ESP_CORE_UNPROTECT();
esp_sys_sem_wait(&e->sem_sync, 0);
ESP_CORE_PROTECT();         /File esp_thread.c, Line 89-91/
after last esp_sys_sem_release?
Because what we need now is the synchronization of semaphores.

Why do you think is needed?

Ok, so what do we gain with your proposal over mine imolementation? I do not understand your point well.

        ESP_CORE_UNPROTECT();               /* Release protection, think if this is necessary, probably shouldn't be here */
        esp_sys_sem_wait(&e->sem_sync, 120000); /* Lock semaphore, should be unlocked before! */
        ESP_CORE_PROTECT();                 /* Protect system again, think if this is necessary, probably shouldn't be here */
        res = msg->fn(msg);                 /* Process this message, check if command started at least */
        if (res == espOK) {                 /* We have valid data and data were sent */
            ESP_CORE_UNPROTECT();           /* Release protection */
            time = esp_sys_sem_wait(&e->sem_sync, msg->block_time); /* Wait for synchronization semaphore */
            ESP_CORE_PROTECT();              /* Protect system again */
            esp_sys_sem_release(&e->sem_sync);  /* Release protection and start over later */
            if (time == ESP_SYS_TIMEOUT) {  /* Sync timeout occurred? */
                res = espTIMEOUT;           /* Timeout on command */
            }
        } else {
            esp_sys_sem_release(&e->sem_sync);  /* We failed, release semaphore automatically */
        }

So what does esp_sys_sem_wait (&e->sem_sync, 120000) mean?
To make the semaphore zero, prepare for synchronization with the process thread.
I don't understand right?

I will give you now full answer, but first please carefully read following points:

Check how I updated your comments here, to make your code in comment visible normally. More info on Github help: https://help.github.com/articles/creating-and-highlighting-code-blocks/
Since you did not download latest GIT changes (git pull) as proposed, I will manually refer now to latest commit file. For historical purposes, it is located on URL below. All line numbers are from this file directly on the link. https://github.com/MaJerle/ESP_AT_Lib/blob/e6f1c32df67923e43b949d880bb3663d60bcc856/src/esp/esp_threads.c
Use documentation for your reference point on inter-thread communication. https://majerle.eu/documentation/esp_at/html/page_appnote.html#sect_thread_comm
Semaphores in AT-Lib are binary only, always used for thread synchronization purposes, nothing else.

Back to topic.

Lines 90 waits for semaphore. This operation must be executed instantly, otherwise there is serious error in the system for some reason. Here, I could add check if there is any kind of timeout, report serious error and do not allow any other command to proceed.
After semaphore is locked, we start sending first command to AT port.
After first command has been sent, we try to lock semaphore again (line 95).. Remember, we cannot lock it again as it was already locked by us, unless someone will release it. Release happens in another function, which, in order to work properly, needs mutual exclusion, thus we have to call esp_core_unlock() before we try to lock semaphore (line 94).
Release happens on line 930 here: https://github.com/MaJerle/ESP_AT_Lib/blob/e6f1c32df67923e43b949d880bb3663d60bcc856/src/esp/esp_int.c
If lock was successful (command finished), we have to manually release semaphore back to default state (line 100) and start over for next command.

12345

source: gharchive/issue

authors: MaJerle zhangxichao

license: MIT


text:
Fix issues with variable fields & add more tests

Hi guys!
Thank you for this extension, it's very useful!
I found few issues and fixed them. Could you review?

@greeflas yes, sure =)

:tada: This PR is included in version 1.2.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:

authors: Yozhef ci-macpaw greeflas

repo: MacPaw/BehatMessengerContext


text:
follow JSON API

What
JSON API uses snake_case
Why
enhanced clarity when CodingKeys can be removed
Affected Areas
Query & Result structs

Code Duplication is pre-existing; all of it is in Tests.

Sorry but I disagree, Swift uses camel case for property names, it should stay consistent in the event a different encoding is used

OK, thanks for explaining!

authors: SunburstEnzo kalafus

repo: MacPaw/OpenAI


text:
flag command working

To Test:

Open an old chat room. Try the /flag [Country Code] command. You should see a notification that says "You have set your flag to [Country]". And the flag should appear next to your name in the Users side panel.
Open a new chat room. Try the /flag [Country Code] command. You should see a notification that says "You have set your flag to [Country]". And the flag should appear next to your name in the Users side panel.
Open other chat rooms (whether old ones or creating new ones) and be sure you can run the flag command in each room and that the flag updates in other open rooms next to your username.
Go to your account page and be sure your Country is set in the Country field.

On the first try, I got that the flag was changed but the UI did not update. It is updated in other rooms though and then after the first time, the flag changes as it should in the UI.
I think it's good to :shipit:


text:
Add support for more remote types

Currently we support only module or import remote type.
There are far more remoteTypes which are yet to be supported. Here's the list: https://github.com/MadaraUchiha-314/rollup-plugin-module-federation/blob/main/packages/rollup-plugin-module-federation/types/index.d.ts#L25

Since we are using the @module-federation/runtime package, we are restricted by the remote types that it supports. Currently the runtime supports only 2 types of remotes esm and global. https://github.com/module-federation/universe/blob/cec30634d9f00d31b053e2089e1a6b4365ea59d4/packages/sdk/src/types/stats.ts#L3


text:
LambdaBetterGrass

Mod name
LambdaBetterGrass
Curseforge link
https://www.curseforge.com/minecraft/mc-mods/lambdabettergrass
Modrinth link
https://modrinth.com/mod/lambdabettergrass
Other link
https://github.com/LambdAurora/LambdaBetterGrass
What it does
Adds "better grass" like Optifine
Why should it be in the modpack
Optifine parity
Why shouldn't it be in the modpack
Rendering API missing
Categories

[ ] Performance optimization
[x] Graphics optimization
[ ] New feature
[x] Optifine parity
[ ] Fixes a bug/dependency

Additional details

Issue I'd like to get fixed first: https://github.com/LambdAurora/LambdaBetterGrass/issues/16

1.16 & 1.17 work. See above mention in the Indium issue for proof.
LambdAurora/LambdaBetterGrass#16 can be worked around by providing a default config disabling corner blending because it is optional.


text:
🛑 Libreddit (libreddit.spike.codes) is down

In c6f5661, Libreddit (libreddit.spike.codes) (https://libreddit.spike.codes) was down:

HTTP code: 429
Response time: 312 ms

Resolved: Libreddit (libreddit.spike.codes) is back up in 9b514e3.

repo: Magic-Services/upptime


text:
🛑 Bitwarden is down

In f9735dc, Bitwarden (https://bitwarden.com) was down:

HTTP code: 503
Response time: 137 ms

Resolved: Bitwarden is back up in d465498.


text:
🛑 Libreddit (libreddit.spike.codes) is down

In 45dd5b1, Libreddit (libreddit.spike.codes) (https://libreddit.spike.codes) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Libreddit (libreddit.spike.codes) is back up in 89d4c63.


text:
🛑 Libreddit (libreddit.spike.codes) is down

In d8f9fb1, Libreddit (libreddit.spike.codes) (https://libreddit.spike.codes) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Libreddit (libreddit.spike.codes) is back up in c6e4e5e.


text:
addXml func

added add xml function to mod importer to add new entries designated by command Add XML to the bottom of the designated xml file. Does not require a "_append" anywhere in the ml file

something suitable will be added to a future version (sggmi as a plugin or fixed XML merge in modimporter)

repo: MagicGonads/sgg-mod-format


text:
Wrong link in the README.md

The link the README.md points to the https://github.com/MaiaVictor/cedille-core/blob/master

FIxed.

authors: MaiaVictor andorp

repo: MaiaVictor/cedille-core


text:
support RHEL

https://catalog.redhat.com/software/containers/search?p=1&build_categories_list=Base Image&product_listings_names=Red Hat Enterprise Linux 8|Red Hat Enterprise Linux 6|Red Hat Enterprise Linux 7
https://zenn.dev/knqyf263/articles/324e17db2310f0

yum update --disableplugin=subscription-manager -y


text:
Add support for .aviv

Add support for .aviv images. They're even better than Webp.

Implemented in version 1.2.0


text:
test: Council tests

This Pull Request create tests for the Council Class.
As you may see, not all methods are tested, since it does not bring any value testing all of them.
I've also created a __mocks__ as described by Jest's Manual Mocks doc.
Closes #22

SonarCloud Dash

repo: Maintenance-of-Votum/Votum


text:
prefix to postfix in cpp

Information about Algorithm
It will convert the expression from prefix form to postfix form
(Type here)
Have you read the Contributing.md and Code of conduct

[x] Yes
[ ] No

Other context

Hi @stutimohta20,
I will wait for your pull request.

repo: MakeContributions/DSA


text:
Outdated?

Just attempted to load this mod into the latest version of Minecraft (1.20.6), and got a huge series of errors.  I've attached the crash report .txt below.  I'm assuming this has something to do with the fact that the mod is only up to 1.20.1.  I don't have any other mods loaded, just the latest Forge API.  Are you planning on updating this soon, or was this crash caused by something else?  I'd really like to try it out and all of my friends' realms are up to 1.20.6!  Thanks in advance!
crash-2024-05-11_14.37.51-fml.txt

Yeah seems like it's incompatible with 1.20.5+. I'll aim to get to it when I have some free time. Thanks for letting me know.

Duplicate of #9


text:
Fix orders order.

Changes

changed the orders order from newest to oldest.

cc @syncrou Can we add sorting to the API? its not critical just something that would be nice in future.

Codecov Report

Merging #162 into master will increase coverage by 0.02%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #162      +/-   ##
==========================================
+ Coverage   82.31%   82.33%   +0.02%
==========================================
  Files          86       86
  Lines         820      821       +1
  Branches       68       68
==========================================
+ Hits          675      676       +1
  Misses        131      131
  Partials       14       14

Impacted Files
Coverage Δ

src/redux/actions/order-actions.js
95.23% <100%> (+0.23%)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 3ba77b3...5e8f827. Read the comment docs.

repo: ManageIQ/catalog-ui


text:
Added 'release' type for build options

'release' builds will put the images to 'upstream_stable' directory. 'nightly' will continue to put the images to 'upstream' directory.
@Fryguy @jrafanie please review.

Looks good @simaishi

authors: jrafanie simaishi

repo: ManageIQ/manageiq-appliance-build


text:
Create 508 compliance scanner gulp task

In support for https://github.com/ManageIQ/manageiq-ui-service/issues/273 we need to add a gulp task that can scan for compliance.

So people know what I am looking into, I am experimenting with https://github.com/yargalot/gulp-accessibility to see if it could work well to help report on the 508 compliance of our codebase.

test comment.  ignore.

test commetn.

authors: chalettu chriskacerguis

repo: ManageIQ/manageiq-ui-service

license_source: bigquery


text:
PT#139690455-Refactor mock-api server, add resources for list/details states

https://www.pivotaltracker.com/story/show/139690455
Mock api now stubs details for each explorer and the entries

@AllenBW , great job on the changes and stubbing out many of our endpoints.

authors: AllenBW chalettu


text:
Control/ Alert,  Real time performance edit form missing

I am trying editing Control/Alert. For Real time performance, there should be form for picking up the condition, but it is missing

@skateman please see the attached screenshot from 5.3.z, When adding Real Time Performance alert on master "Real Time Performance Parameters" box is missing

authors: Ladas h-kataria

repo: ManageIQ/manageiq


text:
Configure -> Configuration error after db reset

I've executed rake db:reset and rake db:seed (on top of git commit fe870036b), and afterwards, it seems that I can't access configure -> configuration (/ops/explorer).
It seems that the error appears in [1], and I also saw a weird error[2] few minutes before that (not sure if related).
Version info:
postgresql-9.4.4-1.fc22.x86_64
ruby 2.0.0p598 (2014-11-13 revision 48408) [x86_64-linux]
[1] Log:
[----] I, [2015-07-23T15:08:43.858683 #29239:42fe8c]  INFO -- : Started GET "/ops/explorer" for 127.0.0.1 at 2015-07-23 15:08:43 +0300
[----] I, [2015-07-23T15:08:43.908603 #29239:42fe8c]  INFO -- : Processing by OpsController#explorer as HTML
[----] W, [2015-07-23T15:08:43.950788 #29239:42fe8c]  WARN -- : DEPRECATION WARNING: Relation#all is deprecated. If you want to eager-load a relation, you can call #load (e.g. Post.where( published: true).load). If you want to get an array of records from a relation, you can call #to_a (e.g. Post.where(published: true).to_a). (called from x_get_tree_custom_kids at /home/
oschreib/dev_env/manageiq/app/presenters/tree_builder_ops_settings.rb:38)
[----] F, [2015-07-23T15:08:43.973995 #29239:42fe8c] FATAL -- : Error caught: [NoMethodError] undefined method evm_tables' for nil:NilClass /home/oschreib/dev_env/manageiq/app/presenters/tree_builder_ops_vmdb.rb:22:in x_get_tree_roots'
/home/oschreib/dev_env/manageiq/app/presenters/tree_builder.rb:244:in x_get_tree_objects' /home/oschreib/dev_env/manageiq/app/presenters/tree_builder.rb:212:in x_build_dynatree'
/home/oschreib/dev_env/manageiq/app/presenters/tree_builder.rb:152:in build_tree' /home/oschreib/dev_env/manageiq/app/presenters/tree_builder.rb:91:in initialize'
/home/oschreib/dev_env/manageiq/app/controllers/ops_controller/db.rb:170:in new' /home/oschreib/dev_env/manageiq/app/controllers/ops_controller/db.rb:170:in db_build_tree'
[2] Log:
[----] E, [2015-07-23T15:03:36.342062 #29126:cefe8c] ERROR -- : PG::UndefinedColumn: ERROR:  column t.reltoastidxid does not exist
LINE 7:              AND i.oid = t.reltoastidxid
^
:            SELECT distinct i.relname, d.indisunique, d.indkey, i.oid

Duplicate of #3550: we're currently not compatible with PG 9.4.

@matthewd @chessbyte I had no idea we're not compatible with PG 9.4.
I see that #3550 is indeed relevant to the [2] log I attached there, but are we sure that [1] is due to the same issue?
Also, as PG 9.4 is the default in Fedora 22, I guess https://github.com/ManageIQ/guides/blob/master/developer_setup.md has to be changed as well, since there's no mention to the fact that PG 9.4 is unsupported.

Ah, I didn't see that there are possibly two issues here. I'll reopen this until someone can make a definitive statement about [1]. I haven't really looked at it, but at a glance, it does sound like it could be related.

@matthewd @oschreib ManageIQ has built-in DBA capabilities that we wrote to monitor our own application.  They depend on internal PostgreSQL tables and columns (here, here, here, and here).  So, we need to address those before we move to PG 9.4.
On top of that, when the UI process the /ops/explorer action, it tries to process all the tabs (even the ones not yet clicked on), and is blowing up processing the Database tab.  That is what I think you are hitting in [1] and [2].

authors: chessbyte matthewd oschreib


text:
Travis tests fails on non-existing path

This error appears a lot : The path `/home/travis/build/ManageIQ/manageiq/gems/pending/gems` does not exist.
https://travis-ci.org/ManageIQ/manageiq/builds/126583739

#8348 fixed the issue.

authors: ZitaNemeckova simaishi