ma-dan / keras-yolo4 Goto Github PK

Source code

from tensorflow import keras
new_model = keras.models.load_model('yolo4_weight.h5')

Error occurred:

Traceback (most recent call last):
  File "/home/source/keras-yolo4/mytest.py", line 13, in <module>
    new_model = keras.models.load_model(h5)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/saving/save.py", line 146, in load_model
    return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/saving/hdf5_format.py", line 212, in load_model_from_hdf5
    custom_objects=custom_objects)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/saving/model_config.py", line 55, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py", line 89, in deserialize
    printable_module_name='layer')
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 192, in deserialize_keras_object
    list(custom_objects.items())))
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1121, in from_config
    process_layer(layer_data)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1105, in process_layer
    layer = deserialize_layer(layer_data, custom_objects=custom_objects)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py", line 89, in deserialize
    printable_module_name='layer')
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 181, in deserialize_keras_object
    config, module_objects, custom_objects, printable_module_name)
  File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 166, in class_and_config_for_serialized_keras_object
    raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
ValueError: Unknown layer: Mish

您好，请问一下，我训练的时候出现这样的提示，会影响训练结果吗，是什么原因导致的呢。

I trained from scratch a new set of weights. I used a set of data that I have used to train Yolo3 which worked well. Creating a new set of weights with train.py results in very low detection rate which does not seem to make sense.

Trying to generate animations in img2img with stablediffusion and ive started getting this error, which is odd as i have not had this error before, if anyone can point me to what i need to fix that'd be greatly appreciated!

Traceback (most recent call last):
File "C:\Users\James\Documents\stable-diffusion-webui-master\modules\ui.py", line 185, in f
res = list(func(*args, **kwargs))
File "C:\Users\James\Documents\stable-diffusion-webui-master\webui.py", line 54, in f
res = func(*args, **kwargs)
File "C:\Users\James\Documents\stable-diffusion-webui-master\modules\img2img.py", line 137, in img2img
processed = modules.scripts.scripts_img2img.run(p, *args)
File "C:\Users\James\Documents\stable-diffusion-webui-master\modules\scripts.py", line 290, in run
processed = script.run(p, *script_args)
File "C:\Users\James\Documents\stable-diffusion-webui-master\scripts\animation.py", line 749, in run
init_img = zoom_at2(init_img, rot_per_frame, int(x_shift_cumulative), int(y_shift_cumulative),
File "C:\Users\James\Documents\stable-diffusion-webui-master\scripts\animation.py", line 34, in zoom_at2
img2 = img.resize((int(w * zoom), int(h * zoom)), Image.Resampling.LANCZOS)
File "C:\Users\James\Documents\stable-diffusion-webui-master\venv\lib\site-packages\PIL\Image.py", line 2082, in resize
return self._new(self.im.resize(size, resample, box))
ValueError: height and width must be > 0

大致看了一下，貌似get_random_data()方法里面并没有实现mosaic的数据增强方法？其他的大部分和keras yolov3的差不多~

Loading weights.
Weights Header: 1329865020 1348031555 1952981061 [1010723949]
Converting 0
Traceback (most recent call last):
File "convert.py", line 173, in
yolo4_model = Yolo4(score, iou, anchors_path, classes_path, model_path, weights_path)
File "convert.py", line 157, in init
self.load_yolo()
File "convert.py", line 130, in load_yolo
buffer=weights_file.read(weights_size * 4))
TypeError: buffer is too small for requested array

@Ma-Dan Hi,
Did you test accuracy of converted YOLOv4-model on MSCOCO?
Is it the same as in the Darknet - 43.5% AP (65.7% AP50) for YOLOv4-608x608?

in model.py, preprocess_true_boxes():
for i in range(3):
anchors_xywh = np.zeros((3, 4))
anchors_xywh[:, 0:2] = np.floor(bbox_xywh_scaled[i, 0:2]).astype(np.int32) + 0.5
anchors_xywh[:, 2:4] = anchors[i]
iou_scale = bbox_iou_data(bbox_xywh_scaled[i][np.newaxis, :], anchors_xywh)
iou.append(iou_scale)

since anchors_xywh is in original scale, we shouldn't use bbox_xywh instead of bbox_xywh_scaled to compute iou?

Traceback (most recent call last):
File "convert.py", line 173, in
yolo4_model = Yolo4(score, iou, anchors_path, classes_path, model_path, weights_path)
File "convert.py", line 157, in init
self.load_yolo()
File "convert.py", line 130, in load_yolo
buffer=weights_file.read(weights_size * 4))

Traceback (most recent call last):
File "test.py", line 69, in
image, boxes, scores, classes = _decode.detect_image(image, True)
File "/home/user/santosh/keras-yolo4/decode_np.py", line 23, in detect_image
boxes, scores, classes = self.predict(pimage, image.shape)
File "/home/user/santosh/keras-yolo4/decode_np.py", line 135, in predict
outs = self._yolo.predict(image)
File "/home/user/anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 1462, in predict
callbacks=callbacks)
File "/home/user/anaconda3/lib/python3.7/site-packages/keras/engine/training_arrays.py", line 324, in predict_loop
batch_outs = f(ins_batch)
File "/home/user/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py", line 3476, in call
run_metadata=self.run_metadata)
File "/home/user/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1472, in call
run_metadata_ptr)
tensorflow.python.framework.errors_impl.UnimplementedError: Fused conv implementation does not support grouped convolutions for now.
[[{{node conv2d_94/BiasAdd}}]]

可否把训练自己数据用的步骤详细写一下？

When I run python test.py
then
Input image filename:'yolo4.png'

I get tis error:
\Traceback (most recent call last):
File "test.py", line 158, in
result = yolo4_model.detect_image(image, model_image_size=model_image_size)
File "test.py", line 80, in detect_image
boxed_image = letterbox_image(image, tuple(reversed(model_image_size)))
File "/home/muhammadmubeen/keras-yolo4/yolo4/utils.py", line 28, in letterbox_image
image = image.resize((nw,nh), Image.BICUBIC)
File "/usr/local/lib/python2.7/dist-packages/PIL/Image.py", line 1883, in resize
im = im.resize(size, resample, box)
File "/usr/local/lib/python2.7/dist-packages/PIL/Image.py", line 1888, in resize
return self._new(self.im.resize(size, resample, box))
ValueError: height and width must be > 0

it works for standard yolov4, but in model.py there is no network for tiny-yolov4, what kind of network that has to be specified? any help for it? Thanks in advance.

I trained with my own data set, and loss felt high and converged slowly

how to capture real camera and detect ?

Hi,

Does anyone know of a way to get the detection information from the model frame by frame and then save that information to a file? I have been trying to figure this out but have not found a good solution yet. Would love to hear your ideas.

Warm Regards

I want to change Backbone network! I heard that It is not good ResNeXt in OD.
I want to change CSPDarknet53! How can I change Backbone?

请问训练该模型使用的GPU是什么型号，显存大小？
GPU=8G显存，能否支持训练batch_size = 4？

keras和tensorflow的版本？

在新版的https://github.com/miemie2013/Keras-YOLOv4代码中，运行出现eval.py程序出现
2020-06-21 20:08:35,249-INFO: Test iter 5800
loading annotations into memory...
Done (t=0.06s)
creating index...
index created!
2020-06-21 20:09:15,991-INFO: Generating json file...
Traceback (most recent call last):
File "eval.py", line 72, in
box_ap = eval(_decode, images, eval_pre_path, anno_file, eval_batch_size, _clsid2catid, draw_image)
File "E:\chongqingchuyuxiangmu\Keras-YOLOv4\tools\cocotools.py", line 167, in eval
box_ap_stats = bbox_eval(anno_file)
File "E:\chongqingchuyuxiangmu\Keras-YOLOv4\tools\cocotools.py", line 86, in bbox_eval
for line in f:
MemoryError
运行test_dev.py正常，但运行eval.py程序之后，出现MemoryError，请问是怎么回事？

3, 5 + num_classes))
TypeError: only integer scalar arrays can be converted to a scalar index
这个问题怎么解决

I have trained yolo.weights (with my custom dataset having 3 classes) and the detections are coming fine while testing with that model. But when i am converting it to keras model, I am getting below error

File "convert.py", line 172, in <module>
    yolo4_model = Yolo4(score, iou, anchors_path, classes_path, model_path, weights_path)
  File "convert.py", line 157, in __init__
    self.load_yolo()
  File "convert.py", line 98, in load_yolo
    buffer=weights_file.read(weights_size * 4))
TypeError: buffer is too small for requested array

Could someone tell me why it is happening and what changes I should make

在model.py的文件中yo'lo4-body中每一个输出层是如何定义的？原版的网络层数我看到只有161层，但是在代码中看到了204层？
并且想知道本次的代码中第二个残差网络输出后是多少层

Epoch 89/150
loss: [70.428894][6.8589654][49.6575623][13.912364]
1234/1234 [==============================] - 638s 517ms/step - loss: 48.12988.202549][5.05134583][33.3962708][9.7549324]]]]]]]
Epoch end eval mAP on weight logs/222/ep089-loss48.130.h5
Epoch 89 mAP 0.2658832599269524
Epoch 90/150
loss: [59.5338707][5.99079227][46.7998619][6.74321651]
1234/1234 [==============================] - 655s 531ms/step - loss: 47.88505.1289482][3.23023152][27.289856][4.60885954]]]]]
Epoch end eval mAP on weight logs/222/ep090-loss47.885.h5
Epoch 90 mAP 0.26986168503362695

in @miemie2013's code

x = layers.ZeroPadding2D(padding=((1, 0), (1, 0)))(x)
x = conv2d_unit(x, i512, 3, strides=2)
s16 = conv2d_unit(x, i256, 1, strides=1)
x = conv2d_unit(x, i256, 1, strides=1)
x = stack_residual_block(x, i256, i256, n=8)
x = conv2d_unit(x, i256, 1, strides=1)
s16 = layers.Concatenate()([x, s16])
x = conv2d_unit(s16, i512, 1, strides=1)

where s16 is the output of Concatenate()

x = layers.UpSampling2D(2)(x)
s16 = conv2d_unit(s16, i256, 1, strides=1, act='leaky')
x = layers.Concatenate()([s16, x])

s16 then computer through conv2d_unit and concatenate with upsampling output x

as for your code:

y38 = DarknetConv2D_BN_Leaky(256, (1,1))(darknet.layers[204].output)
y38 = Concatenate()([y38, y19_upsample])

where darknet.layers[204].output is the output of DarknetConv2D_BN_Mish, not the concatenate layer darknet.layers[201].outputs.

So, I am curious which version is right, or both right?

Hello! Thank you for amazing work!
Could you help me, how to convert the trained weights back to darknet or tensorflow?

Thank you!

thanks for the nice repo.

I am training yolov4 on coco dataset for only the first 7 classes with a split 70:30,
any experiences or hints about the training loss when the model seems acceptable?

below is my traing log, seem a long time to train.

Train on 82801 samples, val on 35486 samples, with batch size 32.
Epoch 1/50

   1/2587 [..............................] - ETA: 28:56:58 - loss: 17721.8926
   2/2587 [..............................] - ETA: 16:33:45 - loss: 17243.4922
   3/2587 [..............................] - ETA: 12:32:28 - loss: 16760.1377
   4/2587 [..............................] - ETA: 10:10:04 - loss: 16299.9021
   5/2587 [..............................] - ETA: 9:24:35 - loss: 15857.3045 
   6/2587 [..............................] - ETA: 9:14:57 - loss: 15430.3268
   7/2587 [..............................] - ETA: 9:07:24 - loss: 15008.8902
   8/2587 [..............................] - ETA: 9:01:51 - loss: 14608.8591
   9/2587 [..............................] - ETA: 9:12:25 - loss: 14219.5904
  10/2587 [..............................] - ETA: 9:20:37 - loss: 13848.0055
  11/2587 [..............................] - ETA: 9:18:39 - loss: 13490.1777
  12/2587 [..............................] - ETA: 8:59:26 - loss: 13150.2271
  13/2587 [..............................] - ETA: 8:44:20 - loss: 12822.8066
  14/2587 [..............................] - ETA: 8:34:12 - loss: 12509.4747
  15/2587 [..............................] - ETA: 8:32:09 - loss: 12211.5387
  16/2587 [..............................] - ETA: 8:26:59 - loss: 11925.6479
  17/2587 [..............................] - ETA: 8:20:37 - loss: 11649.0334
  18/2587 [..............................] - ETA: 8:21:20 - loss: 11381.8880
  19/2587 [..............................] - ETA: 8:14:07 - loss: 11124.9040
  20/2587 [..............................] - ETA: 8:11:29 - loss: 10882.4443
  21/2587 [..............................] - ETA: 8:07:56 - loss: 10645.2164
  22/2587 [..............................] - ETA: 8:03:53 - loss: 10420.4502
  23/2587 [..............................] - ETA: 7:58:37 - loss: 10203.8660
  24/2587 [..............................] - ETA: 7:54:08 - loss: 9995.8066 
  25/2587 [..............................] - ETA: 7:50:39 - loss: 9794.8682
  26/2587 [..............................] - ETA: 7:48:39 - loss: 9599.6988
  27/2587 [..............................] - ETA: 7:45:55 - loss: 9411.8161
  28/2587 [..............................] - ETA: 7:42:36 - loss: 9234.4692
  29/2587 [..............................] - ETA: 7:39:51 - loss: 9060.8342
  30/2587 [..............................] - ETA: 7:35:15 - loss: 8894.1920
  31/2587 [..............................] - ETA: 7:33:45 - loss: 8733.4837
  32/2587 [..............................] - ETA: 7:31:51 - loss: 8577.9887
  33/2587 [..............................] - ETA: 7:29:49 - loss: 8425.8984
  34/2587 [..............................] - ETA: 7:27:43 - loss: 8280.7446
  35/2587 [..............................] - ETA: 7:26:30 - loss: 8139.6654
  36/2587 [..............................] - ETA: 7:26:01 - loss: 8004.0953
  37/2587 [..............................] - ETA: 7:26:13 - loss: 7872.3828
  38/2587 [..............................] - ETA: 7:24:52 - loss: 7746.5956
  39/2587 [..............................] - ETA: 7:23:28 - loss: 7624.6465
  40/2587 [..............................] - ETA: 7:29:19 - loss: 7506.7023
  41/2587 [..............................] - ETA: 7:35:16 - loss: 7391.7025
  42/2587 [..............................] - ETA: 7:34:34 - loss: 7280.5936
  43/2587 [..............................] - ETA: 7:31:58 - loss: 7172.5243
  44/2587 [..............................] - ETA: 7:29:56 - loss: 7067.7942
  45/2587 [..............................] - ETA: 7:30:26 - loss: 6965.9564
  46/2587 [..............................] - ETA: 7:31:08 - loss: 6867.1803
  47/2587 [..............................] - ETA: 7:34:20 - loss: 6771.7925

YOLO V3 keras版本 解冻前3层配置 batchsize 128乘8 解冻后 252层 32乘8，8代表GPU nums 均可以顺利训练
YOLO V3 keras版本 解冻前3层 只能配置 16乘8 解冻后 只能1乘8 ，否则就会报错，这个有办法解决么。如果batchsize不能配置较大的话，训练一个模型50万张图片，需要好几个月，这完全没法忍受啊。
具体报错：（tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[32,256,52,52] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc）

I have converted a custom YOLO4 model to the tensorflow saved_model format. I can successfully use this for single image inference. Now when I feed in a batch of images for client side batching I get the results back in one list without separation of the results into frames so I can't work out which detections go with which frames. Can you provide any info on whether this behavior is expected or how I might remedy it?

loss和val loss总是出现nan

恕我直言qqwweee那版yolo3就是个废仓库，训练不出效果，loss经常出现nan，那段时间让我踩了很多坑。恕我直言这么废的仓库也有那么多star真的让人很无语。算法复现的要求是达到指定精度，而不是让观众成为你的debugger、改进者。广告虽可耻，大家可以看看我复现的这版yolov4，精心设计了损失函数，训练了70000步就没出现过nan，mAP至少可达36%，欢迎点star：

https://github.com/miemie2013/Keras-YOLOv4

Failed to run convert.py. And there is no error message.

此圖的行為不能執行。

from yolo4 import YOLO
yolo = YOLO()

in tf2+, Lambda layer could not be used anymore, is there any solution to solve the issue
especially th error:
ValueError: tf.function-decorated function tried to create variables on non-first call.

能提供个和 v3一样的，tiny版本吗

Tensorflow YOLOv4 训练自己的数据集，深度学习实战群 755860371 共同学习，谢谢