nilboy / tensorflow-yolo Goto Github PK

I downloaded the code and the tiny yolo pretrain model, change nothing but Error Occurs.
Environment :
TensorFlow 1.0
GPU Titan X
Python 2.7

When change the config to use YoloNet there's no such problem, but the pretrain model doesn't fit ...

Hi there, sorry about that to post this here, i was trying to write yolo in torch but struggled the backward function for so many days, but the gradients are always exploding slowly, could you kindly shed a light on my codes? Thank you so much.

gradInput[{ {}, {}, 1, {}, {} }] = self.mse:backward(torch.cmul(self.x_buffer, x, coord_mask), tx)
gradInput[{ {}, {}, 2, {}, {} }] = self.mse:backward(torch.cmul(self.y_buffer, y, coord_mask), ty)
gradInput[{ {}, {}, 3, {}, {} }] = self.mse:backward(torch.cmul(self.w_buffer, w, coord_mask), tw)
gradInput[{ {}, {}, 4, {}, {} }] = self.mse:backward(torch.cmul(self.h_buffer, h, coord_mask), th)
gradInput[{ {}, {}, 5, {}, {} }] = self.mse:backward(torch.cmul(self.conf_buffer, conf, coord_mask), tconf)
gradInput[{ {}, {}, { 6, 5 + nC }, {}, {} }][self.cls_mask] = self.ce:backward(torch.cmul(self.cls_buffer, cls), tcls)

Why only an object can be identified, other objects can't be identified?

Can you release the ckpt file for yolo_net?

When I type in "python tools/train.py -c conf/train.cfg", it report the error:

Traceback (most recent call last):
File "tools/train.py", line 6, in
import yolo
File "./yolo/init.py", line 1, in
import dataset
ModuleNotFoundError: No module named 'dataset'

I'm using anaconda3 BTW.
Thank you for your help!

I've followed the README guide to train this model but after several days of training (1m steps) the resulting performance is sometimes worse than the starting checkpoint. The loss is going down comparing to the first couple steps but doesn't change much after 20000 steps or so. Here is what I have in the end:

2017-04-08 14:51:43.589313: step 999950, loss = 3.99 (57.7 examples/sec; 0.277 s
ec/batch)
2017-04-08 14:51:46.701805: step 999960, loss = 8.01 (54.5 examples/sec; 0.294 s
ec/batch)
2017-04-08 14:51:49.754212: step 999970, loss = 7.63 (48.0 examples/sec; 0.333 s
ec/batch)
2017-04-08 14:51:52.851173: step 999980, loss = 4.31 (58.4 examples/sec; 0.274 s
ec/batch)
2017-04-08 14:51:55.847565: step 999990, loss = 4.15 (55.3 examples/sec; 0.289 s
ec/batch)

But if you compare the results of running demo with yolo_tiny.ckpt and trained model.ckpt you can see that the result is far from perfect.

yolo_tiny.ckpt:

trained model.ckpt:

Anyone else experienced the same behavior?

Hi, the prediction demo works great on images with single objects.

However, it seems it cannot detect multiple objects like the original YOLO version. Am I correct??

It seems that all variables on the graph are trainable because the "train" arg of "_variable_on_cpu" was not sent to "trainable" arg of tf.get_variable. So every time we run train_op, all variable will be updated. Is that correct ?

Hey, I noticed that you replaced
"f.nn.l2_loss(no_I * (p_C - C)) * self.noobject_scale" with
"tf.nn.l2_loss(no_I * (p_C)) * self.noobject_scale".
But as I know, the paper adopted the former one. Why did you make such a change？

I just want to try the demo, but I got this. Can anyone help me ?
2018-02-22 11:17:41.099700: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-02-22 11:17:41.099982: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1208] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.62
pciBusID: 0000:01:00.0
totalMemory: 3.94GiB freeMemory: 3.33GiB
2018-02-22 11:17:41.100000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1308] Adding visible gpu devices: 0
2018-02-22 11:17:41.547405: I tensorflow/core/common_runtime/gpu/gpu_device.cc:989] Creating TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2985 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
Traceback (most recent call last):
File "demo.py", line 77, in
saver.restore(sess, 'models/pretrain/yolo_tiny.ckpt')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1751, in restore
" to the checkpoint files." % save_path)
ValueError: The specified path: models/pretrain/yolo_tiny.ckpt is a file. Please specify only the path prefix to the checkpoint files.

I used this python3 branch to re implement your code. However, i have this error :'( Please help me

NotFoundError (see above for traceback): Unsuccessful TensorSliceReader construc
tor: Failed to find any matching files for F:\yolo_tiny.ckpt
[[Node: save/RestoreV2_5 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:l
ocalhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_5/tensor_na
mes, save/RestoreV2_5/shape_and_slices)]]

Hi
Could you please guide me how to train the net without using pretrained model?

I faced with this error when trying train without pretrained file:
NotFoundError (see above for traceback): Unsuccessful TensorSliceReader constructor: Failed to find any matching files for
[[Node: save/RestoreV2_8 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_8/tensor_names, save/RestoreV2_8/shape_and_slices)]]

Thanks

I have tested your tiny-yolo on pascal_voc 2007 validation set. The result of test is so bad, I think. Do you use
paramters from darknet tiny_yolo or trained by yourself? Can your tiny_yolo reach MAP 50%?

Hello everyone,

When I use this yolo_tiny.ckpt to fine-tune the model in my own dataset, the result is quite bad.
I guess it results of the original kept file.
Is there anyone who can share a better ckpt?

Thank you~

if I want to see the performance like mAp，what I should do?

Hello,

I wanna know if there is anyway to detect less classes (I'm interested in just one).

Thanks!

Hi,

I found there are some types in this model like a person, a cat, and et. I wonder if I could add another type to this model? For example, could I create a type of the panda and train some pictures of panda. I have labelled some pictures in the dataset.

Thanks so much,
Peter

thanks for your well-structure code, but I have a confusion in these lines.

tensorflow-yolo/yolo/net/yolo_tiny_net.py
198-206 lines

can you give a explantion on base_boxes

Hi, I tried to run this code with Branch "python 3". Howerver, as I have used the tensorflow ver1.0, the error "module 'tensorflow' has no attribute 'mul'" occurred in this file("./yolo/net/net.py");

then I replaced "tf.mul" with "tf.matmul", another error "ValueError: Shape must be rank 2 but is rank 0 for 'conv1/weight_loss' Mul'.... " occurred. As I am not good at python programming, I cannot put it right.
Noticing that you are still maintaining this project in Branch "python 2.7", so, can you help me solve this problem?(I ran this project with W7 x64, the python version is 3.5.2)

This looks like nice work! But I'm having some trouble interpreting the results and seeing how you do (and I could do) validation/testing (on validation/test set).
When I follow the instructions for training on the Pascal VOC dataset, exactly as you describe it, the system seems to start training fine, and progress can for example be monitored using TensorBoard (or just watching the command line output).
After about 14000 steps, I get:

2017-03-08 13:54:49.951253: step 13960, loss = 4.10 (49.3 examples/sec; 0.325 sec/batch)
2017-03-08 13:54:53.520895: step 13970, loss = 5.73 (45.7 examples/sec; 0.350 sec/batch)
2017-03-08 13:54:57.180532: step 13980, loss = 6.08 (37.5 examples/sec; 0.427 sec/batch)
2017-03-08 13:55:00.669307: step 13990, loss = 4.97 (50.1 examples/sec; 0.319 sec/batch)
2017-03-08 13:55:04.188500: step 14000, loss = 4.71 (50.0 examples/sec; 0.320 sec/batch)
2017-03-08 13:55:07.815220: step 14010, loss = 5.66 (44.7 examples/sec; 0.358 sec/batch)

But this is similar to the loss performance observed after only around 1300 steps or so.
What is the loss performance I should expect, and at how many steps (more or less)?
Is the conf/train.cfg you provide the exact same config file you used to obtain good performance?

And how do you do validation/testing on a separate dataset? Is there a separate function to call or should train.py be used with different parameters or something?

How to fine tune your model?
I don't have sufficient data to retrain your model from scratch.I want to fine tune your model on my data which has only two classes ?

Environment :
TensorFlow 1.0
Python 3.6 (anaconda3)

I download the code for python3 version, and the pretrained model, and the VOC2007 data, then began to train the model by:

python tools/train.py -c conf/train.cfg

BUT, still get the error which I found in a closed issue (#11)

I also checked yolo_tiny_net.py, and found it has already been changed to:
predicts = tf.concat(3, [class_probs, scales, boxes])

Could you please give me some clue? Thanks a lot!

Hi,

I am running this program and really want to know if I could get the confidence probability of the result from demo.py?

Thanks,
Peter

when I tried the training and demo,the following error happened!
NotFoundError (see above for traceback): Tensor name "conv1/biases" not found in checkpoint files models/pretrain/yolo_tiny.ckpt

Hi,

How can I modify the demo.py file, so the input image would be different not only cat.jpg.
For example, now If I want to test another image I have to change the input in demo.py file, which is [np_img = cv2.imread('cat.jpg')], then, I will be able to run another image.

I want to test another image not cat.jpg?

I have run the demo successfully,but 24 hours later,it's still running,and I am using 1080ti,Losses are always fluctuating between 5 and 10,I don't know whether the train has been finished,so I want to find the epoch_num,but I can't find it,anyone konw this?many thanks!

No loading yolo_tiny.ckpt. Direct training yolo_net, it will occur error ：AssertionError: Model diverged with loss = NaN.

Traceback (most recent call last):
File "E:\python_workspace\tensorflow-yolo\demo.py", line 5, in
from yolo.net.yolo_tiny_net import YoloTinyNet
File "E:\python_workspace\tensorflow-yolo\yolo_init_.py", line 2, in
import net
ModuleNotFoundError: No module named 'net'

I have modified the code for one-object detection. Due to the lack of training samples (about 1,600 images), I add the data augmentation codes in text_dataset.py as follows:

def batch(self):
    """get batch
    Returns:
      images: 4-D ndarray [batch_size, height, width, 3]
      labels: 3-D ndarray [batch_size, max_objects, 5]
      objects_num: 1-D ndarray [batch_size]
    """
    images = []
    labels = []
    objects_num = []
    for i in range(self.batch_size):
      image, label, object_num = self.image_label_queue.get()
      image, label = self._augumentation(image, label, object_num)
      images.append(image)
      labels.append(label)
      objects_num.append(object_num)
    images = np.asarray(images, dtype=np.float32)
    images = images/255 * 2 - 1 # 归一化[-1,1]
    labels = np.asarray(labels, dtype=np.float32)
    objects_num = np.asarray(objects_num, dtype=np.int32)
    return images, labels, objects_num
    
 def _augumentation(self, src_image, src_labels, object_num, bright_range=[0.5,1.5], translation_scale=0.8):
    """image augmentation: brightness, flip, translation.
    Args:
      src_image: 3-D ndarray
      src_labels: 2-D list [self.max_objects, 5] (xcenter, ycenter, w, h, class_num)
      object_num: object number in src_image
      bright_range: brightness adjusting range
      translation_scale: translation scale
    Return:
      image: 3-D ndarray
      labels: 2-D list [self.max_objects, 5] (xcenter, ycenter, w, h, class_num)
    """
    width = src_image.shape[1]
    height = src_image.shape[0]
    image = src_image.copy()


    ## brightness
    bright_ratio = random.random()*(bright_range[1]-bright_range[0]) + bright_range[0]
    image = image*bright_ratio
    image[image<0] = 0
    image[image>255] = 255
    image = np.asarray(image, np.uint8)
    labels = src_labels[:]

    ## mirror (flip)
    flip = random.random()>0.5
    if flip:
      image = cv2.flip(image, 1)
      for i in range(object_num):
      	xc = width - labels[i][0]
      	labels[i][0] = xc

    ## translation
    labels_array = np.asarray(labels, np.float32)
    offset_xmin = -np.min(labels_array[:,0]-labels_array[:,2]/2) #left
    offset_xmax = np.min(width-(labels_array[:,0]+labels_array[:,2]/2)) #right
    offset_ymin = -np.min(labels_array[:,1]-labels_array[:,3]/2) #up
    offset_ymax = np.min(height-(labels_array[:,0]+labels_array[:,3]/2)) #down
    offset_x = int(random.uniform(offset_xmin*translation_scale, offset_xmax*translation_scale))
    offset_y = int(random.uniform(offset_ymin*translation_scale, offset_ymax*translation_scale))

    M = np.float32([[1, 0, offset_x], [0, 1, offset_y]])
    image = cv2.warpAffine(image, M, (width, height))
    image = np.asarray(image, np.uint8)

    for i in range(object_num):
      xc = labels[i][0]
      xc = xc + offset_x
      labels[i][0] = xc

      yc = labels[i][1]
      yc = yc + offset_y
      labels[i][1] = yc

    return image, labels

when I just use the augmentation of brightness and flip, it's ok. But the error occurred when the translation operation is added.
the error happens in yolo_tiny_net.py: (function body1())

objects = tf.pad(objects, temp, "CONSTANT")

the error is that:
InvalidArgumentError: padding must be non-negative: 5 -1

so, is there any problems in my codes? (PS: I have run augmentation code independently and it works well.)

Why you set the first fully connected layer's input dim 49 * 1024?

I'm using your code to locate human eyes. In the first 500 iterations, coord loss drop down from 8.x to 1.x and it doesn't drop after that. Please provide some advice.
Here is the test demo after 230k iterations. It's obvious that CNN could detect pupil but cannot locate it accurately.

Caused by op u'while_1/Pad_1', defined at:
File "tools/train.py", line 22, in
solver = eval(solver_params['name'])(dataset, net, common_params, solver_params)
File "./yolo/solver/yolo_solver.py", line 32, in init
self.construct_graph()
File "./yolo/solver/yolo_solver.py", line 62, in construct_graph
self.total_loss, self.nilboy = self.net.loss(self.predicts, self.labels, self.objects_num)
File "./yolo/net/yolo_tiny_net.py", line 295, in loss
tuple_results = tf.while_loop(self.cond1, self.body1, [tf.constant(0), object_num, [class_loss, object_loss, noobject_loss, coord_loss], predict, label, nilboy])
File "/home/wahaha/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2626, in while_loop
result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "/home/wahaha/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2459, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/home/wahaha/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2409, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "./yolo/net/yolo_tiny_net.py", line 187, in body1
response = tf.pad(response, temp, "CONSTANT")
File "/home/wahaha/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 1646, in pad
return gen_array_ops._pad(tensor, paddings, name=name)
File "/home/wahaha/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2083, in _pad
name=name)
File "/home/wahaha/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/home/wahaha/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/wahaha/anaconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1264, in init
self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): Paddings must be non-negative: 9 -3
[[Node: while_1/Pad_1 = Pad[T=DT_FLOAT, Tpaddings=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](while_1/ones_1, while_1/Reshape_2)]]
[[Node: gradients/local1/add_grad/tuple/control_dependency_1/_191 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_11242_gradients/local1/add_grad/tuple/control_dependency_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]]

What's the problem? @nilboy

Hey, I found the "weight_decay" is initialized in yolo_net.py, but where does it be used?

When I run the demo.py, I get the follow image.

I train the model 20,000 steps. Only a person is identified, the bus and bicycly can't be identified. Other images with many objects are the same. How can I modify the code to identify the all objects? Thank you!

Thanks for your shared code.
When i finetuning YOLO_tiny.ckpt（ python：2.7， tensorflow：1.4） ，the error occurred as following：

'''
2018-01-24 16:00:56.966984: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
Traceback (most recent call last):
File "tools/train.py", line 23, in
solver.solve()
File "./yolo/solver/yolo_solver.py", line 79, in solve
saver1.restore(sess, self.pretrain_path)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1666, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 889, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1317, in _do_run
options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: Tensor name "conv3/weights" not found in checkpoint files /home/wqy/Desktop/csdn/lesson8_up/yolo01/tensorflow-yolo/models/pretrain/YOLO_tiny.ckpt
[[Node: save/RestoreV2_5 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_5/tensor_names, save/RestoreV2_5/shape_and_slices)]]

Caused by op u'save/RestoreV2_5', defined at:
File "tools/train.py", line 23, in
solver.solve()
File "./yolo/solver/yolo_solver.py", line 68, in solve
saver1 = tf.train.Saver(self.net.pretrained_collection, write_version=1)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1218, in init
self.build()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1227, in build
self._build(self._filename, build_save=True, build_restore=True)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1263, in _build
build_save=build_save, build_restore=build_restore)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 751, in _build_internal
restore_sequentially, reshape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 427, in _AddRestoreOps
tensors = self.restore_op(filename_tensor, saveable, preferred_shard)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 267, in restore_op
[spec.tensor.dtype])[0])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 1021, in restore_v2
shape_and_slices=shape_and_slices, dtypes=dtypes, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1470, in init
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access

NotFoundError (see above for traceback): Tensor name "conv3/weights" not found in checkpoint files /home/wqy/Desktop/yolo01/tensorflow-yolo/models/pretrain/YOLO_tiny.ckpt
[[Node: save/RestoreV2_5 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_5/tensor_names, save/RestoreV2_5/shape_and_slices)]]
'''

How can i to solve this problem？Thank you！

Can I use the nilboy information to calculate the accuracy?

Hi, thanks for your great work.
I notice that the output of the yolo is a 7730 tensor, and some of the output means Pr(class|object), and some means Pr(object)， but these output may be <0, so they don't means probability any more.
How to understand these problems?

hi, i want to modify your code for predict one_class target，for example，predict cats，how should i change the code？i did these changed，but it seems wrong
1、modified train.cfg，num_classes=1

2、modified demo.py，i dont understand these code，I think it's probably wrong here

Hi, Nice work!

Can you kindly tell me how you generated/got the pre-trained model below?
yolo_tiny: https://drive.google.com/file/d/0B-yiAeTLLamRekxqVE01Yi1RRlk/view?usp=sharing

hey, I modified the code for one-object detection, the number of training samples is about 1000. the model converges quickly and the class_loss is almost zero, the coord_loss is about 0.5, the noobject_loss is about 0.015, the object_loss is about 0.03, the weight_loss is 1.11. but I found that the model still perform worse on training data. so who can explain the phenomenon？

Hi
I'm new to ML and tensorflow.
I'm trying to use tensorflow-yolo to train model for hand recognition.
No. of classes is 1 which is hand

when I run train.py it returns Padding -ve error.

Caused by op u'while_2/Pad', defined at:
File "tools/train.py", line 22, in
solver = eval(solver_params['name'])(dataset, net, common_params, solver_params)
File "./yolo/solver/yolo_solver.py", line 32, in init
self.construct_graph()
File "./yolo/solver/yolo_solver.py", line 62, in construct_graph
self.total_loss, self.nilboy = self.net.loss(self.predicts, self.labels, self.objects_num)
File "./yolo/net/yolo_tiny_net.py", line 297, in loss
tuple_results = tf.while_loop(self.cond1, self.body1, [tf.constant(0), object_num, [class_loss, object_loss, noobject_loss, coord_loss], predict, label, nilboy])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2775, in while_loop
result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2604, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2554, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "./yolo/net/yolo_tiny_net.py", line 175, in body1
objects = tf.pad(objects, temp, "CONSTANT")
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1679, in pad
return gen_array_ops._pad(tensor, paddings, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1989, in _pad
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1204, in init
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Paddings must be non-negative: 4 -1
[[Node: while_2/Pad = Pad[T=DT_FLOAT, Tpaddings=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](while_2/ones, while_2/Reshape_1)]]
[[Node: gradients/AddN_132/_185 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_49524_gradients/AddN_132", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]]

Do you have any idea cause of this issue?
Regards

import sys

sys.path.append('./')

import time
from yolo.net.yolo_tiny_net import YoloTinyNet 
import tensorflow as tf 
import cv2
import numpy as np

classes_name =  ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train","tvmonitor"]


class Timer(object):
    """A simple timer."""
    def __init__(self):
        self.total_time = 0.
        self.calls = 0
        self.start_time = 0.
        self.diff = 0.
        self.average_time = 0.

    def tic(self):
        # using time.time instead of time.clock because time time.clock
        # does not normalize for multi-threading
        self.start_time = time.time()

    def toc(self, average=True):
        self.diff = time.time() - self.start_time
        self.total_time += self.diff
        self.calls += 1
        self.average_time = self.total_time / self.calls
        if average:
            return self.average_time
        else:
            return self.diff



def process_predicts(resized_img, predicts, thresh=0.12):
	"""
	process the predicts of object detection with one image input.
	
	Args:
		resized_img: resized source image.
		predicts: output of the model.
		thresh: thresh of bounding box confidence.
	Return:
		predicts_dict: {"cat": [[x1, y1, x2, y2, scores1], [...]]}.
	"""
	p_classes = predicts[0, :, :, 0:20] # 20 classes.
	C = predicts[0, :, :, 20:22] # two bounding boxes in one cell.
	coordinate = predicts[0, :, :,22:] # all bounding boxes position.
	
	p_classes = np.reshape(p_classes, (7, 7, 1, 20))
	C = np.reshape(C, (7, 7, 2, 1))
	
	P = C * p_classes # confidencefor all classes of all bounding boxes (cell_size, cell_size, bounding_box_num, class_num) = (7, 7, 2, 1).
	
	predicts_dict = {}
	for i in range(7):
		for j in range(7):
			temp_data = np.zeros_like(P, np.float32)
			temp_data[i, j, :, :] = P[i, j, :, :]
			position = np.argmax(temp_data) # refer to the class num (with maximum confidence) for every bounding box.
			index = np.unravel_index(position, P.shape)
			
			if P[index] > thresh:
				class_num = index[-1]
				coordinate = np.reshape(coordinate, (7, 7, 2, 4)) # (cell_size, cell_size, bbox_num_per_cell, coordinate)[xmin, ymin, xmax, ymax]
				max_coordinate = coordinate[index[0], index[1], index[2], :]
				
				xcenter = max_coordinate[0]
				ycenter = max_coordinate[1]
				w = max_coordinate[2]
				h = max_coordinate[3]
				
				xcenter = (index[1] + xcenter) * (448/7.0)
				ycenter = (index[0] + ycenter) * (448/7.0)
				
				w = w * 448 
				h = h * 448
				xmin = 0 if (xcenter - w/2.0 < 0) else (xcenter - w/2.0)
				ymin = 0 if (ycenter - h/2.0 < 0) else (ycenter - h/2.0)
				xmax = resized_img.shape[0] if (xmin + w) > resized_img.shape[0] else (xmin + w)
				ymax = resized_img.shape[1] if (ymin + h) > resized_img.shape[1] else (ymin + h)
				
				class_name = classes_name[class_num]
				predicts_dict.setdefault(class_name, [])
				predicts_dict[class_name].append([int(xmin), int(ymin), int(xmax), int(ymax), P[index]])
				
	return predicts_dict
	

def non_max_suppress(predicts_dict, threshold=0.5):
    """
    implement non-maximum supression on predict bounding boxes.
    Args:
        predicts_dict: {"cat": [[x1, y1, x2, y2, scores1], [...]]}.
        threshhold: iou threshold
    Return:
        predicts_dict processed by non-maximum suppression
    """
    for object_name, bbox in predicts_dict.items():
        bbox_array = np.array(bbox, dtype=np.float)
        x1, y1, x2, y2, scores = bbox_array[:,0], bbox_array[:,1], bbox_array[:,2], bbox_array[:,3], bbox_array[:,4]
        areas = (x2-x1+1) * (y2-y1+1)
        #print "areas shape = ", areas.shape
        order = scores.argsort()[::-1]
        #print "order = ", order
        keep = []

        while order.size > 0:
            i = order[0]
            keep.append(i)
            xx1 = np.maximum(x1[i], x1[order[1:]])
            yy1 = np.maximum(y1[i], y1[order[1:]])
            xx2 = np.minimum(x2[i], x2[order[1:]])
            yy2 = np.minimum(y2[i], y2[order[1:]])

            inter = np.maximum(0.0, xx2-xx1+1) * np.maximum(0.0, yy2-yy1+1)
            iou = inter/(areas[i]+areas[order[1:]]-inter)
            indexs = np.where(iou<=threshold)[0]
            order = order[indexs+1]
        bbox = bbox_array[keep]
        predicts_dict[object_name] = bbox.tolist()
        predicts_dict = predicts_dict
    return predicts_dict


def plot_result(src_img, predicts_dict):
    """
    plot bounding boxes on source image.
    Args:
        src_img: source image
        predicts_dict: {"cat": [[x1, y1, x2, y2, scores1], [...]]}.
    """
    height_ratio = src_img.shape[0]/448.0
    width_ratio = src_img.shape[1]/448.0
    for object_name, bbox in predicts_dict.items():
        for box in bbox:
            xmin, ymin, xmax, ymax, score = box
            src_xmin = xmin * width_ratio
            src_ymin = ymin * height_ratio
            src_xmax = xmax * width_ratio
            src_ymax = ymax * height_ratio
            score = float("%.3f" %score)

            cv2.rectangle(src_img, (int(src_xmin), int(src_ymin)), (int(src_xmax), int(src_ymax)), (0, 0, 255))
            cv2.putText(src_img, object_name + str(score), (int(src_xmin), int(src_ymin)), 1, 2, (0, 0, 255))

    cv2.imshow("result", src_img)
    #cv2.imwrite("result.jpg", src_img)
  

if __name__ == '__main__':
    common_params = {'image_size': 448, 'num_classes': 20, 'batch_size': 1}
    net_params = {'cell_size': 7, 'boxes_per_cell': 2, 'weight_decay': 0.0005}

    net = YoloTinyNet(common_params, net_params, test=True)

    image = tf.placeholder(tf.float32, (1, 448, 448, 3))
    predicts = net.inference(image)

    sess = tf.Session()

    src_img = cv2.imread("./data/VOCdevkit/VOC2007/JPEGImages/000101.jpg")
    resized_img = cv2.resize(src_img, (448, 448))
    #height_ratio = src_img.shape[0]/448.0
    #width_ratio = src_img.shape[1]/448.0

    # convert to rgb image
    np_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB)
    # convert data type used in tf
    np_img = np_img.astype(np.float32)
    # data normalization and reshape to input tensor
    np_img = np_img / 255.0 * 2 - 1
    np_img = np.reshape(np_img, (1, 448, 448, 3))

    saver = tf.train.Saver(net.trainable_collection)
    saver.restore(sess, 'models/pretrain/yolo_tiny.ckpt')

    timer = Timer()
    timer.tic()

    print('Procession detection...')
    np_predict = sess.run(predicts, feed_dict={image: np_img})
    timer.toc()
    print('One detection took {:.3f}s in average'.format(timer.total_time))
    predicts_dict = process_predicts(resized_img, np_predict)
    print "predict dict: ", predicts_dict
    predicts_dict = non_max_suppress(predicts_dict)
    print "predict dict after non-maximum suppression: ", predicts_dict
    
    plot_result(src_img, predicts_dict)
    cv2.waitKey(0)

I am not sure if there is any problems in this code, At least it runs successfully in my pc. I hope this could help anyone who needs it. And I will be appreciate it if anyone could give some advice.

Traceback (most recent call last):
File "demo.py", line 77, in
saver.restore(sess, 'models/pretrain/yolo_tiny.ckpt')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1457, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 778, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 982, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1032, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: Tensor name "conv5/biases" not found in checkpoint files models/pretrain/yolo_tiny.ckpt
[[Node: save/RestoreV2_8 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_8/tensor_names, save/RestoreV2_8/shape_and_slices)]]

Caused by op u'save/RestoreV2_8', defined at:
File "demo.py", line 75, in
saver = tf.train.Saver(net.trainable_collection)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1056, in init
self.build()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1086, in build
restore_sequentially=self._restore_sequentially)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 691, in build
restore_sequentially, reshape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 407, in _AddRestoreOps
tensors = self.restore_op(filename_tensor, saveable, preferred_shard)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 247, in restore_op
[spec.tensor.dtype])[0])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 669, in restore_v2
dtypes=dtypes, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1228, in init
self._traceback = _extract_stack()

NotFoundError (see above for traceback): Tensor name "conv5/biases" not found in checkpoint files models/pretrain/yolo_tiny.ckpt
[[Node: save/RestoreV2_8 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save/Const_0, save/RestoreV2_8/tensor_names, save/RestoreV2_8/shape_and_slices)]]

Thanks for sharing your work. I tried the demo.py and it seems to work fine. I think I could understand more about YOLO.

However, I am slightly curious why it takes quite long. One single image query took 116 sec (model load 0.4, inference 115.5, post-process 0). I am seeing 9 conv + 3 fc layers. It must be only feed-forward. Such size usually takes that long? Do you have any idea? Is it tensorflow problem?

Hi, I rewrite this project

• upgrade tensorflow version to tensorflow 1.5 and higher
• upgrade it to python3.5

Can I add this project to microsoft sample-for-ai?
https://github.com/LeoCHANGcn/samples-for-ai

Thanks!

Hi, Thanks for sharing your work.
Why is that, it detect only one item even if the object is the same type...

example... if we have 4 persons in a picture, it detects only one of the person not 4 of them ? How can we adjust it to detect 4 of them ? Thanks