Hi,

Thanks a lot for the repo, but I hit the following error when trying the python test_inference.py with the densenet169:

Traceback (most recent call last):
  File "test_inference.py", line 27, in <module>
    model = DenseNet(reduction=0.5, classes=1000, weights_path=weights_path)
  File "densenet169.py", line 78, in DenseNet
    model.load_weights(weights_path, by_name=True)
  File "/home/petteri/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py", line 2498, in load_weights
    load_weights_from_hdf5_group_by_name(f, self.layers)
  File "/home/petteri/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py", line 2976, in load_weights_from_hdf5_group_by_name
    K.batch_set_value(weight_value_tuples)
  File "/home/petteri/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2025, in batch_set_value
    assign_op = x.assign(assign_placeholder)
AttributeError: 'Tensor' object has no attribute 'assign'

So when printing the types of the variables in the saved weights I got the following:

<tf.Variable 'conv1/kernel:0' shape=(7, 7, 3, 64) dtype=float32_ref>
<tf.Variable 'conv1_bn/gamma:0' shape=(64,) dtype=float32_ref>
<tf.Variable 'conv1_bn/beta:0' shape=(64,) dtype=float32_ref>
<tf.Variable 'conv1_bn/moving_mean:0' shape=(64,) dtype=float32_ref>
<tf.Variable 'conv1_bn/moving_variance:0' shape=(64,) dtype=float32_ref>
Tensor("conv1_scale/Const:0", shape=(64,), dtype=float32)

And the batch_set_value(tuples) from the backend hit the Tensor problem with "conv1_scale", is there a fix for this to allow the assigning or are the weights somewhat weirdly saved?:

            if hasattr(x, '_assign_placeholder'):
                assign_placeholder = x._assign_placeholder
                assign_op = x._assign_op
            else:
                assign_placeholder = tf.placeholder(tf_dtype, shape=value.shape)
                assign_op = x.assign(assign_placeholder)
                x._assign_placeholder = assign_placeholder
                x._assign_op = assign_op
            assign_ops.append(assign_op)
            feed_dict[assign_placeholder] = value

Version

DenseNet-Keras git:(master) ipython
Python 2.7.12 (default, Dec  4 2017, 14:50:18)
Type "copyright", "credits" or "license" for more information.

IPython 5.5.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import cv2

In [2]: import tensorflow as tf

In [3]: cv2.__version__
Out[3]: '2.4.13.6'

In [4]: tf.__version__
Out[4]: '1.4.0'

In [5]: import keras
Using TensorFlow backend.

In [6]: keras.__version__
Out[6]: '2.2.2'

DenseNet-Keras git:(master) ls -hl imagenet_models
total 33M
drwxrwxr-x 2 epbox epbox 4.0K 8月  30 15:08 .
drwxrwxr-x 5 epbox epbox 4.0K 8月  30 15:09 ..
-rw-r--r-- 1 epbox epbox  32M 8月  30 15:07 densenet121_weights_tf.h5
-rw-rw-r-- 1 epbox epbox   55 8月  30 14:40 README.md

Error

DenseNet-Keras git:(master) python test_inference.py
Using TensorFlow backend.
/home/epbox/Github/DenseNet-Keras/densenet121.py:45: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (7, 7), strides=(2, 2), use_bias=False, name="conv1")`
  x = Convolution2D(nb_filter, 7, 7, subsample=(2, 2), name='conv1', bias=False)(x)
2018-09-10 14:25:19.720132: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-09-10 14:25:19.821988: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-09-10 14:25:19.822306: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties:
name: GeForce GTX TITAN X major: 5 minor: 2 memoryClockRate(GHz): 1.076
pciBusID: 0000:01:00.0
totalMemory: 11.92GiB freeMemory: 11.64GiB
2018-09-10 14:25:19.822322: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0, compute capability: 5.2)
/home/epbox/Github/DenseNet-Keras/densenet121.py:99: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name="conv2_1_x1")`
  x = Convolution2D(inter_channel, 1, 1, name=conv_name_base+'_x1', bias=False)(x)
/home/epbox/Github/DenseNet-Keras/densenet121.py:109: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name="conv2_1_x2")`
  x = Convolution2D(nb_filter, 3, 3, name=conv_name_base+'_x2', bias=False)(x)
Traceback (most recent call last):
  File "test_inference.py", line 34, in <module>
    model = DenseNet(reduction=0.5, classes=1000, weights_path=weights_path)
  File "/home/epbox/Github/DenseNet-Keras/densenet121.py", line 55, in DenseNet
    x, nb_filter = dense_block(x, stage, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
  File "/home/epbox/Github/DenseNet-Keras/densenet121.py", line 165, in dense_block
    concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))
TypeError: 'module' object is not callable

Hi,

At first, thanks for this repo with DenseNet for Keras. As I understand you ported the architecture and weights from Caffe. I've just a question on actual purpose of your custom layer Scale after BatchNormalization in Keras v2 in the sense don't they perform the same work ?

Hi, this is a nice work. And I have some questions about how to use the model.

What tool is used convert the caffe model? And what order should be used for testing, RGB or BGR. Thanks.

Hello, thanks for your great work. I need your pretrained model for my own experiment. I'll appreciate it if you can give me the access. My google mail address: [email protected]

tested on tensorflow 1.13.1 - 2.0.0 and keras 2.2.4 - 2.3.0 as Merge layer has been removed and concatenate has been separated and some other changes in parameters such as axis and set_image_dim has been replaced

tested on tensorflow 1.13.1 - 2.0.0 and keras 2.2.4 - 2.3.0 as Merge layer has been removed and concatenate has been separated and some other changes in parameters such as axis and set_image_dim has been replaced

tested on tensorflow 1.13.1 - 2.0.0 and keras 2.2.4 - 2.3.0 as Merge layer has been removed and concatenate is now separate and image_dim_ordering has been changed.

Hi, I notice your project at https://github.com/shahsohil/sunets/issues/8. Have you reproduced the paper<DocUNet: Document Image Unwarping via A Stacked U-Net>? I trained the S-UNet for this paper some times,but it doesn't work. Can you give me some suggestions?