yang7879 / attsets Goto Github PK
View Code? Open in Web Editor NEW🔥AttSets in Tensorflow (IJCV 2019)
Home Page: https://doi.org/10.1007/s11263-019-01217-w
License: MIT License
🔥AttSets in Tensorflow (IJCV 2019)
Home Page: https://doi.org/10.1007/s11263-019-01217-w
License: MIT License
Hi, I just experienced pre-trained network and it works fine. But i need to train the network with my own data-set. Please give me a guideline to create a custom data-set for this network.
Also i didn't understood how we can create ground truth. Please help me.
loading files: 03001627
train objs: 4
test objs: 2
X_rgb_train_files_ori: 96
X_rgb_test_files_ori: 48
total_train_batch_num: 1
ep: 0 i: 0 train single rec loss: 0.69298536
Traceback (most recent call last):
File "main_AttSets.py", line 305, in
net.train(data)
File "main_AttSets.py", line 278, in train
X_rgb_batch, Y_vox_batch = data.load_X_Y_test_next_batch(test_mv=1)
File "/Users/ghost/AttSets/tools.py", line 261, in load_X_Y_test_next_batch
idx = random.sample(range(len(self.X_rgb_test_files_ori)/num), self.batch_size)
TypeError: 'float' object cannot be interpreted as an integer
I meet the problem:
DataLossError (see above for traceback): Checksum does not match: stored 769046061 vs. calculated on the restored bytes 1925235876
[[Node: save/RestoreV2_127 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2_127/tensor_names, save/RestoreV2_127/shape_and_slices)]]
[[Node: save/RestoreV2_112/_225 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_454_save/RestoreV2_112", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]]
Do you know why? My tf version is 1.9.0. THX!
Hi, I have tried retraining of pre-trained network and training from scratch using the training code that you have given. After 400 epoches i have saved the model of both. But i got same error in both model while testing. error was in below line
Y_pred = tf.get_default_graph().get_tensor_by_name("r2n/Reshape_9:0")
r2n/Reshape_9 tensor was not there. I have confirmed it by TensorBoard. But while checking the graph generated by pre-trained network which is downloaded shows the same tensor. How it is possible??
One interesting thing is that , I changed the code line as below
Y_pred = tf.get_default_graph().get_tensor_by_name("r2n/Reshape_7:0")
And then tested with my trained network. It give output. Because after training myself r2n block takes 323232 sized voxels from r2n/Reshape_7:0.
Can you explain why it is so???
I am very inspired by your paper, but I have some questions to ask you.
1.For the experiment of single-view reconstruction :
Category problem: do training set and testing set input per category?
View problem: do training set and testing set input single-view which is random chose?
Similarly, For the experiment of multi-view reconstruction :
Category problem: do training set input multiple categories? do testing set input multiple categories?
View problem: do training set input 24 views? do testing set input multiple views (e.g., 1,2,3,...) ?
2.Could you provide the code of full testing? Does full testing mean to go through all the test set?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.