I've been trying to use the VGG-Face descriptor model (http://www.robots.ox.ac.uk/~vgg/software/vgg_face/) for a project of mine. All I want to do is to simply obtain the outputs of the network from an input image.
I haven't used any of MatConvNet, Caffe or PyTorch before and so I picked PyTorch at random. It turns out that the model (of class torch.legacy.nn.Sequential.Sequential) was saved in an older version of PyTorch and the syntax was thus slightly different to the ones on PyTorch's documentation.
I was able to load the lua .t7 model like so:
vgg_net = load_lua('./vgg_face_torch/VGG_FACE.t7', unknown_classes=True)
And loading in the input image:
# load image
image = imread('./ak.png')
# convert to tensor
input = torch.from_numpy(image).float()
Gleefully, I loaded in the image into the model with much anticipation:
# load into vgg_net
output = vgg_net.forward(input)
However, my hopes of it cooperating at all was quickly dashed when the code fails to compile. Leaving behind a cryptic error message:
Traceback (most recent call last):
File "~/Documents/python/vgg-face-test/vgg-pytorch.py", line 25, in <module>
output = vgg_net.forward(input)
File "~/.local/lib/python3.6/site-packages/torch/legacy/nn/Module.py", line 33, in forward
return self.updateOutput(input)
File "~/.local/lib/python3.6/site-packages/torch/utils/serialization/read_lua_file.py", line 235, in updateOutput_patch
return obj.updateOutput(*args)
File "~/.local/lib/python3.6/site-packages/torch/legacy/nn/Sequential.py", line 36, in updateOutput
currentOutput = module.updateOutput(currentOutput)
TypeError: 'NoneType' object is not callable
Of which I am absolutely dumbfounded by.
This is why I sought help on Stackoverflow. I hope someone here could perhaps lend me a hand in setting up the model - not even necessarily in Torch, in fact any working model will do, where I can simply get the description for any particular image.
try output = vgg_net(input) without the forward.
This apparently calls a default method defined in the module but I'm having trouble understanding why this is necessary.
Related
I'm following the guide:
Python + PyTorch + Pygame Reinforcement Learning – Train an AI to Play Snake(YouTube)
But instead of Snake I use 2048.
When running the agent.py I get the error:
File "D:\VisualStudio\MachineLearning\2048\agent.py", line 102, in <module>
train()
File "D:\VisualStudio\MachineLearning\2048\agent.py", line 71, in train
final_move = agent.get_action(state_old)
File "D:\VisualStudio\MachineLearning\2048\agent.py", line 52, in get_action
state0 = torch.tensor(state, dtype=torch.float)
TypeError: must be real number, not NoneType
How can I solve this? I uploaded the code to GitHub(https://github.com/ADude0806/2048) Thank You for your help!
You are returning nothing in agent.get_state method (and also probably not doing other things there you might want to do).
I am trying to generate YOLOv2 model yolo.h5 so that I can load this pre-trained model. I am trying to port Andrew Ng coursera Yolo assignment ( which runs in tensorflow 1.x) to tensorflow 2.3.
I was able to cleanly port it thanks to tensorflow uprade (https://www.tensorflow.org/guide/upgrade), But little did I realize that I cannot download the yolo.h5 file ( either its get corrupted or the download times out) and therefore I thought I should build one and I followed instructions from https://github.com/JudasDie/deeplearning.ai/issues/2.
It looked pretty straight forward as I cloned YAD2k repo and downloaded both the yolo.weights and yolo.cfg.
I ran the following the command as per the instructions:
python yad2k.py yolo.cfg yolo.weights model_data/yolo.h5
But I got the following error:-
Traceback (most recent call last):
_main(parser.parse_args())
File "yad2k.py", line 233, in _main
Lambda(
File "/home/sunny/miniconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line
925, in __call__
return self._functional_construction_call(inputs, args, kwargs,
File "/home/sunny/miniconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line
1117, in _functional_construction_call
outputs = call_fn(cast_inputs, *args, **kwargs)
File "/home/sunny/miniconda3/lib/python3.8/site-packages/tensorflow/python/keras/layers/core.py", line 903, i
n call
result = self.function(inputs, **kwargs)
File "/home/sunny/YAD2K/yad2k/models/keras_yolo.py", line 32, in space_to_depth_x2
return tf.space_to_depth(x, block_size=2)
AttributeError: module 'tensorflow' has no attribute 'space_to_depth'
From the all chats I figured out that the above needs to run in tensorflow 1.x . However it puts me back where I started which is to run it in tensorflow 1.x. I would love to stick with tensorflow 2.3.
Wondering if someone can guide me here. Frankly, to get me going all I need is an model hd5 file. But I thought generating one would be a better learning than to get one.
The above problem goes away when you upgrade all of your code under yad2k repo ( particularly yad2k.py and python files under models folder to tensorflow 2.x. The beautiful upgrade utility provided by tensorflow does the magic for you by replacing the original call to the compatible tf.compat.v1.space_to_depth(input=x, block_size=...)
Therefore for those who are planning to do the hard job of downgrading their tensorflow and keras, I would recommend them to try the tensorflow upgrade. This saves a lot of time.
This takes care of my model h5 file creation. My bad - I didn't think about it when I asking the question.
there's is a bug while running the Tensorflow code, the error code appears like this:
Traceback (most recent call last):
File "app.py", line 76, in <module>
model = deepmoji_emojis(maxlen, PRETRAINED_PATH)
File "/home/lifeofpy/LifeofPy/AI Photographer Project/Text-to-Color/deepmoji/model_def.py", line 35, in deepmoji_emojis
model._make_predict_function()
AttributeError: 'Functional' object has no attribute '_make_predict_function
and the file app.py is like this:
# print('Loading model from {}.'.format(PRETRAINED_PATH))
model = deepmoji_emojis(maxlen, PRETRAINED_PATH)
model.summary()
model._make_predict_function()
I think the error message is occured by the function 'model._make_predict_function',
I would appreciate any comments on this issue. Thanks!
Use: model.make_predict_function()
instead of: model._make_predict_function()
I tried to find _make_predict_function() with Google and it seems it was private function in old Keras in keras.engine.training.py but now Keras is part of tensorflow and function was removed from code. I can't find _make_predict_function() in tensorflow.keras.engine.training.py
Some old posts suggest to use model.predict() instead of model._make_predict_function() before threads but other posts suggest to duplicate model for every thread. But maybe new code in tensorflow resolved problem with running it in threads and maybe it doesn't need this function any more.
I have been following https://github.com/kvfrans/twitch/blob/master/main.py tutorial to create and train a chatbot based on rnn using tensorflow. From what I understand, the tutorials was written on an older version of tensorflow, so some parts are outdated and give me an error like:
Traceback (most recent call last):
File "main.py", line 33, in <module>
outputs, last_state = tf.nn.seq2seq.rnn_decoder(inputs, initialstate, cell, loop_function=None, scope='rnnlm')
AttributeError: 'module' object has no attribute 'seq2seq'
I fixed some of them, but can't figure out what is the alternative to tf.nn.seq2seq.rnn_decoder and what should be the new module's parameters. What I currently fixed:
tf.nn.rnn_cell.BasicLSTMCell(embedsize) changed to
tf.contrib.rnn.BasicLSTMCell(embedsize)
tf.nn.rnn_cell.DropoutWrapper(lstm_cell,keep_prob) changed to tf.contrib.rnn.DropoutWrapper(lstm_cell,keep_prob)
tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * numlayers) changed to
tf.contrib.rnn.MultiRNNCell([lstm_cell] * numlayers)
Can someone please help me figure out what tf.nn.seq2seq.rnn_decoder will be?
I think this is the one you need:
tf.contrib.legacy_seq2seq.rnn_decoder
I have created python script tool and able to select layer by providing feature classes as below.
import arcpy
arcpy.env.workspace = "C:/project/pmms.gdb"
arcpy.SelectLayerByLocation_management('stops', 'intersect', 'adminarea')
But when I used the following code to take user input polygon (FeatureSet), it fails and an error message is given. I have created a parameter of FeatureSet type to allow user to provide interactive polygon input. Please provide your suggestions.
import arcpy
fc = "C:/project/pmms.gdb/stops"
infeat = arcpy.GetParameterAsText(0)
arcpy.SelectLayerByLocation_management(fc, 'intersect', infeat)
Error message:
Traceback (most recent call last):
File "C:\project\scripts\select.py", line 7, in <module>
arcpy.SelectLayerByLocation_management(fc, 'intersect', infeat)
File "c:\program files\arcgis\desktop10.1\arcpy\arcpy\management.py", line 6585, in SelectLayerByLocation
raise e
ExecuteError: Failed to execute. Parameters are not valid.
ERROR 000368: Invalid input data.
Failed to execute (SelectLayerByLocation).
From the ArcGIS help page on the Select Layer By Location function:
The input must be a feature layer; it cannot be a feature class.
Include a Make Feature Layer operation before attempting to select, and it should work as expected.
fc = "C:/project/pmms.gdb/stops"
arcpy.MakeFeatureLayer_management(fc, 'stops')
arcpy.SelectLayerByLocation_management('stops', 'intersect', infeat)
Just make sure you don't already have a layer in your ArcMap table of contents that is called stops (which is presumably why the previous version of your code was working correctly).