CuPy error when pushing / popping pycuda context - pycuda

I am using tensorRT to perform inference with CUDA. I'd like to use CuPy to preprocess some images that I'll feed to the tensorRT engine. The preprocessing function, called my_function, works fine as long as tensorRT is not run between different calls of the my_function method (see code below). Specifically, the issue is not strictly related by tensorRT but by the fact that tensorRT inference requires to be wrapped by push and pop operations of the pycuda context.
With respect to the following code, the last execution of my_function will raise the following error:
File "/home/ubuntu/myfile.py", line 188, in _pre_process_cuda
img = ndimage.zoom(img, scaling_factor)
File "/home/ubuntu/.local/lib/python3.6/site-packages/cupyx/scipy/ndimage/interpolation.py", line 482, in zoom
kern(input, zoom, output)
File "cupy/core/_kernel.pyx", line 822, in cupy.core._kernel.ElementwiseKernel.__call__
File "cupy/cuda/function.pyx", line 196, in cupy.cuda.function.Function.linear_launch
File "cupy/cuda/function.pyx", line 164, in cupy.cuda.function._launch
File "cupy_backends/cuda/api/driver.pyx", line 299, in cupy_backends.cuda.api.driver.launchKernel
File "cupy_backends/cuda/api/driver.pyx", line 124, in cupy_backends.cuda.api.driver.check_status
cupy_backends.cuda.api.driver.CUDADriverError: CUDA_ERROR_INVALID_HANDLE: invalid resource handle
Note: in the following code I haven't reported the entire tensorRT inference code. In fact, simply pushing and popping a pycuda context generates the error
Code:
import numpy as np
import cv2
import time
from PIL import Image
import requests
from io import BytesIO
from matplotlib import pyplot as plt
import cupy as cp
from cupyx.scipy import ndimage
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
def my_function(numpy_frame):
dtype = 'float32'
img = cp.array(numpy_frame, dtype='float32')
# print(img)
img = ndimage.zoom(img, (0.5, 0.5, 3))
img = (cp.array(2, dtype=dtype) / cp.array(255, dtype=dtype)) * img - cp.array(1, dtype=dtype)
img = img.transpose((2, 0, 1))
img = img.ravel()
return img
# load image
url = "https://www.pexels.com/photo/109919/download/?search_query=&tracking_id=411xe21veam"
response = requests.get(url)
img = Image.open(BytesIO(response.content))
img = np.array(img)
# initialize tensorrt
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
trt_runtime = trt.Runtime(TRT_LOGGER)
cfx = cuda.Device(0).make_context()
my_function(img) # ok
my_function(img) # ok
# ----- TENSORRT ---------
cfx.push()
# .... tensorrt inference....
cfx.pop()
# ----- TENSORRT ---------
my_function(img) # <---- error
I even tried to do it other ways, but unfortunately with the same result:
cfx.push()
my_function(img) # ok
cfx.pop()
cfx.push()
my_function(img) # error
cfx.pop()
#admin: if you can think of a better name for this question feel free to edit it :)

There were multiple contexts open. For instance, it seems that all of the following open a context:
import pycuda.autoinit
cfx.cuda.Device(0).make_context()
cfx.push()
So if you run the three command above, then simply running one cfx.pop() won't be enough. You will need to run cfx.pop() three times to pop all the contexts.

Related

How to efficiently rename many files or partially select the names of these files during import?

how to rename the files efficiently by the number in the name (see picture)? I did not succeed with Windows PowerToys and I dont wana click each file and rename to the number (e.g. 290)
or how to read the files in this order and define a name? If I try it with a script (see below) the following output occurs:
[![ValueError: invalid literal for int() with base 10: '211001_164357_P_Scripted_Powermeasurement_Wavelength_automatic_Powermeter1_0'][1]][1]
or how to select only the numbers (290 to 230 - see picture) within the name when reading?
Script:
#import libraries
import pandas as pd
import os
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
data_location = r'C:\Users\...\Characterization_OPO\Data'
data_folder = Path(data_location)
data = {}
allist = list(data_folder.glob('*'))
for i, file in enumerate(allist):
file = str(file)
file_name = file.split('\\')[-1]
wavelength = int(file_name.split('.')[0])
tmp = pd.read_csv(file, skiprows=20, skipfooter=59, index_col="PixelNo")
data[f'{wavelength} nm'] = tmp;
#data.plot(x='Wavelength',y='CCD_1', label=f"{wavelength} nm")
Picture:
I removed all words with windows power rename and than took the last three digits:
for i, file in enumerate(allist):
file = str(file)
file_name = file.split('\\')[-1]
wavelength = int(file_name.split('.')[0])
tmp = pd.read_csv(file, skiprows=26, skipfooter=5)
data[f'{wavelength % 1000} nm'] = tmp;
#data.plot(x='Wavelength',y='CCD_1', label=f"{wavelength} nm")

Issue with scipy.io.wavfile.read and scipy.fftpack.fft

from os.path import dirname, join as pjoin
import scipy.io as sio
from scipy.io import wavfile
from scipy.fftpack import fft
data_dir = pjoin(dirname(sio.__file__), 'tests', 'data')
wav_fname = pjoin(data_dir, 'test-44100Hz-2ch-32bit-float-be.wav')
print(wav_fname)
def create_FFT(fn,size=1000):
sample_rate, X = wavfile.read(fn)
fft_features = abs(fft(X)[:size])
return(sample_rate, X, fft_features)
for wav_fn in wav_fname :
samplerate, data, fft_features = create_FFT(wav_fn)
print(f"number of channels = {data.shape[1]}")
print("fft features are: {}".format(fft_features))
In the above code, if I don't include fft specific code in the create_FFT function, I could read the file and print the number of channels. However, as soon as I include fft specific code, I get an error "FileNotFoundError: [Errno 2] No such file or directory: 'C'"
Any help will be appreciated.
Found the answer. It was with the for loop at the bottom.

overlay a small image on multiple biger images and save them in a different folder

Hi i want to overlay or paste an image on bigger images(have a folder containinf 10 images and want to overlay the smaller images on all 10) and save them in a different folder. I did try somethings but ran into errors.
import scipy.misc
import numpy as np
import os
import cv2
outPath = "C:\darkflow\Augmented Images\augmented_images\.."
cov = cv2.imread("C:\darkflow\Augmented Images\extracted\cover\extracted_cover.jpg")
bgs = [cv2.imread(file) for file in glob.glob("C:\darkflow\Augmented Images\images\*.jpg")]
for bg in bgs:
bg[y_offset:y_offset+s_img.shape[0], x_offset:x_offset+s_img.shape[1]] = cov
f_image = cv2.cvtColor(bg, cv2.COLOR_BGR2RGB)
fullpath = os.path.join(outPath, 'augmented_'+ bg)
misc.imsave(fullpath, f_image)
with this code i get an error : ufunc 'add' did not contain a loop with signature matching types dtype('
I found the answer while looking into the code. My code is
from scipy import ndimage, misc
import scipy.misc
import numpy as np
import os
import cv2
cov = cv2.imread("C:\darkflow\Augmented Images\extracted\cover\extracted_cover.jpg")
bgs = [cv2.imread(file) for file in glob.glob("C:\darkflow\Augmented Images\images\*.jpg")]
d=1
x_offset=100
y_offset= 100
for bg in bgs:
bg[y_offset:y_offset+ cov.shape[0], x_offset:x_offset+ cov.shape[1]] = cov
filename = "images/file_%d.jpg"%d
cv2.imwrite(filename, bg)
d+=1

ValueError: Cannot feed value of shape (1, 2048, 2048, 1) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'

Using TensorFlow I am trying to detect one object(png and grayscale image). I have trained and exported a model.ckpt successfully. Now I am trying to restore the saved model.ckpt for prediction. Here is the script:
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
if tf.__version__ != '1.4.0':
raise ImportError('Please upgrade your tensorflow installation to v1.4.0!')
# This is needed to display the images.
#matplotlib inline
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
from utils import label_map_util
from utils import visualization_utils as vis_util
MODEL_NAME = 'melon_graph'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('training', 'object_detection.pbtxt')
NUM_CLASSES = 1
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width, 1)).astype(np.float64)
# For the sake of simplicity we will use only 2 images:
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'te_data{}.png'.format(i)) for i in range(1, 336) ]
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(image_np,np.squeeze(boxes),np.squeeze(classes).astype(np.float64), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=5)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)
and this is the error
Traceback (most recent call last): File "cochlear_detection.py",
line 81, in
(boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded}) File
"/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py",
line 889, in run
run_metadata_ptr) File "/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py",
line 1096, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape()))) ValueError: Cannot feed value of shape (1, 2048, 2048, 1) for Tensor
'image_tensor:0', which has shape '(?, ?, ?, 3)'

Callbackfunction modelcheckpoint causes error in keras

I seem to get this error when I am using the callback function modelcheckpoint..
I read from a github issue that the solution would be make use of model.get_weight, but I am implicitly only storing that since i am only storing the one with best weight.
Keras only seem to save weights using h5, which make me question is there any other way to do store them using the eras API, if so how? If not, how do i store it?
Made an example to recreate the problem:
#!/usr/bin/python
import glob, os
import sys
from os import listdir
from os.path import isfile, join
import numpy as np
import warnings
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from keras.utils import np_utils
from keras import metrics
import keras
from keras import backend as K
from keras.models import Sequential
from keras.optimizers import SGD, Adam
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten
from keras.layers import Conv1D,Conv2D,MaxPooling2D, MaxPooling1D, Reshape
#from keras.utils.visualize_util import plot
from keras.models import Model
from keras.layers import Input, Dense
from keras.layers.merge import Concatenate, Add
import h5py
import random
import tensorflow as tf
import math
from keras.callbacks import CSVLogger
from keras.callbacks import ModelCheckpoint
if len(sys.argv) < 5:
print "Missing Arguments!"
print "python keras_convolutional_feature_extraction.py <workspace> <totale_frames> <fbank-dim> <window-height> <batch_size>"
print "Example:"
print "python keras_convolutional_feature_extraction.py deltas 15 40 5 100"
sys.exit()
total_frames = int(sys.argv[2])
total_frames_with_deltas = total_frames*3
dim = int(sys.argv[3])
window_height = int(sys.argv[4])
inserted_batch_size = int(sys.argv[5])
stride = 1
splits = ((dim - window_height)+1)/stride
#input_train_data = "/media/carl/E2302E68302E443F/"+str(sys.argv[1])+"/fbank/org_train_total_frames_"+str(total_frames)+"_dim_"+str(dim)+"_winheig_"+str(window_height)+"_batch_"+str(inserted_batch_size)+"_fws_input"
#output_train_data ="/media/carl/E2302E68302E443F/"+str(sys.argv[1])+"/fbank/org_train_total_frames_"+str(total_frames)+"_dim_"+str(dim)+"_winheig_"+str(window_height)+"_batch_"+str(inserted_batch_size)+"_fws_output"
#input_test_data = "/media/carl/E2302E68302E443F/"+str(sys.argv[1])+"/fbank/org_test_total_frames_"+str(total_frames)+"_dim_"+str(dim)+"_winheig_"+str(window_height)+"_batch_"+str(1)+"_fws_input"
#output_test_data = "/media/carl/E2302E68302E443F/"+str(sys.argv[1])+"/fbank/org_test_total_frames_"+str(total_frames)+"_dim_"+str(dim)+"_winheig_"+str(window_height)+"_batch_"+str(1)+"_fws_output"
#train_files =[f for f in listdir(input_train_data) if isfile(join(input_train_data, f))]
#test_files =[f for f in listdir(input_test_data) if isfile(join(input_test_data, f))]
#print len(train_files)
np.random.seed(100)
print "hallo"
def train_generator():
while True:
# input = random.choice(train_files)
# h5f = h5py.File(input_train_data+'/'+input, 'r')
# train_input = h5f['train_input'][:]
# train_output = h5f['train_output'][:]
# h5f.close()
train_input = np.random.randint(100,size=((inserted_batch_size,splits*total_frames_with_deltas,window_height,3)))
train_list_list = []
train_input = train_input.reshape((inserted_batch_size,splits*total_frames_with_deltas,window_height,3))
train_input_list = np.split(train_input,splits*total_frames_with_deltas,axis=1)
for i in range(len(train_input_list)):
train_input_list[i] = train_input_list[i].reshape(inserted_batch_size,window_height,3)
#for i in range(len(train_input_list)):
# train_input_list[i] = train_input_list[i].reshape(inserted_batch_size,33,window_height,1,3)
train_output = np.random.randint(5, size = (1,total_frames,5))
middle = int(math.ceil(total_frames/2))
train_output = train_output[:,middle:middle+1,:].reshape((inserted_batch_size,1,5))
#print train_output.shape
#print len(train_input_list)
#print train_input_list[0].shape
yield (train_input_list, train_output)
print "hallo"
def test_generator():
while True:
# input = random.choice(test_files)
# h5f = h5py.File(input_test_data+'/'+input, 'r')
# test_input = h5f['test_input'][:]
# test_output = h5f['test_output'][:]
# h5f.close()
test_input = np.random.randint(100,size=((inserted_batch_size,splits*total_frames_with_deltas,window_height,3)))
test_input = test_input.reshape((inserted_batch_size,splits*total_frames_with_deltas,window_height,3))
test_input_list = np.split(test_input,splits*total_frames_with_deltas,axis=1)
#test_input_list = np.split(test_input,45,axis=3)
for i in range(len(test_input_list)):
test_input_list[i] = test_input_list[i].reshape(inserted_batch_size,window_height,3)
#for i in range(len(test_input_list)):
# test_input_list[i] = test_input_list[i].reshape(inserted_batch_size,33,window_height,1,3)
test_output = np.random.randint(5, size = (1,total_frames,5))
middle = int(math.ceil(total_frames/2))
test_output = test_output[:,middle:middle+1,:].reshape((inserted_batch_size,1,5))
yield (test_input_list, test_output)
print "hallo"
def fws():
#print "Inside"
# Params:
# batch , lr, decay , momentum, epochs
#
#Input shape: (batch_size,40,45,3)
#output shape: (1,15,50)
# number of unit in conv_feature_map = splitd
next(train_generator())
model_output = []
list_of_input = [Input(shape=(8,3)) for i in range(splits*total_frames_with_deltas)]
output = []
#Conv
skip = total_frames_with_deltas
for steps in range(total_frames_with_deltas):
conv = Conv1D(filters = 100, kernel_size = 8)
column = 0
for _ in range(splits):
#print "column " + str(column) + "steps: " + str(steps)
output.append(conv(list_of_input[(column*skip)+steps]))
column = column + 1
#print len(output)
#print splits*total_frames_with_deltas
conv = []
for section in range(splits):
column = 0
skip = splits
temp = []
for _ in range(total_frames_with_deltas):
temp.append(output[((column*skip)+section)])
column = column + 1
conv.append(Add()(temp))
#print len(conv)
output_conc = Concatenate()(conv)
#print output_conc.get_shape
output_conv = Reshape((splits, -1))(output_conc)
#print output_conv.get_shape
#Pool
pooled = MaxPooling1D(pool_size = 6, strides = 2)(output_conv)
reshape = Reshape((1,-1))(pooled)
#Fc
dense1 = Dense(units = 1024, activation = 'relu', name = "dense_1")(reshape)
#dense2 = Dense(units = 1024, activation = 'relu', name = "dense_2")(dense1)
dense3 = Dense(units = 1024, activation = 'relu', name = "dense_3")(dense1)
final = Dense(units = 5, activation = 'relu', name = "final")(dense3)
model = Model(inputs = list_of_input , outputs = final)
sgd = SGD(lr=0.1, decay=1e-1, momentum=0.9, nesterov=True)
model.compile(loss="categorical_crossentropy", optimizer=sgd , metrics = ['accuracy'])
print "compiled"
model_yaml = model.to_yaml()
with open("model.yaml", "w") as yaml_file:
yaml_file.write(model_yaml)
print "Model saved!"
log= CSVLogger('/home/carl/kaldi-trunk/dnn/experimental/yesno_cnn_50_training_total_frames_'+str(total_frames)+"_dim_"+str(dim)+"_window_height_"+str(window_height)+".csv")
filepath='yesno_cnn_50_training_total_frames_'+str(total_frames)+"_dim_"+str(dim)+"_window_height_"+str(window_height)+"weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_weights_only=True, mode='max')
print "log"
#plot_model(model, to_file='model.png')
print "Fit"
hist_current = model.fit_generator(train_generator(),
steps_per_epoch=444,#len(train_files),
epochs = 10000,
verbose = 1,
validation_data = test_generator(),
validation_steps=44,#len(test_files),
pickle_safe = True,
workers = 4,
callbacks = [log,checkpoint])
fws()
Execute the script by: python name_of_script.py yens 50 40 8 1
which give me a full traceback:
full traceback
Error:
carl#ca-ThinkPad-T420s:~/Dropbox$ python mini.py yesno 50 40 8 1
Using TensorFlow backend.
Couldn't import dot_parser, loading of dot files will not be possible.
hallo
hallo
hallo
compiled
Model saved!
log
Fit
/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:2252: UserWarning: Expected no kwargs, you passed 1
kwargs passed to function are ignored with Tensorflow backend
warnings.warn('\n'.join(msg))
Epoch 1/10000
2017-05-26 13:01:45.851125: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-26 13:01:45.851345: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-26 13:01:45.851392: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
443/444 [============================>.] - ETA: 4s - loss: 100.1266 - acc: 0.3138Epoch 00000: saving model to yesno_cnn_50_training_total_frames_50_dim_40_window_height_8weights-improvement-00-0.48.hdf5
Traceback (most recent call last):
File "mini.py", line 205, in <module>
File "mini.py", line 203, in fws
File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 88, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1933, in fit_generator
callbacks.on_epoch_end(epoch, epoch_logs)
File "/usr/local/lib/python2.7/dist-packages/keras/callbacks.py", line 77, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "/usr/local/lib/python2.7/dist-packages/keras/callbacks.py", line 411, in on_epoch_end
self.model.save_weights(filepath, overwrite=True)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2503, in save_weights
save_weights_to_hdf5_group(f, self.layers)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2746, in save_weights_to_hdf5_group
f.attrs['layer_names'] = [layer.name.encode('utf8') for layer in layers]
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2684)
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2642)
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/attrs.py", line 93, in __setitem__
self.create(name, data=value, dtype=base.guess_dtype(value))
File "/usr/local/lib/python2.7/dist-packages/h5py/_hl/attrs.py", line 183, in create
attr = h5a.create(self._id, self._e(tempname), htype, space)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2684)
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper (/tmp/pip-4rPeHA-build/h5py/_objects.c:2642)
File "h5py/h5a.pyx", line 47, in h5py.h5a.create (/tmp/pip-4rPeHA-build/h5py/h5a.c:1904)
RuntimeError: Unable to create attribute (Object header message is too large)
If you look at the amount of data Keras is trying to save under layer_names attribute (inside the output HDF5 file being create), you will find that it takes more than 64kB.
np.asarray([layer.name.encode('utf8') for layer in model.layers]).nbytes
>> 77100
I quote from https://support.hdfgroup.org/HDF5/faq/limits.html:
Is there an object header limit and how does that affect HDF5 ?
There is a limit (in HDF5-1.8) of the object header, which is 64 KB.
The datatype for a dataset is stored in the object header, so there is
therefore a limit on the size of the datatype that you can have. (See
HDFFV-1089)
The code above was (almost entirely) copied from the traceback:
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2746, in save_weights_to_hdf5_group
f.attrs['layer_names'] = [layer.name.encode('utf8') for layer in layers]
I am using numpy asarray method to get the figure fast but h5py gets similar figure (I guess), see https://github.com/h5py/h5py/blob/master/h5py/_hl/attrs.py#L102 if you want to find exact figure.
Anyway, either you will need to implement your own methods for saving/loading of the weights (or use existing workarounds), or you need to give a really short name to ALL the layers inside your model :), something like this:
list_of_input = [Input(shape=(8,3), name=('i%x' % i)) for i in range(splits*total_frames_with_deltas)]
conv = Conv1D(filters = 100, kernel_size = 8, name='cv%x' % steps)
conv.append(Add(name='add%x' % section)(temp))
output_conc = Concatenate(name='ct')(conv)
output_conv = Reshape((splits, -1), name='rs1')(output_conc)
pooled = MaxPooling1D(pool_size = 6, strides = 2, name='pl')(output_conv)
reshape = Reshape((1,-1), name='rs2')(pooled)
dense1 = Dense(units = 1024, activation = 'relu', name = "d1")(reshape)
dense2 = Dense(units
= 1024, activation = 'relu', name = "d2")(dense1)
dense3 = Dense(units = 1024, activation = 'relu', name = "d3")(dense1)
final = Dense(units = 5, activation = 'relu', name = "fl")(dense3)
You mustn't forget to name all the layers because the (numpy) string array into which the layer names are converted is using the size of the longest string for each individual string in it when it is saved!
After renaming the layers as proposed above (which takes almost 26kB) the model is saved successfully. Hope this elaborate answer helps someone.
Update: I have just made a PR to Keras which should fix the issue without implementing any custom loading/saving methods, see 7508
A simple solution, albeit possibly not the most elegant, could be to run a while loop with epochs = 1.
Get the weights at the end of every epoch together with the accuracy and the loss
Save the weights to file 1 with model.get_weight
if accuracy is greater than at the previous epoch (i.e. loop), store the weights to a different file (file 2)
Run the loop again loading the weights from file 1
Break the loops setting a manual early stopping so that it breaks if the loss does not improve for a certain number of loops
You can use get_weights() together with numpy.save.
It's not the best solution, because it will save several files, but it actually works.
The problem is that you won't have the "optimizer" saved with the current states. But you can perhaps work around that by using smaller learning rates after loading.
Custom callback using numpy.save:
def myCallback(epoch,logs):
global storedLoss
#do your comparisons here using the "logs" var.
print(logs)
if (logs['loss'] < storedLoss):
storedLoss = logs['loss']
for i in range(len(model.layers)):
WandB = model.layers[i].get_weights()
if len (WandB) > 0: #necessary because some layers have no weights
np.save("W" + "-" + str(i), WandB[0],False)
np.save("B" + "-" + str(i), WandB[1],False)
#remember that get and set weights use a list: [weights,biases]
#it may happen (not sure) that there is no bias, and thus you may have to check it (len(WandB)==1).
The logs var brings a dictionary with named metrics, such as "loss", and "accuracy", if you used it.
You can store the losses within the callback in a global var, and compare if each loss is better or worse than the last.
When fitting, use the lambda callback:
from keras.callbacks import LambdaCallback
model.fit(...,callbacks=[LambdaCallback(on_epoch_end=myCallback)])
In the example above, I used the LambdaCallback, which has more possibilities than just on_epoch_end.
For loading, do a similar loop:
#you have to create the model first and then set the layers
def loadModel(model):
for i in range(len(model.layers)):
WandBForCheck = model.layers[i].get_weights()
if len (WandBForCheck) > 0: #necessary because some layers have no weights
W = np.load(Wfile + str(i))
B = np.load(Bfile + str(i))
model.layers[i].set_weights([W,B])
See follow-up at https://github.com/fchollet/keras/issues/6766 and https://github.com/farizrahman4u/keras-contrib/pull/90.
I saw the YAML and the root cause is probably that you have so many Inputs. A few Inputs with many dimensions is preferred to many Inputs, especially if you can use scanning and batch operations to do everything efficiently.
Now, ignoring that entirely, here is how you can save and load your model if it has too much stuff to save as JSON efficiently:
You can pass save_weights_only=True. That won't save optimizer weights, so isn't a great solution.
Just put together a PR for saving model weights and optimizer weights but not configuration. When you want to load, first instantiate and compile the model as you did when you were going to train it, then use load_all_weights to load the model and optimizer weights into that model. I'll try to merge it soon so you can use it from the master branch.
You could use it something like this:
from keras.callbacks import LambdaCallback
from keras_contrib.utils.save_load_utils import save_all_weights, load_all_weights
# do some stuff to create and compile model
# use `save_all_weights` as a callback to checkpoint your model and optimizer weights
model.fit(..., callbacks=[LambdaCallback(on_epoch_end=lambda epoch, logs: save_all_weights(model, "checkpoint-{:05d}.h5".format(epoch))])
# use `load_all_weights` to load model and optimizer weights into an existing model
# if not compiled (no `model.optimizer`), this will just load model weights
load_all_weights(model, 'checkpoint-1337.h5')
So I don't endorse the model, but if you want to get it to save and load anyways this should probably work for you.
As a side note, if you want to save weights in a different format, something like this would work.
pickle.dump([K.get_value(w) for w in model.weights], open( "save.p", "wb" ) )
Cheers
Your model architecture must be too large to be saved.
USE get_weights AND set_weights TO SAVE AND LOAD MODEL, RESPECTIVELY.
Do not use callback model checkpoint. just once the training ends, save its weights with pickle.
Have a look at this link: Unable to save DataFrame to HDF5 ("object header message is too large")