I am trying to add parallel edges between two nodes using NetworkX but it fails with the below error. What am I doing wrong?
import networkx as nx
import graphviz
g1 = nx.MultiGraph()
node1 = 'a'
node2 = 'b'
g1.add_edge(node1,node2,key='one')
g1.add_edge(node1,node2,key='two')
A = nx.to_agraph(g1)
A.add_subgraph()
A.draw('test2.png', prog='dot')
Error:
Traceback (most recent call last):
File "test2.py", line 12, in <module>
A = nx.to_agraph(g1)
File "C:\python27\lib\site-packages\networkx-1.11rc1-py2.7.egg\networkx\drawing\nx_agraph.py", line 152, in to_agraph
A.add_edge(u,v,key=str(key),**str_edgedata)
File "C:\python27\lib\site-packages\pygraphviz\agraph.py", line 481, in add_edge
eh = gv.agedge(self.handle, uh, vh, key, _Action.find)
KeyError: 'agedge: no key'
You can do the same without using graphviz. I do it adding connectionstyle to nx.draw:
import networkx as nx
g1 = nx.DiGraph(directed=True)
node1 = 'a'
node2 = 'b'
g1.add_edge(node1,node2,key=1)
g1.add_edge(node2,node1,key=2)
nx.draw(g1, with_labels=True, arrows = True, connectionstyle='arc3, rad = 0.1')
Your code is working fine, and I attached the output image.
Related
I am trying to train my point cloud data on PointCNN so I need to convert my dataset to hdf5 as used in PointCNN. PointCNN used the modelnet40_ply_hdf5_2048 dataset.
I have tried converting my custom dataset but I am having issues with the label.
I tried this to get the label/shape_names
shape_ids = {}
shape_ids = [line.rstrip() for line in open(os.path.join(PATH, 'filelist1.txt'))]
shape_names = ['_'.join(x.split('_')[0:-1]) for x in shape_ids]
datapath = [(shape_names[i], os.path.join(PATH, shape_names[i], shape_ids[i])) for i
in range(len(shape_ids))]
Convert to h5py file
import numpy as np
from tqdm import tqdm
import h5py
filenames = [line.rstrip() for line in open(os.path.join(PATH))]
f = h5py.File("filename", 'w')
data = np.zeros((len(filenames), 1024, 3))
for i in range(0, len(datapath)):
fn = datapath[i]
cls = classes[datapath[i][0]]
label = np.array([cls]).astype(np.int32)
csvreader = np.genfromtxt("data1/" + filenames[i] + ".csv", delimiter=",").astype(np.float32)
for j in range(0,1024):
data[i,j] = [csvreader[j][0], csvreader[j][1], csvreader[j][2]]
label
dset1 = f.create_dataset("data", data=data, compression="gzip", compression_opts=4)
dset2 = f.create_dataset("label", data=label, compression="gzip", compression_opts=1)
f.close()
It did convert successfully but when I tried to train on PointCNN
PointCNN training
------Building model-------
------Successfully Built model-------
Traceback (most recent call last):
File "train_pytorch.py", line 174, in <module>
current_data, current_label, _ = provider.shuffle_data(current_data, np.squeeze(current_label))
File "provider.py", line 28, in shuffle_data
idx = np.arange(len(labels))
TypeError: len() of unsized object
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.
I am trying to save an chart from excel into a file, which I want to use later in a powerpoint presentation, but the code I am running keeps on coming up with
"AttributeError: 'NoneType' object has no attribute 'save'" .
Have been looking around google/stackoverflow but none of the suggestions I can find actually help, I keep on getting the error.
The code I am trying is below,
import win32com.client
import PIL
folder_path = r'C:/temp/Monthly_Graphs.xlsm'
xlApp = win32com.client.DispatchEx('Excel.Application')
wb = xlApp.Workbooks.Open(folder_path)
xlApp = win32com.client.DispatchEx('Excel.Application')
wb = xlApp.Workbooks.Open(folder_path)
wb.Sheets('Sheet1').Shapes('Sheet1_Pie_Chart').CopyPicture()
pie_image = PIL.ImageGrab.grabclipboard()
pie_image.savefig(r'C:/temp/pie_test.bmp','BMP')
the traceback is below
Traceback (most recent call last):
File "<ipython-input-12-b8e52c17e4d1>", line 1, in <module>
runfile('C:/python/stackoverflow_1.py', wdir='C:/python')
File "C:\Users\xxxxxxx\AppData\Local\conda\conda\envs\py64bit\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile
execfile(filename, namespace)
File "C:\Users\xxxxxxx\AppData\Local\conda\conda\envs\py64bit\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/python/stackoverflow_1.py", line 26, in <module>
pie_image.savefig(r'C:/temp/pie_test.bmp','BMP')
AttributeError: 'NoneType' object has no attribute 'savefig'
Managed to get it to work by referring to the below Q and setting the format of the CopyPicture line. Issue seems to be that excel default copy of the image is not in a format that PIL understands
Python Export Excel Sheet Range as Image
import win32com.client
from PIL import ImageGrab
import win32clipboard as clip
folder_path = r'C:/temp/Monthly_Graphs.xlsm'
xlApp = win32com.client.DispatchEx('Excel.Application')
wb = xlApp.Workbooks.Open(folder_path)
xlApp = win32com.client.DispatchEx('Excel.Application')
wb = xlApp.Workbooks.Open(folder_path)
wb.Sheets('Sheet1').Shapes('Sheet1_Pie_Chart').CopyPicture(Format=clip.CF_BITMAP)
pie_image = ImageGrab.grabclipboard()
pie_image.save(r'C:/temp/pie_test.bmp','BMP')
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)'
I have trained a network model and saved its weights and architecture via checkpoint = ModelCheckpoint(filepath='weights.hdf5') callback. During training, I am using multiple GPUs by calling the funtion below:
def make_parallel(model, gpu_count):
def get_slice(data, idx, parts):
shape = tf.shape(data)
size = tf.concat([ shape[:1] // parts, shape[1:] ],axis=0)
stride = tf.concat([ shape[:1] // parts, shape[1:]*0 ],axis=0)
start = stride * idx
return tf.slice(data, start, size)
outputs_all = []
for i in range(len(model.outputs)):
outputs_all.append([])
#Place a copy of the model on each GPU, each getting a slice of the batch
for i in range(gpu_count):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i) as scope:
inputs = []
#Slice each input into a piece for processing on this GPU
for x in model.inputs:
input_shape = tuple(x.get_shape().as_list())[1:]
slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx':i,'parts':gpu_count})(x)
inputs.append(slice_n)
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
#Save all the outputs for merging back together later
for l in range(len(outputs)):
outputs_all[l].append(outputs[l])
# merge outputs on CPU
with tf.device('/cpu:0'):
merged = []
for outputs in outputs_all:
merged.append(merge(outputs, mode='concat', concat_axis=0))
return Model(input=model.inputs, output=merged)
With the testing code:
from keras.models import Model, load_model
import numpy as np
import tensorflow as tf
model = load_model('cpm_log/deneme.hdf5')
x_test = np.random.randint(0, 255, (1, 368, 368, 3))
output = model.predict(x = x_test, batch_size=1)
print output[4].shape
I got the error below:
Traceback (most recent call last):
File "cpm_test.py", line 5, in <module>
model = load_model('cpm_log/Jun5_1000/deneme.hdf5')
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 240, in load_model
model = model_from_config(model_config, custom_objects=custom_objects)
File "/usr/local/lib/python2.7/dist-packages/keras/models.py", line 301, in model_from_config
return layer_module.deserialize(config, custom_objects=custom_objects)
File "/usr/local/lib/python2.7/dist-packages/keras/layers/__init__.py", line 46, in deserialize
printable_module_name='layer')
File "/usr/local/lib/python2.7/dist-packages/keras/utils/generic_utils.py", line 140, in deserialize_keras_object
list(custom_objects.items())))
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2378, in from_config
process_layer(layer_data)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 2373, in process_layer
layer(input_tensors[0], **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/topology.py", line 578, in __call__
output = self.call(inputs, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/layers/core.py", line 659, in call
return self.function(inputs, **arguments)
File "/home/muhammed/DEV_LIBS/developments/mocap/pose_estimation/training/cpm/multi_gpu.py", line 12, in get_slice
def get_slice(data, idx, parts):
NameError: global name 'tf' is not defined
By inspecting the error output, I decide that the problem is with the parallelization code. However, I can't resolve the issue.
You may need to use custom_objects to enable loading of the model.
import tensorflow as tf
model = load_model('model.h5', custom_objects={'tf': tf,})