I am trying to generate overlap patches from image size (112,112) but i am unable to do so. I have already tried a lot but it didn't work out.
**Code**
import torch
import numpy as np
import torch.nn as nn
from torch import nn
from PIL import Image
import cv2
import os
import math
import torch.nn.functional as F
import torchvision.transforms as T
from timm import create_model
from typing import List
import matplotlib.pyplot as plt
from torchvision import io, transforms
from utils_torch import Image, ImageDraw
from torchvision.transforms.functional import to_pil_image
IMG_SIZE = 112
# PATCH_SIZE = 64
resize = transforms.Resize((IMG_SIZE, IMG_SIZE))
img = resize(io.read_image("Adam_Brody_233.png"))
img = img.to(torch.float32)
image_size = 112
patch_size = 28
ac_patch_size = 12
pad = 4
img = img.unsqueeze(0)
soft_split = nn.Unfold(kernel_size=(ac_patch_size, ac_patch_size), stride=(patch_size, patch_size), padding=(pad, pad))
patches = soft_split(img).transpose(1, 2)
fig, ax = plt.subplots(16, 16)
for i in range(16):
for j in range(16):
sub_img = patches[:, i, j]
ax[i][j].imshow(to_pil_image(sub_img))
ax[i][j].axis('off')
plt.show()
Traceback
Traceback (most recent call last):
File "/home/cvpr/Documents/OPVT/unfold_ours.py", line 32, in <module>
patches = soft_split(img).transpose(1, 2)
File "/home/cvpr/anaconda3/envs/OPVT/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/cvpr/anaconda3/envs/OPVT/lib/python3.7/site-packages/torch/nn/modules/fold.py", line 295, in forward
self.padding, self.stride)
File "/home/cvpr/anaconda3/envs/OPVT/lib/python3.7/site-packages/torch/nn/functional.py", line 3831, in unfold
_pair(dilation), _pair(padding), _pair(stride))
RuntimeError: "im2col_out_cpu" not implemented for 'Byte'
Yes this is an open issue in PyTorch. A simple fix is just to convert your image tensor from ints to floats you can do it like this:
img = img.to(torch.float32)
This should solve your problem
Related
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from google.colab import files
uploded = files.upload()
df = pd.read_csv("quotation_test_data.csv")
print(df)
import seaborn as sns
df . describe() #describe the data as it shows mean , count
df.isnull().sum() #for see is there any column dont have a data
df ["Net profit"].value_counts()
sns.countplot(df['Net profit'])
#spli data into x and y then make it into train and test data
x=df.iloc[: , :-1]
y=df.iloc[: , :-1]
# [:,:-1] the (:) mean the coumln (:-1) this mean like print the alll column except the last one
#npw i will split the data into train and test data will use sklearn
from sklearn.model_selection import train_test_split
x_train , x_test,y_train,y_test = train_test_split(x,y,random_state=100)
x_train.shape
y_train.shape
from sklearn.tree import DecisionTreeClassifier #intilize desion tree
from pandas.core.common import random_state
clf = DecisionTreeClassifier(criterion="gini",max_depth=7,min_samples_split=10,random_state=10) # criteraition
import pandas as pd
data=pd.read_csv('quotation_test_data.csv')
dataconver = data.replace('[^\d.]','', regex=True).astype(float)
i was try to make a desion tree
then this is show for me :
ValueError Traceback (most recent call last)
<ipython-input-75-df55a55b03a4> in <module>
----> 1 dataconver = data.replace('[^\d.]', regex=True).astype(float)
2
7 frames
/usr/local/lib/python3.8/dist-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy, skipna)
1199 if copy or is_object_dtype(arr.dtype) or is_object_dtype(dtype):
1200 # Explicit copy, or required since NumPy can't view from / to object.
-> 1201 return arr.astype(dtype, copy=True)
1202
1203 return arr.astype(dtype, copy=copy)
ValueError: could not convert string to float: 'Kellie Scott'
i was try make desion tree
I was trying to run the main.py but it threw an error about attribution.
My Python version is Python 3.5. I am using the CNTK Docker release 2.6-cpu-python3.5. I cannot update the Python version because of CNTK. It only supports Python 3.5 and will only run in Ubuntu 16.04.
Pandas version: pandas==0.25.3
The Error
Traceback (most recent call last):
File "/workspace/main.py", line 5, in <module>
from model import extract_patches, score_patch, del_cache
File "/workspace/model.py", line 2, in <module>
from regressionModel import extract_features, predict_label
File "/workspace/regressionModel.py", line 26, in <module>
regression_model = read_model['model'][0]
File "/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py", line 2898, in __getitem__
if self.columns.is_unique and key in self.columns:
File "/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py", line 5063, in __getattr__
return object.__getattribute__(self, name)
File "pandas/_libs/properties.pyx", line 65, in pandas._libs.properties.AxisProperty.__get__
File "/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py", line 5063, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute '_data'
main.py
import os
import flask
import numpy as np
from flask import jsonify, request
from model import extract_patches, score_patch, del_cache
app = flask.Flask(__name__)
#app.route('/url/<path:argument>')
def url(argument):
# create a patch folder
patch_path = './patches'
if not os.path.exists(patch_path):
os.mkdir(patch_path)
# get image url from the query string
imageURL = request.url.split('=',1)[1]
# extract patches from imageURL
dimension, face_loc, image_dim = extract_patches(imageURL)
# score each patch
patch_score= score_patch(patch_path)
# delete the downloaded image and the patches from local
del_cache(patch_path)
if os.path.exists('temp.jpg'):
os.remove('temp.jpg')
data = dict()
data['patch_score'] = []
for key in dimension:
tmp = []
tmp[:] = dimension[key]
tmp.append(patch_score[key])
data['patch_score'].append(tmp)
data['image_score'] = round(np.mean(list(patch_score.values())), 2)
data['face_loc'] = face_loc['face_loc']
data['img_dim'] = image_dim
return jsonify(patch_score = str(data['patch_score']), image_score = str(data['image_score']), face_loc = str(data['face_loc']), image_dim = str(data['img_dim']))
if __name__ == '__main__':
app.run(host='0.0.0.0', port = 9580) # port number can be changed in your case
model.py
import getPatches
from regressionModel import extract_features, predict_label
import os
import shutil
def extract_patches(imageURL):
patch_path = './patches'
dimension_dict = dict()
face_dict = dict()
image_dim = []
try:
dim, face, img = getPatches.extract_patches(imageURL, dimension_dict,face_dict, image_dim, patch_path)
print ("extract patches pass")
except:
print ('cannot extract patches from the image')
return dim, face, img
def score_patch(patch_path):
patch_score = dict()
for file in next(os.walk(patch_path))[2]:
file_path = os.path.join(patch_path, file)
score_features = extract_features (file_path)[0].flatten()# extract features from CNTK pretrained model
pred_score_label = predict_label(score_features) # score the extracted features using trained regression model
patch_score[file.split('.')[0]] = float("{0:.2f}".format(pred_score_label[0]))
return patch_score
def infer_label(patch_score, label_mapping):
max_score_name, max_score_value = max(patch_score.items(), key=lambda x:x[1])
pred_label = label_mapping[round(max_score_value)-1]
return pred_label
def del_cache(patch_folder):
shutil.rmtree(patch_folder)
return
regressionModel.py
import numpy as np
import pandas as pd
import cntk as C
from PIL import Image
import pickle
from cntk import load_model, combine
import cntk.io.transforms as xforms
from cntk.logging import graph
from cntk.logging.graph import get_node_outputs
pretrained_model = 'ResNet152_ImageNet_Caffe.model'
pretrained_node_name = 'pool5'
regression_model = 'cntk_regression.dat'
image_width = 224
image_height = 224
# load CNTK pretrained model
#model_file = os.path.join(pretrained_model_path, pretrained_model_name)
loaded_model = load_model(pretrained_model) # a full path is required
node_in_graph = loaded_model.find_by_name(pretrained_node_name)
output_nodes = combine([node_in_graph.owner])
# load the stored regression model
read_model = pd.read_pickle(regression_model)
regression_model = read_model['model'][0]
train_regression = pickle.loads(regression_model)
def extract_features(image_path):
img = Image.open(image_path)
resized = img.resize((image_width, image_height), Image.ANTIALIAS)
bgr_image = np.asarray(resized, dtype=np.float32)[..., [2, 1, 0]]
hwc_format = np.ascontiguousarray(np.rollaxis(bgr_image, 2))
arguments = {loaded_model.arguments[0]: [hwc_format]}
output = output_nodes.eval(arguments)
return output
def predict_label(features):
return train_regression.predict(features.reshape(1,-1))
https://pypi.org/project/cntk/#files has CNTK 2.7 for Python 3.6. Still an obsolete version, but not quite as obsolete.
I want to plot 3D visualizations of DICOM Scans, but I am stuck on this error.
I am using the marching cubes method. First from mesh verts and faces and returned then passed into plt_3d. Modules imported:
import numpy as np
import pydicom as pyd
import os
import matplotlib.pyplot as plt
from glob import glob
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import scipy.ndimage
from skimage import morphology
from skimage import measure
from skimage.transform import resize
from sklearn.cluster import KMeans
from plotly import version
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from plotly.tools import FigureFactory as FF
from plotly.graph_objs import *
init_notebook_mode(connected=True)
imgs_after_resamp is the pixel array(3d) which contains the DICOM Data
def make_mesh(image, threshold=-300, step_size=1):
p = image.transpose(2,1,0)
verts, faces, norm, val = measure.marching_cubes_lewiner(p, threshold,
step_size=step_size, allow_degenerate=True)
print(verts)
return verts, faces
def plotly_3d(verts, faces):
x,y,z = zip(*verts)
fig = FF.create_trisurf(x=x,
y=y,
z=z,
plot_edges=False,
colormap=colormap,
simplices=faces,
backgroundcolor='rgb(64, 64, 64)',
title="Interactive Visualization")
iplot(fig)
def plt_3d(verts, faces):
print(“Drawing”)
x,y,z = zip(*verts)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
mesh = Poly3DCollection(verts[faces], linewidths=0.05, alpha=1)
face_color = [1, 1, 0.9]
mesh.set_facecolor(face_color)
ax.add_collection3d(mesh)
ax.set_xlim(0, max(x))
ax.set_ylim(0, max(y))
ax.set_zlim(0, max(z))
ax.set_axis_bgcolor((0.7, 0.7, 0.7))
plt.show()
v, f = make_mesh(imgs_after_resamp, 350)
new3d=np.vectorize(plt_3d)
new3d(v,f)
ValueError Traceback (most recent call last)
in
----> 1 plot_ve(imgs_re,400)
TypeError: type object argument after * must be an iterable, not numpy.float32
The code is showed below,and the wrong message is also showed below:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import urllib.request
import tensorflow as tf
import numpy as np
IRIS_TRAINING = "iris_training.csv"
IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv"
IRIS_TEST = "iris_test.csv"
IRIS_TEST_RRL = "http://download.tensorflow.org/data/iris_test.csv"
if not os.path.exists(IRIS_TRAINING):
raw = urllib.request.urlopen(IRIS_TRAINING_URL).read()
with open(IRIS_TRAINING, 'w') as f:
f.write(raw)
if not os.path.exists(IRIS_TEST):
raw = urllib.request.urlopen(IRIS_TEST_RRL).read()
with open(IRIS_TEST, 'w') as f:
f.write(raw)
# load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv_without_header(
filename=IRIS_TRAINING,
target_dtype=np.int,
features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_without_header(
filename=IRIS_TEST,
target_dtype=np.int,
features_dtype=np.float32
)
# Specify that all features have real_valued data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
# Build 3 layers DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 30],
n_class=3,
model_dir="/tem/iris_model")
# Define the training imputs
def get_train_inputs():
x = tf.constant(training_set.data)
y = tf.constant(training_set.target)
return x, y
# Fit model
classifier.fit(input_fn=get_train_inputs(), steps=2000)
# Define the test inputs
def get_test_inputs():
x = tf.constant(test_set.data)
y = tf.constant(test_set.target)
return x, y
# Evaluate accuracy
accuracy_score = classifier.evaluate(input_fn=get_test_inputs(), steps=1)["accuracy"]
print("\nTest Accuracy: {0:f}\n".format(accuracy_score))
This prints the following stack-trace:
Traceback (most recent call last):
File "/home/skyfacon/PycharmProjects/LinearFitting/IrisClassification.py", line 35, in <module>
features_dtype=np.float32
File "/home/skyfacon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py", line 69, in load_csv_without_header
data.append(np.asarray(row, dtype=features_dtype))
File "/home/skyfacon/anaconda3/envs/tensorflow/lib/python3.6/site-packages/numpy/core/numeric.py", line 531, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: 'setosa'
Process finished with exit code 1
I would like to know which page you are using as tutorial for this. Because the first page which comes when searching in google is this:
https://www.tensorflow.org/get_started/tflearn
And the difference between this and what you posted is tf.contrib.learn.datasets.base.load_csv_without_header and tf.contrib.learn.datasets.base.load_csv_with_header.
The actual URL or iris data you have specified contains the header. And you are trying to load it as a file without the header. Hence the strings in the header are not able to get converted to float and the error.
Change your code to:
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=IRIS_TRAINING,
target_dtype=np.int,
features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename=IRIS_TEST,
target_dtype=np.int,
features_dtype=np.float32)
I am using numpy and matplotlib in Python3.
The following code is causing the error:
import matplotlib
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.axes import Subplot as plt
from matplotlib import pylab
a=[1,1]
b=[1,1]
fsam = 48000
w, h = freqz(b, a, worN=2000)
plt.plot(((fsam-8000) * 0.5 / np.pi) * w, abs(h), label=" ")
The actual error with matplotlib 1.3.x:
File "/usr/local/lib/python3.2/dist-packages/matplotlib-1.3.x-py3.2-linux-x86_64.egg/matplotlib/axes.py", line 4119, in plot
if not self._hold:
AttributeError: 'numpy.ndarray' object has no attribute '_hold'
The actual error with matplotlib 1.2.0:
Traceback (most recent call last):
File "/home/christoph/audio_measurement/AudioTools/AudioTools.py", line 222, in <module>
main()
File "/home/christoph/audio_measurement/AudioTools/AudioTools.py", line 216, in main
form = AppForm()
File "/home/christoph/audio_measurement/AudioTools/AudioTools.py", line 39, in __init__
self.on_draw()
File "/home/christoph/audio_measurement/AudioTools/AudioTools.py", line 80, in on_draw
self.transfer = Transfer(self.canvas)
File "/home/christoph/audio_measurement/AudioTools/Transfer.py", line 42, in __init__
plt.plot(((fsam-8000) * 0.5 / np.pi) * w, abs(h), label=" ")
File "/usr/local/lib/python3.2/dist-packages/matplotlib/axes.py", line 3995, in plot
if not self._hold: self.cla()
AttributeError: 'numpy.ndarray' object has no attribute '_hold'
Transfer is the class, which plots onto the canvas.
I had a look at the length of the coefficients a and b, but they did not effect the result.
I could not find anything on that. Does anyone know whats going wrong?
Normally I'd use import matplotlib.pyplot as plt with plt.plot, plt.subplot, plt.show, etc -- or even just from pylab import *. Anyway, this line
from matplotlib.axes import Subplot as plt
is the reason you have an unbound plot function that's trying to operate on the ndarray argument. Subplot needs to be instantiated. This should work:
import numpy as np
from scipy.signal import freqz
import matplotlib
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.axes import Subplot
fig = Figure()
ax = Subplot(fig, 111)
fig.add_subplot(ax)
canvas = FigureCanvas(fig)
a=[1,1]
b=[1,1]
fsam = 48000
w, h = freqz(b, a, worN=2000)
ax.plot(((fsam-8000) * 0.5 / np.pi) * w, abs(h), label=" ")
canvas.show()