PyTorch newbie here. I wrote a script (code below) that performs the following operations: load an image, perform a 2D convolution operation and then display the output and the input.
At present I have the image below, which seems off. How can I plot the feature map correctly?
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import matplotlib.pyplot as plt
import imageio
import sys
A = imageio.imread('LiT.png')
# Define how the convolution operation works
conv2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)
image_d = torch.FloatTensor(np.asarray(A.reshape(1, 3, A.shape[0] , A.shape[1])))
fc = conv2(image_d)
fc1 = fc.permute(0, 2, 3, 1).reshape([516, 780, 3])
plt.figure(figsize=(16,8))
plt.subplot(1,2,1)
plt.imshow(A)
plt.subplot(1,2,2)
plt.imshow(fc1.data.numpy())
plt.show()
The issue with your code is this line
image_d = torch.FloatTensor(np.asarray(A.reshape(1, 3, A.shape[0] , A.shape[1])))
You can't just reshape the image you need to transpose the channels. As a remark for the future, if you get a stripy result like you did it's most likely some permutation/transposition or reshaping operation that's not correct.
Other than that I also scaled the input image to [0, 1] to show it properly. Below is the working code:
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import matplotlib.pyplot as plt
import imageio
import sys
A = imageio.imread('LiT.png')
# Define how the convolution operation works
conv2 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1)
# from [H, W, C] to [C, H, W]
transposed_image = A.transpose((2, 0, 1))
# add batch dim
transposed_image = np.expand_dims(transposed_image, 0)
image_d = torch.FloatTensor(transposed_image)
fc = conv2(image_d)
fc1 = fc.permute(0, 2, 3, 1)[0]
result = fc1.data.numpy()
max_ = np.max(result)
min_ = np.min(result)
result -= min_
result /= max_
plt.figure(figsize=(16,8))
plt.subplot(1,2,1)
plt.imshow(A)
plt.subplot(1,2,2)
plt.imshow(result)
plt.show()
To my understanding, the problem lies in how you are permuting channels position in the image by using reshape. Instead, 'np.transpose or tensor.permute should be used. Using torch for permutation:
image_d = torch.FloatTensor(np.asarray(A)).unsqueeze(0).permute(0,3,1,2)
Or, if we want to handle the permutation part in numpy:
image_d = np.transpose(np.asarray(A), (2,0,1))
image_d = torch.FloatTensor(image_d).unsqueeze(0)
Related
When I use the code below to perform Semantic semgentation on my owndataset(40 images) and annotations(1 class(myface) and the annotations in cocojson format) I got just black image no mask and all bits are the same and the model accuracy in all epochs is 67.87% but loss is going down in each epoch :
import cv2
import os
import json
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from tensorflow.keras.models import Model
from pycocotools.coco import COCO
from tensorflow.keras.applications import VGG16
from sklearn.utils import compute_sample_weight
folder_path = "D:\\ImageClassification\\face_semantic_segmentation\\dataset"
filenames = os.listdir(folder_path)
images = []
for filename in filenames:
img = cv2.imread(os.path.join(folder_path, filename))
img = cv2.resize(img, (256, 256))
img = np.array(img)
images.append(img)
x_train = np.array(images)
with open("D:\\ImageClassification\\face_semantic_segmentation\\annotations.json") as f:
coco = json.load(f)
annotations = coco['annotations']
masks = {}
for annotation in annotations:
image_id = annotation['image_id']
if image_id not in masks:
masks[image_id] = []
masks[image_id].append(annotation['segmentation'])
resized_masks = []
for image_id, mask in masks.items():
mask_img = np.zeros((720, 1280), dtype=np.uint8)
for segmentation in mask:
poly = np.array(segmentation).reshape((-1, 1, 2)).astype(np.int32)
cv2.fillPoly(mask_img, [poly], 1)
mask_img = cv2.resize(mask_img, (256, 256))
mask_img = np.stack([mask_img] * 3, axis=-1)
resized_masks.append(mask_img)
y_train = np.array(resized_masks)
y_train.shape
import matplotlib.pyplot as plt
import numpy as np
mask = y_train[4]
mask = np.sum(mask, axis=-1)
plt.imshow(mask)
plt.show()
from segmentation_models import Unet
from segmentation_models import get_preprocessing
from segmentation_models.losses import bce_jaccard_loss
from segmentation_models.metrics import iou_score
from tensorflow.keras.models import Model
BACKBONE = 'resnet50'
preprocess_input = get_preprocessing(BACKBONE)
model = Unet(BACKBONE,classes=2,input_shape=(256,256, 3), encoder_weights='imagenet',activation='sigmoid')
x_train = preprocess_input(x_train)
x = model.layers[-1].output
x = Conv2D(3, (1, 1), activation='sigmoid')(x)
model = Model(inputs=model.input, outputs=x)
model.compile(optimizer='Adam', loss='binary_crossentropy', metrics=['binary_accuracy'])
model.fit(x=x_train,y=y_train,batch_size=32,epochs=50)
import cv2 as cv
import numpy as np
from google.colab.patches import cv2_imshow
from tensorflow.keras.models import load_model
img_color = cv.imread('test3.jpg', cv.IMREAD_COLOR)
img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY)
ret,img_binary = cv.threshold(img_gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
kernel = cv.getStructuringElement( cv.MORPH_RECT, ( 5, 5 ) )
img_binary = cv.morphologyEx(img_binary, cv. MORPH_CLOSE, kernel)
cv2_imshow('digit', img_binary)
cv.waitKey(0)
This is the process of training a model in handwritten and testing the trained model.
I want to load an image using imshow() in colab.
Is there any way to use it without setting the file path?
You can use matplotlib's function for it
import matplotlib.pyplot as plt
%matplotlib inline
fig = plt.gcf()
fig.set_size_inches(18, 10)
plt.axis("off")
plt.rcParams['figure.figsize'] = [20, 10]
plt.imshow(img_binary)
plt.show()
my problem is that I always get the following error when I operate the following code.Strange thing is that, when i set the epochs to 0 the error dosnt show up and I can upload with no problems. Thanks for the Help!
I have already tried anabling third party cockies, which did not help. The strange thing is, that the upload works, if I set the training epochs to 0.
Sometimes the error is google.colab._files is undefined.
I have already tried to use Chrome and Firefox.
import tensorflow as tf
import numpy as np
mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
training_images = training_images.reshape(60000, 28, 28, 1)
training_images = training_images / 255.0
test_images = test_images.reshape(10000, 28, 28, 1)
test_images = test_images / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32,(3,3),activation='relu', input_shape=(28,28,1)),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64,(3,3),activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(training_images,training_labels, epochs=1)
classes = model.predict(test_images)
predicted_classes = np.argmax(classes, axis=1)
print(classes[0])
print(test_labels[0])
mnist = tf.keras.datasets.fashion_mnist
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
import matplotlib.pyplot as plt
plt.imshow(test_images[0], cmap='Greys_r')
import numpy as np
from google.colab import files
from keras.preprocessing import image
import cv2
import matplotlib.pyplot as plt
uploaded = files.upload()
for fn in uploaded.keys():
path = '/content/' + fn
img = cv2.imread(path)
img = cv2.resize(img,(28,28))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
x = image.img_to_array(img, dtype=np.float32)
print("top left pixel value:", x[0,0])
if x[0,0] > 250:
# white background
print("needs to be inverted!")
x -= 255
x *= -1
x = x / 255.0
x = x.reshape(1, 28, 28, 1)
plt.imshow(img, cmap='Greys_r')
plt.show()
classes = model.predict(x)
plt.bar(range(10), classes[0])
plt.show()
print("prediction: class", np.argmax(classes[0]))
TypeError: Cannot read property '_uploadFiles' of undefined
So I found out that it works if you use 2 cells one for the neural network and one for the upload feature.
To allow using Keras model as part of standard tensorflow operations, I create a model using specific placeholder for the input.
However, when trying to do model.predict, I get an error:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [100,84,84,4]
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[100,84,84,4], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
My code is given below:
from keras.layers import Convolution2D, Dense, Input
from keras.models import Model
from keras.optimizers import Nadam
from keras.losses import mean_absolute_error
from keras.activations import relu
import tensorflow as tf
import numpy as np
import gym
state_size = [100, 84, 84, 4]
input_tensor = tf.placeholder(dtype=tf.float32, shape=state_size)
inputL = Input(tensor=input_tensor)
h1 = Convolution2D(filters=32, kernel_size=(5,5), strides=(4,4), activation=relu) (inputL)
h2 = Convolution2D(filters=64, kernel_size=(3,3), strides=(2,2), activation=relu) (h1)
h3 = Convolution2D(filters=64, kernel_size=(3,3), activation=relu) (h2)
h4 = Dense(512, activation=relu) (h3)
out = Dense(18) (h4)
model = Model(inputL, out)
opt = Nadam()
disc_rate=0.99
sess = tf.Session()
dummy_input = np.ones(shape=state_size)
model.compile(opt, mean_absolute_error)
writer = tf.summary.FileWriter('./my_graph', sess.graph)
writer.close()
print(out)
print(model.predict({input_tensor: dummy_input}))
I have also trying feeding the input directly(no dictionary, just the value) - same exception. I can, however, get the model to work like:
print(sess.run( model.output, {input_tensor: dummy_input }))
Is there a way for me to still use normal Keras .predict method?
The following works (we need to initialize global variables):
sess.run(tf.global_variables_initializer()) # initialize
print(sess.run([model.output], feed_dict={input_tensor: dummy_input}))
I am attempting to use the anneal.arff dataset with Python scikit-learn's semisupervised algorithm LabelPropagation. The anneal dataset is categorical data, so I preprocessed it so that the output class for each item of instance
looks like [0. 0. 1. 0. 0.]. This is a numeric list that encodes the output class
as 5 possible values with 0's everywhere, and 1. in the position of the corresponding class. This is what I would expect.
For semi-supervised learning, most of the training data must be unlabeled, so
I modified the training set so that the unlabeled data has output [-1, -1, -1, -1, -1]. I previously tried just using -1, but the code emits the same error as shown below.
I train the classifier as follows, Y_train includes labeled and "unlabeled" data:
lp_model = LabelSpreading(gamma=0.25, max_iter=5)
lp_model.fit(X, Y_train)
I receive the error shown below after calling the fit method:
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\semi_supervised\label_propagation.py", line 221, in fit
X, y = check_X_y(X, y)
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 526, in check_X_y
y = column_or_1d(y, warn=True)
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 562, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (538, 5)
This suggests that something is wrong with the shape of my Y_train list,
but this is the correct shape. What am I doing wrong?
Can LabelPropagation take as training data in this form, or does it only
accept unlabeled data as a scalar -1?
--- edit ---
Here is the code that generates the error. I'm sorry about the confusion over algorithms--I want to use both LabelSpreading and LabelPropagation, and choosing one or the other doesn't fix this error.
from scipy.io import arff
import pandas as pd
import numpy as np
import math
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from copy import deepcopy
from sklearn.semi_supervised import LabelPropagation
from sklearn.semi_supervised import LabelSpreading
f = "../../Documents/UCI/anneal.arff"
dataAsRecArray, meta = arff.loadarff(f)
dataset_raw = pd.DataFrame.from_records(dataAsRecArray)
dataset = pd.get_dummies(dataset_raw)
class_names = [col for col in dataset.columns if 'class_' in col]
print (dataset.shape)
number_of_output_columns = len(class_names)
print (number_of_output_columns)
def run(name, model, dataset, percent):
# Split-out validation dataset
array = dataset.values
X = array[:, 0:-number_of_output_columns]
Y = array[:, -number_of_output_columns:]
validation_size = 0.40
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
num_samples = len(Y_train)
num_labeled_points = math.floor(percent*num_samples)
indices = np.arange(num_samples)
unlabeled_set = indices[num_labeled_points:]
Y_train[unlabeled_set] = [-1, -1, -1, -1, -1]
lp_model = LabelSpreading(gamma=0.25, max_iter=5)
lp_model.fit(X_train, Y_train)
"""
predicted_labels = lp_model.transduction_[unlabeled_set]
print(predicted_labels[:10])
"""
if __name__ == "__main__":
#percentages = [0.1, 0.2, 0.3, 0.4]
percentages = [0.1]
models = []
models.append(('LS', LabelSpreading()))
#models.append(('CART', DecisionTreeClassifier()))
#models.append(('NB', GaussianNB()))
#models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
for percent in percentages:
run(name, model, dataset, percent)
print ("bye")
Your Y_train has shape (538, 5) but should be 1d. LabelPropagation doesn't support multi-label or multi-output multi-class right now.
The error message could be more informative, though :-/