tkshnkmr

import numpy as np
import matplotlib.pyplot as plt 
# image modules
from PIL import Image
import matplotlib.image as mpimg 
import cv2
# PyTorch
import torch
from torch.utils import data
from torchvision import transforms

tkshnkmr

import numpy as np
import matplotlib.pyplot as plt

# Input x, output y
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)

# Creates figure first
my_dpi = 200
fig = plt.figure(figsize=(4, 2), dpi=my_dpi)

tkshnkmr

import numpy as np
import matplotlib.pyplot as plt

# Input x, output y
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)

# Creates figure first
my_dpi = 40
fig = plt.figure(figsize=(20, 10), dpi=my_dpi)

tkshnkmr

import numpy as np
import matplotlib.pyplot as plt

# Input x, output y
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)

# make multiple subplots. 
# you can define the size of figure and dpi (dot per inch, defalt dpi=72)
my_dpi = 50

tkshnkmr

import numpy as np
import matplotlib.pyplot as plt

# Input x, output y
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)

# Creates fig and ax from subplots().
# But only create a single plot 
fig, ax = plt.subplots()

tkshnkmr

import numpy as np
import matplotlib.pyplot as plt

# Input x, output y
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
# Plot
plt.plot(x, y)
# Change the fontsize of ticks
plt.xticks(fontsize=8)

tkshnkmr

# data loader
batch_size = 1
num_workers = 4

data_loader = torch.utils.data.DataLoader(my_dataset,
                                          batch_size=batch_size,
                                          shuffle=False,
                                          num_workers=num_workers
                                         )

tkshnkmr

# data directory
root = "my_data"

# assume we have 3 jpg images
filenames = ['img1.jpg', 'img2.jpg', 'img3.jpg']

# the class of image might be ['black cat', 'tabby cat', 'tabby cat']
labels = [0, 1, 1]

# create own Dataset

tkshnkmr

import os
import torch
from torch.utils import data
from PIL import Image
from torchvision import transforms

class simpleDataset(data.Dataset):
    
    # initialise function of class
    def __init__(self, root, filenames, labels):

tkshnkmr

from torchvision.models.detection.faster_rcnn import FastRCNNPredictor

def get_model_instance_segmentation(num_classes):
    # load an instance segmentation model pre-trained pre-trained on COCO
    model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False)
    # get number of input features for the classifier
    in_features = model.roi_heads.box_predictor.cls_score.in_features
    # replace the pre-trained head with a new one
    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)