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050025b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
import cv2
import os
# define CNN architecture
class CustomCNNModel(nn.Module):
def __init__(self, input_dim, num_classes):
super(CustomCNNModel,self).__init__()
self.input_dim = input_dim
self.num_classes = num_classes
self.conv_layers = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, padding =1, stride=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(32, 64, kernel_size=3, padding =1, stride=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding =1, stride=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding =1, stride=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self._to_linear = None
self._get_conv_output(self.input_dim)
self.fc_layers = nn.Sequential(
nn.Linear(self._to_linear, 512),
nn.ReLU(),
nn.Linear(512, 128),
nn.ReLU(),
nn.Linear(128,self.num_classes)
)
def forward(self, x):
x = self.conv_layers(x)
x = x.view(x.size(0),-1)
x = self.fc_layers(x)
return x
def _get_conv_output(self, input_dim=128):
with torch.no_grad():
dummy_input = torch.zeros(1, 3,input_dim, input_dim) #batch size, no of channels, input
output = self.conv_layers(dummy_input)
self._to_linear = output.view(1, -1).size(1)
# load CNN architecture trained weights
# index to label map
# transformation
class ImageClassifier():
def __init__(self, model_path, class_names=None):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model = CustomCNNModel(input_dim=128, num_classes=3).to(self.device)
self.model.load_state_dict(torch.load(model_path, map_location = self.device))
self.model.eval()
if class_names is None:
self.class_names = {0: 'person', 1: 'Dog', 2: 'Cat'}
else:
self.class_names = class_names
self.transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std = [0.5, 0.5, 0.5])
])
# load image with pillow(pytorch expects pillow)
# prediction
# label map --> class
# opencv, write text on our input image
# return class, output image
def predict(self, image_path):
image = Image.open(image_path).convert("RGB")
image_tensor = self.transform(image).unsqueeze(0).to(self.device) # adds batch size to the image unsqueeze
with torch.no_grad():
output = self.model(image_tensor)
## [[0.3, 0.7, 0.9]] -> 2D tensor
_, predicted = torch.max(output, 1)
label = self.class_names[predicted.item()]
img = cv2.imread(image_path)
cv2.putText(img, label, (10,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
output_path = "output_image.jpg"
cv2.imwrite("output_image.jpg",img)
cwd = os.getcwd()
output_path = os.path.join(cwd, output_path)
return label, output_path
## CNN Architecture
## CNN() model instance
## CNN.load_dict()
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