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import os
import numpy as np
import torch
from PIL import Image
import matplotlib.pyplot as plt
from torchvision import models
from torchvision import transforms
from mlcam_utils import GradCAM, show_cam_on_image
def main():
model = models.mobilenet_v3_large(pretrained=True)
target_layers = [model.features[-1]]
# model = models.vgg16(pretrained=True)
# target_layers = [model.features]
# model = models.resnet34(pretrained=True)
# target_layers = [model.layer4]
# model = models.regnet_y_800mf(pretrained=True)
# target_layers = [model.trunk_output]
# model = models.efficientnet_b0(pretrained=True)
# target_layers = [model.features]
data_transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
# load image
img_path = "both.png"
assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
img = Image.open(img_path).convert('RGB')
img = np.array(img, dtype=np.uint8)
# img = center_crop_img(img, 224)
# [C, H, W]
img_tensor = data_transform(img)
# expand batch dimension
# [C, H, W] -> [N, C, H, W]
input_tensor = torch.unsqueeze(img_tensor, dim=0)
cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False, target_size=(112, 112))
target_category = 281 # tabby, tabby cat
# target_category = 254 # pug, pug-dog
grayscale_cam = cam(input_tensor=input_tensor, target_category=target_category)
grayscale_cam = grayscale_cam[0, :]
visualization = show_cam_on_image(img.astype(dtype=np.float32) / 255.,
grayscale_cam,
use_rgb=True)
plt.imshow(visualization)
plt.show()
if __name__ == '__main__':
main()
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