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grad_mymodel.py
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import os
import sys
import PIL
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn as nn
import torchvision.models as models
from torchvision import transforms
from torchvision.utils import make_grid, save_image
from gradcam.utils import visualize_cam
from gradcam import GradCAM, GradCAMpp
device = 'cuda:1' if torch.cuda.is_available() else 'cpu'
input_path = sys.argv[1]
img_name = os.path.join('./input', input_path)
input_weightpath = sys.argv[2]
weight_path = os.path.join('weight_finetuning_path', input_weightpath)
input_modellayer = int(sys.argv[3])
pil_img = PIL.Image.open(img_name)
pil_img
torch_img = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])(pil_img).to(device)
normed_torch_img = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(torch_img)[None]
if(input_modellayer == 18):
resnet= models.resnet18(pretrained='True')
resnet_ft = models.resnet18(pretrained='True')
if(input_modellayer == 50):
resnet= models.resnet50(pretrained='True')
resnet_ft = models.resnet50(pretrained='True')
if(input_modellayer == 101):
resnet= models.resnet101(pretrained='True')
resnet_ft = models.resnet101(pretrained='True')
if(input_modellayer == 152):
resnet= models.resnet152(pretrained='True')
resnet_ft = models.resnet152(pretrained='True')
#print(type(models))
num_ftrs = resnet_ft.fc.in_features
resnet_ft.fc = nn.Linear(num_ftrs, 311)
resnet_ft.load_state_dict(torch.load(weight_path, map_location = device))
print(resnet.fc)
print(resnet_ft.fc)
configs = [
dict(model_type='resnet', arch=resnet, layer_name='layer4'),
dict(model_type='resnet', arch=resnet_ft, layer_name='layer4'),
]
configs_shallow = [
dict(model_type='resnet', arch=resnet, layer_name='layer1'),
dict(model_type='resnet', arch=resnet_ft, layer_name='layer1'),
]
for config in configs:
config['arch'].to(device).eval()
cams = [
[cls.from_config(**config) for cls in (GradCAM, GradCAMpp)]
for config in configs
]
images = []
for gradcam, gradcam_pp in cams:
mask, _ = gradcam(normed_torch_img)
heatmap, result = visualize_cam(mask, torch_img)
mask_pp, _ = gradcam_pp(normed_torch_img)
heatmap_pp, result_pp = visualize_cam(mask_pp, torch_img)
images.extend([torch_img.cpu(), heatmap, heatmap_pp, result, result_pp])
grid_image = make_grid(images, nrow=5)
img = transforms.ToPILImage()(grid_image)
img.show()