├── .github └── workflows │ └── azure-static-web-apps-yellow-smoke-01409ab1e.yml ├── .gitignore ├── LICENSE ├── README.md ├── grad_cam.py ├── lib ├── gradients.py ├── image_utils.py └── labels.py ├── results ├── cam │ ├── grad_cam.jpg │ └── guided_grad_cam.jpg └── grad │ ├── guided_grad.jpg │ ├── guided_smooth_grad.jpg │ ├── smooth_grad.jpg │ └── vanilla_grad.jpg └── saliency.py /.github/workflows/azure-static-web-apps-yellow-smoke-01409ab1e.yml: -------------------------------------------------------------------------------- 1 | name: Azure Static Web Apps CI/CD 2 | 3 | on: 4 | push: 5 | branches: 6 | - master 7 | pull_request: 8 | types: [opened, synchronize, reopened, closed] 9 | branches: 10 | - master 11 | 12 | jobs: 13 | build_and_deploy_job: 14 | if: github.event_name == 'push' || (github.event_name == 'pull_request' && github.event.action != 'closed') 15 | runs-on: ubuntu-latest 16 | name: Build and Deploy Job 17 | steps: 18 | - uses: actions/checkout@v2 19 | with: 20 | submodules: true 21 | - name: Build And Deploy 22 | id: builddeploy 23 | uses: Azure/static-web-apps-deploy@v0.0.1-preview 24 | with: 25 | azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_YELLOW_SMOKE_01409AB1E }} 26 | repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments) 27 | action: "upload" 28 | ###### Repository/Build Configurations - These values can be configured to match you app requirements. ###### 29 | # For more information regarding Static Web App workflow configurations, please visit: https://aka.ms/swaworkflowconfig 30 | app_location: "/" # App source code path 31 | api_location: "api" # Api source code path - optional 32 | app_artifact_location: "" # Built app content directory - optional 33 | ###### End of Repository/Build Configurations ###### 34 | 35 | close_pull_request_job: 36 | if: github.event_name == 'pull_request' && github.event.action == 'closed' 37 | runs-on: ubuntu-latest 38 | name: Close Pull Request Job 39 | steps: 40 | - name: Close Pull Request 41 | id: closepullrequest 42 | uses: Azure/static-web-apps-deploy@v0.0.1-preview 43 | with: 44 | azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_YELLOW_SMOKE_01409AB1E }} 45 | action: "close" 46 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | .DS_Store 3 | .vscode 4 | .python-version 5 | __pycache__ 6 | images 7 | results_ 8 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Hikaru Sasaki 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SmoothGrad implementation in PyTorch 2 | 3 | PyTorch implementation of [SmoothGrad: removing noise by adding noise](https://arxiv.org/abs/1706.03825). 4 | 5 | |Vanilla Gradients|SmoothGrad|Guided backpropagation|Guided SmoothGrad| 6 | |:-:|:-:|:-:|:-:| 7 | |![](results/grad/vanilla_grad.jpg)|![](results/grad/smooth_grad.jpg)|![](results/grad/guided_grad.jpg)|![](results/grad/guided_smooth_grad.jpg)| 8 | 9 | And other techniques such as the following are implemented. 10 | 11 | + [Vanilla Gradients](https://arxiv.org/abs/1312.6034) 12 | + [Guided backpropagation](https://arxiv.org/abs/1412.6806) 13 | + [Grad-CAM](https://arxiv.org/abs/1610.02391) 14 | 15 | ## Download 16 | ``` 17 | git clone https://github.com/pkdn/pytorch-smoothgrad 18 | ``` 19 | 20 | ## Usage 21 | 22 | Save the saliency maps using `VanillaGrad`, `GuidedBackpropGrad`, `SmoothGrad`, `GuidedBackpropSmoothGrad`. 23 | 24 | ``` 25 | python saliency.py --img 26 | ``` 27 | 28 | Save the Grad-CAM image. 29 | 30 | ``` 31 | python grad_cam.py --img 32 | ``` 33 | 34 | If you do not specify an image path, read the raccoon's data ([scipy.misc.face()](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.misc.face.htmlM)). 35 | 36 | ## Requirements 37 | 38 | + PyTorch 39 | + torch-vision 40 | + numpy 41 | + scipy 42 | + OpenCV 43 | 44 | Environment under Python 3.5.2 is tested. 45 | 46 | ## Acknowledgments 47 | 48 | This code is insipired by [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam). 49 | -------------------------------------------------------------------------------- /grad_cam.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import sys 4 | 5 | import numpy as np 6 | from scipy import misc 7 | import cv2 8 | import torch 9 | import torch.nn as nn 10 | from torch.autograd import Variable 11 | from torchvision.models import vgg16, vgg19 12 | from torchvision.utils import save_image 13 | 14 | from lib.gradients import GradCam, GuidedBackpropGrad 15 | from lib.image_utils import preprocess_image, save_cam_image, save_as_gray_image 16 | from lib.labels import IMAGENET_LABELS 17 | 18 | 19 | def parse_args(): 20 | parser = argparse.ArgumentParser() 21 | parser.add_argument('--cuda', action='store_true', default=False, 22 | help='Use NVIDIA GPU acceleration') 23 | parser.add_argument('--img', type=str, default='', 24 | help='Input image path') 25 | parser.add_argument('--out_dir', type=str, default='./result/cam/', 26 | help='Result directory path') 27 | args = parser.parse_args() 28 | args.cuda = args.cuda and torch.cuda.is_available() 29 | if args.cuda: 30 | print("Using GPU for acceleration") 31 | else: 32 | print("Using CPU for computation") 33 | if args.img: 34 | print('Input image: {}'.format(args.img)) 35 | else: 36 | print('Input image: raccoon face (scipy.misc.face())') 37 | print('Output directory: {}'.format(args.out_dir)) 38 | print() 39 | return args 40 | 41 | 42 | def main(): 43 | args = parse_args() 44 | 45 | if not os.path.exists(args.out_dir): 46 | os.makedirs(args.out_dir) 47 | 48 | target_layer_names = ['35'] 49 | target_index = None 50 | 51 | # Prepare input image 52 | if args.img: 53 | img = cv2.imread(args.img, 1) 54 | else: 55 | img = misc.face() 56 | img = np.float32(cv2.resize(img, (224, 224))) / 255 57 | preprocessed_img = preprocess_image(img, args.cuda) 58 | 59 | model = vgg19(pretrained=True) 60 | if args.cuda: 61 | model.cuda() 62 | 63 | # Prediction 64 | output = model(preprocessed_img) 65 | pred_index = np.argmax(output.data.cpu().numpy()) 66 | print('Prediction: {}'.format(IMAGENET_LABELS[pred_index])) 67 | 68 | # Prepare grad cam 69 | grad_cam = GradCam( 70 | pretrained_model=model, 71 | target_layer_names=target_layer_names, 72 | cuda=args.cuda) 73 | 74 | # Compute grad cam 75 | mask = grad_cam(preprocessed_img, target_index) 76 | 77 | save_cam_image(img, mask, os.path.join(args.out_dir, 'grad_cam.jpg')) 78 | print('Saved Grad-CAM image') 79 | 80 | # Reload preprocessed image 81 | preprocessed_img = preprocess_image(img) 82 | 83 | # Compute guided backpropagation 84 | guided_backprop = GuidedBackpropGrad( 85 | pretrained_model=model, cuda=args.cuda) 86 | guided_backprop_saliency = guided_backprop(preprocessed_img, index=target_index) 87 | 88 | cam_mask = np.zeros(guided_backprop_saliency.shape) 89 | for i in range(guided_backprop_saliency.shape[0]): 90 | cam_mask[i, :, :] = mask 91 | 92 | cam_guided_backprop = np.multiply(cam_mask, guided_backprop_saliency) 93 | save_as_gray_image( 94 | cam_guided_backprop, 95 | os.path.join(args.out_dir, 'guided_grad_cam.jpg')) 96 | print('Saved Guided Grad-CAM image') 97 | 98 | 99 | if __name__ == '__main__': 100 | main() 101 | -------------------------------------------------------------------------------- /lib/gradients.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import torch 4 | import torch.nn as nn 5 | from torch.autograd import Variable 6 | from torch.nn import functional as F 7 | 8 | class VanillaGrad(object): 9 | 10 | def __init__(self, pretrained_model, cuda=False): 11 | self.pretrained_model = pretrained_model 12 | self.features = pretrained_model.features 13 | self.cuda = cuda 14 | #self.pretrained_model.eval() 15 | 16 | def __call__(self, x, index=None): 17 | output = self.pretrained_model(x) 18 | 19 | if index is None: 20 | index = np.argmax(output.data.cpu().numpy()) 21 | 22 | one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) 23 | one_hot[0][index] = 1 24 | if self.cuda: 25 | one_hot = Variable(torch.from_numpy(one_hot).cuda(), requires_grad=True) 26 | else: 27 | one_hot = Variable(torch.from_numpy(one_hot), requires_grad=True) 28 | one_hot = torch.sum(one_hot * output) 29 | 30 | one_hot.backward(retain_variables=True) 31 | 32 | grad = x.grad.data.cpu().numpy() 33 | grad = grad[0, :, :, :] 34 | 35 | return grad 36 | 37 | 38 | class SmoothGrad(VanillaGrad): 39 | 40 | def __init__(self, pretrained_model, cuda=False, stdev_spread=0.15, 41 | n_samples=25, magnitude=True): 42 | super(SmoothGrad, self).__init__(pretrained_model, cuda) 43 | """ 44 | self.pretrained_model = pretrained_model 45 | self.features = pretrained_model.features 46 | self.cuda = cuda 47 | self.pretrained_model.eval() 48 | """ 49 | self.stdev_spread = stdev_spread 50 | self.n_samples = n_samples 51 | self.magnitutde = magnitude 52 | 53 | def __call__(self, x, index=None): 54 | x = x.data.cpu().numpy() 55 | stdev = self.stdev_spread * (np.max(x) - np.min(x)) 56 | total_gradients = np.zeros_like(x) 57 | for i in range(self.n_samples): 58 | noise = np.random.normal(0, stdev, x.shape).astype(np.float32) 59 | x_plus_noise = x + noise 60 | if self.cuda: 61 | x_plus_noise = Variable(torch.from_numpy(x_plus_noise).cuda(), requires_grad=True) 62 | else: 63 | x_plus_noise = Variable(torch.from_numpy(x_plus_noise), requires_grad=True) 64 | output = self.pretrained_model(x_plus_noise) 65 | 66 | if index is None: 67 | index = np.argmax(output.data.cpu().numpy()) 68 | 69 | one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) 70 | one_hot[0][index] = 1 71 | if self.cuda: 72 | one_hot = Variable(torch.from_numpy(one_hot).cuda(), requires_grad=True) 73 | else: 74 | one_hot = Variable(torch.from_numpy(one_hot), requires_grad=True) 75 | one_hot = torch.sum(one_hot * output) 76 | 77 | if x_plus_noise.grad is not None: 78 | x_plus_noise.grad.data.zero_() 79 | one_hot.backward(retain_variables=True) 80 | 81 | grad = x_plus_noise.grad.data.cpu().numpy() 82 | 83 | if self.magnitutde: 84 | total_gradients += (grad * grad) 85 | else: 86 | total_gradients += grad 87 | #if self.visdom: 88 | 89 | avg_gradients = total_gradients[0, :, :, :] / self.n_samples 90 | 91 | return avg_gradients 92 | 93 | 94 | class GuidedBackpropReLU(torch.autograd.Function): 95 | 96 | def __init__(self, inplace=False): 97 | super(GuidedBackpropReLU, self).__init__() 98 | self.inplace = inplace 99 | 100 | def forward(self, input): 101 | pos_mask = (input > 0).type_as(input) 102 | output = torch.addcmul( 103 | torch.zeros(input.size()).type_as(input), 104 | input, 105 | pos_mask) 106 | self.save_for_backward(input, output) 107 | return output 108 | 109 | def backward(self, grad_output): 110 | input, output = self.saved_tensors 111 | 112 | pos_mask_1 = (input > 0).type_as(grad_output) 113 | pos_mask_2 = (grad_output > 0).type_as(grad_output) 114 | grad_input = torch.addcmul( 115 | torch.zeros(input.size()).type_as(input), 116 | torch.addcmul( 117 | torch.zeros(input.size()).type_as(input), grad_output, pos_mask_1), 118 | pos_mask_2) 119 | 120 | return grad_input 121 | 122 | def __repr__(self): 123 | inplace_str = ', inplace' if self.inplace else '' 124 | return self.__class__.__name__ + ' (' \ 125 | + inplace_str + ')' 126 | 127 | 128 | class GuidedBackpropGrad(VanillaGrad): 129 | 130 | def __init__(self, pretrained_model, cuda=False): 131 | super(GuidedBackpropGrad, self).__init__(pretrained_model, cuda) 132 | for idx, module in self.features._modules.items(): 133 | if module.__class__.__name__ is 'ReLU': 134 | self.features._modules[idx] = GuidedBackpropReLU() 135 | 136 | 137 | class GuidedBackpropSmoothGrad(SmoothGrad): 138 | 139 | def __init__(self, pretrained_model, cuda=False, stdev_spread=.15, n_samples=25, magnitude=True): 140 | super(GuidedBackpropSmoothGrad, self).__init__( 141 | pretrained_model, cuda, stdev_spread, n_samples, magnitude) 142 | for idx, module in self.features._modules.items(): 143 | if module.__class__.__name__ is 'ReLU': 144 | self.features._modules[idx] = GuidedBackpropReLU() 145 | 146 | 147 | class FeatureExtractor(object): 148 | 149 | def __init__(self, model, target_layers): 150 | self.model = model 151 | self.features = model.features 152 | self.target_layers = target_layers 153 | self.gradients = [] 154 | 155 | def __call__(self, x): 156 | target_activations, output = self.extract_features(x) 157 | output = output.view(output.size(0), -1) 158 | output = self.model.classifier(output) 159 | return target_activations, output 160 | 161 | def get_gradients(self): 162 | return self.gradients 163 | 164 | def save_gradient(self, grad): 165 | self.gradients.append(grad) 166 | 167 | def extract_features(self, x): 168 | outputs = [] 169 | for name, module in self.features._modules.items(): 170 | x = module(x) 171 | if name in self.target_layers: 172 | x.register_hook(self.save_gradient) 173 | outputs += [x] 174 | return outputs, x 175 | 176 | 177 | class GradCam(object): 178 | 179 | def __init__(self, pretrained_model, target_layer_names, cuda): 180 | self.pretrained_model = pretrained_model 181 | self.cuda = cuda 182 | if self.cuda: 183 | self.pretrained_model.cuda() 184 | self.pretrained_model.eval() 185 | self.extractor = FeatureExtractor(self.pretrained_model, target_layer_names) 186 | 187 | def __call__(self, x, index=None): 188 | features, output = self.extractor(x) 189 | 190 | if index is None: 191 | index = np.argmax(output.data.cpu().numpy()) 192 | 193 | one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) 194 | one_hot[0][index] = 1 195 | one_hot = Variable(torch.from_numpy(one_hot), requires_grad=True) 196 | if self.cuda: 197 | one_hot = one_hot.cuda() 198 | one_hot = torch.sum(one_hot * output) 199 | 200 | self.pretrained_model.zero_grad() 201 | one_hot.backward(retain_variables=True) 202 | 203 | grads = self.extractor.get_gradients()[-1].data.cpu().numpy() 204 | 205 | target = features[-1].data.cpu().numpy()[0, :] 206 | weights = np.mean(grads, axis=(2, 3))[0, :] 207 | cam = np.ones(target.shape[1:], dtype=np.float32) 208 | 209 | for i, w in enumerate(weights): 210 | cam += w * target[i, :, :] 211 | 212 | cam = np.maximum(cam, 0) 213 | cam = cv2.resize(cam, (224, 224)) 214 | cam = cam - np.min(cam) 215 | cam = cam / np.max(cam) 216 | 217 | return cam 218 | -------------------------------------------------------------------------------- /lib/image_utils.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import torch 4 | from torch.autograd import Variable 5 | 6 | 7 | def preprocess_image(img, cuda=False): 8 | means=[0.485, 0.456, 0.406] 9 | stds=[0.229, 0.224, 0.225] 10 | 11 | preprocessed_img = img.copy()[: , :, ::-1] 12 | for i in range(3): 13 | preprocessed_img[:, :, i] = preprocessed_img[:, :, i] - means[i] 14 | preprocessed_img[:, :, i] = preprocessed_img[:, :, i] / stds[i] 15 | preprocessed_img = \ 16 | np.ascontiguousarray(np.transpose(preprocessed_img, (2, 0, 1))) 17 | preprocessed_img = torch.from_numpy(preprocessed_img) 18 | preprocessed_img.unsqueeze_(0) 19 | if cuda: 20 | preprocessed_img = Variable(preprocessed_img.cuda(), requires_grad=True) 21 | else: 22 | preprocessed_img = Variable(preprocessed_img, requires_grad=True) 23 | 24 | return preprocessed_img 25 | 26 | 27 | def save_as_gray_image(img, filename, percentile=99): 28 | img_2d = np.sum(img, axis=0) 29 | span = abs(np.percentile(img_2d, percentile)) 30 | vmin = -span 31 | vmax = span 32 | img_2d = np.clip((img_2d - vmin) / (vmax - vmin), -1, 1) 33 | cv2.imwrite(filename, img_2d * 255) 34 | 35 | return 36 | 37 | 38 | def save_cam_image(img, mask, filename): 39 | heatmap = cv2.applyColorMap(np.uint8(255*mask), cv2.COLORMAP_JET) 40 | heatmap = np.float32(heatmap) / 255 41 | cam = heatmap + np.float32(img) 42 | cam = cam / np.max(cam) 43 | cv2.imwrite(filename, np.uint8(255 * cam)) 44 | -------------------------------------------------------------------------------- /lib/labels.py: -------------------------------------------------------------------------------- 1 | IMAGENET_LABELS = { 2 | 0: 'tench, Tinca tinca', 3 | 1: 'goldfish, Carassius auratus', 4 | 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', 5 | 3: 'tiger shark, Galeocerdo cuvieri', 6 | 4: 'hammerhead, hammerhead shark', 7 | 5: 'electric ray, crampfish, numbfish, torpedo', 8 | 6: 'stingray', 9 | 7: 'cock', 10 | 8: 'hen', 11 | 9: 'ostrich, Struthio camelus', 12 | 10: 'brambling, Fringilla montifringilla', 13 | 11: 'goldfinch, Carduelis carduelis', 14 | 12: 'house finch, linnet, Carpodacus mexicanus', 15 | 13: 'junco, snowbird', 16 | 14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea', 17 | 15: 'robin, American robin, Turdus migratorius', 18 | 16: 'bulbul', 19 | 17: 'jay', 20 | 18: 'magpie', 21 | 19: 'chickadee', 22 | 20: 'water ouzel, dipper', 23 | 21: 'kite', 24 | 22: 'bald eagle, American eagle, Haliaeetus leucocephalus', 25 | 23: 'vulture', 26 | 24: 'great grey owl, great gray owl, Strix nebulosa', 27 | 25: 'European fire salamander, Salamandra salamandra', 28 | 26: 'common newt, Triturus vulgaris', 29 | 27: 'eft', 30 | 28: 'spotted salamander, Ambystoma maculatum', 31 | 29: 'axolotl, mud puppy, Ambystoma mexicanum', 32 | 30: 'bullfrog, Rana catesbeiana', 33 | 31: 'tree frog, tree-frog', 34 | 32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui', 35 | 33: 'loggerhead, loggerhead turtle, Caretta caretta', 36 | 34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea', 37 | 35: 'mud turtle', 38 | 36: 'terrapin', 39 | 37: 'box turtle, box tortoise', 40 | 38: 'banded gecko', 41 | 39: 'common iguana, iguana, Iguana iguana', 42 | 40: 'American chameleon, anole, Anolis carolinensis', 43 | 41: 'whiptail, whiptail lizard', 44 | 42: 'agama', 45 | 43: 'frilled lizard, Chlamydosaurus kingi', 46 | 44: 'alligator lizard', 47 | 45: 'Gila monster, Heloderma suspectum', 48 | 46: 'green lizard, Lacerta viridis', 49 | 47: 'African chameleon, Chamaeleo chamaeleon', 50 | 48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis', 51 | 49: 'African crocodile, Nile crocodile, Crocodylus niloticus', 52 | 50: 'American alligator, Alligator mississipiensis', 53 | 51: 'triceratops', 54 | 52: 'thunder snake, worm snake, Carphophis amoenus', 55 | 53: 'ringneck snake, ring-necked snake, ring snake', 56 | 54: 'hognose snake, puff adder, sand viper', 57 | 55: 'green snake, grass snake', 58 | 56: 'king snake, kingsnake', 59 | 57: 'garter snake, grass snake', 60 | 58: 'water snake', 61 | 59: 'vine snake', 62 | 60: 'night snake, Hypsiglena torquata', 63 | 61: 'boa constrictor, Constrictor constrictor', 64 | 62: 'rock python, rock snake, Python sebae', 65 | 63: 'Indian cobra, Naja naja', 66 | 64: 'green mamba', 67 | 65: 'sea snake', 68 | 66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus', 69 | 67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus', 70 | 68: 'sidewinder, horned rattlesnake, Crotalus cerastes', 71 | 69: 'trilobite', 72 | 70: 'harvestman, daddy longlegs, Phalangium opilio', 73 | 71: 'scorpion', 74 | 72: 'black and gold garden spider, Argiope aurantia', 75 | 73: 'barn spider, Araneus cavaticus', 76 | 74: 'garden spider, Aranea diademata', 77 | 75: 'black widow, Latrodectus mactans', 78 | 76: 'tarantula', 79 | 77: 'wolf spider, hunting spider', 80 | 78: 'tick', 81 | 79: 'centipede', 82 | 80: 'black grouse', 83 | 81: 'ptarmigan', 84 | 82: 'ruffed grouse, partridge, Bonasa umbellus', 85 | 83: 'prairie chicken, prairie grouse, prairie fowl', 86 | 84: 'peacock', 87 | 85: 'quail', 88 | 86: 'partridge', 89 | 87: 'African grey, African gray, Psittacus erithacus', 90 | 88: 'macaw', 91 | 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita', 92 | 90: 'lorikeet', 93 | 91: 'coucal', 94 | 92: 'bee eater', 95 | 93: 'hornbill', 96 | 94: 'hummingbird', 97 | 95: 'jacamar', 98 | 96: 'toucan', 99 | 97: 'drake', 100 | 98: 'red-breasted merganser, Mergus serrator', 101 | 99: 'goose', 102 | 100: 'black swan, Cygnus atratus', 103 | 101: 'tusker', 104 | 102: 'echidna, spiny anteater, anteater', 105 | 103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus', 106 | 104: 'wallaby, brush kangaroo', 107 | 105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus', 108 | 106: 'wombat', 109 | 107: 'jellyfish', 110 | 108: 'sea anemone, anemone', 111 | 109: 'brain coral', 112 | 110: 'flatworm, platyhelminth', 113 | 111: 'nematode, nematode worm, roundworm', 114 | 112: 'conch', 115 | 113: 'snail', 116 | 114: 'slug', 117 | 115: 'sea slug, nudibranch', 118 | 116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore', 119 | 117: 'chambered nautilus, pearly nautilus, nautilus', 120 | 118: 'Dungeness crab, Cancer magister', 121 | 119: 'rock crab, Cancer irroratus', 122 | 120: 'fiddler crab', 123 | 121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica', 124 | 122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus', 125 | 123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish', 126 | 124: 'crayfish, crawfish, crawdad, crawdaddy', 127 | 125: 'hermit crab', 128 | 126: 'isopod', 129 | 127: 'white stork, Ciconia ciconia', 130 | 128: 'black stork, Ciconia nigra', 131 | 129: 'spoonbill', 132 | 130: 'flamingo', 133 | 131: 'little blue heron, Egretta caerulea', 134 | 132: 'American egret, great white heron, Egretta albus', 135 | 133: 'bittern', 136 | 134: 'crane', 137 | 135: 'limpkin, Aramus pictus', 138 | 136: 'European gallinule, Porphyrio porphyrio', 139 | 137: 'American coot, marsh hen, mud hen, water hen, Fulica americana', 140 | 138: 'bustard', 141 | 139: 'ruddy turnstone, Arenaria interpres', 142 | 140: 'red-backed sandpiper, dunlin, Erolia alpina', 143 | 141: 'redshank, Tringa totanus', 144 | 142: 'dowitcher', 145 | 143: 'oystercatcher, oyster catcher', 146 | 144: 'pelican', 147 | 145: 'king penguin, Aptenodytes patagonica', 148 | 146: 'albatross, mollymawk', 149 | 147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus', 150 | 148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca', 151 | 149: 'dugong, Dugong dugon', 152 | 150: 'sea lion', 153 | 151: 'Chihuahua', 154 | 152: 'Japanese spaniel', 155 | 153: 'Maltese dog, Maltese terrier, Maltese', 156 | 154: 'Pekinese, Pekingese, Peke', 157 | 155: 'Shih-Tzu', 158 | 156: 'Blenheim spaniel', 159 | 157: 'papillon', 160 | 158: 'toy terrier', 161 | 159: 'Rhodesian ridgeback', 162 | 160: 'Afghan hound, Afghan', 163 | 161: 'basset, basset hound', 164 | 162: 'beagle', 165 | 163: 'bloodhound, sleuthhound', 166 | 164: 'bluetick', 167 | 165: 'black-and-tan coonhound', 168 | 166: 'Walker hound, Walker foxhound', 169 | 167: 'English foxhound', 170 | 168: 'redbone', 171 | 169: 'borzoi, Russian wolfhound', 172 | 170: 'Irish wolfhound', 173 | 171: 'Italian greyhound', 174 | 172: 'whippet', 175 | 173: 'Ibizan hound, Ibizan Podenco', 176 | 174: 'Norwegian elkhound, elkhound', 177 | 175: 'otterhound, otter hound', 178 | 176: 'Saluki, gazelle hound', 179 | 177: 'Scottish deerhound, deerhound', 180 | 178: 'Weimaraner', 181 | 179: 'Staffordshire bullterrier, Staffordshire bull terrier', 182 | 180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier', 183 | 181: 'Bedlington terrier', 184 | 182: 'Border terrier', 185 | 183: 'Kerry blue terrier', 186 | 184: 'Irish terrier', 187 | 185: 'Norfolk terrier', 188 | 186: 'Norwich terrier', 189 | 187: 'Yorkshire terrier', 190 | 188: 'wire-haired fox terrier', 191 | 189: 'Lakeland terrier', 192 | 190: 'Sealyham terrier, Sealyham', 193 | 191: 'Airedale, Airedale terrier', 194 | 192: 'cairn, cairn terrier', 195 | 193: 'Australian terrier', 196 | 194: 'Dandie Dinmont, Dandie Dinmont terrier', 197 | 195: 'Boston bull, Boston terrier', 198 | 196: 'miniature schnauzer', 199 | 197: 'giant schnauzer', 200 | 198: 'standard schnauzer', 201 | 199: 'Scotch terrier, Scottish terrier, Scottie', 202 | 200: 'Tibetan terrier, chrysanthemum dog', 203 | 201: 'silky terrier, Sydney silky', 204 | 202: 'soft-coated wheaten terrier', 205 | 203: 'West Highland white terrier', 206 | 204: 'Lhasa, Lhasa apso', 207 | 205: 'flat-coated retriever', 208 | 206: 'curly-coated retriever', 209 | 207: 'golden retriever', 210 | 208: 'Labrador retriever', 211 | 209: 'Chesapeake Bay retriever', 212 | 210: 'German short-haired pointer', 213 | 211: 'vizsla, Hungarian pointer', 214 | 212: 'English setter', 215 | 213: 'Irish setter, red setter', 216 | 214: 'Gordon setter', 217 | 215: 'Brittany spaniel', 218 | 216: 'clumber, clumber spaniel', 219 | 217: 'English springer, English springer spaniel', 220 | 218: 'Welsh springer spaniel', 221 | 219: 'cocker spaniel, English cocker spaniel, cocker', 222 | 220: 'Sussex spaniel', 223 | 221: 'Irish water spaniel', 224 | 222: 'kuvasz', 225 | 223: 'schipperke', 226 | 224: 'groenendael', 227 | 225: 'malinois', 228 | 226: 'briard', 229 | 227: 'kelpie', 230 | 228: 'komondor', 231 | 229: 'Old English sheepdog, bobtail', 232 | 230: 'Shetland sheepdog, Shetland sheep dog, Shetland', 233 | 231: 'collie', 234 | 232: 'Border collie', 235 | 233: 'Bouvier des Flandres, Bouviers des Flandres', 236 | 234: 'Rottweiler', 237 | 235: 'German shepherd, German shepherd dog, German police dog, alsatian', 238 | 236: 'Doberman, Doberman pinscher', 239 | 237: 'miniature pinscher', 240 | 238: 'Greater Swiss Mountain dog', 241 | 239: 'Bernese mountain dog', 242 | 240: 'Appenzeller', 243 | 241: 'EntleBucher', 244 | 242: 'boxer', 245 | 243: 'bull mastiff', 246 | 244: 'Tibetan mastiff', 247 | 245: 'French bulldog', 248 | 246: 'Great Dane', 249 | 247: 'Saint Bernard, St Bernard', 250 | 248: 'Eskimo dog, husky', 251 | 249: 'malamute, malemute, Alaskan malamute', 252 | 250: 'Siberian husky', 253 | 251: 'dalmatian, coach dog, carriage dog', 254 | 252: 'affenpinscher, monkey pinscher, monkey dog', 255 | 253: 'basenji', 256 | 254: 'pug, pug-dog', 257 | 255: 'Leonberg', 258 | 256: 'Newfoundland, Newfoundland dog', 259 | 257: 'Great Pyrenees', 260 | 258: 'Samoyed, Samoyede', 261 | 259: 'Pomeranian', 262 | 260: 'chow, chow chow', 263 | 261: 'keeshond', 264 | 262: 'Brabancon griffon', 265 | 263: 'Pembroke, Pembroke Welsh corgi', 266 | 264: 'Cardigan, Cardigan Welsh corgi', 267 | 265: 'toy poodle', 268 | 266: 'miniature poodle', 269 | 267: 'standard poodle', 270 | 268: 'Mexican hairless', 271 | 269: 'timber wolf, grey wolf, gray wolf, Canis lupus', 272 | 270: 'white wolf, Arctic wolf, Canis lupus tundrarum', 273 | 271: 'red wolf, maned wolf, Canis rufus, Canis niger', 274 | 272: 'coyote, prairie wolf, brush wolf, Canis latrans', 275 | 273: 'dingo, warrigal, warragal, Canis dingo', 276 | 274: 'dhole, Cuon alpinus', 277 | 275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus', 278 | 276: 'hyena, hyaena', 279 | 277: 'red fox, Vulpes vulpes', 280 | 278: 'kit fox, Vulpes macrotis', 281 | 279: 'Arctic fox, white fox, Alopex lagopus', 282 | 280: 'grey fox, gray fox, Urocyon cinereoargenteus', 283 | 281: 'tabby, tabby cat', 284 | 282: 'tiger cat', 285 | 283: 'Persian cat', 286 | 284: 'Siamese cat, Siamese', 287 | 285: 'Egyptian cat', 288 | 286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor', 289 | 287: 'lynx, catamount', 290 | 288: 'leopard, Panthera pardus', 291 | 289: 'snow leopard, ounce, Panthera uncia', 292 | 290: 'jaguar, panther, Panthera onca, Felis onca', 293 | 291: 'lion, king of beasts, Panthera leo', 294 | 292: 'tiger, Panthera tigris', 295 | 293: 'cheetah, chetah, Acinonyx jubatus', 296 | 294: 'brown bear, bruin, Ursus arctos', 297 | 295: 'American black bear, black bear, Ursus americanus, Euarctos americanus', 298 | 296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus', 299 | 297: 'sloth bear, Melursus ursinus, Ursus ursinus', 300 | 298: 'mongoose', 301 | 299: 'meerkat, mierkat', 302 | 300: 'tiger beetle', 303 | 301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle', 304 | 302: 'ground beetle, carabid beetle', 305 | 303: 'long-horned beetle, longicorn, longicorn beetle', 306 | 304: 'leaf beetle, chrysomelid', 307 | 305: 'dung beetle', 308 | 306: 'rhinoceros beetle', 309 | 307: 'weevil', 310 | 308: 'fly', 311 | 309: 'bee', 312 | 310: 'ant, emmet, pismire', 313 | 311: 'grasshopper, hopper', 314 | 312: 'cricket', 315 | 313: 'walking stick, walkingstick, stick insect', 316 | 314: 'cockroach, roach', 317 | 315: 'mantis, mantid', 318 | 316: 'cicada, cicala', 319 | 317: 'leafhopper', 320 | 318: 'lacewing, lacewing fly', 321 | 319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", 322 | 320: 'damselfly', 323 | 321: 'admiral', 324 | 322: 'ringlet, ringlet butterfly', 325 | 323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus', 326 | 324: 'cabbage butterfly', 327 | 325: 'sulphur butterfly, sulfur butterfly', 328 | 326: 'lycaenid, lycaenid butterfly', 329 | 327: 'starfish, sea star', 330 | 328: 'sea urchin', 331 | 329: 'sea cucumber, holothurian', 332 | 330: 'wood rabbit, cottontail, cottontail rabbit', 333 | 331: 'hare', 334 | 332: 'Angora, Angora rabbit', 335 | 333: 'hamster', 336 | 334: 'porcupine, hedgehog', 337 | 335: 'fox squirrel, eastern fox squirrel, Sciurus niger', 338 | 336: 'marmot', 339 | 337: 'beaver', 340 | 338: 'guinea pig, Cavia cobaya', 341 | 339: 'sorrel', 342 | 340: 'zebra', 343 | 341: 'hog, pig, grunter, squealer, Sus scrofa', 344 | 342: 'wild boar, boar, Sus scrofa', 345 | 343: 'warthog', 346 | 344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius', 347 | 345: 'ox', 348 | 346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis', 349 | 347: 'bison', 350 | 348: 'ram, tup', 351 | 349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis', 352 | 350: 'ibex, Capra ibex', 353 | 351: 'hartebeest', 354 | 352: 'impala, Aepyceros melampus', 355 | 353: 'gazelle', 356 | 354: 'Arabian camel, dromedary, Camelus dromedarius', 357 | 355: 'llama', 358 | 356: 'weasel', 359 | 357: 'mink', 360 | 358: 'polecat, fitch, foulmart, foumart, Mustela putorius', 361 | 359: 'black-footed ferret, ferret, Mustela nigripes', 362 | 360: 'otter', 363 | 361: 'skunk, polecat, wood pussy', 364 | 362: 'badger', 365 | 363: 'armadillo', 366 | 364: 'three-toed sloth, ai, Bradypus tridactylus', 367 | 365: 'orangutan, orang, orangutang, Pongo pygmaeus', 368 | 366: 'gorilla, Gorilla gorilla', 369 | 367: 'chimpanzee, chimp, Pan troglodytes', 370 | 368: 'gibbon, Hylobates lar', 371 | 369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus', 372 | 370: 'guenon, guenon monkey', 373 | 371: 'patas, hussar monkey, Erythrocebus patas', 374 | 372: 'baboon', 375 | 373: 'macaque', 376 | 374: 'langur', 377 | 375: 'colobus, colobus monkey', 378 | 376: 'proboscis monkey, Nasalis larvatus', 379 | 377: 'marmoset', 380 | 378: 'capuchin, ringtail, Cebus capucinus', 381 | 379: 'howler monkey, howler', 382 | 380: 'titi, titi monkey', 383 | 381: 'spider monkey, Ateles geoffroyi', 384 | 382: 'squirrel monkey, Saimiri sciureus', 385 | 383: 'Madagascar cat, ring-tailed lemur, Lemur catta', 386 | 384: 'indri, indris, Indri indri, Indri brevicaudatus', 387 | 385: 'Indian elephant, Elephas maximus', 388 | 386: 'African elephant, Loxodonta africana', 389 | 387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens', 390 | 388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca', 391 | 389: 'barracouta, snoek', 392 | 390: 'eel', 393 | 391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch', 394 | 392: 'rock beauty, Holocanthus tricolor', 395 | 393: 'anemone fish', 396 | 394: 'sturgeon', 397 | 395: 'gar, garfish, garpike, billfish, Lepisosteus osseus', 398 | 396: 'lionfish', 399 | 397: 'puffer, pufferfish, blowfish, globefish', 400 | 398: 'abacus', 401 | 399: 'abaya', 402 | 400: "academic gown, academic robe, judge's robe", 403 | 401: 'accordion, piano accordion, squeeze box', 404 | 402: 'acoustic guitar', 405 | 403: 'aircraft carrier, carrier, flattop, attack aircraft carrier', 406 | 404: 'airliner', 407 | 405: 'airship, dirigible', 408 | 406: 'altar', 409 | 407: 'ambulance', 410 | 408: 'amphibian, amphibious vehicle', 411 | 409: 'analog clock', 412 | 410: 'apiary, bee house', 413 | 411: 'apron', 414 | 412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin', 415 | 413: 'assault rifle, assault gun', 416 | 414: 'backpack, back pack, knapsack, packsack, rucksack, haversack', 417 | 415: 'bakery, bakeshop, bakehouse', 418 | 416: 'balance beam, beam', 419 | 417: 'balloon', 420 | 418: 'ballpoint, ballpoint pen, ballpen, Biro', 421 | 419: 'Band Aid', 422 | 420: 'banjo', 423 | 421: 'bannister, banister, balustrade, balusters, handrail', 424 | 422: 'barbell', 425 | 423: 'barber chair', 426 | 424: 'barbershop', 427 | 425: 'barn', 428 | 426: 'barometer', 429 | 427: 'barrel, cask', 430 | 428: 'barrow, garden cart, lawn cart, wheelbarrow', 431 | 429: 'baseball', 432 | 430: 'basketball', 433 | 431: 'bassinet', 434 | 432: 'bassoon', 435 | 433: 'bathing cap, swimming cap', 436 | 434: 'bath towel', 437 | 435: 'bathtub, bathing tub, bath, tub', 438 | 436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon', 439 | 437: 'beacon, lighthouse, beacon light, pharos', 440 | 438: 'beaker', 441 | 439: 'bearskin, busby, shako', 442 | 440: 'beer bottle', 443 | 441: 'beer glass', 444 | 442: 'bell cote, bell cot', 445 | 443: 'bib', 446 | 444: 'bicycle-built-for-two, tandem bicycle, tandem', 447 | 445: 'bikini, two-piece', 448 | 446: 'binder, ring-binder', 449 | 447: 'binoculars, field glasses, opera glasses', 450 | 448: 'birdhouse', 451 | 449: 'boathouse', 452 | 450: 'bobsled, bobsleigh, bob', 453 | 451: 'bolo tie, bolo, bola tie, bola', 454 | 452: 'bonnet, poke bonnet', 455 | 453: 'bookcase', 456 | 454: 'bookshop, bookstore, bookstall', 457 | 455: 'bottlecap', 458 | 456: 'bow', 459 | 457: 'bow tie, bow-tie, bowtie', 460 | 458: 'brass, memorial tablet, plaque', 461 | 459: 'brassiere, bra, bandeau', 462 | 460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty', 463 | 461: 'breastplate, aegis, egis', 464 | 462: 'broom', 465 | 463: 'bucket, pail', 466 | 464: 'buckle', 467 | 465: 'bulletproof vest', 468 | 466: 'bullet train, bullet', 469 | 467: 'butcher shop, meat market', 470 | 468: 'cab, hack, taxi, taxicab', 471 | 469: 'caldron, cauldron', 472 | 470: 'candle, taper, wax light', 473 | 471: 'cannon', 474 | 472: 'canoe', 475 | 473: 'can opener, tin opener', 476 | 474: 'cardigan', 477 | 475: 'car mirror', 478 | 476: 'carousel, carrousel, merry-go-round, roundabout, whirligig', 479 | 477: "carpenter's kit, tool kit", 480 | 478: 'carton', 481 | 479: 'car wheel', 482 | 480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM', 483 | 481: 'cassette', 484 | 482: 'cassette player', 485 | 483: 'castle', 486 | 484: 'catamaran', 487 | 485: 'CD player', 488 | 486: 'cello, violoncello', 489 | 487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone', 490 | 488: 'chain', 491 | 489: 'chainlink fence', 492 | 490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour', 493 | 491: 'chain saw, chainsaw', 494 | 492: 'chest', 495 | 493: 'chiffonier, commode', 496 | 494: 'chime, bell, gong', 497 | 495: 'china cabinet, china closet', 498 | 496: 'Christmas stocking', 499 | 497: 'church, church building', 500 | 498: 'cinema, movie theater, movie theatre, movie house, picture palace', 501 | 499: 'cleaver, meat cleaver, chopper', 502 | 500: 'cliff dwelling', 503 | 501: 'cloak', 504 | 502: 'clog, geta, patten, sabot', 505 | 503: 'cocktail shaker', 506 | 504: 'coffee mug', 507 | 505: 'coffeepot', 508 | 506: 'coil, spiral, volute, whorl, helix', 509 | 507: 'combination lock', 510 | 508: 'computer keyboard, keypad', 511 | 509: 'confectionery, confectionary, candy store', 512 | 510: 'container ship, containership, container vessel', 513 | 511: 'convertible', 514 | 512: 'corkscrew, bottle screw', 515 | 513: 'cornet, horn, trumpet, trump', 516 | 514: 'cowboy boot', 517 | 515: 'cowboy hat, ten-gallon hat', 518 | 516: 'cradle', 519 | 517: 'crane', 520 | 518: 'crash helmet', 521 | 519: 'crate', 522 | 520: 'crib, cot', 523 | 521: 'Crock Pot', 524 | 522: 'croquet ball', 525 | 523: 'crutch', 526 | 524: 'cuirass', 527 | 525: 'dam, dike, dyke', 528 | 526: 'desk', 529 | 527: 'desktop computer', 530 | 528: 'dial telephone, dial phone', 531 | 529: 'diaper, nappy, napkin', 532 | 530: 'digital clock', 533 | 531: 'digital watch', 534 | 532: 'dining table, board', 535 | 533: 'dishrag, dishcloth', 536 | 534: 'dishwasher, dish washer, dishwashing machine', 537 | 535: 'disk brake, disc brake', 538 | 536: 'dock, dockage, docking facility', 539 | 537: 'dogsled, dog sled, dog sleigh', 540 | 538: 'dome', 541 | 539: 'doormat, welcome mat', 542 | 540: 'drilling platform, offshore rig', 543 | 541: 'drum, membranophone, tympan', 544 | 542: 'drumstick', 545 | 543: 'dumbbell', 546 | 544: 'Dutch oven', 547 | 545: 'electric fan, blower', 548 | 546: 'electric guitar', 549 | 547: 'electric locomotive', 550 | 548: 'entertainment center', 551 | 549: 'envelope', 552 | 550: 'espresso maker', 553 | 551: 'face powder', 554 | 552: 'feather boa, boa', 555 | 553: 'file, file cabinet, filing cabinet', 556 | 554: 'fireboat', 557 | 555: 'fire engine, fire truck', 558 | 556: 'fire screen, fireguard', 559 | 557: 'flagpole, flagstaff', 560 | 558: 'flute, transverse flute', 561 | 559: 'folding chair', 562 | 560: 'football helmet', 563 | 561: 'forklift', 564 | 562: 'fountain', 565 | 563: 'fountain pen', 566 | 564: 'four-poster', 567 | 565: 'freight car', 568 | 566: 'French horn, horn', 569 | 567: 'frying pan, frypan, skillet', 570 | 568: 'fur coat', 571 | 569: 'garbage truck, dustcart', 572 | 570: 'gasmask, respirator, gas helmet', 573 | 571: 'gas pump, gasoline pump, petrol pump, island dispenser', 574 | 572: 'goblet', 575 | 573: 'go-kart', 576 | 574: 'golf ball', 577 | 575: 'golfcart, golf cart', 578 | 576: 'gondola', 579 | 577: 'gong, tam-tam', 580 | 578: 'gown', 581 | 579: 'grand piano, grand', 582 | 580: 'greenhouse, nursery, glasshouse', 583 | 581: 'grille, radiator grille', 584 | 582: 'grocery store, grocery, food market, market', 585 | 583: 'guillotine', 586 | 584: 'hair slide', 587 | 585: 'hair spray', 588 | 586: 'half track', 589 | 587: 'hammer', 590 | 588: 'hamper', 591 | 589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier', 592 | 590: 'hand-held computer, hand-held microcomputer', 593 | 591: 'handkerchief, hankie, hanky, hankey', 594 | 592: 'hard disc, hard disk, fixed disk', 595 | 593: 'harmonica, mouth organ, harp, mouth harp', 596 | 594: 'harp', 597 | 595: 'harvester, reaper', 598 | 596: 'hatchet', 599 | 597: 'holster', 600 | 598: 'home theater, home theatre', 601 | 599: 'honeycomb', 602 | 600: 'hook, claw', 603 | 601: 'hoopskirt, crinoline', 604 | 602: 'horizontal bar, high bar', 605 | 603: 'horse cart, horse-cart', 606 | 604: 'hourglass', 607 | 605: 'iPod', 608 | 606: 'iron, smoothing iron', 609 | 607: "jack-o'-lantern", 610 | 608: 'jean, blue jean, denim', 611 | 609: 'jeep, landrover', 612 | 610: 'jersey, T-shirt, tee shirt', 613 | 611: 'jigsaw puzzle', 614 | 612: 'jinrikisha, ricksha, rickshaw', 615 | 613: 'joystick', 616 | 614: 'kimono', 617 | 615: 'knee pad', 618 | 616: 'knot', 619 | 617: 'lab coat, laboratory coat', 620 | 618: 'ladle', 621 | 619: 'lampshade, lamp shade', 622 | 620: 'laptop, laptop computer', 623 | 621: 'lawn mower, mower', 624 | 622: 'lens cap, lens cover', 625 | 623: 'letter opener, paper knife, paperknife', 626 | 624: 'library', 627 | 625: 'lifeboat', 628 | 626: 'lighter, light, igniter, ignitor', 629 | 627: 'limousine, limo', 630 | 628: 'liner, ocean liner', 631 | 629: 'lipstick, lip rouge', 632 | 630: 'Loafer', 633 | 631: 'lotion', 634 | 632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system', 635 | 633: "loupe, jeweler's loupe", 636 | 634: 'lumbermill, sawmill', 637 | 635: 'magnetic compass', 638 | 636: 'mailbag, postbag', 639 | 637: 'mailbox, letter box', 640 | 638: 'maillot', 641 | 639: 'maillot, tank suit', 642 | 640: 'manhole cover', 643 | 641: 'maraca', 644 | 642: 'marimba, xylophone', 645 | 643: 'mask', 646 | 644: 'matchstick', 647 | 645: 'maypole', 648 | 646: 'maze, labyrinth', 649 | 647: 'measuring cup', 650 | 648: 'medicine chest, medicine cabinet', 651 | 649: 'megalith, megalithic structure', 652 | 650: 'microphone, mike', 653 | 651: 'microwave, microwave oven', 654 | 652: 'military uniform', 655 | 653: 'milk can', 656 | 654: 'minibus', 657 | 655: 'miniskirt, mini', 658 | 656: 'minivan', 659 | 657: 'missile', 660 | 658: 'mitten', 661 | 659: 'mixing bowl', 662 | 660: 'mobile home, manufactured home', 663 | 661: 'Model T', 664 | 662: 'modem', 665 | 663: 'monastery', 666 | 664: 'monitor', 667 | 665: 'moped', 668 | 666: 'mortar', 669 | 667: 'mortarboard', 670 | 668: 'mosque', 671 | 669: 'mosquito net', 672 | 670: 'motor scooter, scooter', 673 | 671: 'mountain bike, all-terrain bike, off-roader', 674 | 672: 'mountain tent', 675 | 673: 'mouse, computer mouse', 676 | 674: 'mousetrap', 677 | 675: 'moving van', 678 | 676: 'muzzle', 679 | 677: 'nail', 680 | 678: 'neck brace', 681 | 679: 'necklace', 682 | 680: 'nipple', 683 | 681: 'notebook, notebook computer', 684 | 682: 'obelisk', 685 | 683: 'oboe, hautboy, hautbois', 686 | 684: 'ocarina, sweet potato', 687 | 685: 'odometer, hodometer, mileometer, milometer', 688 | 686: 'oil filter', 689 | 687: 'organ, pipe organ', 690 | 688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO', 691 | 689: 'overskirt', 692 | 690: 'oxcart', 693 | 691: 'oxygen mask', 694 | 692: 'packet', 695 | 693: 'paddle, boat paddle', 696 | 694: 'paddlewheel, paddle wheel', 697 | 695: 'padlock', 698 | 696: 'paintbrush', 699 | 697: "pajama, pyjama, pj's, jammies", 700 | 698: 'palace', 701 | 699: 'panpipe, pandean pipe, syrinx', 702 | 700: 'paper towel', 703 | 701: 'parachute, chute', 704 | 702: 'parallel bars, bars', 705 | 703: 'park bench', 706 | 704: 'parking meter', 707 | 705: 'passenger car, coach, carriage', 708 | 706: 'patio, terrace', 709 | 707: 'pay-phone, pay-station', 710 | 708: 'pedestal, plinth, footstall', 711 | 709: 'pencil box, pencil case', 712 | 710: 'pencil sharpener', 713 | 711: 'perfume, essence', 714 | 712: 'Petri dish', 715 | 713: 'photocopier', 716 | 714: 'pick, plectrum, plectron', 717 | 715: 'pickelhaube', 718 | 716: 'picket fence, paling', 719 | 717: 'pickup, pickup truck', 720 | 718: 'pier', 721 | 719: 'piggy bank, penny bank', 722 | 720: 'pill bottle', 723 | 721: 'pillow', 724 | 722: 'ping-pong ball', 725 | 723: 'pinwheel', 726 | 724: 'pirate, pirate ship', 727 | 725: 'pitcher, ewer', 728 | 726: "plane, carpenter's plane, woodworking plane", 729 | 727: 'planetarium', 730 | 728: 'plastic bag', 731 | 729: 'plate rack', 732 | 730: 'plow, plough', 733 | 731: "plunger, plumber's helper", 734 | 732: 'Polaroid camera, Polaroid Land camera', 735 | 733: 'pole', 736 | 734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria', 737 | 735: 'poncho', 738 | 736: 'pool table, billiard table, snooker table', 739 | 737: 'pop bottle, soda bottle', 740 | 738: 'pot, flowerpot', 741 | 739: "potter's wheel", 742 | 740: 'power drill', 743 | 741: 'prayer rug, prayer mat', 744 | 742: 'printer', 745 | 743: 'prison, prison house', 746 | 744: 'projectile, missile', 747 | 745: 'projector', 748 | 746: 'puck, hockey puck', 749 | 747: 'punching bag, punch bag, punching ball, punchball', 750 | 748: 'purse', 751 | 749: 'quill, quill pen', 752 | 750: 'quilt, comforter, comfort, puff', 753 | 751: 'racer, race car, racing car', 754 | 752: 'racket, racquet', 755 | 753: 'radiator', 756 | 754: 'radio, wireless', 757 | 755: 'radio telescope, radio reflector', 758 | 756: 'rain barrel', 759 | 757: 'recreational vehicle, RV, R.V.', 760 | 758: 'reel', 761 | 759: 'reflex camera', 762 | 760: 'refrigerator, icebox', 763 | 761: 'remote control, remote', 764 | 762: 'restaurant, eating house, eating place, eatery', 765 | 763: 'revolver, six-gun, six-shooter', 766 | 764: 'rifle', 767 | 765: 'rocking chair, rocker', 768 | 766: 'rotisserie', 769 | 767: 'rubber eraser, rubber, pencil eraser', 770 | 768: 'rugby ball', 771 | 769: 'rule, ruler', 772 | 770: 'running shoe', 773 | 771: 'safe', 774 | 772: 'safety pin', 775 | 773: 'saltshaker, salt shaker', 776 | 774: 'sandal', 777 | 775: 'sarong', 778 | 776: 'sax, saxophone', 779 | 777: 'scabbard', 780 | 778: 'scale, weighing machine', 781 | 779: 'school bus', 782 | 780: 'schooner', 783 | 781: 'scoreboard', 784 | 782: 'screen, CRT screen', 785 | 783: 'screw', 786 | 784: 'screwdriver', 787 | 785: 'seat belt, seatbelt', 788 | 786: 'sewing machine', 789 | 787: 'shield, buckler', 790 | 788: 'shoe shop, shoe-shop, shoe store', 791 | 789: 'shoji', 792 | 790: 'shopping basket', 793 | 791: 'shopping cart', 794 | 792: 'shovel', 795 | 793: 'shower cap', 796 | 794: 'shower curtain', 797 | 795: 'ski', 798 | 796: 'ski mask', 799 | 797: 'sleeping bag', 800 | 798: 'slide rule, slipstick', 801 | 799: 'sliding door', 802 | 800: 'slot, one-armed bandit', 803 | 801: 'snorkel', 804 | 802: 'snowmobile', 805 | 803: 'snowplow, snowplough', 806 | 804: 'soap dispenser', 807 | 805: 'soccer ball', 808 | 806: 'sock', 809 | 807: 'solar dish, solar collector, solar furnace', 810 | 808: 'sombrero', 811 | 809: 'soup bowl', 812 | 810: 'space bar', 813 | 811: 'space heater', 814 | 812: 'space shuttle', 815 | 813: 'spatula', 816 | 814: 'speedboat', 817 | 815: "spider web, spider's web", 818 | 816: 'spindle', 819 | 817: 'sports car, sport car', 820 | 818: 'spotlight, spot', 821 | 819: 'stage', 822 | 820: 'steam locomotive', 823 | 821: 'steel arch bridge', 824 | 822: 'steel drum', 825 | 823: 'stethoscope', 826 | 824: 'stole', 827 | 825: 'stone wall', 828 | 826: 'stopwatch, stop watch', 829 | 827: 'stove', 830 | 828: 'strainer', 831 | 829: 'streetcar, tram, tramcar, trolley, trolley car', 832 | 830: 'stretcher', 833 | 831: 'studio couch, day bed', 834 | 832: 'stupa, tope', 835 | 833: 'submarine, pigboat, sub, U-boat', 836 | 834: 'suit, suit of clothes', 837 | 835: 'sundial', 838 | 836: 'sunglass', 839 | 837: 'sunglasses, dark glasses, shades', 840 | 838: 'sunscreen, sunblock, sun blocker', 841 | 839: 'suspension bridge', 842 | 840: 'swab, swob, mop', 843 | 841: 'sweatshirt', 844 | 842: 'swimming trunks, bathing trunks', 845 | 843: 'swing', 846 | 844: 'switch, electric switch, electrical switch', 847 | 845: 'syringe', 848 | 846: 'table lamp', 849 | 847: 'tank, army tank, armored combat vehicle, armoured combat vehicle', 850 | 848: 'tape player', 851 | 849: 'teapot', 852 | 850: 'teddy, teddy bear', 853 | 851: 'television, television system', 854 | 852: 'tennis ball', 855 | 853: 'thatch, thatched roof', 856 | 854: 'theater curtain, theatre curtain', 857 | 855: 'thimble', 858 | 856: 'thresher, thrasher, threshing machine', 859 | 857: 'throne', 860 | 858: 'tile roof', 861 | 859: 'toaster', 862 | 860: 'tobacco shop, tobacconist shop, tobacconist', 863 | 861: 'toilet seat', 864 | 862: 'torch', 865 | 863: 'totem pole', 866 | 864: 'tow truck, tow car, wrecker', 867 | 865: 'toyshop', 868 | 866: 'tractor', 869 | 867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi', 870 | 868: 'tray', 871 | 869: 'trench coat', 872 | 870: 'tricycle, trike, velocipede', 873 | 871: 'trimaran', 874 | 872: 'tripod', 875 | 873: 'triumphal arch', 876 | 874: 'trolleybus, trolley coach, trackless trolley', 877 | 875: 'trombone', 878 | 876: 'tub, vat', 879 | 877: 'turnstile', 880 | 878: 'typewriter keyboard', 881 | 879: 'umbrella', 882 | 880: 'unicycle, monocycle', 883 | 881: 'upright, upright piano', 884 | 882: 'vacuum, vacuum cleaner', 885 | 883: 'vase', 886 | 884: 'vault', 887 | 885: 'velvet', 888 | 886: 'vending machine', 889 | 887: 'vestment', 890 | 888: 'viaduct', 891 | 889: 'violin, fiddle', 892 | 890: 'volleyball', 893 | 891: 'waffle iron', 894 | 892: 'wall clock', 895 | 893: 'wallet, billfold, notecase, pocketbook', 896 | 894: 'wardrobe, closet, press', 897 | 895: 'warplane, military plane', 898 | 896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin', 899 | 897: 'washer, automatic washer, washing machine', 900 | 898: 'water bottle', 901 | 899: 'water jug', 902 | 900: 'water tower', 903 | 901: 'whiskey jug', 904 | 902: 'whistle', 905 | 903: 'wig', 906 | 904: 'window screen', 907 | 905: 'window shade', 908 | 906: 'Windsor tie', 909 | 907: 'wine bottle', 910 | 908: 'wing', 911 | 909: 'wok', 912 | 910: 'wooden spoon', 913 | 911: 'wool, woolen, woollen', 914 | 912: 'worm fence, snake fence, snake-rail fence, Virginia fence', 915 | 913: 'wreck', 916 | 914: 'yawl', 917 | 915: 'yurt', 918 | 916: 'web site, website, internet site, site', 919 | 917: 'comic book', 920 | 918: 'crossword puzzle, crossword', 921 | 919: 'street sign', 922 | 920: 'traffic light, traffic signal, stoplight', 923 | 921: 'book jacket, dust cover, dust jacket, dust wrapper', 924 | 922: 'menu', 925 | 923: 'plate', 926 | 924: 'guacamole', 927 | 925: 'consomme', 928 | 926: 'hot pot, hotpot', 929 | 927: 'trifle', 930 | 928: 'ice cream, icecream', 931 | 929: 'ice lolly, lolly, lollipop, popsicle', 932 | 930: 'French loaf', 933 | 931: 'bagel, beigel', 934 | 932: 'pretzel', 935 | 933: 'cheeseburger', 936 | 934: 'hotdog, hot dog, red hot', 937 | 935: 'mashed potato', 938 | 936: 'head cabbage', 939 | 937: 'broccoli', 940 | 938: 'cauliflower', 941 | 939: 'zucchini, courgette', 942 | 940: 'spaghetti squash', 943 | 941: 'acorn squash', 944 | 942: 'butternut squash', 945 | 943: 'cucumber, cuke', 946 | 944: 'artichoke, globe artichoke', 947 | 945: 'bell pepper', 948 | 946: 'cardoon', 949 | 947: 'mushroom', 950 | 948: 'Granny Smith', 951 | 949: 'strawberry', 952 | 950: 'orange', 953 | 951: 'lemon', 954 | 952: 'fig', 955 | 953: 'pineapple, ananas', 956 | 954: 'banana', 957 | 955: 'jackfruit, jak, jack', 958 | 956: 'custard apple', 959 | 957: 'pomegranate', 960 | 958: 'hay', 961 | 959: 'carbonara', 962 | 960: 'chocolate sauce, chocolate syrup', 963 | 961: 'dough', 964 | 962: 'meat loaf, meatloaf', 965 | 963: 'pizza, pizza pie', 966 | 964: 'potpie', 967 | 965: 'burrito', 968 | 966: 'red wine', 969 | 967: 'espresso', 970 | 968: 'cup', 971 | 969: 'eggnog', 972 | 970: 'alp', 973 | 971: 'bubble', 974 | 972: 'cliff, drop, drop-off', 975 | 973: 'coral reef', 976 | 974: 'geyser', 977 | 975: 'lakeside, lakeshore', 978 | 976: 'promontory, headland, head, foreland', 979 | 977: 'sandbar, sand bar', 980 | 978: 'seashore, coast, seacoast, sea-coast', 981 | 979: 'valley, vale', 982 | 980: 'volcano', 983 | 981: 'ballplayer, baseball player', 984 | 982: 'groom, bridegroom', 985 | 983: 'scuba diver', 986 | 984: 'rapeseed', 987 | 985: 'daisy', 988 | 986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", 989 | 987: 'corn', 990 | 988: 'acorn', 991 | 989: 'hip, rose hip, rosehip', 992 | 990: 'buckeye, horse chestnut, conker', 993 | 991: 'coral fungus', 994 | 992: 'agaric', 995 | 993: 'gyromitra', 996 | 994: 'stinkhorn, carrion fungus', 997 | 995: 'earthstar', 998 | 996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa', 999 | 997: 'bolete', 1000 | 998: 'ear, spike, capitulum', 1001 | 999: 'toilet tissue, toilet paper, bathroom tissue' 1002 | } 1003 | 1004 | IMAGENET_LABELS_TO_IDX = {label: idx for idx, label in IMAGENET_LABELS.items()} 1005 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import sys 4 | 5 | import numpy as np 6 | from scipy import misc 7 | import cv2 8 | import torch 9 | import torch.nn as nn 10 | from torch.autograd import Variable 11 | from torchvision.models import vgg16, vgg19 12 | from torchvision.utils import save_image 13 | 14 | from lib.gradients import VanillaGrad, SmoothGrad, GuidedBackpropGrad, GuidedBackpropSmoothGrad 15 | from lib.image_utils import preprocess_image, save_as_gray_image 16 | from lib.labels import IMAGENET_LABELS 17 | 18 | 19 | def parse_args(): 20 | parser = argparse.ArgumentParser() 21 | parser.add_argument('--cuda', action='store_true', default=False, 22 | help='Use NVIDIA GPU acceleration') 23 | parser.add_argument('--img', type=str, default='', 24 | help='Input image path') 25 | parser.add_argument('--out_dir', type=str, default='./result/grad/', 26 | help='Result directory path') 27 | parser.add_argument('--n_samples', type=int, default=10, 28 | help='Sample size of SmoothGrad') 29 | args = parser.parse_args() 30 | args.cuda = args.cuda and torch.cuda.is_available() 31 | if args.cuda: 32 | print("Using GPU for acceleration") 33 | else: 34 | print("Using CPU for computation") 35 | if args.img: 36 | print('Input image: {}'.format(args.img)) 37 | else: 38 | print('Input image: raccoon face (scipy.misc.face())') 39 | print('Output directory: {}'.format(args.out_dir)) 40 | print('Sample size of SmoothGrad: {}'.format(args.n_samples)) 41 | print() 42 | return args 43 | 44 | 45 | def main(): 46 | args = parse_args() 47 | 48 | if not os.path.exists(args.out_dir): 49 | os.makedirs(args.out_dir) 50 | 51 | target_layer_names = ['35'] 52 | target_index = None 53 | 54 | # Prepare input image 55 | if args.img: 56 | img = cv2.imread(args.img, 1) 57 | else: 58 | img = misc.face() 59 | img = np.float32(cv2.resize(img, (224, 224))) / 255 60 | preprocessed_img = preprocess_image(img, args.cuda) 61 | 62 | model = vgg19(pretrained=True) 63 | if args.cuda: 64 | model.cuda() 65 | 66 | # Prediction 67 | output = model(preprocessed_img) 68 | pred_index = np.argmax(output.data.cpu().numpy()) 69 | print('Prediction: {}'.format(IMAGENET_LABELS[pred_index])) 70 | 71 | # Compute vanilla gradient 72 | vanilla_grad = VanillaGrad( 73 | pretrained_model=model, cuda=args.cuda) 74 | vanilla_saliency = vanilla_grad(preprocessed_img, index=target_index) 75 | save_as_gray_image(vanilla_saliency, os.path.join(args.out_dir, 'vanilla_grad.jpg')) 76 | print('Saved vanilla gradient image') 77 | 78 | # Reload preprocessed image 79 | preprocessed_img = preprocess_image(img, args.cuda) 80 | 81 | # Compute guided gradient 82 | guided_grad = GuidedBackpropGrad( 83 | pretrained_model=model, cuda=args.cuda) 84 | guided_saliency = guided_grad(preprocessed_img, index=target_index) 85 | save_as_gray_image(guided_saliency, os.path.join(args.out_dir, 'guided_grad.jpg')) 86 | print('Saved guided backprop gradient image') 87 | 88 | # Reload preprocessed image 89 | preprocessed_img = preprocess_image(img, args.cuda) 90 | 91 | # Compute smooth gradient 92 | smooth_grad = SmoothGrad( 93 | pretrained_model=model, 94 | cuda=args.cuda, 95 | n_samples=args.n_samples, 96 | magnitude=True) 97 | smooth_saliency = smooth_grad(preprocessed_img, index=target_index) 98 | save_as_gray_image(smooth_saliency, os.path.join(args.out_dir, 'smooth_grad.jpg')) 99 | print('Saved smooth gradient image') 100 | 101 | # Reload preprocessed image 102 | preprocessed_img = preprocess_image(img, args.cuda) 103 | 104 | # Compute guided smooth gradient 105 | guided_smooth_grad = GuidedBackpropSmoothGrad( 106 | pretrained_model=model, 107 | cuda=args.cuda, 108 | n_samples=args.n_samples, 109 | magnitude=True) 110 | guided_smooth_saliency = guided_smooth_grad(preprocessed_img, index=target_index) 111 | save_as_gray_image(guided_smooth_saliency, os.path.join(args.out_dir, 'guided_smooth_grad.jpg')) 112 | print('Saved guided backprop smooth gradient image') 113 | 114 | 115 | if __name__ == '__main__': 116 | main() 117 | --------------------------------------------------------------------------------