Spaces:
Sleeping
Sleeping
File size: 5,668 Bytes
2efd69c 1955ab3 2efd69c 1955ab3 2efd69c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
import operator
import torch
from torchvision import transforms
import gradio as gr
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
import gradio as gr
import model
from data_loader import CIFAR_CLASS_LABELS, TEST_TRANSFORM
import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
resnet_18 = model.LitResnet()
state_dict = torch.load("resnet.pth", map_location=torch.device('cpu'))
resnet_18.load_state_dict(state_dict)
resnet_18_model = resnet_18.model
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
def inference(input_img, n_top_classes,
apply_gradcam, transparency=0.5,
target_layer_number = -1):
org_img = input_img
input_img = TEST_TRANSFORM(image=input_img)['image']
input_img = input_img.unsqueeze(0)
outputs = resnet_18_model(input_img)
softmax = torch.nn.Softmax(dim=0)
o = softmax(outputs.flatten())
y = {classes[i]: float(o[i]) for i in range(10)}
sorted_pred = sorted(y.items(), key=operator.itemgetter(1), reverse=True)
sorted_pred = sorted_pred[: n_top_classes]
confidences = {klass: prob for klass, prob in sorted_pred}
if apply_gradcam:
target_layers = [resnet_18_model.layer3[target_layer_number]]
cam = GradCAM(model=resnet_18_model, target_layers=target_layers, use_cuda=False)
grayscale_cam = cam(input_tensor=input_img, targets=None)
grayscale_cam = grayscale_cam[0, :]
visualization = show_cam_on_image(
org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
return (gr.update(value= confidences),
gr.update(value=visualization, visible=True))
return (gr.update(value=confidences),
gr.update(visible=False))
def show_misclasif(see_misclassif, n_images):
if see_misclassif:
subset = torch.load('misclassified_images.pt')
images, actuals, preds = torch.tensor(subset[0])[:20], subset[1], subset[2]
figsize=(n_images, 4)
nrows=2
ncols=n_images//2
fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
fig.suptitle('misclassified images', weight='bold', size=10)
axes = axes.ravel()
for img, actual, pred, ax in zip(images, actuals, preds, axes):
ax.imshow(img)
ax.set_title(
f'Prediction={CIFAR_CLASS_LABELS[pred]}\n Actual={CIFAR_CLASS_LABELS[actual]}',
fontsize=8)
ax.set(xticks=[], yticks=[], xticklabels=[], yticklabels=[])
ax.axis('off')
image_path = "plot.png"
fig.savefig(image_path)
plt.close()
return gr.update(value=image_path, visible=True)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_image = gr.Image(shape=(32, 32), label="Input Image")
n_top_classes = gr.Slider(maximum=10, minimum=1, value=3, step=1,
label="Top n classes to show", interactive=True)
require_gradcam = gr.Checkbox(label="Apply GradCAM",
info="Do you want see the GRAD-CAM visualization")
opacity_gradcam = gr.Slider(0, 1, value=0.5,
label="Opacity of GradCAM")
layer_gradcam = gr.Slider(-2, -1, value=-2, step=1,
label="Which Layer?")
submit = gr.Button("Submit")
with gr.Column():
pred_classes = gr.Label()
grad_cam = gr.Image(shape=(32, 32),
label="Output",visible=False)\
.style(width=128, height=128)
with gr.Row():
with gr.Column():
see_misclassif = gr.Checkbox(label="View misclassified images",
info="Do you want see the miscassified images in the test dataset")
n_misclasif = gr.Slider(maximum=20, minimum=2, value=10, step=2,
label="Number of misclassified images to show",
interactive=True, visible=False)
render = gr.Button("Render", visible=False)
misclasif_display = gr.Image(visible=False)
n_top_classes.postprocess(n_top_classes.value)
submit.click(inference,
inputs=[input_image, n_top_classes, require_gradcam,
opacity_gradcam, layer_gradcam],
outputs=[pred_classes, grad_cam]
)
def turn_on_misclasif(see_misclassif):
if see_misclassif:
return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
see_misclassif.change(turn_on_misclasif, see_misclassif, [n_misclasif, render, misclasif_display])
render.click(show_misclasif, [see_misclassif, n_misclasif], misclasif_display)
gr.Examples(
examples=[
["examples/truck.jpg", 3, True],
["examples/ship.jpg", 3, True],
["examples/dog.jpg", 3, True],
["examples/cat.jpg", 3, True],
["examples/horse.jpg", 3, True],
["examples/airplane.jpg", 3, True],
["examples/bird.jpg", 3, True],
["examples/automobile.jpg", 3, True],
["examples/deer.jpg", 3, True],
["examples/frog.jpg", 3, True],
],
inputs=[input_image, n_top_classes, require_gradcam],
outputs=[pred_classes, grad_cam],
fn=inference,
)
demo.launch()
|