from torchray.attribution.excitation_backprop import contrastive_excitation_backprop
from torchray.benchmark import get_example_data, plot_example
# Obtain example data.
model, x, category_id, _ = get_example_data()
# Contrastive excitation backprop.
saliency = contrastive_excitation_backprop(
model,
x,
category_id,
saliency_layer='features.9',
contrast_layer='features.30',
classifier_layer='classifier.6',
)
# Plots.
plot_example(x, saliency, 'contrastive excitation backprop', category_id)
from torchray.attribution.extremal_perturbation import extremal_perturbation, contrastive_reward
from torchray.benchmark import get_example_data, plot_example
from torchray.utils import get_device
# Obtain example data.
model, x, category_id_1, category_id_2 = get_example_data()
# Run on GPU if available.
device = get_device()
model.to(device)
x = x.to(device)
# Extremal perturbation backprop.
masks_1, _ = extremal_perturbation(
model,
x,
category_id_1,
reward_func=contrastive_reward,
debug=True,
areas=[0.12],
)
masks_2, _ = extremal_perturbation(
model,
x,
category_id_2,
reward_func=contrastive_reward,
debug=True,
areas=[0.05],
)
def app(model = torchvision.models.resnet18().eval(), in_dist_name="in", ood_data_names=["out","out2"], image=True):
# Render the readme as markdown using st.markdown.
st.markdown(get_file_content_as_string("Incepto/dashboard/intro.md"))
layers = get_layers(model)
# Once we have the dependencies, add a selector for the app mode on the sidebar.
if st.sidebar.button("Go to Guide"):
caching.clear_cache()
st.markdown(get_file_content_as_string("Incepto/dashboard/details.md"))
st.sidebar.title("Data Settings")
# select which set of SNPs to explore
dataset = st.sidebar.selectbox(
"Set Dataset:",
(in_dist_name,*ood_data_names),
)
if image:
visualization = st.sidebar.selectbox(
"Set Visualization Type:",
("-", "Color Distribution for Entire Dataset", "Pixel Distribution for Entire Dataset", "Deconvolution", "Excitation Backpropgation","Gradient","Grad-CAM","Guided Backpropagation","Linear Approximation", "Extremal Perturbation", "RISE"),
)
else:
visualization = st.sidebar.selectbox(
"Set Visualization Type:",
("-", "Average Signal for Entire Dataset", "Deconvolution", "Excitation Backpropgation","Gradient","Grad-CAM","Guided Backpropagation","Linear Approximation", "Extremal Perturbation", "RISE"),
)
if image:
if visualization == "Deconvolution":
caching.clear_cache()
saliency_layer=st.selectbox("Select Layer:",tuple(layers))
# st.number_input(label="Enter a channel number:", step=1, min_value=0, value=0)
_, x, category_id, _ = get_example_data()
saliency = deconvnet(model, x.cpu(), category_id, saliency_layer=saliency_layer)
fig = plt.figure(figsize=(40,40))
ax = fig.add_subplot(131)
ax.imshow(np.asarray(saliency.squeeze()))
ax = fig.add_subplot(132)
ax.imshow(np.asarray(x.cpu().squeeze().permute(1,2,0).detach().numpy() ))
st.pyplot(fig)
elif visualization == "Grad-CAM":
with st.spinner("Generating Plot"):
caching.clear_cache()
saliency_layer=st.selectbox("Select Layer:",tuple(layers))
# st.number_input(label="Enter a channel number:", step=1, min_value=0, value=0)
_, x, category_id, _ = get_example_data()
saliency = linear_approx(model, x.cpu(), category_id, saliency_layer=saliency_layer)
fig = plt.figure(figsize=(40,40))
ax = fig.add_subplot(131)
ax.imshow(np.asarray(saliency.squeeze().detach().numpy() ))
ax = fig.add_subplot(132)
ax.imshow(np.asarray(x.cpu().squeeze().permute(1,2,0).detach().numpy() ))
st.pyplot(fig)
elif visualization == "Guided Backpropagation":
with st.spinner("Generating Plot"):
caching.clear_cache()
saliency_layer=st.selectbox("Select Layer:",tuple(layers))
# st.number_input(label="Enter a channel number:", step=1, min_value=0, value=0)
_, x, category_id, _ = get_example_data()
saliency = guided_backprop(model, x.cpu(), category_id, saliency_layer=saliency_layer)
fig = plt.figure(figsize=(40,40))
ax = fig.add_subplot(131)
ax.imshow(np.asarray(saliency.squeeze().detach().numpy() ))
ax = fig.add_subplot(132)
ax.imshow(np.asarray(x.cpu().squeeze().permute(1,2,0).detach().numpy() ))
st.pyplot(fig)
elif visualization == "Gradient":
with st.spinner("Generating Plot"):
caching.clear_cache()
saliency_layer=st.selectbox("Select Layer:",tuple(layers))
# st.number_input(label="Enter a channel number:", step=1, min_value=0, value=0)
_, x, category_id, _ = get_example_data()
saliency = gradient(model, x.cpu(), category_id, saliency_layer=saliency_layer)
fig = plt.figure(figsize=(40,40))
ax = fig.add_subplot(131)
ax.imshow(np.asarray(saliency.squeeze().detach().numpy() ))
ax = fig.add_subplot(132)
ax.imshow(np.asarray(x.cpu().squeeze().permute(1,2,0).detach().numpy() ))
st.pyplot(fig)
elif visualization == "Linear Approximation":
with st.spinner("Generating Plot"):
caching.clear_cache()
saliency_layer=st.selectbox("Select Layer:",tuple(layers))
# st.number_input(label="Enter a channel number:", step=1, min_value=0, value=0)
_, x, category_id, _ = get_example_data()
saliency = gradient(model, x.cpu(), category_id, saliency_layer=saliency_layer)
fig = plt.figure(figsize=(40,40))
ax = fig.add_subplot(131)
ax.imshow(np.asarray(saliency.squeeze().detach().numpy() ))
ax = fig.add_subplot(132)
ax.imshow(np.asarray(x.cpu().squeeze().permute(1,2,0).detach().numpy() ))
st.pyplot(fig)
elif visualization == "Extremal Perturbation":
with st.spinner("Generating Plot"):
caching.clear_cache()
# saliency_layer=st.selectbox("Select Layer:",tuple(layers))
# st.number_input(label="Enter a channel number:", step=1, min_value=0, value=0)
_, x, category_id, _ = get_example_data()
masks_1, _ = extremal_perturbation(
model, x.cpu(), category_id,
reward_func=contrastive_reward,
debug=False,
areas=[0.12],)
fig = plt.figure(figsize=(40,40))
ax = fig.add_subplot(131)
ax.imshow(np.asarray(masks_1.squeeze().detach().numpy() ))
ax = fig.add_subplot(132)
ax.imshow(np.asarray(x.cpu().squeeze().permute(1,2,0).detach().numpy() ))
st.pyplot(fig)
elif visualization == "RISE":
with st.spinner("Generating Plot"):
caching.clear_cache()
# saliency_layer=st.selectbox("Select Layer:",tuple(layers))
# st.number_input(label="Enter a channel number:", step=1, min_value=0, value=0)
_, x, category_id, _ = get_example_data()
saliency = rise(model, x.cpu())
saliency = saliency[:, category_id].unsqueeze(0)
fig = plt.figure(figsize=(40,40))
ax = fig.add_subplot(131)
ax.imshow(np.asarray(saliency.squeeze().detach().numpy() ))
ax = fig.add_subplot(132)
ax.imshow(np.asarray(x.cpu().squeeze().permute(1,2,0).detach().numpy() ))
st.pyplot(fig)
elif visualization == "Color Distribution for Entire Dataset":
with st.spinner("Generating Plot"):
caching.clear_cache()
# saliency_layer=st.selectbox("Select Layer:",tuple(layers))
# st.number_input(label="Enter a channel number:", step=1, min_value=0, value=0)
_, x, category_id, _ = get_example_data()
x = sum(x)/len(x)
image = x.cpu().detach().numpy()
fig = plt.figure()
mpl.rcParams.update({'font.size': 15})
_ = plt.hist(image[:, :, 0].ravel(), bins = 256, color = 'red', alpha = 0.5)
_ = plt.hist(image[:, :, 1].ravel(), bins = 256, color = 'Green', alpha = 0.5)
_ = plt.hist(image[:, :, 2].ravel(), bins = 256, color = 'Blue', alpha = 0.5)
_ = plt.xlabel('Intensity Value')
_ = plt.ylabel('Count')
_ = plt.legend(['Red_Channel', 'Green_Channel', 'Blue_Channel'])
st.pyplot(fig)
elif visualization == "Pixel Distribution for Entire Dataset":
with st.spinner("Generating Plot"):
caching.clear_cache()
_, x, category_id, _ = get_example_data()
x = sum(x)/len(x)
image = x.cpu().detach().numpy()
fig = plt.figure(figsize=(40,40))
plt.ylabel("Count")
plt.xlabel("Intensity Value")
mpl.rcParams.update({'font.size': 55})
ax = plt.hist(x.cpu().detach().numpy().ravel(), bins = 256)
vlo = cv2.Laplacian(x.cpu().detach().numpy().ravel(), cv2.CV_32F).var()
plt.text(1, 1, ('Variance of Laplacian: '+str(vlo)), style='italic', bbox={'facecolor': 'white', 'alpha': 0.5, 'pad': 10})
st.pyplot(fig)
mpl.rcParams.update({'font.size': 15})
if st.sidebar.button("Visualize Model"):
saliency_layer=st.selectbox("Select Layer:",tuple(layers))
filter = st.number_input(label="Enter a filter number:", step=1, min_value=1, value=1)
g_ascent = GradientAscent(model)
g_ascent.use_gpu = False
layer = model.conv1
exec("layer = model.conv1")
print(layer)
img = g_ascent.visualize(layer, filter, title=saliency_layer,return_output=True)[0][0][0]
fig = plt.figure(figsize=(40,40))
ax = fig.add_subplot(131)
ax.imshow(np.asarray(img.cpu().detach().numpy() ))
st.pyplot(fig)
