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# takn from: https://huggingface.co/spaces/frgfm/torch-cam/blob/main/app.py
# streamlit run app.py
from io import BytesIO
import os
import sys
import matplotlib.pyplot as plt
import requests
import streamlit as st
import torch
from PIL import Image
from torchvision import models
from torchvision.transforms.functional import normalize, resize, to_pil_image, to_tensor
from torchvision import transforms
from torchcam.methods import CAM
from torchcam import methods as torchcam_methods
from torchcam.utils import overlay_mask
import os.path as osp
root_path = osp.abspath(osp.join(__file__, osp.pardir))
sys.path.append(root_path)
from utils import get_model
from registry_utils import import_registered_modules
import_registered_modules()
# from torchcam.methods._utils import locate_candidate_layer
CAM_METHODS = [
"CAM",
# "GradCAM",
# "GradCAMpp",
# "SmoothGradCAMpp",
# "ScoreCAM",
# "SSCAM",
# "ISCAM",
# "XGradCAM",
# "LayerCAM",
]
TV_MODELS = [
"resnet18",
# "resnet50",
]
SR_METHODS = ["GFPGAN", "RealESRGAN", "SRResNet", "CodeFormer", "HAT"]
UPSCALE = ["2", "3", "4"]
LABEL_MAP = [
"left_eye",
"right_eye",
]
@torch.no_grad()
def _load_model(model_configs, device="cpu"):
model_path = os.path.join(root_path, model_configs["model_path"])
model_configs.pop("model_path")
model_dict = torch.load(model_path, map_location=device)
model = get_model(model_configs=model_configs)
model.load_state_dict(model_dict)
model = model.to(device)
model = model.eval()
return model
def main():
# Wide mode
st.set_page_config(page_title="Pupil Diameter Estimator", layout="wide")
# Designing the interface
st.title("EyeDentify Playground")
# For newline
st.write("\n")
# Set the columns
cols = st.columns((1, 1))
# cols = st.columns((1, 1, 1))
cols[0].header("Input image")
# cols[1].header("Raw CAM")
cols[-1].header("Prediction")
# Sidebar
# File selection
st.sidebar.title("Input selection")
# Disabling warning
st.set_option("deprecation.showfileUploaderEncoding", False)
# Choose your own image
uploaded_file = st.sidebar.file_uploader(
"Upload files", type=["png", "jpeg", "jpg"]
)
if uploaded_file is not None:
img = Image.open(BytesIO(uploaded_file.read()), mode="r").convert("RGB")
cols[0].image(img, use_column_width=True)
# Model selection
st.sidebar.title("Setup")
tv_model = st.sidebar.selectbox(
"Classification model",
TV_MODELS,
help="Supported models from Torchvision",
)
# class_choices = [
# f"{idx + 1} - {class_name}" for idx, class_name in enumerate(LABEL_MAP)
# ]
# class_selection = st.sidebar.selectbox(
# "Class selection", ["Predicted class (argmax)", *class_choices]
# )
img_configs = {"img_size": [32, 64], "means": None, "stds": None}
# For newline
st.sidebar.write("\n")
if st.sidebar.button("Compute CAM"):
if uploaded_file is None:
st.sidebar.error("Please upload an image first")
else:
with st.spinner("Analyzing..."):
preprocess_steps = [transforms.ToTensor()]
image_size = img_configs["img_size"]
if image_size is not None:
preprocess_steps.append(
transforms.Resize(
[image_size[0], image_size[-1]],
interpolation=transforms.InterpolationMode.BICUBIC,
antialias=True,
)
)
means = img_configs["means"]
stds = img_configs["stds"]
if means is not None and stds is not None:
preprocess_steps.append(transforms.Normalize(means, stds))
preprocess_function = transforms.Compose(preprocess_steps)
input_img = preprocess_function(img)
input_img = input_img.unsqueeze(0).to(device="cpu")
model_configs = {
"model_path": root_path
+ "/pre_trained_models/ResNet18/left_eye.pt",
"registered_model_name": "ResNet18",
"num_classes": 1,
}
registered_model_name = model_configs["registered_model_name"]
# default_layer = ""
if tv_model is not None:
with st.spinner("Loading model..."):
model = _load_model(model_configs)
if torch.cuda.is_available():
model = model.cuda()
if registered_model_name == "ResNet18":
target_layer = model.resnet.layer4[-1].conv2
elif registered_model_name == "ResNet50":
target_layer = model.resnet.layer4[-1].conv3
else:
raise Exception(
f"No target layer available for selected model: {registered_model_name}"
)
# target_layer = st.sidebar.text_input(
# "Target layer",
# default_layer,
# help='If you want to target several layers, add a "+" separator (e.g. "layer3+layer4")',
# )
cam_method = "CAM"
# cam_method = st.sidebar.selectbox(
# "CAM method",
# CAM_METHODS,
# help="The way your class activation map will be computed",
# )
if cam_method is not None:
# cam_extractor = methods.__dict__[cam_method](
# model,
# target_layer=(
# [s.strip() for s in target_layer.split("+")]
# if len(target_layer) > 0
# else None
# ),
# )
cam_extractor = torchcam_methods.__dict__[cam_method](
model,
target_layer=target_layer,
fc_layer=model.resnet.fc,
input_shape=(3, 32, 64),
)
# with torch.no_grad():
# if input_mask is not None:
# out = self.model(input_img, input_mask)
# else:
# out = self.model(input_img)
# activation_map = cam_extractor(class_idx=target_class)
# Forward the image to the model
out = model(input_img)
print("out = ", out)
# Select the target class
# if class_selection == "Predicted class (argmax)":
# class_idx = out.squeeze(0).argmax().item()
# else:
# class_idx = LABEL_MAP.index(class_selection.rpartition(" - ")[-1])
# Retrieve the CAM
# act_maps = cam_extractor(class_idx=target_class)
act_maps = cam_extractor(0, out)
# Fuse the CAMs if there are several
activation_map = (
act_maps[0]
if len(act_maps) == 1
else cam_extractor.fuse_cams(act_maps)
)
# Overlayed CAM
fig, ax = plt.subplots()
result = overlay_mask(
img, to_pil_image(activation_map, mode="F"), alpha=0.5
)
ax.imshow(result)
ax.axis("off")
cols[-1].pyplot(fig)
if __name__ == "__main__":
main()
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