Add application file

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

app.py

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+import streamlit as st
+
+import requests
+from PIL import Image
+from io import BytesIO
+from transformers import (
+    AutoModelForImageClassification,
+    AutoFeatureExtractor,
+    AutoConfig,
+)
+from torchcam.methods import GradCAM
+from torchcam.utils import overlay_mask
+import matplotlib.pyplot as plt
+from torchvision.transforms.functional import to_pil_image
+from torchcam import methods
+
+# TODO I have an error with those
+# CAM_METHODS = ["CAM", "GradCAM", "GradCAMpp", "SmoothGradCAMpp", "ScoreCAM", "SSCAM", "ISCAM", "XGradCAM", "LayerCAM"]
+CAM_METHODS = ["CAM", "GradCAM", "GradCAMpp", "LayerCAM"]
+
+SUPPORTED_MODELS = ["convnext"]
+
+
+def main():
+    # Wide mode
+    st.set_page_config(layout="wide")
+
+    # Designing the interface
+    st.title("TorchCAM 📸 and Transformers 🤗")
+    st.header("Class activation explorer")
+    # For newline
+    st.write("\n")
+    st.write("`torch-cam`: https://github.com/frgfm/torch-cam")
+    st.write("`transformers`: https://github.com/huggingface/transformers")
+    st.write("Upload an image, select your CAM method and hit the Compute Cam button!")
+
+    # For newline
+    st.write("\n")
+    # Set the columns
+    cols = st.columns((1, 1))
+    cols[0].header("Input image")
+    cols[1].header("Overlayed CAM")
+    # 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")
+    else:
+        r = requests.get(
+            "https://i.insider.com/5df126b679d7570ad2044f3e?width=700&format=jpeg&auto=webp"
+        )
+        img = Image.open(BytesIO(r.content))
+    cols[0].image(img, use_column_width=True)
+
+    model_name = st.sidebar.text_input("Model name", "facebook/convnext-tiny-224")
+
+    if model_name is not None:
+        with st.spinner("Loading model..."):
+            config = AutoConfig.from_pretrained(model_name)
+            model_type = config.model_type
+            if model_type not in SUPPORTED_MODELS:
+                st.warning(
+                    f"{model_type} not in supported models: {','.join(SUPPORTED_MODELS)}"
+                )
+            else:
+                feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+                model = AutoModelForImageClassification.from_pretrained(model_name)
+
+    cam_method = st.sidebar.selectbox("CAM method", CAM_METHODS)
+    if cam_method is not None:
+        cam_extractor = methods.__dict__[cam_method](
+            model, target_layer=model.convnext.encoder.stages[-1].layers[-1]
+        )
+
+    # label choices
+    class_choices = [
+        f"{idx + 1} - {class_name}" for idx, class_name in model.config.id2label.items()
+    ]
+    class_selection = st.sidebar.selectbox(
+        "Class selection", ["Predicted class (argmax)"] + class_choices
+    )
+    # for newline
+    st.sidebar.write("\n")
+
+    if st.sidebar.button("Compute CAM"):
+        # compute cam
+        if img is None:
+            st.sidebar.error("Please upload an image first")
+        else:
+            with st.spinner("Analyzing..."):
+                # Set your CAM extractor
+                cam_extractor = GradCAM(
+                    model, target_layer=model.convnext.encoder.stages[-1].layers[-1]
+                )
+                inputs = feature_extractor(img, return_tensors="pt")
+                logits = model(**inputs).logits
+                # select the target class
+                if class_selection == "Predicted class (argmax)":
+                    class_idx = logits.squeeze(0).argmax().item()
+                else:
+                    class_idx = model.config.label2id[
+                        class_selection.rpartition(" - ")[-1]
+                    ]
+                print(class_idx)
+                # run the cam extractor
+                cams = cam_extractor(class_idx, logits)
+                cam = cams[0] if len(cams) == 1 else cam_extractor.fuse_cams(cams)
+                # resize + overlay
+                result = overlay_mask(img, to_pil_image(cam, mode="F"), alpha=0.5)
+                # display it
+                fig, ax = plt.subplots()
+                result = overlay_mask(img, to_pil_image(cam, mode="F"), alpha=0.5)
+                ax.imshow(result)
+                ax.axis("off")
+                cols[1].pyplot(fig)
+                if class_selection == "Predicted class (argmax)":
+                    # show the predicted class
+                    st.markdown(
+                        f"<p style='text align: center'> Predicted class is {config.id2label[class_idx]}</p>",
+                        unsafe_allow_html=True,
+                    )
+
+
+main()

requirements.txt

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+torchcam
+git+https://github.com/huggingface/transformers.git
+streamlit==0.86.2
+torchvision