Upload 6 files

.gitignore

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+lightning_logs
+data
+.ipynb_checkpoints
+__pycache__/

app.py

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+import gradio as gr
+import random
+import numpy as np
+from PIL import Image
+import torch
+import torchvision
+
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+
+from models.resnet_lightning import ResNet
+from utils.data import CIFARDataModule
+from utils.transforms import test_transform
+from utils.common import get_misclassified_data
+
+inv_normalize = torchvision.transforms.Normalize(
+    mean=[-0.50 / 0.23, -0.50 / 0.23, -0.50 / 0.23], std=[1 / 0.23, 1 / 0.23, 1 / 0.23]
+)
+
+datamodule = CIFARDataModule()
+datamodule.setup()
+classes = datamodule.train_dataset.classes
+
+model = ResNet.load_from_checkpoint("model.ckpt")
+model = model.to("cpu")
+
+prediction_image = None
+
+
+def upload_file(files):
+    file_paths = [file.name for file in files]
+    return file_paths
+
+
+def read_image(path):
+    img = Image.open(path)
+    img.load()
+    data = np.asarray(img, dtype="uint8")
+    return data
+
+
+def sample_images():
+    images = []
+    length = len(datamodule.test_dataset)
+    classes = datamodule.train_dataset.classes
+    for i in range(10):
+        idx = random.randint(0, length - 1)
+        image, label = datamodule.test_dataset[idx]
+        image = inv_normalize(image).permute(1, 2, 0).numpy()
+        images.append((image, classes[label]))
+    return images
+
+
+def get_misclassified_images(misclassified_count):
+    misclassified_images = []
+    misclassified_data = get_misclassified_data(
+        model=model,
+        device="cpu",
+        test_loader=datamodule.test_dataloader(),
+        count=misclassified_count,
+    )
+    for i in range(misclassified_count):
+        img = misclassified_data[i][0].squeeze().to("cpu")
+        img = inv_normalize(img)
+        img = np.transpose(img.numpy(), (1, 2, 0))
+        label = f"Label: {classes[misclassified_data[i][1].item()]} | Prediction: {classes[misclassified_data[i][2].item()]}"
+        misclassified_images.append((img, label))
+    return misclassified_images
+
+
+def get_gradcam_images(gradcam_layer, gradcam_count, gradcam_opacity):
+    gradcam_images = []
+    if gradcam_layer == "Layer1":
+        target_layers = [model.layer1[-1]]
+    elif gradcam_layer == "Layer2":
+        target_layers = [model.layer2[-1]]
+    else:
+        target_layers = [model.layer3[-1]]
+
+    cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False)
+    data = get_misclassified_data(
+        model=model,
+        device="cpu",
+        test_loader=datamodule.test_dataloader(),
+        count=gradcam_count,
+    )
+    for i in range(gradcam_count):
+        input_tensor = data[i][0]
+
+        # Get the activations of the layer for the images
+        grayscale_cam = cam(input_tensor=input_tensor, targets=None)
+        grayscale_cam = grayscale_cam[0, :]
+
+        # Get back the original image
+        img = input_tensor.squeeze(0).to("cpu")
+        if inv_normalize is not None:
+            img = inv_normalize(img)
+        rgb_img = np.transpose(img, (1, 2, 0))
+        rgb_img = rgb_img.numpy()
+
+        # Mix the activations on the original image
+        visualization = show_cam_on_image(
+            rgb_img, grayscale_cam, use_rgb=True, image_weight=gradcam_opacity
+        )
+        label = f"Label: {classes[data[i][1].item()]} | Prediction: {classes[data[i][2].item()]}"
+        gradcam_images.append((visualization, label))
+    return gradcam_images
+
+
+def show_hide_misclassified(status):
+    if not status:
+        return {misclassified_count: gr.update(visible=False)}
+    return {misclassified_count: gr.update(visible=True)}
+
+
+def show_hide_gradcam(status):
+    if not status:
+        return [gr.update(visible=False) for i in range(3)]
+    return [gr.update(visible=True) for i in range(3)]
+
+
+def set_prediction_image(evt: gr.SelectData, gallery):
+    global prediction_image
+    if isinstance(gallery[evt.index], dict):
+        prediction_image = gallery[evt.index]["name"]
+    else:
+        prediction_image = gallery[evt.index][0]["name"]
+
+
+def predict(
+    is_misclassified,
+    misclassified_count,
+    is_gradcam,
+    gradcam_count,
+    gradcam_layer,
+    gradcam_opacity,
+    num_classes,
+):
+    misclassified_images = None
+    if is_misclassified:
+        misclassified_images = get_misclassified_images(int(misclassified_count))
+
+    gradcam_images = None
+    if is_gradcam:
+        gradcam_images = get_gradcam_images(
+            gradcam_layer, int(gradcam_count), gradcam_opacity
+        )
+
+    img = read_image(prediction_image)
+    image_transformed = test_transform(image=img)["image"]
+    output = model(image_transformed.unsqueeze(0))
+    preds = torch.softmax(output, dim=1).squeeze().detach().numpy()
+    indices = (
+        output.argsort(descending=True).squeeze().detach().numpy()[: int(num_classes)]
+    )
+    predictions = {classes[i]: round(float(preds[i]), 2) for i in indices}
+
+    return {
+        miscalssfied_output: gr.update(value=misclassified_images),
+        gradcam_output: gr.update(value=gradcam_images),
+        prediction_label: gr.update(value=predictions),
+    }
+
+
+with gr.Blocks() as app:
+    gr.Markdown("## ERA Session12 - CIFAR10 Classification with ResNet")
+    with gr.Row():
+        with gr.Column():
+            with gr.Box():
+                is_misclassified = gr.Checkbox(
+                    label="Misclassified Images", info="Display misclassified images?"
+                )
+                misclassified_count = gr.Dropdown(
+                    choices=["10", "20"],
+                    label="Select Number of Images",
+                    info="Number of Misclassified images",
+                    visible=False,
+                    interactive=True,
+                )
+                is_misclassified.input(
+                    show_hide_misclassified,
+                    inputs=[is_misclassified],
+                    outputs=[misclassified_count],
+                )
+            with gr.Box():
+                is_gradcam = gr.Checkbox(
+                    label="GradCAM Images",
+                    info="Display GradCAM images?",
+                )
+                gradcam_count = gr.Dropdown(
+                    choices=["10", "20"],
+                    label="Select Number of Images",
+                    info="Number of GradCAM images",
+                    interactive=True,
+                    visible=False,
+                )
+                gradcam_layer = gr.Dropdown(
+                    choices=["Layer1", "Layer2", "Layer3"],
+                    label="Select the layer",
+                    info="Please select the layer for which the GradCAM is required",
+                    interactive=True,
+                    visible=False,
+                )
+                gradcam_opacity = gr.Slider(
+                    minimum=0,
+                    maximum=1,
+                    value=0.6,
+                    label="Opacity",
+                    info="Opacity of GradCAM output",
+                    interactive=True,
+                    visible=False,
+                )
+
+                is_gradcam.input(
+                    show_hide_gradcam,
+                    inputs=[is_gradcam],
+                    outputs=[gradcam_count, gradcam_layer, gradcam_opacity],
+                )
+            with gr.Box():
+                # file_output = gr.File(file_types=["image"])
+                with gr.Group():
+                    upload_gallery = gr.Gallery(
+                        value=None,
+                        label="Uploaded images",
+                        show_label=False,
+                        elem_id="gallery_upload",
+                        columns=5,
+                        rows=2,
+                        height="auto",
+                        object_fit="contain",
+                    )
+                    upload_button = gr.UploadButton(
+                        "Click to Upload images",
+                        file_types=["image"],
+                        file_count="multiple",
+                    )
+                    upload_button.upload(upload_file, upload_button, upload_gallery)
+
+                with gr.Group():
+                    sample_gallery = gr.Gallery(
+                        value=sample_images,
+                        label="Sample images",
+                        show_label=True,
+                        elem_id="gallery_sample",
+                        columns=5,
+                        rows=2,
+                        height="auto",
+                        object_fit="contain",
+                    )
+
+                upload_gallery.select(set_prediction_image, inputs=[upload_gallery])
+                sample_gallery.select(set_prediction_image, inputs=[sample_gallery])
+
+            with gr.Box():
+                num_classes = gr.Dropdown(
+                    choices=[str(i + 1) for i in range(10)],
+                    label="Select Number of Top Classes",
+                    info="Number of Top target classes to be shown",
+                )
+            run_btn = gr.Button()
+        with gr.Column():
+            with gr.Box():
+                miscalssfied_output = gr.Gallery(
+                    value=None, label="Misclassified Images", show_label=True
+                )
+            with gr.Box():
+                gradcam_output = gr.Gallery(
+                    value=None, label="GradCAM Images", show_label=True
+                )
+            with gr.Box():
+                prediction_label = gr.Label(value=None, label="Predictions")
+
+        run_btn.click(
+            predict,
+            inputs=[
+                is_misclassified,
+                misclassified_count,
+                is_gradcam,
+                gradcam_count,
+                gradcam_layer,
+                gradcam_opacity,
+                num_classes,
+            ],
+            outputs=[miscalssfied_output, gradcam_output, prediction_label],
+        )
+
+
+app.launch(server_name="0.0.0.0", server_port=9998)

config.toml

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+[data]
+batch_size = 512
+shuffle = true
+num_workers = 4
+
+[training]
+epochs = 20
+batch_size = 512
+optimizer = "adam"
+criterion = "crossentropy"
+lr = 0.003
+weight_decay = 1e-4
+lrfinder = { numiter = 600, endlr = 10, startlr = 1e-2 }

model.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6f3d4b6359778a6dd0c86e85afb1a522aae822ccfeeea9a6fb82aabb124f518d
+size 78938183

requirements.txt

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+absl-py==1.4.0
+adbc-driver-manager==0.5.1
+adbc-driver-sqlite==0.5.1
+aiofiles==23.1.0
+aiohttp==3.8.5
+aiosignal==1.3.1
+albumentations==1.3.1
+altair==5.0.1
+annotated-types==0.5.0
+anyio==3.7.1
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+arrow==1.2.3
+asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1670263926556/work
+async-lru==2.0.4
+async-timeout==4.0.2
+attrs==23.1.0
+Babel==2.12.1
+backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work
+backoff==2.2.1
+backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1687772187254/work
+beautifulsoup4==4.12.2
+black==23.7.0
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+blessed==1.20.0
+cachetools==5.3.1
+certifi==2022.12.7
+cffi==1.15.1
+charset-normalizer==2.1.1
+click==8.1.6
+cloudpickle==2.2.1
+cmake==3.25.0
+connectorx==0.3.1
+contourpy==1.1.0
+croniter==1.4.1
+cycler==0.11.0
+dateutils==0.6.12
+debugpy @ file:///home/builder/ci_310/debugpy_1640789504635/work
+decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
+deepdiff==6.3.1
+defusedxml==0.7.1
+deltalake==0.10.0
+entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work
+exceptiongroup==1.1.2
+executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1667317341051/work
+fastapi==0.100.1
+fastjsonschema==2.18.0
+ffmpy==0.3.1
+filelock==3.12.2
+fonttools==4.41.0
+fqdn==1.5.1
+frozenlist==1.4.0
+fsspec==2023.6.0
+google-auth==2.22.0
+google-auth-oauthlib==1.0.0
+grad-cam==1.4.8
+gradio==3.39.0
+gradio_client==0.3.0
+greenlet==2.0.2
+grpcio==1.56.2
+h11==0.14.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.16.4
+idna==3.4
+imageio==2.31.1
+inquirer==3.1.3
+ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1655369107642/work
+ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1685727741709/work
+ipywidgets==8.0.7
+isoduration==20.11.0
+itsdangerous==2.1.2
+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1669134318875/work
+Jinja2==3.1.2
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+jsonpointer==2.4
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+jsonschema-specifications==2023.7.1
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+jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1686775611663/work
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+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1660814786464/work
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+nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1664684991461/work
+netron==7.0.6
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+notebook_shim==0.2.3
+numpy==1.24.1
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
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+packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1681337016113/work
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+pathspec==0.11.2
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+pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
+Pillow==10.0.0
+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1689538620473/work
+polars==0.18.8
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+prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1688565951714/work
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+psutil @ file:///opt/conda/conda-bld/psutil_1656431268089/work
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+pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work
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+Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1681904169130/work
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+pyparsing==3.0.9
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+pyzmq @ file:///croot/pyzmq_1686601365461/work
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+referencing==0.30.2
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+six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
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+stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
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