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import os
os.system("pip install datasets einops tabulate opencv-python ttach -U")
import os, cv2, time, math
print("=> Loading libraries...")
start = time.time()
import requests, torch
import gradio as gr
from torchvision import transforms
from datasets import load_dataset
from timm.data import create_transform
from timm.models import create_model, load_checkpoint
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"=> Libraries loaded in {time.time()- start:.2f} sec(s).")
print("=> Loading model...")
start = time.time()
size = "b"
img_size = 224
crop_pct = 0.9
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
model = create_model(f"tpmlp_{size}").to(device)
load_checkpoint(model, f"../tpmlp_{size}.pth.tar", True)
model.eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
augs = create_transform(
input_size=(3, 224, 224),
is_training=False,
use_prefetcher=False,
crop_pct=0.9,
)
scale_size = math.floor(img_size / crop_pct)
resize = transforms.Compose([
transforms.Resize(scale_size),
transforms.CenterCrop(img_size),
transforms.ToTensor()
])
normalize = transforms.Normalize(mean=torch.tensor(IMAGENET_DEFAULT_MEAN), std=torch.tensor(IMAGENET_DEFAULT_STD))
def transform(img):
img = resize(img.convert("RGB"))
tensor = normalize(img)
return img, tensor
def predict(inp):
img, inp = transform(inp)
inp = inp.unsqueeze(0)
with GradCAM(model=model, target_layers=[model.layers[3]], use_cuda=device=="cuda") as cam:
grayscale_cam, probs = cam(input_tensor=inp, aug_smooth=False, eigen_smooth=False, return_probs=True)
# Here grayscale_cam has only one image in the batch
grayscale_cam = grayscale_cam[0, :]
probs = probs[0, :]
cam_image = show_cam_on_image(img.permute(1, 2, 0).detach().cpu().numpy(), grayscale_cam, use_rgb=True, image_weight=0.5, colormap=cv2.COLORMAP_TWILIGHT_SHIFTED)
confidences = {labels[i]: float(probs[i]) for i in range(1000)}
return confidences, cam_image
print(f"=> Model (tpmlp_{size}) loaded in {time.time()- start:.2f} sec(s).")
if not os.path.isdir("../example-imgs"):
os.mkdir("../example-imgs")
print("=> Loading examples.")
indices = [
0, # Coucal
2, # Volcano
7, # Sombrero
9, # Balance beam
10, # Sulphur-crested cockatoo
11, # Shower cap
12, # Petri dish INCORRECTLY CLASSIFIED as lens
14, # Angora rabbit
]
ds = load_dataset("imagenet-1k", split="validation", streaming=True)
examples = []; idx = 0
start = time.time()
for data in ds:
if idx == indices:
data['image'].save(f"../example-imgs/{idx}.png")
idx += 1
if idx == max(indices):
break
del ds
print(f"=> Examples loaded in {time.time()- start:.2f} sec(s).")
# demo = gr.Interface(
# fn=predict,
# inputs=gr.inputs.Image(type="pil"),
# outputs=[gr.outputs.Label(num_top_classes=4), gr.outputs.Image(type="numpy")],
# examples=[f"../example-imgs/{idx}.png" for idx in indices],
# )
with gr.Blocks(theme=gr.themes.Monochrome(font=[gr.themes.GoogleFont("DM Sans"), "sans-serif"])) as demo:
gr.HTML("""
<h1 align="center">Interactive Demo</h1>
<h2 align="center">CS-Mixer: A Cross-Scale Vision MLP Model with Spatial–Channel Mixing</h2>
<br><br>
""")
with gr.Row():
input_image = gr.Image(type="pil", min_width=300, label="Input Image")
softmax = gr.Label(num_top_classes=4, min_width=200, label="Model Predictions")
grad_cam = gr.Image(type="numpy", min_width=300, label="Grad-CAM")
with gr.Row():
gr.Button("Predict").click(fn=predict, inputs=input_image, outputs=[softmax, grad_cam])
gr.ClearButton(input_image)
with gr.Row():
gr.Examples([f"../example-imgs/{idx}.png" for idx in indices], inputs=input_image, outputs=[softmax, grad_cam], fn=predict, run_on_click=True)
demo.launch(
share=False, debug=False, allowed_paths=["../example-imgs"], server_name="0.0.0.0", # ssl_verify=False,
server_port=8000, # ssl_certfile="/workspace/openssl/cert.pem", ssl_keyfile="/workspace/openssl/key.pem"
) |