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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
class_names = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]
model = torchvision.models.efficientnet_b2()
model.classifier = nn.Sequential(
nn.Dropout(p=0.3, inplace=True),
nn.Linear(in_features=1408, out_features=7),
)
for param in model.parameters():
param.requires_grad = False
model.load_state_dict(
torch.load(
f="trained_model.pt",
map_location=torch.device("cpu"),
)
)
def preprocessImg(img):
transform = transforms.Compose([
# transforms.Grayscale(),
transforms.Resize((256,256)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
img = transform(img)
return img
def predict(img) -> Tuple[Dict, float]:
"""Transforms and performs a prediction on img and returns prediction and time taken.
"""
start_time = timer()
img = preprocessImg(img).unsqueeze(0)
model.eval()
with torch.inference_mode():
pred_probs = torch.softmax(model(img), dim=1)
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
pred_time = round(timer() - start_time, 5)
return pred_labels_and_probs, pred_time
title = "Facial Expression Classifier"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of facial expressions"
article = "for source code you can visit [my github](https://github.com/Bijan-K/Pytorch-Facial-Expression-Recognition)."
example_list = [["examples/" + example] for example in os.listdir("examples")]
demo = gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=3, label="Predictions"),
gr.Number(label="Prediction time (s)")],
examples=example_list,
title=title,
description=description,
article=article)
demo.launch() |