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import gradio as gr
import random
from PIL import Image, ImageEnhance
import torch
from glob import glob
from torch import nn
from torchvision import transforms
def predict_number(custom_image: Image.Image):
custom_image = ImageEnhance.Contrast(custom_image.convert("L")).enhance(5.0).point(lambda p: 255 if p > 128 else 0).resize((28, 28))
transform = transforms.Compose([
transforms.ToTensor(),
])
model_0.eval()
with torch.inference_mode():
pred = torch.softmax(model_0(transform(custom_image).unsqueeze(0).to(device)), dim=1)
class_names = list("0123456789")
return {class_names[i]: float(pred[0][i]) for i in range(len(class_names))}
class NumberClassifier(nn.Module):
def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:
super().__init__()
self.conv_block_1 = nn.Sequential(
nn.Conv2d(in_channels=input_shape,
out_channels=hidden_units,
kernel_size=2, # how big is the square that's going over the image?
stride=1, # default
), # options = "valid" (no padding) or "same" (output has same shape as input) or int for specific number
nn.ReLU(),
nn.Conv2d(in_channels=hidden_units,
out_channels=hidden_units,
kernel_size=2,
stride=1,
),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2,
stride=2) # default stride value is same as kernel_size
)
self.conv_block_2 = nn.Sequential(
nn.Conv2d(hidden_units, hidden_units, kernel_size=2),
nn.ReLU(),
nn.Conv2d(hidden_units, hidden_units, kernel_size=2),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.classifier = nn.Sequential(
nn.Flatten(),
# Where did this in_features shape come from?
# It's because each layer of our network compresses and changes the shape of our input data.
nn.Linear(in_features=hidden_units*5*5,
out_features=output_shape)
)
def forward(self, x: torch.Tensor):
x = self.conv_block_1(x)
# print(x.shape)
x = self.conv_block_2(x)
# print(x.shape)
x = self.classifier(x)
# print(x.shape)
return x
# return self.classifier(self.conv_block_2(self.conv_block_1(x))) # <- leverage the benefits of operator fusion
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.manual_seed(42)
model_0 = NumberClassifier(input_shape=1, # number of color channels (3 for RGB)
hidden_units=10,
output_shape=10).to(device)
model_0.load_state_dict(torch.load("models/pytorch_num_classifier_final_model_with_EMNIST.pth", map_location=torch.device('cpu')))
title = "Number Classifier Minimal"
description = "An Image feature extractor computer vision model to classify images of handwritten digits."
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
example_list = [[str(filepath)] for filepath in random.sample(glob("examples/*"), k=25)]
example_list
demo = gr.Interface(fn=predict_number,
inputs=gr.Image(type="pil"),
outputs=[gr.Label(num_top_classes=10, label="Predictions")],
examples=example_list,
title=title,
description=description,
article=article)
demo.launch() |