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import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torch
import matplotlib.pyplot as plt
import numpy as np
import gradio as gr
class CNN(nn.Module):
def __init__(self, in_channels=1, num_classes=4):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.batch_norm1 = nn.BatchNorm2d(32)
self.batch_norm2 = nn.BatchNorm2d(64)
self.batch_norm3 = nn.BatchNorm2d(128)
self.dropout = nn.Dropout(0.5)
# Calcular el tamaño de la entrada a la capa fully connected
self.fc1 = nn.Linear(128 * (200 // 8) * (200 // 8), 256)
self.fc2 = nn.Linear(256, num_classes)
def forward(self, x):
x = F.relu(self.batch_norm1(self.conv1(x)))
x = self.pool(x)
x = F.relu(self.batch_norm2(self.conv2(x)))
x = self.pool(x)
x = F.relu(self.batch_norm3(self.conv3(x)))
x = self.pool(x)
x = x.view(x.shape[0], -1) # Aplanar
x = self.dropout(F.relu(self.fc1(x)))
x = self.fc2(x)
return x
model = CNN()
model.load_state_dict(
torch.load("gabriel_complex_modelo.pth", map_location=torch.device("cpu"))
)
def inference(model, imagen, device="cpu"):
label_mapping = {0: "Círculo", 1: "Triángulo", 2: "Cuadrado", 3: "Estrella"}
model.eval() # Ponemos el modelo en modo evaluación
# Realizar la inferencia
with torch.no_grad():
scores = model(imagen) # Output: tensor con logits
probabilities = torch.softmax(
scores, dim=1
) # Convertir logits a probabilidades
_, prediction = scores.max(1) # Obtener la clase con mayor probabilidad
label_predicho = prediction.item()
# Diccionario con las probabilidades
probabilities_dict = {
label_mapping[i]: float(probabilities[0, i]) for i in range(4)
}
return label_mapping[label_predicho], probabilities_dict
def predict(img):
image_array = img["composite"][:, :, 3]
image_array = 255 - image_array
image_tensor = torch.from_numpy(image_array).unsqueeze(0)
transform_to_gray = transforms.Compose(
[
transforms.Resize((200, 200)),
transforms.ConvertImageDtype(dtype=torch.float32), # Convertir a flotante
]
)
image = transform_to_gray(image_tensor)
image = image.unsqueeze(0) # Agregar dimensión extra
# Hacemos la inferencia
label_predict, probabilities = inference(model, image, device="cpu")
print(label_predict)
print(probabilities)
return probabilities # Retorna el diccionario con las probabilidades
with gr.Blocks() as demo:
with gr.Row():
im = gr.Sketchpad(type="numpy", crop_size="1:1")
out = gr.Label()
im.change(predict, outputs=out, inputs=im, show_progress="hidden")
demo.launch(share=True, debug=False)
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