def predict(X):
nn = NN([6, 8, 4, 1])
nn.load_state_dict(torch.load('model_state/state'))
nn.eval()
result = nn.forward(X).detach().numpy().reshape(-1, 1)
result_transformed = scaler.inverse_transform(result)
return result_transformed
best_accuracy = (0, 0)
for epoch in tqdm(range(CHOSEN_EPOCH)):
losses_training = []
for data, target in train_loader:
data = Variable(data)
target = Variable(target)
loss = model.step(data, target)
losses_training.append(loss)
# Mean of the losses of training predictions
training_loss_history.append(np.mean(losses_training))
########## Test at this epoch ##########
model.eval()
accuracy = 0
losses_test = []
class_correct = list(0. for i in range(3))
class_total = list(0. for i in range(3))
for data, target in test_loader:
data = Variable(data)
target = Variable(target)
y_pred_proba, loss = model.predict_proba_and_get_loss(data, target)
losses_test.append(loss)
accuracy += y_pred_proba.data[0][target.data[0]]
loss_test = np.mean(losses_test)
import cv2
from model import NN
import numpy as np
import torch
cap = cv2.VideoCapture(0)
i = 0
classify = 1
labels =[]
Model = NN(batch_size = 1)
Model.load_state_dict(torch.load("1"))
Model.eval()
tardict = {1 : 'Face Detected' , 0 : 'Undetected' }
while True:
i += 1
ret , frame = cap.read()
gray = cv2.cvtColor(frame , cv2.COLOR_RGB2GRAY)
gray = cv2.GaussianBlur(gray, (15,15), 0)
cv2.imshow('feed' , frame)
gray = torch.from_numpy(gray).view(1 , 1, 480 , 640).float()
output = torch.round(Model.forward(gray))
output = output.item()
print (tardict[output])
if output != 0:
input()
if cv2.waitKey(1) & 0xFF == ord('q') :
break
