Thank you Dwight.
I really appreciated.
I am not sure if I understood correctly.
The only model I used is in the models.py inside the DEEPScreen training program.
This *.py was the following :
import torch
import torch.nn as nn
import torch.nn.functional as F
from operator import itemgetter
class CNNModel1(nn.Module):
def init(self, fully_layer_1, fully_layer_2, drop_rate):
super(CNNModel1, self).init()
    self.conv1 = nn.Conv2d(3, 32, 2)
    self.bn1 = nn.BatchNorm2d(32)
    self.conv2 = nn.Conv2d(32, 64, 2)
    self.bn2 = nn.BatchNorm2d(64)
    self.conv3 = nn.Conv2d(64, 128, 2)
    self.bn3 = nn.BatchNorm2d(128)
    self.conv4 = nn.Conv2d(128, 64, 2)
    self.bn4 = nn.BatchNorm2d(64)
    self.conv5 = nn.Conv2d(64, 32, 2)
    self.bn5 = nn.BatchNorm2d(32)

    self.pool = nn.MaxPool2d(2, 2)
    self.drop_rate = drop_rate
    self.fc1 = nn.Linear(32*5*5, fully_layer_1)
    self.fc2 = nn.Linear(fully_layer_1, fully_layer_2)
    self.fc3 = nn.Linear(fully_layer_2, 2)

def forward(self, x):
    # print(x.shape)
    x = self.pool(F.relu(self.bn1(self.conv1(x))))
    # print(x.shape)
    x = self.pool(F.relu(self.bn2(self.conv2(x))))
    # print(x.shape)
    x = self.pool(F.relu(self.bn3(self.conv3(x))))
    # print(x.shape)
    x = self.pool(F.relu(self.bn4(self.conv4(x))))
    # print(x.shape)
    x = self.pool(F.relu(self.bn5(self.conv5(x))))
    # print(x.shape)

    x = x.view(-1, 32*5*5)
    x = F.dropout(F.relu(self.fc1(x)), self.drop_rate)
    x = F.dropout(F.relu(self.fc2(x)), self.drop_rate)
    x = self.fc3(x)

    return x

If that is the case, do you mean to use:
class CNNModel1(nn.Module):
…include the model architecture as above…
model=model()
model.load_state_dict(torch.load(“VA2.pth”))
Assuming that is Ok, next, I supposse I have to also to include some reference to the *.png imgs they are about to be inferred (they are all files into the same directory for convinience). How?
Thanks again
julio