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9d8daba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | import io
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 5)
self.conv2 = nn.Conv2d(32, 64, 5)
#full layer
self.fc1 = nn.Linear(64 * 13 * 13, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2,2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
loaded_model = CNN()
loaded_model.load_state_dict(torch.load("cnn_model.pth")) # it takes the loaded dictionary, not the path file itself
loaded_model.eval()
#transform images
def transform_image(image_bytes):
transform = transforms.Compose(
[transforms.Resize(64), transforms.CenterCrop(64), transforms.ToTensor()]
)
image = Image.open(io.BytesIO(image_bytes))
return transform(image).unsqueeze(0)
def get_prediction(image_tensor):
outputs = loaded_model(image_tensor)
# max returns (value ,index)
_, predicted = torch.max(outputs.data, 1)
return predicted |