diff --git a/bird_cnn/__pycache__/bird_cnn.cpython-314.pyc b/bird_cnn/__pycache__/bird_cnn.cpython-314.pyc
index b941511..5fe7de8 100644
Binary files a/bird_cnn/__pycache__/bird_cnn.cpython-314.pyc and b/bird_cnn/__pycache__/bird_cnn.cpython-314.pyc differ
diff --git a/bird_cnn/__pycache__/server.cpython-314.pyc b/bird_cnn/__pycache__/server.cpython-314.pyc
index 9ae87ea..25b1bfe 100644
Binary files a/bird_cnn/__pycache__/server.cpython-314.pyc and b/bird_cnn/__pycache__/server.cpython-314.pyc differ
diff --git a/bird_cnn/bird_cnn.py b/bird_cnn/bird_cnn.py
index b8cc332..dd49aef 100644
--- a/bird_cnn/bird_cnn.py
+++ b/bird_cnn/bird_cnn.py
@@ -27,14 +27,25 @@ class SeparableConvolution(nn.Module):
class SkipBlock(nn.Module):
def __init__(self, c_in, c_out, kernel_size=3):
super().__init__()
- self.conv = SeparableConvolution(c_in=c_in, c_out=c_out, kernel_size=kernel_size)
+ self.conv = nn.Sequential(
+ nn.Conv2d(c_in, c_out, kernel_size, padding=kernel_size//2),
+ nn.BatchNorm2d(c_out),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(c_out, c_out, kernel_size, padding=kernel_size//2),
+ nn.BatchNorm2d(c_out),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(c_out, c_out, kernel_size, padding=kernel_size//2),
+ nn.BatchNorm2d(c_out),
+ nn.ReLU(inplace=True)
+ )
self.conv_skip = nn.Sequential(
nn.Conv2d(c_in, c_out, 1),
- nn.BatchNorm2d(c_out)
+ nn.BatchNorm2d(c_out),
+ nn.ReLU(inplace=True)
)
def forward(self, x):
- return(F.relu(self.conv_skip(x) + self.conv(x)))
+ return(F.relu(self.conv_skip(x) + self.conv(x), inplace=True))
class Bird_CNN(nn.Module):
@@ -44,7 +55,7 @@ class Bird_CNN(nn.Module):
self.model = nn.Sequential(
nn.Conv2d(c_in, c_hidden, kernel_size=3, padding=1),
nn.BatchNorm2d(c_hidden),
- nn.ReLU(),
+ nn.ReLU(inplace=True),
SkipBlock(c_in=c_hidden, c_out=c_hidden),
SkipBlock(c_in=c_hidden, c_out=c_hidden),
@@ -56,7 +67,8 @@ class Bird_CNN(nn.Module):
SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
- SeparableConvolution(c_in=c_hidden*2, c_out=c_hidden*4, kernel_size=3),
+ nn.Conv2d(c_hidden*2, c_hidden*4, kernel_size=3, padding=1),
+ nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
diff --git a/bird_cnn/build_models/bird_cnn b/bird_cnn/build_models/bird_cnn
index 0d1ca27..6dd0cd9 100644
Binary files a/bird_cnn/build_models/bird_cnn and b/bird_cnn/build_models/bird_cnn differ
diff --git a/bird_cnn/server.py b/bird_cnn/server.py
index 3d82a5d..20cea3c 100644
--- a/bird_cnn/server.py
+++ b/bird_cnn/server.py
@@ -14,7 +14,7 @@ from bird_cnn import Bird_CNN
import threading
BUILD_PATH = "./build_models"
-IMAGE_SIZE = 256
+IMAGE_SIZE = 64
sem = threading.Semaphore(1) #adjust to performance
@@ -35,25 +35,13 @@ transform = transforms.Compose([
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model =Bird_CNN(c_in=3, c_hidden=64, c_out=7)
+model = Bird_CNN(c_in=3, c_hidden=16, c_out=7)
full_path = os.path.join(BUILD_PATH, "bird_cnn")
model.load_state_dict(torch.load(full_path, map_location=torch.device(device)))
model.to(device)
model.eval()
-app = FastAPI()
-
-origins = [
- "*"
-]
-
-app.add_middleware(
- CORSMiddleware,
- allow_origins=origins,
- allow_credentials=True,
- allow_methods=["*"],
- allow_headers=["*"],
-)
+app = FastAPI(root_path="/cnn-api")
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
diff --git a/bird_cnn/main.py b/bird_cnn/train_bird.py
similarity index 85%
rename from bird_cnn/main.py
rename to bird_cnn/train_bird.py
index 24f079e..10f9e6d 100644
--- a/bird_cnn/main.py
+++ b/bird_cnn/train_bird.py
@@ -1,7 +1,9 @@
import torch
import torch.nn as nn
+import torchvision
from torchvision import datasets, transforms
from torch.utils.data import DataLoader, random_split
+import matplotlib.pyplot as plt
from bird_cnn import Bird_CNN, sample, trainCNN
@@ -10,7 +12,7 @@ from enum import Enum
from PIL import Image
SAVE_PATH = "./saved_models"
-IMAGE_SIZE = 256
+IMAGE_SIZE = 64
class bird_species(Enum):
Common_Kingfisher = 0
@@ -34,13 +36,14 @@ val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
-train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
-val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
+
device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
print("Using device", device)
-model = Bird_CNN(c_in=3, c_hidden=64, c_out=7)
+model = Bird_CNN(c_in=3, c_hidden=16, c_out=7)
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
loss_module = nn.CrossEntropyLoss()
diff --git a/bird_cnn/train_mnist.py b/bird_cnn/train_mnist.py
index 63c5813..5e8a82d 100644
--- a/bird_cnn/train_mnist.py
+++ b/bird_cnn/train_mnist.py
@@ -27,13 +27,13 @@ val_dataset = datasets.MNIST(
transform=transform
)
-train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
-val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
print("Using device", device)
-model = Bird_CNN(c_in=1, c_hidden=8, c_out=10)
+model = Bird_CNN(c_in=1, c_hidden=4, c_out=10)
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
loss_module = nn.CrossEntropyLoss()
diff --git a/cnn_website/index.html b/cnn_website/index.html
index 59046b6..dd2f7b1 100644
--- a/cnn_website/index.html
+++ b/cnn_website/index.html
@@ -3,9 +3,9 @@
-
+
- cnn_website
+ Birdy
diff --git a/cnn_website/public/generated_two_no_background.png b/cnn_website/public/generated_two_no_background.png
new file mode 100644
index 0000000..ed64883
Binary files /dev/null and b/cnn_website/public/generated_two_no_background.png differ
diff --git a/cnn_website/public/loading.gif b/cnn_website/public/loading.gif
deleted file mode 100644
index 0e8f9ae..0000000
Binary files a/cnn_website/public/loading.gif and /dev/null differ
diff --git a/cnn_website/src/Bird_CNN.jsx b/cnn_website/src/Bird_CNN.jsx
index c19e921..5f27d19 100644
--- a/cnn_website/src/Bird_CNN.jsx
+++ b/cnn_website/src/Bird_CNN.jsx
@@ -4,8 +4,8 @@ import './Bird_CNN.css';
function Bird_CNN() {
const apiUrl = process.env.NODE_ENV === "development"
- ? "https://api.marvinkrausser.com"
- : "https://api.marvinkrausser.com";
+ ? "https://marvinkrausser.com/api"
+ : "https://marvinkrausser.com/api";
const [file, setFile] = useState(null);
const [birdClass, setBirdClass] = useState(null);
@@ -54,30 +54,33 @@ function Bird_CNN() {
const formData = new FormData();
formData.append("file", file);
+ setLoading(true);
+
try {
- setLoading(true);
const response = await fetch(`${apiUrl}/predict`, {
method: "POST",
body: formData,
});
-
+ } catch (e) {
+ setError(true);
+ return;
+ }
+ finally {
setLoading(false);
-
- if (!response.ok) {
- setError(true);
- }
- else {
- setError(false);
- }
-
- const result = await response.json();
- setBirdClass(result["class"]);
- const confidence = result["confidence"];
- setConfidence(`${Math.round(confidence * 100)}%`);
}
- catch (error) {
- console.error("Upload failed:", error);
+
+ if (!response.ok) {
+ setError(true);
+ return;
}
+ else {
+ setError(false);
+ }
+
+ const result = await response.json();
+ setBirdClass(result["class"]);
+ const confidence = result["confidence"];
+ setConfidence(`${Math.round(confidence * 100)}%`);
};
return (