153 lines
4.8 KiB
Python
153 lines
4.8 KiB
Python
import os
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from tqdm import tqdm
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class SeparableConvolution(nn.Module):
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def __init__(self, c_in, c_out, kernel_size):
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super().__init__()
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self.depthwise = nn.Conv2d(c_in, c_in, kernel_size, groups=c_in, padding=kernel_size//2)
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self.bn1 = nn.BatchNorm2d(c_in)
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self.pointwise = nn.Conv2d(c_in, c_out, kernel_size=1)
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self.bn2 = nn.BatchNorm2d(c_out)
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def forward(self, x):
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x = self.depthwise(x)
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x = self.bn1(x)
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x = F.relu(x)
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x = self.pointwise(x)
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x = self.bn2(x)
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x = F.relu(x)
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return x
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class SkipBlock(nn.Module):
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def __init__(self, c_in, c_out, kernel_size=3):
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(c_in, c_out, kernel_size, padding=kernel_size//2),
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nn.BatchNorm2d(c_out),
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nn.ReLU(inplace=True),
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nn.Conv2d(c_out, c_out, kernel_size, padding=kernel_size//2),
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nn.BatchNorm2d(c_out),
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nn.ReLU(inplace=True),
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nn.Conv2d(c_out, c_out, kernel_size, padding=kernel_size//2),
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nn.BatchNorm2d(c_out),
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nn.ReLU(inplace=True)
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)
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self.conv_skip = nn.Sequential(
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nn.Conv2d(c_in, c_out, 1),
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nn.BatchNorm2d(c_out),
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nn.ReLU(inplace=True)
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)
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def forward(self, x):
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return(F.relu(self.conv_skip(x) + self.conv(x), inplace=True))
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class Bird_CNN(nn.Module):
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def __init__(self, c_in, c_hidden, c_out):
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super().__init__()
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self.model = nn.Sequential(
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nn.Conv2d(c_in, c_hidden, kernel_size=3, padding=1),
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nn.BatchNorm2d(c_hidden),
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nn.ReLU(inplace=True),
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SkipBlock(c_in=c_hidden, c_out=c_hidden),
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SkipBlock(c_in=c_hidden, c_out=c_hidden),
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SkipBlock(c_in=c_hidden, c_out=c_hidden),
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SkipBlock(c_in=c_hidden, c_out=c_hidden),
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SkipBlock(c_in=c_hidden, c_out=c_hidden*2),
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SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
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SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
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SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
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nn.Conv2d(c_hidden*2, c_hidden*4, kernel_size=3, padding=1),
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nn.ReLU(inplace=True),
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nn.AdaptiveAvgPool2d((1, 1)),
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nn.Flatten(),
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nn.Linear(c_hidden*4, c_out),
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nn.Dropout(0.3)
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)
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def forward(self, x):
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return self.model(x)
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def trainCNN(model, optimizer, loss_module, train_data_loader, validation_data_loader, device, num_epochs, SAVE_PATH, save=False):
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best_val = 0
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for epoch in range(num_epochs):
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############
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# Training #
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############
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model.train()
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true_preds, count = 0, 0
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for data_inputs, classes in tqdm(train_data_loader, desc=f"Train Epoch {epoch+1}", leave=False):
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data_inputs = data_inputs.to(device)
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classes = classes.to(device)
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preds = model(data_inputs)
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loss = loss_module(preds, classes)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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true_preds += (preds.argmax(dim=1) == classes).sum().item()
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count += data_inputs.size(0)
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train_acc = true_preds / count
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torch.cuda.empty_cache()
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##############
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# Validation #
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##############
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model.eval()
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true_preds, count = 0, 0
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for data_inputs, classes in tqdm(validation_data_loader, desc=f"Validate Epoch {epoch+1}", leave=False):
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with torch.no_grad():
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data_inputs = data_inputs.to(device)
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classes = classes.to(device)
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preds = model(data_inputs)
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loss = loss_module(preds, classes)
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true_preds += (preds.argmax(dim=1) == classes).sum().item()
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count += data_inputs.size(0)
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val_acc = true_preds / count
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if(save and best_val < val_acc):
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best_val = val_acc
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save_dir = os.path.join(SAVE_PATH, "bird_cnn")
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os.makedirs(save_dir, exist_ok=True)
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save_path = os.path.join(save_dir, f"bird_cnn{epoch+1}")
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torch.save(model.state_dict(), save_path)
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print(f"epoch: {epoch+1} | train accuracy: {int(train_acc * 1000) / 10}% | validation accuracy: {int(val_acc * 1000) / 10}%")
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torch.cuda.empty_cache()
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def sample(model, img, device, SAVE_PATH, model_name="bird_cnn", folder="bird_cnn"):
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with torch.no_grad():
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full_path = os.path.join(SAVE_PATH, folder, model_name)
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state_dict = torch.load(full_path, weights_only=False)
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model.load_state_dict(state_dict)
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model.eval()
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img = img.to(device)
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pred = model(img)
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probs = F.softmax(pred, dim=1)
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return(torch.max(probs, dim=1)) |