mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
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127 lines
4.1 KiB
Python
127 lines
4.1 KiB
Python
import torch
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from tqdm import tqdm
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import numpy as np
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from torch import nn
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from torch import optim
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from torch.utils.data import DataLoader, Dataset
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from utils import save_checkpoint, load_checkpoint, check_accuracy
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from sklearn.metrics import cohen_kappa_score
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import config
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import os
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import pandas as pd
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def make_prediction(model, loader, file):
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preds = []
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filenames = []
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model.eval()
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for x, y, files in tqdm(loader):
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x = x.to(config.DEVICE)
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with torch.no_grad():
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predictions = model(x)
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# Convert MSE floats to integer predictions
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predictions[predictions < 0.5] = 0
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predictions[(predictions >= 0.5) & (predictions < 1.5)] = 1
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predictions[(predictions >= 1.5) & (predictions < 2.5)] = 2
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predictions[(predictions >= 2.5) & (predictions < 3.5)] = 3
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predictions[(predictions >= 3.5) & (predictions < 1000000000000)] = 4
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predictions = predictions.long().view(-1)
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y = y.view(-1)
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preds.append(predictions.cpu().numpy())
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filenames += map(list, zip(files[0], files[1]))
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filenames = [item for sublist in filenames for item in sublist]
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df = pd.DataFrame({"image": filenames, "level": np.concatenate(preds, axis=0)})
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df.to_csv(file, index=False)
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model.train()
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print("Done with predictions")
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class MyDataset(Dataset):
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def __init__(self, csv_file):
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self.csv = pd.read_csv(csv_file)
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def __len__(self):
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return self.csv.shape[0]
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def __getitem__(self, index):
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example = self.csv.iloc[index, :]
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features = example.iloc[: example.shape[0] - 4].to_numpy().astype(np.float32)
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labels = example.iloc[-4:-2].to_numpy().astype(np.int64)
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filenames = example.iloc[-2:].values.tolist()
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return features, labels, filenames
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class MyModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.model = nn.Sequential(
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nn.BatchNorm1d((1536 + 1) * 2),
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nn.Linear((1536+1) * 2, 500),
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nn.BatchNorm1d(500),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(500, 100),
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nn.BatchNorm1d(100),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(100, 2),
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)
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def forward(self, x):
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return self.model(x)
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if __name__ == "__main__":
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model = MyModel().to(config.DEVICE)
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ds = MyDataset(csv_file="train/train_blend.csv")
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loader = DataLoader(ds, batch_size=256, num_workers=3, pin_memory=True, shuffle=True)
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ds_val = MyDataset(csv_file="train/val_blend.csv")
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loader_val = DataLoader(
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ds_val, batch_size=256, num_workers=3, pin_memory=True, shuffle=True
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)
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ds_test = MyDataset(csv_file="train/test_blend.csv")
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loader_test = DataLoader(
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ds_test, batch_size=256, num_workers=2, pin_memory=True, shuffle=False
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)
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optimizer = optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)
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loss_fn = nn.MSELoss()
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if config.LOAD_MODEL and "linear.pth.tar" in os.listdir():
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load_checkpoint(torch.load("linear.pth.tar"), model, optimizer, lr=1e-4)
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model.train()
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for _ in range(5):
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losses = []
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for x, y, files in tqdm(loader_val):
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x = x.to(config.DEVICE).float()
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y = y.to(config.DEVICE).view(-1).float()
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# forward
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scores = model(x).view(-1)
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loss = loss_fn(scores, y)
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losses.append(loss.item())
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# backward
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optimizer.zero_grad()
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loss.backward()
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# gradient descent or adam step
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optimizer.step()
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print(f"Loss: {sum(losses)/len(losses)}")
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if config.SAVE_MODEL:
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checkpoint = {"state_dict": model.state_dict(), "optimizer": optimizer.state_dict()}
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save_checkpoint(checkpoint, filename="linear.pth.tar")
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preds, labels = check_accuracy(loader_val, model)
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print(cohen_kappa_score(labels, preds, weights="quadratic"))
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preds, labels = check_accuracy(loader, model)
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print(cohen_kappa_score(labels, preds, weights="quadratic"))
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make_prediction(model, loader_test, "test_preds.csv")
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