279 lines
8.9 KiB
Python
279 lines
8.9 KiB
Python
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import time
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import copy
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import torch
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import torch.nn as nn
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import torchvision
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import model
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def train(seed=0):
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# Configurable parameters
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epochs = 10
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batch_size = 512
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momentum = 0.9
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weight_decay = 0.256
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weight_decay_bias = 0.004
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ema_update_freq = 5
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ema_rho = 0.99 ** ema_update_freq
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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dtype = torch.float16 if device.type != "cpu" else torch.float32
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# First, the learning rate rises from 0 to 0.002 for the first 194 batches.
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# Next, the learning rate shrinks down to 0.0002 over the next 582 batches.
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lr_schedule = torch.cat([
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torch.linspace(0e+0, 2e-3, 194),
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torch.linspace(2e-3, 2e-4, 582),
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])
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lr_schedule_bias = 64.0 * lr_schedule
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# Print information about hardware on first run
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if seed == 0:
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if device.type == "cuda":
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print("Device :", torch.cuda.get_device_name(device.index))
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print("Dtype :", dtype)
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print()
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# Start measuring time
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start_time = time.perf_counter()
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# Set random seed to increase chance of reproducability
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torch.manual_seed(seed)
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# Setting cudnn.benchmark to True hampers reproducability, but is faster
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torch.backends.cudnn.benchmark = True
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# Load dataset
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train_data, train_targets, valid_data, valid_targets = load_cifar10(device, dtype)
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# Compute special weights for first layer
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weights = model.patch_whitening(train_data[:10000, :, 4:-4, 4:-4])
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# Construct the neural network
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train_model = model.Model(weights, c_in=3, c_out=10, scale_out=0.125)
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# Convert model weights to half precision
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train_model.to(dtype)
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# Convert BatchNorm back to single precision for better accuracy
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for module in train_model.modules():
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if isinstance(module, nn.BatchNorm2d):
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module.float()
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# Upload model to GPU
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train_model.to(device)
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# Collect weights and biases and create nesterov velocity values
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weights = [
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(w, torch.zeros_like(w))
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for w in train_model.parameters()
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if w.requires_grad and len(w.shape) > 1
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]
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biases = [
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(w, torch.zeros_like(w))
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for w in train_model.parameters()
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if w.requires_grad and len(w.shape) <= 1
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]
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# Copy the model for validation
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valid_model = copy.deepcopy(train_model)
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print(f"Preprocessing: {time.perf_counter() - start_time:.2f} seconds")
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# Train and validate
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print("\nepoch batch train time [sec] validation accuracy")
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train_time = 0.0
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batch_count = 0
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for epoch in range(1, epochs + 1):
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# Flush CUDA pipeline for more accurate time measurement
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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start_time = time.perf_counter()
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# Randomly shuffle training data
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indices = torch.randperm(len(train_data), device=device)
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data = train_data[indices]
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targets = train_targets[indices]
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# Crop random 32x32 patches from 40x40 training data
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data = [
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random_crop(data[i : i + batch_size], crop_size=(32, 32))
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for i in range(0, len(data), batch_size)
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]
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data = torch.cat(data)
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# Randomly flip half the training data
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data[: len(data) // 2] = torch.flip(data[: len(data) // 2], [-1])
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for i in range(0, len(data), batch_size):
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# discard partial batches
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if i + batch_size > len(data):
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break
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# Slice batch from data
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inputs = data[i : i + batch_size]
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target = targets[i : i + batch_size]
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batch_count += 1
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# Compute new gradients
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train_model.zero_grad()
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train_model.train(True)
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logits = train_model(inputs)
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loss = model.label_smoothing_loss(logits, target, alpha=0.2)
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loss.sum().backward()
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lr_index = min(batch_count, len(lr_schedule) - 1)
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lr = lr_schedule[lr_index]
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lr_bias = lr_schedule_bias[lr_index]
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# Update weights and biases of training model
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update_nesterov(weights, lr, weight_decay, momentum)
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update_nesterov(biases, lr_bias, weight_decay_bias, momentum)
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# Update validation model with exponential moving averages
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if (i // batch_size % ema_update_freq) == 0:
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update_ema(train_model, valid_model, ema_rho)
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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# Add training time
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train_time += time.perf_counter() - start_time
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valid_correct = []
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for i in range(0, len(valid_data), batch_size):
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valid_model.train(False)
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# Test time agumentation: Test model on regular and flipped data
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regular_inputs = valid_data[i : i + batch_size]
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flipped_inputs = torch.flip(regular_inputs, [-1])
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logits1 = valid_model(regular_inputs).detach()
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logits2 = valid_model(flipped_inputs).detach()
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# Final logits are average of augmented logits
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logits = torch.mean(torch.stack([logits1, logits2], dim=0), dim=0)
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# Compute correct predictions
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correct = logits.max(dim=1)[1] == valid_targets[i : i + batch_size]
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valid_correct.append(correct.detach().type(torch.float64))
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# Accuracy is average number of correct predictions
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valid_acc = torch.mean(torch.cat(valid_correct)).item()
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print(f"{epoch:5} {batch_count:8d} {train_time:19.2f} {valid_acc:22.4f}")
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return valid_acc
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def preprocess_data(data, device, dtype):
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# Convert to torch float16 tensor
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data = torch.tensor(data, device=device).to(dtype)
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# Normalize
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mean = torch.tensor([125.31, 122.95, 113.87], device=device).to(dtype)
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std = torch.tensor([62.99, 62.09, 66.70], device=device).to(dtype)
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data = (data - mean) / std
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# Permute data from NHWC to NCHW format
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data = data.permute(0, 3, 1, 2)
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return data
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def load_cifar10(device, dtype, data_dir="~/data"):
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train = torchvision.datasets.CIFAR10(root=data_dir, download=True)
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valid = torchvision.datasets.CIFAR10(root=data_dir, train=False)
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train_data = preprocess_data(train.data, device, dtype)
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valid_data = preprocess_data(valid.data, device, dtype)
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train_targets = torch.tensor(train.targets).to(device)
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valid_targets = torch.tensor(valid.targets).to(device)
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# Pad 32x32 to 40x40
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train_data = nn.ReflectionPad2d(4)(train_data)
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return train_data, train_targets, valid_data, valid_targets
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def update_ema(train_model, valid_model, rho):
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# The trained model is not used for validation directly. Instead, the
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# validation model weights are updated with exponential moving averages.
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train_weights = train_model.state_dict().values()
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valid_weights = valid_model.state_dict().values()
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for train_weight, valid_weight in zip(train_weights, valid_weights):
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if valid_weight.dtype in [torch.float16, torch.float32]:
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valid_weight *= rho
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valid_weight += (1 - rho) * train_weight
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def update_nesterov(weights, lr, weight_decay, momentum):
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for weight, velocity in weights:
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if weight.requires_grad:
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gradient = weight.grad.data
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weight = weight.data
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gradient.add_(weight, alpha=weight_decay).mul_(-lr)
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velocity.mul_(momentum).add_(gradient)
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weight.add_(gradient.add_(velocity, alpha=momentum))
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def random_crop(data, crop_size):
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crop_h, crop_w = crop_size
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h = data.size(2)
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w = data.size(3)
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x = torch.randint(w - crop_w, size=(1,))[0]
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y = torch.randint(h - crop_h, size=(1,))[0]
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return data[:, :, y : y + crop_h, x : x + crop_w]
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def sha256(path):
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import hashlib
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with open(path, "rb") as f:
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return hashlib.sha256(f.read()).hexdigest()
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def getrelpath(abspath):
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import os
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return os.path.relpath(abspath, os.getcwd())
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def print_info():
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# Knowing this information might improve chance of reproducability
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print("File :", getrelpath(__file__), sha256(__file__))
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print("Model :", getrelpath(model.__file__), sha256(model.__file__))
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print("PyTorch:", torch.__version__)
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def main():
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print_info()
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accuracies = []
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threshold = 0.94
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for run in range(100):
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valid_acc = train(seed=run)
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accuracies.append(valid_acc)
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# Print accumulated results
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within_threshold = sum(acc >= threshold for acc in accuracies)
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acc = threshold * 100.0
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print()
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print(f"{within_threshold} of {run + 1} runs >= {acc} % accuracy")
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mean = sum(accuracies) / len(accuracies)
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variance = sum((acc - mean)**2 for acc in accuracies) / len(accuracies)
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std = variance**0.5
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print(f"Min accuracy: {min(accuracies)}")
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print(f"Max accuracy: {max(accuracies)}")
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print(f"Mean accuracy: {mean} +- {std}")
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print()
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if __name__ == "__main__":
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main()
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