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maml_miniimagenet_test_notravis.py
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maml_miniimagenet_test_notravis.py
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#!/usr/bin/env python3
import unittest
import random
import numpy as np
import torch as th
from torch import nn
from torch import optim
import learn2learn as l2l
def accuracy(predictions, targets):
predictions = predictions.argmax(dim=1).view(targets.shape)
return (predictions == targets).sum().float() / targets.size(0)
def fast_adapt(batch, learner, loss, adaptation_steps, shots, ways, device):
data, labels = batch
data, labels = data.to(device), labels.to(device)
# Separate data into adaptation/evalutation sets
adaptation_indices = th.zeros(data.size(0)).byte()
adaptation_indices[th.arange(shots*ways) * 2] = 1
adaptation_data, adaptation_labels = data[adaptation_indices], labels[adaptation_indices]
evaluation_data, evaluation_labels = data[1 - adaptation_indices], labels[1 - adaptation_indices]
# Adapt the model
for step in range(adaptation_steps):
train_error = loss(learner(adaptation_data), adaptation_labels)
train_error /= len(adaptation_data)
learner.adapt(train_error)
# Evaluate the adapted model
predictions = learner(evaluation_data)
valid_error = loss(predictions, evaluation_labels)
valid_error /= len(evaluation_data)
valid_accuracy = accuracy(predictions, evaluation_labels)
return valid_error, valid_accuracy
def main(
ways=5,
shots=5,
meta_lr=0.003,
fast_lr=0.5,
meta_batch_size=32,
adaptation_steps=1,
num_iterations=60000,
cuda=False,
seed=42,
):
random.seed(seed)
np.random.seed(seed)
th.manual_seed(seed)
device = th.device('cpu')
if cuda and th.cuda.device_count():
th.cuda.manual_seed(seed)
device = th.device('cuda')
# Create Datasets
train_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='train')
valid_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='validation')
test_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='test')
train_dataset = l2l.data.MetaDataset(train_dataset)
valid_dataset = l2l.data.MetaDataset(valid_dataset)
test_dataset = l2l.data.MetaDataset(test_dataset)
train_transforms = [
l2l.data.transforms.NWays(train_dataset, ways),
l2l.data.transforms.KShots(train_dataset, 2*shots),
l2l.data.transforms.LoadData(train_dataset),
l2l.data.transforms.RemapLabels(train_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
]
train_tasks = l2l.data.TaskDataset(train_dataset,
task_transforms=train_transforms,
num_tasks=20000)
valid_transforms = [
l2l.data.transforms.NWays(valid_dataset, ways),
l2l.data.transforms.KShots(valid_dataset, 2*shots),
l2l.data.transforms.LoadData(valid_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
l2l.data.transforms.RemapLabels(valid_dataset),
]
valid_tasks = l2l.data.TaskDataset(valid_dataset,
task_transforms=valid_transforms,
num_tasks=600)
test_transforms = [
l2l.data.transforms.NWays(test_dataset, ways),
l2l.data.transforms.KShots(test_dataset, 2*shots),
l2l.data.transforms.LoadData(test_dataset),
l2l.data.transforms.RemapLabels(test_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
]
test_tasks = l2l.data.TaskDataset(test_dataset,
task_transforms=test_transforms,
num_tasks=600)
# Create model
model = l2l.vision.models.MiniImagenetCNN(ways)
model.to(device)
maml = l2l.algorithms.MAML(model, lr=fast_lr, first_order=False)
opt = optim.Adam(maml.parameters(), meta_lr)
loss = nn.CrossEntropyLoss(size_average=True, reduction='mean')
for iteration in range(num_iterations):
opt.zero_grad()
meta_train_error = 0.0
meta_train_accuracy = 0.0
meta_valid_error = 0.0
meta_valid_accuracy = 0.0
meta_test_error = 0.0
meta_test_accuracy = 0.0
for task in range(meta_batch_size):
# Compute meta-training loss
learner = maml.clone()
batch = train_tasks.sample()
evaluation_error, evaluation_accuracy = fast_adapt(batch,
learner,
loss,
adaptation_steps,
shots,
ways,
device)
evaluation_error.backward()
meta_train_error += evaluation_error.item()
meta_train_accuracy += evaluation_accuracy.item()
# Compute meta-validation loss
learner = maml.clone()
batch = valid_tasks.sample()
evaluation_error, evaluation_accuracy = fast_adapt(batch,
learner,
loss,
adaptation_steps,
shots,
ways,
device)
meta_valid_error += evaluation_error.item()
meta_valid_accuracy += evaluation_accuracy.item()
# Compute meta-testing loss
learner = maml.clone()
batch = test_tasks.sample()
evaluation_error, evaluation_accuracy = fast_adapt(batch,
learner,
loss,
adaptation_steps,
shots,
ways,
device)
meta_test_error += evaluation_error.item()
meta_test_accuracy += evaluation_accuracy.item()
# Print some metrics
print('\n')
print('Iteration', iteration)
print('Meta Train Error', meta_train_error / meta_batch_size)
print('Meta Train Accuracy', meta_train_accuracy / meta_batch_size)
print('Meta Valid Error', meta_valid_error / meta_batch_size)
print('Meta Valid Accuracy', meta_valid_accuracy / meta_batch_size)
print('Meta Test Error', meta_test_error / meta_batch_size)
print('Meta Test Accuracy', meta_test_accuracy / meta_batch_size)
# Average the accumulated gradients and optimize
for p in maml.parameters():
p.grad.data.mul_(1.0 / meta_batch_size)
opt.step()
return meta_train_accuracy, meta_valid_accuracy, meta_test_accuracy
class MAMLMiniImagenetIntegrationTests(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_final_accuracy(self):
train_acc, valid_acc, test_acc = main(num_iterations=1)
self.assertTrue(train_acc >= 0.2)
self.assertTrue(valid_acc >= 0.2)
self.assertTrue(test_acc >= 0.2)
if __name__ == '__main__':
unittest.main()