examples/maml_omniglot/maml-omniglot-higher.py

#!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
This example shows how to use higher to do Model Agnostic Meta Learning (MAML)
for few-shot Omniglot classification.
For more details see the original MAML paper:
https://arxiv.org/abs/1703.03400

This code has been modified from Jackie Loong's PyTorch MAML implementation:
https://github.com/dragen1860/MAML-Pytorch/blob/master/omniglot_train.py

Our MAML++ fork and experiments are available at:
https://github.com/bamos/HowToTrainYourMAMLPytorch
"""

import argparse
import time

import higher
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from support.omniglot_loaders import OmniglotNShot

import torch
import torch.nn.functional as F
import torch.optim as optim
from torch import nn


mpl.use("Agg")
plt.style.use("bmh")


def main():
    argparser = argparse.ArgumentParser()
    argparser.add_argument("--n-way", "--n_way", type=int, help="n way", default=5)
    argparser.add_argument(
        "--k-spt", "--k_spt", type=int, help="k shot for support set", default=5
    )
    argparser.add_argument(
        "--k-qry", "--k_qry", type=int, help="k shot for query set", default=15
    )
    argparser.add_argument("--device", type=str, help="device", default="cuda")
    argparser.add_argument(
        "--task-num",
        "--task_num",
        type=int,
        help="meta batch size, namely task num",
        default=32,
    )
    argparser.add_argument("--seed", type=int, help="random seed", default=1)
    args = argparser.parse_args()

    torch.manual_seed(args.seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(args.seed)
    np.random.seed(args.seed)

    # Set up the Omniglot loader.
    device = args.device
    db = OmniglotNShot(
        "/tmp/omniglot-data",
        batchsz=args.task_num,
        n_way=args.n_way,
        k_shot=args.k_spt,
        k_query=args.k_qry,
        imgsz=28,
        device=device,
    )

    # Create a vanilla PyTorch neural network that will be
    # automatically monkey-patched by higher later.
    # Before higher, models could *not* be created like this
    # and the parameters needed to be manually updated and copied
    # for the updates.
    net = nn.Sequential(
        nn.Conv2d(1, 64, 3),
        nn.BatchNorm2d(64, momentum=1, affine=True),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, 2),
        nn.Conv2d(64, 64, 3),
        nn.BatchNorm2d(64, momentum=1, affine=True),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, 2),
        nn.Conv2d(64, 64, 3),
        nn.BatchNorm2d(64, momentum=1, affine=True),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, 2),
        Flatten(),
        nn.Linear(64, args.n_way),
    ).to(device)

    # We will use Adam to (meta-)optimize the initial parameters
    # to be adapted.
    meta_opt = optim.Adam(net.parameters(), lr=1e-3)

    log = []
    for epoch in range(100):
        train(db, net, device, meta_opt, epoch, log)
        test(db, net, device, epoch, log)
        plot(log)


def train(db, net, device, meta_opt, epoch, log):
    net.train()
    n_train_iter = db.x_train.shape[0] // db.batchsz

    for batch_idx in range(n_train_iter):
        start_time = time.time()
        # Sample a batch of support and query images and labels.
        x_spt, y_spt, x_qry, y_qry = db.next()

        task_num, setsz, c_, h, w = x_spt.size()
        querysz = x_qry.size(1)

        # TODO: Maybe pull this out into a separate module so it
        # doesn't have to be duplicated between `train` and `test`?

        # Initialize the inner optimizer to adapt the parameters to
        # the support set.
        n_inner_iter = 5
        inner_opt = torch.optim.SGD(net.parameters(), lr=1e-1)

        qry_losses = []
        qry_accs = []
        meta_opt.zero_grad()
        for i in range(task_num):
            with higher.innerloop_ctx(net, inner_opt, copy_initial_weights=False) as (
                fnet,
                diffopt,
            ):
                # Optimize the likelihood of the support set by taking
                # gradient steps w.r.t. the model's parameters.
                # This adapts the model's meta-parameters to the task.
                # higher is able to automatically keep copies of
                # your network's parameters as they are being updated.
                for _ in range(n_inner_iter):
                    spt_logits = fnet(x_spt[i])
                    spt_loss = F.cross_entropy(spt_logits, y_spt[i])
                    diffopt.step(spt_loss)

                # The final set of adapted parameters will induce some
                # final loss and accuracy on the query dataset.
                # These will be used to update the model's meta-parameters.
                qry_logits = fnet(x_qry[i])
                qry_loss = F.cross_entropy(qry_logits, y_qry[i])
                qry_losses.append(qry_loss.detach())
                qry_acc = (qry_logits.argmax(dim=1) == y_qry[i]).sum().item() / querysz
                qry_accs.append(qry_acc)

                # print([b.shape for b in fnet[1].buffers()])

                # Update the model's meta-parameters to optimize the query
                # losses across all of the tasks sampled in this batch.
                # This unrolls through the gradient steps.
                qry_loss.backward()

        meta_opt.step()
        qry_losses = sum(qry_losses) / task_num
        qry_accs = 100.0 * sum(qry_accs) / task_num
        i = epoch + float(batch_idx) / n_train_iter
        iter_time = time.time() - start_time
        if batch_idx % 4 == 0:
            print(
                f"[Epoch {i:.2f}] Train Loss: {qry_losses:.2f} | Acc: {qry_accs:.2f} | Time: {iter_time:.2f}"
            )

        log.append(
            {
                "epoch": i,
                "loss": qry_losses,
                "acc": qry_accs,
                "mode": "train",
                "time": time.time(),
            }
        )


def test(db, net, device, epoch, log):
    # Crucially in our testing procedure here, we do *not* fine-tune
    # the model during testing for simplicity.
    # Most research papers using MAML for this task do an extra
    # stage of fine-tuning here that should be added if you are
    # adapting this code for research.
    net.train()
    n_test_iter = db.x_test.shape[0] // db.batchsz

    qry_losses = []
    qry_accs = []

    for _ in range(n_test_iter):
        x_spt, y_spt, x_qry, y_qry = db.next("test")

        task_num, setsz, c_, h, w = x_spt.size()

        # TODO: Maybe pull this out into a separate module so it
        # doesn't have to be duplicated between `train` and `test`?
        n_inner_iter = 5
        inner_opt = torch.optim.SGD(net.parameters(), lr=1e-1)

        for i in range(task_num):
            with higher.innerloop_ctx(net, inner_opt, track_higher_grads=False) as (
                fnet,
                diffopt,
            ):
                # Optimize the likelihood of the support set by taking
                # gradient steps w.r.t. the model's parameters.
                # This adapts the model's meta-parameters to the task.
                for _ in range(n_inner_iter):
                    spt_logits = fnet(x_spt[i])
                    spt_loss = F.cross_entropy(spt_logits, y_spt[i])
                    diffopt.step(spt_loss)

                # The query loss and acc induced by these parameters.
                qry_logits = fnet(x_qry[i]).detach()
                qry_loss = F.cross_entropy(qry_logits, y_qry[i], reduction="none")
                qry_losses.append(qry_loss.detach())
                qry_accs.append((qry_logits.argmax(dim=1) == y_qry[i]).detach())

    qry_losses = torch.cat(qry_losses).mean().item()
    qry_accs = 100.0 * torch.cat(qry_accs).float().mean().item()
    print(f"[Epoch {epoch+1:.2f}] Test Loss: {qry_losses:.2f} | Acc: {qry_accs:.2f}")
    log.append(
        {
            "epoch": epoch + 1,
            "loss": qry_losses,
            "acc": qry_accs,
            "mode": "test",
            "time": time.time(),
        }
    )


def plot(log):
    # Generally you should pull your plotting code out of your training
    # script but we are doing it here for brevity.
    df = pd.DataFrame(log)

    fig, ax = plt.subplots(figsize=(6, 4))
    train_df = df[df["mode"] == "train"]
    test_df = df[df["mode"] == "test"]
    ax.plot(train_df["epoch"], train_df["acc"], label="Train")
    ax.plot(test_df["epoch"], test_df["acc"], label="Test")
    ax.set_xlabel("Epoch")
    ax.set_ylabel("Accuracy")
    ax.set_ylim(70, 100)
    fig.legend(ncol=2, loc="lower right")
    fig.tight_layout()
    fname = "maml-accs.png"
    print(f"--- Plotting accuracy to {fname}")
    fig.savefig(fname)
    plt.close(fig)


# Won't need this after this PR is merged in:
# https://github.com/pytorch/pytorch/pull/22245
class Flatten(nn.Module):
    def forward(self, input):
        return input.view(input.size(0), -1)


if __name__ == "__main__":
    main()