add lmdb dataset support and EpisodicDataset

configs/few-shot/crossdomain.yml

@@ -0,0 +1,66 @@
+name: cross-domain-1
+engine: crossdomain
+result_dir: ./result
+
+distributed:
+  model:
+    # broadcast_buffers: False
+
+misc:
+  random_seed: 1004
+
+checkpoints:
+  interval: 2000
+
+log:
+  logger:
+    level: 20 # DEBUG(10) INFO(20)
+
+model:
+  _type: resnet10
+
+baseline:
+  plusplus: False
+  optimizers:
+    _type: Adam
+  data:
+    dataloader:
+      batch_size: 1024
+      shuffle: True
+      num_workers: 16
+      pin_memory: True
+      drop_last: True
+    dataset:
+      train:
+        path: /data/few-shot/mini_imagenet_full_size/train
+        lmdb_path: /data/few-shot/lmdb/mini-ImageNet/train.lmdb
+        pipeline:
+          - Load
+          - RandomResizedCrop:
+              size: [256, 256]
+          - ColorJitter:
+              brightness: 0.4
+              contrast: 0.4
+              saturation: 0.4
+          - RandomHorizontalFlip
+          - ToTensor
+          - Normalize:
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+      val:
+        path: /data/few-shot/mini_imagenet_full_size/val
+        lmdb_path: /data/few-shot/lmdb/mini-ImageNet/val.lmdb
+        pipeline:
+          - Load
+          - Resize:
+              size: [286, 286]
+          - RandomCrop:
+              size: [256, 256]
+          - ToTensor
+          - Normalize:
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+
+
+
+

data/dataset.py

@@ -1,23 +1,52 @@
 import os
 import pickle
+from collections import defaultdict
 
 import torch
 from torch.utils.data import Dataset
-from torchvision.datasets.folder import has_file_allowed_extension, IMG_EXTENSIONS
+from torchvision.datasets import ImageFolder
+from torchvision.datasets.folder import has_file_allowed_extension, IMG_EXTENSIONS, default_loader
 
 import lmdb
+from tqdm import tqdm
 
 from .transform import transform_pipeline
 from .registry import DATASET
 
 
+def default_transform_way(transform, sample):
+    return [transform(sample[0]), *sample[1:]]
+
+
 class LMDBDataset(Dataset):
-    def __init__(self, lmdb_path, output_transform=None, map_size=2 ** 40, readonly=True, **lmdb_kwargs):
+    def __init__(self, lmdb_path, pipeline=None, transform_way=default_transform_way, map_size=2 ** 40, readonly=True,
+                 **lmdb_kwargs):
+        self.path = lmdb_path
         self.db = lmdb.open(lmdb_path, subdir=os.path.isdir(lmdb_path), map_size=map_size, readonly=readonly,
-                            **lmdb_kwargs)
-        self.output_transform = output_transform
+                            lock=False, **lmdb_kwargs)
+
         with self.db.begin(write=False) as txn:
-            self._len = pickle.loads(txn.get(b"__len__"))
+            self._len = pickle.loads(txn.get(b"$$len$$"))
+            self.done_pipeline = pickle.loads(txn.get(b"$$done_pipeline$$"))
+            if pipeline is None:
+                self.not_done_pipeline = []
+            else:
+                self.not_done_pipeline = self._remain_pipeline(pipeline)
+            self.transform = transform_pipeline(self.not_done_pipeline)
+            self.transform_way = transform_way
+            essential_attr = pickle.loads(txn.get(b"$$essential_attr$$"))
+            for ea in essential_attr:
+                setattr(self, ea, pickle.loads(txn.get(f"${ea}$".encode(encoding="utf-8"))))
+
+    def _remain_pipeline(self, pipeline):
+        for i, dp in enumerate(self.done_pipeline):
+            if pipeline[i] != dp:
+                raise ValueError(
+                    f"pipeline {self.done_pipeline} saved in this lmdb database is not match with pipeline:{pipeline}")
+        return pipeline[len(self.done_pipeline):]
+
+    def __repr__(self):
+        return f"LMDBDataset: {self.path}\nlength: {len(self)}\n{self.transform}"
 
     def __len__(self):
         return self._len
@@ -25,10 +54,77 @@ class LMDBDataset(Dataset):
     def __getitem__(self, idx):
         with self.db.begin(write=False) as txn:
             sample = pickle.loads(txn.get("{}".format(idx).encode()))
-            if self.output_transform is not None:
-                sample = self.output_transform(sample)
+            sample = self.transform_way(self.transform, sample)
             return sample
 
+    @staticmethod
+    def lmdbify(dataset, done_pipeline, lmdb_path):
+        env = lmdb.open(lmdb_path, map_size=2 ** 40, subdir=os.path.isdir(lmdb_path))
+        with env.begin(write=True) as txn:
+            for i in tqdm(range(len(dataset)), ncols=0):
+                txn.put("{}".format(i).encode(), pickle.dumps(dataset[i]))
+            txn.put(b"$$len$$", pickle.dumps(len(dataset)))
+            txn.put(b"$$done_pipeline$$", pickle.dumps(done_pipeline))
+            essential_attr = getattr(dataset, "essential_attr", list())
+            txn.put(b"$$essential_attr$$", pickle.dumps(essential_attr))
+            for ea in essential_attr:
+                txn.put(f"${ea}$".encode(encoding="utf-8"), pickle.dumps(getattr(dataset, ea)))
+
+
+@DATASET.register_module()
+class ImprovedImageFolder(ImageFolder):
+    def __init__(self, root, pipeline):
+        super().__init__(root, transform_pipeline(pipeline), loader=lambda x: x)
+        self.classes_list = defaultdict(list)
+        self.essential_attr = ["classes_list"]
+        for i in range(len(self)):
+            self.classes_list[self.samples[i][-1]].append(i)
+        assert len(self.classes_list) == len(self.classes)
+
+
+class EpisodicDataset(Dataset):
+    def __init__(self, origin_dataset, num_class, num_query, num_support, num_episodes):
+        self.origin = origin_dataset
+        self.num_class = num_class
+        assert self.num_class < len(self.origin.classes_list)
+        self.num_query = num_query  # K
+        self.num_support = num_support  # K
+        self.num_episodes = num_episodes
+
+    def _fetch_list_data(self, id_list):
+        return [self.origin[i][0] for i in id_list]
+
+    def __len__(self):
+        return self.num_episodes
+
+    def __getitem__(self, _):
+        random_classes = torch.randperm(len(self.origin.classes_list))[:self.num_class].tolist()
+        support_set_list = []
+        query_set_list = []
+        target_list = []
+        for tag, c in enumerate(random_classes):
+            image_list = self.origin.classes_list[c]
+
+            if len(image_list) >= self.num_query + self.num_support:
+                # have enough images belong to this class
+                idx_list = torch.randperm(len(image_list))[:self.num_query + self.num_support].tolist()
+            else:
+                idx_list = torch.randint(high=len(image_list), size=(self.num_query + self.num_support,)).tolist()
+
+            support = self._fetch_list_data(map(image_list.__getitem__, idx_list[:self.num_support]))
+            query = self._fetch_list_data(map(image_list.__getitem__, idx_list[self.num_support:]))
+            support_set_list.extend(support)
+            query_set_list.extend(query)
+            target_list.extend([tag] * self.num_query)
+        return {
+            "support": torch.stack(support_set_list),
+            "query": torch.stack(query_set_list),
+            "target": torch.tensor(target_list)
+        }
+
+    def __repr__(self):
+        return f"<EpisodicDataset NE{self.num_episodes} NC{self.num_class} NS{self.num_support} NQ{self.num_query}>"
+
 
 @DATASET.register_module()
 class SingleFolderDataset(Dataset):

engine/crossdomain.py

@@ -0,0 +1,83 @@
+import torch
+import torch.nn as nn
+from torchvision.datasets import ImageFolder
+
+import ignite.distributed as idist
+from ignite.contrib.metrics.gpu_info import GpuInfo
+from ignite.contrib.handlers.tensorboard_logger import TensorboardLogger, global_step_from_engine, OutputHandler, \
+    WeightsScalarHandler, GradsHistHandler, WeightsHistHandler, GradsScalarHandler
+from ignite.engine import create_supervised_evaluator, create_supervised_trainer, Events
+from ignite.metrics import Accuracy, Loss, RunningAverage
+from ignite.contrib.engines.common import save_best_model_by_val_score
+from ignite.contrib.handlers import ProgressBar
+
+from util.build import build_model, build_optimizer
+from util.handler import setup_common_handlers
+from data.transform import transform_pipeline
+from data.dataset import LMDBDataset
+
+
+def baseline_trainer(config, logger):
+    model = build_model(config.model, config.distributed.model)
+    optimizer = build_optimizer(model.parameters(), config.baseline.optimizers)
+    loss_fn = nn.CrossEntropyLoss()
+    trainer = create_supervised_trainer(model, optimizer, loss_fn, idist.device(), non_blocking=True,
+                                        output_transform=lambda x, y, y_pred, loss: (loss.item(), y_pred, y))
+    trainer.logger = logger
+    RunningAverage(output_transform=lambda x: x[0]).attach(trainer, "loss")
+    Accuracy(output_transform=lambda x: (x[1], x[2])).attach(trainer, "acc")
+    ProgressBar(ncols=0).attach(trainer)
+
+    if idist.get_rank() == 0:
+        GpuInfo().attach(trainer, name='gpu')
+
+        tb_logger = TensorboardLogger(log_dir=config.output_dir)
+        tb_logger.attach(
+            trainer,
+            log_handler=OutputHandler(
+                tag="train",
+                metric_names='all',
+                global_step_transform=global_step_from_engine(trainer),
+            ),
+            event_name=Events.EPOCH_COMPLETED
+        )
+        tb_logger.attach(trainer, log_handler=WeightsScalarHandler(model),
+                         event_name=Events.EPOCH_COMPLETED(every=10))
+
+        tb_logger.attach(trainer, log_handler=WeightsHistHandler(model), event_name=Events.EPOCH_COMPLETED(every=25))
+
+        tb_logger.attach(trainer, log_handler=GradsScalarHandler(model),
+                         event_name=Events.EPOCH_COMPLETED(every=10))
+
+        tb_logger.attach(trainer, log_handler=GradsHistHandler(model), event_name=Events.EPOCH_COMPLETED(every=25))
+
+        @trainer.on(Events.COMPLETED)
+        def _():
+            tb_logger.close()
+
+    to_save = dict(model=model, optimizer=optimizer, trainer=trainer)
+    setup_common_handlers(trainer, config.output_dir, print_interval_event=Events.EPOCH_COMPLETED, to_save=to_save,
+                          save_interval_event=Events.EPOCH_COMPLETED(every=25), n_saved=5,
+                          metrics_to_print=["loss", "acc"])
+    return trainer
+
+
+def run(task, config, logger):
+    assert torch.backends.cudnn.enabled
+    torch.backends.cudnn.benchmark = True
+    logger.info(f"start task {task}")
+    if task == "baseline":
+        train_dataset = LMDBDataset(config.baseline.data.dataset.train.lmdb_path,
+                                    pipeline=config.baseline.data.dataset.train.pipeline)
+        # train_dataset = ImageFolder(config.baseline.data.dataset.train.path,
+        #                             transform=transform_pipeline(config.baseline.data.dataset.train.pipeline))
+        logger.info(f"train with dataset:\n{train_dataset}")
+        train_data_loader = idist.auto_dataloader(train_dataset, **config.baseline.data.dataloader)
+        trainer = baseline_trainer(config, logger)
+        try:
+            trainer.run(train_data_loader, max_epochs=400)
+        except Exception:
+            import traceback
+            print(traceback.format_exc())
+    else:
+        return NotImplemented(f"invalid task: {task}")

main.py

@@ -30,7 +30,10 @@ def running(local_rank, config, task, backup_config=False, setup_output_dir=Fals
         output_dir = Path(config.result_dir) / config.name if config.output_dir is None else config.output_dir
         config.output_dir = str(output_dir)
         if output_dir.exists():
-            assert not any(output_dir.iterdir()), "output_dir must be empty"
+            # assert not any(output_dir.iterdir()), "output_dir must be empty"
+            contains = list(output_dir.iterdir())
+            assert (len(contains) == 0) or (len(contains) == 1 and contains[0].name == "config.yml"), \
+                f"output_dir must by empty or only contains config.yml, but now got {len(contains)} files"
         else:
             if idist.get_rank() == 0:
                 output_dir.mkdir(parents=True)

model/__init__.py

@@ -1,2 +1,3 @@
 from model.registry import MODEL
 import model.residual_generator
+import model.fewshot

model/fewshot.py

@@ -0,0 +1,105 @@
+import math
+
+import torch.nn as nn
+
+from .registry import MODEL
+
+
+# --- gaussian initialize ---
+def init_layer(l):
+    # Initialization using fan-in
+    if isinstance(l, nn.Conv2d):
+        n = l.kernel_size[0] * l.kernel_size[1] * l.out_channels
+        l.weight.data.normal_(0, math.sqrt(2.0 / float(n)))
+    elif isinstance(l, nn.BatchNorm2d):
+        l.weight.data.fill_(1)
+        l.bias.data.fill_(0)
+    elif isinstance(l, nn.Linear):
+        l.bias.data.fill_(0)
+
+
+class Flatten(nn.Module):
+    def __init__(self):
+        super(Flatten, self).__init__()
+
+    def forward(self, x):
+        return x.view(x.size(0), -1)
+
+
+class SimpleBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, half_res, leakyrelu=False):
+        super(SimpleBlock, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.block = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2 if half_res else 1, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True) if not leakyrelu else nn.LeakyReLU(0.2, inplace=True),
+            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+        )
+        self.relu = nn.ReLU(inplace=True) if not leakyrelu else nn.LeakyReLU(0.2, inplace=True)
+        if in_channels != out_channels:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, 1, 2 if half_res else 1, bias=False),
+                nn.BatchNorm2d(out_channels)
+            )
+        else:
+            self.shortcut = nn.Identity()
+
+    def forward(self, x):
+        o = self.block(x)
+        return self.relu(o + self.shortcut(x))
+
+
+class ResNet(nn.Module):
+    def __init__(self, block, layers, dims, num_classes=None, classifier_type="linear", flatten=True, leakyrelu=False):
+        super().__init__()
+        assert len(layers) == 4, 'Can have only four stages'
+        self.inplanes = 64
+
+        self.start = nn.Sequential(
+            nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False),
+            nn.BatchNorm2d(self.inplanes),
+            nn.ReLU(inplace=True) if not leakyrelu else nn.LeakyReLU(0.2, inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        )
+
+        trunk = []
+        in_channels = self.inplanes
+        for i in range(4):
+            for j in range(layers[i]):
+                half_res = i >= 1 and j == 0
+                trunk.append(block(in_channels, dims[i], half_res, leakyrelu))
+                in_channels = dims[i]
+        if flatten:
+            trunk.append(nn.AvgPool2d(7))
+            trunk.append(Flatten())
+        if num_classes is not None:
+            if classifier_type == "linear":
+                trunk.append(nn.Linear(in_channels, num_classes))
+            elif classifier_type == "distlinear":
+                pass
+            else:
+                raise ValueError(f"invalid classifier_type:{classifier_type}")
+        self.trunk = nn.Sequential(*trunk)
+        self.apply(init_layer)
+
+    def forward(self, x):
+        return self.trunk(self.start(x))
+
+
+@MODEL.register_module()
+def resnet10(num_classes=None, classifier_type="linear", flatten=True, leakyrelu=False):
+    return ResNet(SimpleBlock, [1, 1, 1, 1], [64, 128, 256, 512], num_classes, classifier_type, flatten, leakyrelu)
+
+
+@MODEL.register_module()
+def resnet18(num_classes=None, classifier_type="linear", flatten=True, leakyrelu=False):
+    return ResNet(SimpleBlock, [2, 2, 2, 2], [64, 128, 256, 512], num_classes, classifier_type, flatten, leakyrelu)
+
+
+@MODEL.register_module()
+def resnet34(num_classes=None, classifier_type="linear", flatten=True, leakyrelu=False):
+    return ResNet(SimpleBlock, [3, 4, 6, 3], [64, 128, 256, 512], num_classes, classifier_type, flatten, leakyrelu)

run.sh

@@ -1,8 +1,14 @@
 #!/usr/bin/env bash
 
 CONFIG=$1
-GPUS=$2
+TASK=$2
+GPUS=$3
 
-#  CUDA_VISIBLE_DEVICES=$GPUS \
-PYTHONPATH=.:$PYTHONPATH OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node="$GPUS" \
-  main.py train "$CONFIG" --backup_config --setup_output_dir --setup_random_seed
+_command="print(len('${GPUS}'.split(',')))"
+GPU_COUNT=$(python3 -c "${_command}")
+
+echo "GPU_COUNT:${GPU_COUNT}"
+
+CUDA_VISIBLE_DEVICES=$GPUS \
+PYTHONPATH=.:$PYTHONPATH OMP_NUM_THREADS=1 python -m torch.distributed.launch --nproc_per_node="$GPU_COUNT" \
+  main.py "$TASK" "$CONFIG" --backup_config --setup_output_dir --setup_random_seed

tool/lmdbify.py

@@ -0,0 +1,20 @@
+import fire
+from omegaconf import OmegaConf
+from data.dataset import ImprovedImageFolder, LMDBDataset
+
+pipeline = """
+pipeline:
+    - Load
+"""
+
+
+def transform(dataset_path, save_path):
+    print(save_path, dataset_path)
+    conf = OmegaConf.create(pipeline)
+    print(conf.pipeline.pretty())
+    origin_dataset = ImprovedImageFolder(dataset_path, conf.pipeline)
+    LMDBDataset.lmdbify(origin_dataset, conf.pipeline, save_path)
+
+
+if __name__ == '__main__':
+    fire.Fire(transform)

util/handler.py

@@ -3,13 +3,13 @@ from pathlib import Path
 import torch
 
 import ignite.distributed as idist
-from ignite.engine import Events
+from ignite.engine import Events, Engine
 from ignite.handlers import Checkpoint, DiskSaver, TerminateOnNan
 from ignite.contrib.handlers import BasicTimeProfiler
 
 
 def setup_common_handlers(
-        trainer,
+        trainer: Engine,
         output_dir=None,
         stop_on_nan=True,
         use_profiler=True,
@@ -39,6 +39,11 @@ def setup_common_handlers(
     :param checkpoint_kwargs:
     :return:
     """
+    @trainer.on(Events.STARTED)
+    @idist.one_rank_only()
+    def print_dataloader_size(engine):
+        engine.logger.info(f"data loader length: {len(engine.state.dataloader)}")
+
     if stop_on_nan:
         trainer.add_event_handler(Events.ITERATION_COMPLETED, TerminateOnNan())
 
@@ -68,6 +73,8 @@ def setup_common_handlers(
         def print_interval(engine):
             print_str = f"epoch:{engine.state.epoch} iter:{engine.state.iteration}\t"
             for m in metrics_to_print:
+                if m not in engine.state.metrics:
+                    continue
                 print_str += f"{m}={engine.state.metrics[m]:.3f} "
             engine.logger.info(print_str)