質問編集履歴
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### 前提・実現したいこと
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----> 5 model = classifier(flags.aug_kwargs)
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725 result = self._slow_forward(*input, **kwargs)
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726 else:
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--> 727 result = self.forward(*input, **kwargs)
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728 for hook in itertools.chain(
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729 _global_forward_hooks.values(),
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TypeError: forward() missing 1 required positional argument: 'targets'
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```
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#モデルの読み込み
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model_path = '.
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model_path = './predictor_last.pt'
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model = classifier(flags.aug_kwargs)
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model.load_state_dict(torch.load(model_path))
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```
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Pytorchで保存したモデルを読み込みたいのですが、エラーが発生してしまいました。
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[こちら](https://tzmi.hatenablog.com/entry/2020/03/05/222813)のGPUでの読み出し方法を参考に
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コードを書いてみたのですが、エラーが発生してしまいました。
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原因についてお分かりの方がいらっしゃいましたら、ご教示いただけますと幸いです。
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AttributeError Traceback (most recent call last)
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<ipython-input-15-64589b233dc6> in <module>
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1 #モデルの読み込み方法
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2 model_path = '../input/detectron2-model/predictor_tf_inception_v3.pt'
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### 該当のソースコード
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```Python
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@dataclass
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class Flags:
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# General
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debug: bool = True
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outdir: str = "results/det"
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device: str = "cuda:0"
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# Data config
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imgdir_name: str = "vinbigdata-chest-xray-resized-png-256x256"
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# split_mode: str = "all_train"
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seed: int = 111
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target_fold: int = 0 # 0~4
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label_smoothing: float = 0.0
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# Model config
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model_name: str = "tf_inception_v3"
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model_mode: str = "normal" # normal, cnn_fixed supported
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# Training config
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epoch: int = 20
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batchsize: int = 8
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valid_batchsize: int = 16
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num_workers: int = 4
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snapshot_freq: int = 5
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ema_decay: float = 0.999 # negative value is to inactivate ema.
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scheduler_type: str = ""
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scheduler_kwargs: Dict[str, Any] = field(default_factory=lambda: {})
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scheduler_trigger: List[Union[int, str]] = field(default_factory=lambda: [1, "iteration"])
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aug_kwargs: Dict[str, Dict[str, Any]] = field(default_factory=lambda: {})
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mixup_prob: float = -1.0 # Apply mixup augmentation when positive value is set.
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def update(self, param_dict: Dict) -> "Flags":
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# Overwrite by `param_dict`
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for key, value in param_dict.items():
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if not hasattr(self, key):
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raise ValueError(f"[ERROR] Unexpected key for flag = {key}")
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setattr(self, key, value)
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return self
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flags_dict = {
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"debug": False, # Change to True for fast debug run!
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"outdir": "results/tmp_debug",
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# Data
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"imgdir_name": "vinbigdata-chest-xray-resized-png-256x256",
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# Model
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"model_name": "tf_inception_v3",#"resnet18",
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# Training
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"num_workers": 4,
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"epoch": 15,
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"batchsize": 8,
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"scheduler_type": "CosineAnnealingWarmRestarts",
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"scheduler_kwargs": {"T_0": 28125}, # 15000 * 15 epoch // (batchsize=8)
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"scheduler_trigger": [1, "iteration"],
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"aug_kwargs": {
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"HorizontalFlip": {"p": 0.5},
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"ShiftScaleRotate": {"scale_limit": 0.15, "rotate_limit": 10, "p": 0.5},
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"RandomBrightnessContrast": {"p": 0.5},
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"CoarseDropout": {"max_holes": 8, "max_height": 25, "max_width": 25, "p": 0.5},
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"Blur": {"blur_limit": [3, 7], "p": 0.5},
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"Downscale": {"scale_min": 0.25, "scale_max": 0.9, "p": 0.3},
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"RandomGamma": {"gamma_limit": [80, 120], "p": 0.6},
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}
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}
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# args = parse()
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print("torch", torch.__version__)
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flags = Flags().update(flags_dict)
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print("flags", flags)
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debug = flags.debug
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outdir = Path(flags.outdir)
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os.makedirs(str(outdir), exist_ok=True)
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flags_dict = dataclasses.asdict(flags)
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save_yaml(str(outdir / "flags.yaml"), flags_dict)
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# --- Read data ---
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inputdir = Path("/kaggle/input")
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datadir = inputdir / "vinbigdata-chest-xray-abnormalities-detection"
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imgdir = inputdir / flags.imgdir_name
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# Read in the data CSV files
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train = pd.read_csv(datadir / "train.csv")
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class CNNFixedPredictor(nn.Module):
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def __init__(self, cnn: nn.Module, num_classes: int = 2):
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super(CNNFixedPredictor, self).__init__()
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self.cnn = cnn
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self.lin = Linear(cnn.num_features, num_classes)
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print("cnn.num_features", cnn.num_features)
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# We do not learn CNN parameters.
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# https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html
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for param in self.cnn.parameters():
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param.requires_grad = False
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def forward(self, x):
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feat = self.cnn(x)
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return self.lin(feat)
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def build_predictor(model_name: str, model_mode: str = "normal"):
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if model_mode == "normal":
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# normal configuration. train all parameters.
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return timm.create_model(model_name, pretrained=True, num_classes=2, in_chans=3)
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elif model_mode == "cnn_fixed":
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# normal configuration. train all parameters.
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# https://rwightman.github.io/pytorch-image-models/feature_extraction/
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timm_model = timm.create_model(model_name, pretrained=True, num_classes=0, in_chans=3)
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return CNNFixedPredictor(timm_model, num_classes=2)
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else:
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raise ValueError(f"[ERROR] Unexpected value model_mode={model_mode}")
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class Classifier(nn.Module):
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"""two class classfication"""
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def __init__(self, predictor, lossfun=cross_entropy_with_logits):
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super().__init__()
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self.predictor = predictor
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self.lossfun = lossfun
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self.prefix = ""
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def forward(self, image, targets):
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outputs = self.predictor(image)
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loss = self.lossfun(outputs, targets)
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metrics = {
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f"{self.prefix}loss": loss.item(),
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f"{self.prefix}acc": accuracy_with_logits(outputs, targets).item()
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}
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ppe.reporting.report(metrics, self)
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return loss, metrics
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def predict(self, data_loader):
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pred = self.predict_proba(data_loader)
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label = torch.argmax(pred, dim=1)
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return label
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def predict_proba(self, data_loader):
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device: torch.device = next(self.parameters()).device
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y_list = []
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self.eval()
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with torch.no_grad():
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for batch in data_loader:
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if isinstance(batch, (tuple, list)):
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# Assumes first argument is "image"
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batch = batch[0].to(device)
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308
|
+
|
|
309
|
+
else:
|
|
310
|
+
|
|
311
|
+
batch = batch.to(device)
|
|
312
|
+
|
|
313
|
+
y = self.predictor(batch)
|
|
314
|
+
|
|
315
|
+
y = torch.softmax(y, dim=-1)
|
|
316
|
+
|
|
317
|
+
y_list.append(y)
|
|
318
|
+
|
|
319
|
+
pred = torch.cat(y_list)
|
|
320
|
+
|
|
321
|
+
return pred
|
|
322
|
+
|
|
323
|
+
|
|
324
|
+
|
|
325
|
+
def create_trainer(model, optimizer, device) -> Engine:
|
|
326
|
+
|
|
327
|
+
model.to(device)
|
|
328
|
+
|
|
329
|
+
|
|
330
|
+
|
|
331
|
+
def update_fn(engine, batch):
|
|
332
|
+
|
|
333
|
+
model.train()
|
|
334
|
+
|
|
335
|
+
optimizer.zero_grad()
|
|
336
|
+
|
|
337
|
+
loss, metrics = model(*[elem.to(device) for elem in batch])
|
|
338
|
+
|
|
339
|
+
loss.backward()
|
|
340
|
+
|
|
341
|
+
optimizer.step()
|
|
342
|
+
|
|
343
|
+
return metrics
|
|
344
|
+
|
|
345
|
+
trainer = Engine(update_fn)
|
|
346
|
+
|
|
347
|
+
return trainer
|
|
348
|
+
|
|
349
|
+
|
|
350
|
+
|
|
351
|
+
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=flags.seed)
|
|
352
|
+
|
|
353
|
+
# skf.get_n_splits(None, None)
|
|
354
|
+
|
|
355
|
+
y = np.array([int(len(d["annotations"]) > 0) for d in dataset_dicts])
|
|
356
|
+
|
|
357
|
+
split_inds = list(skf.split(dataset_dicts, y))
|
|
358
|
+
|
|
359
|
+
train_inds, valid_inds = split_inds[flags.target_fold] # 0th fold
|
|
360
|
+
|
|
361
|
+
train_dataset = VinbigdataTwoClassDataset(
|
|
362
|
+
|
|
363
|
+
[dataset_dicts[i] for i in train_inds],
|
|
364
|
+
|
|
365
|
+
image_transform=Transform(flags.aug_kwargs),
|
|
366
|
+
|
|
367
|
+
mixup_prob=flags.mixup_prob,
|
|
368
|
+
|
|
369
|
+
label_smoothing=flags.label_smoothing,
|
|
370
|
+
|
|
371
|
+
)
|
|
372
|
+
|
|
373
|
+
valid_dataset = VinbigdataTwoClassDataset([dataset_dicts[i] for i in valid_inds])
|
|
374
|
+
|
|
375
|
+
|
|
376
|
+
|
|
377
|
+
train_loader = DataLoader(
|
|
378
|
+
|
|
379
|
+
train_dataset,
|
|
380
|
+
|
|
381
|
+
batch_size=flags.batchsize,
|
|
382
|
+
|
|
383
|
+
num_workers=flags.num_workers,
|
|
384
|
+
|
|
385
|
+
shuffle=True,
|
|
386
|
+
|
|
387
|
+
pin_memory=True,
|
|
388
|
+
|
|
389
|
+
)
|
|
390
|
+
|
|
391
|
+
valid_loader = DataLoader(
|
|
392
|
+
|
|
393
|
+
valid_dataset,
|
|
394
|
+
|
|
395
|
+
batch_size=flags.valid_batchsize,
|
|
396
|
+
|
|
397
|
+
num_workers=flags.num_workers,
|
|
398
|
+
|
|
399
|
+
shuffle=False,
|
|
400
|
+
|
|
401
|
+
pin_memory=True,
|
|
402
|
+
|
|
403
|
+
)
|
|
404
|
+
|
|
405
|
+
|
|
406
|
+
|
|
407
|
+
device = torch.device(flags.device)
|
|
408
|
+
|
|
409
|
+
|
|
410
|
+
|
|
411
|
+
predictor = build_predictor(model_name=flags.model_name, model_mode=flags.model_mode)
|
|
412
|
+
|
|
413
|
+
classifier = Classifier(predictor)
|
|
414
|
+
|
|
415
|
+
model = classifier
|
|
416
|
+
|
|
417
|
+
optimizer = optim.Adam([param for param in model.parameters() if param.requires_grad], lr=1e-3)
|
|
418
|
+
|
|
419
|
+
|
|
420
|
+
|
|
421
|
+
# Train setup
|
|
422
|
+
|
|
423
|
+
trainer = create_trainer(model, optimizer, device)
|
|
424
|
+
|
|
425
|
+
|
|
426
|
+
|
|
427
|
+
ema = EMA(predictor, decay=flags.ema_decay)
|
|
428
|
+
|
|
429
|
+
|
|
430
|
+
|
|
431
|
+
def eval_func(*batch):
|
|
432
|
+
|
|
433
|
+
loss, metrics = model(*[elem.to(device) for elem in batch])
|
|
434
|
+
|
|
435
|
+
# HACKING: report ema value with prefix.
|
|
436
|
+
|
|
437
|
+
if flags.ema_decay > 0:
|
|
438
|
+
|
|
439
|
+
classifier.prefix = "ema_"
|
|
440
|
+
|
|
441
|
+
ema.assign()
|
|
442
|
+
|
|
443
|
+
loss, metrics = model(*[elem.to(device) for elem in batch])
|
|
444
|
+
|
|
445
|
+
ema.resume()
|
|
446
|
+
|
|
447
|
+
classifier.prefix = ""
|
|
448
|
+
|
|
449
|
+
|
|
450
|
+
|
|
451
|
+
valid_evaluator = E.Evaluator(
|
|
452
|
+
|
|
453
|
+
valid_loader, model, progress_bar=False, eval_func=eval_func, device=device
|
|
454
|
+
|
|
455
|
+
)
|
|
456
|
+
|
|
457
|
+
|
|
458
|
+
|
|
459
|
+
# log_trigger = (10 if debug else 1000, "iteration")
|
|
460
|
+
|
|
461
|
+
log_trigger = (1, "epoch")
|
|
462
|
+
|
|
463
|
+
log_report = E.LogReport(trigger=log_trigger)
|
|
464
|
+
|
|
465
|
+
extensions = [
|
|
466
|
+
|
|
467
|
+
log_report,
|
|
468
|
+
|
|
469
|
+
E.ProgressBarNotebook(update_interval=10 if debug else 100), # Show progress bar during training
|
|
470
|
+
|
|
471
|
+
E.PrintReportNotebook(),
|
|
472
|
+
|
|
473
|
+
E.FailOnNonNumber(),
|
|
474
|
+
|
|
475
|
+
]
|
|
476
|
+
|
|
477
|
+
epoch = flags.epoch
|
|
478
|
+
|
|
479
|
+
models = {"main": model}
|
|
480
|
+
|
|
481
|
+
optimizers = {"main": optimizer}
|
|
482
|
+
|
|
483
|
+
manager = IgniteExtensionsManager(
|
|
484
|
+
|
|
485
|
+
trainer, models, optimizers, epoch, extensions=extensions, out_dir=str(outdir),
|
|
486
|
+
|
|
487
|
+
)
|
|
488
|
+
|
|
489
|
+
# Run evaluation for valid dataset in each epoch.
|
|
490
|
+
|
|
491
|
+
manager.extend(valid_evaluator)
|
|
492
|
+
|
|
493
|
+
|
|
494
|
+
|
|
495
|
+
# Save predictor.pt every epoch
|
|
496
|
+
|
|
497
|
+
manager.extend(
|
|
498
|
+
|
|
499
|
+
E.snapshot_object(predictor, "predictor.pt"), trigger=(flags.snapshot_freq, "epoch")
|
|
500
|
+
|
|
501
|
+
)
|
|
502
|
+
|
|
503
|
+
|
|
504
|
+
|
|
505
|
+
manager.extend(E.observe_lr(optimizer=optimizer), trigger=log_trigger)
|
|
506
|
+
|
|
507
|
+
|
|
508
|
+
|
|
509
|
+
if flags.ema_decay > 0:
|
|
510
|
+
|
|
511
|
+
# Exponential moving average
|
|
512
|
+
|
|
513
|
+
manager.extend(lambda manager: ema(), trigger=(1, "iteration"))
|
|
514
|
+
|
|
515
|
+
|
|
516
|
+
|
|
517
|
+
def save_ema_model(manager):
|
|
518
|
+
|
|
519
|
+
ema.assign()
|
|
520
|
+
|
|
521
|
+
#torch.save(predictor.state_dict(), outdir / "predictor_ema.pt")
|
|
522
|
+
|
|
523
|
+
torch.save(predictor.state_dict(), "/kaggle/working/predictor_ema.pt")
|
|
524
|
+
|
|
525
|
+
ema.resume()
|
|
526
|
+
|
|
527
|
+
|
|
528
|
+
|
|
529
|
+
manager.extend(save_ema_model, trigger=(flags.snapshot_freq, "epoch"))
|
|
530
|
+
|
|
531
|
+
|
|
532
|
+
|
|
533
|
+
_ = trainer.run(train_loader, max_epochs=epoch)
|
|
534
|
+
|
|
535
|
+
|
|
536
|
+
|
|
537
|
+
torch.save(predictor.state_dict(), "/kaggle/working/predictor_tf_inception_v3.pt")
|
|
538
|
+
|
|
539
|
+
df = log_report.to_dataframe()
|
|
540
|
+
|
|
541
|
+
df.to_csv("/kaggle/working/log.csv", index=False)
|
|
542
|
+
|
|
543
|
+
|
|
544
|
+
|
|
545
|
+
####################################追記終わり#####################################################
|
|
546
|
+
|
|
547
|
+
|
|
548
|
+
|
|
549
|
+
#モデルの読み込み
|
|
50
550
|
|
|
51
551
|
model_path = '../input/detectron2-model/predictor_tf_inception_v3.pt'
|
|
52
552
|
|
|
53
553
|
model = model.load_state_dict(torch.load(model_path))
|
|
54
554
|
|
|
55
555
|
```
|
|
56
|
-
|
|
57
|
-
|
|
58
|
-
|
|
59
|
-
### 補足情報（FW/ツールのバージョンなど）
|
|
60
|
-
|
|
61
|
-
|
|
62
|
-
|
|
63
|
-
Pytorch: 1.7.1
|
|
64
|
-
|
|
65
|
-
cuda: 10.1
|
|
66
|
-
|
|
67
|
-
kaggleのNotebookを使用しております
|