x = self.effnet.extract_features(image)
x = F.adaptive_avg_pool2d(x, 1).reshape(batch_size, -1)
outputs = self.out(self.dropout(x))
if targets is not None:
loss = nn.CrossEntropyLoss()(outputs, targets)
metrics = self.monitor_metrics(outputs, targets)
return outputs, loss, metrics
return outputs, None, None
train_dataset = ImageDataset(
image_paths=train_image_paths,
targets=train_targets,
resize=(255,255),
augmentations=train_aug,
)
valid_dataset = ImageDataset(
image_paths=valid_image_paths,
targets=valid_targets,
resize=(255,255),
augmentations=valid_aug,
)
model = SnakeModel(num_classes=dfx.breed.nunique())
es = EarlyStopping(
monitor="valid_loss", model_path="model.bin", patience=3, mode="min"
)
model.fit(
valid_image_paths = glob.glob(
os.path.join(INPUT_PATH, f"jpeg-{IMAGE_SIZE}x{IMAGE_SIZE}", "val",
"**", "*.jpeg"),
recursive=True,
)
train_targets = [x.split("/")[-2] for x in train_image_paths]
valid_targets = [x.split("/")[-2] for x in valid_image_paths]
lbl_enc = preprocessing.LabelEncoder()
train_targets = lbl_enc.fit_transform(train_targets)
valid_targets = lbl_enc.transform(valid_targets)
train_dataset = ImageDataset(
image_paths=train_image_paths,
targets=train_targets,
resize=None,
augmentations=train_aug,
)
valid_dataset = ImageDataset(
image_paths=valid_image_paths,
targets=valid_targets,
resize=None,
augmentations=valid_aug,
)
model = FlowerModel(num_classes=len(lbl_enc.classes_))
es = EarlyStopping(
monitor="valid_loss",
model_path=os.path.join(MODEL_PATH, MODEL_NAME + ".bin"),
recursive=True,
)
test_image_paths = glob.glob(
os.path.join(INPUT_PATH, f"jpeg-{IMAGE_SIZE}x{IMAGE_SIZE}", "test", "*.jpeg"),
)
train_targets = [x.split("/")[-2] for x in train_image_paths]
valid_targets = [x.split("/")[-2] for x in valid_image_paths]
train_targets = [CLASSES[c] for c in train_targets]
valid_targets = [CLASSES[c] for c in valid_targets]
train_dataset = ImageDataset(
image_paths=train_image_paths,
targets=train_targets,
augmentations=train_aug,
)
valid_dataset = ImageDataset(
image_paths=valid_image_paths,
targets=valid_targets,
augmentations=valid_aug,
)
test_dataset = ImageDataset(
image_paths=test_image_paths,
targets=[0] * len(test_image_paths),
augmentations=test_aug,
)