def run_stage(self, stage, number):
logging.info(f"start {stage}")
stage_cfg = update_config(self.base_cfg,
Dict(self.base_cfg.stages[stage]))
weights_path = self.get_stage_weights_path(stage)
previous_checkpoint = self.get_best_previous_checkpoint(number)
if previous_checkpoint:
print(f"start from previous {previous_checkpoint}")
pipeline = ImageNetLightningPipeline.load_from_checkpoint_params(
checkpoint_path=previous_checkpoint, hparams=stage_cfg)
else:
pipeline = ImageNetLightningPipeline(stage_cfg)
trainer = object_from_dict(
stage_cfg.trainer,
checkpoint_callback=object_from_dict(stage_cfg.checkpoint,
filepath=weights_path),
logger=object_from_dict(stage_cfg.logger,
path=self.log_path,
run_name=f"{stage}",
version=self.base_cfg.version),
)
trainer.fit(pipeline)
del pipeline, trainer
def main():
# val_dataset = HakunaDataset(mode="val", path=PATHS["data.path"], long_side=320, crop_size=(192, 256))
# val_dataloader = DataLoader(val_dataset, num_workers=4, batch_size=8, collate_fn=fast_collate)
cfg = Dict(Fire(fit))
set_determenistic(cfg.seed)
add_dict = {"val_data": {"batch_size": 8}}
add_dict = Dict(add_dict)
print(add_dict, "\t")
cfg = Dict(update_config(cfg, add_dict))
print("\t")
print(cfg.data)
loader = object_from_dict(cfg.val_data)
batch_size = loader.batch_size
imagenet_mean = np.array([0.485, 0.456, 0.406])
imagenet_std = np.array([0.229, 0.224, 0.225])
# for idx, batch in enumerate(loader):
# images, targets = batch
# images = images.numpy()
# targets = targets.numpy()
# plt.figure()
# for i in range(images.shape[0]):
# plt.subplot(2, 4, i + 1)
# image = np.transpose(images[i], (1, 2, 0))
# plt.title(np.argmax(targets[i]))
# plt.imshow(image)
for images, targets in tqdm(loader, total=len(loader)):
print(images.shape)
print(targets.shape)
img = np.transpose(images.cpu().numpy(), (0, 2, 3, 1))
labels = targets.cpu().numpy()
plt.figure(figsize=(25, 35))
for i in range(batch_size):
plt.subplot(2, 4, i + 1)
shw = np.uint8(
np.clip(255 * (imagenet_mean * img[i] + imagenet_std), 0, 255))
plt.imshow(shw)
plt.show()
def main():
cfg = Dict(Fire(fit))
set_determenistic(cfg.seed)
add_dict = {"data": {"batch_size": 24}}
add_dict = Dict(add_dict)
print(add_dict, "\t")
cfg = Dict(update_config(cfg, add_dict))
print("\t")
print(cfg.data)
loader = object_from_dict(cfg.data, mode="val")
batch_size = loader.batch_size
side = int(np.sqrt(batch_size))
imagenet_mean = np.array([0.485, 0.456, 0.406])
imagenet_std = np.array([0.229, 0.224, 0.225])
for images, targets in tqdm(loader, total=len(loader)):
print(images.shape)
print(targets.shape)
img = np.transpose(images.cpu().numpy(), (0, 2, 3, 1))
labels = targets.cpu().numpy()
plt.figure(figsize=(25, 35))
for i in range(batch_size):
plt.subplot(side, side, i + 1)
shw = np.uint8(
np.clip(255 * (imagenet_mean * img[i] + imagenet_std), 0, 255))
plt.imshow(shw)
plt.show()
break
return torch.utils.data.distributed.DistributedSampler # for pytorch_lightning
def dataset(self): # for pytorch_lightning
return None
if __name__ == "__main__":
cfg = Dict(Fire(fit))
set_determenistic(cfg.seed)
add_dict = {"data": {"batch_size": 25}}
add_dict = Dict(add_dict)
print(add_dict, "\t")
cfg = Dict(update_config(cfg, add_dict))
print("\t")
print(cfg)
loader = object_from_dict(cfg.data, mode="val")
batch_size = loader.batch_size
side = int(np.sqrt(batch_size))
imagenet_mean = np.array([0.485, 0.456, 0.406])
imagenet_std = np.array([0.229, 0.224, 0.225])
for images, targets in tqdm(loader, total=len(loader)):
print(images.shape)
print(targets.shape)