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Update detectron2/engine/defaults.py to make DefaultPredictor able to accept batch predictions #5330

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Update detectron2/engine/defaults.py to make DefaultPredictor able to accept batch predictions #5330

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54 changes: 37 additions & 17 deletions detectron2/engine/defaults.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
import weakref
from collections import OrderedDict
from typing import Optional
import numpy as np
import torch
from fvcore.nn.precise_bn import get_bn_modules
from omegaconf import OmegaConf
Expand Down Expand Up @@ -294,30 +295,49 @@ def __init__(self, cfg):
self.input_format = cfg.INPUT.FORMAT
assert self.input_format in ["RGB", "BGR"], self.input_format

def __call__(self, original_image):
def __call__(self, original_images):
"""
Args:
original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).

original_images (np.ndarray or List[np.ndarray]):
an image of shape (H, W, C) or (B, H, W, C) (in BGR order).
Returns:
predictions (dict):
predictions (dict or List[dict]):
the output of the model for one image only.
See :doc:`/tutorials/models` for details about the format.
"""
with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258
# Apply pre-processing to image.
if self.input_format == "RGB":
# whether the model expects BGR inputs or RGB
original_image = original_image[:, :, ::-1]
height, width = original_image.shape[:2]
image = self.aug.get_transform(original_image).apply_image(original_image)
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
image.to(self.cfg.MODEL.DEVICE)

inputs = {"image": image, "height": height, "width": width}

predictions = self.model([inputs])[0]
return predictions
if isinstance(original_images, np.array):
original_image = original_images
# Apply pre-processing to image.
if self.input_format == "RGB":
# whether the model expects BGR inputs or RGB
original_image = original_image[:, :, ::-1]
height, width = original_image.shape[:2]
image = self.aug.get_transform(original_image).apply_image(original_image)
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
image.to(self.cfg.MODEL.DEVICE)

inputs = {"image": image, "height": height, "width": width}

predictions = self.model([inputs])[0]
return predictions
elif isinstance(original_images, list):
batch_inputs = []
for original_image in original_images:
if self.input_format == "RGB":
# whether the model expects BGR inputs or RGB
original_image = original_image[:, :, ::-1]
height, width = original_image.shape[:2]
image = self.aug.get_transform(original_image).apply_image(original_image)
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
image.to(self.cfg.MODEL.DEVICE)

inputs = {"image": image, "height": height, "width": width}
batch_inputs.append(inputs)
predictions = self.model(batch_inputs)
return predictions
else:
return None


class DefaultTrainer(TrainerBase):
Expand Down