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apply Black 25.11.0 style in fbcode/deeplearning/projects (21/92)

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Summary:
Formats the covered files with pyfmt.

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Reviewed By: itamaro

Differential Revision: D90476315

fbshipit-source-id: ee94c471788b8e7d067813d8b3e0311214d17f3f
This commit is contained in:
Bowie Chen
2026-01-11 23:16:49 -08:00
committed by meta-codesync[bot]
parent 7b89b8fc3f
commit 11dec2936d
69 changed files with 445 additions and 522 deletions
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36
sam3/eval/cgf1_eval.py
View File

@@ -126,9 +126,9 @@ class COCOCustom(COCO):
# MODIFICATION: faster and cached subset check
if not hasattr(self, "img_id_set"):
self.img_id_set = set(self.getImgIds())
assert set(annsImgIds).issubset(
self.img_id_set
), "Results do not correspond to current coco set"
assert set(annsImgIds).issubset(self.img_id_set), (
"Results do not correspond to current coco set"
)
# END MODIFICATION
if "caption" in anns[0]:
imgIds = set([img["id"] for img in res.dataset["images"]]) & set(
@@ -301,9 +301,9 @@ class CGF1Eval(COCOeval):
TP = (match_scores >= thresh).sum()
FP = len(dt) - TP
FN = len(gt) - TP
assert (
FP >= 0 and FN >= 0
), f"FP: {FP}, FN: {FN}, TP: {TP}, match_scores: {match_scores}, len(dt): {len(dt)}, len(gt): {len(gt)}, ious: {ious}"
assert FP >= 0 and FN >= 0, (
f"FP: {FP}, FN: {FN}, TP: {TP}, match_scores: {match_scores}, len(dt): {len(dt)}, len(gt): {len(gt)}, ious: {ious}"
)
TPs.append(TP)
FPs.append(FP)
FNs.append(FN)
@@ -599,9 +599,9 @@ class CGF1Evaluator:
"""
assert len(self.coco_gts) > 0, "No ground truth provided for evaluation."
assert len(self.coco_gts) == len(
self.coco_evals
), "Mismatch in number of ground truths and evaluators."
assert len(self.coco_gts) == len(self.coco_evals), (
"Mismatch in number of ground truths and evaluators."
)
if self.verbose:
print(f"Loading predictions from {pred_file}")
@@ -668,17 +668,17 @@ class CGF1Evaluator:
if len(scorings) == 1:
return scorings[0]
assert (
scorings[0].ndim == 3
), f"Expecting results in [numCats, numAreas, numImgs] format, got {scorings[0].shape}"
assert (
scorings[0].shape[0] == 1
), f"Expecting a single category, got {scorings[0].shape[0]}"
assert scorings[0].ndim == 3, (
f"Expecting results in [numCats, numAreas, numImgs] format, got {scorings[0].shape}"
)
assert scorings[0].shape[0] == 1, (
f"Expecting a single category, got {scorings[0].shape[0]}"
)
for scoring in scorings:
assert (
scoring.shape == scorings[0].shape
), f"Shape mismatch: {scoring.shape}, {scorings[0].shape}"
assert scoring.shape == scorings[0].shape, (
f"Shape mismatch: {scoring.shape}, {scorings[0].shape}"
)
selected_imgs = []
for img_id in range(scorings[0].shape[-1]):

10
sam3/eval/coco_eval.py
View File

@@ -18,19 +18,15 @@ import os
import pickle
from collections import defaultdict
from pathlib import Path
from typing import Any, List, Optional
import numpy as np
import pycocotools.mask as mask_utils
import torch
from iopath.common.file_io import g_pathmgr
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from sam3.train.masks_ops import rle_encode
from sam3.train.utils.distributed import (
all_gather,
gather_to_rank_0_via_filesys,
@@ -755,9 +751,9 @@ def loadRes(self, resFile):
anns = resFile
assert type(anns) == list, "results in not an array of objects"
annsImgIds = [ann["image_id"] for ann in anns]
assert set(annsImgIds) == (
set(annsImgIds) & set(self.getImgIds())
), "Results do not correspond to current coco set"
assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), (
"Results do not correspond to current coco set"
)
if "caption" in anns[0]:
imgIds = set([img["id"] for img in res.dataset["images"]]) & set(
[ann["image_id"] for ann in anns]

6
sam3/eval/coco_writer.py
View File

@@ -83,9 +83,9 @@ class PredictionDumper:
self.merge_predictions = merge_predictions
self.pred_file_evaluators = pred_file_evaluators
if self.pred_file_evaluators is not None:
assert (
merge_predictions
), "merge_predictions must be True if pred_file_evaluators are provided"
assert merge_predictions, (
"merge_predictions must be True if pred_file_evaluators are provided"
)
assert self.dump_dir is not None, "dump_dir must be provided"
if is_main_process():

26
sam3/eval/demo_eval.py
View File

@@ -13,11 +13,9 @@ from typing import Optional
import numpy as np
import pycocotools.mask as maskUtils
from pycocotools.cocoeval import COCOeval
from sam3.eval.coco_eval import CocoEvaluator
from sam3.train.masks_ops import compute_F_measure
from sam3.train.utils.distributed import is_main_process
from scipy.optimize import linear_sum_assignment
@@ -156,9 +154,9 @@ class DemoEval(COCOeval):
TP = (match_scores >= thresh).sum()
FP = len(dt) - TP
FN = len(gt) - TP
assert (
FP >= 0 and FN >= 0
), f"FP: {FP}, FN: {FN}, TP: {TP}, match_scores: {match_scores}, len(dt): {len(dt)}, len(gt): {len(gt)}, ious: {ious}"
assert FP >= 0 and FN >= 0, (
f"FP: {FP}, FN: {FN}, TP: {TP}, match_scores: {match_scores}, len(dt): {len(dt)}, len(gt): {len(gt)}, ious: {ious}"
)
TPs.append(TP)
FPs.append(FP)
FNs.append(FN)
@@ -528,17 +526,17 @@ class DemoEvaluator(CocoEvaluator):
if len(scorings) == 1:
return scorings[0]
assert (
scorings[0].ndim == 3
), f"Expecting results in [numCats, numAreas, numImgs] format, got {scorings[0].shape}"
assert (
scorings[0].shape[0] == 1
), f"Expecting a single category, got {scorings[0].shape[0]}"
assert scorings[0].ndim == 3, (
f"Expecting results in [numCats, numAreas, numImgs] format, got {scorings[0].shape}"
)
assert scorings[0].shape[0] == 1, (
f"Expecting a single category, got {scorings[0].shape[0]}"
)
for scoring in scorings:
assert (
scoring.shape == scorings[0].shape
), f"Shape mismatch: {scoring.shape}, {scorings[0].shape}"
assert scoring.shape == scorings[0].shape, (
f"Shape mismatch: {scoring.shape}, {scorings[0].shape}"
)
selected_imgs = []
for img_id in range(scorings[0].shape[-1]):

7
sam3/eval/hota_eval_toolkit/trackeval/eval.py
View File

@@ -255,9 +255,10 @@ class Evaluator:
if show_progressbar and TQDM_IMPORTED:
seq_list_sorted = sorted(seq_list)
with Pool(config["NUM_PARALLEL_CORES"]) as pool, tqdm.tqdm(
total=len(seq_list)
) as pbar:
with (
Pool(config["NUM_PARALLEL_CORES"]) as pool,
tqdm.tqdm(total=len(seq_list)) as pbar,
):
_eval_sequence = partial(
eval_sequence,
dataset=dataset,

16
sam3/eval/postprocessors.py
View File

@@ -83,9 +83,9 @@ class PostProcessImage(nn.Module):
ret_tensordict: Experimental argument. If true, return a tensordict.TensorDict instead of a list of dictionaries for easier manipulation.
"""
if ret_tensordict:
assert (
consistent is True
), "We don't support returning TensorDict if the outputs have different shapes" # NOTE: It's possible but we don't support it.
assert consistent is True, (
"We don't support returning TensorDict if the outputs have different shapes"
) # NOTE: It's possible but we don't support it.
assert self.detection_threshold <= 0.0, "TODO: implement?"
try:
from tensordict import TensorDict
@@ -118,7 +118,9 @@ class PostProcessImage(nn.Module):
if boxes is None:
assert out_masks is not None
assert not ret_tensordict, "We don't support returning TensorDict if the output does not contain boxes"
assert not ret_tensordict, (
"We don't support returning TensorDict if the output does not contain boxes"
)
B = len(out_masks)
boxes = [None] * B
scores = [None] * B
@@ -418,9 +420,9 @@ class PostProcessAPIVideo(PostProcessImage):
if video_id == -1:
video_id = unique_vid_id.item()
else:
assert (
video_id == unique_vid_id.item()
), "We can only postprocess one video per datapoint"
assert video_id == unique_vid_id.item(), (
"We can only postprocess one video per datapoint"
)
# keeping track of which objects appear in the current frame
obj_ids_per_frame = frame_outs["pred_object_ids"]
assert obj_ids_per_frame.size(-1) == frame_outs["pred_logits"].size(-2)

6
sam3/eval/ytvis_coco_wrapper.py
View File

@@ -95,9 +95,9 @@ class YTVIS(COCO):
anns = resFile
assert type(anns) == list, "results is not an array of objects"
annsImgIds = [ann["image_id"] for ann in anns]
assert set(annsImgIds) == (
set(annsImgIds) & set(self.getImgIds())
), "Results do not correspond to current coco set"
assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), (
"Results do not correspond to current coco set"
)
if "bboxes" in anns[0] and not anns[0]["bboxes"] == []:
res.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
for id, ann in enumerate(anns):

34
sam3/eval/ytvis_eval.py
View File

@@ -109,9 +109,7 @@ class YTVISevalMixin:
) # Num preds x Num GTS x Num frames
inter = inter.sum(-1)
union = union.sum(-1)
assert (
union > 0
).all(), (
assert (union > 0).all(), (
"There exists a tracklet with zero GTs across time. This is suspicious"
)
return inter / union
@@ -136,9 +134,9 @@ class YTVISevalMixin:
iou = inter / union
assert iou >= 0 and iou <= 1, "Encountered an error in IoU computation"
else:
assert np.isclose(inter, 0) and np.isclose(
union, 0
), "Encountered an error in IoU computation"
assert np.isclose(inter, 0) and np.isclose(union, 0), (
"Encountered an error in IoU computation"
)
iou = 1
return iou
@@ -206,16 +204,16 @@ class YTVISResultsWriter:
if len(prediction) == 0:
continue
for k in ["boxes", "scores", "labels"]:
assert (
k in prediction
), f"Expected predictions to have `{k}` key, available keys are {prediction.keys()}"
assert k in prediction, (
f"Expected predictions to have `{k}` key, available keys are {prediction.keys()}"
)
if self.save_per_frame_scores:
assert (
"per_frame_scores" in prediction
), f"Expected predictions to have `per_frame_scores` key, available keys are {prediction.keys()}"
assert xor(
"masks" in prediction, "masks_rle" in prediction
), f"Expected predictions to have either `masks` key or `masks_rle` key, available keys are {prediction.keys()}"
assert "per_frame_scores" in prediction, (
f"Expected predictions to have `per_frame_scores` key, available keys are {prediction.keys()}"
)
assert xor("masks" in prediction, "masks_rle" in prediction), (
f"Expected predictions to have either `masks` key or `masks_rle` key, available keys are {prediction.keys()}"
)
boxes = prediction["boxes"]
boxes = convert_to_xywh(boxes).tolist()
@@ -223,9 +221,9 @@ class YTVISResultsWriter:
labels = prediction["labels"].tolist()
if "masks" in prediction:
masks = prediction["masks"].squeeze(2)
assert (
masks.ndim == 4
), "Expected masks to be of shape(N_preds,T_frames,H,W)"
assert masks.ndim == 4, (
"Expected masks to be of shape(N_preds,T_frames,H,W)"
)
areas = [mask.flatten(1).sum(1).tolist() for mask in masks]
rles = [rle_encode(masklet) for masklet in masks]