HongChuang/ultralytics-ascend
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Fix Windows non-UTF source filenames (#4524)

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Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2023-08-24 03:22:26 +02:00 • committed by GitHub
parent a7419617a6
commit 1db9afc2e5
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16 changed files with 129 additions and 95 deletions
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18
ultralytics/engine/exporter.py
View file

@ -127,7 +127,7 @@ class Exporter:
Attributes:
args (SimpleNamespace): Configuration for the exporter.
save_dir (Path): Directory to save results.
callbacks (list, optional): List of callback functions. Defaults to None.
"""
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
@ -189,7 +189,7 @@ class Exporter:
model.eval()
model.float()
model = model.fuse()
for k, m in model.named_modules():
for m in model.modules():
if isinstance(m, (Detect, RTDETRDecoder)): # Segment and Pose use Detect base class
m.dynamic = self.args.dynamic
m.export = True
@ -427,7 +427,7 @@ class Exporter:
system = 'macos' if MACOS else 'ubuntu' if LINUX else 'windows' # operating system
asset = [x for x in assets if system in x][0] if assets else \
f'https://github.com/pnnx/pnnx/releases/download/20230816/pnnx-20230816-{system}.zip' # fallback
attempt_download_asset(asset, repo='pnnx/pnnx', release='latest')
asset = attempt_download_asset(asset, repo='pnnx/pnnx', release='latest')
unzip_dir = Path(asset).with_suffix('')
pnnx = ROOT / pnnx_filename # new location
(unzip_dir / pnnx_filename).rename(pnnx) # move binary to ROOT
@ -502,9 +502,9 @@ class Exporter:
check_requirements('scikit-learn') # scikit-learn package required for k-means quantization
if mlmodel:
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
else:
elif bits == 8: # mlprogram already quantized to FP16
import coremltools.optimize.coreml as cto
op_config = cto.OpPalettizerConfig(mode=mode, nbits=bits, weight_threshold=512)
op_config = cto.OpPalettizerConfig(mode='kmeans', nbits=bits, weight_threshold=512)
config = cto.OptimizationConfig(global_config=op_config)
ct_model = cto.palettize_weights(ct_model, config=config)
if self.args.nms and self.model.task == 'detect':
@ -839,7 +839,7 @@ class Exporter:
import coremltools as ct # noqa
LOGGER.info(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
batch_size, ch, h, w = list(self.im.shape) # BCHW
_, _, h, w = list(self.im.shape) # BCHW
# Output shapes
spec = model.get_spec()
@ -857,8 +857,8 @@ class Exporter:
# Checks
names = self.metadata['names']
nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height
na, nc = out0_shape
# na, nc = out0.type.multiArrayType.shape # number anchors, classes
_, nc = out0_shape # number of anchors, number of classes
# _, nc = out0.type.multiArrayType.shape
assert len(names) == nc, f'{len(names)} names found for nc={nc}' # check
# Define output shapes (missing)
@ -968,7 +968,7 @@ class IOSDetectModel(torch.nn.Module):
def __init__(self, model, im):
"""Initialize the IOSDetectModel class with a YOLO model and example image."""
super().__init__()
b, c, h, w = im.shape # batch, channel, height, width
_, _, h, w = im.shape # batch, channel, height, width
self.model = model
self.nc = len(model.names) # number of classes
if w == h:

3
ultralytics/engine/predictor.py
View file

@ -343,8 +343,7 @@ class BasePredictor:
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
suffix = '.mp4' if MACOS else '.avi' if WINDOWS else '.avi'
fourcc = 'avc1' if MACOS else 'WMV2' if WINDOWS else 'MJPG'
suffix, fourcc = ('.mp4', 'avc1') if MACOS else ('.avi', 'WMV2') if WINDOWS else ('.avi', 'MJPG')
save_path = str(Path(save_path).with_suffix(suffix))
self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
self.vid_writer[idx].write(im0)