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ultralytics 8.0.47 Docker and reformat updates (#1153)

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Glenn Jocher 2023-02-25 22:49:19 -08:00 • committed by GitHub
parent d4be4cb24b
commit a58f766f94
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41 changed files with 224 additions and 201 deletions
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26
ultralytics/nn/autobackend.py
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@ -136,7 +136,7 @@ class AutoBackend(nn.Module):
batch_dim = get_batch(network)
if batch_dim.is_static:
batch_size = batch_dim.get_length()
executable_network = ie.compile_model(network, device_name='CPU') # device_name="MYRIAD" for Intel NCS2
executable_network = ie.compile_model(network, device_name='CPU') # device_name="MYRIAD" for NCS2
elif engine: # TensorRT
LOGGER.info(f'Loading {w} for TensorRT inference...')
import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download
@ -176,6 +176,8 @@ class AutoBackend(nn.Module):
LOGGER.info(f'Loading {w} for CoreML inference...')
import coremltools as ct
model = ct.models.MLModel(w)
names, stride, task = (model.user_defined_metadata.get(k) for k in ('names', 'stride', 'task'))
names, stride = eval(names), int(stride)
elif saved_model: # TF SavedModel
LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
import tensorflow as tf
@ -185,18 +187,13 @@ class AutoBackend(nn.Module):
LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
import tensorflow as tf
from ultralytics.yolo.engine.exporter import gd_outputs
def wrap_frozen_graph(gd, inputs, outputs):
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), []) # wrapped
ge = x.graph.as_graph_element
return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
def gd_outputs(gd):
name_list, input_list = [], []
for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef
name_list.append(node.name)
input_list.extend(node.input)
return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
gd = tf.Graph().as_graph_def() # TF GraphDef
with open(w, 'rb') as f:
gd.ParseFromString(f.read())
@ -319,10 +316,17 @@ class AutoBackend(nn.Module):
self.context.execute_v2(list(self.binding_addrs.values()))
y = [self.bindings[x].data for x in sorted(self.output_names)]
elif self.coreml: # CoreML
im = im.cpu().numpy()
im = Image.fromarray((im[0] * 255).astype('uint8'))
im = im[0].cpu().numpy()
if self.task == 'classify':
from ultralytics.yolo.data.utils import IMAGENET_MEAN, IMAGENET_STD
# im_pil = Image.fromarray(((im / 6 + 0.5) * 255).astype('uint8'))
for i in range(3):
im[..., i] *= IMAGENET_STD[i]
im[..., i] += IMAGENET_MEAN[i]
im_pil = Image.fromarray((im * 255).astype('uint8'))
# im = im.resize((192, 320), Image.ANTIALIAS)
y = self.model.predict({'image': im}) # coordinates are xywh normalized
y = self.model.predict({'image': im_pil}) # coordinates are xywh normalized
if 'confidence' in y:
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)