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ultralytics 8.0.47 Docker and reformat updates (#1153)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-02-25 22:49:19 -08:00
committed by GitHub
parent d4be4cb24b
commit a58f766f94
41 changed files with 224 additions and 201 deletions
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26
ultralytics/nn/autobackend.py
View File

@ -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)

41
ultralytics/nn/tasks.py
View File

@ -11,7 +11,7 @@ import torch.nn as nn
from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
GhostBottleneck, GhostConv, Segment)
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, yaml_load
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
intersect_dicts, make_divisible, model_info, scale_img, time_sync)
@ -76,7 +76,7 @@ class BaseModel(nn.Module):
None
"""
c = m == self.model[-1] # is final layer, copy input as inplace fix
o = thop.profile(m, inputs=(x.clone() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
o = thop.profile(m, inputs=[x.clone() if c else x], verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
t = time_sync()
for _ in range(10):
m(x.clone() if c else x)
@ -339,14 +339,20 @@ def torch_safe_load(weight):
file = attempt_download_asset(weight) # search online if missing locally
try:
return torch.load(file, map_location='cpu'), file # load
except ModuleNotFoundError as e:
if e.name == 'omegaconf': # e.name is missing module name
LOGGER.warning(f'WARNING ⚠️ {weight} requires {e.name}, which is not in ultralytics requirements.'
f'\nAutoInstall will run now for {e.name} but this feature will be removed in the future.'
f'\nRecommend fixes are to train a new model using updated ultralytics package or to '
f'download updated models from https://github.com/ultralytics/assets/releases/tag/v0.0.0')
if e.name != 'models':
check_requirements(e.name) # install missing module
except ModuleNotFoundError as e: # e.name is missing module name
if e.name == 'models':
raise TypeError(
emojis(f'ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained '
f'with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with '
f'YOLOv8 at https://github.com/ultralytics/ultralytics.'
f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")) from e
LOGGER.warning(f"WARNING ⚠️ {weight} appears to require '{e.name}', which is not in ultralytics requirements."
f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")
check_requirements(e.name) # install missing module
return torch.load(file, map_location='cpu'), file # load
@ -437,22 +443,21 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
args[j] = eval(a) if isinstance(a, str) else a # eval strings
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in {
Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):
c1, c2 = ch[f], args[0]
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
c2 = make_divisible(c2 * gw, 8)
args = [c1, c2, *args[1:]]
if m in {BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x}:
if m in (BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x):
args.insert(2, n) # number of repeats
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum(ch[x] for x in f)
elif m in {Detect, Segment}:
elif m in (Detect, Segment):
args.append([ch[x] for x in f])
if m is Segment:
args[2] = make_divisible(args[2] * gw, 8)
@ -490,11 +495,11 @@ def guess_model_task(model):
def cfg2task(cfg):
# Guess from YAML dictionary
m = cfg['head'][-1][-2].lower() # output module name
if m in ['classify', 'classifier', 'cls', 'fc']:
if m in ('classify', 'classifier', 'cls', 'fc'):
return 'classify'
if m in ['detect']:
if m == 'detect':
return 'detect'
if m in ['segment']:
if m == 'segment':
return 'segment'
# Guess from model cfg