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ultralytics 8.0.41 TF SavedModel and EdgeTPU export (#1034)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
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Glenn Jocher 2023-02-20 01:27:28 +01:00 • committed by GitHub
parent 4b866c9718
commit f6e393c1d2
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64 changed files with 604 additions and 351 deletions
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11
ultralytics/nn/tasks.py
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@ -1,6 +1,5 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import ast
import contextlib
from copy import deepcopy
from pathlib import Path
@ -338,7 +337,7 @@ def torch_safe_load(weight):
file = attempt_download_asset(weight) # search online if missing locally
try:
return torch.load(file, map_location='cpu') # load
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.'
@ -347,7 +346,7 @@ def torch_safe_load(weight):
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
return torch.load(file, map_location='cpu') # load
return torch.load(file, map_location='cpu'), file # load
def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
@ -355,13 +354,13 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
ensemble = Ensemble()
for w in weights if isinstance(weights, list) else [weights]:
ckpt = torch_safe_load(w) # load ckpt
ckpt, w = torch_safe_load(w) # load ckpt
args = {**DEFAULT_CFG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args
model = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
# Model compatibility updates
model.args = args # attach args to model
model.pt_path = weights # attach *.pt file path to model
model.pt_path = w # attach *.pt file path to model
model.task = guess_model_task(model)
if not hasattr(model, 'stride'):
model.stride = torch.tensor([32.])
@ -392,7 +391,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
# Loads a single model weights
ckpt = torch_safe_load(weight) # load ckpt
ckpt, weight = torch_safe_load(weight) # load ckpt
args = {**DEFAULT_CFG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args
model = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model