Remove `formats` variable to avoid `pd` conflict (#7993)
* Remove `formats` variable to avoid `pd` conflict * Update export.pypull/7997/head
parent
1dcb774998
commit
945579699a
10
export.py
10
export.py
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@ -475,9 +475,9 @@ def run(
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):
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):
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t = time.time()
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t = time.time()
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include = [x.lower() for x in include] # to lowercase
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include = [x.lower() for x in include] # to lowercase
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formats = tuple(export_formats()['Argument'][1:]) # --include arguments
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fmts = tuple(export_formats()['Argument'][1:]) # --include arguments
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flags = [x in include for x in formats]
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flags = [x in include for x in fmts]
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assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {formats}'
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assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
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jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags # export booleans
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jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags # export booleans
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file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights
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file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights
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@ -499,7 +499,7 @@ def run(
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im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
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im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
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# Update model
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# Update model
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if half and not (coreml or xml):
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if half and not coreml and not xml:
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im, model = im.half(), model.half() # to FP16
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im, model = im.half(), model.half() # to FP16
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model.train() if train else model.eval() # training mode = no Detect() layer grid construction
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model.train() if train else model.eval() # training mode = no Detect() layer grid construction
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for k, m in model.named_modules():
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for k, m in model.named_modules():
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@ -531,7 +531,7 @@ def run(
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if any((saved_model, pb, tflite, edgetpu, tfjs)):
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if any((saved_model, pb, tflite, edgetpu, tfjs)):
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if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
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if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
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check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
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check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
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assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
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assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
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model, f[5] = export_saved_model(model.cpu(),
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model, f[5] = export_saved_model(model.cpu(),
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im,
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im,
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file,
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file,
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@ -56,9 +56,8 @@ def run(
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pt_only=False, # test PyTorch only
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pt_only=False, # test PyTorch only
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):
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):
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y, t = [], time.time()
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y, t = [], time.time()
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formats = export.export_formats()
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device = select_device(device)
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device = select_device(device)
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for i, (name, f, suffix, gpu) in formats.iterrows(): # index, (name, file, suffix, gpu-capable)
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for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable)
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try:
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try:
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assert i != 9, 'Edge TPU not supported'
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assert i != 9, 'Edge TPU not supported'
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assert i != 10, 'TF.js not supported'
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assert i != 10, 'TF.js not supported'
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@ -104,9 +103,8 @@ def test(
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pt_only=False, # test PyTorch only
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pt_only=False, # test PyTorch only
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):
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):
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y, t = [], time.time()
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y, t = [], time.time()
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formats = export.export_formats()
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device = select_device(device)
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device = select_device(device)
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for i, (name, f, suffix, gpu) in formats.iterrows(): # index, (name, file, suffix, gpu-capable)
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for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable)
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try:
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try:
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w = weights if f == '-' else \
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w = weights if f == '-' else \
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export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights
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export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights
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