FM / helpers

/usr/local/lib64/python3.6/site-packages/caffe2/python/helpers/

/usr/local/lib64/python3.6/site-packages/caffe2/python/helpers

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
algebra.py	1296	0644	edit dl rm
arg_scope.py	1114	0644	edit dl rm
array_helpers.py	648	0644	edit dl rm
control_ops.py	625	0644	edit dl rm
conv.py	10396	0644	edit dl rm
db_input.py	381	0644	edit dl rm
dropout.py	412	0644	edit dl rm
elementwise_linear.py	1385	0644	edit dl rm
fc.py	6405	0644	edit dl rm
nonlinearity.py	1145	0644	edit dl rm
normalization.py	10891	0644	edit dl rm
pooling.py	924	0644	edit dl rm
quantization.py	261	0644	edit dl rm
tools.py	1089	0644	edit dl rm
train.py	2192	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/helpers/tools.py (1089B)

## @package tools
# Module caffe2.python.helpers.tools

def image_input(
    model, blob_in, blob_out, order="NCHW", use_gpu_transform=False, **kwargs
):
    assert 'is_test' in kwargs, "Argument 'is_test' is required"
    if order == "NCHW":
        if (use_gpu_transform):
            kwargs['use_gpu_transform'] = 1 if use_gpu_transform else 0
            # GPU transform will handle NHWC -> NCHW
            outputs = model.net.ImageInput(blob_in, blob_out, **kwargs)
            pass
        else:
            outputs = model.net.ImageInput(
                blob_in, [blob_out[0] + '_nhwc'] + blob_out[1:], **kwargs
            )
            outputs_list = list(outputs)
            outputs_list[0] = model.net.NHWC2NCHW(outputs_list[0], blob_out[0])
            outputs = tuple(outputs_list)
    else:
        outputs = model.net.ImageInput(blob_in, blob_out, **kwargs)
    return outputs

def video_input(model, blob_in, blob_out, **kwargs):
    # size of outputs can vary depending on kwargs
    outputs = model.net.VideoInput(blob_in, blob_out, **kwargs)
    return outputs

cmd: