Source code for gluoncv.utils.export_helper

"""Helper utils for export HybridBlock to symbols."""
from __future__ import absolute_import
import copy
import mxnet as mx
from mxnet.base import MXNetError
from mxnet.gluon import HybridBlock
from mxnet.gluon import nn


class _DefaultPreprocess(HybridBlock):
    """Default preprocess block used by GluonCV.

    The default preprocess block includes:

        - mean [123.675, 116.28, 103.53]

        - std [58.395, 57.12, 57.375]

        - transpose to (B, 3, H, W)

    It is used to transform from resized original images with shape (1, H, W, 3) or (B, H, W, 3)
    in range (0, 255) and RGB color format.

    """
    def __init__(self, **kwargs):
        super(_DefaultPreprocess, self).__init__(**kwargs)
        with self.name_scope():
            mean = mx.nd.array([123.675, 116.28, 103.53]).reshape((1, 1, 1, 3))
            scale = mx.nd.array([58.395, 57.12, 57.375]).reshape((1, 1, 1, 3))
            self.init_mean = self.params.get_constant('init_mean', mean)
            self.init_scale = self.params.get_constant('init_scale', scale)

    # pylint: disable=arguments-differ
    def hybrid_forward(self, F, x, init_mean, init_scale):
        x = F.broadcast_minus(x, init_mean)
        x = F.broadcast_div(x, init_scale)
        x = F.transpose(x, axes=(0, 3, 1, 2))
        return x

[docs]def export_block(path, block, data_shape=None, epoch=0, preprocess=True, layout='HWC', ctx=mx.cpu()): """Helper function to export a HybridBlock to symbol JSON to be used by `SymbolBlock.imports`, `mxnet.mod.Module` or the C++ interface.. Parameters ---------- path : str Path to save model. Two files path-symbol.json and path-xxxx.params will be created, where xxxx is the 4 digits epoch number. block : mxnet.gluon.HybridBlock The hybridizable block. Note that normal gluon.Block is not supported. data_shape : tuple of int, default is None Fake data shape just for export purpose, in format (H, W, C) for 2D data or (T, H, W, C) for 3D data. If you don't specify ``data_shape``, `export_block` will try use some common data_shapes, e.g., (224, 224, 3), (256, 256, 3), (299, 299, 3), (512, 512, 3)... If any of this ``data_shape`` goes through, the export will succeed. epoch : int Epoch number of saved model. preprocess : mxnet.gluon.HybridBlock, default is True. Preprocess block prior to the network. By default (True), it will subtract mean [123.675, 116.28, 103.53], divide std [58.395, 57.12, 57.375], and convert original image (B, H, W, C and range [0, 255]) to tensor (B, C, H, W) as network input. This is the default preprocess behavior of all GluonCV pre-trained models. You can use custom pre-process hybrid block or disable by set ``preprocess=None``. layout : str, default is 'HWC' The layout for raw input data. By default is HWC. Supports 'HWC', 'CHW', 'THWC' and 'CTHW'. Note that image channel order is always RGB. ctx: mx.Context, default mx.cpu() Network context. Returns ------- None """ # input image layout layout = layout.upper() if data_shape is None: if layout == 'HWC': data_shapes = [(s, s, 3) for s in (224, 256, 299, 300, 320, 416, 512, 600)] elif layout == 'CHW': data_shapes = [(3, s, s) for s in (224, 256, 299, 300, 320, 416, 512, 600)] else: raise ValueError('Unable to predict data_shape, please specify.') else: data_shapes = [data_shape] # use deepcopy of network to avoid in-place modification copy_block = block if '_target_generator' in copy_block._children: copy_block = copy.deepcopy(block) copy_block._children.pop('_target_generator') # avoid using some optimized operators that are not yet available outside mxnet if 'box_encode' in mx.sym.contrib.__dict__: box_encode = mx.sym.contrib.box_encode mx.sym.contrib.__dict__.pop('box_encode') else: box_encode = None if preprocess: # add preprocess block if preprocess is True: assert layout == 'HWC', \ "Default preprocess only supports input as HWC, provided {}.".format(layout) preprocess = _DefaultPreprocess() else: if not isinstance(preprocess, HybridBlock): raise TypeError("preprocess must be HybridBlock, given {}".format(type(preprocess))) wrapper_block = nn.HybridSequential() preprocess.initialize(ctx=ctx) wrapper_block.add(preprocess) wrapper_block.add(copy_block) else: wrapper_block = copy_block assert layout in ('CHW', 'CTHW'), \ "Default layout is CHW for 2D models and CTHW for 3D models if preprocess is None," \ + " provided {}.".format(layout) wrapper_block.collect_params().reset_ctx(ctx) # try different data_shape if possible, until one fits the network last_exception = None for dshape in data_shapes: if layout == 'HWC': h, w, c = dshape x = mx.nd.zeros((1, h, w, c), ctx=ctx) elif layout == 'CHW': c, h, w = dshape x = mx.nd.zeros((1, c, h, w), ctx=ctx) elif layout == 'THWC': t, h, w, c = dshape x = mx.nd.zeros((1, t, h, w, c), ctx=ctx) elif layout == 'CTHW': c, t, h, w = dshape x = mx.nd.zeros((1, c, t, h, w), ctx=ctx) else: raise RuntimeError('Input layout %s is not supported yet.' % (layout)) # hybridize and forward once wrapper_block.hybridize() try: wrapper_block(x) wrapper_block.export(path, epoch) last_exception = None break except MXNetError as e: last_exception = e if last_exception is not None: raise RuntimeError(str(last_exception).splitlines()[0])
[docs]def export_tvm(path, block, data_shape, epoch=0, preprocess=True, layout='HWC', ctx=mx.cpu(), target='llvm', opt_level=3, use_autotvm=False): """Helper function to export a HybridBlock to TVM executable. Note that tvm package needs to be installed(https://tvm.ai/). Parameters ---------- path : str Path to save model. Three files path_deploy_lib.tar, path_deploy_graph.json and path_deploy_xxxx.params will be created, where xxxx is the 4 digits epoch number. block : mxnet.gluon.HybridBlock The hybridizable block. Note that normal gluon.Block is not supported. data_shape : tuple of int, required Unlike `export_block`, `data_shape` is required here for the purpose of optimization. If dynamic shape is required, you can use the shape that most fits the inference tasks, but the optimization won't accommodate all situations. epoch : int Epoch number of saved model. preprocess : mxnet.gluon.HybridBlock, default is True. Preprocess block prior to the network. By default (True), it will subtract mean [123.675, 116.28, 103.53], divide std [58.395, 57.12, 57.375], and convert original image (B, H, W, C and range [0, 255]) to tensor (B, C, H, W) as network input. This is the default preprocess behavior of all GluonCV pre-trained models. You can use custom pre-process hybrid block or disable by set ``preprocess=None``. layout : str, default is 'HWC' The layout for raw input data. By default is HWC. Supports 'HWC' and 'CHW'. Note that image channel order is always RGB. ctx: mx.Context, default mx.cpu() Network context. target : str, default is 'llvm' Runtime type for code generation, can be ('llvm', 'cuda', 'opencl', 'metal'...) opt_level : int, default is 3 TVM optimization level, if supported, higher `opt_level` may generate more efficient runtime library, however, some operator may not support high level optimization, which will fallback to lower `opt_level`. use_autotvm : bool, default is False Use autotvm for performance tuning. Note that this can take very long time, since it's a search and model based tuning process. Returns ------- None """ try: import tvm from tvm import autotvm from tvm import relay from tvm.relay import testing from tvm.autotvm.tuner import XGBTuner, RandomTuner import tvm.contrib.graph_runtime as runtime except ImportError: print("TVM package required, please refer https://tvm.ai/ for installation guide.") raise # add preprocess block if necessary if preprocess: # add preprocess block if preprocess is True: preprocess = _DefaultPreprocess() else: if not isinstance(preprocess, HybridBlock): raise TypeError("preprocess must be HybridBlock, given {}".format(type(preprocess))) wrapper_block = nn.HybridSequential() preprocess.initialize(ctx=ctx) wrapper_block.add(preprocess) wrapper_block.add(block) else: wrapper_block = block wrapper_block.collect_params().reset_ctx(ctx) # convert to relay graph sym, params = relay.frontend.from_mxnet(wrapper_block, shape={"data": data_shape}) if use_autotvm: def tune_kernels(tasks, measure_option, tuner='gridsearch', early_stopping=None, log_filename='tuning.log'): for i, tsk in enumerate(tasks): prefix = "[Task %2d/%2d] " % (i+1, len(tasks)) # converting conv2d tasks to conv2d_NCHWc tasks op_name = tsk.workload[0] if op_name == 'conv2d': func_create = 'topi_x86_conv2d_NCHWc' elif op_name == 'depthwise_conv2d_nchw': func_create = 'topi_x86_depthwise_conv2d_NCHWc_from_nchw' else: raise ValueError("Tuning {} is not supported on x86".format(op_name)) task = autotvm.task.create(func_create, args=tsk.args, target=target, template_key='direct') task.workload = tsk.workload # create tuner if tuner in ('xgb', 'xgb-rank'): tuner_obj = XGBTuner(task, loss_type='rank') elif tuner == 'ga': tuner_obj = GATuner(task, pop_size=50) elif tuner == 'random': tuner_obj = RandomTuner(task) elif tuner == 'gridsearch': tuner_obj = GridSearchTuner(task) else: raise ValueError("Invalid tuner: " + tuner) # do tuning n_trial = len(task.config_space) tuner_obj.tune(n_trial=n_trial, early_stopping=early_stopping, measure_option=measure_option, callbacks=[ autotvm.callback.progress_bar(n_trial, prefix=prefix), autotvm.callback.log_to_file(log_filename)]) # tasks = autotvm.task.extract_from_program(sym, target=target, params=params, ops=(relay.op.nn.conv2d,)) logging.warning('Start tunning, this can be slow...') tuning_option = { 'log_filename': 'tune.log', 'tuner': 'random', 'early_stopping': None, 'measure_option': autotvm.measure_option( builder=autotvm.LocalBuilder(), runner=autotvm.LocalRunner(number=10, repeat=1, min_repeat_ms=1000), ), } tune_kernels(tasks, **tuning_option) with autotvm.apply_history_best(log_file): with relay.build_config(opt_level=opt_level): graph, lib, params = relay.build_module.build( sym, target=target, params=params) else: with relay.build_config(opt_level=opt_level): graph, lib, params = relay.build_module.build( sym, target, params=params) # export library, json graph and parameters lib.export_library(path + '_deploy_lib.so') with open(path + '_deploy_graph.json', 'w') as fo: fo.write(graph) with open(path + '_deploy_{:04n}.params'.format(epoch), 'wb') as fo: try: fo.write(relay.compiler.save_param_dict(params)) except AttributeError: fo.write(relay.save_param_dict(params))