Python chainer.function.no_backprop_mode() Examples

def _evaluate_local_single(self, iterator):
        for batch in iterator:
            in_arrays = convert._call_converter(
                self.converter, batch, self.device)

            with function.no_backprop_mode():
                if isinstance(in_arrays, tuple):
                    results = self.calc_local(*in_arrays)
                elif isinstance(in_arrays, dict):
                    results = self.calc_local(**in_arrays)
                else:
                    results = self.calc_local(in_arrays)

            if self._progress_hook:
                self._progress_hook(batch)
            yield results 

def predict(self, images, oversample=True):
        """Computes all the probabilities of given images.

        Args:
            images (iterable of PIL.Image or numpy.ndarray): Input images.
            oversample (bool): If ``True``, it averages results across
                center, corners, and mirrors. Otherwise, it uses only the
                center.

        Returns:
            ~chainer.Variable: Output that contains the class probabilities
            of given images.

        """

        x = concat_examples([prepare(img, size=(256, 256)) for img in images])
        if oversample:
            x = imgproc.oversample(x, crop_dims=(224, 224))
        else:
            x = x[:, :, 16:240, 16:240]
        # Use no_backprop_mode to reduce memory consumption
        with function.no_backprop_mode():
            x = Variable(self.xp.asarray(x))
            y = self(x, layers=['prob'])['prob']
            if oversample:
                n = y.data.shape[0] // 10
                y_shape = y.data.shape[1:]
                y = reshape(y, (n, 10) + y_shape)
                y = sum(y, axis=1) / 10
        return y 

def evaluate(self):
        iterator = self._iterators['main']
        eval_func = self.eval_func or self._targets['main']

        if self.eval_hook:
            self.eval_hook(self)

        if hasattr(iterator, 'reset'):
            iterator.reset()
            it = iterator
        else:
            it = copy.copy(iterator)

        # summary = reporter_module.DictSummary()
        summary = collections.defaultdict(list)

        for batch in it:
            observation = {}
            with reporter_module.report_scope(observation):
                in_arrays = self.converter(batch, self.device)
                with function.no_backprop_mode():
                    if isinstance(in_arrays, tuple):
                        eval_func(*in_arrays)
                    elif isinstance(in_arrays, dict):
                        eval_func(**in_arrays)
                    else:
                        eval_func(in_arrays)
            n_data = len(batch)
            summary['n'].append(n_data)
            # summary.add(observation)
            for k, v in observation.items():
                summary[k].append(v)

        mean = dict()
        ns = summary['n']
        del summary['n']
        for k, vs in summary.items():
            mean[k] = sum(v * n for v, n in zip(vs, ns)) / sum(ns)
        return mean
        # return summary.compute_mean() 

def __call__(self, imgs):
        imgs = self.model.xp.asarray([self.do_transform(img) for img in imgs])

        with using_config("train", False), no_backprop_mode():
            imgs = Variable(imgs)
            predictions = self.model(imgs)

        output = to_cpu(predictions.array if hasattr(predictions, "array") else cupy.asnumpy(predictions))
        return output 

def _evaluate_local(self, iterator):
        # Check whether local eval is all done every 8 rounds
        gather_interval = 8

        all_done = None
        while not all_done:
            all_done = None
            results = None
            for _ in range(gather_interval):
                try:
                    batch = iterator.next()
                    in_arrays = convert._call_converter(
                        self.converter, batch, self.device)

                    with function.no_backprop_mode():
                        if isinstance(in_arrays, tuple):
                            results = self.calc_local(*in_arrays)
                        elif isinstance(in_arrays, dict):
                            results = self.calc_local(**in_arrays)
                        else:
                            results = self.calc_local(in_arrays)

                    if self.comm.rank == self.root and self._progress_hook:
                        self._progress_hook(batch)

                except StopIteration:
                    batch = None
                    results = None

                results = self.comm.gather_obj(results, root=self.root)

                if self.comm.rank == self.root:
                    valid_results = [r for r in results if r is not None]
                    for result in valid_results:
                        yield result

                    all_done = len(valid_results) == 0

            all_done = self.comm.bcast_obj(all_done, root=self.root)
        return 

def predict(self, images, oversample=True):
        """Computes all the probabilities of given images.

        Args:
            images (iterable of PIL.Image or numpy.ndarray): Input images.
                When you specify a color image as a :class:`numpy.ndarray`,
                make sure that color order is RGB.
            oversample (bool): If ``True``, it averages results across
                center, corners, and mirrors. Otherwise, it uses only the
                center.

        Returns:
            ~chainer.Variable: Output that contains the class probabilities
            of given images.

        """

        x = concat_examples([prepare(img, size=(256, 256)) for img in images])
        if oversample:
            x = imgproc.oversample(x, crop_dims=(224, 224))
        else:
            x = x[:, :, 16:240, 16:240]
        # Use no_backprop_mode to reduce memory consumption
        with function.no_backprop_mode(), chainer.using_config('train', False):
            x = Variable(self.xp.asarray(x))
            y = self(x, layers=['prob'])['prob']
            if oversample:
                n = len(y) // 10
                y_shape = y.shape[1:]
                y = reshape(y, (n, 10) + y_shape)
                y = sum(y, axis=1) / 10
        return y 

def predict(self, images, oversample=True):
        """Computes all the probabilities of given images.

        Args:
            images (iterable of PIL.Image or numpy.ndarray): Input images.
                When you specify a color image as a :class:`numpy.ndarray`,
                make sure that color order is RGB.
            oversample (bool): If ``True``, it averages results across
                center, corners, and mirrors. Otherwise, it uses only the
                center.

        Returns:
            ~chainer.Variable: Output that contains the class probabilities
            of given images.

        """

        x = concat_examples([prepare(img, size=(256, 256)) for img in images])
        if oversample:
            x = imgproc.oversample(x, crop_dims=(224, 224))
        else:
            x = x[:, :, 16:240, 16:240]
        # Use no_backprop_mode to reduce memory consumption
        with function.no_backprop_mode(), chainer.using_config('train', False):
            x = Variable(self.xp.asarray(x))
            y = self(x, layers=['prob'])['prob']
            if oversample:
                n = len(y) // 10
                y_shape = y.shape[1:]
                y = reshape(y, (n, 10) + y_shape)
                y = sum(y, axis=1) / 10
        return y 

def predict(self, images, oversample=True):
        """Computes all the probabilities of given images.

        Args:
            images (iterable of PIL.Image or numpy.ndarray): Input images.
                When you specify a color image as a :class:`numpy.ndarray`,
                make sure that color order is RGB.
            oversample (bool): If ``True``, it averages results across
                center, corners, and mirrors. Otherwise, it uses only the
                center.

        Returns:
            ~chainer.Variable: Output that contains the class probabilities
            of given images.

        """

        x = concat_examples([prepare(img, size=(256, 256)) for img in images])
        if oversample:
            x = imgproc.oversample(x, crop_dims=(224, 224))
        else:
            x = x[:, :, 16:240, 16:240]
        # Use no_backprop_mode to reduce memory consumption
        with function.no_backprop_mode(), chainer.using_config('train', False):
            x = Variable(self.xp.asarray(x))
            y = self(x, layers=['prob'])['prob']
            if oversample:
                n = len(y) // 10
                y_shape = y.shape[1:]
                y = reshape(y, (n, 10) + y_shape)
                y = average(y, axis=1)
        return y 

def evaluate(self):
        iterator = self._iterators['main']
        gen = self._targets['gen']

        if self.eval_hook:
            self.eval_hook(self)

        if hasattr(iterator, 'reset'):
            iterator.reset()
            it = iterator
        else:
            it = copy.copy(iterator)

        summary = reporter_module.DictSummary()

        for batch in it:
            observation = {}
            with reporter_module.report_scope(observation):
                xy_proj, xyz, scale = self.converter(batch, self.device)
                xy_proj, xyz = xy_proj[:, 0], xyz[:, 0]
                with function.no_backprop_mode(), \
                        chainer.using_config('train', False):
                    xy_real = chainer.Variable(xy_proj)
                    z_pred = gen(xy_real)
                    z_mse = F.mean_squared_error(z_pred, xyz[:, 2::3])
                    chainer.report({'z_mse': z_mse}, gen)

                    lx = gen.xp.power(xyz[:, 0::3] - xy_proj[:, 0::2], 2)
                    ly = gen.xp.power(xyz[:, 1::3] - xy_proj[:, 1::2], 2)
                    lz = gen.xp.power(xyz[:, 2::3] - z_pred.data, 2)

                    euclidean_distance = gen.xp.sqrt(lx + ly + lz).mean(axis=1)
                    euclidean_distance *= scale[:, 0]
                    euclidean_distance = gen.xp.mean(euclidean_distance)
                    chainer.report(
                        {'euclidean_distance': euclidean_distance}, gen)
            summary.add(observation)

        return summary.compute_mean() 

def evaluate(self):

        iterator = self._iterators['main']
        eval_func = self.eval_func or self._targets['main']

        if self.eval_hook:
            self.eval_hook(self)

        if hasattr(iterator, 'reset'):
            iterator.reset()
            it = iterator
        else:
            it = copy.copy(iterator)

        summary = reporter_module.DictSummary()

        for batch in it:
            observation = {}
            with reporter_module.report_scope(observation):
                row_idx, col_idx, val_idx = [], [], []
                x = cuda.to_gpu(np.array([i[0] for i in batch]))
                labels = [l[1] for l in batch]
                for i in range(len(labels)):
                    l_list = list(set(labels[i]))
                    for y in l_list:
                        row_idx.append(i)
                        col_idx.append(y)
                        val_idx.append(1)
                m = len(labels)
                n = self.class_dim
                t = sp.csr_matrix((val_idx, (row_idx, col_idx)), shape=(m, n), dtype=np.int8).todense()
                t = cuda.to_gpu(t)

                with function.no_backprop_mode():
                    loss = F.sigmoid_cross_entropy(eval_func(x), t)
                    summary.add({MyEvaluator.default_name + '/main/loss':loss})
            summary.add(observation)

        return summary.compute_mean() 

def evaluate(self):
        """Evaluates the model and returns a result dictionary.

        This method runs the evaluation loop over the validation dataset. It
        accumulates the reported values to :class:`~chainer.DictSummary` and
        returns a dictionary whose values are means computed by the summary.

        Note that this function assumes that the main iterator raises
        ``StopIteration`` or code in the evaluation loop raises an exception.
        So, if this assumption is not held, the function could be caught in
        an infinite loop.

        Users can override this method to customize the evaluation routine.

        .. note::

            This method encloses :attr:`eval_func` calls with
            :func:`function.no_backprop_mode` context, so all calculations
            using :class:`~chainer.FunctionNode`\\s inside
            :attr:`eval_func` do not make computational graphs. It is for
            reducing the memory consumption.

        Returns:
            dict: Result dictionary. This dictionary is further reported via
            :func:`~chainer.report` without specifying any observer.

        """
        iterator = self._iterators['main']
        eval_func = self.eval_func or self._targets['main']

        if self.eval_hook:
            self.eval_hook(self)

        if hasattr(iterator, 'reset'):
            iterator.reset()
            it = iterator
        else:
            it = copy.copy(iterator)

        if self.max_num_iterations is not None:
            it = self.fixed_num_iterations_iterator(it)

        summary = reporter_module.DictSummary()

        for batch in it:
            observation = {}
            with reporter_module.report_scope(observation):
                in_arrays = self.converter(batch, self.device)
                with function.no_backprop_mode():
                    if isinstance(in_arrays, tuple):
                        eval_func(*in_arrays)
                    elif isinstance(in_arrays, dict):
                        eval_func(**in_arrays)
                    else:
                        eval_func(in_arrays)

            summary.add(observation)

        return self.calculate_mean_of_summary(summary)