def __init__(self):
super(NetArgmaxWithValue, self).__init__()
axis1 = 0
axis2 = -1
self.argmax1 = P.ArgMaxWithValue(axis1)
self.argmax2 = P.ArgMaxWithValue(axis2)
self.argmax3 = P.ArgMaxWithValue()
def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
super(BboxAssignSample, self).__init__()
cfg = config
self.batch_size = batch_size
self.neg_iou_thr = Tensor(cfg.neg_iou_thr, mstype.float16)
self.pos_iou_thr = Tensor(cfg.pos_iou_thr, mstype.float16)
self.min_pos_iou = Tensor(cfg.min_pos_iou, mstype.float16)
self.zero_thr = Tensor(0.0, mstype.float16)
self.num_bboxes = num_bboxes
self.num_gts = cfg.num_gts
self.num_expected_pos = cfg.num_expected_pos
self.num_expected_neg = cfg.num_expected_neg
self.add_gt_as_proposals = add_gt_as_proposals
if self.add_gt_as_proposals:
self.label_inds = Tensor(np.arange(1, self.num_gts + 1))
self.concat = P.Concat(axis=0)
self.max_gt = P.ArgMaxWithValue(axis=0)
self.max_anchor = P.ArgMaxWithValue(axis=1)
self.sum_inds = P.ReduceSum()
self.iou = P.IOU()
self.greaterequal = P.GreaterEqual()
self.greater = P.Greater()
self.select = P.Select()
self.gatherND = P.GatherNd()
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.logicaland = P.LogicalAnd()
self.less = P.Less()
self.random_choice_with_mask_pos = P.RandomChoiceWithMask(self.num_expected_pos)
self.random_choice_with_mask_neg = P.RandomChoiceWithMask(self.num_expected_neg)
self.reshape = P.Reshape()
self.equal = P.Equal()
self.bounding_box_encode = BoundingBoxEncode()
self.scatterNdUpdate = P.ScatterNdUpdate()
self.scatterNd = P.ScatterNd()
self.logicalnot = P.LogicalNot()
self.tile = P.Tile()
self.zeros_like = P.ZerosLike()
self.assigned_gt_inds = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
self.assigned_gt_zeros = Tensor(np.array(np.zeros(num_bboxes), dtype=np.int32))
self.assigned_gt_ones = Tensor(np.array(np.ones(num_bboxes), dtype=np.int32))
self.assigned_gt_ignores = Tensor(np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
self.assigned_pos_ones = Tensor(np.array(np.ones(self.num_expected_pos), dtype=np.int32))
self.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool))
self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16))
self.check_gt_one = Tensor(np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
self.check_anchor_two = Tensor(np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
self.print = P.Print()
def __init__(self, tag_to_index, batch_size=1, seq_length=128, is_training=True):
super(CRF, self).__init__()
self.target_size = len(tag_to_index)
self.is_training = is_training
self.tag_to_index = tag_to_index
self.batch_size = batch_size
self.seq_length = seq_length
self.START_TAG = "<START>"
self.STOP_TAG = "<STOP>"
self.START_VALUE = Tensor(self.target_size-2, dtype=mstype.int32)
self.STOP_VALUE = Tensor(self.target_size-1, dtype=mstype.int32)
transitions = np.random.normal(size=(self.target_size, self.target_size)).astype(np.float32)
transitions[tag_to_index[self.START_TAG], :] = -10000
transitions[:, tag_to_index[self.STOP_TAG]] = -10000
self.transitions = Parameter(Tensor(transitions), name="transition_matrix")
self.cat = P.Concat(axis=-1)
self.argmax = P.ArgMaxWithValue(axis=-1)
self.log = P.Log()
self.exp = P.Exp()
self.sum = P.ReduceSum()
self.tile = P.Tile()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.reshape = P.Reshape()
self.expand = P.ExpandDims()
self.mean = P.ReduceMean()
init_alphas = np.ones(shape=(self.batch_size, self.target_size)) * -10000.0
init_alphas[:, self.tag_to_index[self.START_TAG]] = 0.
self.init_alphas = Tensor(init_alphas, dtype=mstype.float32)
self.cast = P.Cast()
self.reduce_max = P.ReduceMax(keep_dims=True)
self.on_value = Tensor(1.0, dtype=mstype.float32)
self.off_value = Tensor(0.0, dtype=mstype.float32)
self.onehot = P.OneHot()
def __init__(self, backbone, generate=False):
super(EvalNet, self).__init__(auto_prefix=False)
self.backbone = backbone
self.argmax = P.ArgMaxWithValue()
self.generate = generate
self.topk = P.TopK(sorted=True).shard(((1, 1),))
self.log_softmax = P.Softmax(axis=-1)
def __init__(self, config):
super(CrossEntropyLoss, self).__init__()
self.mean = P.ReduceMean()
self.sum = P.ReduceSum().shard(((config.dp, config.mp),))
self.onehot = P.OneHot().shard(((config.dp, config.mp), (), ()))
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.vocab_size = config.vocab_size
self.max = P.ArgMaxWithValue(axis=-1, keep_dims=True).shard(
((config.dp, config.mp),))
self.eps_const = Tensor(1e-24, mstype.float32)
self.sub = P.Sub().shard(((config.dp, config.mp), (config.dp, 1)))
self.exp = P.Exp().shard(((config.dp, config.mp),))
self.div = P.RealDiv().shard(((config.dp, config.mp), (config.dp, 1)))
self.log = P.Log().shard(((config.dp, config.mp),))
self.add = P.TensorAdd().shard(((config.dp, config.mp), ()))
self.mul = P.Mul().shard(
((config.dp, config.mp), (config.dp, config.mp)))
self.neg = P.Neg().shard(((config.dp, config.mp),))
self.sum2 = P.ReduceSum().shard(((1,),))
self.mul2 = P.Mul().shard(((1,), (1,)))
self.add2 = P.TensorAdd()
self.div2 = P.RealDiv()
if config.stage_num > 1:
self.div2.add_prim_attr("end", 0)
def __init__(self, network):
super(GRUInferCell, self).__init__(auto_prefix=False)
self.network = network
self.argmax = P.ArgMaxWithValue(axis=2)
self.transpose = P.Transpose()
self.teacher_force = Tensor(np.zeros((config.eval_batch_size)),
mstype.bool_)
def __init__(self,
probs=None,
seed=None,
dtype=mstype.int32,
name="Categorical"):
param = dict(locals())
param['param_dict'] = {'probs': probs}
valid_dtype = mstype.uint_type + mstype.int_type + mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Categorical, self).__init__(seed, dtype, name, param)
self._probs = self._add_parameter(probs, 'probs')
if self.probs is not None:
check_rank(self.probs)
check_prob(self.probs)
check_sum_equal_one(probs)
# update is_scalar_batch and broadcast_shape
# drop one dimension
if self.probs.shape[:-1] == ():
self._is_scalar_batch = True
self._broadcast_shape = self._broadcast_shape[:-1]
self.argmax = P.ArgMaxWithValue(axis=-1)
self.broadcast = broadcast_to
self.cast = P.Cast()
self.clip_by_value = C.clip_by_value
self.concat = P.Concat(-1)
self.cumsum = P.CumSum()
self.dtypeop = P.DType()
self.exp = exp_generic
self.expand_dim = P.ExpandDims()
self.fill = P.Fill()
self.gather = P.GatherNd()
self.greater = P.Greater()
self.issubclass = P.IsSubClass()
self.less = P.Less()
self.log = log_generic
self.log_softmax = P.LogSoftmax()
self.logicor = P.LogicalOr()
self.logicand = P.LogicalAnd()
self.multinomial = P.Multinomial(seed=self.seed)
self.reshape = P.Reshape()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.select = P.Select()
self.shape = P.Shape()
self.softmax = P.Softmax()
self.squeeze = P.Squeeze()
self.squeeze_first_axis = P.Squeeze(0)
self.squeeze_last_axis = P.Squeeze(-1)
self.square = P.Square()
self.transpose = P.Transpose()
self.is_nan = P.IsNan()
self.index_type = mstype.int32
self.nan = np.nan
def __init__(self, backbone, config):
super(WithLossCell, self).__init__(auto_prefix=False)
self._backbone = backbone
self.batch_size = config.batch_size
self.onehot = nn.OneHot(depth=config.ch_vocab_size)
self._loss_fn = NLLLoss()
self.max_len = config.max_seq_length
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.argmax = P.ArgMaxWithValue(axis=1, keep_dims=True)
self.print = P.Print()
def __init__(self):
super(openpose_loss, self).__init__()
self.expand_dims = P.ExpandDims()
self.tile = P.Tile()
self.mul = P.Mul()
self.l2_loss = P.L2Loss()
self.square = P.Square()
self.reduceMean = P.ReduceMean()
self.reduceSum = P.ReduceSum()
self.print = P.Print()
self.shape = P.Shape()
self.maxoftensor = P.ArgMaxWithValue(-1)
def __init__(self, config, is_train=True):
super(Seq2Seq, self).__init__()
self.max_len = config.max_seq_length
self.is_train = is_train
self.encoder = Encoder(config, is_train)
self.decoder = Decoder(config, is_train)
self.expanddims = P.ExpandDims()
self.squeeze = P.Squeeze(axis=0)
self.argmax = P.ArgMaxWithValue(axis=int(2), keep_dims=True)
self.concat = P.Concat(axis=1)
self.concat2 = P.Concat(axis=0)
self.select = P.Select()
def __init__(self,
config,
batch_size,
num_classes,
target_means=(0., 0., 0., 0.),
target_stds=(0.1, 0.1, 0.2, 0.2)):
super(RcnnCls, self).__init__()
cfg = config
self.rcnn_loss_cls_weight = Tensor(
np.array(cfg.rcnn_loss_cls_weight).astype(np.float16))
self.rcnn_loss_reg_weight = Tensor(
np.array(cfg.rcnn_loss_reg_weight).astype(np.float16))
self.rcnn_fc_out_channels = cfg.rcnn_fc_out_channels
self.target_means = target_means
self.target_stds = target_stds
self.num_classes = num_classes
self.in_channels = cfg.rcnn_in_channels
self.train_batch_size = batch_size
self.test_batch_size = cfg.test_batch_size
self.fpn_cls = FpnCls(self.in_channels, self.rcnn_fc_out_channels,
self.num_classes, cfg.roi_layer["out_size"])
self.relu = P.ReLU()
self.logicaland = P.LogicalAnd()
self.loss_cls = P.SoftmaxCrossEntropyWithLogits()
self.loss_bbox = P.SmoothL1Loss(beta=1.0)
self.loss_mask = P.SigmoidCrossEntropyWithLogits()
self.reshape = P.Reshape()
self.onehot = P.OneHot()
self.greater = P.Greater()
self.cast = P.Cast()
self.sum_loss = P.ReduceSum()
self.tile = P.Tile()
self.expandims = P.ExpandDims()
self.gather = P.GatherNd()
self.argmax = P.ArgMaxWithValue(axis=1)
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.value = Tensor(1.0, mstype.float16)
self.num_bboxes = (cfg.num_expected_pos_stage2 +
cfg.num_expected_neg_stage2) * batch_size
rmv_first = np.ones((self.num_bboxes, self.num_classes))
rmv_first[:, 0] = np.zeros((self.num_bboxes, ))
self.rmv_first_tensor = Tensor(rmv_first.astype(np.float16))
self.num_bboxes_test = cfg.rpn_max_num * cfg.test_batch_size
def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False):
super(BertSquad, self).__init__()
self.bert = BertSquadModel(config, is_training, num_labels, dropout_prob, use_one_hot_embeddings)
self.loss = CrossEntropyCalculation(is_training)
self.num_labels = num_labels
self.seq_length = config.seq_length
self.is_training = is_training
self.total_num = Parameter(Tensor([0], mstype.float32))
self.start_num = Parameter(Tensor([0], mstype.float32))
self.end_num = Parameter(Tensor([0], mstype.float32))
self.sum = P.ReduceSum()
self.equal = P.Equal()
self.argmax = P.ArgMaxWithValue(axis=1)
self.squeeze = P.Squeeze(axis=-1)
def __init__(self, config, is_training=True):
super(Seq2Seq, self).__init__()
if is_training:
self.batch_size = config.batch_size
else:
self.batch_size = config.eval_batch_size
self.encoder = Encoder(config, is_training=is_training)
self.decoder = Decoder(config, is_training=is_training)
self.expandims = P.ExpandDims()
self.dropout = nn.Dropout()
self.shape = P.Shape()
self.concat = P.Concat(axis=0)
self.argmax = P.ArgMaxWithValue(axis=1, keep_dims=True)
self.squeeze = P.Squeeze(axis=0)
self.sos = Tensor(np.ones((self.batch_size, 1)) * 2, mstype.int32)
self.select = P.Select()
self.text_len = config.max_length
def __init__(self,
temperature=0.07,
contrast_mode='all',
base_temperature=0.07):
super(SupConLoss, self).__init__()
self.temperature = temperature
self.contrast_mode = contrast_mode
self.base_temperature = base_temperature
self.normalize = P.L2Normalize(axis=2)
self.eye = P.Eye()
self.unbind = P.Unstack(axis=1)
self.cat = P.Concat(axis=0)
self.matmul = P.MatMul()
self.div = P.Div()
self.transpose = P.Transpose()
self.maxes = P.ArgMaxWithValue(axis=1, keep_dims=True)
self.tile = P.Tile()
self.scatter = P.ScatterNd()
self.oneslike = P.OnesLike()
self.exp = P.Exp()
self.sum = P.ReduceSum(keep_dims=True)
self.log = P.Log()
self.reshape = P.Reshape()
self.mean = P.ReduceMean()
def __init__(self, config):
super(Mask_Rcnn_Resnet50, self).__init__()
self.train_batch_size = config.batch_size
self.num_classes = config.num_classes
self.anchor_scales = config.anchor_scales
self.anchor_ratios = config.anchor_ratios
self.anchor_strides = config.anchor_strides
self.target_means = tuple(config.rcnn_target_means)
self.target_stds = tuple(config.rcnn_target_stds)
# Anchor generator
anchor_base_sizes = None
self.anchor_base_sizes = list(
self.anchor_strides) if anchor_base_sizes is None else anchor_base_sizes
self.anchor_generators = []
for anchor_base in self.anchor_base_sizes:
self.anchor_generators.append(
AnchorGenerator(anchor_base, self.anchor_scales, self.anchor_ratios))
self.num_anchors = len(self.anchor_ratios) * len(self.anchor_scales)
featmap_sizes = config.feature_shapes
assert len(featmap_sizes) == len(self.anchor_generators)
self.anchor_list = self.get_anchors(featmap_sizes)
# Backbone resnet50
self.backbone = ResNetFea(ResidualBlockUsing,
config.resnet_block,
config.resnet_in_channels,
config.resnet_out_channels,
False)
# Fpn
self.fpn_ncek = FeatPyramidNeck(config.fpn_in_channels,
config.fpn_out_channels,
config.fpn_num_outs)
# Rpn and rpn loss
self.gt_labels_stage1 = Tensor(np.ones((self.train_batch_size, config.num_gts)).astype(np.uint8))
self.rpn_with_loss = RPN(config,
self.train_batch_size,
config.rpn_in_channels,
config.rpn_feat_channels,
config.num_anchors,
config.rpn_cls_out_channels)
# Proposal
self.proposal_generator = Proposal(config,
self.train_batch_size,
config.activate_num_classes,
config.use_sigmoid_cls)
self.proposal_generator.set_train_local(config, True)
self.proposal_generator_test = Proposal(config,
config.test_batch_size,
config.activate_num_classes,
config.use_sigmoid_cls)
self.proposal_generator_test.set_train_local(config, False)
# Assign and sampler stage two
self.bbox_assigner_sampler_for_rcnn = BboxAssignSampleForRcnn(config, self.train_batch_size,
config.num_bboxes_stage2, True)
self.decode = P.BoundingBoxDecode(max_shape=(768, 1280), means=self.target_means, \
stds=self.target_stds)
# Roi
self.roi_align = SingleRoIExtractor(config,
config.roi_layer,
config.roi_align_out_channels,
config.roi_align_featmap_strides,
self.train_batch_size,
config.roi_align_finest_scale,
mask=False)
self.roi_align.set_train_local(config, True)
self.roi_align_mask = SingleRoIExtractor(config,
config.roi_layer,
config.roi_align_out_channels,
config.roi_align_featmap_strides,
self.train_batch_size,
config.roi_align_finest_scale,
mask=True)
self.roi_align_mask.set_train_local(config, True)
self.roi_align_test = SingleRoIExtractor(config,
config.roi_layer,
config.roi_align_out_channels,
config.roi_align_featmap_strides,
1,
config.roi_align_finest_scale,
mask=False)
self.roi_align_test.set_train_local(config, False)
self.roi_align_mask_test = SingleRoIExtractor(config,
config.roi_layer,
config.roi_align_out_channels,
config.roi_align_featmap_strides,
1,
config.roi_align_finest_scale,
mask=True)
self.roi_align_mask_test.set_train_local(config, False)
# Rcnn
self.rcnn_cls = RcnnCls(config, self.train_batch_size, self.num_classes)
self.rcnn_mask = RcnnMask(config, self.train_batch_size, self.num_classes)
# Op declare
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.concat = P.Concat(axis=0)
self.concat_1 = P.Concat(axis=1)
self.concat_2 = P.Concat(axis=2)
self.reshape = P.Reshape()
self.select = P.Select()
self.greater = P.Greater()
self.transpose = P.Transpose()
# Test mode
self.test_batch_size = config.test_batch_size
self.split = P.Split(axis=0, output_num=self.test_batch_size)
self.split_shape = P.Split(axis=0, output_num=4)
self.split_scores = P.Split(axis=1, output_num=self.num_classes)
self.split_fb_mask = P.Split(axis=1, output_num=self.num_classes)
self.split_cls = P.Split(axis=0, output_num=self.num_classes-1)
self.tile = P.Tile()
self.gather = P.GatherNd()
self.rpn_max_num = config.rpn_max_num
self.zeros_for_nms = Tensor(np.zeros((self.rpn_max_num, 3)).astype(np.float16))
self.ones_mask = np.ones((self.rpn_max_num, 1)).astype(np.bool)
self.zeros_mask = np.zeros((self.rpn_max_num, 1)).astype(np.bool)
self.bbox_mask = Tensor(np.concatenate((self.ones_mask, self.zeros_mask,
self.ones_mask, self.zeros_mask), axis=1))
self.nms_pad_mask = Tensor(np.concatenate((self.ones_mask, self.ones_mask,
self.ones_mask, self.ones_mask, self.zeros_mask), axis=1))
self.test_score_thresh = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * config.test_score_thr)
self.test_score_zeros = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * 0)
self.test_box_zeros = Tensor(np.ones((self.rpn_max_num, 4)).astype(np.float16) * -1)
self.test_iou_thr = Tensor(np.ones((self.rpn_max_num, 1)).astype(np.float16) * config.test_iou_thr)
self.test_max_per_img = config.test_max_per_img
self.nms_test = P.NMSWithMask(config.test_iou_thr)
self.softmax = P.Softmax(axis=1)
self.logicand = P.LogicalAnd()
self.oneslike = P.OnesLike()
self.test_topk = P.TopK(sorted=True)
self.test_num_proposal = self.test_batch_size * self.rpn_max_num
# Improve speed
self.concat_start = min(self.num_classes - 2, 55)
self.concat_end = (self.num_classes - 1)
# Init tensor
roi_align_index = [np.array(np.ones((config.num_expected_pos_stage2 + config.num_expected_neg_stage2, 1)) * i,
dtype=np.float16) for i in range(self.train_batch_size)]
roi_align_index_test = [np.array(np.ones((config.rpn_max_num, 1)) * i, dtype=np.float16) \
for i in range(self.test_batch_size)]
self.roi_align_index_tensor = Tensor(np.concatenate(roi_align_index))
self.roi_align_index_test_tensor = Tensor(np.concatenate(roi_align_index_test))
roi_align_index_pos = [np.array(np.ones((config.num_expected_pos_stage2, 1)) * i,
dtype=np.float16) for i in range(self.train_batch_size)]
self.roi_align_index_tensor_pos = Tensor(np.concatenate(roi_align_index_pos))
self.rcnn_loss_cls_weight = Tensor(np.array(config.rcnn_loss_cls_weight).astype(np.float16))
self.rcnn_loss_reg_weight = Tensor(np.array(config.rcnn_loss_reg_weight).astype(np.float16))
self.rcnn_loss_mask_fb_weight = Tensor(np.array(config.rcnn_loss_mask_fb_weight).astype(np.float16))
self.argmax_with_value = P.ArgMaxWithValue(axis=1)
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.onehot = P.OneHot()
self.reducesum = P.ReduceSum()
self.sigmoid = P.Sigmoid()
self.expand_dims = P.ExpandDims()
self.test_mask_fb_zeros = Tensor(np.zeros((self.rpn_max_num, 28, 28)).astype(np.float16))
self.value = Tensor(1.0, mstype.float16)
('Split', {
'block': P.Split(1, 2),
'desc_inputs': [Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))],
'skip': ['backward']}),
('Argmax', {
'block': P.Argmax(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [0],
'skip': ['backward']}),
('Argmin', {
'block': P.Argmin(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [1],
'skip': ['backward']}),
('ArgMaxWithValue', {
'block': P.ArgMaxWithValue(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[1], [1]],
'skip': ['backward']}),
('ArgMinWithValue', {
'block': P.ArgMinWithValue(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[1], [1]],
'skip': ['backward']}),
('Transpose_dim3', {
'block': P.Transpose(),
'desc_const': [(0, 2, 1)],
'desc_inputs': [[1, 2, 3]],
'desc_bprop': [[1, 3, 2]]}),
('Transpose_dim4', {
'block': P.Transpose(),
def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
super(BboxAssignSampleForRcnn, self).__init__()
cfg = config
self.dtype = np.float32
self.ms_type = mstype.float32
self.batch_size = batch_size
self.neg_iou_thr = cfg.neg_iou_thr_stage2
self.pos_iou_thr = cfg.pos_iou_thr_stage2
self.min_pos_iou = cfg.min_pos_iou_stage2
self.num_gts = cfg.num_gts
self.num_bboxes = num_bboxes
self.num_expected_pos = cfg.num_expected_pos_stage2
self.num_expected_neg = cfg.num_expected_neg_stage2
self.num_expected_total = cfg.num_expected_total_stage2
self.add_gt_as_proposals = add_gt_as_proposals
self.label_inds = Tensor(
np.arange(1, self.num_gts + 1).astype(np.int32))
self.add_gt_as_proposals_valid = Tensor(
np.full(self.num_gts, self.add_gt_as_proposals, dtype=np.int32))
self.concat = P.Concat(axis=0)
self.max_gt = P.ArgMaxWithValue(axis=0)
self.max_anchor = P.ArgMaxWithValue(axis=1)
self.sum_inds = P.ReduceSum()
self.iou = P.IOU()
self.greaterequal = P.GreaterEqual()
self.greater = P.Greater()
self.select = P.Select()
self.gatherND = P.GatherNd()
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.logicaland = P.LogicalAnd()
self.less = P.Less()
self.random_choice_with_mask_pos = P.RandomChoiceWithMask(
self.num_expected_pos)
self.random_choice_with_mask_neg = P.RandomChoiceWithMask(
self.num_expected_neg)
self.reshape = P.Reshape()
self.equal = P.Equal()
self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0,
0.0),
stds=(0.1, 0.1, 0.2,
0.2))
self.concat_axis1 = P.Concat(axis=1)
self.logicalnot = P.LogicalNot()
self.tile = P.Tile()
# Check
self.check_gt_one = Tensor(
np.full((self.num_gts, 4), -1, dtype=self.dtype))
self.check_anchor_two = Tensor(
np.full((self.num_bboxes, 4), -2, dtype=self.dtype))
# Init tensor
self.assigned_gt_inds = Tensor(np.full(num_bboxes, -1, dtype=np.int32))
self.assigned_gt_zeros = Tensor(
np.array(np.zeros(num_bboxes), dtype=np.int32))
self.assigned_gt_ones = Tensor(
np.array(np.ones(num_bboxes), dtype=np.int32))
self.assigned_gt_ignores = Tensor(
np.full(num_bboxes, -1, dtype=np.int32))
self.assigned_pos_ones = Tensor(
np.array(np.ones(self.num_expected_pos), dtype=np.int32))
self.gt_ignores = Tensor(np.full(self.num_gts, -1, dtype=np.int32))
self.range_pos_size = Tensor(
np.arange(self.num_expected_pos).astype(self.dtype))
self.check_neg_mask = Tensor(
np.array(np.ones(self.num_expected_neg - self.num_expected_pos),
dtype=np.bool))
self.bboxs_neg_mask = Tensor(
np.zeros((self.num_expected_neg, 4), dtype=self.dtype))
self.labels_neg_mask = Tensor(
np.array(np.zeros(self.num_expected_neg), dtype=np.uint8))
self.reshape_shape_pos = (self.num_expected_pos, 1)
self.reshape_shape_neg = (self.num_expected_neg, 1)
self.scalar_zero = Tensor(0.0, dtype=self.ms_type)
self.scalar_neg_iou_thr = Tensor(self.neg_iou_thr, dtype=self.ms_type)
self.scalar_pos_iou_thr = Tensor(self.pos_iou_thr, dtype=self.ms_type)
self.scalar_min_pos_iou = Tensor(self.min_pos_iou, dtype=self.ms_type)
def __init__(self, network):
super(FastTextInferCell, self).__init__(auto_prefix=False)
self.network = network
self.argmax = P.ArgMaxWithValue(axis=1, keep_dims=True)
self.log_softmax = nn.LogSoftmax(axis=1)
def read_img(img_path):
img_ori = cv2.imread(img_path)
img = cv2.cvtColor(img_ori, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (96, 96))
img = img.transpose(2, 0, 1)
img = np.array([img]).astype(np.float32)/255.
img = Tensor(img)
return img, img_ori
blur_soft = nn.Softmax(0)
kps_soft = nn.Softmax(-1)
reshape = P.Reshape()
argmax = P.ArgMaxWithValue()
def test_trains(args):
'''test trains'''
print('----eval----begin----')
model_path = args.pretrained
result_file = model_path.replace('.ckpt', '.txt')
if os.path.exists(result_file):
os.remove(result_file)
epoch_result = open(result_file, 'a')
epoch_result.write(model_path + '\n')
network = FaceQABackbone()
ckpt_path = model_path
def __init__(self, axis=-1):
super(Softmax, self).__init__()
self.max = P.ArgMaxWithValue(axis=axis, keep_dims=True)
self.sum = P.ReduceSum(keep_dims=True)
self.axis = axis
def __init__(self, axis, keep_dims):
super().__init__()
self.op = op.ArgMaxWithValue(axis=axis, keep_dims=keep_dims)
def __init__(self, axis=0):
super(NetArgmaxWithValueBig, self).__init__()
self.argmax = P.ArgMaxWithValue(axis)
def __init__(self,
config,
representation_size,
batch_size,
num_classes,
target_means=(0., 0., 0., 0.),
target_stds=(0.1, 0.1, 0.2, 0.2)):
super(Rcnn, self).__init__()
cfg = config
self.rcnn_loss_cls_weight = Tensor(
np.array(cfg.rcnn_loss_cls_weight).astype(np.float16))
self.rcnn_loss_reg_weight = Tensor(
np.array(cfg.rcnn_loss_reg_weight).astype(np.float16))
self.rcnn_fc_out_channels = cfg.rcnn_fc_out_channels
self.target_means = target_means
self.target_stds = target_stds
self.num_classes = num_classes
self.in_channels = cfg.rcnn_in_channels
self.train_batch_size = batch_size
self.test_batch_size = cfg.test_batch_size
self.use_ambigous_sample = cfg.use_ambigous_sample
shape_0 = (self.rcnn_fc_out_channels, representation_size)
weights_0 = initializer("XavierUniform",
shape=shape_0[::-1],
dtype=mstype.float16).to_tensor()
shape_1 = (self.rcnn_fc_out_channels, self.rcnn_fc_out_channels)
weights_1 = initializer("XavierUniform",
shape=shape_1[::-1],
dtype=mstype.float16).to_tensor()
self.shared_fc_0 = DenseNoTranpose(representation_size,
self.rcnn_fc_out_channels,
weights_0)
self.shared_fc_1 = DenseNoTranpose(self.rcnn_fc_out_channels,
self.rcnn_fc_out_channels,
weights_1)
cls_weight = initializer(
'Normal',
shape=[num_classes, self.rcnn_fc_out_channels][::-1],
dtype=mstype.float16).to_tensor()
reg_weight = initializer(
'Normal',
shape=[num_classes * 4, self.rcnn_fc_out_channels][::-1],
dtype=mstype.float16).to_tensor()
self.cls_scores = DenseNoTranpose(self.rcnn_fc_out_channels,
num_classes, cls_weight)
self.reg_scores = DenseNoTranpose(self.rcnn_fc_out_channels,
num_classes * 4, reg_weight)
self.flatten = P.Flatten()
self.relu = P.ReLU()
self.logicaland = P.LogicalAnd()
self.loss_cls = P.SoftmaxCrossEntropyWithLogits()
self.loss_bbox = P.SmoothL1Loss(beta=1.0)
self.reshape = P.Reshape()
self.onehot = P.OneHot()
self.greater = P.Greater()
self.equal = P.Equal()
self.cast = P.Cast()
self.sum_loss = P.ReduceSum()
self.tile = P.Tile()
self.expandims = P.ExpandDims()
self.gather = P.GatherNd()
self.argmax = P.ArgMaxWithValue(axis=1)
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.value = Tensor(1.0, mstype.float16)
self.num_bboxes = (cfg.num_expected_pos_stage2 +
cfg.num_expected_neg_stage2) * batch_size
if self.use_ambigous_sample:
self.num_bboxes = (cfg.num_expected_pos_stage2 +
cfg.num_expected_amb_stage2 +
cfg.num_expected_neg_stage2) * batch_size
rmv_first = np.ones((self.num_bboxes, self.num_classes))
rmv_first[:, 0] = np.zeros((self.num_bboxes, ))
self.rmv_first_tensor = Tensor(rmv_first.astype(np.float16))
self.num_bboxes_test = cfg.rpn_max_num * cfg.test_batch_size
range_max = np.arange(self.num_bboxes_test).astype(np.int32)
self.range_max = Tensor(range_max)
def __init__(self, strategy1, strategy2, strategy3):
super().__init__()
self.mul1 = P.Mul().set_strategy(strategy1)
self.arg_max_with_value = P.ArgMaxWithValue(
keep_dims=False, axis=-1).set_strategy(strategy2)
self.mul2 = P.Mul().set_strategy(strategy3)
def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
super(BboxAssignSampleForRcnn, self).__init__()
cfg = config
self.batch_size = batch_size
self.neg_iou_thr = cfg.neg_iou_thr_stage2
self.pos_iou_thr = cfg.pos_iou_thr_stage2
self.min_pos_iou = cfg.min_pos_iou_stage2
self.num_gts = cfg.num_gts
self.num_bboxes = num_bboxes
self.num_expected_pos = cfg.num_expected_pos_stage2
self.num_expected_neg = cfg.num_expected_neg_stage2
self.num_expected_total = cfg.num_expected_total_stage2
self.add_gt_as_proposals = add_gt_as_proposals
self.label_inds = Tensor(
np.arange(1, self.num_gts + 1).astype(np.int32))
self.add_gt_as_proposals_valid = Tensor(
np.array(self.add_gt_as_proposals * np.ones(self.num_gts),
dtype=np.int32))
self.concat = P.Concat(axis=0)
self.max_gt = P.ArgMaxWithValue(axis=0)
self.max_anchor = P.ArgMaxWithValue(axis=1)
self.sum_inds = P.ReduceSum()
self.iou = P.IOU()
self.greaterequal = P.GreaterEqual()
self.greater = P.Greater()
self.select = P.Select()
self.gatherND = P.GatherNd()
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.logicaland = P.LogicalAnd()
self.less = P.Less()
self.random_choice_with_mask_pos = P.RandomChoiceWithMask(
self.num_expected_pos)
self.random_choice_with_mask_neg = P.RandomChoiceWithMask(
self.num_expected_neg)
self.reshape = P.Reshape()
self.equal = P.Equal()
self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0,
0.0),
stds=(0.1, 0.1, 0.2,
0.2))
self.concat_axis1 = P.Concat(axis=1)
self.logicalnot = P.LogicalNot()
self.tile = P.Tile()
# Check
self.check_gt_one = Tensor(
np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16))
self.check_anchor_two = Tensor(
np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16))
# Init tensor
self.assigned_gt_inds = Tensor(
np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
self.assigned_gt_zeros = Tensor(
np.array(np.zeros(num_bboxes), dtype=np.int32))
self.assigned_gt_ones = Tensor(
np.array(np.ones(num_bboxes), dtype=np.int32))
self.assigned_gt_ignores = Tensor(
np.array(-1 * np.ones(num_bboxes), dtype=np.int32))
self.assigned_pos_ones = Tensor(
np.array(np.ones(self.num_expected_pos), dtype=np.int32))
self.gt_ignores = Tensor(
np.array(-1 * np.ones(self.num_gts), dtype=np.int32))
self.range_pos_size = Tensor(
np.arange(self.num_expected_pos).astype(np.float16))
self.check_neg_mask = Tensor(
np.array(np.ones(self.num_expected_neg - self.num_expected_pos),
dtype=np.bool))
self.bboxs_neg_mask = Tensor(
np.zeros((self.num_expected_neg, 4), dtype=np.float16))
self.labels_neg_mask = Tensor(
np.array(np.zeros(self.num_expected_neg), dtype=np.uint8))
self.reshape_shape_pos = (self.num_expected_pos, 1)
self.reshape_shape_neg = (self.num_expected_neg, 1)
self.scalar_zero = Tensor(0.0, dtype=mstype.float16)
self.scalar_neg_iou_thr = Tensor(self.neg_iou_thr,
dtype=mstype.float16)
self.scalar_pos_iou_thr = Tensor(self.pos_iou_thr,
dtype=mstype.float16)
self.scalar_min_pos_iou = Tensor(self.min_pos_iou,
dtype=mstype.float16)
self.expand_dims = P.ExpandDims()
self.split = P.Split(axis=1, output_num=4)
self.concat_last_axis = P.Concat(axis=-1)
self.round = P.Round()
self.image_h_w = Tensor(
[cfg.img_height, cfg.img_width, cfg.img_height, cfg.img_width],
dtype=mstype.float16)
self.range = nn.Range(start=0, limit=cfg.num_expected_pos_stage2)
self.crop_and_resize = P.CropAndResize(method="bilinear_v2")
self.mask_shape = (cfg.mask_shape[0], cfg.mask_shape[1])
self.squeeze_mask_last = P.Squeeze(axis=-1)
