Revert "use tensor.shape bug not paddle.shape(tensor)"

ppdet/modeling/architectures/detr.py

@@ -104,7 +104,7 @@ def _forward(self):
  else:
  bbox, bbox_num, mask = self.post_process(
  preds, self.inputs['im_shape'], self.inputs['scale_factor'],
- self.inputs['image'])[2:].shape
+ paddle.shape(self.inputs['image'])[2:])
 
  output = {'bbox': bbox, 'bbox_num': bbox_num}
  if self.with_mask:

ppdet/modeling/architectures/pose3d_metro.py

@@ -36,7 +36,7 @@ def orthographic_projection(X, camera):
  """
  camera = camera.reshape((-1, 1, 3))
  X_trans = X[:, :, :2] + camera[:, :, 1:]
- shape = X_trans.shape
+ shape = paddle.shape(X_trans)
  X_2d = (camera[:, :, 0] * X_trans.reshape((shape[0], -1))).reshape(shape)
  return X_2d
 

ppdet/modeling/backbones/trans_encoder.py

@@ -37,11 +37,11 @@ def __init__(self, word_size, position_embeddings_size, word_type_size,
  self.dropout = nn.Dropout(dropout_prob)
 
  def forward(self, x, token_type_ids=None, position_ids=None):
- seq_len = x.shape[1]
+ seq_len = paddle.shape(x)[1]
  if position_ids is None:
  position_ids = paddle.arange(seq_len).unsqueeze(0).expand_as(x)
  if token_type_ids is None:
- token_type_ids = paddle.zeros(x.shape)
+ token_type_ids = paddle.zeros(paddle.shape(x))
 
  word_embs = self.word_embeddings(x)
  position_embs = self.position_embeddings(position_ids)
@@ -82,7 +82,7 @@ def forward(self, x, attention_mask, head_mask=None):
  key = self.key(x)
  value = self.value(x)
 
- query_dim1, query_dim2 = query.shape[:-1]
+ query_dim1, query_dim2 = paddle.shape(query)[:-1]
  new_shape = [
  query_dim1, query_dim2, self.num_attention_heads,
  self.attention_head_size
@@ -102,7 +102,7 @@ def forward(self, x, attention_mask, head_mask=None):
 
  context = paddle.matmul(attention_value, value).transpose(perm=(0, 2, 1,
  3))
- ctx_dim1, ctx_dim2 = context.shape[:-2]
+ ctx_dim1, ctx_dim2 = paddle.shape(context)[:-2]
  new_context_shape = [
  ctx_dim1,
  ctx_dim2,
@@ -303,7 +303,7 @@ def init_weights(self, module):
  module.bias.set_value(paddle.zeros(shape=module.bias.shape))
 
  def forward(self, x):
- batchsize, seq_len = x.shape[:2]
+ batchsize, seq_len = paddle.shape(x)[:2]
  input_ids = paddle.zeros((batchsize, seq_len), dtype="int64")
  position_ids = paddle.arange(
  seq_len, dtype="int64").unsqueeze(0).expand_as(input_ids)

ppdet/modeling/backbones/transformer_utils.py

@@ -34,7 +34,7 @@ def drop_path(x, drop_prob=0., training=False):
  if drop_prob == 0. or not training:
  return x
  keep_prob = paddle.to_tensor(1 - drop_prob, dtype=x.dtype)
- shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)
+ shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
  random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
  random_tensor = paddle.floor(random_tensor) # binarize
  output = x.divide(keep_prob) * random_tensor
@@ -85,7 +85,7 @@ def window_partition(x, window_size):
  windows: windows after partition with [B * num_windows, window_size, window_size, C].
  (Hp, Wp): padded height and width before partition
  """
- B, H, W, C = x.shape
+ B, H, W, C = paddle.shape(x)
 
  pad_h = (window_size - H % window_size) % window_size
  pad_w = (window_size - W % window_size) % window_size
@@ -116,7 +116,7 @@ def window_unpartition(x, pad_hw, num_hw, hw):
  Hp, Wp = pad_hw
  num_h, num_w = num_hw
  H, W = hw
- B, window_size, _, C = x.shape
+ B, window_size, _, C = paddle.shape(x)
  B = B // (num_h * num_w)
  x = x.reshape([B, num_h, num_w, window_size, window_size, C])
  x = x.transpose([0, 1, 3, 2, 4, 5]).reshape([B, Hp, Wp, C])

ppdet/modeling/backbones/vision_transformer.py

@@ -121,7 +121,7 @@ def __init__(self,
  self.proj_drop = nn.Dropout(proj_drop)
 
  def forward(self, x, rel_pos_bias=None):
- x_shape = x.shape
+ x_shape = paddle.shape(x)
  N, C = x_shape[1], x_shape[2]
 
  qkv_bias = None

ppdet/modeling/backbones/vit_mae.py

@@ -181,7 +181,7 @@ def add_decomposed_rel_pos(self, attn, q, h, w):
  return attn.reshape([B, h * w, h * w])
 
  def forward(self, x):
- B, H, W, C = x.shape
+ B, H, W, C = paddle.shape(x)
 
  if self.q_bias is not None:
  qkv_bias = paddle.concat(
@@ -567,7 +567,7 @@ def get_2d_sincos_position_embedding(self, h, w, temperature=10000.):
 
  def forward(self, inputs):
  x = self.patch_embed(inputs['image']).transpose([0, 2, 3, 1])
- B, Hp, Wp, _ = x.shape
+ B, Hp, Wp, _ = paddle.shape(x)
 
  if self.use_abs_pos:
  x = x + self.get_2d_sincos_position_embedding(Hp, Wp)

ppdet/modeling/backbones/vitpose.py

@@ -40,7 +40,7 @@ def drop_path(x, drop_prob=0., training=False):
  if drop_prob == 0. or not training:
  return x
  keep_prob = paddle.to_tensor(1.0 - drop_prob).astype(x.dtype)
- shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)
+ shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
  random_tensor = keep_prob + paddle.rand(shape).astype(x.dtype)
  random_tensor = paddle.floor(random_tensor) # binarize
  output = x.divide(keep_prob) * random_tensor
@@ -303,7 +303,7 @@ def _init_weights(self):
 
  def forward_features(self, x):
 
- B = x.shape[0]
+ B = paddle.shape(x)[0]
  x = self.patch_embed(x)
  B, D, Hp, Wp = x.shape
  x = x.flatten(2).transpose([0, 2, 1])

ppdet/modeling/heads/fcosr_head.py

@@ -187,7 +187,7 @@ def _generate_anchors(self, feats):
  if self.trt:
  anchor_points = []
  for feat, stride in zip(feats, self.fpn_strides):
- _, _, h, w = feat.shape
+ _, _, h, w = paddle.shape(feat)
  anchor, _ = anchor_generator(
  feat,
  stride * 4,
@@ -206,7 +206,7 @@ def _generate_anchors(self, feats):
  stride_tensor = []
  num_anchors_list = []
  for feat, stride in zip(feats, self.fpn_strides):
- _, _, h, w = feat.shape
+ _, _, h, w = paddle.shape(feat)
  shift_x = (paddle.arange(end=w) + 0.5) * stride
  shift_y = (paddle.arange(end=h) + 0.5) * stride
  shift_y, shift_x = paddle.meshgrid(shift_y, shift_x)
@@ -263,7 +263,7 @@ def forward_eval(self, feats, target=None):
  cls_pred_list, reg_pred_list = [], []
  anchor_points, _, _ = self._generate_anchors(feats)
  for stride, feat, scale in zip(self.fpn_strides, feats, self.scales):
- b, _, h, w = feat.shape
+ b, _, h, w = paddle.shape(feat)
  # cls
  cls_feat = feat
  for cls_layer in self.stem_cls:

ppdet/modeling/heads/gfl_head.py

@@ -253,7 +253,7 @@ def forward(self, fpn_feats):
  if not self.training:
  cls_score = F.sigmoid(cls_score.transpose([0, 2, 3, 1]))
  bbox_pred = bbox_pred.transpose([0, 2, 3, 1])
- b, cell_h, cell_w, _ = cls_score.shape
+ b, cell_h, cell_w, _ = paddle.shape(cls_score)
  y, x = self.get_single_level_center_point(
  [cell_h, cell_w], stride, cell_offset=self.cell_offset)
  center_points = paddle.stack([x, y], axis=-1)
@@ -515,7 +515,7 @@ def forward(self, fpn_feats):
  if not self.training:
  cls_score = F.sigmoid(cls_score.transpose([0, 2, 3, 1]))
  bbox_pred = bbox_pred.transpose([0, 2, 3, 1])
- b, cell_h, cell_w, _ = cls_score.shape
+ b, cell_h, cell_w, _ = paddle.shape(cls_score)
  y, x = self.get_single_level_center_point(
  [cell_h, cell_w], stride, cell_offset=self.cell_offset)
  center_points = paddle.stack([x, y], axis=-1)

ppdet/modeling/heads/mask_head.py

@@ -224,12 +224,13 @@ def forward_test(self,
  if self.num_classes == 1:
  mask_out = F.sigmoid(mask_logit)[:, 0, :, :]
  else:
- num_masks = mask_logit.shape[0]
+ num_masks = paddle.shape(mask_logit)[0]
  index = paddle.arange(num_masks).cast('int32')
  mask_out = mask_logit[index, labels]
- mask_out_shape = mask_out.shape
- mask_out = paddle.reshape(mask_out, 
- index.shape + [mask_out_shape[-2]] + [mask_out_shape[-1]])
+ mask_out_shape = paddle.shape(mask_out)
+ mask_out = paddle.reshape(mask_out, [
+ paddle.shape(index), mask_out_shape[-2], mask_out_shape[-1]
+ ])
  mask_out = F.sigmoid(mask_out)
  return mask_out
 

ppdet/modeling/heads/pico_head.py

@@ -581,7 +581,7 @@ def forward_train(self, fpn_feats):
 
  cls_score_out = cls_score.transpose([0, 2, 3, 1])
  bbox_pred = reg_pred.transpose([0, 2, 3, 1])
- b, cell_h, cell_w, _ = cls_score_out.shape
+ b, cell_h, cell_w, _ = paddle.shape(cls_score_out)
  y, x = self.get_single_level_center_point(
  [cell_h, cell_w], stride, cell_offset=self.cell_offset)
  center_points = paddle.stack([x, y], axis=-1)

ppdet/modeling/heads/ppyoloe_r_head.py

@@ -137,7 +137,7 @@ def _generate_anchors(self, feats):
  if self.trt:
  anchor_points = []
  for feat, stride in zip(feats, self.fpn_strides):
- _, _, h, w = feat.shape
+ _, _, h, w = paddle.shape(feat)
  anchor, _ = anchor_generator(
  feat,
  stride * 4,
@@ -156,7 +156,7 @@ def _generate_anchors(self, feats):
  stride_tensor = []
  num_anchors_list = []
  for feat, stride in zip(feats, self.fpn_strides):
- _, _, h, w = feat.shape
+ _, _, h, w = paddle.shape(feat)
  shift_x = (paddle.arange(end=w) + 0.5) * stride
  shift_y = (paddle.arange(end=h) + 0.5) * stride
  shift_y, shift_x = paddle.meshgrid(shift_y, shift_x)
@@ -210,7 +210,7 @@ def forward_eval(self, feats):
  cls_score_list, reg_box_list = [], []
  anchor_points, _, _ = self._generate_anchors(feats)
  for i, (feat, stride) in enumerate(zip(feats, self.fpn_strides)):
- b, _, h, w = feat.shape
+ b, _, h, w = paddle.shape(feat)
  l = h * w
  # cls
  avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))

ppdet/modeling/heads/s2anet_head.py

@@ -250,7 +250,7 @@ def forward(self, feats, targets=None):
  for i, feat in enumerate(feats):
  # get shape
  B = feat.shape[0]
- H, W = feat.shape[2], feat.shape[3]
+ H, W = paddle.shape(feat)[2], paddle.shape(feat)[3]
 
  NA = H * W
  num_anchors_list.append(NA)
@@ -324,7 +324,7 @@ def forward(self, feats, targets=None):
 
  def get_bboxes(self, head_outs):
  perd_bboxes_list, pred_scores_list = head_outs
- batch = pred_scores_list[0].shape[0]
+ batch = paddle.shape(pred_scores_list[0])[0]
  bboxes, bbox_num = [], []
  for i in range(batch):
  pred_scores_per_image = [t[i] for t in pred_scores_list]
@@ -712,7 +712,7 @@ def rbox2poly(self, rboxes):
  to
  polys: [x0,y0,x1,y1,x2,y2,x3,y3]
  """
- N = rboxes.shape[0]
+ N = paddle.shape(rboxes)[0]
 
  x_ctr = rboxes[:, 0]
  y_ctr = rboxes[:, 1]

ppdet/modeling/heads/solov2_head.py

@@ -131,16 +131,16 @@ def forward(self, inputs):
  if i == (self.range_level - 1):
  input_feat = input_p
  x_range = paddle.linspace(
- -1, 1, input_feat.shape[-1], dtype='float32')
+ -1, 1, paddle.shape(input_feat)[-1], dtype='float32')
  y_range = paddle.linspace(
- -1, 1, input_feat.shape[-2], dtype='float32')
+ -1, 1, paddle.shape(input_feat)[-2], dtype='float32')
  y, x = paddle.meshgrid([y_range, x_range])
  x = paddle.unsqueeze(x, [0, 1])
  y = paddle.unsqueeze(y, [0, 1])
  y = paddle.expand(
- y, shape=[input_feat.shape[0], 1, -1, -1])
+ y, shape=[paddle.shape(input_feat)[0], 1, -1, -1])
  x = paddle.expand(
- x, shape=[input_feat.shape[0], 1, -1, -1])
+ x, shape=[paddle.shape(input_feat)[0], 1, -1, -1])
  coord_feat = paddle.concat([x, y], axis=1)
  input_p = paddle.concat([input_p, coord_feat], axis=1)
  feat_all_level = paddle.add(feat_all_level,
@@ -271,7 +271,7 @@ def _split_feats(self, feats):
  align_mode=0,
  mode='bilinear'), feats[1], feats[2], feats[3], F.interpolate(
  feats[4],
- size=feats[3].shape[-2:],
+ size=paddle.shape(feats[3])[-2:],
  mode='bilinear',
  align_corners=False,
  align_mode=0))
@@ -300,16 +300,16 @@ def _get_output_single(self, input, idx):
  ins_kernel_feat = input
  # CoordConv
  x_range = paddle.linspace(
- -1, 1, ins_kernel_feat.shape[-1], dtype='float32')
+ -1, 1, paddle.shape(ins_kernel_feat)[-1], dtype='float32')
  y_range = paddle.linspace(
- -1, 1, ins_kernel_feat.shape[-2], dtype='float32')
+ -1, 1, paddle.shape(ins_kernel_feat)[-2], dtype='float32')
  y, x = paddle.meshgrid([y_range, x_range])
  x = paddle.unsqueeze(x, [0, 1])
  y = paddle.unsqueeze(y, [0, 1])
  y = paddle.expand(
- y, shape=[ins_kernel_feat.shape[0], 1, -1, -1])
+ y, shape=[paddle.shape(ins_kernel_feat)[0], 1, -1, -1])
  x = paddle.expand(
- x, shape=[ins_kernel_feat.shape[0], 1, -1, -1])
+ x, shape=[paddle.shape(ins_kernel_feat)[0], 1, -1, -1])
  coord_feat = paddle.concat([x, y], axis=1)
  ins_kernel_feat = paddle.concat([ins_kernel_feat, coord_feat], axis=1)
 
@@ -358,7 +358,7 @@ def get_loss(self, cate_preds, kernel_preds, ins_pred, ins_labels,
  loss_ins (Tensor): The instance loss Tensor of SOLOv2 network.
  loss_cate (Tensor): The category loss Tensor of SOLOv2 network.
  """
- batch_size = grid_order_list[0].shape[0]
+ batch_size = paddle.shape(grid_order_list[0])[0]
  ins_pred_list = []
  for kernel_preds_level, grid_orders_level in zip(kernel_preds,
  grid_order_list):
@@ -368,25 +368,25 @@ def get_loss(self, cate_preds, kernel_preds, ins_pred, ins_labels,
  grid_orders_level = paddle.reshape(grid_orders_level, [-1])
  reshape_pred = paddle.reshape(
  kernel_preds_level,
- shape=(kernel_preds_level.shape[0],
- kernel_preds_level.shape[1], -1))
+ shape=(paddle.shape(kernel_preds_level)[0],
+ paddle.shape(kernel_preds_level)[1], -1))
  reshape_pred = paddle.transpose(reshape_pred, [0, 2, 1])
  reshape_pred = paddle.reshape(
- reshape_pred, shape=(-1, reshape_pred.shape[2]))
+ reshape_pred, shape=(-1, paddle.shape(reshape_pred)[2]))
  gathered_pred = paddle.gather(reshape_pred, index=grid_orders_level)
  gathered_pred = paddle.reshape(
  gathered_pred,
- shape=[batch_size, -1, gathered_pred.shape[1]])
+ shape=[batch_size, -1, paddle.shape(gathered_pred)[1]])
  cur_ins_pred = ins_pred
  cur_ins_pred = paddle.reshape(
  cur_ins_pred,
- shape=(cur_ins_pred.shape[0],
- cur_ins_pred.shape[1], -1))
+ shape=(paddle.shape(cur_ins_pred)[0],
+ paddle.shape(cur_ins_pred)[1], -1))
  ins_pred_conv = paddle.matmul(gathered_pred, cur_ins_pred)
  cur_ins_pred = paddle.reshape(
  ins_pred_conv,
- shape=(-1, ins_pred.shape[-2],
- ins_pred.shape[-1]))
+ shape=(-1, paddle.shape(ins_pred)[-2],
+ paddle.shape(ins_pred)[-1]))
  ins_pred_list.append(cur_ins_pred)
 
  num_ins = paddle.sum(fg_num)
@@ -423,7 +423,7 @@ def get_prediction(self, cate_preds, kernel_preds, seg_pred, im_shape,
  seg_masks (Tensor): The prediction score of each segmentation.
  """
  num_levels = len(cate_preds)
- featmap_size = seg_pred.shape[-2:]
+ featmap_size = paddle.shape(seg_pred)[-2:]
  seg_masks_list = []
  cate_labels_list = []
  cate_scores_list = []
@@ -449,7 +449,7 @@ def get_prediction(self, cate_preds, kernel_preds, seg_pred, im_shape,
  seg_masks, cate_labels, cate_scores = self.get_seg_single(
  cate_pred_list, seg_pred_list, kernel_pred_list, featmap_size,
  im_shape[idx], scale_factor[idx][0])
- bbox_num = cate_labels.shape[0:1]
+ bbox_num = paddle.shape(cate_labels)[0:1]
  return seg_masks, cate_labels, cate_scores, bbox_num
 
  def get_seg_single(self, cate_preds, seg_preds, kernel_preds, featmap_size,
@@ -462,7 +462,7 @@ def get_seg_single(self, cate_preds, seg_preds, kernel_preds, featmap_size,
  w = paddle.cast(im_shape[1], 'int32')
  upsampled_size_out = [featmap_size[0] * 4, featmap_size[1] * 4]
 
- y = paddle.zeros(shape=cate_preds.shape, dtype='float32')
+ y = paddle.zeros(shape=paddle.shape(cate_preds), dtype='float32')
  inds = paddle.where(cate_preds > self.score_threshold, cate_preds, y)
  inds = paddle.nonzero(inds)
  cate_preds = paddle.reshape(cate_preds, shape=[-1])
@@ -507,7 +507,7 @@ def get_seg_single(self, cate_preds, seg_preds, kernel_preds, featmap_size,
  seg_masks = paddle.cast(seg_masks, 'float32')
  sum_masks = paddle.sum(seg_masks, axis=[1, 2])
 
- y = paddle.zeros(shape=sum_masks.shape, dtype='float32')
+ y = paddle.zeros(shape=paddle.shape(sum_masks), dtype='float32')
  keep = paddle.where(sum_masks > strides, sum_masks, y)
  keep = paddle.nonzero(keep)
  keep = paddle.squeeze(keep, axis=[1])

ppdet/modeling/heads/yolof_head.py

@@ -201,7 +201,7 @@ def forward(self, feats, targets=None):
  objectness = self.object_pred(conv_reg_feat)
  bboxes_reg = self.bbox_pred(conv_reg_feat)
 
- N, C, H, W = cls_logits.shape[:]
+ N, C, H, W = paddle.shape(cls_logits)[:]
  cls_logits = cls_logits.reshape((N, self.na, self.num_classes, H, W))
  objectness = objectness.reshape((N, self.na, 1, H, W))
  norm_cls_logits = cls_logits + objectness - paddle.log(