fix hardcoded float16 and .float()

src/transformers/models/deformable_detr/modeling_deformable_detr.py

@@ -523,14 +523,14 @@ def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=N
     def forward(self, pixel_values, pixel_mask):
         if pixel_mask is None:
             raise ValueError("No pixel mask provided")
-        y_embed = pixel_mask.cumsum(1, dtype=torch.float16)
-        x_embed = pixel_mask.cumsum(2, dtype=torch.float16)
+        y_embed = pixel_mask.cumsum(1, dtype=pixel_values.dtype)
+        x_embed = pixel_mask.cumsum(2, dtype=pixel_values.dtype)
         if self.normalize:
             eps = 1e-6
             y_embed = (y_embed - 0.5) / (y_embed[:, -1:, :] + eps) * self.scale
             x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale
 
-        dim_t = torch.arange(self.embedding_dim, dtype=torch.int64, device=pixel_values.device).float()
+        dim_t = torch.arange(self.embedding_dim, dtype=pixel_values.dtype, device=pixel_values.device)
         dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim)
 
         pos_x = x_embed[:, :, :, None] / dim_t
@@ -708,7 +708,7 @@ def forward(
 
         batch_size, num_queries, _ = hidden_states.shape
         batch_size, sequence_length, _ = encoder_hidden_states.shape
-        total_elements = sum(shape[0] * shape[1] for shape in spatial_shapes_list)
+        total_elements = sum(height * width for height, width in spatial_shapes_list)
         if total_elements != sequence_length:
             raise ValueError(
                 "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"
@@ -1609,7 +1609,7 @@ def get_proposal_pos_embed(self, proposals):
         temperature = 10000
         scale = 2 * math.pi
 
-        dim_t = torch.arange(num_pos_feats, dtype=torch.int64, device=proposals.device).float()
+        dim_t = torch.arange(num_pos_feats, dtype=proposals.dtype, device=proposals.device)
         dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
         # batch_size, num_queries, 4
         proposals = proposals.sigmoid() * scale
@@ -1740,7 +1740,9 @@ def forward(
                     source = self.input_proj[level](features[-1][0])
                 else:
                     source = self.input_proj[level](sources[-1])
-                mask = nn.functional.interpolate(pixel_mask[None].float(), size=source.shape[-2:]).to(torch.bool)[0]
+                mask = nn.functional.interpolate(pixel_mask[None].to(pixel_values.dtype), size=source.shape[-2:]).to(
+                    torch.bool
+                )[0]
                 pos_l = self.backbone.position_embedding(source, mask).to(source.dtype)
                 sources.append(source)
                 masks.append(mask)

src/transformers/models/rt_detr/modeling_rt_detr.py

@@ -862,7 +862,7 @@ def forward(
 
         batch_size, num_queries, _ = hidden_states.shape
         batch_size, sequence_length, _ = encoder_hidden_states.shape
-        total_elements = sum(shape[0] * shape[1] for shape in spatial_shapes_list)
+        total_elements = sum(height * width for height, width in spatial_shapes_list)
         if total_elements != sequence_length:
             raise ValueError(
                 "Make sure to align the spatial shapes with the sequence length of the encoder hidden states"