ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -11,8 +11,18 @@ from torch.nn.init import constant_, xavier_uniform_
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from .conv import Conv
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from .utils import _get_clones, inverse_sigmoid, multi_scale_deformable_attn_pytorch
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__all__ = ('TransformerEncoderLayer', 'TransformerLayer', 'TransformerBlock', 'MLPBlock', 'LayerNorm2d', 'AIFI',
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'DeformableTransformerDecoder', 'DeformableTransformerDecoderLayer', 'MSDeformAttn', 'MLP')
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__all__ = (
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"TransformerEncoderLayer",
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"TransformerLayer",
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"TransformerBlock",
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"MLPBlock",
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"LayerNorm2d",
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"AIFI",
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"DeformableTransformerDecoder",
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"DeformableTransformerDecoderLayer",
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"MSDeformAttn",
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"MLP",
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)
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class TransformerEncoderLayer(nn.Module):
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@ -22,9 +32,11 @@ class TransformerEncoderLayer(nn.Module):
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"""Initialize the TransformerEncoderLayer with specified parameters."""
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super().__init__()
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from ...utils.torch_utils import TORCH_1_9
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if not TORCH_1_9:
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raise ModuleNotFoundError(
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'TransformerEncoderLayer() requires torch>=1.9 to use nn.MultiheadAttention(batch_first=True).')
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"TransformerEncoderLayer() requires torch>=1.9 to use nn.MultiheadAttention(batch_first=True)."
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)
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self.ma = nn.MultiheadAttention(c1, num_heads, dropout=dropout, batch_first=True)
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# Implementation of Feedforward model
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self.fc1 = nn.Linear(c1, cm)
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@ -91,12 +103,11 @@ class AIFI(TransformerEncoderLayer):
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"""Builds 2D sine-cosine position embedding."""
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grid_w = torch.arange(int(w), dtype=torch.float32)
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grid_h = torch.arange(int(h), dtype=torch.float32)
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grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing='ij')
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assert embed_dim % 4 == 0, \
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'Embed dimension must be divisible by 4 for 2D sin-cos position embedding'
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grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
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assert embed_dim % 4 == 0, "Embed dimension must be divisible by 4 for 2D sin-cos position embedding"
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pos_dim = embed_dim // 4
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omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
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omega = 1. / (temperature ** omega)
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omega = 1.0 / (temperature**omega)
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out_w = grid_w.flatten()[..., None] @ omega[None]
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out_h = grid_h.flatten()[..., None] @ omega[None]
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@ -213,10 +224,10 @@ class MSDeformAttn(nn.Module):
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"""Initialize MSDeformAttn with the given parameters."""
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super().__init__()
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if d_model % n_heads != 0:
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raise ValueError(f'd_model must be divisible by n_heads, but got {d_model} and {n_heads}')
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raise ValueError(f"d_model must be divisible by n_heads, but got {d_model} and {n_heads}")
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_d_per_head = d_model // n_heads
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# Better to set _d_per_head to a power of 2 which is more efficient in a CUDA implementation
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assert _d_per_head * n_heads == d_model, '`d_model` must be divisible by `n_heads`'
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assert _d_per_head * n_heads == d_model, "`d_model` must be divisible by `n_heads`"
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self.im2col_step = 64
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@ -234,21 +245,24 @@ class MSDeformAttn(nn.Module):
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def _reset_parameters(self):
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"""Reset module parameters."""
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constant_(self.sampling_offsets.weight.data, 0.)
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constant_(self.sampling_offsets.weight.data, 0.0)
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thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
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grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
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grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(
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1, self.n_levels, self.n_points, 1)
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grid_init = (
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(grid_init / grid_init.abs().max(-1, keepdim=True)[0])
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.view(self.n_heads, 1, 1, 2)
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.repeat(1, self.n_levels, self.n_points, 1)
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)
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for i in range(self.n_points):
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grid_init[:, :, i, :] *= i + 1
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with torch.no_grad():
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self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
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constant_(self.attention_weights.weight.data, 0.)
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constant_(self.attention_weights.bias.data, 0.)
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constant_(self.attention_weights.weight.data, 0.0)
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constant_(self.attention_weights.bias.data, 0.0)
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xavier_uniform_(self.value_proj.weight.data)
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constant_(self.value_proj.bias.data, 0.)
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constant_(self.value_proj.bias.data, 0.0)
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xavier_uniform_(self.output_proj.weight.data)
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constant_(self.output_proj.bias.data, 0.)
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constant_(self.output_proj.bias.data, 0.0)
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def forward(self, query, refer_bbox, value, value_shapes, value_mask=None):
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"""
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@ -288,7 +302,7 @@ class MSDeformAttn(nn.Module):
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add = sampling_offsets / self.n_points * refer_bbox[:, :, None, :, None, 2:] * 0.5
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sampling_locations = refer_bbox[:, :, None, :, None, :2] + add
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else:
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raise ValueError(f'Last dim of reference_points must be 2 or 4, but got {num_points}.')
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raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {num_points}.")
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output = multi_scale_deformable_attn_pytorch(value, value_shapes, sampling_locations, attention_weights)
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return self.output_proj(output)
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@ -301,7 +315,7 @@ class DeformableTransformerDecoderLayer(nn.Module):
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https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/deformable_transformer.py
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"""
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def __init__(self, d_model=256, n_heads=8, d_ffn=1024, dropout=0., act=nn.ReLU(), n_levels=4, n_points=4):
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def __init__(self, d_model=256, n_heads=8, d_ffn=1024, dropout=0.0, act=nn.ReLU(), n_levels=4, n_points=4):
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"""Initialize the DeformableTransformerDecoderLayer with the given parameters."""
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super().__init__()
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@ -339,14 +353,16 @@ class DeformableTransformerDecoderLayer(nn.Module):
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# Self attention
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q = k = self.with_pos_embed(embed, query_pos)
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tgt = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), embed.transpose(0, 1),
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attn_mask=attn_mask)[0].transpose(0, 1)
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tgt = self.self_attn(q.transpose(0, 1), k.transpose(0, 1), embed.transpose(0, 1), attn_mask=attn_mask)[
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0
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].transpose(0, 1)
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embed = embed + self.dropout1(tgt)
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embed = self.norm1(embed)
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# Cross attention
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tgt = self.cross_attn(self.with_pos_embed(embed, query_pos), refer_bbox.unsqueeze(2), feats, shapes,
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padding_mask)
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tgt = self.cross_attn(
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self.with_pos_embed(embed, query_pos), refer_bbox.unsqueeze(2), feats, shapes, padding_mask
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)
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embed = embed + self.dropout2(tgt)
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embed = self.norm2(embed)
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@ -370,16 +386,17 @@ class DeformableTransformerDecoder(nn.Module):
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self.eval_idx = eval_idx if eval_idx >= 0 else num_layers + eval_idx
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def forward(
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self,
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embed, # decoder embeddings
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refer_bbox, # anchor
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feats, # image features
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shapes, # feature shapes
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bbox_head,
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score_head,
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pos_mlp,
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attn_mask=None,
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padding_mask=None):
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self,
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embed, # decoder embeddings
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refer_bbox, # anchor
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feats, # image features
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shapes, # feature shapes
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bbox_head,
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score_head,
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pos_mlp,
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attn_mask=None,
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padding_mask=None,
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):
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"""Perform the forward pass through the entire decoder."""
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output = embed
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dec_bboxes = []
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