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ultralytics 8.0.197 save P, R, F1 curves to metrics (#5354)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: erminkev1 <83356055+erminkev1@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
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Glenn Jocher 2023-10-13 02:49:31 +02:00 • committed by GitHub
parent 7fd5dcbd86
commit 12e3eef844
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33 changed files with 337 additions and 195 deletions
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20
ultralytics/models/sam/modules/tiny_encoder.py
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@ -172,7 +172,7 @@ class ConvLayer(nn.Module):
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
# Build blocks
self.blocks = nn.ModuleList([
MBConv(
dim,
@ -182,7 +182,7 @@ class ConvLayer(nn.Module):
drop_path[i] if isinstance(drop_path, list) else drop_path,
) for i in range(depth)])
# patch merging layer
# Patch merging layer
self.downsample = None if downsample is None else downsample(
input_resolution, dim=dim, out_dim=out_dim, activation=activation)
@ -393,11 +393,11 @@ class TinyViTBlock(nn.Module):
pH, pW = H + pad_b, W + pad_r
nH = pH // self.window_size
nW = pW // self.window_size
# window partition
# Window partition
x = x.view(B, nH, self.window_size, nW, self.window_size,
C).transpose(2, 3).reshape(B * nH * nW, self.window_size * self.window_size, C)
x = self.attn(x)
# window reverse
# Window reverse
x = x.view(B, nH, nW, self.window_size, self.window_size, C).transpose(2, 3).reshape(B, pH, pW, C)
if padding:
@ -467,7 +467,7 @@ class BasicLayer(nn.Module):
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
# Build blocks
self.blocks = nn.ModuleList([
TinyViTBlock(
dim=dim,
@ -481,7 +481,7 @@ class BasicLayer(nn.Module):
activation=activation,
) for i in range(depth)])
# patch merging layer
# Patch merging layer
self.downsample = None if downsample is None else downsample(
input_resolution, dim=dim, out_dim=out_dim, activation=activation)
@ -593,10 +593,10 @@ class TinyViT(nn.Module):
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# stochastic depth
# Stochastic depth
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
# build layers
# Build layers
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
kwargs = dict(
@ -628,7 +628,7 @@ class TinyViT(nn.Module):
self.norm_head = nn.LayerNorm(embed_dims[-1])
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else torch.nn.Identity()
# init weights
# Init weights
self.apply(self._init_weights)
self.set_layer_lr_decay(layer_lr_decay)
self.neck = nn.Sequential(
@ -653,7 +653,7 @@ class TinyViT(nn.Module):
"""Sets the learning rate decay for each layer in the TinyViT model."""
decay_rate = layer_lr_decay
# layers -> blocks (depth)
# Layers -> blocks (depth)
depth = sum(self.depths)
lr_scales = [decay_rate ** (depth - i - 1) for i in range(depth)]