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[PT] remove submodule from textnet arch #1436

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merged 4 commits into from
Jan 26, 2024
Merged

[PT] remove submodule from textnet arch #1436

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8504b34
remove submodule from textnet arch
felixdittrich92 Jan 23, 2024
caaef6b
lower prob in det augmentations
felixdittrich92 Jan 23, 2024
dced985
update
felixdittrich92 Jan 25, 2024
8649f63
change urls
felixdittrich92 Jan 26, 2024
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16 changes: 8 additions & 8 deletions doctr/models/classification/textnet/pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,21 +22,21 @@
"std": (0.299, 0.296, 0.301),
"input_shape": (3, 32, 32),
"classes": list(VOCABS["french"]),
"url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_tiny-c23a1b9a.pt&src=0",
"url": "https://github.com/mindee/doctr/releases/download/v0.7.0/textnet_tiny-c5970fe0.pt",
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},
"textnet_small": {
"mean": (0.694, 0.695, 0.693),
"std": (0.299, 0.296, 0.301),
"input_shape": (3, 32, 32),
"classes": list(VOCABS["french"]),
"url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_small-775169f7.pt&src=0",
"url": "https://github.com/mindee/doctr/releases/download/v0.7.0/textnet_small-6e8ab0ce.pt",
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},
"textnet_base": {
"mean": (0.694, 0.695, 0.693),
"std": (0.299, 0.296, 0.301),
"input_shape": (3, 32, 32),
"classes": list(VOCABS["french"]),
"url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_base-6121c044.pt&src=0",
"url": "https://github.com/mindee/doctr/releases/download/v0.7.0/textnet_base-8295dc85.pt",
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},
}

Expand Down Expand Up @@ -66,13 +66,13 @@ def __init__(
*conv_sequence_pt(
in_channels=3, out_channels=64, relu=True, bn=True, kernel_size=3, stride=2, padding=(1, 1)
),
nn.Sequential(*[
*[
nn.Sequential(*[
FASTConvLayer(**params) # type: ignore[arg-type]
for params in [{key: stage[key][i] for key in stage} for i in range(len(stage["in_channels"]))]
])
for stage in stages
]),
],
]

if include_top:
Expand Down Expand Up @@ -167,7 +167,7 @@ def textnet_tiny(pretrained: bool = False, **kwargs: Any) -> TextNet:
"stride": [2, 1, 1, 1],
},
],
ignore_keys=["4.2.weight", "4.2.bias"],
ignore_keys=["7.2.weight", "7.2.bias"],
**kwargs,
)

Expand Down Expand Up @@ -216,7 +216,7 @@ def textnet_small(pretrained: bool = False, **kwargs: Any) -> TextNet:
"stride": [2, 1, 1, 1, 1],
},
],
ignore_keys=["4.2.weight", "4.2.bias"],
ignore_keys=["7.2.weight", "7.2.bias"],
**kwargs,
)

Expand Down Expand Up @@ -270,6 +270,6 @@ def textnet_base(pretrained: bool = False, **kwargs: Any) -> TextNet:
"stride": [2, 1, 1, 1, 1],
},
],
ignore_keys=["4.2.weight", "4.2.bias"],
ignore_keys=["7.2.weight", "7.2.bias"],
**kwargs,
)
10 changes: 5 additions & 5 deletions references/detection/train_pytorch.py
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Expand Up @@ -270,10 +270,10 @@ def main(args):
# Augmentations
T.RandomApply(T.ColorInversion(), 0.1),
T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1),
T.RandomApply(T.RandomShadow(), 0.4),
T.RandomApply(GaussianBlur(kernel_size=3), 0.3),
RandomPhotometricDistort(p=0.1),
RandomGrayscale(p=0.1),
T.RandomApply(T.RandomShadow(), 0.1),
T.RandomApply(GaussianBlur(kernel_size=3), 0.1),
RandomPhotometricDistort(p=0.05),
RandomGrayscale(p=0.05),
]),
sample_transforms=T.SampleCompose(
(
Expand Down Expand Up @@ -442,7 +442,7 @@ def parse_args():
action="store_true",
help="metrics evaluation with straight boxes instead of polygons to save time + memory",
)
parser.add_argument("--sched", type=str, default="cosine", help="scheduler to use")
parser.add_argument("--sched", type=str, default="onecycle", help="scheduler to use")
parser.add_argument("--amp", dest="amp", help="Use Automatic Mixed Precision", action="store_true")
parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
Expand Down
6 changes: 3 additions & 3 deletions references/detection/train_tensorflow.py
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Expand Up @@ -224,12 +224,12 @@ def main(args):
T.RandomApply(T.ColorInversion(), 0.1),
T.RandomJpegQuality(60),
T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1),
T.RandomApply(T.RandomShadow(), 0.4),
T.RandomApply(T.GaussianBlur(kernel_shape=3, std=(0.1, 0.1)), 0.3),
T.RandomApply(T.RandomShadow(), 0.1),
T.RandomApply(T.GaussianBlur(kernel_shape=3, std=(0.1, 0.1)), 0.1),
T.RandomSaturation(0.3),
T.RandomContrast(0.3),
T.RandomBrightness(0.3),
T.RandomApply(T.ToGray(num_output_channels=3), 0.1),
T.RandomApply(T.ToGray(num_output_channels=3), 0.05),
]),
sample_transforms=T.SampleCompose(
(
Expand Down
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