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Add name arg to most Keras applications.
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@fchollet
fchollet committed May 10, 2024
1 parent 4b18bd4 commit 2b7120b
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Showing 12 changed files with 238 additions and 177 deletions.
57 changes: 32 additions & 25 deletions keras/src/applications/convnext.py
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Original file line number Diff line number Diff line change
Expand Up @@ -131,6 +131,7 @@
Defaults to `"softmax"`.
When loading pretrained weights, `classifier_activation` can only
be `None` or `"softmax"`.
name: The name of the model (string).
Returns:
A model instance.
Expand Down Expand Up @@ -333,7 +334,7 @@ def ConvNeXt(
drop_path_rate=0.0,
layer_scale_init_value=1e-6,
default_size=224,
model_name="convnext",
name="convnext",
include_preprocessing=True,
include_top=True,
weights=None,
Expand All @@ -342,6 +343,7 @@ def ConvNeXt(
pooling=None,
classes=1000,
classifier_activation="softmax",
weights_name=None,
):
"""Instantiates ConvNeXt architecture given specific configuration.
Expand All @@ -354,7 +356,7 @@ def ConvNeXt(
layer_scale_init_value: Layer scale coefficient. If 0.0, layer scaling
won't be used.
default_size: Default input image size.
model_name: An optional name for the model.
name: An optional name for the model.
include_preprocessing: boolean denoting whther to
include preprocessing in the model.
When `weights="imagenet"` this should always be `True`.
Expand Down Expand Up @@ -440,7 +442,7 @@ def ConvNeXt(
)
num_channels = input_shape[channel_axis - 1]
if num_channels == 3:
x = PreStem(name=model_name)(x)
x = PreStem(name=name)(x)

# Stem block.
stem = Sequential(
Expand All @@ -449,13 +451,13 @@ def ConvNeXt(
projection_dims[0],
kernel_size=4,
strides=4,
name=model_name + "_stem_conv",
name=name + "_stem_conv",
),
layers.LayerNormalization(
epsilon=1e-6, name=model_name + "_stem_layernorm"
epsilon=1e-6, name=name + "_stem_layernorm"
),
],
name=model_name + "_stem",
name=name + "_stem",
)

# Downsampling blocks.
Expand All @@ -468,16 +470,16 @@ def ConvNeXt(
[
layers.LayerNormalization(
epsilon=1e-6,
name=model_name + "_downsampling_layernorm_" + str(i),
name=name + "_downsampling_layernorm_" + str(i),
),
layers.Conv2D(
projection_dims[i + 1],
kernel_size=2,
strides=2,
name=model_name + "_downsampling_conv_" + str(i),
name=name + "_downsampling_conv_" + str(i),
),
],
name=model_name + "_downsampling_block_" + str(i),
name=name + "_downsampling_block_" + str(i),
)
downsample_layers.append(downsample_layer)

Expand All @@ -499,7 +501,7 @@ def ConvNeXt(
projection_dim=projection_dims[i],
drop_path_rate=depth_drop_rates[cur + j],
layer_scale_init_value=layer_scale_init_value,
name=model_name + f"_stage_{i}_block_{j}",
name=name + f"_stage_{i}_block_{j}",
)(x)
cur += depths[i]

Expand All @@ -508,7 +510,7 @@ def ConvNeXt(
x = Head(
num_classes=classes,
classifier_activation=classifier_activation,
name=model_name,
name=name,
)(x)

else:
Expand All @@ -518,17 +520,17 @@ def ConvNeXt(
x = layers.GlobalMaxPooling2D()(x)
x = layers.LayerNormalization(epsilon=1e-6)(x)

model = Functional(inputs=inputs, outputs=x, name=model_name)
model = Functional(inputs=inputs, outputs=x, name=name)

# Load weights.
if weights == "imagenet":
if include_top:
file_suffix = ".h5"
file_hash = WEIGHTS_HASHES[model_name][0]
file_hash = WEIGHTS_HASHES[weights_name][0]
else:
file_suffix = "_notop.h5"
file_hash = WEIGHTS_HASHES[model_name][1]
file_name = model_name + file_suffix
file_hash = WEIGHTS_HASHES[weights_name][1]
file_name = name + file_suffix
weights_path = file_utils.get_file(
file_name,
BASE_WEIGHTS_PATH + file_name,
Expand All @@ -552,7 +554,6 @@ def ConvNeXt(
]
)
def ConvNeXtTiny(
model_name="convnext_tiny",
include_top=True,
include_preprocessing=True,
weights="imagenet",
Expand All @@ -561,14 +562,16 @@ def ConvNeXtTiny(
pooling=None,
classes=1000,
classifier_activation="softmax",
name="convnext_tiny",
):
return ConvNeXt(
weights_name="convnext_tiny",
depths=MODEL_CONFIGS["tiny"]["depths"],
projection_dims=MODEL_CONFIGS["tiny"]["projection_dims"],
drop_path_rate=0.0,
layer_scale_init_value=1e-6,
default_size=MODEL_CONFIGS["tiny"]["default_size"],
model_name=model_name,
name=name,
include_top=include_top,
include_preprocessing=include_preprocessing,
weights=weights,
Expand All @@ -587,7 +590,6 @@ def ConvNeXtTiny(
]
)
def ConvNeXtSmall(
model_name="convnext_small",
include_top=True,
include_preprocessing=True,
weights="imagenet",
Expand All @@ -596,14 +598,16 @@ def ConvNeXtSmall(
pooling=None,
classes=1000,
classifier_activation="softmax",
name="convnext_small",
):
return ConvNeXt(
weights_name="convnext_small",
depths=MODEL_CONFIGS["small"]["depths"],
projection_dims=MODEL_CONFIGS["small"]["projection_dims"],
drop_path_rate=0.0,
layer_scale_init_value=1e-6,
default_size=MODEL_CONFIGS["small"]["default_size"],
model_name=model_name,
name=name,
include_top=include_top,
include_preprocessing=include_preprocessing,
weights=weights,
Expand All @@ -622,7 +626,6 @@ def ConvNeXtSmall(
]
)
def ConvNeXtBase(
model_name="convnext_base",
include_top=True,
include_preprocessing=True,
weights="imagenet",
Expand All @@ -631,14 +634,16 @@ def ConvNeXtBase(
pooling=None,
classes=1000,
classifier_activation="softmax",
name="convnext_base",
):
return ConvNeXt(
weights_name="convnext_base",
depths=MODEL_CONFIGS["base"]["depths"],
projection_dims=MODEL_CONFIGS["base"]["projection_dims"],
drop_path_rate=0.0,
layer_scale_init_value=1e-6,
default_size=MODEL_CONFIGS["base"]["default_size"],
model_name=model_name,
name=name,
include_top=include_top,
include_preprocessing=include_preprocessing,
weights=weights,
Expand All @@ -657,7 +662,6 @@ def ConvNeXtBase(
]
)
def ConvNeXtLarge(
model_name="convnext_large",
include_top=True,
include_preprocessing=True,
weights="imagenet",
Expand All @@ -666,14 +670,16 @@ def ConvNeXtLarge(
pooling=None,
classes=1000,
classifier_activation="softmax",
name="convnext_large",
):
return ConvNeXt(
weights_name="convnext_large",
depths=MODEL_CONFIGS["large"]["depths"],
projection_dims=MODEL_CONFIGS["large"]["projection_dims"],
drop_path_rate=0.0,
layer_scale_init_value=1e-6,
default_size=MODEL_CONFIGS["large"]["default_size"],
model_name=model_name,
name=name,
include_top=include_top,
include_preprocessing=include_preprocessing,
weights=weights,
Expand All @@ -692,7 +698,6 @@ def ConvNeXtLarge(
]
)
def ConvNeXtXLarge(
model_name="convnext_xlarge",
include_top=True,
include_preprocessing=True,
weights="imagenet",
Expand All @@ -701,14 +706,16 @@ def ConvNeXtXLarge(
pooling=None,
classes=1000,
classifier_activation="softmax",
name="convnext_xlarge",
):
return ConvNeXt(
weights_name="convnext_xlarge",
depths=MODEL_CONFIGS["xlarge"]["depths"],
projection_dims=MODEL_CONFIGS["xlarge"]["projection_dims"],
drop_path_rate=0.0,
layer_scale_init_value=1e-6,
default_size=MODEL_CONFIGS["xlarge"]["default_size"],
model_name=model_name,
name=name,
include_top=include_top,
include_preprocessing=include_preprocessing,
weights=weights,
Expand Down
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