update resnet_family

keras_cv_attention_models/attention_layers/__init__.py

@@ -4,7 +4,7 @@
 from keras_cv_attention_models.coat.coat import ConvPositionalEncoding, ConvRelativePositionalEncoding, layer_norm
 from keras_cv_attention_models.halonet.halonet import HaloAttention
 from keras_cv_attention_models.resnest.resnest import rsoftmax, split_attention_conv2d
-from keras_cv_attention_models.resnext.resnext import groups_depthwise
+from keras_cv_attention_models.resnet_family.resnext import groups_depthwise
 from keras_cv_attention_models.volo.volo import outlook_attention, outlook_attention_simple, BiasLayer, PositionalEmbedding, ClassToken
 from keras_cv_attention_models.mlp_family.mlp_mixer import mlp_block, mixer_block
 from keras_cv_attention_models.mlp_family.res_mlp import ChannelAffine

keras_cv_attention_models/resnet_family/README.md

@@ -0,0 +1,43 @@
+# ___Keras ResNet Family___
+***
+
+## Summary
+  - Keras implementation of [Github facebookresearch/ResNeXt](https://github.com/facebookresearch/ResNeXt). Paper [PDF 1611.05431 Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).
+  - Model weights reloaded from [Tensorflow keras/applications](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py).
+***
+
+## Models
+  | Model                 | Params | Image  resolution | Top1 Acc | Download            |
+  | --------------------- | ------ | ----------------- | -------- | ------------------- |
+  | resnext50 (32x4d)     | 25M    | 224               | 77.74    | [resnext50.h5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnext/resnext50.h5)  |
+  | resnext101 (32x4d)    | 42M    | 224               | 78.73    | [resnext101.h5](https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnext/resnext101.h5)  |
+## Usage
+  ```py
+  from keras_cv_attention_models import resnext
+
+  # Will download and load pretrained imagenet weights.
+  mm = resnext.ResNeXt50(pretrained="imagenet")
+
+  # Run prediction
+  from skimage.data import chelsea
+  imm = keras.applications.imagenet_utils.preprocess_input(chelsea(), mode='tf') # Chelsea the cat
+  pred = mm(tf.expand_dims(tf.image.resize(imm, mm.input_shape[1:3]), 0)).numpy()
+  print(keras.applications.imagenet_utils.decode_predictions(pred)[0])
+  # [('n02124075', 'Egyptian_cat', 0.98292357),
+  #  ('n02123045', 'tabby', 0.009655442),
+  #  ('n02123159', 'tiger_cat', 0.0057404325),
+  #  ('n02127052', 'lynx', 0.00089362176),
+  #  ('n04209239', 'shower_curtain', 0.00013918217)]
+  ```
+  **Set new input resolution**
+  ```py
+  from keras_cv_attention_models import resnext
+  mm = resnext.ResNeXt101(input_shape=(320, 320, 3), num_classes=0)
+  print(mm(np.ones([1, 320, 320, 3])).shape)
+  # (1, 10, 10, 2048)
+
+  mm = resnext.ResNeXt101(input_shape=(512, 512, 3), num_classes=0)
+  print(mm(np.ones([1, 512, 512, 3])).shape)
+  # (1, 16, 16, 2048)
+  ```
+***

keras_cv_attention_models/resnet_family/__init__.py

@@ -0,0 +1,88 @@
+from keras_cv_attention_models.resnet_family.resnext import ResNeXt, ResNeXt50, ResNeXt101, groups_depthwise
+
+
+__head_doc__ = """
+Keras implementation of [Github facebookresearch/ResNeXt](https://github.com/facebookresearch/ResNeXt).
+Paper [PDF 1611.05431 Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).
+"""
+
+__tail_doc__ = """  strides: a `number` or `list`, indicates strides used in the last stack or list value for all stacks.
+      If a number, it will be `[1, 2, 2, strides]`.
+  out_channels: default as `[128, 256, 512, 1024]`. Output channel for each stack.
+  stem_width: output dimension for stem block.
+  deep_stem: Boolean value if use deep stem.
+  stem_downsample: Boolean value if ass `MaxPooling2D` layer after stem block.
+  cardinality: Control channel expansion in each block, the bigger the widder.
+      Also the `groups` number for `groups_depthwise` in each block, bigger `cardinality` leads to less `groups`.
+  input_shape: it should have exactly 3 inputs channels, default `(224, 224, 3)`.
+  num_classes: number of classes to classify images into. Set `0` to exclude top layers.
+  activation: activation used in whole model, default `relu`.
+  classifier_activation: A `str` or callable. The activation function to use on the "top" layer if `num_classes > 0`.
+      Set `classifier_activation=None` to return the logits of the "top" layer.
+      Default is `softmax`.
+  pretrained: one of `None` (random initialization) or 'imagenet' (pre-training on ImageNet).
+      Will try to download and load pre-trained model weights if not None.
+  **kwargs: other parameters if available.
+
+Returns:
+    A `keras.Model` instance.
+"""
+
+ResNeXt.__doc__ = __head_doc__ + """
+Args:
+  num_blocks: number of blocks in each stack.
+  model_name: string, model name.
+""" + __tail_doc__ + """
+Model architectures:
+  | Model          | Params | Image  resolution | Top1 Acc |
+  | -------------- | ------ | ----------------- | -------- |
+  | resnext50      | 25M    | 224               | 77.8     |
+  | resnext101     | 42M    | 224               | 80.9     |
+"""
+
+ResNeXt50.__doc__ = __head_doc__ + """
+Args:
+""" + __tail_doc__
+
+ResNeXt101.__doc__ = ResNeXt50.__doc__
+
+groups_depthwise.__doc__ = __head_doc__ + """
+Grouped depthwise. Callable function, NOT defined as a layer.
+
+Args:
+  inputs: input tensor.
+  groups: number of groups splitted for `DepthwiseConv2D` result.
+  kernel_size: kernel size for `DepthwiseConv2D`.
+  strides: strides for `DepthwiseConv2D`.
+  padding: padding for `DepthwiseConv2D`.
+
+Examples:
+
+>>> from keras_cv_attention_models import attention_layers
+>>> inputs = keras.layers.Input([28, 28, 192])
+>>> nn = attention_layers.groups_depthwise(inputs, groups=32)
+>>> dd = keras.models.Model(inputs, nn)
+>>> dd.output_shape
+(None, 28, 28, 192)
+
+>>> dd.summary()
+_________________________________________________________________
+Layer (type)                 Output Shape              Param #
+=================================================================
+input_2 (InputLayer)         [(None, 28, 28, 192)]     0
+_________________________________________________________________
+zero_padding2d (ZeroPadding2 (None, 30, 30, 192)       0
+_________________________________________________________________
+depthwise_conv2d (DepthwiseC (None, 28, 28, 1152)      10368
+_________________________________________________________________
+reshape (Reshape)            (None, 28, 28, 32, 6, 6)  0
+_________________________________________________________________
+tf.math.reduce_sum (TFOpLamb (None, 28, 28, 32, 6)     0
+_________________________________________________________________
+reshape_1 (Reshape)          (None, 28, 28, 192)       0
+=================================================================
+Total params: 10,368
+Trainable params: 10,368
+Non-trainable params: 0
+_________________________________________________________________
+"""

keras_cv_attention_models/resnet_family/resnet_deep.py

@@ -0,0 +1,49 @@
+from keras_cv_attention_models.aotnet import AotNet
+import os
+
+def ResNetD(num_blocks, input_shape=(224, 224, 3), pretrained="imagenet", deep_stem=True, stem_width=32, strides=2, **kwargs):
+    strides = strides if isinstance(strides, (list, tuple)) else [1, 2, 2, strides]
+    model = AotNet(num_blocks, input_shape=input_shape, deep_stem=deep_stem, stem_width=stem_width, strides=strides, **kwargs)
+    reload_model_weights(model, input_shape, pretrained)
+    return model
+
+
+def reload_model_weights(model, input_shape=(224, 224, 3), pretrained="imagenet"):
+    pretrained_dd = {
+        "resnet50d": ["imagenet"],
+    }
+    if model.name not in pretrained_dd or pretrained not in pretrained_dd[model.name]:
+        print(">>>> No pretraind available, model will be randomly initialized")
+        return
+
+    pre_url = "https://github.com/leondgarse/keras_cv_attention_models/releases/download/resnet_family/{}_{}.h5"
+    url = pre_url.format(model.name, pretrained)
+    file_name = os.path.basename(url)
+    try:
+        pretrained_model = keras.utils.get_file(file_name, url, cache_subdir="models")
+    except:
+        print("[Error] will not load weights, url not found or download failed:", url)
+        return
+    else:
+        print(">>>> Load pretraind from:", pretrained_model)
+        model.load_weights(pretrained_model, by_name=True, skip_mismatch=True)
+
+
+def ResNet50D(input_shape=(224, 224, 3), num_classes=1000, activation="relu", classifier_activation="softmax", pretrained="imagenet", **kwargs):
+    num_blocks = [3, 4, 6, 3]
+    return ResNetD(**locals(), model_name="resnet50d", **kwargs)
+
+
+def ResNet101D(input_shape=(224, 224, 3), num_classes=1000, activation="relu", classifier_activation="softmax", pretrained="imagenet", **kwargs):
+    num_blocks = [3, 4, 23, 3]
+    return ResNetD(**locals(), model_name="resnet101d", **kwargs)
+
+
+def ResNet152D(input_shape=(224, 224, 3), num_classes=1000, activation="relu", classifier_activation="softmax", pretrained="imagenet", **kwargs):
+    num_blocks = [3, 8, 36, 3]
+    return ResNetD(**locals(), model_name="resnet152d", **kwargs)
+
+
+def ResNet200D(input_shape=(224, 224, 3), num_classes=1000, activation="relu", classifier_activation="softmax", pretrained="imagenet", **kwargs):
+    num_blocks = [3, 24, 36, 3]
+    return ResNetD(**locals(), model_name="resnet200d", **kwargs)