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Copy pathconvert_imagenet_weights_to_3D_models.py
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convert_imagenet_weights_to_3D_models.py
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# coding: utf-8
__author__ = 'ZFTurbo: https://kaggle.com/zfturbo'
if __name__ == '__main__':
import os
gpu_use = 4
print('GPU use: {}'.format(gpu_use))
os.environ["KERAS_BACKEND"] = "tensorflow"
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_use)
try:
# tf keras
from tensorflow.keras import backend as K
from classification_models.tfkeras import Classifiers as Classifiers_2D
from classification_models_3D.tfkeras import Classifiers as Classifiers_3D
print('Use TF keras...')
except:
# keras
from keras import backend as K
from classification_models.keras import Classifiers as Classifiers_2D
from classification_models_3D.kkeras import Classifiers as Classifiers_3D
print('Use keras...')
import os
import glob
import hashlib
from keras.applications.efficientnet import EfficientNetB0
from keras.applications.efficientnet import EfficientNetB1
from keras.applications.efficientnet import EfficientNetB2
from keras.applications.efficientnet import EfficientNetB3
from keras.applications.efficientnet import EfficientNetB4
from keras.applications.efficientnet import EfficientNetB5
from keras.applications.efficientnet import EfficientNetB6
from keras.applications.efficientnet import EfficientNetB7
from keras.applications.efficientnet_v2 import *
from keras.applications.convnext import ConvNeXtTiny
from keras.applications.convnext import ConvNeXtSmall
from keras.applications.convnext import ConvNeXtBase
from keras.applications.convnext import ConvNeXtLarge
from keras.applications.convnext import ConvNeXtXLarge
MODELS_PATH = './'
OUTPUT_PATH_CONVERTER = MODELS_PATH + 'converter/'
if not os.path.isdir(OUTPUT_PATH_CONVERTER):
os.mkdir(OUTPUT_PATH_CONVERTER)
def get_model_memory_usage(batch_size, model):
import numpy as np
shapes_mem_count = 0
internal_model_mem_count = 0
for l in model.layers:
layer_type = l.__class__.__name__
if layer_type == 'Model':
internal_model_mem_count += get_model_memory_usage(batch_size, l)
single_layer_mem = 1
out_shape = l.output_shape
if type(out_shape) is list:
out_shape = out_shape[0]
for s in out_shape:
if s is None:
continue
single_layer_mem *= s
shapes_mem_count += single_layer_mem
trainable_count = np.sum([K.count_params(p) for p in model.trainable_weights])
non_trainable_count = np.sum([K.count_params(p) for p in model.non_trainable_weights])
number_size = 4.0
if K.floatx() == 'float16':
number_size = 2.0
if K.floatx() == 'float64':
number_size = 8.0
total_memory = number_size * (batch_size * shapes_mem_count + trainable_count + non_trainable_count)
gbytes = np.round(total_memory / (1024.0 ** 3), 3) + internal_model_mem_count
return gbytes
def convert_weights(m2, m3, out_path, target_channel):
print('Start: {}'.format(m2.name))
for i in range(len(m2.layers)):
layer_2D = m2.layers[i]
layer_3D = m3.layers[i]
print('Extract for [{}]: {} {}'.format(i, layer_2D.__class__.__name__, layer_2D.name))
print('Set for [{}]: {} {}'.format(i, layer_3D.__class__.__name__, layer_3D.name))
if layer_2D.name != layer_3D.name:
print('Warning: different names!')
weights_2D = layer_2D.get_weights()
weights_3D = layer_3D.get_weights()
if layer_2D.__class__.__name__ == 'Conv2D' or \
layer_2D.__class__.__name__ == 'DepthwiseConv2D':
print(type(weights_2D), len(weights_2D), weights_2D[0].shape, weights_3D[0].shape)
print(layer_2D.output_shape)
print(layer_3D.output_shape)
weights_3D[0][...] = 0
if target_channel == 2:
for j in range(weights_3D[0].shape[2]):
weights_3D[0][:, :, j, :, :] = weights_2D[0] / weights_3D[0].shape[2]
if target_channel == 1:
for j in range(weights_3D[0].shape[1]):
weights_3D[0][:, j, :, :, :] = weights_2D[0] / weights_3D[0].shape[1]
else:
for j in range(weights_3D[0].shape[0]):
weights_3D[0][j, :, :, :, :] = weights_2D[0] / weights_3D[0].shape[0]
# Bias
if len(weights_3D) > 1:
print(weights_3D[1].shape, weights_2D[1].shape)
weights_3D[1] = weights_2D[1][:weights_3D[1].shape[0]]
m3.layers[i].set_weights(weights_3D)
elif layer_2D.__class__.__name__ == 'Sequential' and 'convnext' in layer_2D.name:
print('Convnext', type(weights_2D), len(weights_2D), weights_2D[0].shape, weights_3D[0].shape)
print(layer_2D.output_shape)
print(layer_3D.output_shape)
if 'downsampling' in layer_2D.name:
index_w = 2
index_b = 3
layer_norm_0 = 0
layer_norm_1 = 1
else:
index_w = 0
index_b = 1
layer_norm_0 = 2
layer_norm_1 = 3
weights_3D[index_w][...] = 0
if target_channel == 2:
for j in range(weights_3D[index_w].shape[2]):
weights_3D[index_w][:, :, j, :, :] = weights_2D[index_w] / weights_3D[index_w].shape[2]
if target_channel == 1:
for j in range(weights_3D[index_w].shape[1]):
weights_3D[index_w][:, j, :, :, :] = weights_2D[index_w] / weights_3D[index_w].shape[1]
else:
for j in range(weights_3D[index_w].shape[0]):
weights_3D[index_w][j, :, :, :, :] = weights_2D[index_w] / weights_3D[index_w].shape[0]
# Bias
if len(weights_3D) > 1:
print(weights_3D[index_b].shape, weights_2D[index_b].shape)
weights_3D[index_b] = weights_2D[index_b][:weights_3D[index_b].shape[0]]
# layer norm
weights_3D[layer_norm_0] = weights_2D[layer_norm_0]
weights_3D[layer_norm_1] = weights_2D[layer_norm_1]
m3.layers[i].set_weights(weights_3D)
elif layer_2D.__class__.__name__ == 'Normalization' and i == 2:
if len(weights_3D) == 0:
# Effnet v2 (it's in parameters)
pass
else:
m3.layers[i].set_weights(weights_2D)
m3.save(out_path)
def convert_models():
include_top = False
target_channel = 0
shape_size_3D = (64, 64, 64, 3)
# shape_size_3D = (32, 7*32, 7*32, 3)
shape_size_2D = (224, 224, 3)
list_to_check = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'seresnet18', 'seresnet34', 'seresnet50',
'seresnet101', 'seresnet152', 'seresnext50', 'seresnext101', 'senet154', 'resnext50', 'resnext101',
'vgg16', 'vgg19', 'densenet121', 'densenet169', 'densenet201', 'mobilenet', 'mobilenetv2',
'efficientnetb0', 'efficientnetb1', 'efficientnetb2', 'efficientnetb3',
'efficientnetb4', 'efficientnetb5', 'efficientnetb6', 'efficientnetb7', 'efficientnetv2-b0',
'efficientnetv2-b1', 'efficientnetv2-b2', 'efficientnetv2-b3', 'efficientnetv2-s', 'efficientnetv2-m',
'efficientnetv2-l', 'convnext_tiny', 'convnext_small', 'convnext_base', 'convnext_large', 'convnext_xlarge'
]
list_to_check = [
'convnext_tiny', 'convnext_small', 'convnext_base', 'convnext_large', 'convnext_xlarge'
]
for t in list_to_check:
out_path = MODELS_PATH + 'converter/{}_inp_channel_{}_tch_{}_top_{}.h5'.format(t, shape_size_3D[-1], target_channel, include_top)
if os.path.isfile(out_path):
print('Already exists: {}!'.format(out_path))
continue
model3D, preprocess_input = Classifiers_3D.get(t)
model3D = model3D(include_top=include_top,
weights=None,
input_shape=shape_size_3D,
pooling='avg', )
mem = get_model_memory_usage(1, model3D)
print('Model 3D: {} Mem single: {:.2f}'.format(t, mem))
if t in ['efficientnetb0', 'efficientnetb1', 'efficientnetb2', 'efficientnetb3',
'efficientnetb4', 'efficientnetb5', 'efficientnetb6', 'efficientnetb7']:
func = {
'efficientnetb0': EfficientNetB0,
'efficientnetb1': EfficientNetB1,
'efficientnetb2': EfficientNetB2,
'efficientnetb3': EfficientNetB3,
'efficientnetb4': EfficientNetB4,
'efficientnetb5': EfficientNetB5,
'efficientnetb6': EfficientNetB6,
'efficientnetb7': EfficientNetB7,
}
model2D = func[t](
include_top=include_top,
weights='imagenet',
input_shape=shape_size_2D,
pooling='avg',
)
elif t in ['efficientnetv2-b0', 'efficientnetv2-b1', 'efficientnetv2-b2', 'efficientnetv2-b3',
'efficientnetv2-s', 'efficientnetv2-m', 'efficientnetv2-l']:
func = {
'efficientnetv2-b0': EfficientNetV2B0,
'efficientnetv2-b1': EfficientNetV2B1,
'efficientnetv2-b2': EfficientNetV2B2,
'efficientnetv2-b3': EfficientNetV2B3,
'efficientnetv2-s': EfficientNetV2S,
'efficientnetv2-m': EfficientNetV2M,
'efficientnetv2-l': EfficientNetV2L,
}
model2D = func[t](
include_top=include_top,
weights='imagenet',
input_shape=shape_size_2D,
pooling='avg',
)
elif t in ['convnext_tiny', 'convnext_small', 'convnext_base', 'convnext_large', 'convnext_xlarge']:
func = {
'convnext_tiny': ConvNeXtTiny,
'convnext_small': ConvNeXtSmall,
'convnext_base': ConvNeXtBase,
'convnext_large': ConvNeXtLarge,
'convnext_xlarge': ConvNeXtXLarge,
}
model2D = func[t](
include_top=include_top,
weights='imagenet',
input_shape=shape_size_2D,
pooling='avg',
)
else:
model2D, preprocess_input = Classifiers_2D.get(t)
model2D = model2D(
include_top=include_top,
weights='imagenet',
input_shape=shape_size_2D,
pooling='avg',
)
mem = get_model_memory_usage(1, model2D)
print('Model 2D: {} Mem single: {:.2f}'.format(t, mem))
convert_weights(model2D, model3D, out_path, target_channel=target_channel)
K.clear_session()
def md5(fname):
hash_md5 = hashlib.md5()
with open(fname, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_md5.update(chunk)
return hash_md5.hexdigest()
def gen_text_with_links():
list_to_check = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'seresnet18', 'seresnet34', 'seresnet50',
'seresnet101', 'seresnet152', 'seresnext50', 'seresnext101', 'senet154', 'resnext50', 'resnext101',
'vgg16', 'vgg19', 'densenet121', 'densenet169', 'densenet201', 'mobilenet', 'mobilenetv2',
'efficientnetb0', 'efficientnetb1', 'efficientnetb2', 'efficientnetb3',
'efficientnetb4', 'efficientnetb5', 'efficientnetb6', 'efficientnetb7', 'efficientnetv2-b0',
'efficientnetv2-b1', 'efficientnetv2-b2', 'efficientnetv2-b3', 'efficientnetv2-s', 'efficientnetv2-m',
'efficientnetv2-l', 'convnext_tiny', 'convnext_small', 'convnext_base', 'convnext_large', 'convnext_xlarge'
]
for model_name in list_to_check:
files = glob.glob('./converter/{}_*.h5'.format(model_name))
for f in files:
file_name = os.path.basename(f)
arr = file_name[:-3].split('_')
m5 = md5(f)
print('# {}'.format(model_name))
print('{')
print(' \'model\': \'{}\','.format(model_name))
print(' \'dataset\': \'imagenet\','.format(model_name))
print(' \'classes\': 1000,'.format(model_name))
print(' \'include_top\': {},'.format(arr[-1]))
print(' \'url\': \'https://github.com/ZFTurbo/classification_models_3D/releases/download/v1.0.4/{}\','.format(file_name))
print(' \'name\': \'{}\','.format(file_name))
print(' \'md5\': \'{}\','.format(m5))
print('},')
if __name__ == '__main__':
convert_models()
gen_text_with_links()