我（仍在尝试在Tensorflow 2.0后端上使用Keras实现一个简单的Unet网络。
我的模板和蒙版是1536x1536 RGB图像（蒙版是黑白的）。根据this article，可以测量所需的内存量。
我的模型因张量[1,16,1536,1536]上的内存分配错误而崩溃。使用上面文章中给出的方程式，我计算出了该张量所需的内存量：1 * 16 * 1536 * 1536 * 4 = 144 MB。我有可用于Tensorflow的约9 GB的GTX 1080 Ti。怎么了我想念什么吗？
这里几乎是完整的回溯：
2020-03-02 15:59:13.841967: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
2020-03-02 15:59:16.083234: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2020-03-02 15:59:16.087240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-03-02 15:59:16.210856: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.607
pciBusID: 0000:41:00.0
2020-03-02 15:59:16.210988: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2020-03-02 15:59:16.211429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2020-03-02 15:59:16.947775: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-02 15:59:16.947868: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2020-03-02 15:59:16.947922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2020-03-02 15:59:16.948594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8784 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:41:00.0, compute capability: 6.1)
2020-03-02 15:59:16.994676: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.607
pciBusID: 0000:41:00.0
2020-03-02 15:59:16.994849: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2020-03-02 15:59:16.995291: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2020-03-02 15:59:16.995793: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.607
pciBusID: 0000:41:00.0
2020-03-02 15:59:16.995908: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2020-03-02 15:59:16.996301: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2020-03-02 15:59:16.996406: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-02 15:59:16.996491: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2020-03-02 15:59:16.996541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2020-03-02 15:59:16.996942: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8784 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:41:00.0, compute capability: 6.1)
2020-03-02 15:59:18.191834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.607
pciBusID: 0000:41:00.0
2020-03-02 15:59:18.191964: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2020-03-02 15:59:18.192383: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2020-03-02 15:59:18.192499: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-02 15:59:18.192591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2020-03-02 15:59:18.192644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2020-03-02 15:59:18.193053: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:0 with 8784 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:41:00.0, compute capability: 6.1)
Epoch 1/100
2020-03-02 15:59:18.421211: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-03-02 15:59:19.577897: I tensorflow/stream_executor/cuda/cuda_driver.cc:830] failed to allocate 512.00M (536870912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-03-02 15:59:19.616600: I tensorflow/stream_executor/cuda/cuda_driver.cc:830] failed to allocate 460.80M (483183872 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-03-02 15:59:19.638395: W tensorflow/stream_executor/cuda/redzone_allocator.cc:312] Internal: Invoking ptxas not supported on Windows
Relying on driver to perform ptx compilation. This message will be only logged once.
2020-03-02 15:59:19.644478: I tensorflow/stream_executor/cuda/cuda_driver.cc:830] failed to allocate 1.00G (1073741824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-03-02 15:59:19.644601: W tensorflow/core/common_runtime/bfc_allocator.cc:305] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature.
2020-03-02 15:59:19.653644: I tensorflow/stream_executor/cuda/cuda_driver.cc:830] failed to allocate 1.00G (1073741824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-03-02 15:59:19.653767: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 259.00MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2020-03-02 15:59:19.865828: I tensorflow/stream_executor/cuda/cuda_driver.cc:830] failed to allocate 1.00G (1073741824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-03-02 15:59:19.874844: I tensorflow/stream_executor/cuda/cuda_driver.cc:830] failed to allocate 1.00G (1073741824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-03-02 15:59:29.884662: I tensorflow/stream_executor/cuda/cuda_driver.cc:830] failed to allocate 1.00G (1073741824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-03-02 15:59:29.893593: I tensorflow/stream_executor/cuda/cuda_driver.cc:830] failed to allocate 1.00G (1073741824 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory
2020-03-02 15:59:29.893792: W tensorflow/core/common_runtime/bfc_allocator.cc:419] Allocator (GPU_0_bfc) ran out of memory trying to allocate 144.00MiB (rounded to 150994944). Current allocation summary follows.
2020-03-02 15:59:29.919126: I tensorflow/core/common_runtime/bfc_allocator.cc:923] total_region_allocated_bytes_: 1054574080 memory_limit_: 9210949796 available bytes: 8156375716 curr_region_allocation_bytes_: 1073741824
2020-03-02 15:59:29.919304: I tensorflow/core/common_runtime/bfc_allocator.cc:929] Stats:
Limit: 9210949796
InUse: 1010432000
MaxInUse: 1010432000
NumAllocs: 594
MaxAllocSize: 283870720
2020-03-02 15:59:29.919520: W tensorflow/core/common_runtime/bfc_allocator.cc:424] *****__****************xxxxxxxxxx***************xxxxxxxxxx******************************xxxxxxxxxxxx
2020-03-02 15:59:29.919696: W tensorflow/core/framework/op_kernel.cc:1622] OP_REQUIRES failed at conv_ops.cc:947 : Resource exhausted: OOM when allocating tensor with shape[1,16,1536,1536] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
File "E:/Explorium/python/unet_trainer.py", line 82, in <module>
results = model.fit_generator(train_generator, epochs=EPOCHS, steps_per_epoch=STEPS_PER_EPOCH, validation_data=val_generator, validation_steps=VALIDATION_STEPS, callbacks=callbacks)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1297, in fit_generator
steps_name='steps_per_epoch')
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py", line 265, in model_iteration
batch_outs = batch_function(*batch_data)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 973, in train_on_batch
class_weight=class_weight, reset_metrics=reset_metrics)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 264, in train_on_batch
output_loss_metrics=model._output_loss_metrics)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 311, in train_on_batch
output_loss_metrics=output_loss_metrics))
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 252, in _process_single_batch
training=training))
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 127, in _model_loss
outs = model(inputs, **kwargs)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 708, in call
convert_kwargs_to_constants=base_layer_utils.call_context().saving)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 860, in _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\keras\layers\convolutional.py", line 197, in call
outputs = self._convolution_op(inputs, self.kernel)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 1134, in __call__
return self.conv_op(inp, filter)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 639, in __call__
return self.call(inp, filter)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 238, in __call__
name=self.name)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\ops\nn_ops.py", line 2010, in conv2d
name=name)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1031, in conv2d
data_format=data_format, dilations=dilations, name=name, ctx=_ctx)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\ops\gen_nn_ops.py", line 1130, in conv2d_eager_fallback
ctx=_ctx, name=name)
File "C:\Users\E-soft\Anaconda3\envs\Explorium\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute
six.raise_from(core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[1,16,1536,1536] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Conv2D]
Process finished with exit code 1
这是我的模特：
import numpy as np
import os
import cv2
import random
from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Input, BatchNormalization, Activation, Dropout
from tensorflow.python.keras.layers.convolutional import Conv2D, Conv2DTranspose
from tensorflow.python.keras.layers.pooling import MaxPooling2D
from tensorflow.python.keras.layers.merge import concatenate
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)
def data_gen(templates_folder, masks_folder, image_width, batch_size):
counter = 0
images_list = os.listdir(templates_folder)
random.shuffle(images_list)
while True:
templates_pack = np.zeros((batch_size, image_width, image_width, 3)).astype('float')
masks_pack = np.zeros((batch_size, image_width, image_width, 1)).astype('float')
for i in range(counter, counter + batch_size):
template = cv2.imread(templates_folder + '/' + images_list[i]) / 255.
templates_pack[i - counter] = template
mask = cv2.imread(masks_folder + '/' + images_list[i], cv2.IMREAD_GRAYSCALE) / 255.
mask = mask.reshape(image_width, image_width, 1) # Add extra dimension for parity with template size [1536 * 1536 * 3]
masks_pack[i - counter] = mask
counter += batch_size
if counter + batch_size >= len(images_list):
counter = 0
random.shuffle(images_list)
yield templates_pack, masks_pack
def get_unet(input_image, n_filters, kernel_size, dropout=0.5):
conv_1 = Conv2D(filters=n_filters, kernel_size=(kernel_size, kernel_size), data_format="channels_last", activation='relu', kernel_initializer="he_normal", padding="same")(input_image)
conv_1 = BatchNormalization()(conv_1)
conv_2 = Conv2D(filters=n_filters, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_1)
conv_2 = BatchNormalization()(conv_2)
pool_1 = MaxPooling2D(pool_size=(2, 2))(conv_2)
pool_1 = Dropout(dropout * 0.5)(pool_1)
conv_3 = Conv2D(filters=n_filters * 2, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(pool_1)
conv_3 = BatchNormalization()(conv_3)
conv_4 = Conv2D(filters=n_filters * 2, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_3)
conv_4 = BatchNormalization()(conv_4)
pool_2 = MaxPooling2D(pool_size=(2, 2))(conv_4)
pool_2 = Dropout(dropout)(pool_2)
conv_5 = Conv2D(filters=n_filters * 4, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(pool_2)
conv_5 = BatchNormalization()(conv_5)
conv_6 = Conv2D(filters=n_filters * 4, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_5)
conv_6 = BatchNormalization()(conv_6)
pool_3 = MaxPooling2D(pool_size=(2, 2))(conv_6)
pool_3 = Dropout(dropout)(pool_3)
conv_7 = Conv2D(filters=n_filters * 8, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(pool_3)
conv_7 = BatchNormalization()(conv_7)
conv_8 = Conv2D(filters=n_filters * 8, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_7)
conv_8 = BatchNormalization()(conv_8)
pool_4 = MaxPooling2D(pool_size=(2, 2))(conv_8)
pool_4 = Dropout(dropout)(pool_4)
conv_9 = Conv2D(filters=n_filters * 16, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(pool_4)
conv_9 = BatchNormalization()(conv_9)
conv_10 = Conv2D(filters=n_filters * 16, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_9)
conv_10 = BatchNormalization()(conv_10)
upconv_1 = Conv2DTranspose(n_filters * 8, (kernel_size, kernel_size), strides=(2, 2), padding='same')(conv_10)
concat_1 = concatenate([upconv_1, conv_8])
concat_1 = Dropout(dropout)(concat_1)
conv_11 = Conv2D(filters=n_filters * 8, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(concat_1)
conv_11 = BatchNormalization()(conv_11)
conv_12 = Conv2D(filters=n_filters * 8, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_11)
conv_12 = BatchNormalization()(conv_12)
upconv_2 = Conv2DTranspose(n_filters * 4, (kernel_size, kernel_size), strides=(2, 2), padding='same')(conv_12)
concat_2 = concatenate([upconv_2, conv_6])
concat_2 = Dropout(dropout)(concat_2)
conv_13 = Conv2D(filters=n_filters * 4, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(concat_2)
conv_13 = BatchNormalization()(conv_13)
conv_14 = Conv2D(filters=n_filters * 4, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_13)
conv_14 = BatchNormalization()(conv_14)
upconv_3 = Conv2DTranspose(n_filters * 2, (kernel_size, kernel_size), strides=(2, 2), padding='same')(conv_14)
concat_3 = concatenate([upconv_3, conv_4])
concat_3 = Dropout(dropout)(concat_3)
conv_15 = Conv2D(filters=n_filters * 2, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(concat_3)
conv_15 = BatchNormalization()(conv_15)
conv_16 = Conv2D(filters=n_filters * 2, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_15)
conv_16 = BatchNormalization()(conv_16)
upconv_4 = Conv2DTranspose(n_filters * 1, (kernel_size, kernel_size), strides=(2, 2), padding='same')(conv_16)
concat_4 = concatenate([upconv_4, conv_2])
concat_4 = Dropout(dropout)(concat_4)
conv_17 = Conv2D(filters=n_filters * 1, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(concat_4)
conv_17 = BatchNormalization()(conv_17)
conv_18 = Conv2D(filters=n_filters * 1, kernel_size=(kernel_size, kernel_size), activation='relu', kernel_initializer="he_normal", padding="same")(conv_17)
conv_18 = BatchNormalization()(conv_18)
conv_19 = Conv2D(1, (1, 1), activation='sigmoid')(conv_18)
model = Model(inputs=input_image, outputs=conv_19)
return model
callbacks = [EarlyStopping(patience=10, verbose=1),
ReduceLROnPlateau(factor=0.1, patience=3, min_lr=0.00001, verbose=1),
ModelCheckpoint("model-prototype.h5", verbose=1, save_best_only=True, save_weights_only=True)
]
train_templates_path = "E:/train/templates"
train_masks_path = "E:/train/masks"
valid_templates_path = "E:/valid/templates"
valid_masks_path = "E:/valid/masks"
TRAIN_SET_SIZE = len(os.listdir(train_templates_path))
VALID_SET_SIZE = len(os.listdir(valid_templates_path))
BATCH_SIZE = 1
EPOCHS = 100
STEPS_PER_EPOCH = TRAIN_SET_SIZE / BATCH_SIZE
VALIDATION_STEPS = VALID_SET_SIZE / BATCH_SIZE
IMAGE_WIDTH = 1536
train_generator = data_gen(train_templates_path, train_masks_path, IMAGE_WIDTH, batch_size = BATCH_SIZE)
val_generator = data_gen(valid_templates_path, valid_masks_path, IMAGE_WIDTH, batch_size = BATCH_SIZE)
input_image = Input((IMAGE_WIDTH, IMAGE_WIDTH, 3), name='img')
model = get_unet(input_image, n_filters=16, kernel_size = 3, dropout=0.05)
model.compile(optimizer=Adam(lr=0.001), loss="binary_crossentropy", metrics=["accuracy"])
results = model.fit_generator(train_generator, epochs=EPOCHS, steps_per_epoch=STEPS_PER_EPOCH, validation_data=val_generator, validation_steps=VALIDATION_STEPS, callbacks=callbacks)
当然，一个张量可能会占用那么多内存，但是您还必须保留网络中的所有变量以及要反向传播的值。这使计算操作要求变得复杂（尽管并非不可能）。在model.summary()调用结束时检查参数数量，您会发现数量可能会很大。正如评论所言，您的网络很大。
您的问题是图像的尺寸。
这不是模型中的注释，就像其他人所说的那样，而是图像的输入维，需要更多的GPU内存才能进行处理。
您的解决方案是将图像降采样两倍。您需要用完全相同的因子来划分宽度和高度，以保持纵横比，从而使网络甚至可以在较小的图像上进行学习，而不会丢失太多信息并引入失真。
您将可以在768x768的GTX 1080上以batch_size为1进行训练（我有一个GTX 1080Ti，并且我测试了具有多个输入尺寸的多个分段网络）。