我(仍然(试图在Tensorflow 2.0后端使用Keras实现一个简单的Unet网络。
我的模板和遮罩是1536x1536 RGB图像(遮罩是黑白的(。根据这篇文章，可以测量所需的内存量。
我的模型因张量[1，1515361536]上的内存分配错误而崩溃。使用上面文章中给出的等式，我计算出了这个张量所需的内存量：1*16*1536*1536*4=144兆字节。我有GTX 1080 Ti，约9 GB可用于Tensorflow。怎么了？我是不是错过了什么？
以下是一个几乎完整的回溯：
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:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginetraining.py", line 1297, in fit_generator
steps_name='steps_per_epoch')
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginetraining_generator.py", line 265, in model_iteration
batch_outs = batch_function(*batch_data)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginetraining.py", line 973, in train_on_batch
class_weight=class_weight, reset_metrics=reset_metrics)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginetraining_v2_utils.py", line 264, in train_on_batch
output_loss_metrics=model._output_loss_metrics)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginetraining_eager.py", line 311, in train_on_batch
output_loss_metrics=output_loss_metrics))
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginetraining_eager.py", line 252, in _process_single_batch
training=training))
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginetraining_eager.py", line 127, in _model_loss
outs = model(inputs, **kwargs)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginebase_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginenetwork.py", line 708, in call
convert_kwargs_to_constants=base_layer_utils.call_context().saving)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginenetwork.py", line 860, in _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkerasenginebase_layer.py", line 891, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonkeraslayersconvolutional.py", line 197, in call
outputs = self._convolution_op(inputs, self.kernel)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonopsnn_ops.py", line 1134, in __call__
return self.conv_op(inp, filter)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonopsnn_ops.py", line 639, in __call__
return self.call(inp, filter)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonopsnn_ops.py", line 238, in __call__
name=self.name)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonopsnn_ops.py", line 2010, in conv2d
name=name)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonopsgen_nn_ops.py", line 1031, in conv2d
data_format=data_format, dilations=dilations, name=name, ctx=_ctx)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythonopsgen_nn_ops.py", line 1130, in conv2d_eager_fallback
ctx=_ctx, name=name)
File "C:UsersE-softAnaconda3envsExploriumlibsite-packagestensorflow_corepythoneagerexecute.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
这是我的型号：
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)
您案例中的问题是图像的维度。
这不是评论中其他人所说的模型尺寸，而是图像的输入尺寸，需要更多的GPU内存才能进行处理。
在您的情况下，解决方案是使用因子2对图像进行下采样。你需要用完全相同的因子来划分宽度和高度，以保持纵横比，从而使网络即使在较小的图像上也能学习，而不会丢失太多信息并引入失真。
您将能够在768x768上的GTX 1080上使用batch_size 1进行训练(我有一个GTX 1080Ti，我测试了几个具有几个输入维度的分割网络(。如果由于某些原因，您的GPU消耗被其他进程(如YT或类似进程(占用，那么将其减少到512x512肯定会起作用(即使在batch_size=1上使用768x768也应该起作用(
当然，一个张量可能会占用那么多内存，但你也必须保存网络中的所有变量，以及要反向传播的值。这使得计算操作需求变得复杂(尽管并非不可能(。您的网络的操作空间相当大。