為什么即使批量大小為 1 也會出現內存分配錯誤？

Question

我（仍在）嘗試在 Tensorflow 2.0 后端使用 Keras 實現一個簡單的 Unet 網絡。

我的模板和蒙版是 1536x1536 RGB 圖像（蒙版是黑白的）。 根據這篇文章，可以測量所需的內存量。

我的模型因張量 [1,16,1536,1536] 上的內存分配錯誤而崩潰。 使用上面文章中給出的方程，我計算了這個張量所需的內存量：1 * 16 * 1536 * 1536 * 4 = 144 MB。 我有 GTX 1080 Ti，大約 9 GB 可用於 Tensorflow。 怎么了？ 我錯過了什么嗎？

這是一個幾乎完整的回溯：

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:\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

這是我的模型：

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)

Answer 1

您的情況的問題是圖像的尺寸。

它不像評論中的其他人那樣是模型的維度，而是圖像的輸入維度，它需要更多的 GPU 內存才能進行處理。

在您的情況下，解決方案是以兩倍的系數對圖像進行下采樣。 您需要使用完全相同的因子來划分寬度和高度，以保持縱橫比，從而使網絡即使在較小的圖像上也能學習，而不會丟失太多信息並引入失真。

您將能夠在 768x768 的 GTX 1080 上以 1 的 batch_size 進行訓練（我有一個 GTX 1080Ti，我測試了多個具有多個輸入維度的分割網絡）。 如果由於某些原因您的 GPU 消耗被其他進程（例如 YT 或類似進程）占用，那么將其減少到 512x512 肯定會起作用（即使在 batch_size=1 上使用 768x768 也應該起作用）

Answer 2

當然，一個張量可能會占用那么多內存，但您還必須保存網絡中的所有變量，以及要反向傳播的值。 這使得計算操作要求變得復雜（盡管並非不可能）。 你的網絡的操作空間是相當大的。