Моя обучающая модель представляет собой модифицированную версию U- Net с целью добавить фильтр к изображению размером 512x1808.
проблема, с которой я сталкиваюсь, заключается в том, что у меня постоянно возникают проблемы с памятью, когда я вызываю model.predict, но это нормально, когда я вызываю model.fit для обучения данных.
вот модель:
## create model
IMG_WIDTH = width_padded #2048
IMG_HEIGHT = height #512
IMG_CHANNELS = 3 #RGB
inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
#encoder path
c1a = tf.keras.layers.Conv2D(16, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(inputs)
c1a = tf.keras.layers.Dropout(0.1)(c1a)
c1a = tf.keras.layers.Conv2D(16, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(c1a)
p1a = tf.keras.layers.MaxPooling2D((2,2))(c1a)
c1b = tf.keras.layers.Conv2D(16, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(c1a)
c1b = tf.keras.layers.Dropout(0.1)(c1b)
c1b = tf.keras.layers.Conv2D(16, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(c1b)
p1b = tf.keras.layers.MaxPooling2D((2,2))(c1b)
c1 = tf.keras.layers.Conv2D(16, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(c1b)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3,3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)
p1 = tf.keras.layers.MaxPooling2D((2,2))(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)
p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)
#decoder path
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
u9 = tf.keras.layers.concatenate([u9, c1a], axis=3)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)
c9 = tf.keras.layers.Dropout(0.1)(c9)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)
outputs = tf.keras.layers.Conv2D(3, (1, 1), activation='sigmoid')(c9)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
обучающий вызов:
train_in, train_out, test_in, test_out = dataset['train_in'], dataset['train_out'], dataset['test_in'], dataset['test_out']
results = model.fit(train_in, train_out, validation_split=0.1, batch_size=1, epochs=5, callbacks=callbacks)
прогнозирующий вызов:
preds_train = new_model.predict(dataset["test_in"])
plt.imshow(preds_train)
и сообщение об ошибке:
020-06-18 17:07:30.212907: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-06-18 17:07:31.394251: 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.
2020-06-18 17:07:31.457663: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.39GiB 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.
2020-06-18 17:07:31.469003: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.39GiB 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.
2020-06-18 17:07:31.744585: W tensorflow/core/common_runtime/bfc_allocator.cc:239] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.02GiB 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.
2020-06-18 17:07:31.753330: W tensorflow/core/kernels/conv_ops.cc:1014] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once.
2020-06-18 17:07:42.400811: W tensorflow/core/common_runtime/bfc_allocator.cc:419] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.00GiB (rounded to 2147483648). Current allocation summary follows.
2020-06-18 17:07:42.406709: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (256): Total Chunks: 74, Chunks in use: 73. 18.5KiB allocated for chunks. 18.3KiB in use in bin. 8.0KiB client-requested in use in bin.
2020-06-18 17:07:42.413456: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (512): Total Chunks: 15, Chunks in use: 15. 7.5KiB allocated for chunks. 7.5KiB in use in bin. 7.5KiB client-requested in use in bin.
2020-06-18 17:07:42.430835: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (1024): Total Chunks: 10, Chunks in use: 10. 12.5KiB allocated for chunks. 12.5KiB in use in bin. 12.1KiB client-requested in use in bin.
2020-06-18 17:07:42.445773: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (2048): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-06-18 17:07:42.457267: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (4096): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-06-18 17:07:42.466993: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (8192): Total Chunks: 21, Chunks in use: 21. 189.5KiB allocated for chunks. 189.5KiB in use in bin. 186.0KiB client-requested in use in
bin.
2020-06-18 17:07:42.473657: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (16384): Total Chunks: 6, Chunks in use: 6. 108.0KiB allocated for chunks. 108.0KiB in use in bin. 108.0KiB client-requested in use in bin.
2020-06-18 17:07:42.479695: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (32768): Total Chunks: 9, Chunks in use: 9. 312.0KiB allocated for chunks. 312.0KiB in use in bin. 312.0KiB client-requested in use in bin.
2020-06-18 17:07:42.493219: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (65536): Total Chunks: 6, Chunks in use: 6. 432.0KiB allocated for chunks. 432.0KiB in use in bin. 432.0KiB client-requested in use in bin.
2020-06-18 17:07:42.500604: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (131072): Total Chunks: 10, Chunks in use: 9. 1.45MiB allocated for chunks. 1.22MiB in use in bin. 1.22MiB client-requested in use in bin.
2020-06-18 17:07:42.509323: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (262144): Total Chunks: 6, Chunks in use: 6. 1.69MiB allocated for chunks. 1.69MiB in use in bin. 1.69MiB client-requested in use
in bin.
2020-06-18 17:07:42.516243: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (524288): Total Chunks: 9, Chunks in use: 9. 5.20MiB allocated for chunks. 5.20MiB in use in bin. 4.88MiB client-requested in use
in bin.
2020-06-18 17:07:42.531832: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (1048576): Total Chunks: 6, Chunks in use: 5. 7.16MiB allocated for chunks. 5.63MiB in use in bin. 5.63MiB client-requested in use
in bin.
2020-06-18 17:07:42.542340: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (2097152): Total Chunks: 4, Chunks in use: 4. 10.46MiB allocated for chunks. 10.46MiB in use in bin. 7.88MiB client-requested in use in bin.
2020-06-18 17:07:42.548178: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (4194304): Total Chunks: 1, Chunks in use: 0. 4.00MiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-06-18 17:07:42.565222: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (8388608): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-06-18 17:07:42.571730: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (16777216): Total Chunks: 1, Chunks in use: 1. 24.00MiB allocated for chunks. 24.00MiB in use in bin. 24.00MiB client-requested in use in bin.
2020-06-18 17:07:42.585429: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (33554432): Total Chunks: 2, Chunks in use: 0. 72.00MiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-06-18 17:07:42.591711: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (67108864): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-06-18 17:07:42.599001: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (134217728): Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-06-18 17:07:42.608031: I tensorflow/core/common_runtime/bfc_allocator.cc:869] Bin (268435456): Total Chunks: 5, Chunks in use: 2. 6.00GiB allocated for chunks. 4.00GiB in use in bin. 4.00GiB client-requested in use
in bin.
2020-06-18 17:07:42.614548: I tensorflow/core/common_runtime/bfc_allocator.cc:885] Bin for 2.00GiB was 256.00MiB, Chunk State:
2020-06-18 17:07:42.625221: I tensorflow/core/common_runtime/bfc_allocator.cc:891] Size: 512.00MiB | Requested Size: 384.00MiB | in_use: 0 | bin_num: 20
2020-06-18 17:07:42.633311: I tensorflow/core/common_runtime/bfc_allocator.cc:891] Size: 512.00MiB | Requested Size: 384.00MiB | in_use: 0 | bin_num: 20
2020-06-18 17:07:42.638322: I tensorflow/core/common_runtime/bfc_allocator.cc:891] Size: 1.00GiB | Requested Size: 0B | in_use: 0 | bin_num: 20, prev: Size: 2.00GiB | Requested Size: 2.00GiB | in_use: 1 | bin_num: -1
2020-06-18 17:07:42.649208: I tensorflow/core/common_runtime/bfc_allocator.cc:898] Next region of size 1048576
2020-06-18 17:07:42.660619: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0000000710E00000 next 1 of size 1280
2020-06-18 17:07:42.663664: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0000000710E00500 next 2 of size 256
2020-06-18 17:07:42.675899: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0000000710E00600 next 173 of size 256
2020-06-18 17:07:42.679636: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0000000710E00700 next 4 of size 256
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0000000710EBE900 next 48 of size 256
2020-06-18 17:07:42.895535: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0000000710EBEA00 next 49 of size 18432
2020-06-18 17:07:42.898600: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0000000710EC3200 next 50 of size 256
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2020-06-18 17:07:42.931868: I tensorflow/core/common_runtime/bfc_allocator.cc:905] Free at 0000000710EC5B00 next 175 of size 256
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2020-06-18 17:07:42.944828: I tensorflow/core/common_runtime/bfc_allocator.cc:905] Free at 0000000710EC5D00 next 18446744073709551615 of size 238336
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2020-06-18 17:07:43.006865: I tensorflow/core/common_runtime/bfc_allocator.cc:905] Free at 000000071171C200 next 56 of size 1606400
2020-06-18 17:07:43.011424: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 00000007118A4500 next 112 of size 256
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2020-06-18 17:07:43.047870: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 00000007118A9800 next 64 of size 9216
2020-06-18 17:07:43.058766: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 00000007118ABC00 next 63 of size 256
2020-06-18 17:07:43.107214: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 00000007118AE600 next 76 of size 512
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2020-06-18 17:07:43.129575: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 00000007118AF200 next 113 of size 524288
2020-06-18 17:07:43.140727: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 000000071192F200 next 85 of size 1179648
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2020-06-18 17:07:43.879021: I tensorflow/core/common_runtime/bfc_allocator.cc:905] Free at 0000000738E00000 next 18446744073709551615 of size 536870912
2020-06-18 17:07:43.882678: I tensorflow/core/common_runtime/bfc_allocator.cc:898] Next region of size 2147483648
2020-06-18 17:07:43.893653: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 0000000758E00000 next 18446744073709551615 of size 2147483648
2020-06-18 17:07:43.897406: I tensorflow/core/common_runtime/bfc_allocator.cc:898] Next region of size 3223725568
2020-06-18 17:07:43.910485: I tensorflow/core/common_runtime/bfc_allocator.cc:905] InUse at 00000007EC400000 next 183 of size 2147483648
2020-06-18 17:07:43.914438: I tensorflow/core/common_runtime/bfc_allocator.cc:905] Free at 000000086C400000 next 18446744073709551615 of size 1076241920
2020-06-18 17:07:43.925183: I tensorflow/core/common_runtime/bfc_allocator.cc:914] Summary of in-use Chunks by size:
2020-06-18 17:07:43.928750: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 73 Chunks of size 256 totalling 18.3KiB
2020-06-18 17:07:43.931900: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 15 Chunks of size 512 totalling 7.5KiB
2020-06-18 17:07:43.943729: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 6 Chunks of size 1024 totalling 6.0KiB
2020-06-18 17:07:43.946789: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 1280 totalling 1.3KiB
2020-06-18 17:07:43.949717: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 3 Chunks of size 1792 totalling 5.3KiB
2020-06-18 17:07:43.961057: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 3 Chunks of size 8192 totalling 24.0KiB
2020-06-18 17:07:43.964209: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 16 Chunks of size 9216 totalling 144.0KiB
2020-06-18 17:07:43.977017: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 2 Chunks of size 11008 totalling 21.5KiB
2020-06-18 17:07:43.980707: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 6 Chunks of size 18432 totalling 108.0KiB
2020-06-18 17:07:43.993934: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 3 Chunks of size 32768 totalling 96.0KiB
2020-06-18 17:07:43.997585: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 6 Chunks of size 36864 totalling 216.0KiB
2020-06-18 17:07:44.009845: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 6 Chunks of size 73728 totalling 432.0KiB
2020-06-18 17:07:44.013957: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 3 Chunks of size 131072 totalling 384.0KiB
2020-06-18 17:07:44.024972: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 6 Chunks of size 147456 totalling 864.0KiB
2020-06-18 17:07:44.029296: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 6 Chunks of size 294912 totalling 1.69MiB
2020-06-18 17:07:44.033041: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 524288 totalling 512.0KiB
2020-06-18 17:07:44.042342: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 580608 totalling 567.0KiB
2020-06-18 17:07:44.045601: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 6 Chunks of size 589824 totalling 3.38MiB
2020-06-18 17:07:44.048650: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 804608 totalling 785.8KiB
2020-06-18 17:07:44.060413: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 5 Chunks of size 1179648 totalling 5.63MiB
2020-06-18 17:07:44.063842: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 2097152 totalling 2.00MiB
2020-06-18 17:07:44.066961: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 2359296 totalling 2.25MiB
2020-06-18 17:07:44.077720: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 2690560 totalling 2.57MiB
2020-06-18 17:07:44.081227: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 3819776 totalling 3.64MiB
2020-06-18 17:07:44.091829: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 1 Chunks of size 25165824 totalling 24.00MiB
2020-06-18 17:07:44.095323: I tensorflow/core/common_runtime/bfc_allocator.cc:917] 2 Chunks of size 2147483648 totalling 4.00GiB
2020-06-18 17:07:44.098446: I tensorflow/core/common_runtime/bfc_allocator.cc:921] Sum Total of in-use chunks: 4.05GiB
2020-06-18 17:07:44.109398: I tensorflow/core/common_runtime/bfc_allocator.cc:923] total_region_allocated_bytes_: 6578120192 memory_limit_: 6578120295 available bytes: 103 curr_region_allocation_bytes_: 4294967296
2020-06-18 17:07:44.114692: I tensorflow/core/common_runtime/bfc_allocator.cc:929] Stats:
Limit: 6578120295
InUse: 4346599680
MaxInUse: 4799586304
NumAllocs: 1210
MaxAllocSize: 2164260864
2020-06-18 17:07:44.130208: W tensorflow/core/common_runtime/bfc_allocator.cc:424] **________________******************************************************************________________
2020-06-18 17:07:44.140209: W tensorflow/core/framework/op_kernel.cc:1622] OP_REQUIRES failed at conv_ops.cc:501 : Resource exhausted: OOM when allocating tensor with shape[32,16,512,2048] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
2020-06-18 17:07:44.149440: W tensorflow/core/common_runtime/base_collective_executor.cc:216] BaseCollectiveExecutor::StartAbort Resource exhausted: OOM when allocating tensor with shape[32,16,512,2048] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node model/StatefulPartitionedCall/StatefulPartitionedCall/conv2d_3/StatefulPartitionedCall/Conv2D}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Traceback (most recent call last):
File "c:/Users/shini/Documents/GitHub/skinner/load_test_sample.py", line 126, in <module>
preds_train = new_model.predict(dataset["test_in"])
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 909, in predict
use_multiprocessing=use_multiprocessing)
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 462, in predict
steps=steps, callbacks=callbacks, **kwargs)
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 444, in _model_iteration
total_epochs=1)
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 123, in run_one_epoch
batch_outs = execution_function(iterator)
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 86, in execution_function
distributed_function(input_fn))
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 457, in __call__
result = self._call(*args, **kwds)
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 526, in _call
return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\eager\function.py", line 1141, in _filtered_call
self.captured_inputs)
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\eager\function.py", line 1224, in _call_flat
ctx, args, cancellation_manager=cancellation_manager)
File "C:\Users\shini\anaconda3\envs\hope2\lib\site-packages\tensorflow_core\python\eager\function.py", line 511, in call
ctx=ctx)
File "C:\Users\shini\anaconda3\envs\hope2\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[32,16,512,2048] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node model/StatefulPartitionedCall/StatefulPartitionedCall/conv2d_3/StatefulPartitionedCall/Conv2D}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[Op:__inference_distributed_function_8302]
Function call stack:
distributed_function
Я пытался использовать tf.config.experimental.set_memory_growth(gpu[0], True), чтобы решить эту проблему, но безрезультатно. Я хочу сказать, что модель слишком велика или что-то в этом роде, но я бы ожидал, что обучение не удастся, если бы это было так. dataset["test_in"] - это тип np.memmap, если это важно. но я действительно в тупике