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added AMD/Intel cards support via DirectX12 ( DirectML backend )

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iperov 2021-04-22 18:19:15 +04:00
parent fc4a49c3e7
commit fdb143ff47
7 changed files with 166 additions and 116 deletions
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6
models/Model_Quick96/Model.py
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@ -31,7 +31,7 @@ class QModel(ModelBase):
masked_training = True
models_opt_on_gpu = len(devices) >= 1 and all([dev.total_mem_gb >= 4 for dev in devices])
models_opt_device = '/GPU:0' if models_opt_on_gpu and self.is_training else '/CPU:0'
models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
input_ch = 3
@ -96,7 +96,7 @@ class QModel(ModelBase):
gpu_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
with tf.device( f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
@ -190,7 +190,7 @@ class QModel(ModelBase):
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device( f'/GPU:0' if len(devices) != 0 else f'/CPU:0'):
with tf.device( nn.tf_default_device_name if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)