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85 行
2.4 KiB
85 行
2.4 KiB
from mlagents.torch_utils import torch
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from mlagents.trainers.torch.layers import (
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Swish,
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linear_layer,
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lstm_layer,
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Initialization,
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LSTM,
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LayerNorm,
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)
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def test_swish():
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layer = Swish()
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input_tensor = torch.Tensor([[1, 2, 3], [4, 5, 6]])
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target_tensor = torch.mul(input_tensor, torch.sigmoid(input_tensor))
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assert torch.all(torch.eq(layer(input_tensor), target_tensor))
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def test_initialization_layer():
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torch.manual_seed(0)
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# Test Zero
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layer = linear_layer(
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3, 4, kernel_init=Initialization.Zero, bias_init=Initialization.Zero
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)
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assert torch.all(torch.eq(layer.weight.data, torch.zeros_like(layer.weight.data)))
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assert torch.all(torch.eq(layer.bias.data, torch.zeros_like(layer.bias.data)))
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def test_lstm_layer():
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torch.manual_seed(0)
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# Test zero for LSTM
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layer = lstm_layer(
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4, 4, kernel_init=Initialization.Zero, bias_init=Initialization.Zero
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)
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for name, param in layer.named_parameters():
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if "weight" in name:
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assert torch.all(torch.eq(param.data, torch.zeros_like(param.data)))
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elif "bias" in name:
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assert torch.all(
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torch.eq(param.data[4:8], torch.ones_like(param.data[4:8]))
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)
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def test_lstm_class():
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torch.manual_seed(0)
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input_size = 12
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memory_size = 64
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batch_size = 8
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seq_len = 16
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lstm = LSTM(input_size, memory_size)
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assert lstm.memory_size == memory_size
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sample_input = torch.ones((batch_size, seq_len, input_size))
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sample_memories = torch.ones((1, batch_size, memory_size))
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out, mem = lstm(sample_input, sample_memories)
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# Hidden size should be half of memory_size
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assert out.shape == (batch_size, seq_len, memory_size // 2)
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assert mem.shape == (1, batch_size, memory_size)
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def test_layer_norm():
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torch.manual_seed(0)
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torch_ln = torch.nn.LayerNorm(10, elementwise_affine=False)
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cust_ln = LayerNorm()
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sample_input = torch.rand(10)
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assert torch.all(
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torch.isclose(
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torch_ln(sample_input), cust_ln(sample_input), atol=1e-5, rtol=0.0
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)
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)
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sample_input = torch.rand((4, 10))
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assert torch.all(
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torch.isclose(
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torch_ln(sample_input), cust_ln(sample_input), atol=1e-5, rtol=0.0
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)
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)
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sample_input = torch.rand((7, 6, 10))
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assert torch.all(
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torch.isclose(
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torch_ln(sample_input), cust_ln(sample_input), atol=1e-5, rtol=0.0
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)
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)
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