def paddle_grucell():
np.random.seed(SEED)
x = np.random.rand(1, 230).astype(np.float32)
y = np.random.rand(1, 96).astype(np.float32)
# np.save('org.npy', x)
with fluid.dygraph.guard():
layer = nn.GRUCell(input_size=230, hidden_size=96)
# sd = np.load('lstm.npy', allow_pickle=True).tolist()
# lstm.set_state_dict(sd)
state_dict = layer.state_dict()
sd = OrderedDict()
for key, value in state_dict.items():
v = value.numpy()
print(key, value.shape, np.sum(v), np.mean(v), np.max(v),
np.min(v))
sd[key] = v
np.save('gru_cell.npy', sd)
inp = fluid.dygraph.to_variable(x)
prev_hidden = fluid.dygraph.to_variable(y)
ret, hidden = layer(inp, prev_hidden)
print(ret - hidden)
return ret.numpy()
def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
super(AttentionGRUCell, self).__init__()
self.i2h = nn.Linear(input_size, hidden_size, bias_attr=False)
self.h2h = nn.Linear(hidden_size, hidden_size)
self.score = nn.Linear(hidden_size, 1, bias_attr=False)
self.rnn = nn.GRUCell(
input_size=input_size + num_embeddings, hidden_size=hidden_size)
self.hidden_size = hidden_size
def __init__(self, in_dim, dropout):
super(AttentivePooling, self).__init__()
self.compute_logits = nn.Sequential(nn.Linear(2 * in_dim, 1),
nn.LeakyReLU())
self.project_nodes = nn.Sequential(nn.Dropout(dropout),
nn.Linear(in_dim, in_dim))
self.pool = pgl.nn.GraphPool(pool_type='sum')
self.gru = nn.GRUCell(in_dim, in_dim)
def __init__(self, i_size: int, h_size: int):
super().__init__()
hidden_size = h_size * 3
self.fw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
self.fw_bn = nn.BatchNorm1D(hidden_size,
bias_attr=None,
data_format='NLC')
self.bw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
self.bw_bn = nn.BatchNorm1D(hidden_size,
bias_attr=None,
data_format='NLC')
self.fw_cell = nn.GRUCell(input_size=hidden_size, hidden_size=h_size)
self.bw_cell = nn.GRUCell(input_size=hidden_size, hidden_size=h_size)
self.fw_rnn = nn.RNN(self.fw_cell, is_reverse=False,
time_major=False) # [B, T, D]
self.bw_rnn = nn.RNN(self.fw_cell, is_reverse=True,
time_major=False) # [B, T, D]
def __init__(self, sDim, xDim, yDim, attDim):
super(DecoderUnit, self).__init__()
self.sDim = sDim
self.xDim = xDim
self.yDim = yDim
self.attDim = attDim
self.emdDim = attDim
self.attention_unit = AttentionUnit(sDim, xDim, attDim)
self.tgt_embedding = nn.Embedding(
yDim + 1, self.emdDim, weight_attr=nn.initializer.Normal(
std=0.01)) # the last is used for <BOS>
self.gru = nn.GRUCell(input_size=xDim + self.emdDim, hidden_size=sDim)
self.fc = nn.Linear(sDim,
yDim,
weight_attr=nn.initializer.Normal(std=0.01),
bias_attr=nn.initializer.Constant(value=0))
self.embed_fc = nn.Linear(300, self.sDim)
def func_test_layer_str(self):
module = nn.ELU(0.2)
self.assertEqual(str(module), 'ELU(alpha=0.2)')
module = nn.CELU(0.2)
self.assertEqual(str(module), 'CELU(alpha=0.2)')
module = nn.GELU(True)
self.assertEqual(str(module), 'GELU(approximate=True)')
module = nn.Hardshrink()
self.assertEqual(str(module), 'Hardshrink(threshold=0.5)')
module = nn.Hardswish(name="Hardswish")
self.assertEqual(str(module), 'Hardswish(name=Hardswish)')
module = nn.Tanh(name="Tanh")
self.assertEqual(str(module), 'Tanh(name=Tanh)')
module = nn.Hardtanh(name="Hardtanh")
self.assertEqual(str(module),
'Hardtanh(min=-1.0, max=1.0, name=Hardtanh)')
module = nn.PReLU(1, 0.25, name="PReLU", data_format="NCHW")
self.assertEqual(
str(module),
'PReLU(num_parameters=1, data_format=NCHW, init=0.25, dtype=float32, name=PReLU)'
)
module = nn.ReLU()
self.assertEqual(str(module), 'ReLU()')
module = nn.ReLU6()
self.assertEqual(str(module), 'ReLU6()')
module = nn.SELU()
self.assertEqual(
str(module),
'SELU(scale=1.0507009873554805, alpha=1.6732632423543772)')
module = nn.LeakyReLU()
self.assertEqual(str(module), 'LeakyReLU(negative_slope=0.01)')
module = nn.Sigmoid()
self.assertEqual(str(module), 'Sigmoid()')
module = nn.Hardsigmoid()
self.assertEqual(str(module), 'Hardsigmoid()')
module = nn.Softplus()
self.assertEqual(str(module), 'Softplus(beta=1, threshold=20)')
module = nn.Softshrink()
self.assertEqual(str(module), 'Softshrink(threshold=0.5)')
module = nn.Softsign()
self.assertEqual(str(module), 'Softsign()')
module = nn.Swish()
self.assertEqual(str(module), 'Swish()')
module = nn.Tanhshrink()
self.assertEqual(str(module), 'Tanhshrink()')
module = nn.ThresholdedReLU()
self.assertEqual(str(module), 'ThresholdedReLU(threshold=1.0)')
module = nn.LogSigmoid()
self.assertEqual(str(module), 'LogSigmoid()')
module = nn.Softmax()
self.assertEqual(str(module), 'Softmax(axis=-1)')
module = nn.LogSoftmax()
self.assertEqual(str(module), 'LogSoftmax(axis=-1)')
module = nn.Maxout(groups=2)
self.assertEqual(str(module), 'Maxout(groups=2, axis=1)')
module = nn.Linear(2, 4, name='linear')
self.assertEqual(
str(module),
'Linear(in_features=2, out_features=4, dtype=float32, name=linear)'
)
module = nn.Upsample(size=[12, 12])
self.assertEqual(
str(module),
'Upsample(size=[12, 12], mode=nearest, align_corners=False, align_mode=0, data_format=NCHW)'
)
module = nn.UpsamplingNearest2D(size=[12, 12])
self.assertEqual(
str(module),
'UpsamplingNearest2D(size=[12, 12], data_format=NCHW)')
module = nn.UpsamplingBilinear2D(size=[12, 12])
self.assertEqual(
str(module),
'UpsamplingBilinear2D(size=[12, 12], data_format=NCHW)')
module = nn.Bilinear(in1_features=5, in2_features=4, out_features=1000)
self.assertEqual(
str(module),
'Bilinear(in1_features=5, in2_features=4, out_features=1000, dtype=float32)'
)
module = nn.Dropout(p=0.5)
self.assertEqual(str(module),
'Dropout(p=0.5, axis=None, mode=upscale_in_train)')
module = nn.Dropout2D(p=0.5)
self.assertEqual(str(module), 'Dropout2D(p=0.5, data_format=NCHW)')
module = nn.Dropout3D(p=0.5)
self.assertEqual(str(module), 'Dropout3D(p=0.5, data_format=NCDHW)')
module = nn.AlphaDropout(p=0.5)
self.assertEqual(str(module), 'AlphaDropout(p=0.5)')
module = nn.Pad1D(padding=[1, 2], mode='constant')
self.assertEqual(
str(module),
'Pad1D(padding=[1, 2], mode=constant, value=0.0, data_format=NCL)')
module = nn.Pad2D(padding=[1, 0, 1, 2], mode='constant')
self.assertEqual(
str(module),
'Pad2D(padding=[1, 0, 1, 2], mode=constant, value=0.0, data_format=NCHW)'
)
module = nn.ZeroPad2D(padding=[1, 0, 1, 2])
self.assertEqual(str(module),
'ZeroPad2D(padding=[1, 0, 1, 2], data_format=NCHW)')
module = nn.Pad3D(padding=[1, 0, 1, 2, 0, 0], mode='constant')
self.assertEqual(
str(module),
'Pad3D(padding=[1, 0, 1, 2, 0, 0], mode=constant, value=0.0, data_format=NCDHW)'
)
module = nn.CosineSimilarity(axis=0)
self.assertEqual(str(module), 'CosineSimilarity(axis=0, eps=1e-08)')
module = nn.Embedding(10, 3, sparse=True)
self.assertEqual(str(module), 'Embedding(10, 3, sparse=True)')
module = nn.Conv1D(3, 2, 3)
self.assertEqual(str(module),
'Conv1D(3, 2, kernel_size=[3], data_format=NCL)')
module = nn.Conv1DTranspose(2, 1, 2)
self.assertEqual(
str(module),
'Conv1DTranspose(2, 1, kernel_size=[2], data_format=NCL)')
module = nn.Conv2D(4, 6, (3, 3))
self.assertEqual(str(module),
'Conv2D(4, 6, kernel_size=[3, 3], data_format=NCHW)')
module = nn.Conv2DTranspose(4, 6, (3, 3))
self.assertEqual(
str(module),
'Conv2DTranspose(4, 6, kernel_size=[3, 3], data_format=NCHW)')
module = nn.Conv3D(4, 6, (3, 3, 3))
self.assertEqual(
str(module),
'Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)')
module = nn.Conv3DTranspose(4, 6, (3, 3, 3))
self.assertEqual(
str(module),
'Conv3DTranspose(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)')
module = nn.PairwiseDistance()
self.assertEqual(str(module), 'PairwiseDistance(p=2.0)')
module = nn.InstanceNorm1D(2)
self.assertEqual(str(module),
'InstanceNorm1D(num_features=2, epsilon=1e-05)')
module = nn.InstanceNorm2D(2)
self.assertEqual(str(module),
'InstanceNorm2D(num_features=2, epsilon=1e-05)')
module = nn.InstanceNorm3D(2)
self.assertEqual(str(module),
'InstanceNorm3D(num_features=2, epsilon=1e-05)')
module = nn.GroupNorm(num_channels=6, num_groups=6)
self.assertEqual(
str(module),
'GroupNorm(num_groups=6, num_channels=6, epsilon=1e-05)')
module = nn.LayerNorm([2, 2, 3])
self.assertEqual(
str(module),
'LayerNorm(normalized_shape=[2, 2, 3], epsilon=1e-05)')
module = nn.BatchNorm1D(1)
self.assertEqual(
str(module),
'BatchNorm1D(num_features=1, momentum=0.9, epsilon=1e-05, data_format=NCL)'
)
module = nn.BatchNorm2D(1)
self.assertEqual(
str(module),
'BatchNorm2D(num_features=1, momentum=0.9, epsilon=1e-05)')
module = nn.BatchNorm3D(1)
self.assertEqual(
str(module),
'BatchNorm3D(num_features=1, momentum=0.9, epsilon=1e-05, data_format=NCDHW)'
)
module = nn.SyncBatchNorm(2)
self.assertEqual(
str(module),
'SyncBatchNorm(num_features=2, momentum=0.9, epsilon=1e-05)')
module = nn.LocalResponseNorm(size=5)
self.assertEqual(
str(module),
'LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=1.0)')
module = nn.AvgPool1D(kernel_size=2, stride=2, padding=0)
self.assertEqual(str(module),
'AvgPool1D(kernel_size=2, stride=2, padding=0)')
module = nn.AvgPool2D(kernel_size=2, stride=2, padding=0)
self.assertEqual(str(module),
'AvgPool2D(kernel_size=2, stride=2, padding=0)')
module = nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
self.assertEqual(str(module),
'AvgPool3D(kernel_size=2, stride=2, padding=0)')
module = nn.MaxPool1D(kernel_size=2, stride=2, padding=0)
self.assertEqual(str(module),
'MaxPool1D(kernel_size=2, stride=2, padding=0)')
module = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
self.assertEqual(str(module),
'MaxPool2D(kernel_size=2, stride=2, padding=0)')
module = nn.MaxPool3D(kernel_size=2, stride=2, padding=0)
self.assertEqual(str(module),
'MaxPool3D(kernel_size=2, stride=2, padding=0)')
module = nn.AdaptiveAvgPool1D(output_size=16)
self.assertEqual(str(module), 'AdaptiveAvgPool1D(output_size=16)')
module = nn.AdaptiveAvgPool2D(output_size=3)
self.assertEqual(str(module), 'AdaptiveAvgPool2D(output_size=3)')
module = nn.AdaptiveAvgPool3D(output_size=3)
self.assertEqual(str(module), 'AdaptiveAvgPool3D(output_size=3)')
module = nn.AdaptiveMaxPool1D(output_size=16, return_mask=True)
self.assertEqual(
str(module), 'AdaptiveMaxPool1D(output_size=16, return_mask=True)')
module = nn.AdaptiveMaxPool2D(output_size=3, return_mask=True)
self.assertEqual(str(module),
'AdaptiveMaxPool2D(output_size=3, return_mask=True)')
module = nn.AdaptiveMaxPool3D(output_size=3, return_mask=True)
self.assertEqual(str(module),
'AdaptiveMaxPool3D(output_size=3, return_mask=True)')
module = nn.SimpleRNNCell(16, 32)
self.assertEqual(str(module), 'SimpleRNNCell(16, 32)')
module = nn.LSTMCell(16, 32)
self.assertEqual(str(module), 'LSTMCell(16, 32)')
module = nn.GRUCell(16, 32)
self.assertEqual(str(module), 'GRUCell(16, 32)')
module = nn.PixelShuffle(3)
self.assertEqual(str(module), 'PixelShuffle(upscale_factor=3)')
module = nn.SimpleRNN(16, 32, 2)
self.assertEqual(
str(module),
'SimpleRNN(16, 32, num_layers=2\n (0): RNN(\n (cell): SimpleRNNCell(16, 32)\n )\n (1): RNN(\n (cell): SimpleRNNCell(32, 32)\n )\n)'
)
module = nn.LSTM(16, 32, 2)
self.assertEqual(
str(module),
'LSTM(16, 32, num_layers=2\n (0): RNN(\n (cell): LSTMCell(16, 32)\n )\n (1): RNN(\n (cell): LSTMCell(32, 32)\n )\n)'
)
module = nn.GRU(16, 32, 2)
self.assertEqual(
str(module),
'GRU(16, 32, num_layers=2\n (0): RNN(\n (cell): GRUCell(16, 32)\n )\n (1): RNN(\n (cell): GRUCell(32, 32)\n )\n)'
)
module1 = nn.Sequential(
('conv1', nn.Conv2D(1, 20, 5)), ('relu1', nn.ReLU()),
('conv2', nn.Conv2D(20, 64, 5)), ('relu2', nn.ReLU()))
self.assertEqual(
str(module1),
'Sequential(\n '\
'(conv1): Conv2D(1, 20, kernel_size=[5, 5], data_format=NCHW)\n '\
'(relu1): ReLU()\n '\
'(conv2): Conv2D(20, 64, kernel_size=[5, 5], data_format=NCHW)\n '\
'(relu2): ReLU()\n)'
)
module2 = nn.Sequential(
nn.Conv3DTranspose(4, 6, (3, 3, 3)),
nn.AvgPool3D(kernel_size=2, stride=2, padding=0),
nn.Tanh(name="Tanh"), module1, nn.Conv3D(4, 6, (3, 3, 3)),
nn.MaxPool3D(kernel_size=2, stride=2, padding=0), nn.GELU(True))
self.assertEqual(
str(module2),
'Sequential(\n '\
'(0): Conv3DTranspose(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)\n '\
'(1): AvgPool3D(kernel_size=2, stride=2, padding=0)\n '\
'(2): Tanh(name=Tanh)\n '\
'(3): Sequential(\n (conv1): Conv2D(1, 20, kernel_size=[5, 5], data_format=NCHW)\n (relu1): ReLU()\n'\
' (conv2): Conv2D(20, 64, kernel_size=[5, 5], data_format=NCHW)\n (relu2): ReLU()\n )\n '\
'(4): Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)\n '\
'(5): MaxPool3D(kernel_size=2, stride=2, padding=0)\n '\
'(6): GELU(approximate=True)\n)'
)