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Fixed working with doubles bug #87

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Fixed working with doubles bug #87

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2 changes: 1 addition & 1 deletion experiment_params/train_config_default.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ device: 'cuda:0' # Will use this device if available, otherwise wil use cpu
# Define networks architectures
networks:
variational: True
dtype : "float"
dtype : "double"
encoder:
hidden_conv_layers: 6
n_filters: [32, 64, 64, 64, 64, 64, 64] # first + hidden
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6 changes: 3 additions & 3 deletions hamiltonian_generative_network.py
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Expand Up @@ -74,7 +74,7 @@ def forward(self, rollout_batch, n_steps=None, variational=True):
prediction_shape = list(rollout_batch.shape)
prediction_shape[1] = n_steps + 1 # Count the first one
prediction = HgnResult(batch_shape=torch.Size(prediction_shape),
device=self.device)
device=self.device, dtype=self.dtype)
prediction.set_input(rollout_batch)

# Concat along channel dimension
Expand Down Expand Up @@ -165,13 +165,13 @@ def get_random_sample(self, n_steps, img_shape=(32, 32)):
# Sample from a normal distribution the latent representation of the rollout
latent_shape = (1, self.encoder.out_mean.out_channels, img_shape[0],
img_shape[1])
latent_representation = torch.randn(latent_shape).to(self.device)
latent_representation = torch.randn(latent_shape).to(self.device).type(self.dtype)

# Instantiate prediction object
prediction_shape = (1, n_steps, self.channels, img_shape[0],
img_shape[1])
prediction = HgnResult(batch_shape=torch.Size(prediction_shape),
device=self.device)
device=self.device, dtype=self.dtype)

prediction.set_z(z_sample=latent_representation)

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5 changes: 3 additions & 2 deletions utilities/hgn_result.py
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Expand Up @@ -14,14 +14,15 @@ class HgnResult():
"""Class to bundle HGN guessed output information.
"""

def __init__(self, batch_shape, device):
def __init__(self, batch_shape, device, dtype):
"""Instantiate the HgnResult that will contain all the information of the forward pass
over a single batch of rollouts.

Args:
batch_shape (torch.Size): Shape of a batch of reconstructed rollouts, returned by
batch.shape.
device (str): String with the device to use. E.g. 'cuda:0', 'cpu'.
dtype (torch.dtype): Data type
"""
self.input = None
self.z_mean = None
Expand All @@ -30,7 +31,7 @@ def __init__(self, batch_shape, device):
self.q_s = []
self.p_s = []
self.energies = [] # Estimated energy of the system by the Hamiltonian network
self.reconstructed_rollout = torch.empty(batch_shape).to(device)
self.reconstructed_rollout = torch.empty(batch_shape, dtype=dtype).to(device)
self.reconstruction_ptr = 0

def set_input(self, rollout):
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