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i am getting following error.
ValueError: Expected input batch_size (56180) to match target batch_size (100).
My model's input is 3 channel(RGB) 227x227 images
And batch size is 100.
And following is summary.
torch.Size([100, 3, 227, 227])
torch.Size([100, 10, 111, 111])
torch.Size([100, 20, 53, 53])
torch.Size([56180, 100])
torch.Size([56180, 64])
torch.Size([56180, 64])
torch.Size([56180, 32])
torch.Size([56180, 32])
torch.Size([56180, 1])
This is binary classification(True, False), so i make final output is 1
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
#input image 227x227x3
self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(100, 64)
self.fc3 = nn.Linear(64, 32)
self.fc6 = nn.Linear(32, 1)
def forward(self, x):
print(x.shape)
x = F.relu(F.max_pool2d(self.conv1(x), 2))
print(x.shape)
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
print(x.shape)
x = x.view(-1, x.size(0))
print(x.shape)
x = F.relu(self.fc1(x))
print(x.shape)
x = F.dropout(x, training=self.training)
print(x.shape)
x = self.fc3(x)
print(x.shape)
x = F.dropout(x, training=self.training)
print(x.shape)
x = self.fc6(x)
print(x.shape)
return x
def train(model, train_loader, optimizer):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(DEVICE), target.to(DEVICE)
optimizer.zero_grad()
output = model(data)
target = target.unsqueeze(-1)
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
My question is that i have 100 batch images so that target(Y) is 100 units. But Why i am getting 56180 unit result?
Change the view function (in forward method):
x = x.view(x.size(0), -1)
The batch size must be in the 0 dimension.
Your forward method should be defined like this:
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc3(x)
x = F.dropout(x, training=self.training)
x = self.fc6(x)
return x
There is a class where everything is set to 32x32 image format Taken from here
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 6, 5) # here I changed the image channel from 3 to 1
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 64, 5)
self.fc1 = nn.Linear(64 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 22) # here I changed the number of output neurons from 10 to 22
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
How to change all this under resolution 96 to 96? Channel 1 (grayscale)?
At resolution 32x32 the output of conv2 is shaped (1, 64, 5, 5). On the other hand, if the input is at resolution 96x96, it will be (1, 64, 21, 21). This means fc1 needs to have 28_224 input neurons.
>>> self.fc1 = nn.Linear(64 * 21 * 21, 120)
Alternatively, you can use nn.LazyLinear which will infer this number for you, based on the first inference.
>>> self.fc1 = nn.Linear(120)
I am trying to use this architecture:
class Net(BaseFeaturesExtractor):
def __init__(self, observation_space: gym.spaces.Box, features_dim: int = 512):
super(Net, self).__init__(observation_space, features_dim)
self.conv1 = nn.Conv2d(1, 64, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 64, 3, stride=1)
self.conv4 = nn.Conv2d(64, 64, 3, stride=1)
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(64)
self.bn4 = nn.BatchNorm2d(64)
self.fc1 = nn.Linear(64 * (7 - 4) * (6 - 4), 128)
self.fc_bn1 = nn.BatchNorm1d(128)
self.fc2 = nn.Linear(128, 64)
self.fc_bn2 = nn.BatchNorm1d(64)
self.fc3 = nn.Linear(64, 7)
self.fc4 = nn.Linear(64, 1)
def forward(self, s):
# s: batch_size x board_x x board_y
s = s.view(-1, 1, 7, 6) # batch_size x 1 x board_x x board_y
s = F.relu(self.bn1(self.conv1(s))) # batch_size x num_channels x board_x x board_y
s = F.relu(self.bn2(self.conv2(s))) # batch_size x num_channels x board_x x board_y
s = F.relu(self.bn3(self.conv3(s))) # batch_size x num_channels x (board_x-2) x (board_y-2)
s = F.relu(self.bn4(self.conv4(s))) # batch_size x num_channels x (board_x-4) x (board_y-4)
s = s.view(-1,64 * (7 - 4) * (6 - 4))
s = F.dropout(
F.relu(self.fc_bn1(self.fc1(s))),
p=0.3,
training=self.training) # batch_size x 128
s = F.dropout(
F.relu(self.fc_bn2(self.fc2(s))),
p=0.3,
training=self.training) # batch_size x 64
pi = self.fc3(s) # batch_size x action_size
v = self.fc4(s) # batch_size x 1
return F.log_softmax(pi, dim=1), th.tanh(v)
When I am trying to use this architecture, I am getting following error:
Traceback (most recent call last):
File "/Users/joe/Documents/JUPYTER/ConnectX/training3.py", line 130, in <module>
learner.learn(total_timesteps=iterations, callback=eval_callback)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/ppo/ppo.py", line 264, in learn
reset_num_timesteps=reset_num_timesteps,
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/on_policy_algorithm.py", line 222, in learn
continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/on_policy_algorithm.py", line 154, in collect_rollouts
actions, values, log_probs = self.policy.forward(obs_tensor)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/policies.py", line 545, in forward
latent_pi, latent_vf, latent_sde = self._get_latent(obs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/policies.py", line 564, in _get_latent
latent_pi, latent_vf = self.mlp_extractor(features)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/stable_baselines3/common/torch_layers.py", line 220, in forward
shared_latent = self.shared_net(features)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/container.py", line 117, in forward
input = module(input)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 93, in forward
return F.linear(input, self.weight, self.bias)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/torch/nn/functional.py", line 1688, in linear
if input.dim() == 2 and bias is not None:
AttributeError: 'tuple' object has no attribute 'dim'
How this problem can be fixed?
I tried to reproduce a small working code based on the class definitions given by you and I was able to get the outputs from the model. Here is the following code:
# BaseFeaturesExtractor class
import gym
import torch as th
from torch import nn
class BaseFeaturesExtractor(nn.Module):
"""
Base class that represents a features extractor.
:param observation_space:
:param features_dim: Number of features extracted.
"""
def __init__(self, observation_space: gym.Space, features_dim: int = 0):
super(BaseFeaturesExtractor, self).__init__()
assert features_dim > 0
self._observation_space = observation_space
self._features_dim = features_dim
#property
def features_dim(self) -> int:
return self._features_dim
def forward(self, observations: th.Tensor) -> th.Tensor:
raise NotImplementedError()
# Net class
class Net(BaseFeaturesExtractor):
def __init__(self, observation_space: gym.spaces.Box, features_dim: int = 512):
super(Net, self).__init__(observation_space, features_dim)
self.conv1 = nn.Conv2d(1, 64, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
self.conv3 = nn.Conv2d(64, 64, 3, stride=1)
self.conv4 = nn.Conv2d(64, 64, 3, stride=1)
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(64)
self.bn4 = nn.BatchNorm2d(64)
self.fc1 = nn.Linear(64 * (7 - 4) * (6 - 4), 128)
self.fc_bn1 = nn.BatchNorm1d(128)
self.fc2 = nn.Linear(128, 64)
self.fc_bn2 = nn.BatchNorm1d(64)
self.fc3 = nn.Linear(64, 7)
self.fc4 = nn.Linear(64, 1)
def forward(self, s):
# s: batch_size x board_x x board_y
s = s.view(-1, 1, 7, 6) # batch_size x 1 x board_x x board_y
s = F.relu(self.bn1(self.conv1(s))) # batch_size x num_channels x board_x x board_y
s = F.relu(self.bn2(self.conv2(s))) # batch_size x num_channels x board_x x board_y
s = F.relu(self.bn3(self.conv3(s))) # batch_size x num_channels x (board_x-2) x (board_y-2)
s = F.relu(self.bn4(self.conv4(s))) # batch_size x num_channels x (board_x-4) x (board_y-4)
s = s.view(-1,64 * (7 - 4) * (6 - 4))
s = F.dropout(
F.relu(self.fc_bn1(self.fc1(s))),
p=0.3,
training=self.training) # batch_size x 128
s = F.dropout(
F.relu(self.fc_bn2(self.fc2(s))),
p=0.3,
training=self.training) # batch_size x 64
pi = self.fc3(s) # batch_size x action_size
v = self.fc4(s) # batch_size x 1
return F.log_softmax(pi, dim=1), th.tanh(v)
# Minimal code to reproduce a forward pass
import numpy as np
import torch
import torch.nn.functional as F
params = gym.spaces.Box(np.array([-1,0,0]), np.array([+1,+1,+1]))
model = Net(params)
inputs = torch.randn(2, 1, 7, 6)
outputs = model(inputs)
print(outputs[0].shape, outputs[1].shape) # prints (torch.Size([2, 7]), torch.Size([2, 1]))
i am trying to get the input and output information of a network. When debugging, i got this error, Runtime, shape ‘[-1, 400]’ is invalid for input of size 384. I tried different values, but can’t find the correct value. Is there a way to solve this issue? Thanks.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
input_shape = (3, 21,21)
dummy_input = torch.randn(6,*input_shape)
graph = torch.jit._get_trace_graph(model, args=dummy_input, _force_outplace=False, _return_inputs_states=False)
Error message:
RuntimeError: shape '[-1, 400]' is invalid for input of size 384
The shape of the tensor after the convolutional layers is [6,16,2,2]. So you cannot reshape it to 16*5*5 before feeding them to the linear layers. You should change your network to the one given below if you want to use the same filter sizes as the original in the convolutional layers.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*2*2, 120) # changed the size
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16*2*2) # changed the size
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
I am trying to do create CNN for regression purpose. Input is image data.
For learning purpose , i have 10 image of shape (10,3,448,448), where 10 are images, 3 are channel and 448 are hieght and width.
Output lables are (10,245).
Here is my architecture
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=5)
self.conv2 = nn.Conv2d(32, 32, kernel_size=5)
self.conv3 = nn.Conv2d(32,64, kernel_size=5)
self.fc1 = nn.Linear(3*3*64, 256)
self.fc2 = nn.Linear(256, 245)
def forward(self, x):
x = F.relu(self.conv1(x))
#x = F.dropout(x, p=0.5, training=self.training)
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = F.dropout(x, p=0.5, training=self.training)
x = F.relu(F.max_pool2d(self.conv3(x),2))
x = F.dropout(x, p=0.5, training=self.training)
x = x.view(-1,3*3*64 )
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return x
cnn = CNN()
print(cnn)
it = iter(train_loader)
X_batch, y_batch = next(it)
print(cnn.forward(X_batch).shape)
Using batch size 2 i am expecting data shape produced by model is (2,245). But it is producing data of shape (2592, 245)
after self.conv3 you have tensors of shape [2, 64, 108, 108] which produces [2592, 576] after reshape. So this is where 2592 comes from.
Change the lines:
"self.fc1 = nn.Linear(3*3*64, 256)"
and
"x = x.view(-1,3*3*64)"
so that they use proper image size after the layers.
below is the fixed code:
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=5)
self.conv2 = nn.Conv2d(32, 32, kernel_size=5)
self.conv3 = nn.Conv2d(32,64, kernel_size=5)
self.fc1 = nn.Linear(108*108*64, 256)
self.fc2 = nn.Linear(256, 245)
def forward(self, x):
print (x.shape)
x = F.relu(self.conv1(x))
print (x.shape)
#x = F.dropout(x, p=0.5, training=self.training)
x = F.relu(F.max_pool2d(self.conv2(x), 2))
print (x.shape)
x = F.dropout(x, p=0.5, training=self.training)
print (x.shape)
x = F.relu(F.max_pool2d(self.conv3(x),2))
print (x.shape)
x = F.dropout(x, p=0.5, training=self.training)
print (x.shape)
x = x.view(-1,108*108*64 )
print (x.shape)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return x
cnn = CNN()
print(cnn)
# X_batch, y_batch = next(it)
print(cnn.forward(X_batch).shape)