When running in a Docker environment, the following log is output and there is no result. #108
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When running in a Docker environment, the following log is output and there is no result.
2024-09-03 12:36:59 Using GPUs: [] 2024-09-03 12:36:59 Using OpenAI API with model gpt-4o-2024-05-13. 2024-09-03 12:36:59 2024-09-03 12:36:59 Generating idea 1/1 2024-09-03 12:36:59 Iteration 1/3 2024-09-03 12:36:59 {'Name': 'conditional_generation', 'Title': 'Controllable Generation with Conditional Embeddings in Diffusion Models', 'Experiment': "In this experiment, we introduce conditional embeddings to the denoiser network to enable controllable generation. We will add a new embedding layer for the conditional vector and modify the forward pass of the MLPDenoiser to include this conditional embedding. We will then train the model on the existing 2D datasets, with the conditional vector specifying different modes of the data distribution. The results will be evaluated based on the model's ability to generate samples biased towards the specified modes.", 'Interestingness': 8, 'Feasibility': 7, 'Novelty': 7} 2024-09-03 12:36:59 Iteration 2/3 2024-09-03 12:41:28 2024-09-03 12:41:28 <<<<<<< SEARCH 2024-09-03 12:41:28 scheduler = CosineAnnealingLR(optimizer, T_max=config.num_train_steps) 2024-09-03 12:41:28 ======= 2024-09-03 12:41:28 scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1000, 2024-09-03 12:41:28 gamma=0.1) 2024-09-03 12:41:28 >>>>>>> REPLACE 2024-09-03 12:41:28 2024-09-03 12:41:28 2024-09-03 12:41:28 Please run the command python experiment.py --out_dir=run_1 and let me know the 2024-09-03 12:41:28 results. 2024-09-03 12:41:28 2024-09-03 12:45:29 1. **Run 1**: StepLR with step_size=1000 and gamma=0.1 2024-09-03 12:45:29 2. **Run 2**: ExponentialLR with gamma=0.99 2024-09-03 12:45:29 3. **Run 3**: ReduceLROnPlateau with patience=10 and factor=0.1 2024-09-03 12:45:29 4. **Run 4**: CyclicLR with base_lr=1e-4 and max_lr=1e-3 2024-09-03 12:45:29 2024-09-03 12:45:29 ### Implementation 2024-09-03 12:45:29 2024-09-03 12:45:29 Let's start with **Run 1**: StepLR with step_size=1000 and gamma=0.1. 2024-09-03 12:45:29 2024-09-03 12:45:29 #### Step 1: Modify `experiment.py` to use StepLR 2024-09-03 12:45:29 2024-09-03 12:45:29 experiment.py 2024-09-03 12:45:29 ```python 2024-09-03 12:45:29 <<<<<<< SEARCH 2024-09-03 12:45:29 scheduler = CosineAnnealingLR(optimizer, T_max=config.num_train_steps) 2024-09-03 12:45:29 ======= 2024-09-03 12:45:29 scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1000, gamma=0.1) 2024-09-03 12:45:29 >>>>>>> REPLACE 2024-09-03 12:45:29 ``` 2024-09-03 12:45:29 2024-09-03 12:45:29 Please run the command `python experiment.py --out_dir=run_1` and let me know the results. 2024-09-03 12:45:29 Experiment Description for Run 1 2024-09-03 12:45:29 2024-09-03 12:45:29 Update notes.txt 2024-09-03 12:45:29 2024-09-03 12:45:29 notes.txt 2024-09-03 12:45:29 2024-09-03 12:45:29 2024-09-03 12:45:29 <<<<<<< SEARCH 2024-09-03 12:45:29 Description: Baseline results. 2024-09-03 12:45:29 ======= 2024-09-03 12:45:29 Description: Baseline results. 2024-09-03 12:45:29 2024-09-03 12:45:29 ## Run 1: StepLR with step_size=1000 and gamma=0.1 2024-09-03 12:45:29 Results: {'circle': {'training_time': 49.43490982055664, 'eval_loss': 2024-09-03 12:45:29 0.45279408530201143, 'inference_time': 3.512603759765625, 'kl_divergence': 2024-09-03 12:45:29 0.37352884410994314}, 'dino': {'training_time': 51.082834005355835, 'eval_loss 2024-09-03 12:45:29 0.7123583286924435, 'inference_time': 3.617689609527588, 'kl_divergence': 2024-09-03 12:45:29 4.161902163032134}, 'line': {'training_time': 53.97785544395447, 'eval_loss': 2024-09-03 12:45:29 0.805292817485302, 'inference_time': 3.6049094200134277, 'kl_divergence': 2024-09-03 12:45:29 0.16017912127875994}, 'moons': {'training_time': 52.51440739631653, 'eval_loss 2024-09-03 12:45:29 0.6206451436442793, 'inference_time': 3.5346617698669434, 'kl_divergence': 2024-09-03 12:45:29 0.12766315859985514}} 2024-09-03 12:45:29 2024-09-03 12:45:29 experiment.py 2024-09-03 12:45:29 2024-09-03 12:45:29 2024-09-03 12:45:29 <<<<<<< SEARCH 2024-09-03 12:45:29 scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1000, 2024-09-03 12:45:29 gamma=0.1) 2024-09-03 12:45:29 ======= 2024-09-03 12:45:29 scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, 2024-09-03 12:45:29 gamma=0.99) 2024-09-03 12:45:29 >>>>>>> REPLACE 2024-09-03 12:45:29 2024-09-03 12:45:29 2024-09-03 12:45:29 Please run the command python experiment.py --out_dir=run_2 and let me know the 2024-09-03 12:45:29 results. 2024-09-03 12:45:29 2024-09-03 12:49:34 2024-09-03 12:49:34 **Run 1**: We tested the `StepLR` learning rate scheduler with `step_size=1000` and `gamma=0.1`. This scheduler decreases the learning rate by a factor of 0.1 every 1000 steps. The results are as follows: 2024-09-03 12:49:34 2024-09-03 13:24:39 experiment.py 2024-09-03 13:24:39 ```python 2024-09-03 13:24:39 <<<<<<< SEARCH 2024-09-03 13:24:39 self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0) 2024-09-03 13:24:39 >>>>>>> REPLACE 2024-09-03 13:24:39 2024-09-03 13:24:39 ^^^ Expected `=======` not >>>>>>> REPLACE 2024-09-03 13:24:52 Let's correct the error by moving the self.conditional_mlp assignment after the 2024-09-03 13:24:52 super().__init__() call in the MLPDenoiser class. 2024-09-03 13:24:52 2024-09-03 13:24:52 Here is the correct SEARCH/REPLACE block: 2024-09-03 13:24:52 2024-09-03 13:24:52 experiment.py 2024-09-03 13:24:52 2024-09-03 13:24:52 2024-09-03 13:24:52 <<<<<<< SEARCH 2024-09-03 13:24:52 conditional_dim: int = 2, 2024-09-03 13:24:52 ): 2024-09-03 13:24:52 self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0) 2024-09-03 13:24:52 ======= 2024-09-03 13:24:52 conditional_dim: int = 2, 2024-09-03 13:24:52 ): 2024-09-03 13:24:52 >>>>>>> REPLACE 2024-09-03 13:24:52 super().__init__() 2024-09-03 13:24:52 self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0) 2024-09-03 13:24:52 >>>>>>> REPLACE 2024-09-03 13:24:52 2024-09-03 13:24:52 2024-09-03 13:24:52 Please update the file with the above change and re-run the experiment using the 2024-09-03 13:24:52 command python experiment.py --out_dir=run_1. 2024-09-03 13:24:52 2024-09-03 13:24:52 Tokens: 7,063 sent, 163 received. Cost: $0.04 message, $0.11 session. 2024-09-03 13:24:52 Applied edit to experiment.py 2024-09-03 13:24:55 Training model... 2024-09-03 13:24:55 Traceback (most recent call last): 2024-09-03 13:24:55 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 227, in <module> 2024-09-03 13:24:55 noise_pred = model(noisy, timesteps) 2024-09-03 13:24:55 ^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:24:55 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl 2024-09-03 13:24:55 return self._call_impl(*args, **kwargs) 2024-09-03 13:24:55 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:24:55 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl 2024-09-03 13:24:55 return forward_call(*args, **kwargs) 2024-09-03 13:24:55 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:24:55 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 76, in forward 2024-09-03 13:24:55 conditional_emb = self.conditional_mlp(x[:, 2]) 2024-09-03 13:24:55 ^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:24:55 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1729, in __getattr__ 2024-09-03 13:24:55 raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") 2024-09-03 13:24:55 AttributeError: 'MLPDenoiser' object has no attribute 'conditional_mlp' 2024-09-03 13:24:55 2024-09-03 13:25:08 Let's correct the error by moving the `self.conditional_mlp` assignment after the `super().__init__()` call in the `MLPDenoiser` class. 2024-09-03 13:25:08 2024-09-03 13:25:08 Here is the correct *SEARCH/REPLACE* block: 2024-09-03 13:25:08 2024-09-03 13:25:08 experiment.py 2024-09-03 13:25:08 ```python 2024-09-03 13:25:08 <<<<<<< SEARCH 2024-09-03 13:25:08 conditional_dim: int = 2, 2024-09-03 13:25:08 ): 2024-09-03 13:25:08 self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0) 2024-09-03 13:25:08 ======= 2024-09-03 13:25:08 conditional_dim: int = 2, 2024-09-03 13:25:08 ): 2024-09-03 13:25:08 >>>>>>> REPLACE 2024-09-03 13:25:08 super().__init__() 2024-09-03 13:25:08 self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0) 2024-09-03 13:25:08 >>>>>>> REPLACE 2024-09-03 13:25:08 ``` 2024-09-03 13:25:08 2024-09-03 13:25:08 Please update the file with the above change and re-run the experiment using the command `python experiment.py --out_dir=run_1`. 2024-09-03 13:25:08 Run 1 failed with return code 1 2024-09-03 13:25:08 Run failed with the following error Traceback (most recent call last): 2024-09-03 13:25:08 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 227, in <module> 2024-09-03 13:25:08 noise_pred = model(noisy, timesteps) 2024-09-03 13:25:08 ^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:08 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl 2024-09-03 13:25:08 return self._call_impl(*args, **kwargs) 2024-09-03 13:25:08 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:08 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl 2024-09-03 13:25:08 return forward_call(*args, **kwargs) 2024-09-03 13:25:08 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:08 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 76, in forward 2024-09-03 13:25:08 conditional_emb = self.conditional_mlp(x[:, 2]) 2024-09-03 13:25:08 ^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:08 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1729, in __getattr__ 2024-09-03 13:25:08 raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") 2024-09-03 13:25:08 AttributeError: 'MLPDenoiser' object has no attribute 'conditional_mlp' 2024-09-03 13:25:08 2024-09-03 13:25:08 The error indicates that the conditional_mlp attribute is not being properly 2024-09-03 13:25:08 initialized in the MLPDenoiser class. This is likely because we missed adding 2024-09-03 13:25:08 the initialization of self.conditional_mlp in the constructor. 2024-09-03 13:25:08 2024-09-03 13:25:08 Let's fix this by ensuring that self.conditional_mlp is properly initialized in 2024-09-03 13:25:08 the MLPDenoiser class constructor. 2024-09-03 13:25:08 2024-09-03 13:25:08 experiment.py 2024-09-03 13:25:08 2024-09-03 13:25:08 2024-09-03 13:25:08 <<<<<<< SEARCH 2024-09-03 13:25:08 self.network = nn.Sequential( 2024-09-03 13:25:08 ======= 2024-09-03 13:25:08 self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0) 2024-09-03 13:25:08 self.network = nn.Sequential( 2024-09-03 13:25:08 >>>>>>> REPLACE 2024-09-03 13:25:08 2024-09-03 13:25:08 2024-09-03 13:25:08 Please update the file with the above change and re-run the experiment using the 2024-09-03 13:25:08 command python experiment.py --out_dir=run_1. 2024-09-03 13:25:08 2024-09-03 13:25:08 Tokens: 7,524 sent, 153 received. Cost: $0.04 message, $0.15 session. 2024-09-03 13:25:08 Applied edit to experiment.py 2024-09-03 13:25:10 Training model... 2024-09-03 13:25:10 Traceback (most recent call last): 2024-09-03 13:25:10 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 228, in <module> 2024-09-03 13:25:10 noise_pred = model(noisy, timesteps) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl 2024-09-03 13:25:10 return self._call_impl(*args, **kwargs) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl 2024-09-03 13:25:10 return forward_call(*args, **kwargs) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 79, in forward 2024-09-03 13:25:10 return self.network(emb) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl 2024-09-03 13:25:10 return self._call_impl(*args, **kwargs) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl 2024-09-03 13:25:10 return forward_call(*args, **kwargs) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/container.py", line 219, in forward 2024-09-03 13:25:10 input = module(input) 2024-09-03 13:25:10 ^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl 2024-09-03 13:25:10 return self._call_impl(*args, **kwargs) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl 2024-09-03 13:25:10 return forward_call(*args, **kwargs) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/linear.py", line 117, in forward 2024-09-03 13:25:10 return F.linear(input, self.weight, self.bias) 2024-09-03 13:25:10 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:10 RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 384x256) 2024-09-03 13:25:10 2024-09-03 13:25:26 The error indicates that the `conditional_mlp` attribute is not being properly initialized in the `MLPDenoiser` class. This is likely because we missed adding the initialization of `self.conditional_mlp` in the constructor. 2024-09-03 13:25:26 2024-09-03 13:25:26 Let's fix this by ensuring that `self.conditional_mlp` is properly initialized in the `MLPDenoiser` class constructor. 2024-09-03 13:25:26 2024-09-03 13:25:26 experiment.py 2024-09-03 13:25:26 ```python 2024-09-03 13:25:26 <<<<<<< SEARCH 2024-09-03 13:25:26 self.network = nn.Sequential( 2024-09-03 13:25:26 ======= 2024-09-03 13:25:26 self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0) 2024-09-03 13:25:26 self.network = nn.Sequential( 2024-09-03 13:25:26 >>>>>>> REPLACE 2024-09-03 13:25:26 ``` 2024-09-03 13:25:26 2024-09-03 13:25:26 Please update the file with the above change and re-run the experiment using the command `python experiment.py --out_dir=run_1`. 2024-09-03 13:25:26 Run 1 failed with return code 1 2024-09-03 13:25:26 Run failed with the following error Traceback (most recent call last): 2024-09-03 13:25:26 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 228, in <module> 2024-09-03 13:25:26 noise_pred = model(noisy, timesteps) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl 2024-09-03 13:25:26 return self._call_impl(*args, **kwargs) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl 2024-09-03 13:25:26 return forward_call(*args, **kwargs) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 79, in forward 2024-09-03 13:25:26 return self.network(emb) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl 2024-09-03 13:25:26 return self._call_impl(*args, **kwargs) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl 2024-09-03 13:25:26 return forward_call(*args, **kwargs) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/container.py", line 219, in forward 2024-09-03 13:25:26 input = module(input) 2024-09-03 13:25:26 ^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl 2024-09-03 13:25:26 return self._call_impl(*args, **kwargs) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl 2024-09-03 13:25:26 return forward_call(*args, **kwargs) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/linear.py", line 117, in forward 2024-09-03 13:25:26 return F.linear(input, self.weight, self.bias) 2024-09-03 13:25:26 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:26 RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 384x256) 2024-09-03 13:25:26 2024-09-03 13:25:26 The error indicates that the shapes of the matrices being multiplied do not 2024-09-03 13:25:26 match. This is likely because the concatenated embedding vector has a different 2024-09-03 13:25:26 size than expected by the first linear layer in the self.network. 2024-09-03 13:25:26 2024-09-03 13:25:26 Since we are concatenating four embeddings (x1_emb, x2_emb, t_emb, and 2024-09-03 13:25:26 conditional_emb), each of size embedding_dim, the total size of the concatenated 2024-09-03 13:25:26 embedding vector should be embedding_dim * 4. 2024-09-03 13:25:26 2024-09-03 13:25:26 Let's fix this by updating the input size of the first linear layer in the 2024-09-03 13:25:26 self.network to embedding_dim * 4. 2024-09-03 13:25:26 2024-09-03 13:25:26 Here is the SEARCH/REPLACE block: 2024-09-03 13:25:26 2024-09-03 13:25:26 experiment.py 2024-09-03 13:25:26 2024-09-03 13:25:26 2024-09-03 13:25:26 <<<<<<< SEARCH 2024-09-03 13:25:26 nn.Linear(embedding_dim * 3, hidden_dim), 2024-09-03 13:25:26 ======= 2024-09-03 13:25:26 nn.Linear(embedding_dim * 4, hidden_dim), 2024-09-03 13:25:26 >>>>>>> REPLACE 2024-09-03 13:25:26 2024-09-03 13:25:26 2024-09-03 13:25:26 Please update the file with the above change and re-run the experiment using the 2024-09-03 13:25:26 command python experiment.py --out_dir=run_1. 2024-09-03 13:25:26 2024-09-03 13:25:26 Tokens: 6,906 sent, 201 received. Cost: $0.04 message, $0.19 session. 2024-09-03 13:25:26 Applied edit to experiment.py 2024-09-03 13:25:29 Training model... 2024-09-03 13:25:29 /app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py:229: UserWarning: Using a target size (torch.Size([256, 3])) that is different to the input size (torch.Size([256, 2])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. 2024-09-03 13:25:29 loss = F.mse_loss(noise_pred, noise) 2024-09-03 13:25:29 Traceback (most recent call last): 2024-09-03 13:25:29 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 229, in <module> 2024-09-03 13:25:29 loss = F.mse_loss(noise_pred, noise) 2024-09-03 13:25:29 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:29 File "/usr/local/lib/python3.11/site-packages/torch/nn/functional.py", line 3383, in mse_loss 2024-09-03 13:25:29 expanded_input, expanded_target = torch.broadcast_tensors(input, target) 2024-09-03 13:25:29 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:29 File "/usr/local/lib/python3.11/site-packages/torch/functional.py", line 77, in broadcast_tensors 2024-09-03 13:25:29 return _VF.broadcast_tensors(tensors) # type: ignore[attr-defined] 2024-09-03 13:25:29 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:29 RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 1 2024-09-03 13:25:29 2024-09-03 13:25:29 The error indicates that the shapes of the matrices being multiplied do not match. This is likely because the concatenated embedding vector has a different size than expected by the first linear layer in the `self.network`. 2024-09-03 13:25:29 2024-09-03 13:25:29 Since we are concatenating four embeddings (`x1_emb`, `x2_emb`, `t_emb`, and `conditional_emb`), each of size `embedding_dim`, the total size of the concatenated embedding vector should be `embedding_dim * 4`. 2024-09-03 13:25:29 2024-09-03 13:25:29 Let's fix this by updating the input size of the first linear layer in the `self.network` to `embedding_dim * 4`. 2024-09-03 13:25:29 2024-09-03 13:25:29 Here is the *SEARCH/REPLACE* block: 2024-09-03 13:25:29 2024-09-03 13:25:29 experiment.py 2024-09-03 13:25:29 ```python 2024-09-03 13:25:29 <<<<<<< SEARCH 2024-09-03 13:25:29 nn.Linear(embedding_dim * 3, hidden_dim), 2024-09-03 13:25:29 ======= 2024-09-03 13:25:29 nn.Linear(embedding_dim * 4, hidden_dim), 2024-09-03 13:25:29 >>>>>>> REPLACE 2024-09-03 13:25:29 ``` 2024-09-03 13:25:29 2024-09-03 13:25:29 Please update the file with the above change and re-run the experiment using the command `python experiment.py --out_dir=run_1`. 2024-09-03 13:25:29 Run 1 failed with return code 1 2024-09-03 13:25:29 Run failed with the following error /app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py:229: UserWarning: Using a target size (torch.Size([256, 3])) that is different to the input size (torch.Size([256, 2])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size. 2024-09-03 13:25:29 loss = F.mse_loss(noise_pred, noise) 2024-09-03 13:25:29 Traceback (most recent call last): 2024-09-03 13:25:29 File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 229, in <module> 2024-09-03 13:25:29 loss = F.mse_loss(noise_pred, noise) 2024-09-03 13:25:29 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:29 File "/usr/local/lib/python3.11/site-packages/torch/nn/functional.py", line 3383, in mse_loss 2024-09-03 13:25:29 expanded_input, expanded_target = torch.broadcast_tensors(input, target) 2024-09-03 13:25:29 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:29 File "/usr/local/lib/python3.11/site-packages/torch/functional.py", line 77, in broadcast_tensors 2024-09-03 13:25:29 return _VF.broadcast_tensors(tensors) # type: ignore[attr-defined] 2024-09-03 13:25:29 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 2024-09-03 13:25:29 RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 1 2024-09-03 13:25:29 2024-09-03 13:25:29 Max iterations reached 2024-09-03 13:25:29 Not all experiments completed. 2024-09-03 13:25:29 Experiments failed for idea 20240903_042411_conditional_generation 2024-09-03 13:25:29 FINISHED IDEA 2024-09-03 13:25:29 Completed idea: conditional_generation, Success: False 2024-09-03 13:25:29 All ideas evaluated.The text was updated successfully, but these errors were encountered: