Refactor env queue, Training diffusion works (Still not converging)

lerobot/configs/policy/diffusion.yaml

@@ -21,7 +21,7 @@ past_action_visible: False
 keypoint_visible_rate: 1.0
 obs_as_global_cond: True
 
-eval_episodes: 50
+eval_episodes: 1
 eval_freq: 10000
 save_freq: 100000
 log_freq: 250
@@ -40,8 +40,8 @@ policy:
   num_inference_steps: 100
   obs_as_global_cond: ${obs_as_global_cond}
   # crop_shape: null
-  diffusion_step_embed_dim: 128
-  down_dims: [512, 1024, 2048]
+  diffusion_step_embed_dim: 256  # before 128
+  down_dims: [256, 512, 1024]  # before [512, 1024, 2048]
   kernel_size: 5
   n_groups: 8
   cond_predict_scale: True
@@ -62,7 +62,7 @@ policy:
   grad_clip_norm: 0
 
 noise_scheduler:
-  # _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler
+  _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler
   num_train_timesteps: 100
   beta_start: 0.0001
   beta_end: 0.02
@@ -74,16 +74,16 @@ noise_scheduler:
 obs_encoder:
   # _target_: diffusion_policy.model.vision.multi_image_obs_encoder.MultiImageObsEncoder
   shape_meta: ${shape_meta}
-  resize_shape: null
-  crop_shape: [76, 76]
+  # resize_shape: null
+  # crop_shape: [76, 76]
   # constant center crop
-  random_crop: True
+  # random_crop: True
   use_group_norm: True
   share_rgb_model: False
-  imagenet_norm: False  # TODO(rcadene): was set to True
+  imagenet_norm: True
 
 rgb_model:
-  #_target_: diffusion_policy.model.vision.model_getter.get_resnet
+  _target_: diffusion_policy.model.vision.model_getter.get_resnet
   name: resnet18
   weights: null