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Code:
import cv2 import math import torch import torchvision import numpy as np import matplotlib.pyplot as plt from torchsummary import summary from torch.utils.mobile_optimizer import optimize_for_mobile device=None seed=0 lr=1e-4 weight_decay=1e-4 lr_step_period=15 model_name="r2plus1d_18" weights = "file_path.pt" np.random.seed(seed) torch.manual_seed(seed) if device is None: print("in if cond") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = torchvision.models.video.__dict__[model_name](pretrained=True) model.fc = torch.nn.Linear(model.fc.in_features, 1) model.fc.bias.data[0] = 55.6 if device.type == "cuda": model = torch.nn.DataParallel(model) model.to(device) if weights is not None: checkpoint = torch.load(weights) model.load_state_dict(checkpoint['state_dict']) model.eval() summary(model, (3, 1, 112, 112)) example = torch.rand((1, 3, 1, 112, 112)) traced_script_module = torch.jit.trace(model, example) traced_script_module_optimized = optimize_for_mobile(traced_script_module) traced_script_module_optimized._save_for_lite_interpreter("path_to_save.ptl")
The sanity check problem can be avoided by specifying check_trace= False But in that case the save_for_lite_interpreter fails:
RuntimeError: Could not export Python function call 'Scatter'. Remove calls to Python functions before export.
Using pytorch documentation to export the EF prediction model for Android
Is this as issue in the input shape? What should be the exact input shape for the first layer?
The text was updated successfully, but these errors were encountered:
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Code:
The sanity check problem can be avoided by specifying check_trace= False
But in that case the save_for_lite_interpreter fails:
RuntimeError:
Could not export Python function call 'Scatter'. Remove calls to Python functions before export.
Using pytorch documentation to export the EF prediction model for Android
Is this as issue in the input shape? What should be the exact input shape for the first layer?
The text was updated successfully, but these errors were encountered: